Lauren A. Taylor, Annabel Xulin Tan, Caitlin E. Coyle, Chima Ndumele, Erika Rogan, Maureen Canavan, Leslie A. Curry, Elizabeth H. Bradley Published: August 17, 2016
Abstract We summarized the recently published, peer-reviewed literature that examined the impact of investments in social services or investments in integrated models of health care and social services on health outcomes and health care spending. Of 39 articles that met criteria for inclusion in the review, 32 (82%) reported some significant positive effects on either health outcomes (N = 20), health care costs (N = 5), or both (N = 7). Of the remaining 7 (18%) studies, 3 had non-significant results, 2 had mixed results, and 2 had negative results in which the interventions were associated with poorer health outcomes. Our analysis of the literature indicates that several interventions in the areas of housing, income support, nutrition support, and care coordination and community outreach have had positive impact in terms of health improvements or health care spending reductions. These interventions may be of interest to health care policymakers and practitioners seeking to leverage social services to improve health or reduce costs. Further testing of models that achieve better outcomes at less cost is needed. Citation: Taylor LA, Tan AX, Coyle CE, Ndumele C, Rogan E, Canavan M, et al. (2016) Leveraging the Social Determinants of Health: What Works? PLoS ONE 11(8): e0160217. http://doi.org/10.1371/journal.pone.0160217 Editor: Huso Yi, The Chinese University of Hong Kong, HONG KONG Received: May 2, 2016; Accepted: July 16, 2016; Published: August 17, 2016 Copyright: © 2016 Taylor 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. Funding: The study was funded by Blue Cross Blue Shield of Massachusetts Foundation Inc (BCBS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors received funding from the Blue Cross Blue Shield of Massachusetts Foundation Inc (BCBS), a commercial company, for this study. There are no patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.
Introduction Social determinants of health have taken center stage in recent health policy discussions, particularly with the growing emphasis on global payment, accountable care organizations (ACO), and other initiatives focused on improving population health. Health care providers are increasingly being asked to measure impact in terms of the health outcomes of the population they serve. Given that medical care influences a relatively small portion of overall health [2, 3], ACO and value-based financing models face substantial challenges in equipping health care providers to achieve improvements in the population’s health. Many researchers have examined the relative contributions of health care services, genetics, behaviors, environment and social factors in promoting health and reducing premature mortality [3–6]. Overwhelmingly, studies find that non-medical factors including social, behavioral and environmental determinants of health consistently play a substantially larger role than medical factors. Similar patterns hold for specific health outcomes, including burdensome, high-cost diseases such as heart disease, stroke and diabetes [7–9], although the relative contributions may vary by 5–10 percent depending on the health outcome in question. Despite the evidence, an enduring challenge of the social determinants of health literature has been translating its insights into actionable recommendations. The literature is replete with studies dating to the 1970s that indicate that poor social determinants of health are harmful to health both in the short and longer-term [10–14] as well as a growing body of literature which demonstrates positive impact of favorable social conditions on health outcomes [15–17]. Nevertheless, the literature has not yet been reviewed comprehensively to generate an integrated, evidence-based summary of how to best address the social determinants to achieve positive health effects without increasing, and perhaps even decreasing, health care spending. Accordingly, we sought to synthesize the existing empirical evidence about the impact of social service interventions on health outcomes and health care spending, with particular attention to identifying programs and practices that achieved both improvements in health as well as potential reductions in health care spending.
Methods We summarized the peer-reviewed literature that examined the impact of investments in social services or investments in integrated models of health care and social services on health outcomes and health care spending. We used the PubMed database to execute our initial search and included relevant literature published in English between January 2004 and October 2014. We ran a number of search strings comprised of a combination of social and health keywords. Social service keywords included were: “social service,” “social spending,” “social welfare,” “housing,” “education,” “income support,” “nutrition,” “food stamp,” “SNAP,” “public safety,” and “transportation.” Health and health care keywords were: “health,” “health outcomes,” “health saving,” “health costs,” and “health spending,” and “health expenditure.” Eligibility criteria included: 1) inclusion of a social service intervention, or a health care intervention that specifically targeted a social, behavioral, or environmental determinant of health, 2) quantitative measurement of a health outcome, health care costs, or both; and 3) well-documented study design. We also reported utilization outcomes such as hospital admissions and emergency department visits, as these can influence health care spending. We excluded papers that examined health behaviors (e.g., cigarettes smoked, steps walked) rather than health outcomes. The search yielded 123 unique articles. Screening and analysis was conducted by three members of the research team (LT, CC, CN) who met frequently to review decisions and disagreements, which were resolved through negotiated consensus. A total of 80 of the 123 studies were excluded for not meeting eligibility criteria based on a review of their abstracts, leaving 43 articles for full article review. Upon further investigation, the review of the 43 full-length articles resulted in exclusion of an additional 4 articles, yielding a sample of 39 articles for analysis (Fig 1). These 39 articles were reviewed independently by the three members of the research team (LT, CC, CN) to record data on study design, sample characteristics, geographic location, description of the social service intervention, and empirical findings related to intervention-associated changes in health outcomes or health care spending. Because the 39 articles reported a range of health outcomes and cost metrics, the data were unable to accommodate a statistical meta-analysis.
Fig 1. Sampling Schematic.
Of the 39 articles, 32 (82%) reported some significant positive effects on either health outcomes (N = 20), health care costs (N = 5), or both (N = 7). Of the remaining 7 (18%) studies, 3 had non-significant results, 2 had mixed results, and 2 had negative results in which the interventions were associated with poorer health outcomes. No particular study design, intervention, or population was recurrent among studies with non-significant, mixed, and negative findings. For instance, one of the studies with negative findings was a randomized controlled trial evaluating a housing intervention on the mental health of male adolescents  while the other was cross-sectional study evaluating SNAP participation on the Body Mass Index of low-income adults . Among the 32 studies with positive outcomes, 10 (31%) were related to housing support, 7 (22%) to nutritional support, 4 (13%) to income support, 8 (25%) to care coordination and community outreach programs, and 3 (9%) to other types of interventions (Table 1). Study designs included cohort studies (N = 15), randomized controlled trials (N = 12), cross-sectional (N = 4), pre-post interventions (N = 6), quasi-experimental (N = 1), and post-test only evaluation (N = 1). We identified no studies explicitly examining the health effects of transportation or public safety interventions. Approximately 72% of all studies included in this review focused on low-income populations. We have summarized key findings and associations between programs and health outcomes/spending in greater depth (Table 2).
Table 1. Summary of findings in the literature (N = 39).
Table 2. Summary of associations between programs and health outcomes/spending.
http://doi.org/10.1371/journal.pone.0160217.t002 Housing Support
Overall, of 12 studies evaluating housing intervention, 4 studies– 3 from the US [20–22] and 1 from the UK –reported both improved health outcomes and reduced health care costs. Five additional studies showed improvement in health outcomes including obesity and diabetes among women with children , asthma among adults , self-reported health status among adults , mobility among low income older adults  and HIV outcomes . One study found that the provision of housing was significantly associated with lower health care spending among the chronically homeless with severe alcohol addiction . One study with non-significant results pertained to health care costs as a result of a housing intervention that included the provision of housing and case management for homeless adults with chronic illness , and one study reported significantly negative results pertaining to health outcomes, which reported poorer mental health outcomes among the adolescent boys in the intervention group who were offered supportive housing in a neighborhood other than their own . Nutritional Support
Of the 11 studies related to nutritional support interventions, seven studies reported significantly improved health outcomes. Six studies were based in the US [31–36] and one was conducted in Canada . No studies reported decreased health care costs associated with nutritional support interventions. Two studies reported null findings: one showed no significant relationship between food stamp recipients and the prevalence of diabetes, Medicare spending, or hospital utilization rates , and one reported no significant association between food assistance via a federallyfunded nutritional assistance program and probability of overweight/obesity . One study reported significantly increased obesity associated with food assistance in a sample of adult SNAP recipients, and one study  reported mixed results, showing that participation in the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) was associated with significantly higher birth weights, but this result was sensitive to model estimation parameters such that this result was only observed in fixed effects models. Income Support
We found four studies related to income support, all of which demonstrated a positive relationship between income support interventions and health outcomes, or both health outcomes and health care costs. A total of three studies reported Supplemental Security Income had a positive effect on health outcomes including infant mortality , disability rates among the elderly , and mental health among former beneficiaries . We found one study that showed both improved health outcomes and decreased health care costs; the Low Income Home Energy Assistance Program (LIHEAP) was associated with decreased probability of overweight and obesity among children and lower hospital admission rates. Care coordination and community outreach
Of the nine studies we reviewed with care coordination and community outreach interventions, four showed decreased health care costs associated with the intervention [45–48]. Two other care coordination interventions were shown to have a significantly positive impact on health outcomes, specifically on all-cause mortality in mothers  and on birth-weight among African American mothers , and two community outreach studies were associated with lower health care costs and better health outcomes [51, 52]. In these cases, community outreach interventions included a mobile health clinic and home-delivered meals. We also found one study with mixed results, showing that care coordination interventions reduced health care costs, but did not significantly impact quality-of-life related health outcomes . Other
We found three studies related to interventions with major educational components that were associated with improved health outcomes, especially among children. Two of these studies were based on the MEND trial, a multicomponent trial comprised of educational and physical activities designed to combat obesity among children aged 7–13 years [54, 55]. The third study was related to the Carolina Abecedarian Project, a program that was originally designed to promote cognitive development among disadvantaged children, which was ultimately shown to lower risk factors for cardiovascular and metabolic diseases among participants .
Discussion Our analysis of the literature indicates that several interventions in the areas of housing, income support, nutritional support, and care coordination and community outreach have had positive impact. These interventions should be of interest to health care policymakers and practitioners seeking to leverage social services to improve health or reduce costs. Importantly, 100% of the studies evaluating income support programs, 88% of the care coordination and community outreach interventions, 83% of the housing support programs, and 64% of the nutritional support programs evaluated had statistically significant, positive effects on health outcomes alone or on both health outcomes and health care spending. Furthermore, the direction and magnitude of the results were robust across different study designs. Among randomized control trials and quasi-experimental studies, 85% of interventions showed positive health impacts or reductions in health spending. Overall, a small number of studies met criteria to be included in our sample. This not only suggests the need for additional interventional research addressing both health and cost outcomes, but also points to the opportunity for pushing the existing body of evidence on population health to the next level of intervention development. Findings from this work, the majority of which was conducted with low-income populations, suggest that keeping a population healthy may require medical providers to link with unconventional partners such as housing authorities, food banks, and schools. The inclusion of researchers in crosssector partnerships to document impact with empirically sound methods may further sustain and ultimately help to scale interventions targeted at the social determinants of health. Moreover, while case managers and care coordinators have become a ubiquitous feature of many health care systems, the literature provides an impetus for potentially expanding the scope of services that case managers and care coordinators manage. Careful consideration of these issues may be particularly prudent among health systems that have transitioned to value-based financing or accountable care models, where health outcomes have been explicitly prioritized in performance metrics. The literature highlights the “wrong pocket problem” , in which the savings that accompany health improvements do not accrue to the investor. In economic circles, this challenge is more commonly termed an externality. Many social service interventions (e.g., income support, housing) generate positive health outcomes, yet social service sectors receive little if any reward for their contribution to the creation of health in the population. Similarly, depending on its payer and contract mix, a health care organization that contributes to a person’s health does not reap the full social benefit from those health improvements. Thus, the wrong pocket problem discourages cross-sector collaboration when in fact the literature reviewed here suggests a high degree of mutual dependence and potential reward from coordinated health care and social services. These are questions we can and should be wrestling with more explicitly, particularly as literature like this empirically demonstrates the broad range of inputs required to create health. Despite the substantial consistency of the findings overall, several limitations and gaps were apparent in the literature. First, the number of studies that examine impact on health care spending was relatively modest. As policymakers, payers, and providers seek to support programs that address the social determinants of health, understanding the health care cost offsets will be critical for widespread endorsement. Second, few studies examine interventions related to transportation services, public safety, education, and income support programs; and the majority of studies examine impacts on low-income groups, limiting the generalizability of findings. Third, several studies would be strengthened by better comparison groups, larger samples, and more sophisticated analytical methods to address potential confounding influences. These limitations of the literature, continue to hamper the translation of these findings into policy or practice . In summary, we found substantial evidence of improved health outcomes and/or reduced health care spending related to interventions that addressed housing, nutrition, income support, and care coordination and community outreach needs. At the same time, this literature can be improved in scope and rigor. Further studies, particularly examining a broader set of interventions with methods to determine causal effects on both health outcomes and health care spending, are needed to produce a comprehensive understanding of the degree to which interventions to address the social determinants of health care improve health and reduce health care costs.
Author Contributions Conceptualization: LT AXT CC CN EHB. Methodology: LT AXT CC CN EHB. Writing - original draft: LT AXT EHB. Writing - review & editing: LT AXT CC CN ER MC LC EHB.
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