Social Determinants of Health: What’s a Healthcare System to Do? : Journal of Healthcare Management

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Social Determinants of Health

What’s a Healthcare System to Do?

Gottlieb, Laura MD; Fichtenberg, Caroline PhD; Alderwick, Hugh; Adler, Nancy PhD

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Journal of Healthcare Management: July-August 2019 - Volume 64 - Issue 4 - p 243-257
doi: 10.1097/JHM-D-18-00160
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A sea change in our understanding of health and its determinants has occurred over the past three decades. A substantial literature now documents how individuals’ genetic and behavioral risks operate in the context of social, political, and environmental conditions that alter access to resources such as healthy food, safety, financial resources, quality education, and gainful employment— all of which shape health outcomes over the life course (Adler et al., 1994; Adler & Stewart, 2010b; Bach, Pham, Schrag, Tate, & Hargraves, 2004; Backlund, Sorlie, & Johnson, 1999; Crimmins & Saito, 2001; Ecob & Smith, 1999; Feldman, Makuc, Kleinman, & Cornoni-Huntley, 1989; Girotra, Cram, & Popescu, 2012; Jha, Orav, & Epstein, 2011; Jha, Orav, Li, & Epstein, 2007; Jha, Orav, Zheng, & Epstein, 2008; Kaplan et al., 1971; Karasek, Baker, Marxer, Ahlbom, & Theorell, 1981; Lantz et al., 1998; Marmot et al., 1991, 2008; Marmot, Ryff, Bumpass, Shipley, & Marks, 1997; National Academies of Sciences, Engineering, and Medicine, 2016b; Pappas, Queen, Hadden, & Fisher, 1993; Robert & House, 1996; Smith & Egger, 1992).

Models depicting when and how social determinants of health (SDH) affect both health and healthcare use (Adler & Stewart, 2010a) have led to calls to address adverse SDH as a way to reduce health inequities, improve heath, and control healthcare costs (American Academy of Pediatrics Council on Community, 2016; Canadian Medical Association, n.d.; Institute of Medicine of the National Academies Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records, 2014). Despite this interest, healthcare leaders are frequently making ad hoc decisions on how they will incorporate this new research into care delivery (Alderwick, Gottlieb, Fichtenberg, & Adler, 2018; DeMilto & Nakashian, 2016; Institute for Alternative Futures, 2012). Their decisions are made harder by a lack of information about the costs and benefits of different strategies and of the interventions they entail (Bickerdike, Booth, Wilson, Farley, & Wright, 2017; Gottlieb, Wing, & Adler, 2017). A better understanding of the range of options for addressing adverse SDH may help guide decisions about relevant healthcare system activities.

We propose four complementary strategies for healthcare system action on SDH (Table 1). The first two strategies entail modifying patient care, including by adapting clinical decision-making in light of data on patients’ social risks (social risk–informed care) and by intervening to reduce patients’ social risks (social risk–targeted care). These two patient care strategies are focused on clinical services or other programs directed toward improving health for specific patient populations (Skinner, Berkeley, Taylor, Shaw & Kelleher, 2018; Steenkamer, Drewes, Heijink, Baan, & Struijs, 2017)—an approach sometimes referred to as “population health management” (Skinner et al., 2018).

Strategies for Healthcare Systems to Invest in Social Determinants of Health

Two additional strategies are focused on influencing SDH at a wider community level, such as the population living in a county or region—not just patients served by a hospital or health plan. These approaches are more closely aligned with a broader definition of population health improvement that includes action on SDH and the range of other factors shaping population health outcomes and their distribution across communities (Kindig & Stoddart, 2003). Community-level strategies include both financial investments and multisector partnerships that can contribute to community health improvements. Healthcare leaders exploring opportunities to incorporate SDH-related activities should understand the challenges and interplay of these different strategies.


Social Risk–Informed Care

One approach to incorporating the growing awareness of SDH’s influence on health involves more routinely incorporating SDH data into clinical decision-making. Social and economic hardships can pose barriers to accessing high-quality care and adhering to treatment recommendations. Adaptations to traditional healthcare delivery according to information about patients’ social circumstances may help reduce these barriers. Though a handful of these modifications are already incorporated into clinical practice—such as using interpreters to overcome language barriers between patients and providers, decreasing panel sizes for patients with social complexity (Gottlieb, Wing, et al., 2017), or providing evening and weekend access hours— others are less common. If it were more readily available at the point of care, patient information about living environments, employment, literacy, and education could be used methodically to inform a wide range of treatment decisions (Bachur, Singer, Hayes-Conroy, & Horwitz, 2018). For instance, data on patients’ food insecurity could influence medication choices (e.g., aspirin or nonsteroidal anti-inflammatory medications should be taken with meals; Eisenstein, 2018; Yaheya & Ismail, 2009) and dosing (e.g., basal versus mealtime insulin apportioning should be made according to food availability; Seligman, Bolger, Guzman, Lopez, & Bibbins-Domingo, 2014). Similarly, the selection and dosing of medications could be influenced by data on patients’ working patterns (e.g., avoid diuretics for patients in jobs without bathroom breaks; Reinhold & Earl, 2012) or housing status (e.g., avoid doxycycline for patients who cannot avoid sun exposure; Simman & Raynolds, 2012). SDH information could also influence behavior change recommendations. For instance, safety-net providers could avoid offering prescriptions for outdoor activity to patients living in unsafe neighborhoods (Bennett et al., 2007) and incorporate financial security status into motivational interviewing for tobacco cessation. In the Los Angeles County University of Southern California (LAC-USC) Innovations Hub, multilanguage audio consents are being created for low-literacy patients. LAC-USC clinics also maximize the use of point-of-care testing, which can increase visit efficiency for patients who experience socioeconomic barriers to in-person follow-up visits.

In a recent trial, social risk data were collected from patients of 215 primary care practitioners serving in non-safety net settings. In postvisit diaries, the providers noted that having social information improved interactions with more than half of their patients; the providers reported using the social data in almost a quarter of care plans, including by being more mindful of medication costs and remembering to offer transportation services (Tong et al., 2018). Although relevant to all consumers, recognizing and mitigating the influence of SDH factors may be especially relevant to patients who face more financial barriers to healthcare access and treatment.

Social risk–informed care has the fewest barriers to entry for healthcare systems seeking to reduce patients’ social risk factors. It primarily considers the provision of medical care with adjustments to improve access and quality according to social risk data. Some healthcare providers (especially those serving low-income communities) have already incorporated social risk–informed care modifications without explicitly recognizing that their practice decisions incorporate SDH information. Sharpened focus on SDH at a national level, however, provides an opportunity to recognize, evaluate, and potentially incentivize these activities so that they can become routine throughout the U.S. healthcare system.

Social Risk–Targeted Care

A second strategy for incorporating an awareness of SDH into patient care involves leveraging clinical encounters to reduce patients’ social and economic barriers to health and health-promoting activities. This is social risk–targeted care. Addressing social risk factors in clinical settings is not novel—it can be traced back through time to innovators like Rudolph Virchow in the 1850s (Pridan, 1964) and the community health center movement of the 1960s and 1970s (Lefkowitz, 2007). As modern healthcare systems adopt more social risk–targeted care strategies, they sometimes rely on clinicians to identify and respond to patients’ social adversity (e.g., housing or food insecurity). Increasingly, however, healthcare systems incorporate “link workers” such as care managers, social workers, community health workers, peer navigators, or volunteers to screen patients for unmet social risks and help them navigate relevant services (Berkowitz, Hulberg, Standish, Reznor, & Atlas, 2017; Fleegler, Lieu, Wise, & Muret-Wagstaff, 2007; Garg, Sarkar, Marino, Onie, & Solomon, 2010; Garg, Toy, Tripodis, Silverstein, & Freeman, 2015; Hassan et al., 2015; Kangovi et al., 2014; Pinto & Bloch, 2017; Sandel et al., 2010). In some settings, these staff members refer patients to internal health system resources, such as medical–legal partnerships (MLPs) (Beck et al., 2012; Cohen et al., 2010; Sandel et al., 2010; Teufel et al., 2012; Tsai et al., 2016; Weintraub et al., 2010), food pharmacies or on-site food banks (Beck et al., 2014; Colorado Department of Health Care Policy & Financing, 2015; Hussein and Collins, 2016; Smith et al., 2017), or on-site tax preparation services (Basu, Kee, Buchanan, & Sadowski, 2012; Beck et al., 2014; Buchanan, Kee, Sadowski, & Garcia, 2009; Colorado Department of Health Care Policy & Financing, 2015; Garg et al., 2015; Gottlieb, Hessler, et al., 2016; Hole, Marcil, & Vinci, 2017; Hussein & Collins, 2016; Rosenheck, Kasprow, Frisman, & Liu-Mares, 2003; Sadowski, Kee, VanderWeele, & Buchanan, 2009; Smith et al., 2017). They also may provide vouchers for food, housing, or transportation (Basu et al., 2012; Buchanan et al., 2009; Dorr & Townley, 2017; Mercy Housing & the Low Income Investment Fund, 2017; Rosenheck et al., 2003; Sadowski et al., 2009). In other settings, link workers also facilitate referrals to external resources, such as community or government assistance programs around food, housing, and employment (Basu et al., 2012; Beck et al., 2014; Buchanan et al., 2009; Colorado Department of Health Care Policy & Financing, 2015; Garg et al., 2015; Gottlieb, Hessler, et al., 2016; Hole et al., 2017; Hussein & Collins, 2016; Rosenheck et al., 2003; Sadowski et al., 2009; Smith et al., 2017). A growing number of innovative payment mechanisms enable health systems to contract with community-assistance programs for specific social services, which frequently can facilitate social risk–targeted care (Bachrach, Guyer, & Levin, 2016; Machledt, 2017; Miller, Nath, & Line, 2017; Musumeci, Rudowitz, Hinton, Antonisse, & Hall, 2018).

Scaling patient care interventions around SDH—whether social risk–informed or social risk–targeted care—depends on system-level changes. At a minimum, these interventions rely on the availability of patients’ social risk data at the point of care, which requires processes and protocols to screen patients for social risk or to incorporate neighborhood-based information into electronic health records. When social data are available, organizational infrastructure will be required to routinely couple SDH data with medical care decision-making. This could include clinical decision support tools that guide medication selection or other referral and treatment choices (Gold et al., 2018), staff education and training (e.g., on how to ask patients about social risks and to understand how patients prioritize identified risks) (Colvin et al., 2016), dedicated social interventions workflows and infrastructure (e.g., electronic referral systems) (Adler & Stead, 2015; Bazemore et al., 2016; Colvin et al., 2016; Fierman et al., 2016; Gottlieb, Tirozzi, Manchanda, Burns, & Sandel, 2015; Gottlieb, Tobey, Cantor, Hessler, & Adler, 2016; Klein et al., 2014; McCalmont et al., 2016; Nguyen, Chan, Makam, Stieglitz, & Amarasingham, 2015; Real, Beck, Spaulding, Sucharew, & Klein, 2016), and performance measurement and payment models that incentivize social care-related interventions (e.g., payment models that reward care outcomes and enable funding for nonmedical services) (Gottlieb, Garcia, Wing, & Manchanda, 2016).

An important caveat to both patient-level strategies is the lack of evidence on their effectiveness. Despite ample evidence connecting social and economic circumstances and health, research evaluating whether and how these kinds of interventions affect health outcomes is nascent. Two recent reviews of the evidence on social interventions in clinical settings—one in the United States (Gottlieb, Wing, et al., 2017) and one in the United Kingdom (Bickerdike et al., 2017)—concluded that the evidence lags behind enthusiasm for these activities. Most evaluations have focused on intermediate process outcomes (e.g., number of patients served) and have been of poor quality (Bickerdike et al., 2017; Gottlieb, Wing, et al., 2017). Evaluations also have failed to distinguish the added value of components of the intervention related to social or economic needs, such as food or employment supports, from the value of medical care components, such as care management or health education (Gottlieb, Quinones-Rivera, Manchanda, Wing, & Ackerman, 2017). Given the diversity of interventions that could be undertaken to affect patients’ social circumstances, more targeted research is required to inform future investments in this area. This includes research on how much these approaches depend on the quality and availability of existing community infrastructure and the added value of providing these services in healthcare contexts.


Social risk–targeted and social risk–informed care both are strategies for addressing social adversity in the traditional wheelhouse of medicine: patient care. But healthcare systems operating under value-based payment models or investing in population health outcomes for other reasons may also consider opportunities to intervene on social conditions at the community level as a complementary strategy to patient-level initiatives. These strategies extend the reach of healthcare organizations beyond their own patients to include the health and well-being of people living in a geographical area.

Financial Investments

One approach is to use the healthcare system’s financial resources to improve community conditions. Health systems have historically done this by means of community benefit philanthropy—for example, through grants to community-based organizations. In recent years, another model has emerged that leverages the economic influence of health systems as “anchor institutions” such as major employers, purchasers, and investors (Norris & Howard, 2015; Rosenbaum, 2016). Health systems aiming to improve community-level SDH using their economic influence may make different choices regarding operations (e.g., by increasing local hiring and procurement), or in longer term investment portfolios (e.g., by investing in local businesses ventures or low-income housing) (AcademyHealth, 2017; Begun & Potthoff, 2017; Dubb, McKinley, & Howard, 2013). Though nonprofit hospitals’ community benefit philanthropy has remained relatively flat despite changes in community benefit rules implemented alongside the Affordable Care Act (Young, Flaherty, Zepeda, Singh, & Rosen Cramer, 2018), impact investment strategies (investments that generate financial returns alongside social returns) appear to be changing. Dignity Health’s community investment program, for example, invests up to 5% of the organization's investable assets in affordable housing and economic development, including small business development (Hacke & Dean, 2017). Kaiser Permanente committed $200 million in impact investment funds to address housing and homelessness issues (Kaiser Permanente, 2018).


Other community-level strategies will demand that health systems stretch beyond financial investments to engage in more comprehensive multisector partnerships. Though these partnerships—whether formal or informal—may include financial investments, they can present additional opportunities for health systems to engage in and share accountability for community-level health outcomes (Mongeon, Levi, & Heinrich, 2017). Many different types of “accountable communities for health” (ACH) have developed in recent years, with varying levels of health system involvement. In some, health systems have restricted their work to improving social risk–targeted care for specific patient populations; in others, health systems are core partners in promoting broader community-level goals (Levi et al., 2018). Curiously, the new community benefit requirements for nonprofit hospitals—although not associated with changes in actual community health spending—have nonetheless been associated with increased hospital engagement in community partnership activities (Laymon, Shah, Leep, Elligers, & Kumar, 2015). Cincinnati Children’s Hospital, for instance, targets reductions in asthma, infant mortality, and obesity by joining—and in some cases catalyzing—broad coalitions of community, government, and healthcare partners. The hope in Cincinnati and other places involved in regional collaborations is that partners will achieve better outcomes by combining their resources and capabilities. Despite some promising success stories (Beck et al., 2014; Cooper, Craig, Gaynor, & Van Reenen, 2015), multisector partnerships can be costly, difficult to coordinate, and challenging to sustain (Siegel, Erickson, Milstein, & Pritchard, 2018; Sullivan & Skelcher, 2002).

Community-level strategies—whether financial investments or more comprehensive partnerships—may be “high risk/high gain” opportunities: despite being focused on broader populations, these activities may have the greatest long-term payoffs if they result in improvements to community-level SDH (Bradley & Taylor, 2013). As is the case with social risk–informed and social risk–targeted care, however, little rigorously collected evidence illustrates whether and how these more community-level engagement strategies contribute to improvements in health and health equity (Hayes, Mann, Morgan, Kelly, & Weightman, 2012; Ndumbe-Eyoh & Moffat, 2013; Smith et al., 2009)


We have described four different strategies for healthcare systems interested in translating the growing awareness of SDH into healthcare activities and investments. Both of the strategies rooted in clinical care delivery—especially social risk–informed care—may raise fewer concerns about scope creep and therefore be relatively easier to adopt. These patient care strategies, however, will pose challenges related to clinical workflows and workforce, and require wider changes in healthcare policy and payment to ensure sustainability. Both social risk–informed and social risk– targeted care could be incentivized by new social risk–adjustment and value-based payment models (Cavanaugh & Lazio, 2016; Centers for Medicare & Medicaid Services, 2018; Executive Office of Health & Human Services, 2016; National Academies of Sciences, Engineering, and Medicine, 2016a; National Quality Forum, 2017; Office of the Assistant Secretary for Planning and Evaluation, 2016), as well as by national initiatives encouraging the collection of social risk data in clinical care (National Academies of Science, Engineering, and Medicine, 2014).

In contrast, community-level strategies may offer the greatest potential to improve population health but extend the role of healthcare systems beyond their traditional focus and capabilities. Community-level strategies are also likely to require policy changes to enable healthcare systems to sustain long-term investments. Despite the absence of strong effectiveness evidence, many health systems and payers are already experimenting with these strategies to incorporate SDH into the business of healthcare (Beaton, 2018; Gottlieb, Quinones-Rivera, Manchanda, Wing, & Ackerman, 2017; Gottlieb, Garcia, et al., 2016; UnitedHealthcare MyConnections, 2016). We now need to document the impact of these SDH-related activities to ensure healthcare resources are spent wisely. Though each provides a unique entry point for engagement, the strategies are interdependent and likely mutually reinforcing. For example, the success of social risk–targeted care depends on the availability of social services to which patients can be linked. Given this interdependence, healthcare systems wanting to intervene on social risk factors will need to consider multifaceted approaches. An evaluation lens that both recognizes the interdependence and better articulates the value of each of these unique strategies will help implementers understand the true costs and benefits of improving social conditions using healthcare resources.


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