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Social Determinants of Health and Diabetes: A Scientific Review https://doi.org/10.2337/dci20-0053

Decades of research have demonstrated that diabetes affects racial and ethnic minor- ity and low-income adult populations in the U.S. disproportionately, with relatively intractable patterns seen in these populations’ higher risk of diabetes and rates of diabetes complications and mortality (1). With a health care shift toward greater emphasis on population health outcomes and value-based care, social determinants of health (SDOH) have risen to the forefront as essential intervention targets to achieve health equity (2–4). Most recently, the COVID-19 pandemic has highlighted unequal vulnerabilities borne by racial and ethnic minority groups and by disadvantaged communities. In the wake of concurrent pandemic and racial injustice events in the U.S., the American College of Physicians, American Academy of Pediatrics, Society of General Internal Medicine, National Academy of Medicine, and other professional organizations have published statements on SDOH (5–8), and calls to action focus on amelioration of these determinants at individual, organizational, and policy levels (9–11). In diabetes, understanding andmitigating the impact of SDOH are priorities due to

disease prevalence, economic costs, and disproportionate population burden (12–14). In 2013, the American Diabetes Association (ADA) published a scientific statement on socioecological determinants of prediabetes and type 2 diabetes (15). Toward the goal of understanding and advancing opportunities for improvement among the population withdiabetes through addressing SDOH, ADA convened the current SDOHanddiabetes writing committee, prepandemic, to review the literature on 1) associations of SDOH with diabetes risk and outcomes and 2) impact of interventions targeting amelioration of SDOH on diabetes outcomes. This article begins with an overview of key definitions and SDOH frameworks. The literature review focuses primarily onU.S.-based studies of adults with diabetes and on five SDOH: socioeconomic status (education, income, occupation); neighborhood and physical environment (housing, built environment, toxic environmental exposures); food environment (food insecurity, food access); health care (access, affordability, quality); and social context (social cohesion, social capital, social support).This reviewconcludeswithrecommendations for linkagesacross health care and community sectors from national advisory committees, recommen- dations for diabetes research, and recommendations for research to inform practice.

DEFINITIONS OF HEALTH DISPARITIES, HEALTH EQUITY, AND SDOH

Table1displaysdefinitionsof key terms.Differences indiabetes riskandoutcomescan result frommultiple contributors, including biological, clinical, and nonclinical factors (1). A substantial body of scientific literature demonstrates the adverse impact of a particular type of difference, health disparities (16) in diabetes (1,17,18). A pre- ponderance of health disparities research in the U.S. has examined disparities by race and ethnicity (3,19). Internationally, the term health equity has traditionally been used to encompass the range of population inequalities resulting from demographic and economic characteristics, and this term is used increasingly in the U.S. (20–24). Addressing healthy equity necessitates an understanding of social and environmental factors that combined account for 50% to 60% of health outcomes (22,25). These social and environmental factors collectively are known as SDOH (21,26,27).

SDOH NOMENCLATURES AND CONTEXTUAL FACTORS

The writing committee reviewed the following commonly referenced SDOH frame- works for classifications and terminology: the World Health Organization (WHO) Commission on Social Determinants of Health (28), Healthy People 2020 (29,30), the

1Department of Medicine, Johns Hopkins Univer- sity, Baltimore, MD 2Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Medical Insti- tutions, Baltimore, MD 3Department of Psychiatry and Behavioral Sci- ences, University of California San Francisco, San Francisco, CA 4Division of General Medicine and Clinical Epi- demiology,UniversityofNorthCarolinaatChapel Hill, Chapel Hill, NC 5Department of Medicine, University of Chicago, Chicago, IL 6Departments of Epidemiology and Behavioral and Community Health Sciences, University of Pittsburgh, Pittsburgh, PA 7Department of Environmental Health Sciences, Columbia University, New York, NY 8National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 9The Brown School and The School of Medicine, Washington University in St. Louis, St. Louis, MO

Corresponding author: Felicia Hill-Briggs, fbriggs3@ jhmi.edu

Received 25 September 2020 and accepted 25 September 2020

© 2020 by the American Diabetes Association. Readersmayuse this article as longas thework is properly cited, the use is educational and not for profit, and the work is not altered. More infor- mation is availableathttps://www.diabetesjournals .org/content/license.

See accompanying articles, pp. XX, XX, XX, and XX.

Felicia Hill-Briggs,1,2 Nancy E. Adler,3

Seth A. Berkowitz,4 Marshall H. Chin,5

Tiffany L. Gary-Webb,6 Ana Navas-Acien,7

Pamela L. Thornton,8 and

Debra Haire-Joshu9

Diabetes Care 1

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Diabetes Care Publish Ahead of Print, published online November 2, 2020

County Health Rankings Model (31,32), and Kaiser Family Foundation Social De- terminants of Health factors (33). No single consensus set of factors de-

fine SDOH. Nomenclatures used by each framework, for shared SDOH factors, are depicted in Fig. 1. Common among the frameworks is placement of economic and socioeconomic determinants as fore- most. Food SDOH factors (e.g., insecurity and access) are classified as economic stability (29), neighborhood and built environment (29), material circumstan- ces (28), or as an independent category (33). Housing is classified as material circumstances (28), economic stability (29), or neighborhood and built envi- ronment (33). Environmental exposures (i.e., toxins, air pollution, and water quality) are classified as neighborhood and physical environment (31) or built environment (29). Social environment is represented as community and societal context (29,33), social cohesion and so- cial capital (28), or social factors (31). Health care is a SDOH in the WHO, Healthy People 2020, and County Health Rankings models, with access factors as primary, with or without quality of care factors. Each framework posits complex inter-

actions among SDOH factors. WHO ad- ditionally maps causal priority among the determinants. For example, the up- stream sociopolitical context and result- ing socioeconomic position are root cause, structural determinants of health

inequities that work through interme- diary sets of determinants to cause health inequities. WHO positions the health system as an SDOH that plays a role in mediating impact of intermediary deter- minants on health outcomes (28).

REVIEW OF SDOH AND DIABETES

SDOH inclusion in this review was deter- mined by representation within one or more existing SDOH frameworks and presence of a sufficient body of literature to demonstrate influence of the deter- minant on diabetes. The reviewed SDOH are shown in Table 2. Studies of primar- ily adult populations are described, and the terms type 2 diabetes mellitus (T2DM), type 1 diabetes mellitus (T1DM), and “diabetes” (indicating either amixed T2DMandT1DMsampleor diabetes case ascertainment methods that do not en- able specifying clinical diagnostic type) are used in accordance with the termi- nology used in the respective studies.

Socioeconomic Status and Diabetes Socioeconomic status (SES) is a multidi- mensional construct that includes edu- cational, economic, andoccupational status (34–36). SES is a consistently strong pre- dictorofdiseaseonset andprogressionat all levels of SES for many diseases, in- cluding diabetes (37). SES is linked to virtually all of the established SDOH. It is associated with the extent to which in- dividuals and communities can access material resources including health care,

housing, transportation, andnutritious food and social resources such as political power, social engagement, and control.

The three components of SES are in- tercorrelated (38), but each aspect has unique implications for health. Each com- ponent can be assessed at the individual or population level (39). For example, economic status is often measured by determining a person’s own income. However, it is also assessed by the in- come of the household in which the person resides and by the income level of the community (e.g., mean household income of the census track in which a person resides) as a proxy for the indi- vidual’s household income. Census-level household income also operates as a contextual variable, reflecting the com- position and available resources in a de- fined area.

Educational status can be quantified either in years of schooling or highest degree earned. It may be assessed at the level of the individual (e.g., the person’s own educational attainment), the house- hold (the highest grade completed by anyone in the household), or the com- munity (e.g., percent of high school or college graduates in a census track). Quantity of education does not capture differences in quality of education that may be relevant to SES measurement (38). Literacy has emerged as a measure of educational quality and as potentially more reflective of SES than years of schooling among African Americans and

Table 1—Definitions

Term Definition

Health disparities A particular type of health difference that is closely linkedwith social, economic, and/or environmental disadvantage. Health disparities adversely affect groups of people who have systematically experiencedgreaterobstacles tohealthbasedon their racial or ethnic group; religion; socioeconomic status; sex; age;mental health; cognitive, sensory, or physical disability; sexual orientation or gender identity; geographic location;orother characteristicshistorically linked todiscriminationorexclusion (16).

Health equity Equity is the absence of avoidable, unfair, or remediable differences among groups of people, whether those groups are defined socially, economically, demographically, or geographically or by other means of stratification. “Health equity” or “equity in health” implies that ideally everyone should have a fair opportunity to attain their full health potential and that no one should be disadvantaged from achieving this potential (24).

Health equity is attainment of the highest level of health for all people. Achieving health equity requires valuing everyone equallywith focused and ongoing societal efforts to address avoidable inequalities, historical and contemporary injustices, and the elimination of health and health care disparities (23).

Social determinants of health (SDOH) The social determinantsof health are the conditions inwhichpeople areborn, grow, live,work, andage. These circumstances are shaped by the distribution of money, power, and resources at global, national, and local levels. The social determinants of health are mostly responsible for health inequitiesdthe unfair and avoidable differences in health status seenwithin and between countries (26).

2 Social Determinants of Health and Diabetes Diabetes Care

low-income Whites (40,41). Health liter- acy, which is directly associated with literacy and is context specific (42–44), and literacy are included as SDOH in Healthy People 2020 (29). Occupation is itself multidimensional.

It has been measured as employment status (e.g., employed vs. unemployed), stability (e.g., job insecurity), job type (e.g., manual vs. nonmanual, prestige of the occupation), and working conditions (e.g., shiftwork, number of hoursworked, job demands, and control) (39,45). For members of large organizations, occupa- tional hierarchies of job titles capture work conditions as well as qualifications and pay (e.g., civil service grades).

AssociationsofSESWithDiabetes Incidence,

Prevalence, and Outcomes

Income, education, and occupation show a graded association with diabetes prev- alence and complications across all levels of SES, up to the very top. Those lower on

the SES ladder are more likely to develop T2DM, experience more complications, and die sooner than those higher up on the SES ladder (46,47). The higher a person’s income, the greater their edu- cational attainment, and the higher their occupational grade, the less likely they are to develop T2DM or to experience its complications. The gradient is steeper at the bottom, however, and research has focusedprimarilyonthosewiththe lowest levels of income and education. Income.Prevalence of diabetes increases on a gradient from highest to lowest income (48,49). In data from theNational Health Interview Survey (NHIS) covering 2011–2014, Beckles and Chou (50) found increasing diabetes prevalence at lower levels of income as reflected in the levels of ratio of income to poverty level. Com- pared with those with high income, the relative percentage difference in preva- lence of diabetes for those classified as

middle income, near poor, and poor, was 40.0%, 74.1%, and 100.4%, respectively. The difference in diabetes prevalence by income was greater during this time pe- riod than it had been in a prior period (1999–2002), pointing towideningdispar- ities in diabetes prevalence associated with income.

At the neighborhood level, differences in diabetes prevalence by census track are attributable to SES (51,52). For ex- ample, in a recent study by Kolak et al. (52), rate of T2DM was found to be significantly higher and concentrated in census tracts characterized by factors in- cluding lower incomes, lower high school graduationrates,moresingle-parenthouse- holds, and crowded housing. Living in neighborhood census tracts with lower educational attainment, lower annual income, and larger percentage of house- holds receiving Supplemental Nutrition Assistance Program benefits has been

Figure 1—Nomenclatures for shared determinants among four social determinants of health frameworks, theWorld Health Organization Commission on the Social Determinants of Health, the U.S. Department of Health and Human Services Healthy People 2020, the County Health Rankings Model, and the Kaiser Family Foundation Social Determinants of Health framework.

Table 2—SDOH and component factors included in the diabetes review

Socioeconomic status Neighborhood and physical environment Food environment Health care Social context

Education Housing Food security Access Social cohesion Social capital Social support

Income Built environment Food access Affordability QualityOccupation Toxic environmental exposures Food availability

care.diabetesjournals.org Hill-Briggs and Associates 3

associated with higher risk of progres- sion to T2DM among adults with pre- diabetes (53). Gaskin et al. (49) examined the in-

teractionof individual povertywith neigh- borhoodpoverty and found that, compared with nonpoor adults living in nonpoor neighborhoods, poor adults living in nonpoor neighborhoods have increased odds of having diabetes, and poor adults living in poor neighborhoods have two- fold higher odds of having diabetes. In addition, a race-poverty-place gradient was observed. Compared with nonpoor Whites in nonpoor neighborhoods, odds of diabetes were highest for poor Whites in poor neighborhoods (odds ratio [OR] 2.51, 95% CI 5 1.31–4.81), followed by poor Blacks in poor neighborhoods and nonpoor Blacks in poor neighborhoods (OR 2.45, 95% CI 1.50–4.01, and OR 2.49, 95%CI1.48–4.19), andfinally poorWhites in nonpoor neighborhoods (OR 1.73, 95% CI 1.16–2.57) (49). Adults with T2DM who have a family

income below the federal poverty level have a twofold higher risk of diabetes- related mortality compared with their counterparts in the highest family in- come levels (54). This pattern of diabe- tes-related mortality has been observed specifically in adults with T1DM as well (55). A meta-analysis by Bijlsma-Rutte etal. (56)observedan inverseassociation between income and HbA1c levels in people with T2DM, with a pooled mean difference inHbA1cof0.20%(95%CI20.05 to 0.46) betweenpeoplewith lowandhigh income. Low income is associated with a higher risk of experiencing diabetic ketoa- cidosis amongyouthandadultswithT1DM (57) and with higher HbA1c levels, partic- ularly among racial/ethnic minority youth with lower SES (58,59). Education. Age-adjusted incidence of di- agnosed diabetes in adults is associated also with educational level in a stepwise pattern. Diabetes incidence is highest (10.4 per 1,000 persons) for adults with less than a high school education, 7.8 per 1,000 persons for those with a terminal high school education, and 5.3 per 1,000 persons for those with more than a high school education (60). Di- abetes prevalence in the adult U.S. pop- ulation is similarly inversely associated with educational level in a stepwise pat- tern. In the U.S., the age-adjusted prev- alence of diagnosed diabetes is 12.6% for those with less than a high school

education, 9.5% for those with a high school education, and 7.2% for those with more than a high school education (61). Having a college education or more is associated with the lowest odds of diabetes (62). Mirroring findings on in- come, temporal trends in diabetes prev- alence at different levels of education show increasing disparities in prevalence associated with educational attainment (50).

The risk of diabetes-related mortality demonstrates a gradient from lowest to highest education level. Compared with adults with a college degree or higher, having less than high school education is associated with a twofold higher mor- tality fromdiabetes (relative hazard 2.05, 95% CI 1.78–2.35) (54). In adults with T1DM, not having a college degree is associated with a threefold higher mor- tality fromdiabetes comparedwithcoun- terpartswithacollegedegree (63). Lower educational level is associatedwithhigher HbA1c,with ameta-analysis (56) reporting a pooled mean difference in HbA1c of 0.26% (95% CI, 0.09–0.43) between peo- ple with low and high educational levels. Regarding literacy/health literacy as a SDOH, Marciano et al. (64) conducted a meta-analysis of 61 studies of 18,905 adults with T1DM or T2DM to determine associations of health literacy with sev- eral diabetes outcomes and found that higher levels of health literacy were sig- nificantly associated with lower HbA1c levels and better diabetes knowledge, but not with more frequent self-man- agement activities. Occupation.Systematic reviews andmeta- analyses have examined several aspects of occupation in relation to diabetes risk, although most of this research has been conducted outside of theU.S. Ferrie et al. (65) conducted a meta-analysis of asso- ciations of job insecurity with incident diabetes and foundanassociation of high job insecurity with higher risk of incident diabetes (OR 1.19, 95% CI 1.09–1.30). A meta-analysis by Varanka-Ruuska et al. (66) found that unemployment was as- sociated with increased odds of both prediabetes (OR 1.58, 95% CI 1.07– 2.35) and T2DM (OR 1.72, 95% CI 1.14–2.58). Exposure to shift work is associated with higher risk of diabetes than working normal daytime schedules (67). A meta-analysis by Kivimäki et al. (68) reported an association of longwork hours ($55 h per week) as compared

with standard work hours (35–40 h per week) with higher incident diabetes in adults with low SES but not in adults with high SES. A U.S. population-based survey on diabetes and occupation found high- est prevalence of diabetes among trans- portationworkers and lowest prevalence of diabetes among physicians (69,70).

SES Interventions and Diabetes Outcomes

To date, there is no body of literature describing impact of change in income, change to higher educational status, or different employment/occupational sta- tus on diabetes outcomes, although in- comeandwagechanges, and jobchanges and loss, do occur naturalistically. Sim- ilarly, no diabetes outcomes have been reported from interventions directly tar- geting living wages, early childhood ed- ucation, educational quality, or educational access for poor children and families. Studies have examined diabetes self- management interventions in the setting of low literacy/health literacy, particu- larly among racial/ethnic minority adults with T2DM and have demonstrated ef- fectiveness of low-literacy adaptions (71) and health literacy and numeracy tools in improving diabetes knowledge and self- care (72–74). A meta-analysis of nine intervention trials with 1,874 adults with T2DM found that literacy-sensitive interventionswereassociatedwithasmall but statistically significant decrease in HbA1c (–0.18%; 95% CI –0.36 to –0.004) in comparison with usual clinical care (75) in patients regardless of health literacy status. Literacy-adapted education and tools may need to be combined with more comprehensive evidence-based be- havioral self-management intervention approaches to achieve substantive clinical improvements in racial/ethnic minority populations with T2DM and low liter- acy/health literacy (76,77). In conclusion, despitethe long-standingevidence forSES as a key determinant both of diabetes risk andoutcomes, systematic investigationof impact on diabetes of change in SES remains a gap in the literature.

Neighborhood and Physical Environment and Diabetes The neighborhood environment inwhich one lives has been of major interest as a setting in which to understand contex- tual and multilevel influences on health (78).DiezRouxandMair(78)havedescribed the role of historical and contemporary

4 Social Determinants of Health and Diabetes Diabetes Care

residential segregation by race, ethnicity, and SES as the socioeconomic and political context that produced the patterns of un- equal resource distribution resulting in neighborhood environments that maintain health inequities. Tung et al. (79) also discuss the multiple intricacies associated with how race, place, and poverty con- verge in a dynamic way across various spatial contexts and circumstances to influence health and propose that un- derstanding the intersection of these contextual influences is needed to pre- vent diabetes inequities. Neighborhood and physical environment factors of housing, built environment, and envi- ronmental exposures are reviewed.

Housing

Stable housing is a key indicator of eco- nomic stability (80) and a core SDOH (80). Housing instability refers to a spectrum of situations that can range from living in one’s car, staying with relatives or friends, having trouble paying rent, suf- fering evictions or frequent moves, pay- ing more than 50% of income in rent, and living in crowded conditions (histor- ically defined as having more than one person per room) to homelessnessdthe most extreme form of unstable housing (81–85). Homelessness is defined as “lacking a regular nighttime residence or having a primary nighttime residence that is a temporary shelter or other place not designed for sleeping” (86). Asof2020, theU.S. government reported 567,715 or 17 of 10,000 people in the country are homeless; African Americans accounted for 40% of people experienc- ing homelessness, while those identify- ing as Hispanic or Latino comprised 22% of the homeless population (87). A com- mon theme in conceptual models linking housing instability to poor health is that the instability inherent to the situation makes it difficult to attend to preventive services and self-care (83,88–90), leading to worse control of chronic conditions, higher use of acute-care services like emergencydepartments, andhigher like- lihood of complications (91–93).

Associations of Housing Instability With Diabe-

tes Incidence, Prevalence, andOutcomes. The prevalence of diabetes among those with housing instability in theU.S., andwhether it differs from that among those without housing instability, is not known. A key limitation for the field is that there is no single, accepted definition of housing

instability or a commonly used assess- ment instrument. Further, because hous- ing instability is more likely to occur among individualswith lowerSESdwhich is independently associated with higher diabetesprevalencedit isunclearwhether housing instability is causally related to developing diabetes. One systematic re- view did not find higher diabetes preva- lence than in the general population among persons experiencing homeless- ness, estimating approximately 8% prev- alence in adults who do and do not experience homelessness (94). A recent study using nationally representative data from individuals seen in community health centers found that approximately 37% of individuals with diabetes re- ported housing instability. This study also found that individuals with diabetes and housing instability were more likely to self-report having an emergency depart- ment visit or hospitalization for their di- abetes (adjusted OR 5.17, 95% CI 2.08– 12.87) (82). A cross-sectional study in a single health care system found that housing instability among individuals with diabetes was associated with higher outpatient utilization (incident rate ra- tio 1.31, 95% CI 1.14–1.51) (95). Though not specific to diabetes, additional work has linked housing instability to poor health outcomes and reduced health care access (91,96–100). A longitudinal study in the Department of Veterans Affairs (VA) health care system found that experiencing homelessness was as- sociated with higher adjusted odds of having an HbA1c .8.0% and .9.0%. Vijayaraghavan et al. (84) identified un- stable housing as a key barrier to diabetes careamonglow-incomeindividuals.There was an observed linear decrease in di- abetes self-efficacy as housing instability increased (b-coefficient 20.94, 95% CI 21.88 to 20.01, P , 0.01), which was partially mediated by food insecurity. Qualitative work has found that unsta- blehousingmakes itmoredifficult to en- gage in self-care, follow self-management routines, afford diabetes medications and supplies, and eat healthy foods (91,92). Choice of medication is important, and considerations should include medication cost and the ability to store medication and diabetes care supplies safely. Brooks et al. provide a narrative review of con- siderations for diabetes treatment among individuals experiencing homelessness (101).

Housing Instability Interventions and Diabetes

Outcomes. Given its expense, housing is one of the most difficult health-related social needs to intervene upon. Housing intervention studies reporting diabetes outcomes are few; however, there is some high-quality evidence for housing interventions. The Moving To Opportu- nity forFairHousingDemonstrationProject (MTO), a randomized social experiment conducted through the Department of Housing and Urban Development, in partnership with behavioral scientists andother federal agencies, was designed to determinewhat impactmoving from a high-poverty to a low-poverty census tract would have on multiple outcomes (102,103). In 1994–1998, MTO random- ized 4,498 women with children living in public housing within high-poverty cen- sustracts infivecities tooneofthreestudy arms. The 1,788 women in the experi- mental arm received Section 8 vouchers usable only in low-poverty areas (census tractswith,10%of thepopulationbelow the poverty line) along with counseling and assistance in finding a private rental unit. The 1,312 women in the Section 8 arm received traditional unrestricted vouchers and the usual briefing the local Section 8 program provided. The 1,398 women in the control arm received no vouchers but continued to receive MTO project-based assistance. Those who re- ceivedvouchers could choosewhether to use the vouchers or not. Findings from the follow-up survey in 2008 through 2010 found a 21.6% relative reduction in prevalence of anelevatedHbA1c (.6.5%) in the group that moved to low-poverty census tracts compared with the control group, with an absolute difference of 4.31 percentage points (95% CI 27.82 to 20.80). The low-poverty group also had relative reductions of 13.0% in prev- alenceof BMI$35and relative reduction of 19.1% in BMI $40 kg/m2, with abso- lute differences of 4.61 percentage points (95% CI 28.54 to 20.69) and 3.38 per- centage points (95% CI26.39 to20.36), respectively (102). Theusualvouchersand control arms did not differ. Other MTO outcomes among the group randomized to low-poverty census tracts included higher housing quality, education, em- ployment, andearningsaswell asmultiple additional improvements to child and adult health (103). A 10–15year follow-up study found substantial and sustained reductions in diabetes prevalence, rates

care.diabetesjournals.org Hill-Briggs and Associates 5

of extreme obesity, and improvement in mentalhealthoutcomesamongtheadults who received vouchers to move to low- poverty neighborhoods and reduction in extreme obesity among the adults who received Section 8 vouchers (104). While not specific todiabetes, ameta-analysisof randomized trials that provided low-barrier housing support for individuals experienc- ing homelessness found significant reduc- tions in health care utilization (105). Housing interventions may facilitate

access to diabetes care. The Collabora- tive Initiative to End Chronic Homeless- nessprovidedadultswhowerechronically homeless with permanent housing and supportive primary health care and men- tal health services (106). Placed persons weremore likely toreceiveevaluationand management services (relative risk [RR] 1.03, 95% CI 1.01–1.04) than unplaced persons (107). Placed persons were more likely to receive HbA1c tests (RR 1.10, 95% CI 1.02–1.19) and lipid tests (RR 1.09, 95% CI 1.02–1.17), while for those without baseline diabetes placement was associ- ated with lower risk of new diabetes diagnoses (RR 0.87, 95% CI 0.76–0.99). Keene et al. (91) suggest the relationship of stable housing to diabetes manage- ment is due to its role as a foundation for prioritizing care and allowing for the routinization of diabetes management, critical to disease control. This suggests the benefits of supportive and stable housing may be extended to diabetes care and prevention. A naturalistic qual- itative study of the impact of transitioning to rental-assisted housing among low- income, housing-insecure adultswith T2DM reported that rental assistance afforded individuals more environmental and fi- nancial control over life circumstances, thereby enabling diabetes routines and allocation of financial resources to diabe- tes care (108).

Built Environment

The built environment, as defined by the U.S. Centers for Disease Control and Prevention (CDC), includes the physical parts ofwherepeople live andwork, such as infrastructure, buildings, streets, and open spaces (109). Here, built environ- ment factors of walkability and green- space are reviewed.

Associations of Built Environment with Diabe-

tes Incidence, Prevalence, and Outcomes. A robust literature has demonstrated as- sociations of the built environment with

obesity-related outcomes (110–113). However, a smaller body of research has examined associations of the built environment with diabetes specifically. Smalls et al. (114) reported significant associations of both walking environ- ment (b 5 20.040) and neighborhood activities (b520.104) with exercise in a southeastern U.S. population with dia- betes. A recent U.S. review and meta- analysis by Chandrabose et al. (113) examined longitudinal studies of the built environment and cardiometabolic health. Results showed strong evidence for impact of walkability on T2DM out- comes, with four of seven studies (57%) showing significant findings in the ag- gregated analyses using objective and perceived measures of walkability. Al- though the methods to determine me- diation by physical activity in most studies were ineffective to make con- clusions, one study tested the indirect effect of walkability on 10-year change in HbA1c and found a partial mediation effect for self-reported physical activity using structural equation modeling. For other measures of built environment, such as neighborhood recreational fa- cilities or destinations/routes, therewas insufficient data to examine the rela- tionship with T2DM outcomes. A larger body of research on built environment and diabetes has been conducted in countries outside of the U.S (110–112). In these studies, neighborhood physical activity (PA) environments, specifically better walkability of neighborhoods and access to greenspace, have been consis- tently associated with lower risk of T2DM and better outcomes (115,116). Numer- ous studies have been conducted on walkability measured by macroscale as- pects of theneighborhood, includinghigher population density, land use mix, and aesthetics, to microscale aspects, includ- ing sidewalks, street connectivity, and street safety. A review by Bilal et al. (115) on walkability and diabetes incidence and prevalence found that more walk- able neighborhoods are associated with a lower incidence and prevalence of T2DM. Similarly, Twohig-Bennett and Jones (117) conducted a systematic re- view and meta-analysis examining the relationship to diabetes outcomes of “high” and “low” exposure to green- space in neighborhoods (defined as open, undeveloped land with natural vegetation and/or spaces such as parks

and tree-lined areas). The meta-analysis, representing 462,220 participants, showed an association of high exposure with reductions in the incidence of T2DM (OR 0.71, 95% CI 0.61–0.85) (117). After dec- ades of research, many built environ- ment factors related to PA and obesity risk have been identified for consider- ation in urban planning (118). Neighborhood-Level Interventions on the

Built Environment and Diabetes Outcomes.

Because it is often not feasible or ethical to randomize neighborhoods to receive certain structural interventions, natural experiment designs are used in which the researcher does not control or with- hold intervention allocation to particular areas; rather, natural or predetermined variation in allocation occurs, often as a result of policy intervention (119). Several review articles of natural experiments summarize the benefits of policy andbuilt environment changes on obesity-related outcomes(112)anddietandPAoutcomes (120,121).Thestrongestdiet-relatedstudies were those that evaluated regulations to the food environment, and the strongest PA-related studies were those that im- proved infrastructure for active transport. Although this literature does not directly address diabetes outcomes, improve- ments in obesity and diet and PA behav- iors are relevant to populations with diabetes and warrant rigorous evaluation (122).

Toxic Environmental Exposures

Toxic environmental exposures can be naturally occurring (e.g., arsenic in pri- vate wells, radon) or introduced into the environment through human activity (e.g., pollution, synthetic pesticides) (123). Marginalized communities in theU.S. are disproportionately exposed to environ- mental agents that have evidence of an association with diabetes, including air pollution, environmental toxicants, and ambient noise (124–129), and subgroups that generate the least pollution have highest exposures (130).

Factors contributing to inequities in toxic environmental exposures include residential segregation and inequity in goods and services, due in part to sys- temic racism inenvironmental regulation and opportunities (128,130–133). Ex- planatory factors are closer proximity of underserved neighborhoods to nearby pollution sources, poor enforcement of regulations, and inadequate response to

6 Social Determinants of Health and Diabetes Diabetes Care

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w o rk in g co n d it io n s. C re at e so ci al

p ro te ct io n p o lic y su p p o rt iv e o f al l.

Ta ck le th e in eq

u it ab le d is tr ib u ti o n o fp

o w er ,m

o n ey ,a nd

re so u rc es

C re at e a st ro n g p u b lic

se ct o r th at

is co m m it te d , ca p ab le , an d ad eq

u at el y fi n an ce d . En su re

le gi ti m ac y, sp ac e, an d su p p o rt fo rc iv il so ci et y, fo ra n ac co u n ta b le p ri va te se ct o r, an d fo rt h e

p u b lic

to ag re e to

re in ve st m en

t in

co lle ct iv e ac ti o n .

M ea su re

an d u n d er st an d th e p ro b le m

an d as se ss

th e

im p ac t o f ac ti o n

A ck n o w le d ge

th er e is a p ro b le m .E n su re

th at

h ea lt h in eq

u it y is m ea su re d .D

ev el o p n at io n al

an d gl o b al h ea lt h eq

u it y su rv ei lla n ce

sy st em

s fo r ro u ti n e m o n it o ri n g o fh

ea lt h in eq

u it y an d

th e so ci al d et er m in an ts o f h ea lt h .E va lu at e th e h ea lt h eq

u it y im

p ac t o f p o lic y an d ac ti o n .

En su re

st ro n ge r fo cu s o n so ci al

d et er m in an ts

in p u b lic

h ea lt h re se ar ch .

C o m m it te e o n R ec o m m en

d ed

So ci al

an d B eh

av io ra l

D o m ai n s an d M ea su re s fo r El ec tr o n ic H ea lt h

R ec o rd s, In st it u te

o f M ed

ic in e,

N A SE M

(2 01 4)

(8 0)

St an d ar d iz e d at a co lle ct io n an d m ea su re m en

t to

fa ci lit at e th e cr it ic al u se

an d ex ch an ge

o f in fo rm

at io n

o n so ci al

an d b eh

av io ra l d et er m in an ts

o f h ea lt h

O ffi ce

of th e N at io na lC oo

rd in at or fo rH

ea lt h In fo rm

at io n Te ch no

lo gy

an d th e CM

S sh ou

ld in cl ud

e th e re co m m en de d st an da rd iz ed

m ea su re s in th e ce rt ifi ca ti on

an d m ea ni ng fu lu se

re gu la ti on

s: C o m m o n ly u se d m ea su re s: ra ce

an d et h ni ci ty ,* re si d en

ti al ad d re ss ,* al co h o lu se ,t o b ac co

u se

A d d it io n al

re co m m en

d ed

m ea su re s: ce n su s tr ac t- m ed

ia n in co m e, * ed

u ca ti o n ,*

fi n an ci al

re so u rc e st ra in ,*

so ci al

co n n ec ti o n s an d so ci al

is o la ti o n ,*

d ep

re ss io n , in ti m at e p ar tn er

vi o le n ce , p h ys ic al

ac ti vi ty , st re ss

C o m m it te e o n Ed u ca ti n g H ea lt h Pr o fe ss io n al s to

A d d re ss

th e So ci al

D et er m in an ts

o f H ea lt h , N A SE M

(2 01

6) (3 01

)

C re at e a le ar n in g en

vi ro n m en

tf o rh

ea lt h p ro fe ss io n al s to

fo st er

co m m u n it y co lla b o ra ti o n s

H ea lt h p ro fe ss io n al

ed u ca to rs

sh o u ld

cr ea te

lif el o n g le ar n er s w h o ap p re ci at e th e va lu e o f

re la ti o n sh ip s an d co lla b o ra ti o n s fo r u n d er st an di n g an d ad d re ss in g co m m u n it y- id en

ti fi ed

n ee d s an d fo r st re n gt h en

in g co m m u n it y as se ts .

Pr ep

ar e h ea lt h p ro fe ss io n al s to

ta ke

ac ti o n o n SD

O H

To p re p ar e h ea lt h p ro fe ss io n al st o ta ke

ac ti o n o n th e so ci al d et er m in an ts o fh ea lt h in ,w

it h ,a n d

ac ro ss co m m u n it ie s, h ea lt h p ro fe ss io n al an d ed

u ca ti o n al as so ci at io n s an d o rg an iz at io n s at

th e gl o b al , re gi o n al , an d n at io n al

le ve ls sh o u ld

ap p ly

[f ra m ew

o rk s fo r] p ar tn er in g w it h

co m m u n it ie st o in cr ea se th e in cl u si vi ty an d d iv er si ty o ft h e h ea lt h p ro fe ss io n al st u d en

tb o d y

an d fa cu lt y.

In te gr at e SD

O H in to

o rg an iz at io n al

m is si o n an d va lu es

G ov er n m en

ts an d in d iv id u al

m in is tr ie s (e .g ., si gn at o ri es

o f th e R io

D ec la ra ti o n ), h ea lt h

p ro fe ss io n al an d ed

u ca ti o n al as so ci at io n s an d o rg an iz at io n s, an d co m m u n it y gr o u p s sh o u ld

fo st er

an en

ab lin g en

vi ro n m en

t th at

su p p o rt s an d va lu es

th e in te gr at io n o f th e so ci al

d et er m in an ts

fr am

ew o rk

p ri n ci p le s in to

th ei r m is si o n , cu lt u re , an d w o rk .

B u ild

th e ev id en

ce b as e fo r SD

O H le ar n in g, in te rv en

ti o n ,

an d ev al u at io n ap p ro ac h es

G ov er n m en

ts , h ea lt h p ro fe ss io n al

an d ed

u ca ti o n al

as so ci at io n s an d o rg an iz at io n s, an d

co m m u n it y o rg an iz at io n s sh o u ld u se

[a so ci al d et er m in an ts ]f ra m ew

o rk an d m o d el to

gu id e

an d su p p o rt ev al u at io n re se ar ch

ai m ed

at id en

ti fy in g an d ill u st ra ti n g ef fe ct iv e ap p ro ac h es

fo r le ar n in g ab o u t th e so ci al

d et er m in an ts

o f h ea lt h in

an d w it h co m m u n it ie s w h ile

im p ro vi n g h ea lt h o u tc o m es , th er eb

y b u ild in g th e ev id en

ce b as e.

C o m m it te e o n In te gr at in g So ci al

N ee d s C ar e In to

th e

D el iv er y o f H ea lt h C ar e to

Im p ro ve

th e N at io n ’s

H ea lt h , N A SE M

(2 01

9) (5 )

D es ig n h ea lt h ca re

d el iv er y to

in te gr at e so ci al ca re

in to

h ea lt h ca re , gu id ed

b y th e fi ve

h ea lt h ca re

sy st em

ac ti vi ti es d aw

ar en

es s, ad ju st m en

t, as si st an ce ,

al ig n m en

t, an d ad vo ca cy

Es ta b lis h o rg an iz at io n al

co m m it m en

t to

ad d re ss in g d is p ar it ie s an d h ea lt h -r el at ed

so ci al

n ee d s. In co rp o ra te

st ra te gi es

fo r sc re en

in g an d as se ss in g fo r so ci al ri sk

fa ct o rs an d n ee d s.

In co rp o ra te

so ci al ri sk

in to

ca re

d ec is io n s u si n g p at ie n t- ce n te re d ca re . Es ta b lis h lin ka ge s

b et w ee n h ea lt h ca re

an d so ci al se rv ic e p ro vi d er s. In cl u d e so ci al ca re

w o rk er s in te am

ca re .

D ev el o p in fr as tr u ct u re fo rc ar e in te gr at io n ,i n cl u d in g fi n an ci n g o fr ef er ra lr el at io n sh ip s w it h

se le ct

so ci al

p ro vi d er s.

B u ild

a w o rk fo rc e to

in te gr at e so ci al ca re

in to

h ea lt h ca re

d el iv er y

So ci al

w o rk er s an d o th er

so ci al

ca re

w o rk fo rc es

sh o u ld

b e p ro vi d er s el ig ib le

fo r

re im

b u rs em

en t fr o m

p ay er s. In te gr at e SD

O H co m p et en

ci es

in m ed

ic al

an d h ea lt h

p ro fe ss io n al

cr ed

en ti al in g.

C on

ti nu

ed on

p. 8

care.diabetesjournals.org Hill-Briggs and Associates 7

T a b le

3 — C o n ti n u e d

C o m m it te e

R ec o m m en

d ed

ac ti o n s

D es cr ip ti o n

D ev el o p a d ig it al

in fr as tr u ct u re

th at

is in te ro p er ab le

b et w ee n h ea lt h ca re

an d so ci al

ca re

o rg an iz at io n s

Es ta b lis h A C A -r ec o m m en

d ed

d ig it al in fr as tr u ct u re

fo r so ci al ca re .T h e O ffi ce

o f th e N at io n al

C o o rd in at o rs h o u ld su p p o rt id en

ti fi ca ti o n o fi n te ro p er ab le ,s ec u re ,p la tf o rm

s fo ru

se ac ro ss

h ea lt h an d so ci al

ca re

co m m u n it ie s. Th e Fe d er al

H ea lt h In fo rm

at io n Te ch n o lo gy

C o o rd in at in g C o m m it te e sh o u ld

fa ci lit at e d at a sh ar in g ac ro ss

d o m ai n s (e .g ., h ea lt h ca re ,

h o u si n g,

an d ed

u ca ti o n ). A n al yt ic an d te ch n o lo gy

im p le m en

ta ti o n m u st

h av e an

ex p lic it

fo cu s o n eq

u it y to

av oi d u n in te n d ed

co n se q u en

ce s su ch

as p er p et u at io n o r ag gr av at io n o f

d is cr im

in at io n , b ia s, an d m ar gi n al iz at io n .

Fi n an ce

th e in te gr at io n o f h ea lt h ca re

an d so ci al

ca re

C M S sh o u ld

d efi

n e an d u se

fl ex ib ili ty

in w h at

so ci al

ca re

co n st it u te s M ed

ic ai d -c o ve re d

se rv ic es .H

ea lt h sy st em

s, p ay er s, an d go ve rn m en

ts sh o u ld co n si d er

co lle ct iv e fi n an ci n g to

sp re ad

ri sk

an d cr ea te

sh ar ed

re tu rn s o n in ve st m en

ts in so ci al ca re .H

ea lt h sy st em

s su b je ct

to co m m u n it y b en

efi t re gu la ti o n s sh o u ld

co m p ly w it h th o se

re gu la ti o n s an d sh o u ld

al ig n

th ei r h o sp it al

lic en

si n g re q u ir em

en ts

an d p u b lic

re p o rt in g w it h co m m u n it y b en

efi ts

re gu la ti o n s an d sh o u ld

lin k th ei r co m m u n it y b en

efi ts

p ro vi d in g so ci al

ca re .

Fu n d ,c o n d u ct ,a n d tr an sl at e re se ar ch

an d ev al u at io n o n

th e ef fe ct iv en

es s an d im

p le m en

ta ti o n o f so ci al

ca re

p ra ct ic es

in h ea lt h ca re

se tt in gs

Fe d er al (e .g ., N IH ,A

H R Q ,P C O R I) an d st at e ag en

ci es ,p ay er s, p ro vi d er s, d el iv er y sy st em

s, an d

fo u n d at io n s sh o u ld co n tr ib u te

to ad va n ci n g re se ar ch

an d ev al u at io n o fs o ci al ca re

th ro u gh

fu n d in g o p p o rt u n it ie s, re se ar ch er

su p p o rt (i .e ., cu lt iv at e h ea lt h se rv ic es , so ci al sc ie n ce s,

an d cr o ss -d is ci p lin ar y re se ar ch er s) ,a nd

u se

o fe xp er im

en ta lt ri al s, ra p id le ar n in g cy cl es ,a n d

d is se m in at io n o fl ea rn in gs .C M S sh o u ld fu lly

fi n an ce

in d ep

en d en

t st at e w ai ve r ev al u at io n s

to en

su re

ro b u st ev al u at io n o f so ci al ca re

an d h ea lt h ca re

in te gr at io n p ilo t p ro gr am

s an d

d is se m in at io n .

A H R Q , A ge n cy

fo r H ea lt h ca re

R es ea rc h an d Q u al it y;

N IH , N at io n al

In st it u te s o f H ea lt h ; PC

O R I, Pa ti en

t- C en

te re d O u tc om

es R es ea rc h In st it u te . *S D O H m ea su re s.

8 Social Determinants of Health and Diabetes Diabetes Care

community complaints (134–138). In ru- ral and suburban communities, including Native American Indian communities, unregulated private wells are a source of water contaminants including arsenic and other metals/metalloids, pesticides, and hazardous chemicals, affecting mil- lions of people (139–141). Both food packaging and fast-food consumption, which can be high in low-income neigh- borhoods, can expose people to chem- icals known to be endocrine disrupters (142–145). Examples include chemicals released from plastic packaging during microwave heating (142), higher urinary phthalate levels associatedwith fast food (145), and higher urinary bisphenol A levels from canned foods (146). Certain personal care and cosmetic products, which are a source of phthalates and metals (e.g., skin-lightening products, which are high in mercury), are dispro- portionately marketed to marginalized population subgroups (147).

Associations of Environmental Risk Factors

With Diabetes Incidence, Prevalence, and

Outcomes. In 2011, the National Toxicol- ogy Program at the National Institute of Environmental Health Sciences convened an international workshop to evaluate the experimental and epidemiologic ev- idence on the relationship of environ- mental chemicals with obesity, diabetes, and metabolic syndrome (148–150). Ev- idencewasdeemedstrongest for arsenic, with relative risks of diabetes found to range from 1.11 to 10.05 in different studies (median 2.69) at high arsenic exposure levels. More recent systematic reviews and meta-analyses present the growing literature examining multiple groups of chemicals (148,151) or specific groups of chemicals (152–154). Overall, the evidence supports an increased risk of diabetes in populations exposed to environmental chemicals including arse- nic, persistent organic pollutants, phtha- lates, and possibly bisphenol. In 11 prospective studies of air pollu-

tion exposure and incident diabetes in adults, the pooled hazard ratio (HR) (95% CI) per 10 mg/m3 increment particulate matter of ,2.5 mm aerodynamic diam- eter was 1.10 (1.04–1.17) (155). Other reviews have reached conclusions con- sistent with this increased diabetes risk finding (156–158). The epidemiologic evidence is also supported by animal experiments showing that air pollution exposure can increase susceptibility to

insulin resistance and T2DM (159–161). These findings highlight that populations more exposed to air pollution are also disproportionately at risk for developing diabetes.

There is epidemiologic and experimen- tal evidence that environmental exposures increase susceptibility to cardiovascular disease (CVD) in people with diabetes. Theevidence is extensive for air pollution exposures (162,163). For example, in Medicare patients, a daily increase of 10 mg/m3 in particulate matter ,10 mm of aerodynamic diameter was asso- ciated with 2.01% increase in CVD hos- pitalizations for those with diabetes compared with 0.94% increase among thosewithout diabetes (162). Short-term increases in air pollution exposure are also related to higher risk of stroke mortality in patients with diabetes com- pared with those without (164). In an experimental model, mice with diabe- tes exposed to diesel exhaust particles showed increased cardiovascular sus- ceptibility compared with mice without diabetes (165). In natural experiments in human populations, air pollution expo- sure also resulted in increased vascular reactivity (166) and inflammation in pa- tients with diabetes comparedwith those without (167). In addition to air pollution, some evidence is also available for met- als. In theStrongHeart StudyofAmerican Indian adults followed since 1989–1991, the risk of CVD associated with higher exposure to arsenic and cadmium was higher among participants with diabetes compared with those without diabetes (168,169). In a clinical trial in patients with a previousmyocardial infarction (Tri- al toAssessChelationTherapy [TACT]), the beneficial effects of repeated chelation with disodium edetate on cardiovascular outcomes were greater in patients with diabetes (170). Environmental Exposures Interventions and

Diabetes. Few studies have evaluated the effect of population-based or clinical interventions related to environmental exposures and diabetes prevention or control. The increased risk of diabetes in populations exposed to environmental chemicals and the increased suscepti- bility for diabetes complications in in- dividuals with diabetes exposed to air pollution potentially provides an oppor- tunity for prevention and treatment that can be particularly relevant for themost vulnerable populations. For example, a

comparison of preterm births among four studies in different countries, be- fore and after the implementation of smoke-free legislation, has shown re- ductions in diabetes risk (pooled risk change 218.4%, 95% CI 218.8 to 22), although the long-term benefits have not yet been evaluated (171).

Because individuals generally have limited control over environmental agents, themosteffective interventionswill beat the population level, through policy and regulation, with a particular focus on protecting marginalized and underserved populations. There is evidence that de- clines in air pollution levels and metal exposures have contributed to improve- ments in CVD development (172,173); benefits for diabetes development are pending. Research is also needed to test intermediate strategies at the clinical level, such as exposure screening (e.g., asking about living near highways or using private wells for drinking water) and recommendations to test air or water, reduce known sources of exposure (e.g., minimize packaged foods, avoid heating food in plastic containers, and minimize use of certain cosmetic products), and make home interventions (e.g., install filters for air or water contaminants) (174–176).

Food Environment and Diabetes The food environment can be defined as the physical presence of food that affects a person’s diet; a person’s prox- imity to food store locations; the distri- bution of food stores, food service, and any physical entity by which foodmay be obtained; or a connected system that allows access to food (177). It is the “collective physical, economic, policy and sociocultural surroundings, oppor- tunities and conditions that influence people’s food and beverage choices and nutritional status” (178). It is also referred to as the community food en- vironment (e.g., number, type, location, and accessibility of food outlets such as food stores, markets, or both) and the consumer-level environment (e.g., health- ful, affordable foods in stores, markets, or both), which interact to affect food choices and diet quality (179,180). Key dimensions of the food environment include accessibility, availability, afford- ability, and quality (181–184). These factors, which define the quality of the food environment, are of particular

care.diabetesjournals.org Hill-Briggs and Associates 9

importance inmarginalized communities, which may have poor access to super- markets and healthy foods but abundant access to fast-food outlets and energy- dense foods and are often dispropor- tionately impacted by physical hazards (e.g., vacant houses, traffic, and crime) (78). At their root, differences in the food environment can be caused by government policies and incentives, and the legacy of such policies as red- lining and segregation.

Associations of Food Environment With

Diabetes Incidence, Prevalence, and

Outcomes

FoodAccessandAvailability.Cross-sectional studies have shown associations between food access, availability, geographic char- acteristics, and T2DM prevalence. Ahern et al. (185) examined 3,128 counties across the U.S. for food access (assessed as percent of households with no car living more than one mile from a grocery store) and food availability (assessed as numberof fast-food restaurants, full-service restaurants, grocery stores, convenience stores, and per capita sales in dollars from local farms made directly to consumers). Higher access to foodwas associatedwith lower T2DMrates inmetro andnonmetro counties, and higher availability of full- service restaurants andgrocery storesand lower availability of fast-food and conve- nience stores were associated with lower

diabetes rates (185). Haynes-Maslow and Leone (186) similarly found availability of full-service restaurants to be associ- ated with lower prevalence of diabetes in adults and availability of fast-food restau- rants generally to be associated with higher diabetes prevalence. However, the study reported variability in associa- tions amongnumerous foodenvironment characteristics based on county composi- tion (lowpoverty/lowminority, lowpoverty/ medium minority, high poverty/low mi- nority), highlighting complexities in un- derstanding patterns among variables of county socioeconomic status, demo- graphics, food availability, and diabetes prevalence (186).

Several observational, longitudinal studies report neighborhood resources in general, and access and availability of the food environment in particular, as associated with diabetes prevalence and incidence (187). A systematic review by den Braver et al. (188) found availability of fast-food outlets and convenience stores to be associated with higher T2DM risk/prevalence and perceived healthfulness of the food environment tobeassociatedwith lowerdiabetes risk/ prevalence, but no associationwas found between density of grocery stores and T2DM risk/prevalence. Heterogeneity across the studies prevented the conduct of meta-analyses. Gabreab et al. (189)

examined neighborhood, social, and physical environments and T2DM in 3,700 African Americans through the Jackson Heart Study and found higher density of unfavorable food stores was associated with a 34% higher T2DM in- cidence after adjusting for individual- level risk factors. In a longitudinal employee cohort, Herrick et al. (190) found that living in a zip codewith higher supermarket density was associated with a reduction in T2DM risk, while zip codes with a higher percentage of poverty and zip codes with higher walk- ability scores were both associated with higher diabetes risk. Christine et al. (191) reported long-term exposure to residen- tial environments that offer resources to support healthy diets and PA was asso- ciated with a lower incidence of T2DM, although results varied by measurement method.

Studies have also examined both food andPAenvironments in combination and diabetes risk. Meyer et al. (192) com- bined measures of neighborhood food andPAenvironments andweight-related outcomes (N 5 14,379) of the Coronary Artery Risk Development in Young Adults (CARDIA) study, examining population density–specific (less than vs. greater than 1,750 people per square kilometer) clustersofneighborhood indicators: road connectivity, parks and PA facilities,

Table 4—Examples of resources on SDOH available for health care organizations and health care professionals

Organization Resource

Centers for Disease Control and Prevention (CDC) Tools for Putting Social Determinants of Health Into Action (https://www.cdc.gov/ socialdeterminants/tools/index.htm)

National Academies of Science, Engineering, and Medicine

Questions for conducting social andbehavioraldeterminantassessmentand frequencies for assessing

Adler NE, Stead WW. Patients in contextdEHR capture of social and behavioral determinants of health. N Engl J Med 2015;372:698–701. DOI: 10.1056/NEJMp1413945

National Institutes of Health (NIH) Division of Extramural Affairs

The Neighborhood AtlasdFree social determinants of health data for all! Kind AJH, Buckingham W. Making neighborhood disadvantage metrics accessible: the

neighborhood atlas. N Engl J Med 2018;378:2456–2458. PMCID: PMC6051533

American Academy of Family Physicians The EveryONE Project’s Neighborhood Navigator Toolkit (https://www.aafp.org/patient- care/social-determinants-of-health/everyone-project/neighborhood-navigator/ training-videos.html)

American College of Physicians Addressing Social Determinants to Improve Patient Care and Promote Health Equity: An American College of Physicians Position Paper. DOI: 10.7326/M17-2441

American Medical Association Podcast: Social determinants of health: What they are and what they aren’t (https://www .ama-assn.org/delivering-care/patient-support-advocacy/social-determinants-health- what-they-are-what-they-arent)

Nonprofit services 211: A service of the United Way that continuously identifies links for all “211” health and human services referral services in the U.S.

HealthLeads: A nonprofit offering tools, training and resources for integrating SDOH into accountable care

Aunt Bertha: A service that provides links to hundreds of programs serving every U.S. zip code. Free basic use.

10 Social Determinants of Health and Diabetes Diabetes Care

and food stores/restaurants. In lower– population density areas, higher food and PA resource diversity relative to other clusters was significantly associated with higher diet quality (192). In higher– population density areas, a cluster with relatively more natural food/specialty stores, fewer convenience stores, and more PA resources was associated with higher diet quality. Neighborhood clus- ters were inconsistently associated with BMI or insulin resistance and not associated with fast-food consump- tion, orwalking, biking, or running (192). Tabaei et al. (193) examined associations of residential socioeconomic, food, and

built environments with glycemic con- trol in adults with diabetes ascertained from the New York City A1C Registry from2007 to 2013. Individualswho lived continuously in the most advantaged residential areas, including greater ratio of healthy food outlets to unhealthy food outlets and residential walkability, achieved increasedglycemic control and took less time to achieve glycemic con- trol compared with the individuals who lived continuously in the least advan- taged residential areas (193). Food Affordability. Kern et al. (194) note that it is reasonable to expect that large differences in price between healthy and

unhealthy foods would lead to differ- ences in purchasing patterns and result- ing diets and that those differences would bemore prominent for individuals of lower SES. In a longitudinal study, they examined food affordability and neigh- borhood price of healthier food relative to unhealthy food and its association with T2DMand insulin resistance. Higher prices of healthy foods relative to un- healthy foods were found to be associated with lower odds of having a high-quality diet; however, there was no association with diabetes incidence or prevalence (194). More studies are needed in this area.

Table 5—SDOH and diabetes research recommendations Research recommendation 1 Consensus isneededaround languageandmetricsassociatedwithSDOHand

diabetes care that move beyond health care and capture the impact of social advantage and disadvantage in population settings. Clarity and consistency in measurement, evaluation, and reporting of progress will allow for appropriate planning of interventions, allocation of resources, and analysis of impact in achieving equity goals.

Establish consensus core SDOH definitions and metrics

Research recommendation 2 Examinations of potential differences in pathways or impacts of SDOHbased on characteristics including diabetes type or diagnostic category (e.g., T1DM vs. T2DM, gestational diabetes mellitus, prediabetes), age group (e.g., children and youth, adults, older adults), and different SES (wealthy vs. middle class vs. poor) are needed. In addition, complexities of SDOH pathways and impacts for different racial/ethnic groups, based on historical drivers and policies, warrant elucidation to inform intervention and mitigation strategies.

Examine specificities in SDOH pathways and impacts among different populations with diabetes

Research recommendation 3 Multisector partnerships, comprising academic institutions, government sectors (e.g., housing, education, justice), and public health entities are required inorder todesignand testobservational and interventionstudies to better understand and intervene on SDOH as root causes of diabetes disparities. Priorities need to move from compensatory to the next- generation of research that will be larger in scope, addressing foundational causes of disparities (e.g., policy, systems change), and tested over time across sectors. Complex studies, examining the interactive effects of multifaceted systems that influence SDOH, will also transformandmove translational efforts toward large-scale solutions that promote equity for all populations andmitigate the influence of SDOH on diabetes outcomes.

Prioritize a next generation of research that targets SDOH as the root cause of diabetes inequities

Research recommendation 4 For clinical research programs, dissemination and implementationmethods will shorten the translation gap from discovery to impact of evidence- based interventions by addressing the complexity of integrating and adapting evidence-based practices to real-world community and clinical settings. Thiswill assureall populationsbenefit from thebillionsofU.S. tax dollars spent on research to prevent diabetes and to improve diabetes population health.

Use dissemination and implementation science to ensure SDOH considerations are embedded within diabetes research and evaluation studies

Research studiesmust also consider thepotential influenceof either positive or negative SDOH (e.g., wealth or economic security vs. poverty, food security vs. insecurity, stable vs. unstable housing) on intervention appropriateness and outcomes, on study recruitment and participation, and on study outcomes and conclusions.

Research recommendation 5 Training on SDOH and their influence on diabetes prevention and treatment is needed. Training priorities include interdisciplinary science,multisector collaboration research approaches, and methods to advance root cause research on SDOH. Additionally, increasing diversity among research workforces, and fostering educational experiences encompassing multisector partners will develop a workforce that is congruent with promoting diabetes health equity.

Train researchers in methodologies and experimental techniques for multisector and next generation SDOH intervention studies

care.diabetesjournals.org Hill-Briggs and Associates 11

FoodInsecurity.Food insecurity is defined as not having adequate quantity and quality of food at all times for all house- hold members to have an active, healthy life (195,196). Approximately 20% of di- abetes patients report household food insecurity (197), and food insecurity is a risk factor for poor diabetes manage- ment (196). Researchers have investi- gated several pathways through which food insecurity may worsen T2DM out- comes (198–200). First, in the nutritional pathway, food insecurity is associated with lower diet quality (201), which is in turn associated with higher HbA1c. Food insecurity incentivizes more affordable, energy-dense foods that can directly raise serum glucose (e.g., refined carbo- hydrates, processed snacks and sweets, sugar-sweetened beverages, etc.) and may lead to greater insulin resistance (202,203). Conversely, low or inconsis- tent food availability can increase risk of hypoglycemia. Second, via a compensa- tory pathway, behavioral strategies nec- essary to cope with the immediate problem of food insecurity can inadver- tently undermine T2DM management. For example, financial resources that might otherwise have been used for medications or diabetes care supplies are diverted to meet dietary needs (197,204–206). Third, through the psy- chological pathway, the state of food insecurity, in whichmeeting basic needs is outside an individual’s control, under- mines self-efficacy and increases depres- sive symptoms and diabetes distress (207–210). Several studies have reported a relationship between food insecurity and adverse diabetes outcomes (211,212), and a review by Barnard et al. (213) has suggested that food insecurity among patients with and at high risk for T2DM may be particularly toxic because, in addition to issues of accessing sufficient calories overall, the dietary quality of the foods eaten is evenmore important than for the general population. Several cross- sectional studies report a relationship between food insecurity and T2DM di- abetes outcomes (214–216), including poor metabolic control (217,218), expe- rience of severe hypoglycemia in low- income and low-education samples (218), lower diabetes self-management behav- ioral adherence and worse glycemic con- trol (219), and increased outpatient visits but not increased emergency department/ inpatient visits (95,212).

Food Environment Interventions and

Diabetes

Three studies reported food bank and pantry interventions with food inse- cure clients with T2DM (196,220,221). Seligman et al. (196) conducted a pilot program in Texas, California, and Ohio with a pre/post design, encompassing provision of diabetes-appropriate food, blood glucosemonitoring, self-management support, and primary care referrals. The study resulted in improvements in HbA1c, fruit and vegetable consumption, self- efficacy and medication adherence. In a randomized controlled trial of the inter- vention, Seligman et al. (220) found im- provements in nutritional consumption, food security, and distress but no clinical changes. Palar et al. (221) found reduction in BMI but not HbA1c and better nutri- tional and psychosocial outcomes.

Studies have examined effect of su- permarket gain or loss on T2DM out- comes. A study conducted within the setting of the Kaiser Permanente North- ern California Diabetes Registry linked clinical measures to metrics from a geo- graphic information system based on partic- ipants’ residential addresses (115,222,223). Results over 4 years of tracking super- market change in low-income neighbor- hoods showed that relative to no change in supermarket presence, supermarket loss was associated with worse HbA1c trajectories, especially among thosewith highest HbA1c. Supermarket gain in neigh- borhoods was associated with marginally betterHbA1coutcomes, but only for those with near-normal HbA1c baseline values (223). In a natural experiment design, the Pittsburgh Hill/Homewood Study on Eat- ing, Shopping, and Health (PHRESH) tested the effects of adding a supermarket, along with other neighborhood invest- ments, on cardiometabolic risk factors among a randomly selected cohort of residents from two low-income, urban, and predominately African American matched neighborhoods (222,224). Re- sults for the intervention neighborhood (receiving the supermarket) showed im- proved perceived access to healthy food (225), and the prevalence of diabetes increased less in the neighborhood with the supermarket than in the comparison neighborhood. Since the initiation of the supermarket, many other investments including greenspace, housing, and com- mercial spaces have been implemented in the intervention neighborhood (226).

Results of these neighborhood invest- ments onmeasuredBMI, bloodpressure, HbA1c, and HDL cholesterol will be forth- coming. In sum, food environment fac- tors of foodunavailability, inaccessibility, and insecurity each demonstrate asso- ciations with worse diabetes risk and outcomes, and interventions including di- abetes-targeted food and self-management care at food banks and pantries and increasing grocery store presence in low-income neighborhoods are few, but collectively they demonstrate the potential to impact diabetes risk, clinical outcomes, and psychosocial outcomes.

Health Care and Diabetes Health care as a SDOH includes access, affordability, and quality of care factors. In the U.S., these factors are highly cor- related with race/ethnicity, SES, and place/ geographic region (19).

Associations of Health Care With Diabetes

Incidence, Prevalence, and Outcomes

Access. In population-based studies, hav- ing health insurance is the strongest predictor of whether adults with diabe- tes have access to diabetes screenings and care (227). Uninsured adults in the U.S. population have a higher likelihood of having undiagnosed diabetes than adults with insurance (228). Compared with insured adults with diabetes, the uninsured have 60% fewer office visits with a physician, are prescribed 52% fewer medications, and have 168% more emer- gency department visits (229). Liese et al. (230) found that, among adolescents and young adults with T1DM or T2DM, com- pared with having private insurance, hav- ing state or federal health insurance was associated with higher HbA1c values by 0.68%, and having no insurance was as- sociated with higher HbA1c by 1.34%. Having insurance has also been found to attenuate associations of financial barriers with higher HbA1c (231).

Geographic access to adult and pedi- atric endocrinologists varies substantially by state and county in the U.S (232), with disparities in access in many of the geo- graphic regions with highest diabetes prevalence and socioeconomic disadvan- tage (232,233). Similarly, factors that increase odds of having a diabetes self- managementeducationprogram inageo- graphic area include a higher percentage of the population with at least a high school education, a higher percentage of

12 Social Determinants of Health and Diabetes Diabetes Care

insured individuals, and a lower rate of unemployment (234). DeVoe et al. (235) found that among adults with diabetes, having both insurance and a usual source of care, rather than one or the other, conferred the greatest odds of receiving at least minimum diabetes health care. Be- ing uninsured and without a usual source of care was associated with three to five times lower odds of adults receiving an HbA1c screen, blood pressure check, or access to urgent carewhen needed (235). Among adolescents and young adults with diabetes who had state or federal health insurance, not having any usual source of provider (primary care or diabetes spe- cialist) was associated with higher HbA1c than having a usual source of provider, andHbA1cwas similar whether in primary care or specialist care (230). Affordability. On average, health care costs of people with diabetes are 2.3 times those of people without diabetes (229). Approximately 14% to 20% of adults with diabetes report reducing or delaying medications due to cost (236–238). Among adults with diabetes who are prescribed insulin, rates may be.25% (236,239). Cost-related or cost- reducing nonadherence (CRN) is associ- ated with income, insured status, and type of insurance. Adults with diabetes with an annual household income of ,$50,000 are more likely to engage in CRN than their counterparts with in- come $$50,000, and uninsured adults withdiabetes aremore likely to engage in CRN than those with insurance (236). Within a diabetes clinic population of adults with T1DM or T2DM prescribed insulin, odds of CRN were three times higher for those with Medicaid or no insurance compared with those with Medicare (239). Piette et al. (240) found differences based on health system model. Compared with VA patients with diabetes, risk of CRN was found to be almost three times higher for privately insured patients and four to eight times higher for patients with Medicare, Med- icaid, or no health insurance (240). Higher financial stress, financial insecu- rity, and financial barriers are associ- ated with likelihood of CRN (231,238). People with CRN experience poorer di- abetesmanagement, higher HbA1c, and decreased functional status (231,240). Deaths have been reported from in- sulin CRN among youth and adults with T1DM (241).

Quality. Having insurance is the stron- gest single predictor of whether adults with diabetes are likely to meet indi- vidual quality measures of diabetes care (242). Sociodemographic disparities in care quality are well documented in national reports and recommendations (2) and appear to remain consistent over time (243). In a U.S. population-based study of achievement of a composite diabetes treatment goal from 2005 to 2016, data from 2013 to 2016 showed that non-Hispanic Blacks had lower odds of achieving a composite diabetes quality measure than non-Hispanic Whites (ad- justed OR 0.57, 95% CI 0.39–0.83), and women had lower odds than men (ad- justed OR 0.60, 95% CI 0.45–0.80), with no improvement in diabetes treatment gaps fromprior time periods (2005–2008 and 2009–2012), especially for minori- ties, women, and younger adults (227). Within insured settings, disparities have been reported amongBlacks as compared with Whitesdin measures including di- lated eye exam taken; LDL test taken; LDL, bloodpressure,orHbA1ccontrol;andstatin therapy (244–246). A study of 21 VA fa- cilities found Blacks with diabetes were more likely than Whites with diabetes to receive care at lower-performing facilities overall, which explained some racial differ- ences in diabetes quality measures (246).

Health Care Interventions and Diabetes

Community Health Workers. Several sys- tematic reviews have concluded that community health worker (CHW) inter- ventions using trained lay workforces are effective for multiple outcomes in underserved African American and His- panic adults with T2DM and comorbid conditions (247–250). CHWs have been integrated into care delivery (251,252) with reimbursement in some states (253). Roles of CHWs include patient naviga- tion, appointment scheduling, visit atten- dance, patient education, home-based monitoring, assessment of social needs and connectionwith social services, social support, and advocacy (252,254). Re- ported outcomes include better diabetes knowledge and self-care behaviors, in- creasedqualityof life, reducedemergency visits and hospitalizations, reduced costs, and modest improvements in glycemic control (247–250,255), using home-based or integrated health team delivery models (252,256). A majority of the CHW interventions designed for adult

populations with diabetes have been diabetes-focused in content and goals and have utilized structured curricula (254); however, one series of studies reported use of a standardized, all-con- dition CHW intervention and foundmod- est gain in diabetes outcomes along with additional health benefits (257,258). Organizational Interventions. Systematic reviews report improvements in quality of diabetes care among racial/ethnic mi- norities resulting from quality improve- ment employing health information technology (i.e., patient registries in the electronic health record, computerized decision support for providers, reminders, centralized outreach for diabetes pa- tients overdue for specific services) (245,259,260). There is also evidence of effectiveness of self-management in- terventions delivered directly to under- served patients with diabetes when interventions are designed to overcome barriers. For example, the Centers for Medicare & Medicaid Services (CMS)- sponsoredNationalDiabetes Prevention Program (DPP) Medicaid demonstration foundCDC-recognizedDPP lifestylechange programs were effective in achieving per- formance measures among Medicaid re- cipients in Maryland and Oregon, and additional strategies (i.e., transportation assistance and child care) facilitated the highretentionreportedoverthe12months of DPP visits (261). In a series of studies, a problem-based self-management training addressing multiple life barriers to care in low-income and minority populations was adapted for low literacy and prevalent diabetes-related functional limitations (e.g., low vision, physical disability, and mild cognitive impairment) that impede self-management education (73,262). The approach has proven effective in improv- ingclinicaloutcomes(HbA1c,bloodpressure), self-care behaviors, and self-management knowledge and problem-solving skills in low-income, racial/ethnic minority, and ru- ral populations (76,263,264). Policy.Studies have examined the impact of the Affordable Care Act (ACA) on insurance coverage and health care ac- cess for patients with diabetes (265). Analyses of NHIS data from 2009 and 2016 found an increase nationwide of 770,000 more adults with diabetes aged 18 to 64 years with health insurance coverage in 2016, with a significant in- crease in coverage seen among Whites, Blacks, and Hispanics, people with family

care.diabetesjournals.org Hill-Briggs and Associates 13

income ,$35,000, and people across educational attainment strata (less than high school and more than high school) (266). Among people with diabetes in the lowest income strata, the proportion of income spent on health costs decreased significantly from 6.3% to 4.8% (266). Other studies found increased access to care, diabetes management, and health status among people with diabetes in Medicaid expansion states as compared with their counterparts in non–Medicaid expansion states (267); increased rates of diabetes detection and diagnosis among Medicaid patients with undiag- nosed diabetes in states with Medicaid expansion (268); and reduction in cost- related medication nonadherence rates and uninsured rates among people with diabetes following ACA (269).

Social Context and Diabetes Severalmultidimensional factors shape the social environment as a determi- nant of health (270), including social capital, social cohesion, and social sup- port (28,29). Social capital is defined as the features of social structures that serve as resources for collective action (e.g., interpersonal trust, reciprocity norms, andmutual aid) (271–273). Bond- ing social capital refers to trusting and co-operative relations between mem- bers of a network who see themselves as being similar in terms of their shared social identity; by contrast, bridging so- cial capital refers to aspects of respect andmutuality betweenpeoplewhodo not share social identities (e.g., differing by race/ethnicity, social class, age) (274–276). Racism, discrimination, and inclusion ver- susexclusionaremacro-levelsocialcapital factors that impact health (28). Social cohesion refers to the extent of

connectednessandsolidarityamonggroups in a community (271,277) andhas twodi- mensions: reduction of inequalities and patterns of social exclusion of population subgroups from full participation in so- ciety (278) and strengthening of social re- lationshipsand interactions (279–281). Social cohesion actions facilitate the goal of keep- ing the society united, not only through so- cial relations, community ties,and intergroup harmony but also through reducing bias and discrimination toward economically dis- advantaged groups within a society, such as women and ethnic minorities (28). Social support describes experiences in

individuals’ formal and informal personal

relationships as well as their perceptions of those relationships. Categories include emotional support, tangible support, in- formational support, and companionship (282–285). Social support is theorized to operate by either buffering the effects of poor health or by directly impacting health (285,286).

AssociationsofSocialContextWithDiabetes

Incidence, Prevalence, and Outcomes

A systematic review by Flôr et al. (287) concluded that social capital was posi- tively associated with diabetes control among different populations, indepen- dent of the quality or quantity of social capital. However, the few studies avail- able and variations among populations and measures limit the ability to draw firm conclusions related to dimensions of social capital and whether the associa- tion is the same at the individual or neighborhood level (272,288–290). Ge- breab et al. (189), using data from the Jackson Heart Study, examined social cohesion, measured as trust in neigh- bors, shared values with neighbors, will- ingness to help neighbors, and extent to which neighbors get along. The study revealed higher neighborhood social co- hesion was associated with a 22% lower incidence of T2DM (189). Studies dem- onstrating the relationship between so- cial support anddiabeteshaveassociated increased social support with better gly- cemic control and improvedquality of life (291–295), while lack of social support has been associated with increased mor- tality and diabetes-related complications (291).

A number of studies suggest social cohesion, social capital, and social sup- port may influencedor be influenced bydracism and discrimination (296). Racism interacts with other social enti- ties, creating a set of dynamic, interde- pendent components that reinforce each other, sustaining racial inequities and promoting both institutional- and individual- level discrimination across various sectors of society impacting diabetes incidence (296,297). For example, Whitaker et al. (298) documented associations of major and everyday discrimination experien- ces with incident diabetes among a di- verse sample of 5,310 middle-aged to older adults from the Multi-Ethnic Study of Atherosclerosis. The Black Women’s Health Study found that, when compared with women in the lowest quartile of

exposure, those in the highest quartile of exposure to everyday racism had a 31% increased risk of diabetes (HR 1.31, 95% CI 1.20–1.42), and women with the high- est exposure to lifetime racismhad a 16% increased risk (HR 1.16, 95% CI 1.05– 1.27); both associations were mediated byBMI (298,299). Furtherwork is needed to understand themultiple ways that the social environment influences inequities in diabetes outcomes.

Social Context Interventions and Diabetes

Outcomes

To our knowledge, there is no empirical research on social capital or social co- hesion interventions and impact on di- abetes outcomes, but abodyof literature has examined effects of social support. The systematic review by Strom and Egede (284) of 18 observational studies of adults with T2DM found that higher levels of social support were associated with outcomes including better glycemic control, knowledge, treatment adher- ence, quality of life, diagnosis awareness and acceptance, and stress reduction (284). Lack of social support has been linked with increased mortality and di- abetes-related complications in T2DM (291,295). Strom and Egede’s review of 16 social support intervention studies demonstrated improved diabetes-related outcomes (clinical, psychosocial, and/or self-management behavior change) in adults with T2DM, and improvements in clinical outcomes (HbA1c, blood pressure, lipids) appeared to be unrelated to the source or delivery (i.e., peer support, cou- ples/spouse, or nurse manager).

With regard to preferencesdin a study conducted before the coronavirus disease 2019 pandemicdSarkar et al. (300) found that, compared with White adults with diabetes, Hispanics with di- abetes preferred telephone-based and group support (including promotoras), while African Americans demonstrated more variability in their preferences (i.e., telephone, group, internet). Reliance on support from family and community tended to be higher in minority popula- tions, whileWhites reliedmore onmedia and health care professionals (300).

LINKAGES ACROSS HEALTH CARE AND COMMUNITY SECTORS TO ADDRESS SDOH

International and U.S. national commit- tees have convened to provide guidance

14 Social Determinants of Health and Diabetes Diabetes Care

onSDOH intervention approaches. These expert committee recommendations are not specific to any disease; rather, they are applicable to all conditions and pop- ulations of health inequity. Table 3 dis- plays recommendations from the WHO Commission on Social Determinants of Health (27), the National Academies of Science, Engineering, and Medicine (In- stitute ofMedicine, NASEM) Committee on the Recommended Social and Be- havioral Domains andMeasures for Elec- tronic Health Records (80), the NASEM Committee on Educating Health Profes- sionals to Address the Social Determi- nants of Health (301), and the NASEM Committee on Integrating Social Needs Care into the Delivery of Health Care to Improve the Nation’s Health (5). TheWHO recommendations are unique

in their emphasis on root-cause, multi- sector interventions designed to remove the SDOH as a barrier to health equity. The NASEM recommendations are based in the health care sector and, collectively, focus on integration of SDOH into the health care mission, operations, and fi- nancial model. Accountable care organ- izations, value-based purchasing, and shared savings programs could be in- tentionally designed to support and in- centivize health care systems to address patients’ health-related social needs as a strategy to improve health outcomes (5). The Accountable Health Communities is one current CMS demonstration project examining impact on health care costs of three models for health care response to SDOH through linkages with community services: awareness (screening for social needs within the health care setting and patient referral to services using an in- ventoryofavailable local community serv- ices), assistance (screening, referral, plus navigation to enable access to and use of communityservices),andalignment(screen- ing, referral, community service navigation, plus partner alignment using a “backbone” organization for capacity building, data sharing among community and health care partners, and scaling of services) (302). Many health care systems are uti- lizing electronic medical records and health information exchanges to capture SDOH data and commercially available SDOH algorithms to identify patients at social risk and trigger service referrals (303). NASEMprovided assessment ques- tions to capture SDOH domains and frequencies for assessment (304) with

evidence of feasibility (305). In addition, Table 4 displays publicly available resour- ces and tools to aid providers in address- ing individual patients’ social needs.

DISCUSSION

There is SDOH evidence supporting as- sociations of SES, neighborhood and physical environment, food environ- ment, health care, and social context with diabetes-related outcomes. Inequi- ties in living and working conditions and the environments in which people reside have a direct impact on biological and behavioral outcomes associated with di- abetes prevention and control (12,48). Life-course exposure based on the length of time one spends living in resource- deprived environmentsddefined by pov- erty, lack of quality education, or lack of health caredsignificantly impacts dispar- ities in diabetes risk, diagnosis, and out- comes (12,48,306). Although the review reports SDOH intervention studies for aspects of housing, built and food envi- ronment, and health care, there appears to be relatively limited U.S.-based re- search examining impact on diabetes of interventions designed to target educa- tion, income, occupation, toxic environ- mental exposures, social cohesion, and social capital.

In the U.S., integrating social context into health care delivery has become a pri- ority strategy (5–8). A clinical context alone, however, is too narrow to accom- modate systemic SDOH influences. Struc- tural and legal interventions are needed to address root causes driving SDOH (27,307). Similarly, additional emphasis is neededonanextgenerationof research that prioritizes interventions impacting the root causes of diabetes inequities, rather than compensatory interventions assisting the individual to adapt to in- equities (18,308). Forexample, in theU.S., proficient literacy and resulting health literacyare disproportionately low inmar- ginalized populations and communities (42), with historical sociopolitical root causes. U.S. antiliteracy laws for Blacks, whichprohibitedBlacks frombeingtaught to read or write, persisted until the 1930s in some states (309,310), and laws pro- hibitingAfricanAmericans fromattending public and private schools Whites at- tended continued until 1954 and 1976, re- spectively (311). Although adapting health materials for low-literacy suitability is an

effective intervention to compensate for centuries of legal racial discrimination in educational access and quality, a next- generation intervention might target the education sector and implement delivery of high-quality early education to all within both the public and private school systems and with equitable ed- ucational funding for sociodemographic populations. Similarly, while partner- ships to bring bags of healthy groceries to low-income families living in food deserts are important to compensate for food deserts, a next-generation ap- proach might target historical redlining and zoning policies that are the root cause of absence of supermarkets and fresh foodmarkets inminorityand lower- income neighborhoods (312–314).

The review has limitations. First, the undertaking was designed to summarize literature on the range of SDOH identi- fied as having impact on diabetes out- comes. As such, this article describes findings from systematic reviews and meta-analyses as well as more recent published studies on the named SDOH; it was not designed as a primary systematic review of all published research on the topic. Second are limitations of the re- search itself, including wide variability in measures and definitions used in studies within an SDOH area, making it more difficult to describe outcomes for an SDOH area in a consistent or uniform manner or to report quantitative out- comes derived from meta-analyses. Third, this review was U.S.-focused; con- clusions from SDOH research in other countries, which in some instances may utilize more standardly defined SDOH variables (e.g., occupation) are not part of this initial review. Finally, the many complexitiesofSDOHandtheirpotentially different pathways and impacts on pop- ulationsarebeyondthescopeofthis initial review and require attention to specificity in designs of future SDOH research in diabetes.

Recommendations for SDOH research in diabetes resulting from this SDOH re- view are described in Table 5 and include establishing consensus SDOH definitions and metrics, designing studies to exam- ine specificities based on populations, prioritizing next-generation interven- tions, embedding SDOH context within dissemination and implementation sci- ence in diabetes, and training research- ers in methodological techniques for

care.diabetesjournals.org Hill-Briggs and Associates 15

future SDOH intervention studies. By ad- dressing these critical elements, there is potential for progress to be realized in achieving greater health equity in diabe- tes and across health outcomes that are socially determined.

Acknowledgments. The authors express appre- ciation to Malaika I. Hill and Mindy Saraco of the American Diabetes Association; Elizabeth A. Vrany, Johns Hopkins University School of Medicine; and Shelly Johnson, Washington University in St. Louis, for providing technical assistance for this review. Funding. F.H.-B. is supported inpart by the Johns Hopkins Institute for Clinical and Translational Research (ICTR), which is funded in part by grant UL1TR003098 from the National Center for Ad- vancing Translational Sciences (NCATS), a compo- nent of the National Institutes of Health (NIH) and NIH Roadmap forMedical Research. F.H.-B. is also supported inpart byNIHNationalHeart, Lung, and Blood Institute (NHLBI) grant T32HL07180. D.H.-J. is supported in part by NIH National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) grant P30DK092950. M.H.C. is supported in part by NIH and NIDDK grant P30DK092949. T.L.G.-W. is supported in part by NHLBI grant R01HL131531. A.N.-A. is supported in part by Na- tional Institute of Environmental Health Sciences grants P42ES010349 and P30ES009089. S.A.B. is supported in part by NIDDK grant K23DK109200. The findings and conclusion in this report are

those of the authors and do not necessarily rep- resent the official position of the Johns Hopkins ICTR,NCATS, NIH,NIDDK, or any other institution mentioned in the article. Duality of Interests. M.H.C. reports being co- coordinator of the “Bridging the Gap: Reducing Disparities in Diabetes Care National Program Office,” supported by the Merck Foundation, a consultant to the Patient-Centered Outcomes Group, and amember of the Bristol-Myers Squibb Company Health Equity Advisory Board. S.A.B. received personal fees for service on an advisory board about prioritizing food insecurity research topics for the Aspen Institute. T.L.G.-W. received personal fees for service on an advisory board about prioritizing food insecurity research topics for the Aspen Institute. No other potential conflicts of interests relevant to this article were reported. Author Contributions. F.H.-B. researched data and wrote the manuscript. N.E.A. contributed to writing and reviewing/editing the manuscript. S.A.B. researcheddataandcontributedtowriting and reviewing/editing the manuscript. M.H.C. contributed to writing and reviewing/editing the manuscript. T.L.G.-W. researched data and con- tributed to writing and reviewing/editing the manuscript. A.N.-A. researched data and con- tributed to writing the manuscript. P.L.T. re- searched data and contributed to writing and reviewing/editing the manuscript. D.H.-J. re- searched data and contributed to writing and reviewing/editing the manuscript.

References 1. Golden SH, Brown A, Cauley JA, et al. Health disparities in endocrine disorders: biological, clinical, and nonclinical factorsdan Endocrine

Society scientific statement. J Clin Endocrinol Metab 2012;97:E1579–E1639 2. Centers for Medicare & Medicaid Services, Office of Minority Health. The CMS Equity Plan for Improving Quality in Medicare, September 2015. Accessed 25 October 2020. Available from https://www.cms.gov/About-CMS/Agency-Information/ OMH/OMH_Dwnld-CMS_EquityPlanforMedicare_ 090615.pdf 3. U.S. Department of Health and Human Serv- ices, Office of the Secretary, Office of the As- sistantSecretary forPlanningandEvaluation, and Office of Minority Health. HHS Action Plan to Reduce Racial and Ethnic Health Disparities: Implementation Progress Report 2011-2014. Washington,DC,Officeof theAssistant Secretary for Planning and Evaluation, 2015 4. ChinMH.Creating thebusinesscase forachieving health equity. J Gen Intern Med 2016;31:792–796 5. National Academies of Sciences, Engineering, and Medicine. Integrating Social Care Into the Delivery of Health Care: Moving Upstream to Improve the Nation’s Health. Washington, DC, National Academies Press, 2019 6. Byhoff E, Kangovi S, Berkowitz SA, et al.; Society of General Internal Medicine. A Society of General Internal Medicine position state- ment on the internists’ role in social determi- nants of health. J Gen Intern Med 2020;35: 2721–2727 7. Council on Community Pediatrics; Duffee JH, Kuo A, Gitterman BA. Poverty and child health in theUnited States. Pediatrics 2016;137:e20160339 8. Daniel H, Bornstein SS, Kane GC; Health and Public Policy Committee of the American College of Physicians. Addressing social determinants to improve patient care and promote health equity: an American College of Physicians position pa- per. Ann Intern Med 2018;168:577–578 9. PeekME, VelaMB, ChinMH. Practical lessons for teaching about race and racism: successfully leading free, frank, and fearless discussions. Acad Med. 1 September 2020 [Epub ahead of print]. DOI: 10.1097/ACM.0000000000003710 10. Vela M, Blackman D, Burnet D, et al. Ra- cializedviolenceandhealth care’s call to action, 6 June 2020. KevinMD.com. Accessed 7 June 2020. Available fromhttps://www.kevinmd.com/blog/ 2020/06/racialized-violence-and-health-cares- call-to-action.html 11. ChinMH, King PT, Jones RG, et al. Lessons for achieving health equity comparing Aotearoa/ NewZealandand theUnitedStates.Health Policy 2018;122:837–853 12. Haire-Joshu D, Hill-Briggs F. The next gen- eration of diabetes translation: a path to health equity. AnnuRev Public Health 2019;40:391–410 13. Golden SH,Maruthur N,Mathioudakis N, et al. The case for diabetes population health improve- ment: evidence-basedprogramming for population outcomes in diabetes. Curr Diab Rep 2017;17:51 14. Hill-Briggs F. 2018 Health Care & Education Presidential Address: the American Diabetes Association in the era of health care transfor- mation. Diabetes Care 2019;42:352–358 15. Hill JO, Galloway JM, Goley A, et al. Scientific statement: socioecological determinants of pre- diabetes and type 2 diabetes. Diabetes Care 2013;36:2430–2439 16. U.S. Department of Health and Human Serv- ices. Section IV:AdvisoryCommitteefindingsand recommendations. In The Secretary’s Advisory

Committee on National Health Promotion and Disease Prevention Objectives for 2020. Phase I report,2008. Accessed 25 October 2020. Avail- able from http://www.healthypeople.gov/sites/ default/files/PhaseI_0.pdf 17. Dankwa-Mullan I, Rhee KB, Williams K, et al. The science of eliminating health disparities: summary and analysis of the NIH summit recom- mendations. Am J Public Health 2010;100(Suppl. 1):S12–S18 18. Thornton PL, Kumanyika SK, Gregg EW, et al. New research directions on disparities in obesity and type 2 diabetes. Ann N Y Acad Sci 2020;1461:5–24 19. Institute of Medicine Committee on Under- standing Eliminating Racial Ethnic Disparities in Health Care. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Smedley BD, Stith AY, Nelson AR, Eds. Wash- ington, DC, National Academies Press, 2003 20. Braveman P, Arkin E, Orleans T, Proctor D, Plough A. What Is Health Equity? And What Difference Does a Definition Make? Princeton, NJ, Robert Wood Johnson Foundation, 2017 21. U.S. Department of Health and Human Serv- ices. Healthy People 2020: An Opportunity to Address the Societal Determinants of Health in the United States. Secretary’s Advisory Commit- tee on National Health Promotion and Disease Prevention Objectives for 2020, 26 July 2010. Accessed 25 October 2020. Available from http://www.healthypeople.gov/2010/hp2020/ advisory/SocietalDeterminantsHealth.htm 22. Marmot M, Allen JJ. Social determinants of health equity. Am J Public Health 2014; 104(Suppl. 4):S517–S519 23. U.S. Department of Health and Human Serv- ices, Office of Disease Prevention and Health Promotion. Foundation healthmeasures: dispar- ities. Accessed 25 October 2020. Available from https://www.healthypeople.gov/2020/about/ foundation-health-measures/Disparities 24. World Health Organization. Health equity. Accessed 25 October 2020. Available from https://www.who.int/topics/health_equity/en/ 25. County Health Rankings & Roadmaps. Mea- sures&datasources,2019.Accessed2Februrary2020. Available from https://www.countyhealthrankings .org/explore-health-rankings/measures-data-sources 26. World Health Organization. About social determinants of health, 2020. Accessed 7 Febru- ary 2020. Available from https://www.who.int/ social_determinants/sdh_definition/en/ 27. Commission on the Social Determinants of Health. Closing the gap in a generation: health equity through action on the social determinants of health. Final report of the Commission on Social Determinants of Health. Geneva, World Health Orga- nization, 2008. Accessed 25 October 2020. Available from https://www.who.int/social_determinants/ final_report/csdh_finalreport_2008.pdf 28. Solar O, Irwin A. A conceptual framework for action on the social determinants of health. Social Determinants of Health Discussion Paper 2 (Policy and Practice). Geneva, World Health Organiza- tion, 2010. Accessed 25 October 2020. Available from https://www.who.int/sdhconference/ resources/ConceptualframeworkforactiononSDH_ eng.pdf 29. U.S. Department of Health and Human Serv- ices, Office of Disease Prevention and Health Pro- motion. Healthy people 2020: social determinants of health. Accessed 25 October 2020. Available

16 Social Determinants of Health and Diabetes Diabetes Care

from https://www.healthypeople.gov/2020/topics- objectives/topic/social-determinants-of-health 30. Centers for Disease Control and Prevention. About social determinants of health (SDOH). Accessed 14 August 2020. Available from https:// www.cdc.gov/socialdeterminants/about.html. 31. Remington PL, Catlin BB, Gennuso KP. The County Health Rankings: rationale and methods. Popul Health Metr 2015;13:11 32. County Health Rankings & Roadmaps. County Health Rankings Model, 2014. Accessed 25 October 2020. Available from https:// www.countyhealthrankings.org/resources/county- health-rankings-model 33. Artiga S, Hinton E. Beyond health care: the role of social determinants in promoting health and health equity. Issue Brief, May 2018. Kaiser Family Foundation. Accessed 25 October 2020. Available from http://files.kff.org/attachment/ issue-brief-beyond-health-care 34. Saegert SC, Adler NA, Bullock HE, Cauce AM, Liu WM, Wyche KF. Report of the American Psychological Association Task Force on Socio- economic Status. Washington, DC, American Psychological Association, 2007 35. DuttonDB, Levine S. Socioeconomic status and health: overview, methodological cri- tique, and reformulation. In Pathways to Health: The Role of Social Factors. Bunker P, Gomby DS, Kehrer BH, Eds. Menlo Park, CA, The Henry J. Kaiser Family Foundation, 1989, pp. 29–69 36. Adler NE, Boyce T, Chesney MA, et al. Socioeconomic status and health. The chal- lenge of the gradient. Am Psychol 1994;49: 15–24 37. Adler NE, Newman K. Socioeconomic dis- parities in health: pathways and policies. Health Aff (Millwood) 2002;21:60–76 38. Braveman PA, Cubbin C, Egerter S, et al. Socioeconomic status inhealth research:onesize does not fit all. JAMA 2005;294:2879–2888 39. Shavers VL.Measurement of socioeconomic status in health disparities research. J Natl Med Assoc 2007;99:1013–1023 40. Dotson VM, Kitner-Triolo MH, Evans MK, Zonderman AB. Effects of race and socioeco- nomic status on the relative influence of edu- cation and literacy on cognitive functioning. J Int Neuropsychol Soc 2009;15:580–589 41. Sisco S, Gross AL, Shih RA, et al. The role of early-life educational quality and literacy in ex- plaining racial disparities in cognition in late life. J Gerontol B Psychol Sci Soc Sci 2015;70:557–567 42. Rudd RE. Health literacy skills of U.S. adults. Am J Health Behav 2007;31(Suppl. 1):S8–S18 43. KutnerM, Greenberg E, Jin Y, Boyle B, Hsu Y, Dunleavy E (Eds.). Literacy in Everyday Life: Results From the 2003 National Assessment of Adult Literacy (NCES 2007–480). Washington, DC,NationalCenter for EducationStatistics, 2007 44. KutnerM, Greenburg E, Jin Y, Paulsen C. The Health Literacy of America’s Adults: Results From the 2003 National Assessment of Adult Literacy (NCES 2006-483).Washington, DC, National Cen- ter for Education Statistics, 2006 45. Gornick ME. 2. Measuring the effects of socioeconomic statusonhealthcare. InGuidance for the National Healthcare Disparities Report. Swift EK, Ed. Washington, DC, National Acade- mies Press, 2002

46. Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A. Type 2 diabetes incidence and socio-economic position: a systematic review and meta-analysis. Int J Epidemiol 2011;40:804– 818 47. Brown AF, Ettner SL, Piette J, et al. Socio- economic position and health among persons with diabetes mellitus: a conceptual framework and reviewof the literature. Epidemiol Rev 2004; 26:63–77 48. Braveman PA, Cubbin C, Egerter S, Williams DR, Pamuk E. Socioeconomic disparities in health in theUnitedStates:what thepatterns tell us.Am J Public Health 2010;100(Suppl. 1):S186–S196 49. Gaskin DJ, Thorpe RJ Jr, McGinty EE, et al. Disparities in diabetes: the nexus of race, pov- erty, and place. Am J Public Health 2014;104: 2147–2155 50. Beckles GL, Chou CF. Disparities in the prev- alence of diagnosed diabetes – United States, 1999-2002 and 2011-2014. MMWRMorb Mortal Wkly Rep 2016;65:1265–1269 51. Drewnowski A, Rehm CD, Moudon AV, ArterburnD. The geography of diabetes by census tract in a large sample of insured adults in King County,Washington, 2005-2006. Prev Chronic Dis 2014;11:E125 52. Kolak M, Abraham G, Talen MR. Mapping census tract clusters of type 2 diabetes in a primary care population. Prev Chronic Dis 2019; 16:E59 53. Schmittdiel JA, Dyer WT, Marshall CJ, Bivins R. Using neighborhood-level census data to pre- dict diabetes progression in patients with laboratory-defined prediabetes. Perm J 2018; 22:18–096 54. Saydah S, Lochner K. Socioeconomic status and risk of diabetes-related mortality in the U.S. Public Health Rep 2010;125:377–388 55. Scott A, Chambers D, Goyder E, O’Cathain A. Socioeconomic inequalities inmortality,morbidity and diabetes management for adults with type 1 diabetes: a systematic review. PLoS One 2017;12: e0177210 56. Bijlsma-Rutte A, Rutters F, Elders PJM, Bot SDM,NijpelsG. Socio-economic status andHbA1c in type2 diabetes: a systematic reviewandmeta- analysis. DiabetesMetab Res Rev 2018;34:e3008 57. Lindner LME, Rathmann W, Rosenbauer J. Inequalities in glycaemic control, hypoglycaemia and diabetic ketoacidosis according to socio- economic status and area-level deprivation in type 1 diabetes mellitus: a systematic review. Diabet Med 2018;35:12–32 58. Borschuk AP, Everhart RS. Health disparities among youth with type 1 diabetes: a systematic review of the current literature. Fam Syst Health 2015;33:297–313 59. Walker RJ, Garacci E, Palatnik A, Ozieh MN, Egede LE. The longitudinal influence of social determinants of health on glycemic control in elderly adults with diabetes. Diabetes Care 2020; 43:759–766 60. Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2017. At- lanta, GA, Centers for Disease Control and Pre- vention, US Department of Health and Human Services, 2017. Accessed 25 October 2020. Avail- able from https://dev.diabetes.org/sites/default/ files/2019-06/cdc-statistics-report-2017.pdf 61. Centers for Disease Contol and Prevention. Diabetes Report Card 2017. Atlanta, GA, Centers

for Disease Control and Prevention, US Depart- ment of Health andHuman Services, 2018. Accessed 25 October 2020. Available from https://www.cdc .gov/diabetes/pdfs/library/diabetesreportcard2017- 508.pdf 62. Borrell LN, Dallo FJ, White K. Education and diabetes in a racially and ethnically diverse pop- ulation. Am J Public Health 2006;96:1637–1642 63. Secrest AM, Costacou T, Gutelius B, Miller RG, Songer TJ, Orchard TJ. Association of socio- economic status with mortality in type 1 di- abetes: the Pittsburgh Epidemiology of Diabetes Complications Study. Ann Epidemiol 2011;21: 367–373 64. Marciano L, Camerini AL, Schulz PJ. The role of health literacy in diabetes knowledge, self- care, andglycemic control: ameta-analysis. J Gen Intern Med 2019;34:1007–1017 65. Ferrie JE, Virtanen M, Jokela M, et al.; IPD- Work Consortium. Job insecurity and risk of diabetes: a meta-analysis of individual partici- pant data. CMAJ 2016;188:E447–E455 66. Varanka-Ruuska T, Rautio N, Lehtiniemi H, et al. The association of unemployment with glucose metabolism: a systematic review and meta-analysis. Int J Public Health 2018;63:435– 446 67. Gan Y, Yang C, Tong X, et al. Shift work and diabetes mellitus: a meta-analysis of observa- tional studies. Occup Environ Med 2015;72:72– 78 68. KivimakiM,VirtanenM,Kawachi I, et al. Long working hours, socioeconomic status, and the riskof incident type2diabetes:ameta-analysisof published and unpublished data from 222 120 individuals. Lancet Diabetes Endocrinol 2015;3: 27–34 69. Gallup-Sharecare. The Face of Diabetes in the United States, State of AmericanWell-being, 2017. Accessed 25 October 2020. Available from https://wellbeingindex.sharecare.com/wp-content/ uploads/2017/12/The-Face-of-Diabetes-in-the-United- States-2017.pdf 70. Witters D, Liu D. Diabetes rate greatest among transportation workers. Gallup, Inc., 2017. Accessed 25 October 2020. Available from https://news.gallup.com/poll/214097/ diabetes-rate-greatest-among-transportation- workers.aspx 71. Hill-Briggs F, Schumann KP, DikeO. Five-step methodology for evaluation and adaptation of print patient health information to meet the , 5th grade readability criterion. Med Care 2012; 50:294–301 72. Cavanaugh KL. Health literacy in diabetes care: explanation, evidence and equipment. Di- abetes Manag (Lond) 2011;1:191–199 73. Hill-Briggs F, Renosky R, Lazo M, et al. De- velopment and pilot evaluation of literacy-adapted diabetes and CVD education in urban, diabetic African Americans. J Gen Intern Med 2008;23: 1491–1494 74. White RO, Eden S, Wallston KA, et al. Health communication, self-care, and treatment satis- faction among low-income diabetes patients in a public health setting. Patient Educ Couns 2015; 98:144–149 75. Kim SH, Lee A. Health-literacy-sensitive di- abetes self-management interventions: a sys- tematic review and meta-analysis. Worldviews Evid Based Nurs 2016;13:324–333

care.diabetesjournals.org Hill-Briggs and Associates 17

76. Hill-Briggs F, LazoM,PeyrotM,et al. Effectof problem-solving-based diabetes self-management trainingondiabetes control in a low incomepatient sample. J Gen Intern Med 2011;26:972–978 77. Cavanaugh K, Wallston KA, Gebretsadik T, et al. Addressing literacy and numeracy to im- prove diabetes care: two randomized controlled trials. Diabetes Care 2009;32:2149–2155 78. Diez Roux AV, Mair C. Neighborhoods and health. Ann N Y Acad Sci 2010;1186:125–145 79. Tung EL, Cagney KA, Peek ME, Chin MH. Spatial context and health inequity: reconfigur- ing race, place, andpoverty. JUrbanHealth 2017; 94:757–763 80. Institute of Medicine. Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2. Washington, DC, The National Academies Press, 2014 81. Baggett TP, Berkowitz SA, Fung V, Gaeta JM. Prevalence of housing problems among commu- nity health center patients. JAMA2018;319:717– 719 82. Berkowitz SA, Kalkhoran S, Edwards ST, Essien UR, Baggett TP. Unstable housing and diabetes-related emergency department visits and hospitalization: a nationally representative study of safety-net clinic patients. Diabetes Care 2018;41:933–939 83. Kushel MB, Gupta R, Gee L, Haas JS. Housing instability and food insecurity as barriers to health care among low-income Americans. J Gen Intern Med 2006;21:71–77 84. Vijayaraghavan M, Jacobs EA, Seligman H, Fernandez A. The association between housing instability, food insecurity, and diabetes self- efficacy in low-income adults. J Health Care Poor Underserved 2011;22:1279–1291 85. Children’s Health Watch. Our survey. Ac- cessed 25 October 2020. Available from https:// childrenshealthwatch.org/methods/our-survey/ 86. U.S. Congress. H.R.558 - Stewart B. McKinney homeless assistance act, 1987. Accessed 25 October 2020. Available from https://www .congress.gov/bill/100th-congress/house-bill/558 87. Henry M, Watt R, Mahathey A, Ouellette J, Sitler A; Abt Associates. The 2019 Annual Home- less Assessment Report (AHAR) to Congress; Part 1: Point-in-Time Estimates of Homelessness, January 2020. U.S. Department of Housing and Urban Development. Accessed 25 October 2020. Available from https://www.hudexchange.info/ resource/5948/2019-ahar-part-1-pit-estimates- of-homelessness-in-the-us/ 88. Cutts DB, Meyers AF, Black MM, et al. US Housing insecurity and the health of very young children. Am J Public Health 2011;101:1508–1514 89. Frederick TJ, Chwalek M, Hughes J, Karabanow J, Kidd S. How stable is stable? De- fining and measuring housing stability. J Com- munity Psychol 2014;42:964–979 90. Meltzer R, Schwartz A. Housing affordability and health: evidence from New York City. Hous Policy Debate 2016;26:80–104 91. Keene DE, Guo M, Murillo S. “That wasn’t really a place to worry about diabetes”: housing access and diabetes self-management among low-income adults. Soc Sci Med 2018;197:71–77 92. Quensell ML, Taira DA, Seto TB, Braun KL, Sentell TL. “I need my own place to get better”: patient perspectives on the role of housing in potentially preventable hospitalizations. J Health Care Poor Underserved 2017;28:784–797

93. Gelberg L, Andersen RM, Leake BD. The Behavioral Model for Vulnerable Populations: application tomedical care use andoutcomes for homelesspeople.HealthServRes2000;34:1273– 1302 94. Bernstein RS, Meurer LN, Plumb EJ, Jackson JL. Diabetes and hypertension prevalence in homeless adults in the United States: a system- atic review andmeta-analysis. Am J Public Health 2015;105:e46–e60 95. Berkowitz SA, Meigs JB, DeWalt D, et al. Material need insecurities, control of diabetes mellitus, and use of health care resources: results of the Measuring Economic Insecurity in Diabetes study. JAMA Intern Med 2015;175: 257–265 96. Burgard SA, Seefeldt KS, Zelner S. Housing instability andhealth:findings fromtheMichigan Recession andRecoveryStudy. Soc SciMed2012; 75:2215–2224 97. Charkhchi P, Fazeli Dehkordy S, Carlos RC. Housing and food insecurity, care access, and health status among the chronically ill: an anal- ysis of the Behavioral Risk Factor Surveillance System. J Gen Intern Med 2018;33:644–650 98. Stahre M, VanEenwyk J, Siegel P, Njai R. Housing insecurity and the association with health outcomes and unhealthy behaviors, Washington State, 2011. Prev Chronic Dis 2015;12:140511 99. Gibson M, Petticrew M, Bambra C, Sowden AJ,Wright KE,WhiteheadM. Housing and health inequalities: a synthesis of systematic reviews of interventions aimed at different pathways link- ing housing and health. Health Place 2011;17: 175–184 100. Shaw M. Housing and public health. Annu Rev Public Health 2004;25:397–418 101. Brooks LK, KalyanaramanN,MalekR.Diabetes care for patients experiencing homelessness: beyond metformin and sulfonylureas. Am J Med 2019;132:408–412 102. Ludwig J, Sanbonmatsu L, Gennetian L, et al. Neighborhoods, obesity, and diabetesda randomized social experiment. N Engl J Med 2011;365:1509–1519 103. Orr L, Feins JD, Jacob R, et al. Moving to Opportunity for Fair Housing Demonstration Program: Interim Impacts Evaluation. Washing- ton, DC, U.S. Department of Housing and Urban Development Office of Policy Development and Research, 2003. Accessed 25 October 2020. Available from https://www.huduser.gov/Publications/ pdf/MTOFullReport.pdf 104. Sanbonmatsu L,Marvokov J, PorterN, et al. The long-term effects of Moving to Opportunity on adult health and economic self-sufficiency. Cityscape2012;14:109–136 105. Baxter AJ, Tweed EJ, Katikireddi SV, Thomson H. Effects of Housing First approaches on health and well-being of adults who are homeless or at risk of homelessness: systematic review and meta-analysis of randomised con- trolled trials. J Epidemiol Community Health 2019;73:379–387 106. Tsai J, Gelberg L, Rosenheck RA. Changes in physical health after supported housing: results from the Collaborative Initiative to End Chronic Homelessness. J Gen Intern Med 2019;34:1703– 1708 107. Lim S, Miller-Archie SA, Singh TP, Wu WY, Walters SC, Gould LH. Supportive housing and its

relationship with diabetes diagnosis and man- agement among homeless persons in New York City. Am J Epidemiol 2019;188:1120–1129 108. KeeneDE,HenryM,GormleyC,NdumeleC. ‘Then I found housing and everything changed’: transitions to rent-assisted housing and diabe- tes self-management. Cityscape 2018;20:107– 118 109. Centers for Disease Control and Preven- tion. Built environment assessment tool manual, 2019. Accessed 9 March 2020. Available from https://www.cdc.gov/nccdphp/dnpao/state- local-programs/built-environment-assessment/ index.htm 110. Drewnowski A, Buszkiewicz J, Aggarwal A, Rose C, Gupta S, Bradshaw A. Obesity and the built environment: a reappraisal. Obesity (Silver Spring) 2020;28:22–30 111. Martin A, Ogilvie D, Suhrcke M. Evaluating causal relationships between urban built envi- ronment characteristics and obesity: a method- ological review of observational studies. Int J Behav Nutr Phys Act 2014;11:142 112. Mayne SL, Auchincloss AH, Michael YL. Impact of policy and built environment changes on obesity-related outcomes: a systematic re- view of naturally occurring experiments. Obes Rev 2015;16:362–375 113. Chandrabose M, Rachele JN, Gunn L, et al. Built environment and cardio-metabolic health: systematic review and meta-analysis of longitudinal studies. Obes Rev 2019;20: 41–54 114. Smalls BL, Gregory CM, Zoller JS, Egede LE. Assessing the relationship between neighbor- hood factors and diabetes related health out- comes and self-care behaviors. BMC Health Serv Res 2015;15:445 115. Bilal U, Auchincloss AH, Diez-Roux AV. Neighborhood environments and diabetes risk and control. Curr Diab Rep 2018;18:62 116. Leal C, Chaix B. The influence of geographic life environments on cardiometabolic risk fac- tors: a systematic review, a methodological assessment and a research agenda. Obes Rev 2011;12:217–230 117. Twohig-Bennett C, Jones A. The health benefits of the great outdoors: a systematic review and meta-analysis of greenspace expo- sureandhealthoutcomes.EnvironRes2018;166: 628–637 118. Durand CP, AndalibM, DuntonGF,Wolch J, Pentz MA. A systematic review of built environ- ment factors related to physical activity and obesity risk: implications for smart growth urban planning. Obes Rev 2011;12:e173–e182 119. Petticrew M, Cummins S, Ferrell C, et al. Natural experiments: an underused tool for public health? Public Health 2005;119:751–757 120. MacMillan F, George ES, Feng X, et al. Do natural experiments of changes in neighborhood built environment impact physical activity and diet? a systematic review. Int J EnvironRes Public Health 2018;15:217 121. Benton JS, Anderson J, Hunter RF, French DP. The effect of changing the built environment on physical activity: a quantitative review of the risk of bias in natural experiments. Int J Behav Nutr Phys Act 2016;13:107 122. Amuda AT, Berkowitz SA. Diabetes and the built environment: evidence and policies. Curr Diab Rep 2019;19:35

18 Social Determinants of Health and Diabetes Diabetes Care

123. Landrigan PJ, Fuller R, AcostaNJR, et al. The Lancet Commission on pollution and health. Lancet 2018;391:462–512 124. Casey JA, Morello-Frosch R, Mennitt DJ, Fristrup K, Ogburn EL, James P. Race/ethnicity, socioeconomic status, residential segregation, and spatial variation in noise exposure in the contiguous United States. Environ Health Per- spect 2017;125:077017 125. Evans GW, Kantrowitz E. Socioeconomic status and health: the potential role of environ- mental risk exposure. Annu Rev Public Health 2002;23:303–331 126. Hajat A, Hsia C, O’Neill MS. Socioeconomic disparities and air pollution exposure: a global review. Curr Environ Health Rep 2015;2:440– 450 127. Miao Q, Chen D, Buzzelli M, Aronson KJ. Environmental equity research: review with fo- cus on outdoor air pollution research methods and analytic tools. Arch Environ Occup Health 2015;70:47–55 128. Mohai P, Lantz PM, Morenoff J, House JS, Mero RP. Racial and socioeconomic disparities in residential proximity to polluting industrial fa- cilities: evidence from the Americans’ Changing Lives Study. Am J Public Health 2009;99(Suppl. 3): S649–S656 129. Perez AC, Grafton B, Mohai P, Hardin R, Hintzen K, Orvis S. Evolution of the environ- mental justice movement: activism, formaliza- tion and differentiation. Environ Res Lett 2015; 10:105002 130. Tessum CW, Apte JS, Goodkind AL, et al. Inequity in consumption of goods and services adds to racial-ethnic disparities in air pollution exposure. Proc Natl Acad Sci U S A 2019;116: 6001–6006 131. Apelberg BJ, Buckley TJ, White RH. Socio- economic and racial disparities in cancer risk from air toxics in Maryland. Environ Health Per- spect 2005;113:693–699 132. Hilpert M, Johnson M, Kioumourtzoglou MA, et al. A new approach for inferring traffic- related air pollution: use of radar-calibrated crowd-sourced traffic data. Environ Int 2019; 127:142–159 133. Jones MR, Diez-Roux AV, Hajat A, et al. Race/ethnicity, residential segregation, and ex- posure to ambient air pollution: theMulti-Ethnic Study of Atherosclerosis (MESA). Am J Public Health 2014;104:2130–2137 134. Bellavia A, Zota AR, Valeri L, James-Todd T. Multiple mediators approach to study environ- mental chemicals as determinants of health disparities. Environ Epidemiol 2018;2:e015 135. Dodd-Butera T, Beaman M, Brash M. En- vironmental health equity: a concept analysis. Annu Rev Nurs Res 2019;38:183–202 136. Gee GC, Payne-Sturges DC. Environmental health disparities: a framework integrating psy- chosocial and environmental concepts. Environ Health Perspect 2004;112:1645–1653 137. Krometis LA, Gohlke J, Kolivras K, Satterwhite E,Marmagas SW,Marr LC. Environmental health disparities in the Central Appalachian region of the United States. Rev Environ Health 2017;32: 253–266 138. Lewis J, Hoover J, MacKenzie D.Mining and environmental health disparities in Native Amer- ican communities. Curr EnvironHealth Rep2017; 4:130–141

139. Powers M, Yracheta J, Harvey D, et al. Arsenic in groundwater in private wells in rural North Dakota and South Dakota: water quality assessment for an intervention trial. Environ Res 2019;168:41–47 140. Fox MA, Nachman KE, Anderson B, Lam J, Resnick B.Meeting the public health challengeof protecting private wells: proceedings and rec- ommendations from an expert panel workshop. Sci Total Environ 2016;554-555:113–118 141. Lee D, Murphy HM. Private wells and rural health: groundwater contaminants of emerging concern. Curr Environ Health Rep 2020;7:129– 139 142. Groh KJ, Backhaus T, Carney-Almroth B, et al. Overview of known plastic packaging- associated chemicals and their hazards. Sci Total Environ 2019;651:3253–3268 143. Nguyen VK, Kahana A, Heidt J, et al. A comprehensive analysis of racial disparities in chemical biomarker concentrations in United States women, 1999-2014. Environ Int 2020; 137:105496 144. Varshavsky JR,Morello-Frosch R,Woodruff TJ, Zota AR. Dietary sources of cumulative phtha- lates exposure among the U.S. general popula- tion inNHANES2005-2014.Environ Int 2018;115: 417–429 145. Zota AR, Phillips CA, Mitro SD. Recent fast food consumption and bisphenol a and phtha- lates exposures among the U.S. population in NHANES, 2003-2010. Environ Health Perspect 2016;124:1521–1528 146. Hartle JC, Navas-Acien A, Lawrence RS. The consumption of canned food and beverages and urinary Bisphenol A concentrations in NHANES 2003-2008. Environ Res 2016;150:375–382 147. Zota AR, Shamasunder B. The environmen- tal injustice of beauty: framing chemical ex- posures from beauty products as a health disparities concern. Am J Obstet Gynecol 2017; 217:418.e1–418.e6 148. Kuo CC,Moon K, Thayer KA, Navas-Acien A. Environmental chemicals and type 2 diabetes: an updated systematic review of the epidemiologic evidence. Curr Diab Rep 2013;13:831–849 149. Maull EA, Ahsan H, Edwards J, et al. Eval- uation of the association between arsenic and diabetes: a National Toxicology Program work- shop review. Environ Health Perspect 2012;120: 1658–1670 150. Thayer KA, Heindel JJ, Bucher JR, Gallo MA. Role of environmental chemicals in diabetes and obesity: a National Toxicology Program work- shop review. Environ Health Perspect 2012;120: 779–789 151. SongY,ChouEL,BaeckerA,etal. Endocrine- disrupting chemicals, risk of type 2 diabetes, and diabetes-related metabolic traits: a systematic review and meta-analysis. J Diabetes 2016;8: 516–532 152. Evangelou E, Ntritsos G, Chondrogiorgi M, et al. Exposure to pesticides and diabetes: a sys- tematic review and meta-analysis. Environ Int 2016;91:60–68 153. Jaacks LM, Staimez LR. Association of per- sistent organic pollutants and non-persistent pesticides with diabetes and diabetes-related health outcomes in Asia: a systematic review. Environ Int 2015;76:57–70 154. Radke EG, Galizia A, Thayer KA, Cooper GS. Phthalate exposure and metabolic effects:

a systematic review of the human epidemiolog- ical evidence. Environ Int 2019;132:104768 155. Yang BY, Fan S, Thiering E, et al. Ambient air pollution and diabetes: a systematic review and meta-analysis. Environ Res 2020;180:108817 156. Eze IC, Hemkens LG, Bucher HC, et al. As- sociation between ambient air pollution and di- abetes mellitus in Europe and North America: systematic review and meta-analysis. Environ Health Perspect 2015;123:381–389 157. Janghorbani M, Momeni F, MansourianM. Systematic review and metaanalysis of air pol- lution exposure and risk of diabetes. Eur J Epidemiol 2014;29:231–242 158. Liu F, Chen G, Huo W, et al. Associations between long-term exposure to ambient air pollution and risk of type 2 diabetes mellitus: a systematic review and meta-analysis. Environ Pollut 2019;252(Pt B):1235–1245 159. Liu C, Bai Y, Xu X, et al. Exaggerated effects of particulate matter air pollution in genetic type II diabetes mellitus. Part Fibre Toxicol 2014;11:27 160. Liu C, FonkenLK,WangA, et al. Central IKKb inhibition prevents air pollution mediated pe- ripheral inflammation andexaggeration of type II diabetes. Part Fibre Toxicol 2014;11:53 161. Liu C, Ying Z, Harkema J, SunQ, Rajagopalan S. Epidemiological and experimental links be- tween air pollution and type 2 diabetes. Toxicol Pathol 2013;41:361–373 162. Zanobetti A, Schwartz J. Are diabeticsmore susceptible to the health effects of airborne particles? Am J Respir Crit Care Med 2001; 164:831–833 163. Zanobetti A, Schwartz J. Cardiovascular damage by airborne particles: are diabetics more susceptible? Epidemiology 2002;13:588– 592 164. Zeka A, Zanobetti A, Schwartz J. Individual- level modifiers of the effects of particulate matter on daily mortality. Am J Epidemiol 2006;163: 849–859 165. NemmarA, SubramaniyanD, Yasin J, Ali BH. Impact of experimental type 1 diabetes mellitus on systemic and coagulation vulnerability inmice acutely exposed to diesel exhaust particles. Part Fibre Toxicol 2013;10:14 166. O’Neill MS, Veves A, Zanobetti A, et al. Diabetesenhances vulnerability toparticulateair pollution-associated impairment in vascular re- activity and endothelial function. Circulation 2005;111:2913–2920 167. O’Neill MS, Veves A, Sarnat JA, et al. Air pollution and inflammation in type 2 diabetes: a mechanism for susceptibility. Occup Environ Med 2007;64:373–379 168. Moon KA, Guallar E, Umans JG, et al. Association between exposure to low to mod- erate arsenic levels and incident cardiovascular disease. A prospective cohort study. Ann Intern Med 2013;159:649–659 169. Tellez-Plaza M, Guallar E, Howard BV, et al. Cadmium exposure and incident cardiovascular disease. Epidemiology 2013;24:421–429 170. Lamas GA, Goertz C, Boineau R, et al.; TACT Investigators. Effect of disodium EDTA chelation regimen on cardiovascular events in patients with previous myocardial infarction: the TACT randomized trial. JAMA 2013;309:1241–1250 171. Been JV, Nurmatov UB, Cox B, Nawrot TS, van Schayck CP, Sheikh A. Effect of smoke-free

care.diabetesjournals.org Hill-Briggs and Associates 19

legislation on perinatal and child health: a sys- tematic review and meta-analysis. Lancet 2014; 383:1549–1560 172. PetersonGCL, Hogrefe C, CorriganAE, Neas LM,Mathur R, Rappold AG. Impact of reductions in emissions from major source sectors on fine particulate matter–related cardiovascular mor- tality. Environ Health Perspect 2020;128:17005 173. Ruiz-Hernandez A, Navas-Acien A, Pastor- Barriuso R, et al. Declining exposures to lead and cadmium contribute to explaining the reduction of cardiovascular mortality in the US population, 1988-2004. Int J Epidemiol 2017;46:1903–1912 174. Flanagan SV, Braman S, Puelle R, et al. Leveraging health care communication channels forenvironmentalhealthoutreach inNewJersey. J Public Health Manag Pract 2020;26:E23–E26 175. Hadley MB, Vedanthan R, Fuster V. Air pollution and cardiovascular disease: a window ofopportunity.NatRevCardiol 2018;15:193–194 176. Wong KH, Durrani TS. Exposures to endo- crine disrupting chemicals in consumer products-a guide for pediatricians. Curr Probl Pediatr Adolesc Health Care 2017;47:107–118 177. Centers for Disease Contol and Prevention. General food environment resources, 2010. Ac- cessed 10 July 2019. Available from https://www .cdc.gov/healthyplaces/healthtopics/healthyfood/ general.htm 178. Swinburn B, Sacks G, Vandevijvere S, et al.; INFORMAS. INFORMAS (International Network for Food and Obesity/non-communicable dis- eases Research,Monitoring andAction Support): overview and key principles. Obes Rev 2013; 14(Suppl. 1):1–12 179. Glanz K, Sallis JF, Saelens BE, Frank LD. Nutrition Environment Measures Survey in stores (NEMS-S): development and evaluation. Am J Prev Med 2007;32:282–289 180. U.S. Department of Agriculture, Economic Research Service. Food environment atlas. Ac- cessed 10 July 2019. Available fromhttps://www .ers.usda.gov/data-products/food-environment- atlas/2019 181. HerforthA,AhmedS.Thefoodenvironment, its effects on dietary consumption, and potential for measurement within agriculture-nutrition in- terventions. Food Secur 2015;7:505–520 182. Lytle LA, Sokol RL. Measures of the food environment: a systematic review of the field, 2007-2015. Health Place 2017;44:18–34 183. McKinnon RA, Reedy J, Morrissette MA, Lytle LA, Yaroch AL. Measures of the food envi- ronment: a compilation of the literature, 1990- 2007. Am J Prev Med 2009;36(Suppl.):S124–S133 184. Turner C, Aggarwal A, Walls H, et al. Con- cepts and critical perspectives for food envi- ronment research: a global framework with implications for action in low- andmiddle-income countries. Glob Food Secur 2018;18:93–101 185. Ahern M, Brown C, Dukas S. A national study of the association between food environ- ments and county-level health outcomes. J Rural Health 2011;27:367–379 186. Haynes-Maslow L, Leone LA. Examining the relationship between the food environment and adult diabetes prevalence by county economic and racial composition: an ecological study. BMC Public Health 2017;17:648 187. Auchincloss AH,Diez RouxAV,MujahidMS, Shen M, Bertoni AG, Carnethon MR. Neighbor- hood resources for physical activity and healthy

foods and incidence of type 2 diabetes mellitus: the Multi-Ethnic Study of Atherosclerosis. Arch Intern Med 2009;169:1698–1704 188. den Braver NR, Lakerveld J, Rutters F, Schoonmade LJ, Brug J, Beulens JWJ. Built en- vironmental characteristics and diabetes: a sys- tematic review and meta-analysis. BMC Med 2018;16:12 189. Gebreab SY, Hickson DA, Sims M, et al. Neighborhood social and physical environments and type 2 diabetes mellitus in African Ameri- cans: the JacksonHeart Study.Health Place 2017; 43:128–137 190. Herrick CJ, Yount BW, Eyler AA. Implications of supermarket access, neighbourhoodwalkabil- ity and poverty rates for diabetes risk in an employee population. Public Health Nutr 2016; 19:2040–2048 191. Christine PJ, Auchincloss AH, Bertoni AG, et al. Longitudinal associations between neigh- borhood physical and social environments and incident type 2 diabetes mellitus: the Multi- Ethnic Study of Atherosclerosis (MESA). JAMA Intern Med 2015;175:1311–1320 192. Meyer KA, Boone-Heinonen J, Duffey KJ, et al. Combined measure of neighborhood food and physical activity environments and weight- related outcomes: the CARDIA study. Health Place 2015;33:9–18 193. Tabaei BP, Rundle AG, Wu WY, et al. Asso- ciations of residential socioeconomic, food, and built environments with glycemic control in per- sonswith diabetes inNewYork City from2007– 2013. Am J Epidemiol 2018;187:736–745 194. Kern DM, Auchincloss AH, Stehr MF, et al. Neighborhood price of healthier food relative to unhealthy food and its association with type 2 diabetes and insulin resistance: the Multi-Ethnic Study of Atherosclerosis. Prev Med 2018;106: 122–129 195. Coleman-Jensen A, Gregory C, Singh A. Household food security in the United States in2013 (EconomicResearchReportNo.173).U.S. Department of Agriculture, Economic Research Service, 2014. Accessed 25 October 2020. Avail- able from https://www.ers.usda.gov/webdocs/ publications/45265/48787_err173.pdf?v50 196. Seligman HK, Lyles C, Marshall MB, et al. A pilot food bank intervention featuring diabetes- appropriate food improved glycemic control among clients in three states. Health Aff (Mill- wood) 2015;34:1956–1963 197. Berkowitz SA, Seligman HK, Choudhry NK. Treat or eat: food insecurity, cost-related med- ication underuse, and unmet needs. Am J Med 2014;127:303–310.e3 198. Walker RJ, Gebregziabher M, Martin-Harris B, Egede LE. Quantifying direct effects of social determinants of health on glycemic control in adultswith type2diabetes.Diabetes TechnolTher 2015;17:80–87 199. Walker RJ, Williams JS, Egede LE. Pathways between food insecurity andglycaemic control in individuals with type 2 diabetes. Public Health Nutr 2018;21:3237–3244 200. Seligman HK, Schillinger D. Hunger and socioeconomic disparities in chronic disease. N Engl J Med 2010;363:6–9 201. Orr CJ, Keyserling TC, Ammerman AS, Berkowitz SA. Diet quality trends among adults with diabetes by socioeconomic status in the U.S.: 1999-2014. BMC Endocr Disord 2019;19:54

202. Drewnowski A, Eichelsdoerfer P. Can low- income Americans afford a healthy diet? Nutr Today 2010;44:246–249 203. Darmon N, Drewnowski A. Contribution of food prices and diet cost to socioeconomic disparities in diet quality and health: a systematic review and analysis. Nutr Rev 2015;73:643–660 204. Bomberg EM, Neuhaus J, Hake MM, Engelhard EM, Seligman HK. Food preferences and coping strategies among diabetic and non- diabetic households servedbyUS foodpantries. J Hunger Environ Nutr 2019;14:4–17 205. Billimek J, Sorkin DH. Food insecurity, pro- cesses of care, and self-reported medication underuse inpatientswith type2diabetes: results from the California Health Interview Survey. Health Serv Res 2012;47:2159–2168 206. Herman D, Afulani P, Coleman-Jensen A, Harrison GG. Food insecurity and cost-related medication underuse among nonelderly adults in anationally representative sample. AmJPublic Health 2015;105:e48–e59 207. Silverman J, Krieger J, Kiefer M, Hebert P, Robinson J, Nelson K. The relationship between food insecurity and depression, diabetes distress and medication adherence among low-income patients with poorly-controlled diabetes. J Gen Intern Med 2015;30:1476–1480 208. Leung CW, Epel ES, Willett WC, Rimm EB, Laraia BA. Household food insecurity is positively associated with depression among low-income supplemental nutrition assistance program par- ticipants and income-eligible nonparticipants. J Nutr 2015;145:622–627 209. Arenas DJ, Thomas A,Wang J, DeLisser HM. A systematic review and meta-analysis of de- pression, anxiety, and sleep disorders in US adults with food insecurity. J Gen Intern Med 2019;34:2874–2882 210. Kinsey EW, Dupuis R, Oberle M, Cannuscio CC, Hillier A. Chronic disease self-management within the monthly benefit cycle of the Supple- mental Nutrition Assistance Program. Public Health Nutr 2019;22:2248–2259 211. Seligman HK, Bindman AB, Vittinghoff E, Kanaya AM, Kushel MB. Food insecurity is as- sociated with diabetes mellitus: results from the National Health Examination and Nutrition Ex- amination Survey (NHANES) 1999-2002. J Gen Intern Med 2007;22:1018–1023 212. Berkowitz SA, Karter AJ, Corbie-Smith G, etal. Food insecurity, food “deserts,”andglycemic control in patients with diabetes: a longitudinal analysis. Diabetes Care 2018;41:1188–1195 213. Barnard LS,WexlerDJ,DeWaltD,Berkowitz SA. Material need support interventions for di- abetes prevention and control: a systematic re- view. Curr Diab Rep 2015;15:574 214. Seligman HK, Bolger AF, Guzman D, López A, Bibbins-Domingo K. Exhaustion of food budg- ets at month’s end and hospital admissions for hypoglycemia. Health Aff (Millwood) 2014;33: 116–123 215. Seligman HK, Davis TC, Schillinger D, Wolf MS. Food insecurity is associated with hypogly- cemia and poor diabetes self-management in a low-income sample with diabetes. J Health Care Poor Underserved 2010;21:1227–1233 216. SeligmanHK, Jacobs EA, López A, Tschann J, Fernandez A. Food insecurity and glycemic con- trol among low-income patients with type 2 diabetes. Diabetes Care 2012;35:233–238

20 Social Determinants of Health and Diabetes Diabetes Care

217. Berkowitz SA, Baggett TP, Wexler DJ, Huskey KW, Wee CC. Food insecurity and met- abolic control among U.S. adults with diabetes. Diabetes Care 2013;36:3093–3099 218. Berkowitz SA, Karter AJ, Lyles CR, et al. Low socioeconomic status is associated with in- creased risk for hypoglycemia in diabetes pa- tients: the Diabetes Study of Northern California (DISTANCE). J Health Care Poor Underserved 2014;25:478–490 219. HeermanWJ,WallstonKA,OsbornCY, et al. Food insecurity is associated with diabetes self- care behaviours and glycaemic control. Diabet Med 2016;33:844–850 220. Seligman HK, Smith M, Rosenmoss S, Marshall MB, Waxman E. Comprehensive dia- betes self-management support from food banks: a randomized controlled trial. Am J Public Health 2018;108:1227–1234 221. Palar K, Napoles T, Hufstedler LL, et al. Comprehensive and medically appropriate food support is associated with improved HIV and diabetes health. J Urban Health 2017;94:87–99 222. Richardson AS, Ghosh-DastidarM, Beckman R, et al. Can the introduction of a full-service supermarket in a food desert improve residents’ economic status and health? Ann Epidemiol 2017; 27:771–776 223. Zhang YT, Mujahid MS, Laraia BA, et al. Association between neighborhood supermar- ket presence and glycated hemoglobin levels among patients with type 2 diabetes mellitus. Am J Epidemiol 2017;185:1297–1303 224. Gary-Webb TL, Egnot NS, Nugroho A, Dubowitz T, Troxel WM. Changes in perceptions of neighborhood environment and cardiometa- bolic outcomes in two predominantly African American neighborhoods. BMC Public Health 2020;20:52 225. Dubowitz T, Ghosh-Dastidar M, Cohen DA, et al. Diet and perceptions change with super- market introduction in a food desert, but not because of supermarket use. Health Aff (Mill- wood) 2015;34:1858–1868 226. Baird MD, Schwartz HL, Hunter GP, et al. Does large-scale neighborhood reinvestment work? Effects of public–private real estate in- vestment on local sales prices, rental prices, and crime rates. Hous Policy Debate 2020;30:164– 190 227. Kazemian P, Shebl FM, McCann N, Walensky RP, Wexler DJ. Evaluation of the cas- cade of diabetes care in the United States, 2005- 2016. JAMA Intern Med 2019;179:1376–1385 228. Danaei G, Friedman AB, Oza S, Murray CJL, Ezzati M. Diabetes prevalence and diagnosis in US states: analysis of health surveys. Popul Health Metr 2009;7:16 229. American Diabetes Association. Economic costs of diabetes in the U.S. in 2017. Diabetes Care 2018;41:917–928 230. Liese AD, Ma X, Reid L, et al. Health care access and glycemic control in youth and young adults with type 1 and type 2 diabetes in South Carolina. Pediatr Diabetes 2019;20:321–329 231. Ngo-Metzger Q, Sorkin DH, Billimek J, Greenfield S, Kaplan SH. The effects of financial pressures on adherence and glucose control among racial/ethnically diverse patients with diabetes. J Gen Intern Med 2012;27:432–437 232. Lu H, Holt JB, Cheng YJ, Zhang X, Onufrak S, Croft JB. Population-based geographic access to

endocrinologists in theUnited States, 2012. BMC Health Serv Res 2015;15:541 233. Lòpez-DeFede A, Stewart JE. Diagnosed diabetes prevalence and risk factor rankings, by state, 2014–2016: a ring map visualization. Prev Chronic Dis 2019;16:E44 234. Rutledge SA, Masalovich S, Blacher RJ, Saunders MM. Diabetes self-management edu- cation programs in nonmetropolitan countiesd United States, 2016. MMWR Surveill Summ 2017;66:1–6 235. DeVoe JE, Tillotson CJ, Wallace LS. Usual sourceof careas ahealth insurance substitute for U.S. adults with diabetes? Diabetes Care 2009; 32:983–989 236. Kang H, Lobo JM, Kim S, Sohn M-W. Cost- related medication non-adherence among U.S. adults with diabetes. Diabetes Res Clin Pract 2018;143:24–33 237. Lessem SE, Pendley RP. QuickStats: per- centage of adults aged$45 years who reduced or delayed medication to save money in the past 12 months among those who were pre- scribed medication, by diagnosed diabetes status and agedNational Health Interview Survey, 2015. MMWR Morb Mortal Wkly Rep 2017;66:679 238. Patel MR, Piette JD, Resnicow K, Kowalski- Dobson T, Heisler M. Social determinants of health, cost-related non-adherence, and cost- reducing behaviors among adults with diabetes: findings from the National Health Interview Survey. Med Care 2016;54:796–803 239. Herkert D, Vijayakumar P, Luo J, et al. Cost- related insulin underuse among patients with diabetes. JAMA Intern Med 2019;179:112–114 240. Piette JD,Wagner TH, PotterMB, Schillinger D. Health insurance status, cost-related medica- tion underuse, and outcomes among diabetes patients in three systems of care.Med Care 2004; 42:102–109 241. Rosenthal E. When high prices mean need- less death. JAMA Intern Med 2019;179:114–115 242. Doucette ED, Salas J, Wang J, Scherrer JF. Insurance coverage and diabetes quality indica- tors among patients with diabetes in the US general population. Prim Care Diabetes 2017; 11:515–521 243. Ali MK, Shah MK. Age and age-old dispar- ities in diabetes care persist. JAMA Intern Med 2019;179:1386–1387 244. Brown AF, Gregg EW, Stevens MR, et al. Race, ethnicity, socioeconomic position, and quality of care for adults with diabetes enrolled in managed care: the Translating Research Into Action for Diabetes (TRIAD) study. Diabetes Care 2005;28:2864–2870 245. Sequist TD, AdamsA, ZhangF, Ross-Degnan D, Ayanian JZ. Effect of quality improvement on racial disparities in diabetes care. Arch Intern Med 2006;166:675–681 246. Heisler M, Smith DM, Hayward RA, Krein SL, Kerr EA. Racial disparities in diabetes care pro- cesses, outcomes, and treatment intensity. Med Care 2003;41:1221–1232 247. Hunt CW, Grant JS, Appel SJ. An integrative review of community health advisors in type 2 diabetes. J Community Health 2011;36:883–893 248. Little TV, Wang ML, Castro EM, Jiménez J, Rosal MC. Community health worker interven- tions for Latinoswith type2diabetes: a systematic

review of randomized controlled trials. Curr Diab Rep 2014;14:558 249. Norris SL, Chowdhury FM, Van Le K, et al. Effectiveness of community health workers in the care of persons with diabetes. Diabet Med 2006;23:544–556 250. Shah M, Kaselitz E, Heisler M. The role of community health workers in diabetes: update on current literature. Curr Diab Rep 2013;13: 163–171 251. Islam N, Nadkarni SK, Zahn D, Skillman M, Kwon SC, Trinh-Shevrin C. Integrating community health workers within Patient Protection and Affordable Care Act implementation. J Public Health Manag Pract 2015;21:42–50 252. Centers for Disease Control and Prevention. Addressing Chronic Disease Through Community Health Workers: A Policy and Systems-Level Approach; Second Edition. Atlanta, GA, National Center for Chronic Disease Prevention and Health Promotion, Division for Heart Disease and Stroke Prevention, April 2015. Accessed 25 October 2020. Available from https://www.cdc.gov/dhdsp/docs/ chw_brief.pdf 253. National Association of Chronic Disease Directors. Community programs linked to clinical services – community health workers: reim- bursement/advocacy. Accessed 10 June 2020. Available fromhttps://www.chronicdisease.org/ mpage/domain4_chw_ra 254. Egbujie BA, Delobelle PA, Levitt N, Puoane T, Sanders D, van Wyk B. Role of community health workers in type 2 diabetes mellitus self- management: a scoping review. PLoS One 2018; 13:e0198424 255. Palmas W, March D, Darakjy S, et al. Com- munity health worker interventions to improve glycemic control in people with diabetes: a sys- tematic review and meta-analysis. J Gen Intern Med 2015;30:1004–1012 256. Gary TL, Batts-Turner M, Yeh HC, et al. The effects of a nurse casemanager anda community health worker team on diabetic control, emer- gency department visits, and hospitalizations among urban African Americans with type 2 diabetes mellitus: a randomized controlled trial. Arch Intern Med 2009;169:1788–1794 257. Kangovi S, Mitra N, Grande D, Huo H, Smith RA, Long JA. Community health worker support for disadvantaged patients withmultiple chronic diseases: a randomized clinical trial. Am J Public Health 2017;107:1660–1667 258. Kangovi S, Mitra N, Norton L, et al. Effect of community health worker support on clinical outcomes of low-incomepatients across primary care facilities: a randomized clinical trial. JAMA Intern Med 2018;178:1635–1643 259. Peek ME, Cargill A, Huang ES. Diabetes health disparities: a systematic review of health care interventions. Med Care Res Rev 2007; 64(Suppl.):101S–156S 260. Ricci-Cabello I, Ruiz-Pérez I, Nevot-Cordero A, Rodrı́guez-BarrancoM, Sordo L, GonçalvesDC. Health care interventions to improve the quality of diabetes care in African Americans: a system- atic review and meta-analysis. Diabetes Care 2013;36:760–768 261. Porterfield D, Jacobs S, Farrell K, et al. Evaluation of the Medicaid Coverage for the National Diabetes Prevention Program Demon- stration Project: Final Report. RTI International, November 2018. Accessed 25 October 2020.

care.diabetesjournals.org Hill-Briggs and Associates 21

Available from https://cdn.ymaws.com/www .chronicdisease.org/resource/resmgr/diabetes_ dpp_materials/medicaid_demonstration_proje .pdf 262. Hill-Briggs F, Lazo M, Renosky R, Ewing C. Usability of adiabetesand cardiovascular disease education module in an African American, di- abetic samplewith physical, visual, and cognitive impairment. Rehabil Psychol 2008;53:1–8 263. Lilly CL, Bryant LL, Leary JM, et al.; MSHA. Evaluation of the effectiveness of a problem- solving intervention addressing barriers to cardiovascular disease prevention behaviors in 3 underserved populations: Colorado, North Carolina, West Virginia, 2009. Prev Chronic Dis 2014;11:E32 264. Boulware LE, EphraimPL, Hill-Briggs F, et al. Hypertension self-management in socially dis- advantaged African Americans: the Achieving Blood Pressure Control Together (ACT) random- ized comparative effectiveness trial. J Gen Intern Med 2020;35:142–152 265. Brown DS, Delavar A. The Affordable Care Act and insurance coverage for persons with diabetes in the United States. J Hosp Manag Health Policy 2018;2:2 266. Casagrande SS, McEwen LN, Herman WH. Changes in health insurance coverage under the Affordable Care Act: a national sample of U.S. adults with diabetes, 2009 and 2016. Diabetes Care 2018;41:956–962 267. Lee J, Callaghan T, Ory M, Zhao H, Bolin JN. The impact of Medicaid expansion on diabetes management. Diabetes Care 2020;43:1094–1101 268. KaufmanHW,ChenZ, FonsecaVA,McPhaul MJ. Surge in newly identified diabetes among medicaid patients in 2014 within Medicaid ex- pansion states under the Affordable Care Act. Diabetes Care 2015;38:833–837 269. Zhang JX, Bhaumik D, Huang ES, Meltzer DO. Change in insurance status and cost-related medication non-adherence among older U.S. adults with diabetes from 2010 to 2014. J Health Med Econ 2018;4:7 270. TunstallH,MitchellR,Gibbs J,Platt S,Dorling D. Is economic adversity always a killer? Disad- vantaged areaswith relatively lowmortality rates. J Epidemiol Community Health 2007;61:337–343 271. Kawachi I, Berkman L. Social cohesion, social capital,andhealth. InSocialEpidemiology.Berkman LF, Kawachi I, Eds. New York, Oxford University Press, 2000, p. 174 272. WhiteheadM, Diderichsen F. Social capital and health: tip-toeing through the minefield of evidence. Lancet 2001;358:165–166 273. Hawe P, Shiell A. Social capital and health promotion: a review. Soc SciMed2000;51:871–885 274. Szreter S, Woolcock M. Health by associ- ation? Social capital, social theory, and the politicaleconomyofpublichealth. Int J Epidemiol 2004;33:650–667 275. Gittell R, Vidal A. Community Organizing: Building Social Capital as aDevelopment Strategy. Thousand Oaks, CA, Sage Publications, Inc., 1998 276. Portes A. Social capital: its origins and applications in modern sociology. Annu Rev Sociol 1998;24:1–24 277. van Staveren IP, Pervaiz Z, Chaudhary AR. Diversity, inclusiveness and social cohesion. ISS Working Paper Series/General Series. 2013. Ac- cessed 25 October 2020. Available from http:// hdl.handle.net/1765/50480

278. World Health Organization. Social deter- minants of health: evidence on social determi- nants of health. Accessed 25 October 2020. Available from https://www.who.int/social_ determinants/themes/en/ 279. Berger-Schmitt R. Considering social cohe- sion in quality of life assessments: concept and measurement. Soc Indic Res 2002;58:403–428 280. Berger-Schmitt R, Noll HH. Conceptual framework and structure of a European system of social indicator. In EuroReporting Working Paper#9.Mannheim,Centre for SurveyResearch and Methodology, 2000 281. Chuang Y-C, Chuang K-Y, Yang T-H. Social cohesion matters in health. Int J Equity Health 2013;12:87 282. van Dam HA, van der Horst FG, Knoops L, Ryckman RM, Crebolder HF, van den Borne BH. Social support in diabetes: a systematic review of controlled intervention studies. Patient Educ Couns 2005;59:1–12 283. Taylor SE. Social support: a review. In The Handbook of Health Psychology. Friedman MS, Ed. New York, Oxford University Press, 2011, pp. 189–214 284. Strom JL, Egede LE. The impact of social support on outcomes in adult patients with type 2 diabetes: a systematic review. Curr Diab Rep 2012;12:769–781 285. Ford ME, Tilley BC, McDonald PE. Social support among African-American adults with diabetes, part 2: a review. J Natl Med Assoc 1998;90:425–432 286. Thoits PA. Social support an psychological well-being: theoretical possibilities. In Social Support: Theory, Research, and Application. Sarason IG, Sarason BR, Eds. Hingram, MA, Kluwer, 1985, pp. 53–72 287. FlôrCR,BaldoniNR,Aquino JA, etal.What is the association between social capital and di- abetes mellitus? A systematic review. Diabetes Metab Syndr 2018;12:601–605 288. Farajzadegan Z, JafariN,Nazer S, Keyvanara M, Zamani A. Social capitalda neglected issue in diabetes control: a cross-sectional survey in Iran. Health Soc Care Community 2013;21:98–103 289. Long JA, Field S, Armstrong K, Chang VW, Metlay JP. Social capital and glucose control. J Community Health 2010;35:519–526 290. Mendoza-Nú~nez VM, Flores-Bello C, Correa- Mu~noz E, Retana-U Galde R, Ruiz-Ramos M. Re- lationship between social support networks and diabetes control and its impact on the quality of life in older community-dwelling Mexicans. Nutr Hosp 2016;33:1312–1316 291. Ciechanowski P, Russo J, Katon WJ, et al. Relationship styles andmortality in patients with diabetes. Diabetes Care 2010;33:539–544 292. Trief P, Sandberg JG, Ploutz-Snyder R, et al. Promoting couples collaboration in type 2 di- abetes: the diabetes support project pilot data. Fam Syst Health 2011;29:253–261 293. Roblin DW. The potential of cellular tech- nology tomediate social networks for support of chronic disease self-management. J Health Com- mun 2011;16(Suppl. 1):59–76 294. Tang TS, BrownMB, Funnell MM, Anderson RM. Social support, quality of life, and self- care behaviors among African Americanswith type 2 diabetes. Diabetes Educ 2008;34:266– 276

295. ZhangX,NorrisSL,GreggEW,BecklesG.Social support and mortality among older persons with diabetes. Diabetes Educ 2007;33:273–281 296. WilliamsDR, Lawrence JA,Davis BA. Racism and health: evidence and needed research. Annu Rev Public Health 2019;40:105–125 297. Reskin B. The race discrimination system. Annu Rev Sociol 2012;38:17–35 298. Whitaker KM, Everson-Rose SA, Pankow JS, et al. Experiences of discrimination and incident type 2 diabetes mellitus: the Multi-Ethnic Study of Atherosclerosis (MESA). Am J Epidemiol 2017; 186:445–455 299. Bacon KL, Stuver SO, Cozier YC, Palmer JR, Rosenberg L, Ruiz-Narváez EA. Perceived racism and incident diabetes in the BlackWomen’s Health Study. Diabetologia 2017;60:2221–2225 300. Sarkar U, Piette JD, Gonzales R, et al. Prefer- ences for self-management support: findings from a survey of diabetes patients in safety-net health systems. Patient Educ Couns 2008;70:102–110 301. National Academies of Sciences, Engineer- ing, and Medicine. A Framework for Educating Health Professionals to Address the Social Deter- minants of Health.Washington, DC, TheNational Academies Press, 2016 302. Alley DE, Asomugha CN, Conway PH, Sanghavi DM. Accountable health communitiesdaddressing social needs throughMedicare andMedicaid. N Engl J Med 2016;374:8–11 303. Gottlieb LM, Tirozzi KJ, Manchanda R, Burns AR, SandelMT.Moving electronicmedical records upstream: incorporating social determinants of health. Am J Prev Med 2015;48:215–218 304. Adler NE, Stead WW. Patients in contextd EHR capture of social and behavioral determi- nants of health. N Engl J Med 2015;372:698–701 305. Giuse NB, Koonce TY, Kusnoor SV, et al. Institute of Medicine measures of social and behavioral determinants of health: a feasibility study. Am J Prev Med 2017;52:199–206 306. DixonB, Pe~naM-M, Taveras EM. Lifecourse approach to racial/ethnic disparities in childhood obesity. Adv Nutr 2012;3:73–82 307. Gostin LO, Hodge JG Jr, Levin DE. Legal interventions to address us reductions in life expectancy. JAMA 2020;324:1037–1038 308. Thomas SB, Quinn SC, Butler J, Fryer CS, Garza MA. Toward a fourth generation of dispar- ities research to achieve health equity. Annu Rev Public Health 2011;32:399–416 309. Angulo AJ (Ed.). Miseducation: A History of Ignorance-Making in America and Abroad. Balti- more, MD, Johns Hopkins University Press, 2016 310. Douglass F. Chapter X, Learning to Read. In Life and Times of Frederick Douglass. New York, Citadel Press, 1983 311. Bell D. Silent Covenants: Brown v. Board of Education and the Unfulfilled Hopes for Racial Reform. New York, Oxford University Press, 2004 312. Rothstein E. The Color of Law: A Forgotten History of How Our Government Segregated Amer- ica. New York, W.W. Norton and Co. Inc., 2017 313. Federal Housing Authority. Underwriting Manual. Underwriting and Valuation Procedure Under Title II of the National Housing Act. Wash- ington, DC, Federal Housing Administration, 1936 314. Eisenhauer E. In poor health: supermarket redlining and urban nutrition. GeoJournal 2001; 53:125–133

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