health and social care act 2012 case study

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Never Again?: The story of the Health and Social Care Act 2012

‘Never Again?’ tells the story of how and why the Health and Social Care Act 2012 – by far the most controversial piece of NHS legislation in more tha

‘Never Again?’ tells the story of how and why the Health and Social Care Act 2012 – by far the most controversial piece of NHS legislation in more than two decades – became law.

It relates the story of a political thriller – from the legislation’s origins 20 years ago, through the development of the 2010 white paper “Liberating the NHS” and the resultant bill; a bill so controversial that it appeared at times as though the Government might lose it.

It does so from the view point of opponents and critics, but also from the point of view of the man with whom this legislation is uniquely identified – the current health secretary.

On the way, it explains just what it was that Andrew Lansley was trying to do and why the bill was so vast and controversial.

It details the events that shaped it – most notably the Coalition’s now partly forgotten “programme for government”. That document – cooked up purely by the politicians in Downing Street over 12 days immediately after the election in May 2010 – radically reshaped the health secretary’s plans.

Sorting out the “disaster” in the “programme for government” turned what would have been merely a large shift of power and accountability within the NHS into a huge structural upheaval: one that allowed the reforms to be written up as the biggest reorganisation in the 63-year history of the NHS; and one that could become this Government’s “poll tax”.

‘Never Again?’, in particular, is a story of coalition government and coalition policy making. The act is uniquely identified with Andrew Lansley, but without the Liberal Democrats it would have been a very different bill. At the same time, without the Liberal Democrats, there would have been much less fertile ground within government for opponents to sow the seeds of their dissent. Without them, it would have undergone fewer amendments. And yet, in another twist to the coalition tale, without Liberal Democrat votes the legislation would not have passed.

‘Never Again?’ recounts:

• how Andrew Lansley was banned from talking about the detail of his plans ahead of the election
• what happened at the meeting that called “the pause” on the legislation
• how Sir David Nicholson came to be appointed chief executive designate of the NHS Commissioning Board
• how Andy Burnham revived the opposition to the bill
• and how the Coalition finessed its legislation through the House of Lords.

‘Never Again?’ also seeks to draw some early lessons from what is widely regarded as a “car crash” of both politics and policy making. But at the same time it explains why the health secretary believes that never again – or at least for the foreseeable future – will the NHS need to undergo another big structural change and raises the possibility that Andrew Lansley could yet emerge as something of a hero of public sector reform.

Lessons learned

Nicholas Timmins’ case study draws out 10 specific lessons from the story of the Health and Social Care Act. In a separate commentary, Learning the lessons from 'Never Again?' , we link those lessons to earlier work that the Institute for Government has done on:

1. policy making in opposition 2. making coalition government work 3. transitions to government 4. the role of the centre of government 5. being an effective minister 6. better policy making.

This timeline is published alongside Never Again? The story of the Health and Social Care Act 2012 by Nicholas Timmins for the Institute for Government and The King’s Fund.  It is a visual guide through the events that led to the act reaching the statute book. It sets current NHS reforms within a  historical context - from the creation of the internal market in 1989, to the establishment of the first Foundation Trust in 2004, to the passing of the most recent controversial legislation.

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• Research article
• Open access
• Published: 17 February 2017

Commissioning for health improvement following the 2012 health and social care reforms in England: what has changed?

• E. W. Gadsby 1 ,
• S. Peckham 1 ,
• A. Coleman 2 ,
• D. Bramwell 2 ,
• N. Perkins 2 &
• L. M. Jenkins 1  

BMC Public Health volume  17 , Article number:  211 ( 2017 ) Cite this article

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The wide-ranging program of reforms brought about by the Health and Social Care Act (2012) in England fundamentally changed the operation of the public health system, moving responsibility for the commissioning and delivery of services from the National Health Service to locally elected councils and a new national public health agency. This paper explores the ways in which the reforms have altered public health commissioning.

We conducted multi-methods research over 33 months, incorporating national surveys of Directors of Public Health and local council elected members at two time-points, and in-depth case studies in five purposively selected geographical areas.

Public health commissioning responsibilities have changed and become more fragmented, being split amongst a range of different organisations, most of which were newly created in 2013. There is much change in the way public health commissioning is done, in who is doing it, and in what is commissioned, since the reforms. There is wider consultation on decisions in the local council setting than in the NHS, and elected members now have a strong influence on public health prioritisation. There is more (and different) scrutiny being applied to public health contracts, and most councils have embarked on wide-ranging changes to the health improvement services they commission. Public health money is being used in different ways as councils are adapting to increasing financial constraint.

Conclusions

Our findings suggest that, while some of the intended opportunities to improve population health and create a more joined-up system with clearer leadership have been achieved, fragmentation, dispersed decision-making and uncertainties regarding funding remain significant challenges. There have been profound changes in commissioning processes, with consequences for what health improvement services are ultimately commissioned. Time (and further research) will tell if any of these changes lead to improved population health outcomes and reduced health inequalities, but many of the opportunities brought about by the reforms are threatened by the continued flux in the system.

Peer Review reports

The UK government elected in 2010 embarked on a wide-ranging program of reforms to the health and social care systems in England. The Health and Social Care Act (2012) formed the centrepiece of the reforms, introducing extensive changes to the organisation, structure and delivery of health services. As part of these changes, key public health functions were transferred from the National Health Service (NHS) to local government councils. This transfer included specialist public health staff and the budget for commissioning a range of public health services, including sexual health services, public health nursing, drug and alcohol treatment, smoking cessation and weight management services. In addition, a national public health agency (Public Health England, PHE) was established, as the national leadership body for public health to provide national campaigns and co-ordinate health protection, and as an active partner in local initiatives where appropriate [ 1 ].

During the reforms, the government highlighted a number of issues that lay behind inadequate population health outcomes. It felt the system was fragmented, lacked integration and synergies across services and had overlapping responsibilities [ 2 , 3 ]. It also felt the system disempowered public health professionals, insufficiently valuing their skills [ 3 ]. Crucially, the government argued that there was an insufficient focus on the root causes of ill health, and pointed to a lack of accountability with regards to outcomes. Issues faced in England chimed with cross-cutting themes that emerged from a review of public health in Europe, notably: the importance of inter-sectoral working, the existence of wide inequalities between and within countries in Europe, and the knowledge gaps around what public health policies and interventions are being implemented where, and which are most effective [ 4 ].

In 2012, a new European health policy framework was developed, to support action across government and society to improve the health and well-being of populations, reduce health inequalities, strengthen public health and ensure people-centred health systems [ 5 ]. It was in this context that the UK government set out, in the English reforms, to clarify responsibilities and accountabilities, empower people and communities, and focus on the evidence of what works. They wanted a greater emphasis, at all levels, on disease prevention, and a more joined up approach, with clearer leadership. In addition, the need to achieve better results with less money was an undercurrent to the entire health and social care reforms, driven by the government’s aim to reduce their budget deficit.

Prior to the reforms in England, Primary Care Trusts (PCTs) were the NHS bodies responsible for commissioning – strategic planning and purchasing - most health services, including for public health [ 6 ]. Until 2011, PCTs also directly managed the vast majority of NHS community health services, such as district nursing, health visiting and children’s services. In 2013, PCTs were abolished and replaced by a new NHS commissioning architecture, locally led by Clinical Commissioning Groups (CCGs), and nationally led by a new independent NHS commissioning board (NHS England) [ 7 ].

Within PCTs, public health specialists tended to provide a lead role in developing strategies for meeting local health needs, and specialist clinical and public health advice to inform PCT commissioning. Whilst public health was (and remains) an inter-organisational function, with much close working between PCTs and local councils, funding remained predominantly from NHS sources, with most decisions about services and expenditure taken within an executive decision-making framework by Directors of Public Health supported by PCT Boards [ 6 , 8 ].

Public health services are now funded by a public health budget, separate from the budget managed through NHS England for healthcare. This budget is decided by the Department of Health (DoH), and managed by PHE. PHE funds public health activity either through allocations to upper-tier and unitary councils, by commissioning services via NHS England, or by commissioning or providing services itself. Most locally delivered public health activities are now commissioned or provided by local councils. The structure of local government in England is complex: there are 27 areas where services are split between upper-tier county councils (taking responsibility for social care, education, transportation and strategic planning), and smaller district councils (covering e.g. housing, leisure, environmental health and planning), and there are 125 unitary councils that provide the full range of services. All of these councils are run by elected councillors, usually affiliated to a political party, who represent and engage their local population, make key decisions, contribute to policy/strategy review and development, and conduct overview and scrutiny roles. Councils have the freedom to innovate and to make changes locally, under the ‘general power of competence’, introduced by the Localism Act 2011 [ 9 ]. There are important differences, then, in the context in which local public health commissioning is now done.

The Health and Social Care Act also introduced Health and Wellbeing Boards (HWBs) as statutory sub-committees of local councils. These boards were intended to bring together the key NHS, public health and social care leaders in each local council area to work together to co-ordinate commissioning of their services. They are thus an important part of the new health commissioning landscape [ 10 ].

A House of Commons Health Committee inquiry on public health post-2013, launched October 2015, is starting to raise some important issues related to the structures, organisation, funding and delivery of public health following the reforms [ 11 ]. However, to date, little academic attention has been paid to the impact of the reforms on public health commissioning in England. This article examines key changes to the public health system following the reforms, and explores the broad function of commissioning for health improvement within the new system. It highlights some important changes in the way public health commissioning is now undertaken, in who is doing it, and in what is commissioned. It draws on findings from a 3-year research study funded by the DoH, which examined the impact of structural changes on the functioning of the public health system, and on the approaches taken to improving the public’s health. The article critically examines these findings in the context of the intentions of the reforms to create a more joined-up system with clearer leadership and greater opportunities to improve population health.

The PHOENIX study was a 3-year research project to examine the impact of structural reforms on the functioning of the public health system in England. It was an exploratory study that took place from the time of transition (April 2013), and so could explore the ways in which the planning, organisation, commissioning and delivery of health improvement services were changing over time as the new structures bedded in. One of its objectives was to examine approaches taken to commissioning within the new system, using obesity as a focal topic.

The study incorporated multiple methods. In a scoping review [ 12 ], we analysed policy documents and responses to the reforms from key stakeholders [ 13 ], developed a picture of how the new structures were developing, and collated demographic and other data on all 152 upper-tier and unitary local councils in England. This review identified the key themes to follow up on in the next phase of the research. It also enabled the purposive selection of local councils for later case study research, conducted from March 2014 to September 2015, in five areas. The areas were purposively selected for maximum variation across a a range of characteristics related to the councils and the populations they serve (including council type, size, urban or rural location, varied socio-demographic and economic circumstances, obesity prevalence and different political control) in order to provide a diverse range of cases. The five areas (described in Table  1 ) encompassed 13 different councils, including unitary, upper-tier and a sample of lower-tier (district) councils, some of which had a variety of different sharing arrangements. This enabled an examination of multiple perspectives and inter- and intra-organisational relationships.

Within the case study areas, 103 semi-structured interviews were conducted (see Table  1 ) with 36 council public health staff; 18 elected members; 25 council non-public health staff; 13 provider organisation staff; six CCG staff and three other staff at regional levels. Three members of the research team were allocated across the case study sites to enable each researcher to develop a deep understanding of and good relationships within each area. Fifteen meetings were observed and documentary evidence was collated to enrich our understanding of the case study areas. A further five interviews were conducted with key informants outside of the case study areas, particularly to explore national and regional level issues and relationships with/within PHE.

In the autumn of 2015, a web-based questionnaire was sent to all Directors of Public Health (DsPH), and to councillors in all 152 upper-tier and unitary councils who had a public health brief. Usable responses were received from 49% of DsPH and 32% of elected members. The questionnaire was broadly a repeat of a survey conducted the previous year (not reported in this paper). The distribution of responses from local councils was highly representative overall. Data was analysed using SPSS.

Qualitative data was analysed on a case and theme-based approach, using NVIVO 10. Multi-investigator, multi-site and multi-method triangulation was used in an ongoing and iterative process of bringing together and interrogating the data. Reflexive, narrative accounts of each case study area were shared with the research team, which was made up of experts in public health, local government, ethnography, and public policy. Rich interpretations of emergent themes across the cases were developed collaboratively, paying particular attention to roles and relationships, power/autonomy, and decision-making processes. Analysis drew on a number of integrative theoretical frameworks, employing concepts and ideas drawn from a number of different paradigms [ 14 , 15 ]. Ongoing analysis of the data allowed shifts in focus according to the interplay between theory, concepts and data, enabling sensitivity to the constantly changing field of study.

Ethical approval was granted by the university research ethics committee, and research governance approvals were obtained for each case study site.

Throughout analysis, commissioning was considered as one of the broad aspects of public health activity. As a theme, it included identifying needs, reviewing service provision, deciding priorities, procuring services, and managing performance. Our research set out to examine the context for commissioning, the people/organisations involved in commissioning activities, the processes involved, and any evidence of things changing.

The context for commissioning

The transfer of public health staff and resources into local councils from PCTs was far from straightforward, and often accompanied other system reorganisations. For instance, in one of our case study areas, staff in a PCT were separated into a council public health team, one of three CCGs, or into a provider trust. One children’s public health commissioner who was formerly in the children’s joint commissioning team in the PCT with commissioning responsibilities for the whole of the 0–19 pathway, was transferred to a council team. Her former commissioning responsibilities were split amongst different organisations, and she was now responsible only for certain elements of the healthy child programme. She explained the resulting confusion:

“… It has caused fragmentation of the system and certainly for the 0–19 pathway or services for children, you know, the health services for children. It has meant that different parts of the system are now responsible for commissioning different elements of it …, which is challenging” (senior public health commissioner, council, site B).

This also had implications for the sharing of information between health and council commissioners, which this officer described as being “much more difficult for us now”.

Some public health staff chose to join PHE or NHS England, and some became part of new commissioning support organisations. There was much confusion over where staff should be transferred to (sometimes depending on the proportion of their time spent on service commissioning versus service provision), and around the organisation of budgets. There were instances where this tested relationships between councils and CCGs.

Local councils received their public health staff, resources and duties at a time of unprecedented cuts to their budgets [ 16 ]. These cuts precipitated ongoing restructures within councils which sought to streamline their organisations and reduce staffing costs. The positioning of public health teams within councils varied. Our survey found that 26% ( N  = 73) of the public health teams were distinct public health directorates; 52% were sections of another directorate; and 22% had other arrangements, including merged, distributed and mixed models. DsPH also had different levels of access to key council decision-making bodies (53% of DsPH respondents were members of the council’s most senior corporate management team), and different line-management structures (47% said that they were managerially responsible to the council’s chief executive; 53% were managed by a range of other directorate heads). Consequently, DsPH were not always in the best place for strategic influence in the council.

Commissioning processes and people involved

Decision-making within councils was found to be very different to that within PCTs. Decisions about how to spend money were subject to a greater range of decision makers and wider consultation, both across the council and amongst the public, than before. Elected members are the key decision makers within councils; the role of officers, including those in public health, is to support them. Elected members, therefore, were influencing the priorities and actions of the public health team, sometimes overtly and sometimes more subtly. 92% of elected members responding to our survey ( N  = 38) said they felt always able (45%) or quite often able (47%) to influence the priorities of the public health team. In our case studies, we saw how this influence might operate more subtly, perhaps according to the ideologies and interests of the elected member, or the politics of the council. For instance, in one Conservative-led council, the elected member explained that he would have a very difficult job persuading his cabinet to significantly increase spending on smoking cessation: “They’re not particularly interested in it, they think … ‘oh well if people smoke themselves silly, let them smoke themselves silly’” (elected member, council, site A).

Compared with the NHS, local councils take different approaches to prioritisation and commissioning, influenced in part by over 15 years of implementing ‘Best Value’ Footnote 1 . The processes of commissioning (and new procurement laws) within a council have had to be learned by incoming public health staff. At the same time, public health staff have tried to educate councillors in public health commissioning.

Several commissioning officers who had worked within councils prior to the reforms (e.g. in adult or children’s social care directorates) and who moved, following the reforms, into the public health teams, talked about differences they observed in how commissioning was done. One, referring to her incoming public health colleagues, explained:

“We were faced with a lot of ignorance about commissioning - local authority style commissioning and business processes - amongst our colleagues… I was shocked actually by the lack of understanding of what we had been doing or what we did [as local authority commissioners]” (commissioner, council, site B) .

Another talked about the differences between commissioning in PCTs and commissioning in the councils. She explained that “public health has commissioning responsibilities now in a way that they didn’t in the old PCT”. She described commissioning in the former PCTs as comparatively less ‘robust’, with less accountability, and less scrutiny of performance and outcomes data:

“ there’s much stronger scrutiny in local government and that’s all areas of business and it’s something that we’ve had to really work with our providers in NHS specifically around understanding” (commissioner, council, site A) .

From the point of view of providers, however, the sometimes rather narrow outcomes-based scrutiny that services were now subjected to was not always appropriate for complex public health interventions. For instance, the provider of a range of obesity prevention services in one of our case study areas complained that the focus on outcomes in terms of body mass index reductions belied the fact that most of their time and resources were spent on engaging communities and developing relationships with schools and others. The outcomes of this type of activity, however, are impossible to measure.

Having a distinct public health grant for the first time enabled DsPH to take a different approach – a more strategic approach - to the allocation of the public health budget. A public health officer in one of our sites described how, in the PCT, they were sometimes left ‘scrabbling’ around for funds, when public health priorities and PCT priorities were not always well matched. However, with a ring-fenced budget, they were able to plan how best to match spending against their local priorities. The leader of a council in site A explained how they were prepared to completely shake up the way in which the public health grant was spent: “ We’ve got to start at reviewing; is that delivering to the right priorities or not? Is it value for money or not? And what should we stop doing and what should we start doing? ” Indeed, this process of whole-scale service reviews for specific areas (such as obesity) was demanded by councillors in all of our case study areas. For public health officers, this sometimes gave them the freedom to pursue quite different approaches.

Decision-making across the local system following the reforms was intended to be more co-ordinated. However, with commissioning responsibilities now fragmented between NHS England, PHE, local councils and CCGs, our research found that co-ordination was proving to be difficult. Moreover, the lack of clarity about responsibilities sometimes led to delays in the commissioning of services, and/or tensions in the relationships between organisations. Commissioning across an obesity pathway, for instance, involves councils (for broad obesity prevention and non-intensive weight management services), CCGs (for specialist obesity services) and NHS England (bariatric services) [ 17 , 18 ]. Across England, we know that there are significant gaps in this pathway, with a particular lack of specialist obesity services [ 19 , 20 ]. Following the reforms, there was a great deal of confusion about whose responsibility it was to commission these services.

It is clear that, as with many public health interventions, if weight management and obesity prevention services are to achieve their objectives, primary and community care providers play a vital role. The presence, absence, type and success of health improvement services commissioned by councils have important implications for NHS work. However, there is now a greater disconnect between public health officers and NHS commissioners. In response to our survey, 48% of DsPH ( N  = 69) said they felt ‘less able’ to influence local CCGs than before the reforms. In our case study sites, we found that evidence of meaningful engagement between public health teams and CCGs was limited. This HWB chair felt that CCGs had become disengaged from public health:

“ I think we’ve got to persuade the CCG that, in particular, public health is everybody’s business, it’s not just the local authority’s business. … they see public health as a separate entity at the moment, and not part of an integrated health economy” (Chair HWB, council, site C) .

HWBs were meant to be the mechanism for co-ordinating commissioning across NHS, social care and public health at the strategic level. Our survey found that amongst DsPH ( N  = 65), 48% felt the HWB was ‘definitely’ instrumental in identifying the main health and wellbeing priorities, and 45% felt it had ‘definitely’ strengthened relationships between commissioning organisations. However, less than 5% felt that the HWB was ‘definitely’ making difficult decisions, and only 28% felt that it had ‘definitely’ begun to address the wider determinants of health. A further complication with co-ordinating across the system and addressing wider determinants is that in two-tier councils, many of the functions that public health are expected to work across are based in multiple lower-tier district councils. Public health officers must therefore build relationships with a greater number of different organisations, all with their own priorities and ideas. In addition, these district councils often have a limited voice on HWBs. It is perhaps partly for this reason that some HWBs were not seen to be significantly engaging with the public health agenda. As this HWB chair explained:

“We have a very strong focus on integration, Better Care Fund – all that side of things. I’m conscious sometimes of an element of criticism … there’s always a challenge to say, ‘Are you actually thinking enough about long term determinants and all the sort of public health agenda’ …” (Chair HWB, council, site A).

What has changed?

Our research suggested that, as a result of the reforms, public health commissioning was changing on a number of levels. Firstly, money was being used in different ways. One indication of this was the way in which the ring-fenced public health budget was being used to invest in other departments in the majority of councils (see Fig.  1 ). Given the huge cuts councils were having to make, most DsPH felt that, now the public health budget was contained within the council, it was expected to contribute to the overall savings they needed to make. Many seemed reconciled that the budget would now be used to fund other services – in many cases, services that would have been cut (e.g. children’s centres) had public health funding not been available. And in our case studies, public health officers talked about the opportunities this sometimes presented, in terms of embedding public health activities and objectives within other council services and providing more joined-up ways of thinking and working.

Use of public health budget to invest in other council departments in previous 12 months

Secondly, there were many changes being made to the commissioning of health improvement services (see Fig.  2 ). The move to local government prompted public health commissioners to look at services and contracts anew. In addition, councils tended towards shorter contracts and more frequent retendering of services than the NHS. All our respondents had started the process of retendering within 2 years. But we also saw the majority of responding authorities ( N  = 64–67) having set up new services (73%), changed provider of existing services (90%), re-designed existing services (94%) and de-commissioned services (69%). In our case study areas we saw that extensive commissioning changes were sometimes occurring as a result of changes in local area arrangements, for instance, where several areas (former PCTs) were brought together into one (council). Other commissioning changes, however, were as a result of service reviews that were very critical of service outcomes.

Changes made by councils to services commissioned under public health budget in last 12 months

Our surveys asked for more information about changes that were being made to obesity commissioning. DsPH commented that they were wanting to move away from ineffective schemes, increase their focus on children, use new providers and create a more integrated pathway. All these changes were resulting in insecurity in the provider landscape.

Finally, there were changes to the size and profile of the public health teams responsible for commissioning health improvement services. DsPH were asked whether there had been changes in the last 12 months to the size and composition of their public health team. 28% ( N  = 72) reported that they had fewer public health specialists. 15% reported they had more business managers/commissioning support staff, and 22% ( N  = 54) said they had more ‘other’ staff (not falling into the DPH, specialist, analyst or commissioning support categories). In our case study sites, public health officers talked of the need to address skill gaps within their team in response to working in the new environment. In one of our sites, for instance, the public health commissioning team (made up of non-public health specialists) had been significantly bolstered. The team of public health specialists had been correspondingly reduced.

It was not easy to tell, at this juncture, whether these observed changes in commissioning had resulted in a significantly different set of activities being commissioned. However, there were early signs of some general shifts occurring. In three of our case study areas, we observed a shift towards the commissioning of more holistic ‘healthy lifestyle’ services, bringing together weight management, smoking cessation, alcohol reduction, sexual health services, and so on. In two of our councils, we saw a shift (at least in rhetoric) towards ‘whole council’ approaches, for instance, where they were seeking to address a broader range of factors influencing obesity, particularly by working across council departments. We witnessed a greater recognition of public health objectives and expected outcomes in a wider range of council services as a result of public health investment. And we saw public health staff working hard to influence the wider workforce. Particularly during the transition phase, as public health were settling into their new homes, a number of programmes including learning events, information sharing, and engagement events were targeted at elected members and non-public health officers across the council.

The reforms expressed a clear intention to simplify and streamline a previously complex, fragmented system. The transfer of public health responsibilities into local councils was to ensure that public health outcomes were embedded across a council’s functions. The creation of HWBs was to ensure strategic direction across organisations.

The functions of the now extinct PCTs were spread across CCGs, councils and provider organisations, creating a more complex organisational picture than before the reforms, with more complex accountability and governance structures. Moreover, there was continued upheaval in the system, with elements such as CCGs and public health teams merging, the PHE regional tier ‘downsizing’, and local councils constantly restructuring as they tried to cope with substantial budget cuts. Fragmentation is a problem common to many health systems, and is a condition related to the tendency within health care planning to focus and act on the parts without adequately appreciating their relation to the evolving whole [ 21 ]. There is a constant challenge to create a system focused on relationships across the whole – whole people, whole systems, whole communities. It is often these relationships across the whole that suffer in the context of financial restraint and continual change [ 22 ].

The move of public health into local councils in England created a new working environment for commissioners, public health practitioners and providers. Our findings have demonstrated how, in this new environment, existing public health capacity has been both freed and stifled. Public health professionals have the opportunity to take on a more significant role in shaping local places, but will need to find a balance between ‘service’ public health and academic ‘social medicine’ [ 23 ].

The considerable literature on decentralisation suggests that the transfer of authority and resources to local government might offer significant opportunities to improve access to health and other care services, to provide services that are better aligned to needs and local preferences, and to allow for increased flexibility and transparency [ 24 – 26 ]. However, the reforms in England simply moved public health responsibilities at the local level from executive decision-making bodies (PCTs) to democratically governed councils. Whilst PCTs had the same ‘local’ focus as councils, they were historically more directly accountable to central government, and, with a few exceptions, were poor at developing local ‘bottom-up’ methods for making NHS services more user-responsive [ 27 ]. Councils, on the other hand, have been subject to a longer experience of competitive tendering and service commissioning than the NHS [ 28 ], and tend to have a more structured approach for community engagement and user-involvement embedded in their organisational culture [ 29 ]. As a result, there appears to have been a shift in how public health commissioning is performed, from a more specialist-led investment approach to a more ‘business’-orientated approach adopted by many local councils, using best value frameworks.

In the new environment, there seems to be more opportunity for variation across the country in what activity is commissioned, and in who provides it (as well as how, where and to whom). The Localism Agenda [ 9 ] gives councils more freedom to innovate, to both drive down costs and meet local needs [ 30 ]. Considerable discretion was afforded to individual councils to interpret the full and detailed scope of their new functions and services [ 31 ]. This was important, given the independence of councils as democratic organisations, but it means that public health decision-making is now less amenable to central government control.

In the absence of strong central control, it is important to question the extent to which local problems can be solved locally without risking geographical inequity of services which underpin basic human rights [ 32 , 33 ]. For the next couple of years, the annual public health budget devolved to local government in England will be around £3.3 billion (reducing by an average of 3.9% every year in real terms until 2020) [ 34 ]. Prior to the reforms, this budget would have been spent by PCTs, who were accountable for that spend to the DoH, via regional NHS authorities (now abolished) who were mainly concerned with overall NHS expenditure and financial sustainability of NHS healthcare services. Following the reforms, whilst the public health outcomes framework gives a clear sense of outcomes the DoH expects to see, the accountability for spending money is much weaker. Beyond a basic report to the Department on how the budget has been spent, there is very little role for formal state-driven accountability. In a way most uncharacteristic of the NHS, PHE has emphasised that it is there to support local councils, not performance manage them. Instead, there is a reliance on sector-led improvement, whereby councils review and support each other’s performance [ 35 ]. In addition, public health commissioning is coming under much closer scrutiny from elected members within the local council. Our research supported the idea that we can expect to see increasing variation in services, but it is far from clear what impact this will have on variations in outcomes.

Public health officers moving from the NHS to local councils have sometimes struggled to adjust to this different relationship with central government. From the point of view of commissioners, the lack of guidance and clarity from Government was often found to be unhelpful. In particular, public health officers expressed the need for more timely information, for instance, regarding responsibilities for commissioning across the fragmented system, or how the in-year budget cuts would be implemented [ 36 ]. In the absence of detailed information, public health teams were sometimes forced to make commissioning decisions based more on expediency than on need. In the new system, the DoH is defined as the ‘system leader’, improving people’s health and wellbeing through its stewardship of the public health system [ 37 ]. The concept of health stewardship implies a broad over-arching responsibility over the functioning of the system as a whole and, ultimately, over the health of the population [ 38 ]. However, we suggest that central government in England has yet to resolve some important stewardship issues, particularly around its role in securing resources, balancing competing interests and demands, and assuring delivery in the context of localism and the move of public health into local government. Moreover, there was little in our research to suggest that PHE have sufficient capacity, or have yet developed the strong relationships required, to provide meaningful support to local partners in the delivery of their vision.

Public health officers have also had to adjust to different roles and relationships relative to other actors at local level. Directors of public health were previously key decision makers on the executive boards of PCTs. Whilst they were often the first to be pushed back if cuts were required or budgets exceeded, DsPH had clear authority with regards to public health prioritisation. Following the reforms, they are expert advisers to elected members. Leadership for public health is more dispersed; decision-making is now more complex, and arguably subject to greater political ideology and personal interest. There may also be unforeseen consequences arising from the outcomes-based scrutiny of complex public health interventions, and from increased insecurity within the provider landscape. Many public health interventions require a long time-frame in which to bring about significant population health improvements. This doesn’t sit well with the short-termism of contemporary politics. As local councils struggle to cope with tighter budgets, public health officers may find it harder to convince their elected members of the added value of some of the public health services they commission.

Our research has highlighted the huge amount of change occurring in the commissioning (and decommissioning) of health improvement services in England. Whilst it will be important for the wider health system that key public health services are protected and improved (for instance, in smoking cessation, weight management and sexual health services), the public health specialists will need to capitalise on bringing about positive change through closer integration with the strategy and activity of the council. Commissioning for health improvement requires commissioners to focus on the modifiable determinants of health, taking a pro-active approach to improving individuals’ life chances and reducing social inequalities, rather than waiting until people are already ill and commissioning reactively. Local councils, due to their wider scope and responsibilities, are better placed than the NHS with its largely clinical orientation, to address a broad range of determinants, such as lifestyles, community, local economy and activities [ 30 ]. Our research, like the many case studies highlighted in a range of Local Government Association reports [ 39 – 42 ] has identified a range of positive examples where stronger and more direct public health involvement and influence across councils has brought about new opportunities. In their new ‘home’, and with the right support from their council, public health officers can be afforded the freedom to approach public health challenges in new ways. Local councils are also more adept than NHS organisations at broader level consultation and community engagement, which might afford new opportunities in line with the Ottawa Charter recommendations for public participation and empowerment [ 43 ].

The NHS continues to have a vital part to play in population health improvement, and the reforms hoped to bring about improved synergies between public health, NHS and social care. However, with public health moving ‘arms-length’ to the NHS, both health services commissioners and providers are becoming more remote to the local public health systems. Moreover, the vital co-ordination role of HWBs is not always working well locally [ 10 ]. Some health improvement services could, as a result, end up being disconnected from each other and from wider support. Similarly, services that are crucial to the achievement of health service objectives (such as reducing premature mortality from the major causes of death), but which are commissioned or provided by the council (e.g. weight management, smoking cessation and alcohol services), are at risk of being cut or changed. Our research has highlighted that there is much change in the way public health commissioning is done, who is doing it, and what is commissioned. Time (and further research) will tell if these changes are to result in improved outcomes and reduced inequalities. However, until there is a strong sense of shared ownership across local systems, and ‘whole system’ commissioning at local level, any opportunities afforded by the reforms to the public health system might be outweighed by the challenges of fragmentation and budget cuts.

We found that the system created by the reforms was confused, continually changing, and - from the point of view of commissioning - more fragmented than before. In some ways, the move of public health into councils has brought about some of the opportunities associated with decentralisation – in particular, allowing for increased flexibility. However, most public health commissioners were essentially moved from one local organisation (NHS), to another (council), so the comparisons with decentralisation are limited. In this new local environment, former public health capacity has been at the same time freed and stifled. Public health commissioning is being more strongly influenced by a new set of decision-makers in the form of democratically elected councillors, with their own local knowledge, ideologies, and experiences. Meanwhile, many councils are bringing a more business-oriented approach to bear on public health commissioning, with greater scrutiny of outcomes in relation to spend. This is challenging the public health specialists and provider organisations, and changing the shape of health improvement services. Whilst we can expect to see increasing change and variation in services across England, it is far from clear what impact this will have on outcomes and on variations in outcomes.

The Duty of Best Value makes clear that councils should consider overall value – including social value – when considering service provision. Under the general Duty of Best Value, local authorities should “make arrangements to secure continuous improvement in the way in which its functions are exercised, having regard to a combination of economy, efficiency and effectiveness” https://www.gov.uk/government/publications/best-value-statutory-guidance--4.

Abbreviations

Clinical Commissioning Group

Department of Health

Directors of Public Health

Health and Wellbeing Board

National Health Service

Primary care trust

Public Health England

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Acknowledgements

We are grateful to our case study sites for allowing us to spend so much time with them and for being so open in discussing their work. We are also grateful to the survey and interview respondents for giving up their valuable time to respond to our questions. The project’s stakeholder group have provided valuable guidance and support throughout the research.

This research on which this article is based was funded by the UK Department of Health. The views expressed are those of the researchers and not necessarily those of the Department of Health.

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Data will not be shared due to the difficulties of ensuring anonymity of sites and individuals.

Authors’ contributions

SP managed the research on which this article is based. EG, AC, DB, NP and LJ carried out the research. LJ conducted the analysis of the surveys. EG, AC, DB, NP and SP were involved in analysing the case study data. EG drafted the manuscript. EG, SP, AC, DB, NP and LJ helped to interpret the data and edit the manuscripts. EG, SP, AC, DB, NP and LJ approved the final version of the manuscript.

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The authors declare that they have no competing interests.

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Not applicable.

Ethics approval and consent to participate

Approval was sought and granted from the University of Kent’s School of Social Policy, Sociology and Social Research Ethics Board (SRCEA No. 112), and research governance approval was obtained for each case study site in respect of NHS interviewees from the Health Research Authority (15 July 2015/182754). Written, signed consent was obtained from the heads (leaders and/or chief executives) of all local councils within the case study areas and every individual interviewed within the study.

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Gadsby, E.W., Peckham, S., Coleman, A. et al. Commissioning for health improvement following the 2012 health and social care reforms in England: what has changed?. BMC Public Health 17 , 211 (2017). https://doi.org/10.1186/s12889-017-4122-1

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Peer-reviewed

Research Article

Association between the 2012 Health and Social Care Act and specialist visits and hospitalisations in England: A controlled interrupted time series analysis

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, United Kingdom, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America

Roles Investigation, Methodology, Writing – review & editing

Affiliation Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Affiliation Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, United Kingdom

Roles Conceptualization, Investigation, Methodology, Supervision, Validation, Writing – review & editing

• James A. Lopez Bernal, 
• Christine Y. Lu, 
• Antonio Gasparrini, 
• Steven Cummins, 
• J. Frank Wharham, 
• Steven B. Soumerai

• Published: November 14, 2017
• https://doi.org/10.1371/journal.pmed.1002427
• Reader Comments

26 Feb 2018: The PLOS Medicine Staff (2018) Correction: Association between the 2012 Health and Social Care Act and specialist visits and hospitalisations in England: A controlled interrupted time series analysis. PLOS Medicine 15(2): e1002527. https://doi.org/10.1371/journal.pmed.1002527 View correction

The 2012 Health and Social Care Act (HSCA) in England led to among the largest healthcare reforms in the history of the National Health Service (NHS). It gave control of £67 billion of the NHS budget for secondary care to general practitioner (GP) led Clinical Commissioning Groups (CCGs). An expected outcome was that patient care would shift away from expensive hospital and specialist settings, towards less expensive community-based models. However, there is little evidence for the effectiveness of this approach. In this study, we aimed to assess the association between the NHS reforms and hospital admissions and outpatient specialist visits.

Methods and findings

We conducted a controlled interrupted time series analysis to examine rates of outpatient specialist visits and inpatient hospitalisations before and after the implementation of the HSCA. We used national routine hospital administrative data (Hospital Episode Statistics) on all NHS outpatient specialist visits and inpatient hospital admissions in England between 2007 and 2015 (with a mean of 26.8 million new outpatient visits and 14.9 million inpatient admissions per year). As a control series, we used equivalent data on hospital attendances in Scotland. Primary outcomes were: total, elective, and emergency hospitalisations, and total and GP-referred specialist visits. Both countries had stable trends in all outcomes at baseline. In England, after the policy, there was a 1.1% (95% CI 0.7%–1.5%; p < 0.001) increase in total specialist visits per quarter and a 1.6% increase in GP-referred specialist visits (95% CI 1.2%–2.0%; p < 0.001) per quarter, equivalent to 12.7% (647,000 over the 5,105,000 expected) and 19.1% (507,000 over the 2,658,000 expected) more visits per quarter by the end of 2015, respectively. In Scotland, there was no change in specialist visits. Neither country experienced a change in trends in hospitalisations: change in slope for total, elective, and emergency hospitalisations were −0.2% (95% CI −0.6%–0.2%; p = 0.257), −0.2% (95% CI −0.6%–0.1%; p = 0.235), and 0.0% (95% CI −0.5%–0.4%; p = 0.866) per quarter in England. We are unable to exclude confounding due to other events occurring around the time of the policy. However, we limited the likelihood of such confounding by including relevant control series, in which no changes were seen.

Conclusions

Our findings suggest that giving control of healthcare budgets to GP-led CCGs was not associated with a reduction in overall hospitalisations and was associated with an increase in specialist visits.

Author summary

Why was this study done.

• In 2012, the government introduced major reforms to the National Health Service (NHS) in England, which handed budgets for specialist care to GP-led organisations known as Clinical Commissioning Groups.
• This gave GPs a major new role in purchasing hospital-based specialist medical care for patients, in addition to their existing role as “gatekeepers” to specialist care.
• An expected effect of this policy was that there would be a shift in care away from expensive hospital-based care and towards the community.
• Our study aimed to evaluate the potential impact of these reforms on levels of hospital activity including outpatient visits to specialists and inpatient admissions.

What did the researchers do and find?

• We examined trends in all NHS specialist visits and hospital admissions between 2007 and 2015 in order to examine changes in trends following the reforms.
• We included equivalent trends in Scotland, where the reforms did not occur, as a control series.
• We found no change in hospital admissions in either country.
• However, in England we found an increase in the trend of outpatient specialist visits following the reforms, equivalent to approximately 3.7 million additional specialist visits between the time the policy was implemented and the end of the study period (compared to expected).

What do these findings mean?

• Our findings suggest that giving control of healthcare budgets to GP-led CCGs was not linked to a decrease in hospital admissions and was associated with an increase in outpatient specialist visits.
• Further research is needed to establish the appropriateness of these visits and the reasons for the increase.
• However, these findings suggest that other interventions may be needed in order to shift more patient care into the community.

Citation: Lopez Bernal JA, Lu CY, Gasparrini A, Cummins S, Wharham JF, Soumerai SB (2017) Association between the 2012 Health and Social Care Act and specialist visits and hospitalisations in England: A controlled interrupted time series analysis. PLoS Med 14(11): e1002427. https://doi.org/10.1371/journal.pmed.1002427

Academic Editor: Aziz Sheikh, Edinburgh University, UNITED KINGDOM

Received: April 26, 2017; Accepted: October 5, 2017; Published: November 14, 2017

Copyright: © 2017 Lopez Bernal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This study was funded by a UK Medical Research Council Population Health Scientist Fellowship awarded to JLB – Grant Ref: MR/L011891/1, https://www.mrc.ac.uk/ . No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: CCG, Clinical Commissioning Group; CITS, controlled interrupted time series; DH, Department of Health; GP, general practitioner; HES, Hospital Episode Statistics; HSCA, Health and Social Care Act; NHS, National Health Service; PCT, primary care trust; RECORD, REporting of studies Conducted using Observational Routinely-collected health Data; SHA, Strategic Health Authority; SMR, Scottish Medical Records

Introduction

The 2012 Health and Social Care Act (HSCA) in England has been described as “the biggest and most far reaching [reorganisation] in the history of the NHS” [ 1 , 2 ]. The reforms centred around the introduction of general practitioner (GP) led Clinical Commissioning Groups (CCGs), which received about two-thirds of the National Health Service (NHS) budget (£66.8 billion in 2015–2016) to commission (plan and contract) secondary care, including hospital and specialist services [ 1 ]. CCGs represent all GP practices in their local area, and the key difference from the previous commissioning structures was purported to be a major new role for GPs as key decision makers in the commissioning process [ 1 , 3 , 4 ].

Health policy experts and parliamentary and professional bodies have hypothesised that GP-led commissioning could potentially lead to reductions in referrals to specialist care, as either an intended or unintended consequence of the Act [ 5 – 10 ]. They theorise that by giving the gatekeepers, who control access to specialist care, a greater role in budget holding and the purchasing of specialist care, they may be incentivised to reduce referrals [ 5 , 6 ]. Indeed, Smith and Mays suggest that the primary rationale for GP-led commissioning is to encourage a shift away from expensive secondary care towards more community-based care [ 6 ]. Furthermore, 2 out of the 3 main reasons cited by the government for introducing the reforms centred around a need to control costs, although the mechanisms by which GP-led CCGs would achieve cost savings were not made explicit [ 4 ]. While the potential for much-needed cost savings in the NHS as a result of reduced secondary care activity has been viewed positively, some—including the National Audit Office and the Royal College of Surgeons—have raised concerns that a reduction in referrals as a consequence of the HSCA and policies introduced by CCGs could result in inequitable rationing of care and missed diagnoses and that their role in commissioning presents GPs with a conflict of interest [ 7 – 10 ].

CCGs and GPs could reduce secondary care activity through various means, including restricting referral criteria, developing community-based care models, investing in preventative healthcare, or promoting services to prevent readmissions [ 6 , 9 , 10 ]. There also exist potential incentives for them to do so: reducing expensive care would allow CCGs to invest savings in other services. Furthermore CCGs are required to maintain a surplus; otherwise, they cannot access additional funding in the form of a “Quality Premium” of up to £5 per person within the population covered by the CCG person [ 11 ]. In addition, there are incentives to individual GPs: savings from reduced specialist visits and hospitalisations could allow investment in community-based services provided by GP practices themselves; also, some CCGs have introduced direct payments of £6,000–£11,000 to GP practices for reducing referral rates [ 7 , 8 ]. Nevertheless, whether these provide a real incentive in practice depends on how engaged GPs feel with the new commissioning organisations, how much responsibility they feel for their budgets, and how much influence they have on the commissioning process. Previous policies that have begun with an intention to place GPs at the centre of commissioning have ultimately resulted in the formation of bureaucratic bodies that have become detached from local practitioners [ 6 ]. Furthermore, given existing evidence that increasing GP workload may increase referral rates, it is possible that the increased administrative burden associated with their new commissioning role could instead result in an increase in referrals [ 12 , 13 ].

We use a controlled interrupted time series (CITS) design to compare changes in the trends of specialist referrals and hospital admissions in England before and after the HSCA with those in Scotland, where the reforms did not occur. We hypothesise that the 2012 HSCA was associated with a reduction in specialist visits and hospitalisations.

Ethical approval was obtained from the London School of Hygiene and Tropical Medicine Observational/Interventions Research Ethics Committee (LSHTM Ethics Ref: 10505).

The intervention

The 2012 HSCA introduced broad ranging and complex reforms to the NHS and public health services in England. These have been described in more detail elsewhere [ 1 , 2 , 4 , 14 , 15 ]. The principal change was in the way secondary care services were commissioned within the NHS. Prior to 2012, regional healthcare administrative bodies known as primary care trusts (PCTs) and Strategic Health Authorities (SHAs) were responsible for all commissioning. These were abolished as part of the Act and were replaced by CCGs. CCGs are led by a governing body, which includes a representative from each member GP practice, lay members, a secondary care doctor, and a registered nurse [ 3 ]. CCGs were first introduced in shadow form (working alongside PCTs) in April 2012 following the enactment of the HSCA; they then took over full budgetary responsibility in March 2013 [ 1 ].

While a control is not required in interrupted time series studies, the primary comparison being between preintervention and postintervention trends within the study population, a control population can help to exclude additional confounding events and cointerventions. Healthcare is a largely devolved power in the United Kingdom and the HSCA only applied to England; therefore, we considered the other 3 nations of the UK (Northern Ireland, Scotland, and Wales) as potential controls. These are neighbouring countries with similar population demographics ( S1 Table ), similar health systems, and shared political structures. Data equivalent to those in England were not available from Northern Ireland; therefore, it was excluded. We chose Scotland as the control, as preintervention data were more stable than for Wales. We also include an analysis as a supplementary appendix with Wales as the control ( S2 Table , S1 and S2 Figs).

Data and study population

We obtained quarterly data on all hospital admissions and outpatient specialist visits in NHS hospitals in England between April 2007 and December 2015 from the Health and Social Care Information Centre: Hospital Episode Statistics (HES) [ 16 ]. Hospital admissions include all inpatients in NHS hospitals as well as NHS-funded inpatients in the private sector. NHS outpatient activity in England is hospital based; specialist visit data include outpatients in English NHS hospitals and NHS-funded outpatients in the private sector. Our outcomes were total hospital admissions, elective (planned) and emergency (unplanned) admissions, total first specialist visits (excluding follow-up appointments), and GP-referred first specialist visits. Equivalent data for Scotland were obtained from the NHS Scotland Information Services Division: Scottish Medical Records (SMR) [ 17 ]. We obtained demographic data for England and Scotland from the Office for National Statistics including midyear population estimates (for denominators), age and sex distribution, crude birth rate, and crude death rate [ 18 ]. The Scottish hospital admission data did not include obstetric and psychiatric hospitals and the outpatient visit data did not include visits to nurses, dentists, or other allied health professionals. We therefore excluded these categories from the English data to make the 2 datasets comparable. Data quality reports identified a coding error in the outpatient data prior to April 2010 (3 years before the introduction of the policy); we therefore excluded these data from the analysis [ 19 ]. The raw data are provided in the supplementary appendix ( S1 and S2 Data ). A complete list of the data codes and algorithms used in the data extraction is also provided in the supplementary appendix ( S3 and S4 data ).

Statistical analysis

We used a CITS design, which allowed us to control both for preintervention trends in the outcome and for potential confounding events that would have affected both the control and the study groups. Although a Poisson distribution is assumed for individual counts, we had very large numbers and the aggregate data were well approximated by a Gaussian distribution (log transformed). Therefore, we used a simple linear segmented regression model to estimate the change in trend in hospital admissions and outpatient visits following the introduction of the HSCA [ 20 ]. In order to account for the year during which CCGs were in shadow form, we allowed a one-year phase-in period by excluding the second quarter of 2012 to the first quarter of 2013 from the analysis. We modelled the association as a slope change rather than an immediate level change because choice of providers and referral patterns were likely to change gradually when existing contracts expired and new models of care developed [ 1 ]. Adjustments were made for any seasonal effect using a Fourier term [ 20 ].

We first estimated the slope changes in England and in Scotland independently. We then used an interaction model for the CITS to estimate the additional trend change in England over and above any change in Scotland, while controlling for any difference in the preintervention trends of the 2 groups ( S1 Text ). We examined the preintervention data a priori for linearity and autocorrelation at different lags using scatterplots, plots of residuals, and partial autocorrelation functions [ 20 ]. A linear trend provided a reasonable fit for all outcomes in the primary model. We included an autoregressive term at the appropriate lag to adjust for any detected autocorrelation. All analyses were conducted using Stata version 14.

The original study protocol from the ethics application is available as a supplementary appendix ( S1 Protocol ). The analysis has only differed from this protocol in that Scotland was selected as the primary control, as it had the most stable data; Wales was included as an additional control following reviewers’ recommendations. Northern Ireland was not included as equivalent data to that in England was not available. Furthermore, in this protocol, we also proposed including patient experience measures as secondary outcomes; this was ultimately not included within the current study but we plan to conduct a future study looking at the potential impact on patient experience.

This study is reported as per the REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement ( S1 Checklist ).

Population characteristics

Age and sex distributions were similar in both England and Scotland ( Table 1 and S1 Table ). Both populations were slowly aging; the proportion aged 60 or older increased from 21.6% to 23.0% in England and from 22.3% to 24.0% in Scotland. The crude birth rate was consistently about 1.8 per 1,000 higher in England than in Scotland while the crude death rate was consistently about 1.5 per 1,000 lower.

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https://doi.org/10.1371/journal.pmed.1002427.t001

Changes in outpatient specialist visits

Changes in trends of specialist visits are shown in Fig 1 and Table 2 . Absolute counts and the rate per 1,000 for each quarter are presented in Table 3 . In England, total specialist visits rose slowly by 0.5% per quarter (from 84.7 per 1,000 in quarter 2 [Q2] 2010 to 87.2 per 1,000 in Q1 2012) in the baseline. After the intervention, they rose approximately 3.6 times faster at 1.5% per quarter (from 90.0 per 1,000 in Q2 2013 to 104.6 per 1,000 in Q4 2015). This was equivalent to an increase in slope (additional quarterly increase) of 1.1% (95% CI 0.7%–1.5%), which resulted in a 12.7% higher rate of specialist visits (647,000 additional visits) by the end of the postintervention period in Q4 2015, compared to the underlying (counterfactual) trend. The slope increase was even more marked for GP-referred visits. During the preintervention period, these had a flat trend at 48.3 visits per 1,000 per quarter (trend 1.000, 95% CI 0.998–1.002). After the intervention, this trend increased by 1.6% per quarter (from 49.1 per 1,000 in Q2 2013 to 57.6 per 1,000 in Q4 2015). This was equivalent to an increase in slope of 1.6% (95% CI 1.2%–2.0%) per quarter, which resulted in a 19.1% higher than expected rate of specialist visits (507,000 additional visits) by the end of the study period. For those outcomes that showed strong evidence of a trend change (total and GP-referred specialist visits in England), we have presented observed compared to expected counts in the postintervention period in Table 4 . Total specialist visits had weak evidence of seasonal effect with peaks during Q3 (Fourier sin wave p = 0.055, cos wave p = 0.010).

Red o = England, blue x = Scotland. Lines = deseasonalized linear trend. Vertical lines delineate the intervention phase (between quarter 2 [Q2] 2012 and Q2 2013). The data underlying this figure are presented in Table 3 . GP, general practitioner.

https://doi.org/10.1371/journal.pmed.1002427.g001

https://doi.org/10.1371/journal.pmed.1002427.t002

https://doi.org/10.1371/journal.pmed.1002427.t003

https://doi.org/10.1371/journal.pmed.1002427.t004

Specialist visits in Scotland, however, showed no significant change after the policy. Total specialist visits and GP-referred specialist visits almost level at about 72 per 1,000 per quarter (preintervention trend 1.002, 95% CI 0.999–1.006; postintervention trend 1.000, 95% CI 0.996–1.003) and 47 per 1,000 per quarter (preintervention trend 1.002, 95% CI 0.998–1.005, postintervention trend 1.000, 95% CI 0.996–1.003), respectively, throughout the study period.

After controlling for trends in Scotland, the CITS analysis produced similar results. The magnitude of the change in slope in total specialist visits in England increased slightly to 1.4% (95% CI 0.6%–2.1%) per quarter (a 15.9% higher rate by the end of the study period). The change in trend in GP-referred specialist visits increased to 1.9% (95% CI 1.1%–2.7%) per quarter (a 22.5% higher rate than expected by the end of the study period).

The magnitude of the differential increase in trend in England was even greater when using Wales as a control series, although this was partly due to an independent reduction in the trend in Wales ( S2 Table and S1 Fig ).

Changes in inpatient hospitalisations

Changes in trends in hospitalisations following the HSCA are shown in Fig 2 and Table 2 . Absolute counts and the rate per 1,000 for each quarter are presented in Table 5 . In England, there were slowly increasing trends in all hospitalisations during the baseline period. Total hospitalisations increased by 0.5% per quarter (from 60.1 per 1,000 in Q2 2007 to 65.5 per 1,000 in Q1 2012), elective admissions increased by 0.6% per quarter (from 31.7 to 35.7 per 1,000), and emergency admissions increased by 0.3% per quarter (from 22.9 to 24.5 per 1,000). Total hospitalisations and emergency hospitalisations had a seasonal effect with winter peaks in Q4 (emergency hospitalisations Fourier terms: sin wave p = 0.002, cos wave p = 0.039). There were no statistically significant changes in any of these trends following the HSCA. Slope changes were −0.2% (95% CI −0.6%–0.2%), −0.2% (95% CI −0.6%–0.1%), and 0.0 (95% CI −0.5%–0.4%) per quarter for total, elective, and emergency hospitalisations, respectively.

Red o = England, blue x = Scotland. Lines = deseasonalized linear trend. Vertical lines delineate the intervention phase (between quarter 2 [Q2] 2012 and Q2 2013). The data underlying this figure are presented in Table 5 .

https://doi.org/10.1371/journal.pmed.1002427.g002

https://doi.org/10.1371/journal.pmed.1002427.t005

Trends in Scotland were flatter during the baseline. Total hospitalisations increased by 0.2% per quarter (from 54.0 to 55.9 per 1,000), elective admissions increased by 0.20% per quarter (from 29.9 to 31.1 per 1,000), and emergency admissions increased by 0.2% per quarter (from 24.1 to 24.8 per 1,000). Again, there was no evidence of any change in these trends after the HSCA: slope changes were −0.3% (95% CI −0.7%–0.2%), −0.1% (95% CI −0.7%–0.4%) and −0.5% (95% CI −1.1%–0.1%), respectively.

The results of the CITS analysis were again similar. The differential slope changes in England (that is, the additional quarterly change following the HSCA after controlling for trends in Scotland) were: 0.0% (95% CI −0.6%–0.6%) per quarter for total hospitalisations, −0.1% (95% CI −0.7%–0.5%) per quarter for elective hospitalisations, and 0.2% (95% CI −0.5%–1.0%) per quarter for emergency hospitalisations.

Results using Wales as a control instead of Scotland were similar ( S2 Table and S2 Fig ).

To our knowledge, this is the first study of the potential impact on secondary care activity of a universal, national policy that gave control of an unprecedented two-thirds of the English NHS budget to GP-led CCGs. Contrary to the underlying hypothesis, we found no evidence of a reduction in hospitalisations or specialist visits in England following the HSCA. Moreover, we found evidence of an increase over and above the underlying trend in specialist visits in England, with no comparable increase in Scotland, where this policy did not occur. This increase was equivalent to approximately 3.7 million additional specialist visits since the policy was implemented (compared to those expected), of which the majority (approximately 2.9 million) were GP referred.

We used a robust CITS design. By modelling long-term underlying trends, we controlled for secular changes in practice and artefactual changes due to regression to the mean. Selection bias is only an issue in the unlikely event that the population changed suddenly and substantially in contrast to the underlying trend and differentially from trends in the control. S1 Table shows that population characteristics maintained stable trends over the study period, suggesting that this is not an alternative explanation for our findings. Furthermore, we controlled for unknown confounding events coincident with the policy by including Scotland as a comparator. Our study is also based on a very large population with stable trends in the outcomes before and after the intervention; therefore, we are well powered to detect effects. Finally, the compulsory nature and large scale of the intervention again limits selection bias and increases both the internal and external validity of our results.

Our study has several limitations. First, it is possible that the observed changes in trends could have been due to other concurrent policies targeting these outcomes but that did not occur in the control population. Following a literature review, we found one such national policy: an “enhanced service” encouraging GPs to provide extra support for patients deemed at risk of unplanned admission to the hospital; however, this was introduced a year after the Act and only targeted 1 of our outcomes (emergency admissions) in which we did not see a change [ 21 ]. We also considered the fact that the Act included a broad range of changes alongside GP-led commissioning and that observed changes in trends might be due to other aspects of the reforms. However, most of the other changes were support structures for the changes to commissioning (such as accountability systems and services regulating specialist care providers) that would be considered integral to the intervention itself, or structural changes to public health and preventative services that are likely to have little direct impact on hospitalisations or specialist visits [ 2 , 4 ]. Second, smaller-scale effects on certain specialties or diagnoses may have been diluted by the scale of our data. However, as the first study of this nationwide policy, and given the government’s aim to address rising demands and treatment costs within the NHS as a whole [ 4 ], our goal was to examine the association between the policy and trends in specialist visits and hospitalisations. Third, while we have nearly 3 years of postintervention data, it is possible that some effects of GP-led commissioning have not yet become evident. For example, GPs may have chosen to invest more in preventative services, which can take several years to result in population-level reductions in disease. Finally, our study uses routine data that were not specifically created to answer this research question. However, we use the data in high-level aggregate analysis and only use final, rather than provisional, data, which are regarded as complete. Therefore, quarterly changes are unlikely to be due to issues such as data completeness or misclassification [ 22 ].

Following the introduction of the HSCA, the Department of Health (DH) called for research to evaluate its impact [ 23 ]. Nevertheless, initial proposals were rejected, and, while the DH has published an evaluation focussing on the processes of the reforms, we were unable to find any studies looking at the impact of this policy on hospital activity [ 23 , 24 ]. There have been studies of previous policies that handed greater budgetary responsibility to GPs in the UK and in Israel [ 25 – 32 ]. However, the results of these studies are mixed and difficult to interpret, as all used simple pre-post designs, which do not take into account underlying trends in hospitalisations or specialist visits, and they examined smaller policies, which were voluntary and subject to volunteer selection bias. The lack of control for underlying trends in these studies is particularly important because study groups often appear to have had unusually high referral rates prior to the intervention (partly because budget allocations based on existing referral rates incentivized practices to inflate referrals before becoming budget holders) [ 27 ]. Any reduction could therefore have simply been due to regression to the mean.

Our findings suggest that, on a national scale, the concerns raised around restrictions in access to specialist services and rationing of care have not been realised. However, the lack of decrease in hospitalisations and the unanticipated increase in specialist visits also suggest the theorised shift in care away from hospitals to less expensive community settings does not appear to have occurred and, if anything, the increase in specialist visits may have led to cost increases. There are a number of possible reasons why specialist visits and hospitalisations did not decrease. First, while CCGs intended to increase clinical involvement in commissioning, survey evidence suggests that some GPs do not feel fully engaged with their CCG [ 33 ]. For example, the majority of GPs are CCG members but do not have a formal role in the governing body, and this group reported much lower levels of influence and ownership than governing body members. A lack of engagement with members may mean that many GPs feel detached from their CCG and under little pressure to make cost savings or unable to influence the way local health services are managed [ 33 ]. Second, the financial incentive for CCGs to reduce costs and GPs to change referral patterns may have been too small or too indirect, and, while practice income may have increased by shifting some care from hospitals to community-based care provided by GPs, concerns about potential conflicts of interest could have discouraged this [ 7 ]. Finally, it is also possible that referrals to specialists were already appropriate prior to the intervention, resulting in little scope for further reduction. This is supported by evidence that variations in referral rates in the NHS are primarily explained by characteristics of the patient population and not factors affecting GP services [ 34 ].

The increase in specialist visits in our study was surprising and may be an unintended consequence of the policy. We identified annual data on NHS costs for outpatient specialist visits from an independent source ( S3 Fig ). This also appears to show an increase in costs, corroborating our findings regarding upward trends in specialist visits. One explanation might be that the new responsibility for managing budgets has inadvertently increased administrative workload for GPs, resulting in less time to see patients. Under such circumstances, GPs may reduce their threshold for referral to avoid missing a diagnosis. There is some existing evidence to suggest that increased workload and reduced consultation time is associated with increased referral rates [ 12 , 13 ], although other studies have shown no effect [ 35 ]. We considered decreasing GP numbers or increasing supply of specialists as other potential explanations for this finding. However, although there was a slight decrease in the number of full-time equivalent GPs (from 0.69 to 0.67 per 1,000 population) between 2009 and 2010, this does not coincide with the increase in specialist visits, and the number of GPs remained stable from 2010 and, in fact, increased back to 0.69 per 1,000 population in 2014 [ 36 ]. Number of specialists (full-time equivalent consultants) increased gradually over the study period from 0.67 per 1,000 population in 2009 to 0.76 per 1,000 population in 2014 and there was no deviation in this trend around the introduction of the HSCA [ 37 ].

In conclusion, we found no evidence that the introduction of GP-led commissioning in England was associated with a reduction in overall hospitalisations or specialist visits. In fact, there was an increase in specialist visits, which appears to have been paralleled by an increase in expenditure. This study begins to decipher the macro effects of these significant reforms to the organisation of NHS commissioning. However, many questions remain unanswered. Examples include the appropriateness of any change in rates of specialist visits and hospitalisations, the effect of this change on health outcomes, whether changes differed according to CCG and why, and the generalizability of our findings to other health systems. This study alone is unable to determine whether the HSCA can be regarded as a good or bad policy, and further research is needed to evaluate other important outcomes such as costs and quality of care. Nevertheless, in the context of similar findings from other large-scale health policy experiments [ 38 ], more effort may be needed to target specific costly or poorly evidenced practices (such as tonsillectomy, tympanostomy, or antibiotics prescribed for viral infections) rather than to count on broad, system-wide policy changes that often have unintended consequences.

Supporting information

S1 table. population characteristics of england and scotland, 2007–2014..

https://doi.org/10.1371/journal.pmed.1002427.s001

S2 Table. Trend changes in specialist visits and hospitalisations following the intervention in England versus Wales.

Coefficients for trend change are relative change in the slope gradient following the intervention. Trend change study versus control is the slope change in England over and above any change in Wales accounting for differences in baseline trends. All segmented regression models used log transformed Gaussian distribution.

https://doi.org/10.1371/journal.pmed.1002427.s002

S1 Fig. Time series of outpatient specialist visits in England and Wales.

Red o = England, blue x = Wales. Lines = deseasonalized linear trend. Vertical lines delineate the intervention phase (between quarter 2 [Q2] 2014 and Q2 2013).

https://doi.org/10.1371/journal.pmed.1002427.s003

S2 Fig. Time series of inpatient hospitalisations in England and Wales.

https://doi.org/10.1371/journal.pmed.1002427.s004

S3 Fig. National Health Service (NHS) reference costs.

https://doi.org/10.1371/journal.pmed.1002427.s005

S1 Text. Controlled interrupted time series model.

https://doi.org/10.1371/journal.pmed.1002427.s006

S1 Checklist. REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement.

https://doi.org/10.1371/journal.pmed.1002427.s007

S1 Protocol.

https://doi.org/10.1371/journal.pmed.1002427.s008

https://doi.org/10.1371/journal.pmed.1002427.s009

https://doi.org/10.1371/journal.pmed.1002427.s010

https://doi.org/10.1371/journal.pmed.1002427.s011

https://doi.org/10.1371/journal.pmed.1002427.s012

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• 2. Health and Social Care Act. Health and Social Care Act 2012 [Chapter 7]. Available from: http://www.legislation.gov.uk/ukpga/2012/7/pdfs/ukpga_20120007_en.pdf .
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• 17. NHS Scotland Information Services Division. SMR Datasets [14th March 2016]. Available from: http://www.ndc.scot.nhs.uk/Data-Dictionary/SMR-Datasets/ .
• 18. Office for National Statistics. Population estimates [14th March 2016]. Available from: http://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates .
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• 21. NHS England. Avoiding unplanned admissions enhanced service: Guidance and audit requirements 2014 [05/07/2014]. Available from: http://www.nhsemployers.org/~/media/Employers/Publications/Avoiding%20unplanned%20admissions%20guidance%202014-15.pdf .
• 22. Health and Social Care Information Centre. Hospital Episode Statistics (HES) Analysis Guide 2015. Available from: http://content.digital.nhs.uk/media/1592/HES-analysis-guide/pdf/HES_Analysis_Guide_Jan_2014.pdf .
• 24. Department of Health. Post-legislative assessment of the Health and Social Care Act 2012: Memorandum to the House of Commons Health Select Committee. In: Health Do, editor. 2014.
• 33. Robertson R, Ross S, Bennett L, Holder H, Gosling J, Curry N. Risk or reward? The changing role of CCGs in general practice. King's Fund and Nuffield Trust, 2015.
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Was the Health and Social Care Act as bad as we thought?

The Health and Social Care Act 2012 set out the single biggest collection of reforms that the NHS had seen since its creation in 1948.

Focusing on patient-centred care, the legislation looked to improve quality and outcomes whilst reducing inequalities through clinically-led commissioning, but it was highly controversial and critics were quick to lambast it as a disaster .

With commentators scrutinising the act for a supposed erosion of the NHS’s core value - free universal provision at the point of delivery - nearly a decade later we ask: was it really as bad as we thought?

“ It cost billions of pounds and there were significant criticisms of its aims and impact, but the promotion and digitisation of healthcare is a legacy that has fundamentally shifted the NHS’s approach. ”

The biggest piece of legislation of its time

When the UK Government introduced the principles of the Health and Social Care Act in a 2010 white paper, its key aim was to modernise the NHS to meet rising demands, increased treatment costs and to decentralise health spending to specially-formed Clinical Commissioning Groups (CCGs). A crucial objective was to build sustainability for the future.

This was a substantial piece of legislation and it was bound to run into issues, not only because it worked towards the “transferring of public assets and revenue streams to public sector workers ”, but also because it was set to save billions per year in clunky administrative costs and shake-up healthcare management as we knew it.

What didn’t work?

Significant overspend.

In theory, the move towards clinically-led commissioning sounded positive - putting funding straight into the hands of those providing the services to use it how they best saw fit. This would mean that, for example, instead of going through a lengthy procurement process, CCGs could commission services directly for their communities with limited red tape.

However, not only did the proportion of CCGs reporting a budget deficit jump from 15% to 29% between 2016 and 2017, some reported deficits of up to £60m - totalling over £100,000 in excess spending per day. This was perhaps expected in part as non-specialists were now in charge of running the business side of healthcare, but had real impact on healthcare provision and decisions about what to fund or cut to balance the books.

Worsening health inequality

One of the Health and Social Care Act’s key aims was to reduce health inequalities across the entirety of England, primarily through the involvement of local authorities, who now had increased public health functions. This meant CCGs could respond to their specific geographical health concerns, for example to work on interventions to mitigate higher levels of child obesity rates and lung cancer prevalence in poorer areas of England.

However, best practice did not ensue across all 223 trusts. CCGs were responsible for the full financial risk , and research undertaken in 2016 showed that health inequalities were persisting with patchy success on the part of CCGs to reduce it. In fact, in September 2021, Health Secretary Sajid Javid admitted that the COVID-19 pandemic had laid bare underlying health inequalities in England, which is suffering from “the disease of disparity”.

What did work?

Local consultation.

In some ways, being able to target location-specific health needs by giving funding to those with the most on-the-ground insight was positive for many communities, and CCGs have been successful in some localities.

Hull CCG , for example, canvassed local people about their healthcare with a view to incorporating suggestions into their service offering. The CCG also appointed “patient ambassadors” who worked directly in the community and fed back patient voices on local interventions as time went on. This led to patient and community-centered service redesign and informed planning for new services.

The starting blocks for NHS Digital

NHS Digital - formerly the Health and Social Care Information Centre - was constructed on the back of the Health and Social Care Act 2012, and its establishment saw an overhaul in development and operation of digital technology and data services across the NHS in England. The rationale was that by having the right IT services in place, patients would get better care, and data could be used to further improve healthcare outcomes.

NHS Digital has been instrumental in creating technological infrastructure to keep the health service running and link systems together, publishing standards like the Data Security and Protection Toolkit, and using health and care data to improve understanding of health problems and support medical research.

Foundations for integrated care

The Health and Social Care Act also created the foundations for the 2021 Health and Care Bill, which will see the mandatory introduction of Integrated Care Systems (ICSs) across England. These Bill proposes a knitting together of local government, individual NHS trusts, and social care providers with the aim of created an integrated care framework that favours shared responsibility of health and care providers following years of patchwork care in the community. If voted through by parliament, ICSs could be mandated as early as 2022.

How successful was it on balance?

The Health and Social Care Act 2012 restructured the NHS. It cost billions of pounds and there were significant criticisms of its aims and impact, but the promotion and digitisation of healthcare is a legacy that has fundamentally shifted the NHS’s approach.

There were some leaps of progress catalysed by the act, but there is still a long way to go in ensuring best practice and minimum standards for making digital truly work for the NHS. Undoubtedly, the NHS is improving in both its approach and delivery of healthcare, becoming a more modern service, fit for the needs of a more diverse population with complex needs, but only time will tell whether the foundations built by 2012’s Health and Social Care Act are strong enough to support what comes next or will require multiple overhauls as time goes on.

If you want to hear direct from health and care leaders about what they think the NHS needs to do to make digital a working reality, why not sign up to our insights series, which has seen interviews with key figures from NHSX, The Royal Marsden, and more? Click here to sign up.

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White Square: A Perfect Storm in Moscow

Brian Patterson was the lead developer of a large office project in Moscow when the global financial crisis hit. His project, which had looked like it would be jaw-droppingly profitable just months before, was suddenly thrown into turmoil, and he faced trouble on all fronts. His local development partner wanted to sell in order to shore up its failing balance sheet, his world-class anchor tenant suddenly reneged on its pre-lease agreement, the contractor was running months behind schedule, and the project’s bank was looking for any excuse to pull the construction loan.

Just months earlier, the project pro forma had projected hundreds of millions of dollars in profit. Suddenly there were serious questions around whether the project could even be completed. And if it could, what rent and cap rate values could be assumed to determine if it made sense to continue development? Patterson needed to make some assumptions to determine whether or not to accept a sale offer that had been drudged up by his local partner. And if he decided to turn down the sale offer, he needed to find a way forward through a maze of (i) diverging interests amongst his partners and (ii) project development problems.

As the economic and financial system faced global turmoil and threatened collapse, Patterson had to decide whether to keep developing the project – at significant risk to both the project and his personal career – or to sell for a modest profit and live to fight another day.

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• Volume 9, Issue 4
• Exploring the impacts of the 2012 Health and Social Care Act reforms to commissioning on clinical activity in the English NHS: a mixed methods study of cervical screening
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• Jonathan Hammond 1 , 2 , 3 ,
• Thomas Mason 1 , 2 , 3 , 4 ,
• Matt Sutton 1 , 2 , 3 ,
• Alex Hall 2 , 5 ,
• Nicholas Mays 6 ,
• Anna Coleman 1 , 2 , 3 ,
• Pauline Allen 7 ,
• Lynsey Warwick-Giles 1 , 2 , 3 ,
• Kath Checkland 1 , 2 , 3
• 1 Division of Population Health, Health Services Research, and Primary Care , University of Manchester , Manchester , UK
• 2 School of Health Sciences , University of Manchester , Manchester , UK
• 3 Faculty of Biology, Medicine and Health , University of Manchester , Manchester , UK
• 4 Manchester Centre for Health Economics , University of Manchester , Manchester , UK
• 5 Division of Nursing, Midwifery and Social Work , University of Manchester , Manchester , UK
• 6 Public Health and Policy , London School of Hygiene and Tropical Medicine , London , UK
• 7 Health Services Research Unit , London , UK
• Correspondence to Dr Jonathan Hammond; jonathan.hammond{at}manchester.ac.uk

Objectives Explore the impact of changes to commissioning introduced in England by the Health and Social Care Act 2012 (HSCA) on cervical screening activity in areas identified empirically as particularly affected organisationally by the reforms.

Methods Qualitative followed by quantitative methods. Qualitative: semi-structured interviews (with NHS commissioners, managers, clinicians, senior administrative staff from Clinical Commissioning Groups (CCGs), local authorities, service providers), observations of commissioning meetings in two metropolitan areas of England. Quantitative: triple-difference analysis of national administrative data. Variability in the expected effects of HSCA on commissioning was measured by comparing CCGs working with one local authority with CCGs working with multiple local authorities. To control for unmeasured confounders, differential changes over time in cervical screening rates (among women, 25–64 years) between CCGs more and less likely to have been affected by HSCA commissioning organisational change were compared with another outcome—unassisted birth rates—largely unaffected by HSCA changes.

Results Interviewees identified that cervical screening commissioning and provision was more complex and ‘fragmented’, with responsibilities less certain, following the HSCA. Interviewees predicted this would reduce cervical screening rates in some areas more than others. Quantitative findings supported these predictions. Areas where CCGs dealt with multiple local authorities experienced a larger decline in cervical screening rates (1.4%) than those dealing with one local authority (1.0%). Over the same period, unassisted deliveries decreased by 1.6% and 2.0%, respectively, in the two groups.

Conclusions Arrangements for commissioning and delivering cervical screening were disrupted and made more complex by the HSCA. Areas most affected saw a greater decline in screening rates than others. The fact that this was identified qualitatively and then confirmed quantitatively strengthens this finding. The study suggests large-scale health system reforms may have unintended consequences, and that complex commissioning arrangements may be problematic.

• health policy
• health system reform
• mixed methods
• commissioning

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjopen-2018-024156

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Strengths and limitations of this study

Few studies have investigated in detail the impacts of large-scale health system change.

This study combines detailed qualitative data exploring impacts on the system with quantitative exploration of important outcomes, supporting causal inference.

Based on qualitative findings, we developed a quantitative measure for assessing the extent of disruption to the English NHS commissioning system as a result of the 2012 Health and Social Care Act.

We found that cervical screening rates decreased more post-Act in areas that had experienced higher levels of disruption.

Introduction  

Structural reorganisations of publicly financed healthcare systems, driven by central government or other state agencies, are frequently employed with the objective of improving healthcare delivery, and thus population health outcomes, while reducing or containing costs. 1 However, such endeavours can be disruptive and expensive. 2 It is important to understand what possible impacts these reorganisations have in order to understand their value. 3

In the English National Health Service (NHS), attempts to evaluate the impact of reorganisations have typically used operational indicators (eg, bed availability, number of staff) and measures of clinical activity because their improvement was the stated goal of government policy (eg, The NHS Plan 4 ). Other studies have attempted to assess the impacts of reforms by measuring their effects on prices, quality and quantity of provision. 5 Most studies have relied on quantitative analysis of measures that were explicitly targeted by policy reforms. There is a need for approaches which combine qualitative and quantitative methods to generate a deeper understanding of the impacts of structural reorganisation. 6

The most recent structural reorganisation of the English NHS, the Health and Social Care Act 7 (hereafter ‘HSCA’ or ‘the Act’), was introduced in April 2013 and included wide-ranging changes to the health services commissioning system. We explore whether changes to the commissioning of cervical screening services resulting from the Act affected uptake. This analysis uses a relatively novel mixed methods approach. An initial ‘bottom-up’ qualitative analysis allowed us to identify problematic issues associated with the HSCA for those working locally in the health service commissioning system. This process highlighted the disruption to established commissioning arrangements and cervical screening as a clinical activity, which may be specifically affected by this disruption. We then developed a quantitative investigation to explore this more fully. Together these analyses allow us to infer causation.

The HSCA and changes to cervical screening commissioning

The HSCA is regarded as one of the most wide-ranging legislative reforms in the history of the English NHS. 8  Primary Care Trusts (PCTs), 152 organisations previously responsible for the commissioning of primary, community and secondary health services from providers on behalf of local populations, were abolished. Their commissioning functions were split between three groups of organisations: 211 (now 195) newly created Clinical Commissioning Groups (CCGs), membership organisations constituted by general practitioner (GP) (family doctor) practices, given responsibility for commissioning services for their local populations; NHS England (NHSE), a new arm’s-length governmental body with responsibility for authorising and assessing CCGs and commissioning some services at a national level; and top-tier and single-tier elected local authorities, which took responsibility for the majority of public health services for the first time since 1974. In addition, Public Health England (PHE) was created as an executive agency of the Department of Health, to unify the diverse public health profession and provide expert support for local public health services.

In some service areas, the transfer of commissioning responsibilities was relatively straightforward (eg, the commissioning of routine orthopaedic surgery was passed from PCTs to CCGs with minimal alteration to the bundle of services involved). In other service areas, the transfers were much more complex, particularly the commissioning of national screening programmes and sexual health services, as a result of changes to public health commissioning. Pre-HSCA, national screening programmes and sexual health services were both commissioned by PCTs. Cervical screening was largely provided by GP practices, which received additional funding linked to levels of activity, 9 but some women opted to have their cervical smears in PCT-commissioned sexual health clinics. Post-HSCA, responsibility for public health services, including most sexual health services, was transferred to local authorities. The underlying programme theory (ie, the explicit expectation about how the policy would work 10 ) was that local authorities would be better placed to address the wider determinants of health and well-being than the NHS because they could link public health services with their existing responsibilities, such as for transport and housing. 11 NHSE took responsibility for commissioning national screening programmes. 8 NHSE’s regional teams are responsible for commissioning screening programmes, supported by PHE staff ‘embedded’ within NHSE’s screening and immunisation teams. 12 There was no identifiable underlying programme theory for this specific change to screening programme commissioning. However, it is notable that, in contrast to the emphasis placed on localism associated with the creation of CCGs and with the transfer of public health to local authorities, screening commissioning became more centralised as a consequence of the HSCA.

Table 1 shows the organisations with responsibilities of relevance to the commissioning of sexual health, including cervical cancer screening services, pre-HSCA and post-HSCA. It illustrates how responsibility for such services, previously commissioned by PCTs, was split between different agencies. This increased complexity and fragmentation of responsibilities had the potential to disrupt service commissioning. 13

• View inline

Organisations of significance to the commissioning of cervical screening pre-HSCA and post-HSCA

This analysis comes from a study designed to foster emergent interplay between qualitative and quantitative data analysis. 14 15 The focus on cervical screening was not established at the project design stage but driven by the initial qualitative interview findings related to sexual health commissioning arrangements and screening activity post-HSCA. These findings prompted us to consider a quantitative exploration of predictions made by interviewees relating to potential changes in cervical screening activity.

Study context and design

This analysis forms part of a longitudinal project, with data collected between January 2015 and December 2017, into the effect of the HSCA on the commissioning system in England. We combined a qualitative and quantitative exploration of the commissioning of services in two large, socioeconomically diverse metropolitan areas of England with a national level quantitative study of commissioning outcomes. We used a sequential mixed methods approach in which initial qualitative data collection and analysis were used to shape an ensuing quantitative analysis using routinely available data. We therefore present the qualitative and quantitative methods and findings in the order undertaken, and integrate them in the ’Discussion' section.

Patient and public involvement

Our interest in exploring the impact of systemic commissioning change on cervical screening rates was driven initially by concerns expressed by interviewees about potentially negative consequences for patients relating to new arrangements. Patients were not directly involved in the design of, or recruitment to, the overarching project, but an advisory group including a patient representative met regularly throughout the project and played an important role in supporting its development. We presented our initial qualitative findings relating to cervical screening, and early ideas for developing a mixed methods investigation, to our advisory group and were encouraged by our patient representative to pursue this. The results of the broader project were disseminated to participants, and the advisory group, in the form of a series of short reports focusing on specific areas of commissioning and the final report.

Qualitative component

Setting, participants, sampling and data collection.

The qualitative component took place between March 2015 and August 2017, focussing on two metropolitan ‘health economies’ covering a geographical population and a group of commissioning organisations and providers with close operational links. Across both areas, we conducted 143 interviews (each typically an hour in length), 93 of which related to sexual health commissioning, with clinical and non-clinical commissioners, managers, clinicians and senior administrative staff from CCGs, local authorities, service providers and third sector organisations. Organisations and participants were sampled purposively for variation in type and role. We identified participants through organisational websites, personal contacts and through ‘snowballing’ in which we asked participants to recommend other potential participants. Additionally, 8 hours of meetings of an interorganisational sexual health coordinating group involving sexual health commissioners and providers were observed in one of the areas.

Interviews focused on the commissioning system pre-HSCA and post-HSCA, exploring continuities and changes to personal and organisational roles, key issues and challenges, accountability and performance management, interorganisational relationships and communication and commissioning decision-making. Interviews took place either in person (usually in participants’ offices) or over the telephone. All interviews were audio-recorded and all interviewees were provided with written information about the study before consenting to participate.

Data analysis

Audio recordings of interviews were transcribed verbatim, and observational notes from meetings were produced contemporaneously. Transcripts and observational notes were imported into NVivo V.10 software and analysed thematically by JH and AH. 16 This involved repeated readings of transcripts to become sufficiently familiar with their contents, identifying initial codes and coding chunks of data, searching for themes and then iteratively defining and reconstituting themes. Our findings (see below) contained some predictions made by participants regarding changes in cervical screening activity as a consequence of the Act. This prompted us to explore these predictions in a quantitative analysis, which we now describe.

Quantitative component

Interviewees identified that the HSCA had introduced confusion over responsibility for the commissioning of cervical screening services, and had increased variability of provision. They hypothesised that cervical screening rates might be reduced by the new commissioning arrangements; this prompted discussions among the research team and advisory group about developing a way of testing these predictions quantitatively as far as routine data would permit. As the Act had been implemented in all areas simultaneously, removing the scope for a quasi-experimental approach, we sought to identify a measure of variability in the extent to which the Act would have been expected to make commissioning more difficult in each area. One of the features of the post-HSCA system was that some, but not all, CCGs were established which crossed local authority boundaries. Some CCGs related to as many as three separate local authorities. Local authorities were now directly involved in sexual health services commissioning. Findings revealed that CCGs experienced extra challenges when they had to engage with more than one local authority. This suggested that the burden of additional interorganisational coordination might have consequences for commissioning.

As each local authority developed its own approach to cervical screening in its local sexual health clinics, we explored the possibility that GP practices located in CCGs which had to work with more than one local authority might experience lower screening rates compared with practices located in CCGs which had only to deal with one local authority. We compare the demographic characteristics of these two groups in table 2 . The 89 CCGs dealing with more than one local authority had a slightly older population profile than the 119 CCGs which dealt with only one local authority but were otherwise highly comparable.

Clinical Commissioning Group (CCG) demographic characteristics depending on the number of local authorities that the CCG needs to work with

Because cervical screening rates may be influenced by other factors that we cannot observe and may change over time in different ways between the two groups of CCGs, we also compared screening rates with an outcome that was unlikely to have been affected by the introduction of the HSCA. We used unassisted births (ie, uncomplicated deliveries which did not require any intervention) as a percentage of all maternal deliveries, since the commissioning of maternity services was largely unchanged by the Act.

We applied a triple-difference approach. The triple difference represents (the change over time in cervical screening rates for CCGs working with only one local authority minus the change over time in cervical screening rates for CCGs working with more than one local authority) minus (the change over time in unassisted birth rates for CCGs working with only one local authority minus the change over time in unassisted birth rates for CCGs working with more than one local authority).

The screening rate is defined as the percentage of women aged between 25 and 64 years who had received a cervical screening test in the preceding 5 years. This indicator was derived from annual, practice-level data from the Quality and Outcomes Framework, 2009–10 to 2015–16. The comparison indicator is unassisted births as a percentage of all maternal deliveries. This indicator was produced using operation codes in Hospital Episode Statistics for 2009–10 to 2015–16. We aggregated the spell-level data by general practice and financial year.

The key assumption underpinning the triple-difference estimator is that, conditional on the other variables in the model, the differences in the changes over time in the intervention indicator (cervical screening) between the ‘exposed’ and the ‘control’ areas (in this case, CCGs working with one local authority vs CCGs working with more than one) would have been the same as the differences in the changes over time in the comparison indicator (unassisted births) between the exposed and control areas in the absence of the intervention. This is a more complex version of the ‘parallel trends’ assumption required for the double-difference, or difference-in-differences, estimator. 17

A popular test of this assumption in the double-difference case is that there are parallel trends over time in the outcomes in the intervention and comparison group in the preintervention period. For our triple-difference case, we used an F-test to assess the joint significance of interactions between the year effects and the binary variable representing the combination of exposed area and treated indicator in the preperiod.

We also used the lagged dependent variable (LDV) estimator. This model is estimated only on data in the postintervention period and is a less biased estimator of treatment effects when the assumption of parallel pretrends does not hold. 18 We set up the LDV model to generate the equivalent impact estimate as the triple-difference model. The model included: dummy variables for year; values of the dependent variable in each of the preintervention periods; a dummy variable classifying practices depending on whether they were located in CCGs working with more than one local authority; interactions between year and condition dummies; interactions between values of the dependent variable in the preintervention period and the condition dummy and an interaction between the dummy variable classifying practices depending on whether they were located in CCGs working with more than one local authority and the condition dummy. The final term is the impact estimate, showing whether cervical screening was differentially affected after the introduction of the reforms for local authorities working with multiple CCGs.

We estimated the regression models in Stata V.14.1 using dummy variable weighted least squares regression with fixed effects for practice-indicator combinations. The ways in which these models are estimated using regression analyses are described formally in the technical online  supplementary appendix . As the dependent variable is a proportion, and constrained to lie between 0 and 1, we used the empirical logit transformation and back-transformed the coefficients and associated 95% CIs using the mean value of the cervical screening rate. 19 We clustered the SEs at the GP practice level. 20 The general form of the STATA command is: areg {depvar} {indepvars} [aw=denom], robust absorb(practicexindicator) cluster(practice).

Supplementary file 1

Qualitative findings.

Interviewees told us that CCGs working with more than one local authority experienced a number of challenges, including: finding sufficient capacity to engage in multiple meetings of the same type with different local authorities; managing additional collaborative relationships; working with organisations experiencing different financial pressures from each other with different approaches to public health spending; and attempting to develop integrated health and social care arrangements with one local authority that did not have unintended and undesirable consequences for plans with another. The following extract illustrates issues relating to difficulties commissioning a single service offer for CCG patients and the additional resources required for a CCG working with multiple local authorities. (Interview data extracts are denoted by square brackets with numerical participant ID, participant’s organisation type, Area (1 or 2) and month and year of the interview.)

We do have two sets of safeguarding arrangements. So I guess at one level, one can say there is a risk of and there are examples of services being subtly different. Equally, you’ve got to service two times the number of these processes, which can be quite labour-intensive. [2778, CCG, Area 1, April 2015]

In our analysis relating directly to issues surrounding the commissioning and provision of cervical screening post-HSCA, we identified two main themes: confusion and uncertainty regarding budgets and responsibilities, and potential impacts on cervical screening rates. Many of the issues discussed below are likely to be exacerbated when the number of interacting commissioning organisations in a local area are increased.

Confusion and uncertainty regarding budgets and responsibilities

Before the HSCA, both cervical screening and sexual health services were commissioned by PCTs. As one screening and immunisation lead outlined, cervical screening tests (sometimes referred to as smear tests) were provided by GP practices, but patients could usually also have them at sexual health clinics [17685, NHSE, Area 2, December 2016]. Whereas pre-HSCA PCTs held the budget for both cervical screening and sexual health services, following the Act these budgets were separated. This meant that the local authority budget and responsibility for sexual health did not extend to cervical screening. One local authority public health consultant reported that, in spite of this, PHE was sending letters to patients explicitly stating that they could choose to attend either their GP practice or their local sexual health clinic for their cervical screening test. This highlights confusion regarding commissioning arrangements and budgetary responsibility:

Public Health England were writing around to people saying …you’re due your smear, you can go to your general practice or you can go to your local sexual health clinic. And we said, but we don’t have the money for them to do that, they can’t come here routinely unless you’re going to pay us for that. Public Health England, the screening people, they have the money to pay for the smears. But in all the moving around of the budgets, the money for smears that were taken outside general practice doesn’t seem to be anywhere. [8384, local authority, Area 1, November 2015]

One participant from NHSE offered a different perspective. He argued that the public health budget of each local authority reflected the levels of cervical screening activity that had taken place in its sexual health clinics pre-HSCA. However, this is not clear because, in the past, the funding was not ‘disaggregated’ [4058, NHSE, Area 1, June 2015]. Therefore, it is not possible to establish what the pre-HSCA sexual health component of the public health budget covered.

…they (local authorities) think they’re not being paid for it (cervical screening). But, actually, in truth, whatever they were doing at the point of transition if they were doing loads of cervical smears they were just doing loads of cervical smears, so they had the money. There wasn’t a problem when they were doing them before, it’s just the money wasn’t disaggregated. However local authorities have been put under significant pressure in their public health teams to reduce their budgets. So these kinds of things are examples where you can say it’s not our responsibility so therefore we’re taking that element out. [4058, NHSE, Area 1, June 2015]

The above quote illustrates a phenomenon reported by a number of participants that local authorities had reprocured their sexual health services and had taken a position that they would not commission their sexual health provider(s) to do routine cervical screening, because it was not their commissioning responsibility. However, as one member of a screening team in Area 2 illustrated, NHSE was also reluctant to explicitly commission sexual health services to provide cervical screening, seemingly because of administrative challenges relating to numerous low-value contracts with providers:

So cervical screening, we could go to every sexual health provider and have a separate contract. The difficulty again becomes around commissioning capacity. So, I think we’ve got [x] local authorities, so we have [x] separate contracts all very low value, it’s about 1000 screens in each, so you’re talking maybe [x] £20 000 contracts or something. So, it’s a very bitty way of doing stuff. So, we could still do it and we could pay for it, but in terms of the amount of paperwork or the amount of outcomes it becomes potentially unmanageable. [17685, PHE/NHSE, Area 2, December 2016]

This participant went on to indicate that he would prefer local authorities to commission cervical screening as part of their sexual health contracts, but acknowledged the political difficulties for local authorities to justify spending money on an area of service that was not formally their responsibility, especially given the context of diminishing local authority budgets:

In a way, wouldn’t it be so much easier if the local authorities just included it as part of their normal service? But their argument would be that’s not our role, and how can we defend to the (elected) councillors that we’re spending money on stuff that we don’t have to, that someone else is meant to be spending money on? And our argument is well, it’s just so much simpler and it’s not a lot of money. That’s the kind of discussion. And it eventually ends up with them withdrawing money and us saying well, we’re not buying it either then. [17685, PHE/NHSE, Area 2, December 2016]

Potential impacts on cervical screening rates

One local authority commissioner suggested that the policy of his local authority was to continue to facilitate opportunistic cervical screening tests at sexual health clinics, but not routine tests, because to provide the latter would have a detrimental impact on other sexual health services that the local authority was now obligated to commission (“if we don’t say no to (routine) smears, we’ll be turning (other) people away, symptomatic patients away, or women needing contraception away. And that’s our duty" [8384, local authority, Area 1, November 2015]). He reported that local CCGs complained about this discontinuation of routine cervical screening at sexual health clinics, because there was insufficient capacity within general practice for CCGs to meet their cervical screening targets, and thus they required sexual health clinics to provide a proportion of cervical screening activity. One screening consultant developed this point by suggesting that some localities would see a substantial reduction in screening activity because of a lack of capacity within primary care:

…in some local authorities where the sexual health service is no longer doing cervical screening (it) will have a small impact but not a huge impact, in other areas, it will have a big impact on coverage, we’ll see activity go down around it, because the workload is just going to come straight back to primary care, and in different areas primary care didn’t realise this was happening, the re-commissioning, hasn’t got the capability and the capacity… [18352, PHE/NHSE, Area 1, January 2017]

Another screening consultant reflected that changes to NHSE ‘footprints’ (ie, the abolition of Area Teams and the new, more regional focus of the organisation) had implications for the provision of cervical screening:

…say we wanted to sort out cervical screening coverage in GP practices, in (name of PCT) you’ve got [ x ] GP practices, bottom 20 per cent you could talk to the [ y ] practices. In my new patch we’ve got (many more than x ) practices. So you have to think in a completely different way. [17685, NHSE, Area 2, December 2016]

Several participants from different localities in both geographical areas pointed to long-standing challenges in ensuring good uptake rates for screening among their diverse local populations. There were concerns that these challenges would be exacerbated by a reduction in choice for women about where they could go for cervical screening tests:

…you should have an integrated sexual health service where predominantly women can go in and get seen in one episode, in one place for all their sexual health needs, be that sexually transmitted infection testing and treatment and contraception. So I think probably in the past people worked very hard to get things like cervical screening into these services so that the needs of those women who perhaps wouldn’t go to their local GP could be met in an environment they felt happy with. My feeling is now… that perhaps the type of women who traditionally would have gone for cervical screening (at their sexual health clinic) might not feel so comfortable in that environment (of the GP practice). So particularly, say, a lady from a South Asian background who goes to a single handed male GP with no practice nurse, that’s the kind of traditional person who might have gone to a family planning clinic for their cervical screening. [9742, local authority, Area 2, January 2016]

The HSCA separated commissioning responsibilities for some types of services, including sexual health. Our study participants told us that this had introduced complexity and confusion surrounding cervical screening commissioning, and they expressed concern that screening rates would decline as a result, with some areas potentially affected more than others due to differences in local contextual conditions. In order to explore this further, we designed a quantitative analysis to test the proposition that CCGs most affected by this increase in complexity would have a greater decline in screening rates. Based on the findings from our interviews that working with more than one local authority acted to increase the complexity associated with the commissioning role, we compared screening rates between those CCGs which relate to a single local authority and those required to work with two or more local authorities.

Quantitative findings

There were 14.1 million women eligible for screening in England in 2016. 21 Cervical screening rates decreased over time and the decline predated the implementation of the HSCA in April 2013. Unassisted delivery rates also declined over time. The relative decline between the first year (2009–10) and the last year (2015–16) for unassisted deliveries (−4.17%) was larger than for cervical screening (−2.70%) ( table 3 ).

Numbers of general practices and mean rates of cervical screening and unassisted deliveries by year and by the number of LAs with which CCGs had to coordinate commissioning

The changes in cervical screening rates over time were similar for practices in CCGs dealing with a single local authority (−2.53%) compared with practices in CCGs working with multiple local authorities (−2.87%). Figure 1 illustrates the trends in rates of cervical screening in the preintervention and postintervention periods for CCGs depending on the number of local authorities they worked with. There is a noticeable and sharp decline in the rates in both groups between 2011–12 and 2012–13.

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Uptake (%) of cervical screening pre-HSCA and post-HSCA. HSCA, Health and Social Care Act 2012; LA, local authority.

Comparing the unadjusted averages for the pre-HSCA and post-HSCA years, cervical screening rates decreased by 0.39% more for GP practices located in CCGs working with multiple local authorities compared with practices in CCGs working with a single local authority. Unassisted birth rates decreased by 0.40% less for GP practices in CCGs working with multiple local authorities compared with GP practices in CCGs working with a single local authority. As maternity services were largely unaffected by the HSCA, we assumed that these differential changes captured the unmeasured population influences that confound comparisons of the changes in the two groups of CCGs. Relative to the decreases in unassisted delivery rates, GP practices in CCGs working with multiple local authorities experienced a decrease in cervical screening rates of 0.79% compared with practices in CCGs working with a single local authority ( table 4 ).

Rates of cervical screening and unassisted birth for CCGs working with one and more than one LA, before and after the introduction of the HSCA

The results were qualitatively similar when we estimated the formal triple difference (for all years and 2011–12 onwards only) and lagged dependent variable regression models ( table 5 ). The triple-difference estimates show that there was a differentially larger decline of 0.62% (95% CI −0.941 to −0.297) (model 1) in cervical screening rates for practices located in CCGs working with more than one local authority. The decrease is smaller using the shorter preperiod (0.259%; 95% CI −0.573 to 0.052, model 2).

Triple-difference regression results

The direction of result is robust to the model specification and, although we rejected the assumption of parallel trends for model 1 (all years), we could not reject the assumption for model 2 (2011–12 onwards). We also found a similar result in model 3 using the lagged dependent variable estimator, which yields unbiased estimates when pretrends cannot be assumed to be parallel.

The results are also robust to different groupings of the number of local authorities that CCGs work with. Table 5 includes model estimates comparing CCGs working with one or two local authorities with CCGs working with more than two local authorities. The direction of results is equivalent; and the scale and significance are either equivalent or increased. The same pattern is repeated in terms of tests of parallel trends. We cannot reject the null hypothesis of parallel trends for model 2 and the LDV estimation is preferable to model 1 in which we can reject the null hypothesis of parallel trends.

We conducted a mixed methods study exploring the impact of changes associated with the HSCA in the English NHS on cervical screening rates. We carried out qualitative interviews with senior figures from a variety of relevant organisations in two large, socioeconomically diverse areas of England. Analysis of these interviews suggested that cervical screening commissioning had become more complex, with responsibilities between organisations less certain, as a consequence of the HSCA. Some interviewees predicted there would be a reduction in cervical screening rates in particular areas. These findings prompted the development of an analysis to explore these issues quantitatively via a triple-difference regression analysis of publicly available data on cervical screening activity. To control for unmeasured confounders, we compared cervical screening rates with trends in unassisted birth rates because the commissioning of maternity services was unchanged pre-HSCA and post-HSCA.

Interviewees suggested a number of factors that might contribute to a reduction in cervical screening activity. Sexual health service commissioning responsibility had shifted to local authorities while NHSE was made responsible for commissioning national screening services, including cervical screening. Faced with financial austerity and cuts to their budgets, many local authorities were retendering their sexual health services with sexual health service providers but not including routine cervical screening. NHSE was also seemingly reluctant to commission sexual health clinics to perform cervical screening tests because this would entail a multitude of low-value contracts with numerous providers. This would be administratively laborious and practically difficult given the large size of NHSE’s administrative areas and small numbers of NHSE commissioning staff in each area.

The quantitative analysis was designed to explore whether cervical screening activity had declined in areas most affected by commissioning organisational change. GP practices located in CCGs dealing with multiple local authorities, and therefore most exposed to increased commissioning complexity and potential disruption in services because of the lack of clarity of the roles of different organisations, experienced a larger decrease over time in cervical screening rates compared with practices in CCGs dealing with a single local authority. The opposite pattern was observed for unassisted births, which decreased more over time in the CCGs dealing with a single local authority. The triple-difference analyses confirmed that the effects were statistically significant and robust to different model specifications.

We have demonstrated unintended consequences arising out of a large-scale health system reform. Taken together, our findings suggest that there is an urgent need for clarification as to who holds the budget, and therefore who should be commissioning, cervical screening in the English NHS, and for local agreements to ensure that issues over funding and budgets do not disrupt screening programmes. More broadly, the issues we have identified in this study are of value to policy makers and system leaders in other health systems. The current study suggests that there are particular problems associated with service commissioning where coordination is required between multiple commissioners. This suggests that future commissioning reforms should include assessment of the likely impact on coordination, and a presumption in favour of commissioning all required services for geographical populations where possible. This may also have implications for mixed health systems, in which multiple payers (including public and private insurers as well as out of pocket payments) are responsible for services. In such systems achieving desirable population coverage for services such as screening may require specific coordination efforts.

Potential confounders and study strengths

We took 2009 as our starting point for pre-HSCA cervical screening activity. Two potential confounders to our results were considered. First, the high-profile case of Jade Goody, a reality TV star who was diagnosed with cervical cancer in August 2008 and died in March 2009. The contemporaneous media attention and publicity was linked with a substantial increase in cervical screening rates (around an extra half a million women) during the time between Goody’s diagnosis and death. However, previous impacts of high-profile cases of celebrity cancer diagnoses on population behaviour have tended to be brief and immediate rather than longer-lasting, and, therefore, we are confident that from 2010, rates of cervical screening returned towards underlying trends. 22 Second, the UK’s Human Papillomavirus (HPV) vaccination programme was introduced in 2008 in order to reduce the incidence of cervical cancer. 23 The vaccine is offered to all girls aged 12–13 years, and figures for 2008–14 show high uptake rates of just under 90%. It is likely that this vaccination programme will contribute to a reduction in cervical screening activity in future. However, the first cohort of women in the programme, that is, those aged 12–13 years in 2008, were aged only 21–22 years in 2016–17, hence too young to have been invited for routine cervical screening (which begins at age 25) at the time of the study. We can, therefore, be confident that any changes to cervical screening rates cannot yet be attributed directly to the HPV programme, but any future research into cervical screening rates needs to take this into account.

We considered whether the results were sensitive to the group of CCGs in terms of the number of local authorities they worked with. The direction of results was the same, and the strength and significance was increased, comparing CCGs working with one or two local authorities with those CCGs working with more than two local authorities. We also considered whether the results were sensitive to the choice of comparison indicator (unassisted births) for maternity services. We tested whether the results would hold for another indicator of maternity services: the rate of deliveries by caesarean section. We observed the same direction and significance of results for this indicator as well.

The average age of mothers at delivery is likely to be younger than the average age of women attending for cervical screening. For our analysis, we require that differential changes in maternity indicators between CCGs with simple and CCGs with complex local authority relationships are a good proxy for other factors influencing cervical screening rates. We have confirmed the empirical validity of this assumption by looking for parallel trends in the period before the HSCA, but we can never be entirely sure of its validity.

The findings presented here come from a longitudinal study of major healthcare system reform conducted by a multidisciplinary research team. The nature of this study facilitated the development of the relatively novel, sequential mixed methods approach in which the claims made in qualitative interviews could be tested in a subsequent quantitative analysis. There is a reinforcing effect in this analytical approach, which provides a strong cumulative indication that in areas of the country where complexity and coordination issues linked to the HSCA were more likely to occur there was an associated reduction in cervical screening rates.

Acknowledgments

The authors would like to thank the research participants for their involvement, and acknowledge the valuable advice of the Project Advisory Group.

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• 23. ↵ Public Health England . Human Papillomavirus (HPV) Vaccine coverage in England, 2008/09 to 2013/14. 2015 : Public Health England , 2015 .

Contributors KC designed the study with input from MS, NM, PA, AC. JH, AH, LW-G gathered and analysed the qualitative data. TM and MS designed the quantitative evaluation and conducted this analysis. JH drafted the manuscript to which all authors made substantial contributions. All authors approved the final version and agree to be accountable for all aspects of the analysis.

Funding The report is based on independent research commissioned and funded by the NIHR Policy Research Programme (‘Understanding the new commissioning system in England: contexts, mechanisms and outcomes’, PR-R6-1113-25001).

Disclaimer The views expressed in the publication are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health, arm’s-length bodies or other government departments.

Competing interests None declared.

Ethics approval Ethical approval was granted by one of The University of Manchester Research Ethics Committees (application 15085) in March 2015. Participants were provided written information about the study, provided written consent or gave consent verbally at the beginning of telephone interviews.

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement No additional data are available.

Patient consent for publication Not required.

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Intraurban social risk and mortality patterns during extreme heat events: A case study of Moscow, 2010-2017

Affiliations.

• 1 Russian Presidential Academy of National Economy and Public Administration, 119571, Prospect Vernadskogo, 84, Moscow, Russian Federation; Lomonosov Moscow State University, Faculty of Geography, 119991, Leninskiye gory, 1, Moscow, Russia. Electronic address: [email protected].
• 2 Lomonosov Moscow State University, Faculty of Geography, 119991, Leninskiye gory, 1, Moscow, Russia. Electronic address: [email protected].
• 3 Lomonosov Moscow State University, Faculty of Geography, 119991, Leninskiye gory, 1, Moscow, Russia; Lomonosov Moscow State University, Research Computing Center, 119234, Leninskiye gory, 1c4, Moscow, Russia; A.M. Obukhov Institute of Atmospheric Physics Russian Academy of Science, 119017, Pyzhyovskiy Pereulok, 3, Moscow, Russia; Moscow Center of Fundamental and Applied Mathematics, GSP-1, Leninskie gory, 1, bld.1, 199991, Moscow, Russia. Electronic address: [email protected].
• 4 Lomonosov Moscow State University, Faculty of Geography, 119991, Leninskiye gory, 1, Moscow, Russia. Electronic address: [email protected].
• 5 Russian Presidential Academy of National Economy and Public Administration, 119571, Prospect Vernadskogo, 84, Moscow, Russian Federation; Lomonosov Moscow State University, Faculty of Geography, 119991, Leninskiye gory, 1, Moscow, Russia. Electronic address: [email protected].
• 6 National Research University Higher School of Economics, International Laboratory for Population and Health, 101000, Myasnitskaya st., 20, Moscow, Russia. Electronic address: [email protected].
• 7 National Research University Higher School of Economics, International Laboratory for Population and Health, 101000, Myasnitskaya st., 20, Moscow, Russia. Electronic address: [email protected].
• 8 Lomonosov Moscow State University, Faculty of Geography, 119991, Leninskiye gory, 1, Moscow, Russia; National Research University Higher School of Economics, Faculty of Geography and Geoinformation Technology, 109028, Pokrovsky bvd, 11, Moscow, Russia. Electronic address: [email protected].
• PMID: 32992266
• DOI: 10.1016/j.healthplace.2020.102429

There is currently an increase in the number of heat waves occurring worldwide. Moscow experienced the effects of an extreme heat wave in 2010, which resulted in more than 10,000 extra deaths and significant economic damage. This study conducted a comprehensive assessment of the social risks existing during the occurrence of heat waves and allowed us to identify the spatial heterogeneity of the city in terms of thermal risk and the consequences for public health. Using a detailed simulation of the meteorological regime based on the COSMO-CLM regional climate model and the physiologically equivalent temperature (PET), a spatial assessment of thermal stress in the summer of 2010 was carried out. Based on statistical data, the components of social risk (vulnerabilities and adaptive capacity of the population) were calculated and mapped. We also performed an analysis of their changes in 2010-2017. A significant differentiation of the territory of Moscow has been revealed in terms of the thermal stress and vulnerability of the population to heat waves. The spatial pattern of thermal stress agrees quite well with the excess deaths observed during the period from July to August 2010. The identified negative trend of increasing vulnerability of the population has grown in most districts of Moscow. The adaptive capacity has been reduced in most of Moscow. The growth of adaptive capacity mainly affects the most prosperous areas of the city.

Keywords: Adaptive capacity; Heat wave; Physiologically equivalent temperature (PET); Urban heat island (UHI); Vulnerability.

Copyright © 2020 Elsevier Ltd. All rights reserved.

Publication types

• Research Support, Non-U.S. Gov't
• Extreme Heat* / adverse effects
• Hot Temperature
• Moscow / epidemiology

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National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Population Health and Public Health Practice; Committee on Community-Based Solutions to Promote Health Equity in the United States; Baciu A, Negussie Y, Geller A, et al., editors. Communities in Action: Pathways to Health Equity. Washington (DC): National Academies Press (US); 2017 Jan 11.

Communities in Action: Pathways to Health Equity.

• Hardcopy Version at National Academies Press

3 The Root Causes of Health Inequity

Health inequity, categories and examples of which were discussed in the previous chapter, arises from social, economic, environmental, and structural disparities that contribute to intergroup differences in health outcomes both within and between societies. The report identifies two main clusters of root causes of health inequity. The first is the intrapersonal, interpersonal, institutional, and systemic mechanisms that organize the distribution of power and resources differentially across lines of race, gender, class, sexual orientation, gender expression, and other dimensions of individual and group identity (see the following section on such structural inequities for examples). The second, and more fundamental root cause of health inequity, is the unequal allocation of power and resources—including goods, services, and societal attention—which manifest in unequal social, economic, and environmental conditions, also called the social determinants of health. Box 3-1 includes the definitions of structural inequities and the social determinants of health.

Definitions.

The factors that make up the root causes of health inequity are diverse, complex, evolving, and interdependent in nature. It is important to understand the underlying causes and conditions of health inequities to inform equally complex and effective interventions to promote health equity.

The fields of public health and population health science have accumulated a robust body of literature over the past few decades that elucidates how social, political, economic, and environmental conditions and context contribute to health inequities. Furthermore, there is mounting evidence that focusing programs, policies, and investments on addressing these conditions can improve the health of vulnerable populations and reduce health disparities ( Bradley et al., 2016 ; Braveman and Gottlieb, 2014 ; Thornton et al., 2016 ; Williams and Mohammed, 2013 ). This literature is discussed below in the sections on structural inequities and the social determinants of health.

• HOW STRUCTURAL INEQUITIES, SOCIAL DETERMINANTS OF HEALTH, AND HEALTH EQUITY CONNECT

Health inequities are systematic differences in the opportunities groups have to achieve optimal health, leading to unfair and avoidable differences in health outcomes ( Braveman, 2006 ; WHO, 2011 ). The dimensions of social identity and location that organize or “structure” differential access to opportunities for health include race and ethnicity, gender, employment and socioeconomic status, disability and immigration status, geography, and more. Structural inequities are the personal, interpersonal, institutional, and systemic drivers—such as, racism, sexism, classism, able-ism, xenophobia, and homophobia—that make those identities salient to the fair distribution of health opportunities and outcomes. Policies that foster inequities at all levels (from organization to community to county, state, and nation) are critical drivers of structural inequities. The social, environmental, economic, and cultural determinants of health are the terrain on which structural inequities produce health inequities. These multiple determinants are the conditions in which people live, including access to good food, water, and housing; the quality of schools, workplaces, and neighborhoods; and the composition of social networks and nature of social relations.

So, for example, the effect of interpersonal, institutional, and systemic biases in policies and practices (structural inequities) is the “sorting” of people into resource-rich or resource-poor neighborhoods and K–12 schools (education itself being a key determinant of health ( Woolf et al., 2007 ) largely on the basis of race and socioeconomic status. Because the quality of neighborhoods and schools significantly shapes the life trajectory and the health of the adults and children, race- and class-differentiated access to clean, safe, resource-rich neighborhoods and schools is an important factor in producing health inequity. Such structural inequities give rise to large and preventable differences in health metrics such as life expectancy, with research indicating that one's zip code is more important to health than one's genetic code ( RWJF, 2009 ).

The impact of structural inequities follows individuals “from womb to tomb.” For example, African American women are more likely to give birth to low-birthweight infants, and their newborns experience higher infant death rates that are not associated with any biological differences, even after accounting for socioeconomic factors ( Braveman, 2008 ; Hamilton et al., 2016 ; Mathews et al., 2015 ). Although the science is still evolving, it is hypothesized that the chronic stress associated with being treated differently by society is responsible for these persistent differential birth outcomes ( Christian, 2012 ; El-Sayed et al., 2015 ; Strutz et al., 2014 ; Witt et al., 2015 ). In elementary school there are persistent differences across racial and ethnic divisions in rates of discipline and levels of reading attainment, rates that are not associated with any differences in intelligence metrics ( Howard, 2010 ; Losen et al., 2015 ; Reardon et al., 2012 ; Skiba et al., 2011 ; Smith and Harper, 2015 ). There also are race and class differences in adverse childhood experiences and chronic stress and trauma, which are known to affect learning ability and school performance, as well as structural inequities in environmental exposures, such as lead, which ultimately can lead to differences in intelligence quotient (IQ) ( Aizer et al., 2015 ; Bethell et al., 2014 ; Jimenez et al., 2016 ; Levy et al., 2016 ). One of the strongest predictors of life expectancy is high school graduation, which varies dramatically along class and race and ethnicity divisions, as do the rates of college and vocational school participation—all of which shape employment, income, and individual and intergenerational wealth ( Olshansky et al., 2012 ). Structural inequities affect hiring policies, with both implicit and explicit biases creating differential opportunities along racial, gender, and physical ability divisions. Lending policies continue to create differences in home ownership, small business development, and other asset development ( Pager and Shepherd, 2008 ). Structural inequities create differences in the ability to participate and have a voice in policy and political decision making, and even to participate in the arguably most fundamental aspect of our democracy, voting ( Blakely et al., 2001 ; Carter and Reardon, 2014 ). And implicit biases create differential health care service offerings and delivery and affect the effectiveness of care provided, including a lack of cultural competence ( IOM and NRC, 2003 ; Sabin et al., 2009 ).

For many people, the challenges that structural inequities pose limit the scope of opportunities they have for reaching their full health potential. The health of communities is dependent on the determinants of health.

• STRUCTURAL INEQUITIES

As described above, structural inequities refers to the systematic disadvantage of one social group compared to other groups with whom they coexist that are deeply embedded in the fabric of society. In Figure 3-1 , the outermost circle and background indicate the context in which health inequities exist. Structural inequities encompass policy, law, governance, and culture and refer to race, ethnicity, gender or gender identity, class, sexual orientation, and other domains. These inequities produce systematic disadvantages, which lead to inequitable experiences of the social determinants of health (the next circle in the report model, which is discussed in detail later in this chapter) and ultimately shape health outcomes.

Report conceptual model for community solutions to promote health equity. NOTE: Structural inequities are highlighted to convey the focus of this section.

Historical Perspective and Contemporary Perceptions

Whether with respect to race, ethnicity, gender, class, or other markers of human difference, the prevailing American narrative often draws a sharp line between the United States' “past” and its “present,” with the 1960s and 1970s marking a crucial before-and-after moment in that narrative. This narrative asserts that until the 1950s, U.S. history was shaped by the impacts of past slavery, Indian removal, lack of rights for women, Jim Crow segregation, periods of nativist restrictions on immigration and waves of mass deportation of Hispanic immigrants, eugenics, the internment of Japanese Americans, the Chinese exclusion policies, the criminalization of “homosexual acts,” and more ( Gee and Ford, 2011 ; Gee et al., 2009 ). White women and people of color were effectively barred from many occupations and could not vote, serve on juries, or run for office. People with disabilities suffered widespread discrimination, institutionalization, and social exclusion.

Civil rights, women's liberation, gay rights, and disability rights movements and their aftermaths may contribute to a narrative that social, political, and cultural institutions have made progress toward equity, diversity, or inclusion. Highlights of progress include the Civil Rights Act of 1964, the Voting Rights Act of 1965, the Fair Housing Act, Title IX of the Education Amendments of 1972, the Americans with Disabilities Act, the Patient Protection and Affordable Care Act, and, most recently, the Supreme Court case 1 that legalized marriage equality in the United States. With a few notable exceptions—undocumented immigrants and Muslims, for example—these advances in law and policy have been mirrored by the liberalization of attitudes toward previously marginalized identity groups.

Today, polls and surveys indicate that most Americans believe that interpersonal and societal bias on the basis of identity no longer shapes individual or group social outcomes. For example, 6 in 10 respondents to a recent national poll said they thought the country has struck a “reasonable balance” or even gone “too far” in “accepting transgender people” ( Polling Report, n.d. ). In 2015, 72 percent of respondents, including 81 percent of whites, said they believe that “blacks have as good a chance as white people in your community to get any kind of job for which they are qualified” ( Polling Report, n.d. ). In another poll, a total of 72 percent agreed that “women and men have equal trouble finding good-paying jobs” (64 percent) or that men have more trouble (8 percent) ( Ms. Foundation for Women, 2015 ). However, when broken down by racial and ethnic categories, the polls tell a different narrative. A recent survey revealed that 70 percent of African Americans, compared with 36 percent of whites, believe that racial discrimination is a major reason that African Americans have a harder time getting ahead than whites ( Pew Research Center, 2016 ). Furthermore, African Americans (66 percent) and Hispanics (64 percent) are more likely than whites (43 percent) to say that racism is a big problem ( DiJulio et al., 2015 ). Here, perceptions among African Americans and whites have not changed substantially; however, Hispanics are much more likely to now say that racism is a big problem (46 percent in 1995 versus 64 percent in 2015) ( DiJulio et al., 2015 ).

Perceptions are confirmed by the persistence of disparities along the lines of socioeconomic position, gender, race, ethnicity, immigration status, geography, and the like has been well documented. Why? For one, historical inequities continue to ramify into the present. To understand how historical patterns continue to affect life chances for certain groups, historians and economists have attempted to calculate the amount of wealth transmitted from one generation to the next ( Margo, 1990 ). They find that the baseline inequities contribute to intergenerational transfers of disadvantage and advantage for African Americans and whites, respectively ( Chetty et al., 2014 ; Darity et al., 2001 ). The inequities also reproduce the conditions in which disparities develop ( Rodriguez et al., 2015 ).

Though inequities may occur on the basis of socioeconomic status, gender, and other factors, we illustrate these points through the lens of racism, in part because disparities based on race and ethnicity remain the most persistent and difficult to address ( Williams and Mohammed, 2009 ). Racial factors play an important role in structuring socioeconomic disparities ( Farmer and Ferraro, 2005 ); therefore, addressing socioeconomic factors without addressing racism is unlikely to remedy these inequities ( Kaufman et al., 1997 ).

Racism is an umbrella concept that encompasses specific mechanisms that operate at the intrapersonal, interpersonal, institutional, and systemic levels 2 of a socioecological framework (see Figure 3-2 ). Because it is not possible to enumerate all of the mechanisms here, several are described below to illustrate racism mechanisms at different socioecological levels. Stereotype threat, for example, is an intrapersonal mechanism. It “refers to the risk of confirming negative stereotypes about an individual's racial, ethnic, gender, or cultural group” ( Glossary of Education Reform, 2013 ). Stereotype threat manifests as self-doubt that can lead the individual to perform worse than she or he might otherwise be expected to—in the context of test-taking, for example. Implicit biases—unconscious cognitive biases that shape both attitudes and behaviors—operate interpersonally (discussed in further detail below) ( Staats et al., 2016 ). Racial profiling often operates at the institutional level, as with the well-documented institutionalization of stop-and-frisk practices on Hispanic and African American individuals by the New York City Police Department ( Gelman et al., 2007 ).

Social ecological model with examples of racism constructs. NOTES: The mechanisms by which the social determinants of health operate differ with respect to the level. For the intrapersonal level, these mechanisms are individual knowledge, attitudes/beliefs, (more...)

Finally, systemic mechanisms, which may operate at the community level or higher (e.g., through policy), are those whose effects are interactive, rather than singular, in nature. For example, racial segregation of neighborhoods might well be due in part to personal preferences and behavior of landlords, renters, buyers, and sellers. However, historically, segregation was created by legislation, which was reinforced by the policies and practices of economic institutions and housing agencies (e.g., discriminatory banking practices and redlining), as well as enforced by the judicial system and legitimized by churches and other cultural institutions ( Charles, 2003 ; Gee and Ford, 2011 ; Williams and Collins, 2001 ). In other words, segregation was, and remains, an interaction and cumulative “product,” one not easily located in any one actor or institution. Residential segregation remains a root cause of racial disparities in health today ( Williams and Collins, 2001 ).

Racism is not an attribute of minority groups; rather, it is an aspect of the social context and is linked with the differential power relations among racial and ethnic groups ( Guess, 2006 ). Consider the location of environmental hazards in or near minority communities. Placing a hazard in a minority community not only increases the risk of adverse exposures for the residents of that community, it also ensures the reduction of risk for residents of the nonminority community ( Cushing et al., 2015 ; Taylor, 2014 ). Recognizing this, the two communities could work together toward an alternative that precludes having the hazard in the first place, an alternative that disadvantages neither group.

Most studies of racism are based on African American samples; however, other populations may be at risk for manifestations of racism that differ from the African American experience. Asians, Hispanics, and, more recently, Arabs and Muslims are subject to assumptions that they are not U.S. citizens and, therefore, lack the rights and social entitlements that other U.S. residents claim ( Chou and Feagin, 2015 ; Cobas et al., 2009 ; Feldman, 2015 ; Gee et al., 2009 ; Johnson, 2002 ; Khan and Ecklund, 2013 ). The implications of this include threats or actual physical violence against members of these groups. For instance, researchers have found that in the months immediately following September 11, 2001, U.S. women with Arabic surnames who were residing in California experienced increases in both racial microaggressions (i.e., seemingly minor forms of “everyday racism”) and in poor birth outcomes compared to the 6 months preceding 9/11, while women of other U.S. ethnic groups did not ( Kulwicki et al., 2008 ; Lauderdale, 2006 ). For Native Americans, because tribes are independent nations, the issues of racism need to be considered to intersect with those of sovereignty ( Berger, 2009 ; Massie, 2016 ; Sundeen, 2016 ).

The evidence linking racism to health disparities is expanding rapidly. A variety of both general and disease-specific mechanisms have been identified; they link racism to outcomes in mental health, cardiovascular disease, birth defects, and other outcomes ( Paradies, 2006a ; Pascoe and Smart Richman, 2009 ; Shavers et al., 2012 ; Williams and Mohammed, 2009 ). Which racism mechanisms matter most depends in part on the disease and, to a lesser degree, the population. The vast majority of studies focus on the role of discrimination; that is racially disparate treatment from another individual or, in some cases, from an institution. Among the studies not focused on discrimination, the majority examine segregation. Generally, findings show that members of all groups, including whites, report experiencing racial discrimination, with levels typically, though not always, higher among African Americans and, to a lesser degree, Hispanics than among whites. Gender differences in some perceptions about and responses to racism have also been observed ( Otiniano Verissimo et al., 2014 ). Three major mechanisms by which systemic racism influences health equity—discrimination (including implicit bias), segregation, and historical trauma—are discussed in more detail in the following paragraphs.

Discrimination

The mechanisms by which discrimination operates include overt, intentional treatment as well as inadvertent, subconscious treatment of individuals in ways that systematically differ so that minorities are treated worse than nonminorities. Recent meta-analyses suggest that racial discrimination has deleterious effects on the physical and mental health of individuals ( Gee et al., 2009 ; Paradies, 2006a ; Pascoe and Smart Richman, 2009 ; Priest et al., 2013 ; Williams and Mohammed, 2009 ). Significant percentages of members of racial and ethnic minority populations report experiencing discrimination in health care and non-health care settings ( Mays et al., 2007 ). Greater proportions of African Americans than members of other groups report either experiencing discrimination personally or perceiving it as affecting African Americans in general, even if they have not experienced it personally. Hate crimes motivated by race or ethnicity bias disproportionately affect Hispanics and African Americans ( UCR, 2015 ) (see the public safety section in this chapter for more on hate crimes).

Discrimination is generally associated with worse mental health ( Berger and Sarnyai, 2015 ; Gee et al., 2009 ; Paradies, 2006b ; Williams and Mohammed, 2009 ); greater engagement in risky behaviors ( Gee et al., 2009 ; Paradies, 2006b ; Williams and Mohammed, 2009 ); decreased neurological responses ( Harrell et al., 2003 ; Mays et al., 2007 ) and other biomarkers signaling the dysregulation of allostatic load; hypertension-related outcomes ( Sims et al., 2012 ), though some evidence suggests racism does not drive these outcomes ( Roberts et al., 2008 ); reduced likelihood of some health protecting behaviors ( Pascoe and Smart Richman, 2009 ); and poorer birth-related outcomes such as preterm delivery ( Alhusen et al., 2016 ). Paradoxically, despite higher levels of exposure to discrimination, the mental health consequences may be less severe among African Americans than they are among members of other groups, especially Asian populations ( Gee et al., 2009 ; Williams and Mohammed, 2009 ). Researchers have suggested that African Americans draw on reserves of resilience in ways that temper the effects of discrimination on mental health ( Brown and Tylka, 2011 ).

Though people may experience overt forms of racism (e.g., being unfairly fired on the basis of race), the adverse health effects of racism appear to stem primarily from the stress of chronic exposure to seemingly minor forms of “everyday racism” (i.e., racial microaggressions), such as being treated with less respect by others, being stopped by police for no apparent reason, or being monitored by salespeople while shopping ( APA, 2016 ; Sue et al., 2007 ; Williams et al., 2003 ). The chronic exposure contributes to stress-related physiological effects. Thus, discrimination appears to exert its greatest effects not because of exposure to a single life traumatic incident but because people must mentally and physically contend with or be prepared to contend with seemingly minor insults and assaults on a near continual basis ( APA, 2016 ). The implications appear to be greatest for stress-related conditions such as those tied to hypertension, mental health outcomes, substance abuse behaviors, and birth-related outcomes (e.g., low birth weight and premature birth) than for other outcomes ( Williams and Mohammed, 2009 ).

Higher socioeconomic status (SES) does not protect racial and ethnic minorities from discriminatory exposures. In fact, it may increase opportunities for exposure to discrimination. The concept of “John Henryism” is used to describe an intensely active way of tackling racial and other life challenges ( James, 1994 ). Though the evidence is mixed, John Henryism may contribute to worse cardiovascular outcomes among African American males who respond to racism by working even harder to disprove racial stereotypes ( Flaskerud, 2012 ; Subramanyam et al., 2013 ).

Implicit bias John Dovidio defines implicit bias—a mechanism of unconscious discrimination—as a form of racial or other bias that operates beneath the level of consciousness ( Dovidio et al., 2002 ). Research conducted over more than four decades finds that individuals hold racial biases of which they are not aware and, importantly, that discriminatory behaviors can be predicted based on this construct ( Staats et al., 2016 ). The effects are greatest in situations marked by ambiguity, stress, and time constraints ( Bertrand et al., 2005 ; Dovidio and Gaertner, 2000 ). Implicit bias is not an arbitrary personal preference that individuals hold; for example, “I just happen to prefer pears over apples.” Rather, the nature and direction of individuals' biases are structured by the racial stratification and norms of society. As a result, they are predictable.

Much of the public health literature has focused on the implicit biases of health care providers, who with little time to devote to each patient can provide care that is systematically worse for African American patients than for white patients even though the health care provider never intended to do so ( IOM and NRC, 2003 ; van Ryn and Burke, 2000 ). The evidence is clear that unconscious racialized perceptions contribute to differences in how various individual actors, including health care providers, perceive others and treat them. Based on psychology lab experiments, functional magnetic resonance imaging (fMRI) pictures of the brain, and other tools, researchers find that white providers hold implicit biases against African Americans and that, to a lesser degree, some minority providers may also hold these biases ( Hall et al., 2015 ). Although not limited to health care professionals, the biases lead providers to link negative characteristics (e.g., bad) and emotions (e.g., fear) with people or images they perceive as being African American ( Zestcott et al., 2016 ). As a result of such implicit biases, physicians treat patients differently depending on the patient's race, ethnicity, gender, or other assumed or actual characteristics ( IOM and NRC, 2003 ; Zestcott et al., 2016 ).

Given the importance of implicit bias, researchers have considered the role of health care provider–patient racial and ethnic concordance. Even if patients have similar clinical profiles, their care may differ systematically based on their race or ethnicity and that of their health care provider ( Betancourt et al., 2014 ; van Ryn and Fu, 2003 ; Zestcott et al., 2016 ). The evidence on whether and how patient–provider concordance contributes to health disparities is mixed ( van Ryn and Fu, 2003 ). Qualitative and quantitative findings suggest that patients do not necessarily prefer providers of the same race or ethnicity; they prefer a provider who treats them with respect ( Dale et al., 2010 ; Ibrahim et al., 2004 ; Schnittker and Liang, 2006 ; Volandes et al., 2008 ). Providers appear to evaluate African American patients more negatively than they do similar white patients; seem to perceive them as more likely to participate in risky health behaviors; and may be less willing to prescribe them pain medications and narcotics medications ( van Ryn and Fu, 2003 ). In a video-based study conducted among primary care providers, the odds ratio of providers referring simulated African American patients to otherwise identical white patients for cardiac catheterization was 0.6 ( Schulman et al., 1999 ). Some evidence suggests minority providers deliver more equitable care to their diverse patients than white providers. For instance, a longitudinal study among African American and white HIV-positive patients enrolled in HIV care found that white doctors took longer to prescribe protease inhibitors (an effective HIV medication) for their African American patients than for their clinically similar white patients. Providers prescribed them on average 162 days earlier for white patients than for comparable African American patients ( King et al., 2004 ). Among African American providers, there was no difference between African American and white patients in how long before providers prescribed the medications.

Racial and ethnic minority providers play an important role in addressing disparities because they help bridge cultural gulfs ( Butler et al., 2014 ; Cooper et al., 2003 ; Lehman et al., 2012 ), and greater proportions of them serve minority and socially disadvantaged communities ( Cooper and Powe, 2004 ); however, these providers are underrepresented in the health professions, and they face challenges that may constrain their professional development and the quality of care they are able to provide ( Landrine and Corral, 2009 ). Specifically, they are more likely to serve patients in resource-poorer areas and lack professional privileges associated with academic and other resource-rich institutions. The structural inequities have implications not only for individual clinicians but also for the patients and communities they serve. Pipeline programs that grow the numbers of minority providers may help to address underrepresentation in the health professions. The available data suggest that pipeline participants are more likely to care for poor or underserved patients when they join the workforce ( McDougle et al., 2015 ). Supporting the professional development of and expanding the resources and tools available to providers working in resource-poor communities seems to be one option for improving access to and quality of care; however, the literature does not clearly elucidate the relationship between health care workforce pipeline programs (e.g., to grow the numbers of minority providers) and their impact on the social determinants of health for poor and underserved communities ( Brown et al., 2005 ; Smith et al., 2009 ). A commitment to equity is not enough to remedy the discriminatory treatment that results from implicit biases because the inadvertent discriminatory behavior co-occurs alongside deeply held personal commitments to equity. Identifying implicit biases and acknowledging them is one of the most effective steps that can be taken to address their effects ( Zestcott et al., 2016 ). Trainings can help health care providers identify their implicit biases. Well-planned allocations of resources, including time, may afford them sufficient opportunity to account for it while serving diverse persons/patients.

Segregation

Residential segregation—that is, the degree to which groups live separately from one another ( Massey and Denton, 1988 )—can exacerbate the rates of disease among minorities, and social isolation can reduce the public's sense of urgency about the need to intervene ( Acevedo-Garcia, 2000 ; Wallace and Wallace, 1997 ). The effects of racial segregation differ from those of socioeconomic segregation. Lower SES whites are more likely to live in areas with a range of SES levels, which affords even the poorest residents of these communities access to shared resources (e.g., parks, schools) that buffer against the effects of poverty ( APA Task Force on Socioeconomic Status, 2007 ; North Carolina Institute of Medicine Task Force on Prevention, 2009 ). By contrast, racial and ethnic minorities are more likely to live in areas of concentrated poverty ( Bishaw, 2011 ). Indeed, if shared resources are of poor quality, they may compound the low SES challenges an individual faces. Racial segregation contributes to disparities in a variety of ways. It limits the socioeconomic resources available to residents of minority neighborhoods as employers and higher SES individuals leave the neighborhoods; it reduces health care provider density in predominately African American communities, which affects access to health care ( Gaskin et al., 2012 ); it constrains opportunities to engage in recommended health behaviors such as walking; it may be associated with greater density of alcohol outlets, tobacco advertisements, and fast food outlets in African American and other minority neighborhoods ( Berke et al., 2010 ; Hackbarth et al., 1995 ; Kwate, 2008 ; LaVeist and Wallace, 2000 ); it increases the risk for exposure to environmental hazards ( Brulle and Pellow, 2006 ); and it contributes to the mental and physical consequences of prevalent violence, including gun violence and aggressive policing ( Landrine and Corral, 2009 ; Massey and Denton, 1989 ; Polednak, 1996 ).

Historical Trauma

Historical trauma, “a collective complex trauma inflicted on a group of people who share a specific group identity or affiliation” ( Evans-Campbell, 2008, p. 320 ), manifests from the past treatment of certain racial and ethnic groups, especially Native Americans. This is another form of structural (i.e., systemic) racism that continues to shape the opportunities, risks, and health outcomes of these populations today ( Gee and Ford, 2011 ; Gee and Payne-Sturges, 2004 ; Heart et al., 2011 ). The past consignment of Native Americans to reservations with limited resources continues to constrain physical and mental health in these communities; however, the methods to support research on this topic have not yet been fully developed ( Heart et al., 2011 ). Additional details on the health of Native Americans are presented in Chapter 2 and Appendix A .

Interventions

The literature includes a small number of tested interventions. Interventions to address the health consequences of racism need not target racism in order to address the disparities it helps to produce. Furthermore, despite the deeply rooted nature of racism, communities are taking action to address the issue. (See Box 3-2 for a brief example of a community targeting structural racism and Box 3-3 for guidance on how to start a conversation about race.) Policy interventions and multi-sectoral efforts may be necessary to address structural factors such as segregation.

Addressing Structural Racism in Everett, Massachusetts, Through Improving Community–Police Interactions.

How to Start a Conversation on Race and Health (Excerpted from Culture of Health Prize Winner, Everett, Massachusetts).

Examples of interventions that target racism include the following:

• Dismantling racism by addressing factors in organizational settings and environments that “directly and indirectly contribute to racial health care disparities” ( Griffith et al., 2010, p. 370 ); see work by Derek Griffith ( Griffith et al., 2007 , 2010 ).
• The Undoing Racism project ( Yonas et al., 2006 ), which integrates community-based participatory research with the “undoing racism” process, which is built around community organizing.
• The Praxis Project, 3 a national organization whose mission is to build healthy communities by transforming the power relationships and structures that affect lives. The organization's comprehensive strategy for change includes policy advocacy, local organizing, strategic communications, and community research.

Although there is not a robust evidence base from which to draw solutions for implicit bias and its effects, there are promising strategies. For example, there is emerging evidence that mindfulness-based interventions have the potential to reduce implicit bias ( Kang et al., 2014 ; Levesque and Brown, 2007 ; Lueke and Gibson, 2014 ). One promising avenue of research involves models of self-regulation and executive control on interracial interaction ( Richeson and Shelton, 2003 ). Mindfulness has been shown to work on the cognitive brain function attentional processes involved in executive function, which is involved in decision making ( Lueke and Gibson, 2014 ; Malinowski, 2013 ). A key component of mindfulness is paying attention with intention and without judgment.

There is also existing literature that points to the need for community-based interventions to mitigate implicit bias within the context of criminal justice and community safety ( Correll et al., 2002 , 2007 ; La Vigne et al., 2014 ; Richardson and Goff, 2013 ). According to the National Initiative for Building Community Trust and Justice, implicit bias can shape the outcomes of interactions between police and residents, which in turn result in pervasive practices that focus suspicion on specific populations ( National Initiative for Building Community Trust and Justice, 2015 ). As discussed later in this chapter, the criminal justice system is a key actor and setting in shaping health inequity (see also Chapters 6 and 7 for more on criminal justice system as policy context and as a partner, respectively). Law enforcement agencies in communities around the country have employed strategies such as “principled policing” and policy changes and trainings to strengthen police–community relations ( Gilbert et al., 2016 ; Jones, 2016 ).

The Perception Institute, 4 an organization committed to generating evidence-based solutions for bias in education, health care, media, workplace, law enforcement, and civil justice, published a report authored by Godsil et al. (2014) in which promising interventions for implicit bias are highlighted ( Godsil et al., 2014 ). Among these interventions was a multipronged approach to reducing implicit bias that Devine and colleagues (2012) found to be successful and the “first evidence that a controlled, randomized intervention can produce enduring reductions in implicit bias” ( Devine et al., 2012, p. 1271 ). The multiple strategies of the intervention tested included stereotype replacement, counter-stereotype imaging, individuation, perspective taking, and increasing opportunities for contact. As discussed above, there is an emerging body of literature that is beginning to highlight promising solutions for implicit bias; however, that research base needs to be expanded further.

Recommendation 3-1: The committee recommends that research funders 5 support research on (a) health disparities that examines the multiple effects of structural racism (e.g., segregation) and implicit and explicit bias across different categories of marginalized status on health and health care delivery; and (b) effective strategies to reduce and mitigate the effects of explicit and implicit bias.

This could include implicit and explicit bias across race, ethnicity, gender identity, disability status, age, sexual orientation, and other marginalized groups.

There have been promising developments in the search for interventions to address implicit bias, but more research is needed, and engaging community members in this and other aspects of research on health disparities is important for ethical and practical reasons ( Minkler et al., 2010 ; Mosavel et al., 2011 ; Salway et al., 2015 ). In the context of implicit bias in workplaces and business settings, including individuals with relevant expertise in informing and conducting the research could also be helpful. Therefore, teams could be composed of such nontraditional participants as community members and local business leaders, in addition to academic researchers.

Conclusion 3-1: To reduce the adverse effects and the level of implicit bias among stakeholders in the community (such as health care workers, social service workers, employers, police officers, and educators), the committee concludes, based on its judgment, that community-based programs are best suited to mitigate the adverse effects of implicit bias. Successful community programs would be tailored to the needs of the community. However, proven strategies and efficacious interventions to reduce the effects of or mitigate effects of implicit bias are lacking. Therefore: Recommendation 3-2: The committee recommends that research funders support and academic institutions convene multidisciplinary research teams that include nonacademics to (a) understand the cognitive and affective processes of implicit bias and (b) test interventions that disrupt and change these processes toward sustainable solutions.

• SOCIAL DETERMINANTS OF HEALTH

As described earlier, structural inequities are produced on the basis of social identity (e.g., race, gender, and sexual orientation), and the social determinants of health are the “terrain” on which the effects play out. Traditionally, the most well-known and cited of the factors that shape health outcomes are the individual-level behavioral factors (e.g., smoking, physical activity, nutrition habits, and alcohol and drug use) that the evidence shows are proximally associated with individual health status and outcomes. As stated in Chapter 1 , understanding the social determinants of health requires a shift toward a more upstream perspective (i.e., the conditions that provide the context within which an individual's behaviors are shaped). Again, consider the metaphor of a fish, and the role of the conditions of the fishbowl in influencing the fish's well-being, and the analogy to human beings and conditions in which people live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks. These environments and settings (e.g., school, workplace, neighborhood, and church) have been referred to as “place.” In addition to the more material attributes of “place,” the patterns of social engagement, social capital, social cohesion, and sense of security and well-being are also affected by where people live ( Braveman and Gottlieb, 2014 ; Healthy People 2020, 2016 ). Although the term “social determinants of health” is widely used in the literature, the term may incorrectly suggest that such factors are immutable. It is important to note that the factors included among the social determinants of health are indeed modifiable and that they can be influenced by social, economic, and political processes and policies. In fact, there are communities throughout the United States that have prioritized addressing the social determinants of health and are demonstrating how specific upstream strategies lead to improved community conditions and health-related outcomes. (See Chapter 5 for an in depth examination of nine community examples.) Although it might be more accurate to refer to social “contributing factors” for health, the committee continues to use the widely accepted word “determinants” in this report.

For the purposes of this report, the committee has identified nine social determinants of health (see report conceptual model, Figure 3-3 ) that the literature shows fundamentally influence health outcomes at the community level. These determinants are education, income and wealth, employment, health systems and services, housing, the physical environment, transporation, the social environment, and public safety ( Table 3-1 provides a brief definition of each).

Report framework for community solutions to promote health equity. NOTE: The social (and other) determinants of health are highlighted to convey the focus of this section.

The Social (and Other) Determinants of Health .

There is a vast and growing body of literature on the social, economic, and environmental determinants of health and their impacts on health outcomes ( Braveman and Gottlieb, 2014 ; Braveman et al., 2011 ; CSDH, 2008 ; Marmot et al., 2010 ). Often, the evidence is in the form of cross-sectional analyses, and the pathways to health outcomes are not always clearly delineated, in part due to the complexity of the mechanisms and the long time periods it takes to observe outcomes ( Braveman and Gottlieb, 2014 ). Therefore, the literature is not sufficient to establish a causal relationship between each of these determinants and health, but the determinants certainly are correlated with and contribute to health outcomes. While this report focuses on the community level, it should be made clear that the social determinants of health operate at multiple levels throughout the life course ( IOM, 2006 ). This includes the individual level (knowledge, attitudes/beliefs, skills), family and community level (friends and social networks), institutional level (relationships among organizations), and systemic level (national, state, and local policies, laws, and regulations) (see Figure 3-2 , the social ecological model adapted from McLeroy et al. [1988] ). Furthermore, the various levels of influence that the social determinants of health have can occur simultaneously and interact with one another ( IOM, 2006 ). In addition to the multiple levels of influence, there is a diversity of actors, sectors, settings, and stakeholders that interact with and shape the social determinants of health. This adds an additional layer of complexity to the factors that shape health disparities.

The following sections describe each of these nine determinants and how they shape health outcomes, as well as the disparities within these social determinants of health that contribute to health inequity. To highlight the ongoing work of communities that seek to address the conditions in which members live, learn, work, and play, this section will feature brief examples of communities for each determinant of health.

Education, as it pertains to health, can be conceptualized as a process and as an outcome. The process of educational attainment takes place in many settings and levels (e.g., the home/family, school, and community), while the outcome can be described as a sum of knowledge, skills, and capacities that can influence the other social determinants of health, or health, more directly ( Davis et al., 2016 ). Within the current social determinants of health literature, the primary focus on education is on educational attainment as an outcome (i.e., years of schooling, high school completion, and number of degrees obtained) and how it relates to health outcomes.

There is an extensive body of research that consistently demonstrates a positive correlation between educational attainment and health status indicators, such as life expectancy, obesity, morbidity from acute and chronic diseases, health behaviors (e.g., smoking status, heavy drinking physical activity, preventive services or screening behavior, automobile and home safety) and more ( Baum et al., 2013 ; Cutler and Lleras-Muney, 2006 , 2010 ; Feinstein et al., 2006 ; Krueger et al., 2015 ; Rostron et al., 2010 ). Educational attainment also has an intergenerational effect, in which the education of the parents, particularly maternal education, is linked to their children's health and well-being ( Cutler and Lleras-Muney, 2006 ). For example, research suggests that babies born to mothers who have not completed high school are twice as likely to die before their first birthday as babies who are born to college graduates ( Egerter et al., 2011b ; Mathews and MacDorman, 2007 ). Death rates are declining among the most-educated Americans, accompanied by steady or increasing death rates among the least educated ( Jemal et al., 2008 ). The findings on the association between education and health are consistent with population health literature within the international context as well ( Baker et al., 2011 ; Furnee et al., 2008 ; Marmot et al., 2010 ).

Even more noteworthy about the education and health relationship is the graded association that is observed across populations with varying education levels, commonly referred to as the “education gradient.” In the United States the gradient in health outcomes by educational attainment has steepened over the last four decades in all regions of the United States ( Goldman and Smith, 2011 ; Montez and Berkman, 2014 ; Olshansky et al., 2012 ), producing a larger gap in health status between Americans with high and low education. Specifically, trends in data suggest that, over time, the disparities in mortality and life expectancy by education level have been increasing ( Meara et al., 2008 ; Olshansky et al., 2012 ). Meara et al. found that approximately 20 percent of this trend was attributable to differential trends in smoking-related diseases in the 1980s and 1990s, despite the overall population increases in life expectancy during these two decades ( Meara et al., 2008 ). Economic trends and shifting patterns of employment, in which skilled jobs linked to educational attainment are associated with increased income, also have implications for health ( NRC, 2012 ). This makes the connection between education and health, mediated by employment opportunities, even more important and worth exploring.

Data from the Behavioral Risk Factor Surveillance System reveal that across all racial groups, adults with higher levels of educational attainment are less likely to rate their own health as less than very good ( Egerter et al., 2011b ). While the education gradient is present across racial and ethnic groups, it is important to keep in mind that the rates of educational attainment vary across different racial and ethnic groups. For the 2013–2014 academic year, the high school graduation rate for white students was 87.2 percent as compared with 76.3 percent among Hispanics, 72.5 percent among African Americans, and 70 percent among Native Americans ( Kena et al., 2016 ). These rates are consistent with high school diploma and bachelor degree achievement gaps that have persisted since the late 1990s (see Figures 3-4 and 3-5 ).

Percentage of 25- to 29-year-olds who completed at least a high school diploma or its equivalent, by race and ethnicity: Selected years, 1995–2015. NOTE: Race categories exclude persons of Hispanic ethnicity. Prior to 2005, separate data on persons (more...)

Percentage of U.S.-born population ages 25 years and older with a bachelor's degree or higher by race and Hispanic origin, 1988–2015. SOURCE: Ryan and Bauman, 2016.

Although the literature linking education and health is robust, there is still some debate as to whether or not this relationship is a causal one ( Baker et al., 2011 ; Fujiwara and Kawachi, 2009 ; Grossman, 2015 ). Issues that have been raised in the course of this debate include the role of reverse causation and the potential influence of any unobserved third variables ( Grossman, 2015 ). The association between education and health is clearly bidirectional. Education outcomes are substantially affected by health ( Cutler and Lleras-Muney, 2006 ). Students living in community conditions that contribute to hunger, chronic stress, or lack of attention to visual or hearing needs are likely to have problems concentrating in class ( Evans and Schamberg, 2009 ). Unmanaged health conditions (e.g., asthma, dental pain, acute illnesses, mental health issues, etc.) give rise to chronic absenteeism, which in turn is highly correlated with underachievement ( Ginsburg et al., 2014 ). In short, health issues are much more than minor distractions in the lives of students, especially students living in low-income communities.

Disparities in Education

Educational attainment, common measures of which include high school diploma or bachelor's degree, has increased for all race groups and Hispanics since 1988, according to U.S. Census estimates ( Ryan and Bauman, 2016 ). Despite this overall progress, the gaps between these groups have remained the same for some and increased for others. For example, in 1988 African Americans and Hispanics attained bachelor's degrees at very similar rates; however, by 2015 the percentage gap between African Americans and Hispanics had reached 7 percent, with rates of completion at 22 percent and 15 percent, respectively ( Ryan and Bauman, 2016 ). Furthermore, there has been little to no progress in closing the gap of achievement between whites and African Americans ( Ryan and Bauman, 2016 ).

A recent study of school trends conducted by the U.S. Government Accountability Office (GAO) found that there has been a large increase in schools that are distinguished by the poverty and race of their student bodies ( GAO, 2016 ). The percent of K–12 schools with students who are poor and are mostly African American or Hispanic grew from 9 percent to 16 percent from 2000 to 2013. These schools were the most racially and economically concentrated among all schools, with 75 to 100 percent of the students African American or Hispanic and eligible for free or reduced-price lunch—a commonly used indicator of poverty. Moreover, compared with other schools, these schools offered disproportionately fewer math, science, and college preparatory courses and had disproportionately higher rates of students who were held back in 9th grade, suspended, or expelled ( GAO, 2016 ).

One gap in educational achievement that has successfully been narrowed over the past five decades is the gender disparity in bachelor's degree attainment, in which men historically had higher achievement rates ( Crissey et al., 2007 ). In 2015 the percentage of men ages 25 or older with a bachelor's degree or higher was not statistically different from that of women, with women leading by one percentage point ( Ryan and Bauman, 2016 ).

The evidence suggests that disparities in education are apparent early in the life course, which reflects broader societal inequities ( Garcia, 2015 ). In education, these early disparities are evidenced by wide gaps in vocabulary between children from low-income and those from middle- or upper-income families. Children from low-income families may have 600 fewer words in their vocabulary by age 3, a gap that grows to as many as 4,000 words by age 7 ( Christ and Wang, 2010 ). These word gaps directly affect literacy levels and reading achievement ( Marulis and Neuman, 2010 ). There is substantial evidence that children who do not read at grade level by 7 or 8 years of age are much more likely to struggle academically ( Chall et al., 1990 ). Both high school graduation rates and participation in postsecondary education opportunities are correlated with early literacy levels. Hence, attention to and investments in early childhood education are generally viewed as an important way to reduce disparities in education ( Barnett, 2013 ).

Although the association between education and health is clear, the mechanisms by which educational attainment might improve health are not so clearly understood. A keen understanding of the mechanisms could help to inform the most cost-effective and targeted policies or solutions that seek to improve health and, ultimately, promote health equity ( Picker, 2007 ). Egerter et al. (2011b) identified multiple interrelated pathways through which education can affect health, based on the literature (see Figure 3-6 ). The three major pathways are the following:

Pathways through which education can affect health. SOURCE: Egerter et al., 2011b. Used with permission from the Robert Wood Johnson Foundation.

• Education increases health knowledge, literacy, coping, and problem solving, thereby influencing health behaviors;
• Research indicates that each additional year of education leads to almost 11 percent more income annually ( Rouse and Barrow, 2006 ), which can secure safer working environments and benefits such as health insurance and sick leave.
• Education has also been linked to human capital, a systematic way of thinking that benefits every decision, which could positively affect health decisions ( Cutler and Lleras-Muney, 2006 ; Lundborg et al., 2012 , 2016 ).

In this framework, note that educational attainment is a predictor of health and can either improve or hinder health outcomes depending on educational attainment. This suggests that policies and practices proven to increase academic performance and reduce education disparities are important to reducing health disparities. (See Box 3-4 for an example of a community school working to improve educational outcomes.) Intervening early is generally considered a high-impact strategy ( Barnett, 2013 ). However, interventions that support academic achievement in high schools and in postsecondary settings are also important to increasing educational attainment ( Balfanz et al., 2007 ; Carnahan, 1994 ; Kirst and Venezia, 2004 ; Louie, 2007 ). One of the key factors in both high school and college completion rates has to do with how well students transition from one level of the education system to another ( Rosenbaum and Person, 2003 ).

Reagan High School: A Community School.

Income and Wealth

Income can be defined broadly as the amount of money earned in a single year from employment, government assistance, retirement and pension payments, and interest or dividends from investments or other assets ( Davis et al., 2016 ). Income can fluctuate greatly from year to year depending on life stage and employment status. Wealth, or economic assets accumulated over time, is calculated by subtracting outstanding debts and liabilities from the cash value of currently owned assets—such as houses, land, cars, savings accounts, pension plans, stocks and other financial investments, and businesses. Wealth measured at a single time period may provide a more complete picture than income of a person's economic resources. Moreover, wealth has an intergenerational component, which can have implications for who has access to wealth and who does not ( De Nardi, 2002 ).

Access to financial resources, be it income or wealth, affects health by buffering individuals against the financial threat of large medical bills while also facilitating access to health-promoting resources such as access to healthy neighborhoods, homes, land uses, and parks ( Davis et al., 2016 ). Income can predict a number of health outcomes and indicators, such as life expectancy, infant mortality, asthma, heart conditions, obesity, and many others ( Woolf et al., 2015 ).

Income Inequality and Concentration of Poverty

Income inequality is rising in the United States at a rate that is among the highest in the economically developed countries in the north ( OECD, 2015 ). The past few decades have seen dramatic rises in income inequality. In 1970, 17 percent of families lived in upper-income areas, 65 percent in middle-income areas, and 19 percent in lowest-income areas; in 2012, 30 percent of families lived in upper-income areas, 41 percent in middle-income areas, and 30 percent in lowest-income areas ( Reardon and Bischoff, 2016 ). In 2013, the top 10 percent of workers earned an average income 19 times that of the average income earned by the bottom 10 percent of workers; in the 1990s and 1980s, this ratio was 12.5 to 1 and 11 to 1, respectively ( OECD, 2015 ). Furthermore, households earning in the bottom 10 percent have not benefited from overall increases in household income over the past few decades; the average inflation-adjusted income for this population was 3.3 percent lower in 2012 than in 1985 ( OECD, 2015 ). Disparities in life expectancy gains have also increased alongside the rise in income inequality. From 2001 to 2014, life expectancy for the top 5 percent of income earners rose by about 3 years while life expectancy for the bottom 5 percent of income earners saw no increase ( Chetty et al., 2016 ).

Not only are income and wealth determinants of health, but the concentration of poverty in certain neighborhoods is important to recognize as a factor that shapes the conditions in which people live. Concentrated poverty , measured by the proportion of people in a given geographic area living in poverty, can be used to describe areas (e.g., census tracts) where a high proportion of residents are poor ( Shapiro et al., 2015 ). Concentrated poverty disproportionately affects racial and ethnic minorities across all of the social determinants of health. For example, National Equity Atlas data reveal that in about half of the largest 100 cities in the United States, most African American and Hispanic students attend schools where at least 75 percent of all students qualify as poor or low-income under federal guidelines ( Boschma, 2016 ). Given that concentrated poverty is tightly correlated with gaps in educational achievement, this has implications for educational outcomes and health ( Boschma and Brownstein, 2016 ).

Disparities Related to Income Inequality

In 2012, of the 12 million full-time low-income workers between the ages of 25 and 64, 56 percent were racial and ethnic minorities ( Ross, 2016b ). Regional percentages varied from 23 percent in Honolulu, Hawaii, to 65 percent in Brownsville, Texas ( Ross, 2016a ). Figure 3-7 shows the proportion of low-income workers of racial and ethnic minority groups across different regions of the United States. The burden faced by low-income people suggests that efforts to advance health equity through income and wealth will need to take into consideration rising income inequality as well as significant geographic variation.

The share of people of color below 200 percent of poverty ranges. SOURCE: Woolf et al., 2015. Used with permission from PolicyLink, figure from article by Angel Ross, New Data Highlights Vast and Persistent Racial Inequities in Who Experiences Poverty (more...)

Chetty and colleagues published the largest study of its kind, using 1.4 billion income tax and Social Security records to report the association between income level and life expectancy from 1999 through 2014 ( Chetty et al., 2016 ). Consistent with previous findings ( NASEM, 2015 ; Waldron, 2007 ; Woolf et al., 2015 ), they found that higher income is related to higher life expectancy and that lower income is related to lower life expectancy. The gap in life expectancy for the richest and poorest 1 percent of individuals was 14.6 years for men and 10.1 years for women. A novel contribution of the study is its examination of the income–longevity relationship across time and local areas. In certain local areas, the effect of being at the bottom of the income gradient is more pronounced than in others, with four- to five-fold differences. This strong local component reinforces the notion suggested by the literature that place matters. Trends in life expectancy also varied geographically, with some areas experiencing improvements and others declines. Others have commented on the limitations of the study ( Deaton, 2016 ; McGinnis, 2016 ; Woolf and Purnell, 2016 ).

Zonderman et al. take the findings of this study a step further by considering the role of race and gender differences in the relationship between poverty and mortality. They found that while African American men below poverty status had 2.66 times higher risk of mortality than African American men living above poverty status, white men below poverty status had approximately the same risk as white men living above poverty status ( Zonderman et al., 2016 ). Both African American women and white women living below poverty status were at an increased mortality risk relative to those living above poverty status ( Zonderman et al., 2016 ).

Infant mortality rates in the United States rank among the highest for developed nations ( NRC and IOM, 2013 ), and mortality rates for infants born to low-income mothers are even higher. Studies have shown an inverse correlation between family income and infant mortality ( Singh and Yu, 1995 ) as well as a positive correlation between income inequality (measured with the Gini coefficient) and infant mortality ( Olson et al., 2010 ). Infants born to low-income mothers have the highest rates of low birth weight ( Blumenshine et al., 2010 ; Dubay et al., 2001 ).

Chronic diseases are more prevalent among low-income people than among the overall U.S. population. Low-income adults have higher rates of heart disease, diabetes, stroke, and other diseases and conditions relative to adults earning higher levels of income ( Woolf et al., 2015 ).

Researchers have offered various hypotheses about the multiple mechanisms by which income can affect health. Woolf et al. suggest that among others, these mechanisms include more income providing the opportunity to afford health care services and health insurance; greater resources affording a healthy lifestyle and access to place-based benefits known as the social determinants of health; and economic disadvantage and hardship leading to stress and harmful physiological effects on the body ( Woolf et al., 2015 ). Evans and Kim identify “multiple risk exposure” as a potential mechanism for the socioeconomic status and health gradient. This is the convergence among populations with low socioeconomic status of multiple physical and psychosocial risk factors such as poor housing and neighborhood quality, pollutants and toxins, crowding and congestion, noise exposure, and adverse interpersonal relationships ( Evans and Kim, 2010 ).

Wealth affects health through mechanisms that are not necessarily monetary, such as power and prestige, attitudes and behavior, and social capital ( Pollack et al., 2013 ). Even in the absence of income, wealth can provide resources and a safety net that is not available to those without it. (See Box 3-5 for an example of an initiative seeking to build income and wealth in communities around the country.)

Family Independence Initiative: The Power of Information and Investment in Families Who Take Initiative.

Employment is the level or absence of adequate participation in a job or workforce, including the range of occupation, unemployment, and underemployment. Work influences health not only by exposing employees to certain physical environments but also by providing a setting where healthy activities and behaviors can be promoted ( An et al., 2011 ). For most adults, employment is the main source of income, thus providing access to homes, neighborhoods, and other conditions or services that promote health. The features of a worksite, the nature of the work, the amount of earnings or income, and how the work is organized can affect worker mental and physical health ( An et al., 2011 ; Clougherty et al., 2010 ). Many Americans also obtain health insurance through their workplace, accounting for another potential impact on health and wellbeing. While the correlation between employment and health has been well established, there appears to be a bidirectional relationship between employment and health, as health also affects one's ability to participate in and maintain stable employment ( Davis et al., 2016 ; Goodman, 2015 ). Not only that, but a healthy workforce is a prerequisite for economic success in any industry ( Doyle et al., 2005 ).

The existing literature on the social determinants of health makes it clear that there is a positive correlation between SES and health ( Adler and Stewart, 2010a ; Braveman et al., 2005 ; Conti et al., 2010 ; Dow and Rehkopf, 2010 ; Pampel et al., 2010 ; Williams et al., 2010 ). Occupational status, a composite of the power, income, and educational requirements associated with various positions in the occupational structure, is a core component of a person's SES ( Burgard and Stewart, 2003 ; Clougherty et al., 2010 ). Occupational status can be indicative of the types of tangible benefits, hazards, income, fringe benefits, degree of control over work, and level of exposure to harmful physical environments associated with a job ( Clougherty et al., 2010 ). While the mechanisms by which occupational status influences health have not clearly been delineated, there is evidence that the type of job does affect such health outcomes as hypertension risk and obesity ( An et al., 2011 ; Clougherty et al., 2010 ).

On the other end of the spectrum, unemployment is associated with poor psychological well-being ( McKee-Ryan et al., 2005 ; Paul and Moser, 2009 ). Zhang and Bhavsar (2013) examined the literature to illuminate the causality, effect size, and moderating factors of the relationship between unemployment as a risk factor and mental illness as an outcome. The authors reported that unemployment does precede mental illness, but more research is required to determine the effect size ( Zhang and Bhavsar, 2013 ). There is also evidence to suggest that emerging adults who are unemployed are three times as likely to suffer from depression as their employed counterparts ( McGee and Thompson, 2015 ). Burgard and colleagues found that even after controlling for significant social background factors (e.g., gender, race, education, maternal education, income, and more), involuntary job loss was associated with poorer overall self-rated health and more depressive symptoms ( Burgard et al., 2007 ).

Disparities in Employment

Employment data show disparities in unemployment rates across various racial and ethnic groups and geographic regions, despite the overall progress that has been made in reducing unemployment nationally ( Wilson, 2016 ). During the fourth quarter of 2015, the highest state-level unemployment rate was 13.1 percent for African Americans (Illinois), 11.9 percent for Hispanics (Massachusetts), 6.7 percent for whites (West Virginia), and 4.3 percent for Asians (New York) ( Wilson, 2016 ). Figure 3-8 shows how disparities in unemployment by race and ethnicity have persisted for more than 40 years, with the exception of whites and Asians. Disparities in employment between African Americans and whites persist even when level of education, a major predictor of employment, is held equal between the two groups ( Buffie, 2015 ).

Unemployment rates by race and Hispanic or Latino ethnicity, 1973–2013 annual averages. NOTE: People whose ethnicity is identified as Hispanic or Latino may be of any race. Data for Asians are only available since 2000. SOURCE: BLS, 2014.

Among the employed, there are systematic differences in wages and earnings by race, ethnicity, and gender. According to the U.S. Bureau of Labor Statistics, in 2013 the median usual weekly earnings 6 were $578 for Hispanics, $629 for African Americans, $802 for whites, and $942 for Asians ( BLS, 2014 ). These disparities are consistent across almost all occupational groups. The widest gap in median usual weekly earnings was found between Hispanic women and Asian men, who made $541 and $1,059, respectively ( BLS, 2014 ).

As with income, the distribution of occupations tends to differ across racial and ethnic groups (see Figure 3-9 ). Whereas half of Asians worked in management, professional, and related occupations in 2013, only 29 and 20 percent of African Americans and Hispanics, respectively, worked in those professions ( BLS, 2014 ).

Employed people by occupation, race, and Hispanic or Latino ethnicity, 2013 annual averages. NOTE: People whose ethnicity is identified as Hispanic or Latino may be of any race. Data may not sum to 100 percent due to rounding. SOURCE: BLS, 2014.

The literature suggests that there are three potential mechanisms through which employment affects health:

Physical aspects of work and the workplace

Psychosocial aspects of work and how work is organized

Work-related resources and opportunities ( An et al., 2011 ; Clougherty et al., 2010 )

The nature of work and the conditions of a workplace can increase the risk of injury or illness depending on the type of job. For employees in specific sectors (e.g., air transportation, nursing facilities, using motorized vehicles and equipment, trucking services, hospitals, grocery stores, department stores, food services), the risk of occupational injury is higher ( An et al., 2011 ). This is especially true for operators, laborers, fabricators, and laborers ( An et al., 2011 ). Occupational health can also be shaped by the physical nature of the tasks involved in a given work setting. For example, the health impact of a job that requires intense, laborious physical activity will be different than of a job in which the tasks are primarily sedentary. There is also emerging evidence suggesting that women working hourly jobs bear a larger burden due to hazardous conditions in the workplace than their male counterparts on outcomes such as hypertension, the risk of injury, injury severity, rates of absenteeism, and the time to return to work after illness ( Clougherty et al., 2010 ; Hill et al., 2008 ).

The psychosocial aspects and organization of one's job can influence both mental and physical health. The factors that make up this pathway can include work schedules, commute to work, degree of control in work, the balance between effort and rewards, organizational justice, social support at work, and gender and racial discrimination ( An et al., 2011 ). Longer commute times specifically affect low-income populations, as the cost burden of commuting for the working poor is much higher than for other workers and makes up a larger portion of their household budgets ( Roberto, 2008 ).

The resources and opportunities associated with work can have lasting implications for health. Higher-paying jobs are more likely than lower-paying jobs to provide workers with safe work environments and offer benefits such as health insurance, workplace health promotion programs, and sick leave ( An et al., 2011 ). Box 3-6 briefly describes a program that aims to increase “green” employment opportunities for underserved individuals in a community.

Green Jobs Central Oklahoma.

Health Systems and Services

Health care is arguably the most well-known determinant of health, and it is traditionally the area where efforts to improve health have been focused ( Heiman and Artiga, 2015 ). Over the past few decades there has been a paradigm shift that reflects “health” care over “sick” care. The idea is to promote access to effective and affordable care that is also culturally and linguistically appropriate. Health care spans a wide range of services, including preventative care, chronic disease management, emergency services, mental health services, dental care, and, more recently, the promotion of community services and conditions that promote health over the lifespan.

Although screening, disease management, and clinical care play an integral role in health outcomes, social and economic factors contribute to health outcomes almost twice as much as clinical care does ( Heiman and Artiga, 2015 ; Hood et al., 2016 ; McGinnis et al., 2002 ; Schroeder, 2007 ). For example, by some estimates, social and environmental factors proportionally contribute to the risk of premature death twice as much as health care does ( Heiman and Artiga, 2015 ; McGinnis et al., 2002 ; Schroeder, 2007 ). That being said, in March 2002, the Institute of Medicine released a report that demonstrated that even in the face of equal access to health care, minority groups suffer differences in quality of health. The noted differences were lumped into the categories of patient preferences and clinical appropriateness, the ecology of health systems and discrimination, bias, and stereotyping ( IOM and NRC, 2003 ). Our health systems are working to better understand and address these differences and appreciate the importance of moving beyond individualized care to care that affects families, communities, and populations ( Derose et al., 2011 ). This new focus on improving the health of populations has been accompanied by a welcome shift from siloed care to a health care structure that is interprofessional, multisectoral and considers social, economic, structural and other barriers to health ( NASEM, 2016 ).

Arriving at the place of shared understanding concerning the health care needs of individuals, families, and communities has required taking a broader look at health. The triple aim, a framework that aims to optimize health system performance, has helped conceptualize this look, bringing to the forefront the elements that matter most, considering per capita cost, improving the health care experience for patients, and focusing on population health ( Stiefel and Nolan, 2012 ). In addition to helping create new health care opportunities, the Patient Protection and Affordable Care Act (ACA) has helped mitigate the challenge of access to care. According to the U.S. Centers for Disease Control and Prevention (CDC), the proportion of people in 2015 without health insurance had dropped below 10 percent ( Cohen et al., 2016c ).

Continuing the momentum of improving access to culturally competent and linguistically appropriate care will be a crucial step to improving the health of populations. Culturally and linguistically appropriate care includes high-quality care and clear communication regardless of socioeconomic or cultural background ( Betancourt and Green, 2010 ). There is limited research studying whether there is a link between culturally appropriate care and health outcomes, but data do exist that indicate that behavioral and attitudinal elements of cultural competence facilitate higher-quality relationships between physicians and patients ( Paez et al., 2009 ). Making cultural competency training a part of the all types of providers' (e.g., physicians, nurses, medical assistants, dentists, pharmacists, social workers, psychologists) education experience, as well as making it a requirement for licensure for providers ( Like, 2011 ), may have the potential to link quality and safety. Continued work is needed to figure out how to translate increased access to care into improved health outcomes and increased health equity.

In light of the ACA's emphasis on access to improving quality, health outcomes, and population health, it makes sense to look at the environments in which patients live. 7 If the social determinants of health are not addressed in a multi-sectoral approach by educational systems, health systems, communities and others, the country will fall short of the triple aim. The Robert Wood Johnson Foundation's Culture of Health Action Framework has identified action areas meant to work together to address issues of equity, well-being, and improved population health ( RWJF, 2015b ). Social determinants of health are woven through these action areas. In fact, research shows that social determinants of health play a larger role in health outcomes than do medical advances ( Hood et al., 2016 ; Woolf et al., 2007 ).

Disparities

While some disparities in access to care have been narrowing, gaps persist among certain groups of the population. For example, the gaps in insurance that existed between poor and nonpoor households and between African Americans and whites or Hispanics and whites decreased between 2010 and 2015 ( AHRQ, 2016 ). However, systematic differences in access to care still exist and negatively affect poor households and racial and ethnic minority groups, including Hispanics and African Americans ( NCHS, 2016 ) (see Figure 3-10 ). In fact, in 2013 people living below the federal poverty level had worse access to care than people in high-income households across all access measures 8 ( NCHS, 2016 ). People living in low-income households are at an elevated risk of poor health, and access to care is vital for this vulnerable population. The ACA authorized states to expand Medicaid coverage to adults with low incomes up to 138 percent of the poverty level. From 2013 to 2014, the percent of adults who were uninsured declined in all states, with the decline in the number of uninsured being greater in the states that opted to expand their Medicaid programs ( NCHS, 2016 ).

FIGURE 3-10

Percent of adults ages 18–64 with no health insurance coverage by race and Hispanic origin: United States, 1999–June 2015. SOURCE: NCHS, 2016.

Racial and ethnic disparities in mental health services exist as well. Members of racial and ethnic minority groups are less likely than whites to receive necessary mental health care and more likely to receive poor-quality care when treated. Specifically, minority patients are less likely than whites to receive the best available treatments for depression and anxiety ( McGuire and Miranda, 2008 ). Among the barriers to access to care, the lack of culturally competent care can be a barrier for specific racial and ethnic groups who face stigma due to cultural norms ( Wahowiak, 2015 ).

The health care system has an important role to play in addressing the social determinants of health. At the community level, it can partner with community-based organizations and explore locally based interventions ( Heiman and Artiga, 2015 ), creating payment models that take into account social determinants and implementing service delivery models that lend themselves to more community engagement and intervention. Health care systems can center equity by involving the community in decision making, allocating resources to act on the determinants of health in mind, and increasing community-based spending ( Baum et al., 2009 ). Communities can be viewed as places of change for health systems, allowing for work both at micro and macro levels. (See Box 3-7 for an example of a community-based health system.) Cost-effective interventions to reduce health disparities and promote health equity should be recognized and explored, including attention to the structural barriers that affect access to health services.

Kokua Kalihi Valley Comprehensive Family Services.

Housing, as a social determinant of health, refers to the availability or lack of availability of high-quality, safe, and affordable housing for residents at varying income levels. Housing also encompasses the density within a housing unit and within a geographic area, as well as the overall level of segregation and diversity in an area based on racial and ethnic classifications or SES. Housing affects health because of the physical conditions within homes (e.g., lead, particulates, allergens), the conditions in a multi-residence structure (an apartment building or town home), the neighborhoods surrounding homes, and housing affordability, which affects financial stability and the overall ability of families to make healthy choices ( Krieger and Higgins, 2002 ). The Center for Housing Policy has outlined 10 hypotheses on how affordable housing can support health improvement ( Maqbool et al., 2015 ). These range from affordable housing freeing up resources for better nutrition and health care spending to stable housing reducing stress and the likelihood of poor health outcomes (e.g., for mental health or the management of chronic disease).

There is substantive evidence that the physical conditions in homes are important contributors to health outcomes ( Cox et al., 2011 ; WHO, 2006 ). The World Health Organization (WHO) assessed the evidence in 2005 and found that sufficient evidence was available to estimate the burden of disease for physical factors, such as temperature extremes; chemical factors, such as environmental tobacco smoke and lead; biological factors, such as mold and dust mites; and building factors associated with injuries and accidents. Since 2005 research has added to the areas where the WHO found some, but not sufficient, evidence to estimate the burden of disease, including more clarity on the relationship between rodent allergens and asthma ( Ahluwalia et al., 2013 ; American College of Allergy Asthma and Immunology, 2014 ; Sedaghat et al., 2016 ). Data from the National Health and Nutrition Examination Survey show a decrease in blood lead levels between 1976 and 2002, with a steep drop between 1978 and 1988, probably due to lead being phased out of gasoline, and later a more gradual decrease, perhaps due to a reduction in the use of lead-based paint in housing ( Jacobs et al., 2009 ). Conditions in multiunit residential buildings, including whether indoor smoking is permitted, are another dimension of housing that can affect health outcomes. Box 3-8 introduces the revitalization efforts of one multiunit apartment complex in a community in Minnesota.

Renovating the Rolling Hills Apartment Complex, St. Paul, Minnesota.

Neighborhoods matter for a number of reasons, including their influence on physical safety and access to opportunity. The U.S. Department of Housing and Urban Development's (HUD's) Moving to Opportunity program was a 10-year demonstration program, which provided grants to public housing authorities in Baltimore, Boston, Chicago, Los Angeles, and New York City to implement an experimental study—a randomized controlled trial of a housing intervention. Housing authorities

randomly selected experimental groups of households with children [to] receive housing counseling and vouchers that must be used in areas with less than 10 percent poverty. Families chosen for the experimental group receive tenant-based Section 8 rental assistance that helps pay their rent, as well as housing counseling to help them find and successfully use housing in low-poverty areas. Two control groups are included to test the effects of the program: one group already receiving Section 8 assistance and another just coming into the Section 8 program. ( HUD, n.d .)

Homeless Populations

For homeless people, a lack of stable housing contributes to disparities in the social determinants. In addition to having direct ties with lack of employment and income, a lack of housing is also associated with greater barriers to education, lower levels of food security, and reduced public safety. Compared to the overall population, homeless people have shorter life expectancies, which are attributable to higher rates of substance abuse, infectious disease, and violence ( Baggett et al., 2013 ). Infectious diseases—including HIV, tuberculosis, and heart disease—have all been linked to shorter life expectancies among homeless people ( Fazel et al., 2014 ). Other studies have found drug overdose, cancer, and heart disease to be the greatest causes of death among the homeless, with greater barriers to and lower rates of screening, diagnosis, and treatment as contributing factors ( Baggett et al., 2013 ).

The Changing American City

Neighborhoods generally change slowly, but urban neighborhoods are seeing dramatic shifts in demographics and property value and over time are becoming more segregated by income ( Zuk et al., 2015 ). Gentrification—the process of renewal and rebuilding, which precedes the influx of new, more affluent residents—is a trend that is being observed in urban centers around the country ( McKinnish et al., 2010 ; Phillips et al., 2014 ; Sturtevant, 2014 ). While the literature linking the process of gentrification to health outcomes is not definitive, there is substantial evidence that connects displacement and health outcomes ( Zuk et al., 2015 ). Displacement can occur as a direct result of a policy or program ( Freeman and Braconi, 2002 ), because of recent development and property value increases in an area, or as a result of exclusion from a property for various reasons ( Levy et al., 2006 ).

Displacement has major implications for housing, other social determinants, and the health of communities. According to the CDC, displacement exacerbates health disparities by limiting access to healthy housing, healthy food options, transportation, quality schools, bicycle and walk paths, exercise facilities, and social networks ( CDC, 2013 ). Displacement leads to poor housing conditions, including overcrowding and exposure to substandard housing with hazardous conditions (e.g., lead, mold, pests) ( Phillips et al., 2014 ). Displacement can result in financial hardship, reducing disposable income for essential goods and services. This can have a negative impact on the health of the displaced population, with income being a significant determinant of health ( CDC, 2013 ).

Physical Environment

The physical environment reflects the place, including the human-made physical components, design, permitted use of space, and the natural environment. Specific features of the physical or built environment include, but are not limited to, parks and open space, what is sold and how it is promoted, how a place looks and feels, air, water, soil, and arts and cultural expression ( Davis et al., 2016 ). All of these physical factors shape the safety, accessibility, and livability of any locale, thus providing the context in which people live, learn, work, and play. This has direct implications for health. The physical environment contributes to 10 percent of health outcomes ( Remington et al., 2015 ). Additionally, 40 percent of health outcomes depend on social and economic factors, which are intricately tied to the features of the physical environment ( Remington et al., 2015 ). Inequities observed between the different physical environments of states, towns, and neighborhoods contribute to disparate health outcomes among their populations.

Exposure to a harmful physical environment is a well-documented threat to community health. Such threats include environmental exposures such as lead, particulate matter, proximity to toxic sites, water contamination, air pollution, and more—all of which are known to increase the incidence of respiratory diseases, various types of cancer, and negative birth outcomes and to decrease life expectancy ( Wigle et al., 2007 ). Low-income communities and communities of color have an elevated risk of exposure to environmental hazards ( Evans and Kantrowitz, 2002 ). In response to these inequities, the field of environmental justice seeks to achieve the “fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income, with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies” ( EPA, 2016 ). Emerging considerations for low-income communities include the resulting gentrification and potential displacement of families when neighborhoods undergo revitalization that is driven by environmental clean-up efforts ( Anguelovski, 2016 ).

Built Environment: Parks and Green Space

Access to green space has been demonstrated to positively affect health in many contexts. Such green space includes both parks and observable greenery. Living in the presence of more green space is associated with a reduced risk of mortality ( Villeneuve et al., 2012 ). Nature has been shown to relieve stress and refocus the mind. Spending time in parks has been shown to improve mental health ( Cohen et al., 2016a ; Sturm and Cohen, 2014 ).

Beyond their benefits to mental health and reductions in stress, parks provide opportunities for increased physical activity. Local parks departments manage more than 108,000 outdoor public park facilities across the nation, many of them containing open space, jogging paths, and exercise equipment ( Cohen et al., 2016b ). According to Cohen et al., the average neighborhood park of 8.8 acres averaged 1,533 hours of active use per week ( Cohen et al., 2016b ). Individuals who are not as physically active face a greater risk of heart disease, diabetes, and cancer ( James et al., 2016 ). In fact, about 9 percent of premature deaths in the United States are attributable to inactivity ( Lee et al., 2012 ).

The usage of neighborhood parks and the associated health benefits are not equally distributed across communities. Research shows that recreational facilities are much less common in low-income and minority communities, though parks are more evenly distributed ( Diez Roux et al., 2007 ). Moreover, the size and quality of park facilities vary based on race and income ( Abercrombie et al., 2008 ). Accordingly, in low-income communities, residents are less likely to use parks ( Cohen et al., 2016a ). Beyond race and income, other disparities exist in park use. While seniors represent 20 percent of the population, they account for only 4 percent of park users ( Cohen et al., 2016a ). Proximity to park facilities also matters, as evidenced by a decrease in physical activity by more than half when distance between one's home and the park doubles ( Giles-Corti and Donovan, 2002 ).

Food Environment

The food environment refers to the availability of food venues such as supermarkets, grocery stores, corner stores, and farmer's markets, including food quality and affordability. In communities described as food deserts, there is limited access to affordable and quality food. When there are fewer supermarkets, fruit and vegetable intake is lower, and prices are higher ( Powell et al., 2007 ). This makes achieving a healthy diet difficult for local residents. Research indicates that a poor diet is associated with the development of cancer, diabetes, hypertension, birth defects, and heart disease ( Willett et al., 2006 ).

The distribution of supermarkets is not equitable in the United States. Neighborhoods housing residents of lower socioeconomic status often have fewer supermarkets. Discrepancies also exist between racial and ethnic groups ( Powell et al., 2007 ). Underserved communities turn to small grocery or corner stores to serve their food needs, but these businesses rarely provide the healthy selection offered by larger supermarkets. Moreover, food is most often higher priced in such stores.

Access to and the density of alcohol outlets are also associated with health outcomes in communities. In local areas where liquor store density is higher, alcohol consumption rates in the community are also higher ( Pereiram et al., 2013 ). Alcoholism has been linked to diseases such as cancer, anemia, and mental illnesses. Moreover, alcohol outlets can serve as nuisance businesses, with their clientele bothering others in the neighborhood, decreasing the sense of security, and detracting from social cohesion. There is also evidence that links high-density alcohol outlet areas with higher rates of crime and substance use. In urban environments, a higher concentration of liquor stores is found in low-income, African American, and Hispanic communities, contributing to an elevated risk of alcohol-associated disorders in these neighborhoods ( Berke et al., 2010 ).

A Changing Climate

Climate change has become a public health concern ( Wang and Horton, 2015 ). There is a growing recognition that the physical environment is undergoing changes caused by human activity, such as through the production of greenhouse gases ( IPCC, 2014 ). Human health is intricately linked to the places where we live, learn, work, and play. The air we breathe, the surrounding temperature, the availability of food, and whether there is access to clean water are all important ingredients to a healthy life, and the changing climate will affect all of these areas ( Luber et al., 2014 ).

Not only do polluting emissions make air quality worse in the short term, but climate change itself will worsen air quality. Poor air quality exacerbates previous health conditions such as asthma and chronic obstructive pulmonary disease, and air pollution is associated with cardiovascular disease and many other illnesses. The changing climate is also causing a shift in seasons, which can affect pollen production and therefore seasonal allergies. Overall, with the changing climate there will be more extreme weather events such as increasing drought, vulnerability to wildfires, floods, hurricanes, and winter storms—all with subsequent health impacts from displacement, stress, or primary physical harm. The changing temperature is even having an impact on infectious diseases. New infectious diseases that spread via a vector, such as a tick or mosquito, have the potential to emerge in previously non-affected areas. There is also a risk for an increase in food-related and waterborne illness caused by the changing temperatures and the survival of various infectious agents. Food insecurity, which is already a challenge in many locations, is at risk of worsening due to higher food prices, poorer nutritional content, and new challenges with distribution.

Although climate change will affect everyone, certain communities and groups will be more vulnerable to these effects. People with preexisting medical conditions, children, elderly populations, and low-income groups are at increased risk for poor outcomes. Existing health disparities that are due to social, economic, and environmental factors have the potential to be even more affected by climate change.

However, climate change also presents a significant opportunity. Given the existential threat to humanity, there is now a great deal of momentum to mitigate and adapt to climate change. Companies are pursuing new business opportunities, governments are forming international agreements, and policies are being implemented at the national, sub-national, state, regional, and local levels to affect change. Many of these policies to adapt to and mitigate climate change are also the key components in creating healthier, more equitable, and resilient communities. There are many co-benefits, and the policies, if implemented correctly, have the potential to significantly improve health outcomes and reduce health disparities ( Rudolph et al., 2015 ). Examples of climate change mitigation and adaptation policies with co-benefits to build healthier, more equitable places include

• Improving access to public transit;
• Promoting flexible workplace transit;
• Creating more complete streets for better pedestrian and bicycle use;
• Implementing urban greening programs;
• Reducing urban heat islands through green space, cool roofs, and cool pavements;
• Promoting sustainable food systems and improved access;
• Building more walkable, dense, affordable housing and amenities;
• Reducing greenhouse gases;
• Promoting weatherizing homes, energy efficiency, and green buildings; and
• Greening fleets and reducing emissions.

Climate change will affect the physical environment in unprecedented ways. To mitigate and adapt to climate change will require multi-sector collaboration and approaches to effect systems change. Many of the same multi-sector partners required to address the social determinants of health also are already partnering on related climate change work in their communities, creating a substantial opportunity for change (see Box 3-9 for an example of a community engaged in climate change–related work).

A Community Addressing Climate Change, Food Insecurity, and Improving Health Equity—Achieving Co-Benefits.

Transportation

In the social determinants of health literature, transportation is typically discussed as a feature of the physical (or built) environment ( TRB and IOM, 2005 ). This report highlights transportation as a separate determinant of health because of its multifaceted nature: pollution and greenhouse gas production; motor vehicle–related deaths and injuries; mobility and access to employment and vital goods and services; and active transportation. Transportation consists of the network, services, and infrastructure necessary to provide residents with the means to get from one place to another ( Davis et al., 2016 ), and it is also vital to accessing goods, services (including health and social services), social networks, and employment. If designed and maintained properly, transportation facilitates safe mobility and is accessible to all residents, regardless of geographic location, age, or disability status. However, current research suggests that transportation costs are a barrier to mobility for households in poverty, which are disproportionately represented by African Americans and Hispanics ( FHWA, 2014 ). Long commute times and high transportation costs are significant barriers to employment and financial stability ( Roberto, 2008 ). Brookings researchers have concluded, based on analyses of census data, that the suburbanization of poverty is disproportionately affecting proximity to jobs for poor and minority populations as compared with their nonpoor and white peers ( Kneebone and Holmes, 2015 ; Zimmerman et al., 2015 ).

Transportation presents unevenly distributed negative externalities, including air pollution, noise, and motor vehicle–related injuries and deaths that are more prevalent in low-income and minority communities with poor infrastructure ( Bell and Cohen, 2014 ; US DOT, 2015 ). Low-income and minority populations are more likely to live near environmental hazards, including transportation-related sources of pollution and toxic emissions such as roadways, bus depots, and ports ( McConville, 2013 ; NEJAC, 2009 ; Perez et al., 2012 ). See, for example, Shepard (2005/2006) on the high concentration of bus depots in West Harlem, which also has one of the highest rates of asthma in the nation. The Regional Asthma Management and Prevention collaborative, in Oakland, California, and the California Environmental Protection Agency's Air Resources Board, among others, have described the evidence on the relationship between asthma and exposures to diesel and other air pollution ( California EPA, 2016 ; RAMP, 2009 ).

Active transportation—the promotion of walking and cycling for transportation complemented by public transportation or any other active mode—is a form of transportation that reduces environmental barriers to physical activity and can improve health outcomes ( Besser and Dannenberg, 2005 ; Dannenberg et al., 2011 ). Since the mid-20th century, road design and transportation planning have centered on the automobile, with multiple and interconnected consequences for health and equity ( IOM, 2014 ).

The relationship between physical activity and health is well established and was summarized by the U.S. Surgeon General's 1996 report Physical Activity and Health ( HHS, 1996 ) and the U.S. Task Force on Community Preventive Services ( U.S. Task Force on Community Preventive Services, 2001 ). The evidence on the relationship among active transportation, physical activity, and health has been accumulating more recently. In a 2005 report from the Transportation Research Board and the Institute of Medicine, the authoring committee stated that “[r]esearch has not yet identified causal relationships to a point that would enable the committee to provide guidance about cost beneficial investments or state unequivocally that certain changes to the built environment would lead to more physical activity or be the most efficient ways of increasing such activity” ( TRB and IOM, 2005, p. 10 ). Since then, Pucher et al. (2010) found “statistically significant negative relationships” between active travel (walking and cycling) and self-reported obesity as well as between active travel and diabetes ( Pucher et al., 2010 ).

McCormack and Shiell conducted a systematic review of 20 cross-sectional studies and 13 quasi-experimental studies and concluded that most associations “between the built environment and physical activity were in the expected direction or null” ( McCormack and Shiell, 2011 ). They also found that physical activity was considerably influenced by “land use mix, connectivity and population density and overall neighborhood design” and that “the built environment was more likely to be associated with transportation walking compared with other types of physical activity including recreational walking” ( McCormack and Shiell, 2011 ).

CDC has developed a set of transportation recommendations that address all of the facets described above and has also developed a Transportation Health Impact Assessment Toolkit. 9 The CDC and the U.S. Department of Transportation (DOT) have also developed a Transportation and Health Tool to share indicator data on transportation and health. 10

There have been multiple national initiatives in the past two to three decades aiming to improve livability and sustainability in places across the United States, and transportation equity is a mainstay of much of this work. (See Box 3-10 for an example of a regional transportation planning agency that seeks to improve access to transportation.) Initiatives have ranged from the federal Sustainable Communities Partnership, 11 launched by the DOT, HUD, and the U.S. Environmental Protection Agency in 2009 to help U.S. communities “improve access to affordable housing, increase transportation options, and lower transportation costs while protecting the environment,” to Safe Routes to School, which aims to improve children's safety while walking and riding bicycles. 12

The Nashville Metropolitan Planning Organization.

Social Environment

How the social environment is conceptualized varies depending on the source ( Barnett and Casper, 2001 ; Healthy People 2020, 2016 ). However, there are common elements identified by the literature that collectively shape a community's social environment as a determinant of health. For the purposes of this report, the social environment can be thought of as reflecting the individuals, families, businesses, and organizations within a community; the interactions among them; and norms and culture. It can include social networks, capital, cohesion, trust, participation, and willingness to act for the common good in relation to health. Social cohesion refers to the extent of connectedness and solidarity among groups in a community, while social capital is defined as the features of social structures (e.g., interpersonal trust, norms of reciprocity, and mutual aid) that serve as resources for individuals and facilitate collective action ( Kawachi and Berkman, 2000 ).

A 2008 systematic review found associations between trust as an indicator of social cohesion and better physical health, especially with respect to self-rated health. Furthermore, it revealed a pattern in which the association between social capital and better health outcomes was especially salient in inegalitarian countries (i.e., countries with a high degree of economic inequity), such as the United States, as opposed to more egalitarian societies ( Kim et al., 2008 ).

The social environment in a community is often measured as it relates to mental health outcomes. For example, social connections between neighbors (i.e., greater social cohesion, social capital, and reciprocal exchanges between neighbors) are protective against depression ( Diez Roux and Mair, 2010 ). Factors such as exposure to violence, hazardous conditions, and residential instability are all associated with depression and depressive symptoms ( Diez Roux and Mair, 2010 ).

It is important to note that high levels of social capital and a strong presence of social networks are not necessarily guarantors of a healthy community. In fact, they can be sources of strain as well as support ( Pearce and Smith, 2003 ). Some studies explore the potential drawbacks of social capital, such as the contagion of high-risk behaviors (e.g., suicidal ideation, injection drug use, alcohol and drug use among adolescents, smoking, and obesity) ( Bearman and Moody, 2004 ; Christakis and Fowler, 2007 ; Friedman and Aral, 2001 ; Valente et al., 2004 ).

McNeill et al. (2006) postulate that the following are mechanisms by which features of the social environment influence health behaviors:

• Social support and social networks enable or constrain the adoption of health-promoting behaviors; provide access to resources and material goods; provide individual and coping responses; buffer negative health outcomes; and restrict contact to infectious diseases.
• Social cohesion and social capital shape the ability to enforce and reinforce group or social norms for positive health behaviors and the provision of tangible support (e.g., transportation).

The social environment interacts with features of the physical environment at the neighborhood level to shape health behaviors, stress, and, ultimately, health outcomes ( Diez Roux and Mair, 2010 ). For example, a built environment that is poor in quality (i.e., low walkability, fewer parks or open space, unsafe transportation) can contribute to a lack of structural opportunities for social interactions, resulting in limited social networks in a community ( Suglia et al., 2016 ). Other research points to the role of physical activity as a potential pathway by which the social environment affects health outcomes such as obesity ( Suglia et al., 2016 ).

At the community level, an important element of the social environment that can mediate health outcomes is the presence of neighborhood stressors. While the occurrence of stress is a daily facet of life that all people experience, chronic or toxic stress, in which the burden of stress accumulates, is a factor in the expression of disease ( McEwen, 2012 ). Stressful experiences are particularly critical during early stages of life, as evidenced by the adverse childhood experiences study ( Felitti et al., 1998 ), and are associated with abnormal brain development ( IOM, 2000 ; Shonkoff and Garner, 2012 ). For low-income communities, stressors are salient because of the lack of resources, the presence of environmental hazards, unemployment, and exposure to violence, among other factors ( McEwen, 2012 ; Steptoe and Feldman, 2001 ). (See Box 3-11 for an example of a community working to combat these stressors.) This applies as well to children in low-income households, who are more likely to experience multiple stressors that can harm health and development ( Evans and Kim, 2010 ), mediated by chronic stress ( Evans et al., 2011 ).

Cowlitz Community Network.

Chronic stress due to adverse neighborhood and family conditions has been linked to the academic achievement gap, in which children living in poverty fall behind those in better-resourced neighborhoods ( Evans et al., 2011 ; Zimmerman and Woolf, 2014 ). Furthermore, stress and poor health in childhood are associated with decreased cognitive development, increased tobacco and drug use, and a higher risk of cardiovascular disease, diabetes, depression, and other conditions ( County Health Rankings, 2016 ).

Public Safety

Public safety and violence are significant, intertwined social determinants of health, but they are also each significant indicators of health and community well-being in their own right. Public safety refers to the safety and protection of the public, and it is often characterized as the absence of violence in public settings ( Davis et al., 2016 ). Since the late 1960s, homicide and suicide (another form of violence) have consistently ranked among the top leading causes of death in the United States ( Dahlberg and Mercy, 2009 ).

Violent victimization affects health by causing psychological and physical injury, which can lead to disability and, in some cases, premature death. Beyond the risk of injury and death, violent victimization also has far-reaching health consequences for individuals, families, and neighborhoods. Furthermore, research shows that simply being exposed to violence can have detrimental effects on physical and psychological well-being ( Felitti et al., 1998 ; Pinderhughes et al., 2015 ). Violent victimization and exposure to violence have been linked to poor health outcomes, including chronic diseases (e.g., ischemic heart disease, cancer, stroke, chronic obstructive lung disease, diabetes, and hepatitis), asthma-related symptoms, obesity, posttraumatic stress disorder, depression, and substance abuse ( Prevention Institute, 2011 ). For youth in schools, the data suggest that there is a cumulative effect of exposure to violence, with multiple exposures to violence being associated with higher rates of youth reporting their health as “fair” or “poor” ( Egerter et al., 2011a ). There is also research that indicates a link between neighborhood crime rates and adverse birth outcomes such as preterm birth and low birth weight ( Egerter et al., 2011a ).

Violence and the fear of violence can negatively affect other social determinants that further undermine community health. Violence rates can lead to population loss, decreased property values and investments in the built environment, increased health care costs, and the disruption of the provision of social services ( Massetti and Vivolo, 2010 ; Velez et al., 2012 ). In addition, violence in communities is associated with reduced engagement in behaviors that are known to promote health, such as physical activity and park use ( Cohen et al., 2010 ).

The perception of safety is a key indicator of violence in a community that is associated with health. For example, people who describe their neighborhoods as not safe are almost three times more likely to be physically inactive than those who describe their neighborhood as extremely safe ( Prevention Institute, 2011 ). The perception of safety is also important for mental health. There is research that suggests that perceived danger and the fear of violence can influence stress, substance use, anger, anxiety, and feelings of insecurity—all of which compromise the psychological well-being of a community ( Moiduddin and Massey, 2008 ; Perkins and Taylor, 1996 ). At the community level, fear of crime and violence can undermine social organization, social cohesion, and civic participation—all key elements in a social environment that is conducive to optimal health ( Perkins and Taylor, 1996 ). Low perception of safety can also undermine the efforts of a community to improve the built environment through the availability of parks and open space to promote physical activity ( Cohen et al., 2016a ; Weiss et al., 2011 ).

Violence is not a phenomenon that affects all communities equally, nor is it distributed randomly. The widespread disparity in the occurrence of violence is a major facet of health inequity in the United States. Low-income communities are disproportionately affected by violence and by the many effects that it can have on physical and mental well-being. The conditions of low-income communities (concentrated poverty, low housing values, and high schools with low graduation rates among others), foster violence and put residents at an increased risk of death from homicide ( Prevention Institute, 2011 ). This holds true for other types of violence as well. Living in poor U.S. neighborhoods puts African American and white women at an increased risk for intimate partner violence compared with women who reside in areas that are not impoverished ( Prevention Institute, 2011 ).

Criminologists attribute the disparities in neighborhood violence not to the kinds of people living in certain neighborhoods but to the vast differences in social and economic conditions that characterize communities in the United States. Some refer to these differences as “divergent social worlds” and the “racial–spatial divide” ( Peterson and Krivo, 2010 ). This is because there are specific racial and ethnic groups, such as African Americans, Hispanics, and Native Americans, who are vastly overrepresented in communities that are at risk for violence because of the social and economic conditions. Residential segregation, which has been perpetuated by discriminatory housing and mortgage market practices, affects the quality of neighborhoods by increasing poverty, poor housing conditions, and social disorder and by limiting economic opportunity for residents ( Prevention Institute, 2011 ).

As a result of the racial–spatial divide in community conditions, the violent crime rate in majority nonwhite neighborhoods is two to five times higher than in majority white neighborhoods. This is especially true for youth of color, particularly males. Overall homicide rates among 10- to 24-year-old African American males (60.7 per 100,000) and Hispanic males (20.6 per 100,000) exceed that of white males in the same age group (3.5 per 100,000) ( Prevention Institute, 2011 ). African American males 15 to 19 years old are six times as likely to be homicide victims as their white peers ( Prevention Institute, 2011 ). More specifically, African American males ages 15 to 19 are almost four times as likely to be victims of firearm-related homicides as white males ( Prevention Institute, 2011 ). In terms of exposure to violence, African American and Hispanic youth are more likely to be exposed to shootings, riots, domestic violence, and murder than their white counterparts ( Prevention Institute, 2011 ). This has major implications for trauma in communities that are predominantly African American or Hispanic. Native American communities also suffer from a disproportionately high violent crime rate that is two to three times higher than the national average ( Prevention Institute, 2011 ). Box 3-12 briefly describes a public health–oriented model to address violence in communities.

The Cure Violence Health Model.

Child Abuse and Neglect

Child abuse and neglect are two important measures of community violence that can affect physical and mental health. The Institute of Medicine and the National Research Council published a report (2014) that cited abuse and neglect during childhood as a contributor to the following health-related outcomes: problems with growth and motor development, lower self-reported health, gastrointestinal symptoms, obesity, delinquency and violence, and alcohol abuse ( IOM and NRC, 2014 ).

In 1998, Felitti and colleagues published a pivotal study which demonstrated a link between adverse childhood experiences and the leading causes of death in adults at the time. The authors found a strong, graded association between the amount of exposure to abuse or household dysfunction and multiple risk factors (e.g., smoking, severe obesity, physical inactivity, depressed mood, and suicide attempts) for several leading causes of death ( Felitti et al., 1998 ). Child abuse and neglect not only affect health directly, they also affect outcomes within the other social determinants of health, such as education, work, and social relationships ( IOM and NRC, 2014 ). While the overall rates of child maltreatment have been declining since 2002, rates are still much higher for African American (14.3 per 1,000), Native American (11.4 per 1,000), multiracial (10.1 per 1,000), and Hispanic (8.6 per 1,000) children than for white children (7.9 per 1,000) ( IOM and NRC, 2014 ; Prevention Institute, 2011 ). Child abuse and neglect are often accompanied by family stressors and other forms of family violence ( IOM and NRC, 2014 ). As discussed above, the conditions of concentrated poverty in a neighborhood are associated with violence incidence. According to the Prevention Institute, the higher the percentage of families living below the federal poverty level in a neighborhood, the higher the rate of child maltreatment ( Prevention Institute, 2011 ).

Hate Crimes

Hate crimes, which may or may not involve physical violence, are often motivated by some bias against a perceived characteristic. 13 An FBI analysis of single-bias hate crime incidents revealed that in 2014, 48.3 percent of victims were targeted because of the offender's bias against race, and 62.7 percent of those victims were targeted because of anti-African American bias ( UCR, 2015 ). Among hate crimes motivated by bias toward a particular ethnicity in 2014, almost 48 percent of the victims were targeted because of anti-Hispanic bias ( UCR, 2015 ).

As is the case with other types of violence, exposure to hate crime violence can have pernicious effects on health. For lesbian, gay, bisexual, and transgender (LGBT) persons specifically, exposure to hate crimes at the community level has been linked to increased rates of suicide among youth, marijuana use, and all-cause mortality ( Duncan and Hatzenbuehler, 2014 ; Duncan et al., 2014 ; Hatzenbuehler et al., 2014 ). Discrimination in general, which by definition is the driving factor behind the perpetration of hate crimes, has been shown to affect the health of individuals and communities. Whether it be perceived discrimination in everyday encounters or systemic discrimination in housing policies, this type of unequal treatment has been associated with major depression, psychological distress, stress, increased pregnancy risk, mortality, hypertension, and more health-related outcomes ( Dolezsar et al., 2014 ; Galea et al., 2011 ; Kessler et al., 1999 ; Padela and Heisler, 2010 ; Sims et al., 2012 ).

Criminal Justice System

The criminal justice system is a key actor, setting, and driver of public safety as it relates to health equity. Specifically, the criminal justice system's role in the mass incarceration of racial and ethnic minorities is an important factor when examining the social determinants of health ( NRC, 2014 ). The past 40–50 years have seen a large-scale expansion of incarceration, which has had lasting effects on families and communities ( Cloud, 2014 ; Drake, 2013 ). This expansion has affected racial and ethnic minority groups, and particularly men ( Drake, 2013 ). Research suggests that disproportionately more Hispanics and African Americans are confined in jails and prisons than would be predicted by their arrest rates and that Hispanic and African American juveniles are more likely than white juveniles to be referred to adult court rather than juvenile court ( Harris, 2009 ).

When those who were formerly incarcerated are released back into their communities, successful reentry is hindered by a number of obstacles, such as stigma, limited employment and housing opportunities, and the lack of a cohesive social network ( Lyons and Pettit, 2011 ). All of these factors are vital to achieving optimal health, and for communities with high rates of incarceration, the absence of these opportunities can lead to a diminished capacity to combat crime and mobilize for resources ( Clear, 2008 ). It is important to examine the patterns and effects of mass incarceration because it not only affects the health of incarcerated populations but also has a detrimental effect on multiple determinants of health in communities. Mass incarceration has contributed to the breakdown of educational opportunities, family structures, economic mobility, housing options, and neighborhood cohesion, especially in low-income communities of color ( Cloud, 2014 ). Neal and Rick examined U.S. Census data from 1960 to 2010 and found that although great progress was made in closing the black–white education and employment gap up until the 1980s, that progress then came to a halt in large part due to rising incarceration rates ( Neal and Rick, 2014 ). In addition, communities with high levels of incarceration have higher rates of lifetime major depressive disorder and generalized anxiety disorder ( Hatzenbuehler et al., 2015 ).

Wildeman estimated the effects of incarceration on population-level infant mortality rates, and his findings suggest that if incarceration rates remained the same as they were in 1973, the infant mortality rate in 2003 would have been 7.8 percent lower and the absolute African American–white disparity in infant mortality would have been 14.8 percent lower ( Wildeman, 2012 ). A keen understanding of the precise mechanisms by which incarceration affects the health of specific populations and contributes to health inequity is needed to reduce disparities in key health outcomes such as infant mortality.

• CONCLUDING OBSERVATIONS

The root causes of health inequity begin with historical and contemporary inequities that have been shaped by institutional and societal structures, policies, and norms in the United States. As discussed in this chapter, these deeply rooted inequities have shaped inequitable experiences of the social and other determinants of health: education, income and wealth, employment, health systems and services, housing, the physical environment, transportation, the social environment, and public safety.

Conclusion 3-2: Based on its review of the evidence, the committee concludes that health inequities are the result of more than individual choice or random occurrence. They are the result of the historic and ongoing interplay of inequitable structures, policies, and norms that shape lives.

These structures, policies, and norms—such as segregation, redlining and foreclosure, and implicit bias—play out on the terrain of the social, economic, environmental, and cultural determinants of health.

What Can Academic Research Do?

The current public health interest in the role of place, including communities, stems from significant empirical epidemiological evidence. As discussed in this chapter, there are a range of factors that contribute to health and that need to be more extensively studied. These include factors beyond the individual domain, such as living and working conditions and economic policies at the local, state, and national levels that are intimately connected to health and well-being. Likewise, the American Public Health Association's (APHA's) 2014 and 2015 conference themes on the geography of health and health in all policies, respectively, reflect a growing recognition of the need for action on social and environmental factors in order to achieve the goal of becoming the healthiest nation in one generation ( APHA, 2016 ).

At a meeting of the National Academies of Sciences, Engineering, and Medicine's Roundtable on Population Health Improvement in 2013, David Williams asked, “How could we expect that the lives and health of our patients would improve if they continued to live in the same conditions that contributed to their illness?” ( IOM, 2013 ). His question points to a fundamental challenge to improving the public's health and promoting health equity. This recognition that inequities in social arrangements and community factors shape life opportunities is not new; it was asserted as early as 1906 by W. E. B. Du Bois in his address regarding the role of social status and life conditions in shaping health and inequities. Du Bois reported findings from the 11th Atlanta Conference on the Study of the Negro Problem held at Atlanta University, which in part concluded that “the present difference in mortality seems to be sufficiently explained by conditions of life” ( DuBois, 1906 ).

Despite the increasingly widespread recognition in the field, many public health efforts continue to target individuals and are most often disease specific. The existing approaches to prevention and health promotion are still “catching up” with what is known about the social determinants of health and population health. Kindig and Stoddart pointed out that “much of public health activity, in the United States at least, does not have such a broad mandate” ( Kindig and Stoddart, 2003, p. 382 ). Building the science base for how to move upstream to improve population health has begun. While our understanding of the role of the social determinants of health, including features of the physical and social environments, has greatly improved over the last several decades, the scientific progress has not been so great on how, when, and where to intervene. Progress on how to move upstream in taking action has developed much more slowly than progress in the ability to describe the role of context and community-level factors that shape the major causes of morbidity, mortality, and well-being ( Amaro, 2014 ).

Improving the science of population health interventions, place-based approaches, and strategies to improve health equity will require a workforce of scientists and practitioners equipped to develop the requisite knowledge base and practice tools. As Kindig and Stoddart noted, social epidemiology has made highly important contributions to our understanding of the social determinants of health and population health but “does not have the breadth, or imply all of the multiple interactions and pathways” involved in population health ( Kindig and Stoddart, 2003, p. 382 ). Diez Roux and Mair describe social epidemiology's most critical conceptual and methodological challenges as well as promising directions in studying neighborhood health effects ( Diez Roux and Mair, 2010 ). Specifically, models for the training of population and place-based scientists and practitioners are needed to develop the research required to guide upstream approaches—including place-based interventions—that will address the contextual factors that shape major public health problems such as obesity, interpersonal violence, infant and maternal health, cardiovascular diseases, infectious diseases, substance abuse, and mental health disorders. For example, training models such as the interdisciplinary team science McArthur Model described by Adler and Stewart could be expanded to integrate public health practitioners and community leaders alongside research leaders ( Adler and Stewart, 2010b ).

Translating knowledge on the social determinants of health into practice requires at least four essential areas of expertise:

An understanding of theories that articulate the complex mechanisms of action in the social determinants of health and how place influences health.

Expertise in the design of community-level interventions and in models of community–academic partnerships.

Expertise in the complex issues of study design, measurement, and analytic methods in assessing changes resulting from interventions focused on population-level impacts and community-level health improvement.

Expertise and understanding of various socio-demographic groups, cultures, and varied sector stakeholders and drivers that shape sustained stakeholder engagement in improving population health and community conditions.

Considering the distinct fields of expertise required for these components and theory, the approaches to intervention and measurement stem from different disciplines and have often been developed without significant interchange. Researchers face significant challenges. Thus, academic institutions involved in the training of population and place-based scientists need to integrate these diverse bodies of knowledge—including theory, methods, and tools from diverse disciplines. Models for the transdisciplinary training of researchers, practitioners, and community partners are needed. Academic institutions need to develop models for intra-professional workforce training on place-based and community-level implementation science and evaluation that target improving population health and addressing health inequities. See Chapter 7 for more on the role of academic research in community solutions to promote health equity.

The social determinants of health, while interdependent and complex, are made up of mutable factors that shape the conditions in which one lives, learns, works, plays, worships, and ages. As highlighted in the boxes throughout this chapter, communities around the country are taking it upon themselves to address these conditions. Chapter 4 will discuss why communities are powerful agents of change, along with discussing the conditions necessary for successful and sustainable outcomes. Chapter 5 will provide an in-depth overview of nine communities that are addressing the root causes of health inequities.

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Obergefell v. Hodges , 576 U.S. (2015).

In 2000 Dr. Camara Jones developed a theoretical framework for the multiple levels of racism and used an allegory of a garden to illustrate the mechanisms through which these levels operate ( Jones, 2000 ).

For more information, see http://www ​.thepraxisproject.org (accessed October 20, 2016).

For more information, see https://perception ​.org (accessed October 18, 2016).

Funders include government agencies, private foundations, and other sources such as academic centers of higher education.

These represent earnings for full-time wage and salary workers only.

As access to care improves, it will be increasingly important to monitor potential disparities with respect to the nature of care that people receive. This is especially true for chronic conditions that require long-term engagement with the health care system.

Measures of access to care tracked in the 2015 National Healthcare Quality and Disparities Report include having health insurance, having a usual source of care, encountering difficulties when seeking care, and receiving care as soon as wanted.

For more information, see https://www ​.cdc.gov/healthyplaces ​/transportation ​/hia_toolkit.htm (accessed September 21, 2016).

For more information, see https://www ​.transportation ​.gov/transportation-health-tool (accessed September 21, 2016).

For more information, see https://www ​.sustainablecommunities ​.gov/mission/about-us (accessed September 21, 2016).

For more information, see http://www ​.saferoutesinfo.org (accessed September 21, 2016).

The Hate Crimes Statistics Act (28 U.S.C. § 534) defines hate crimes as “crimes that manifest evidence of prejudice based on race, gender or gender identity, religion, disability, sexual orientation, or ethnicity.”

• Cite this Page National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Population Health and Public Health Practice; Committee on Community-Based Solutions to Promote Health Equity in the United States; Baciu A, Negussie Y, Geller A, et al., editors. Communities in Action: Pathways to Health Equity. Washington (DC): National Academies Press (US); 2017 Jan 11. 3, The Root Causes of Health Inequity.
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