Nonmedical Factors That Influence Diabetes Complications and Multilevel Strategies to Reduce Disparities and Improve Outcomes in the U.S.

Abstract

The development of diabetes complications is complex and multifactorial. Although advances in pharmacologic interventions and technology have improved diabetes management, nonmedical factors continue to drive persistent disparities in complications across the U.S. Using a socioecological framework, we examine how nonmedical factors operating at individual, organizational, community, and policy levels contribute to rising complications rates. We synthesize multilevel evidence-based interventions, real-world examples, and emerging opportunities that address these drivers. Approaches include culturally and linguistically tailored, digitally delivered diabetes education; systematic screening and documentation of social drivers within health care systems; use of health information systems technology; training for health care providers; multisector community partnerships that leverage social care resources; and policy incentives that integrate medical and social care. Coordinated interventions across socioecological levels are essential to move beyond traditional clinical approaches toward equitable, sustainable diabetes care that reduces complications.

Graphical Abstract

There are more than 38 million people living with diabetes in the U.S., with more than 1 million newly diagnosed each year (1). Despite remarkable advances in glucose-lowering therapies and diabetes technologies, the burden of diabetes complications remains substantial and among some populations is worsening. Persistent hyperglycemia drives both established microvascular (e.g., diabetic retinopathy, chronic kidney disease, neuropathy) and macrovascular (e.g., myocardial infarction, stroke, peripheral artery disease, heart failure) complications, with vascular damage typically accelerated by coexisting obesity, hypertension, and dyslipidemia—comorbidities that are common in type 2 diabetes and growing in type 1 diabetes (2-9). Emerging complications such as obesity-related cancers, Alzheimer disease and related dementias, obstructive sleep apnea, metabolic dysfunction–associated fatty liver disease, and infection (e.g., kidney infection, osteomyelitis and foot infection, pneumonia, influenza, tuberculosis, skin infection, and general sepsis) are also rising in prevalence (10-12) (Fig. 1). In the U.S., the landscape of diabetes complications has grown in complexity, with recent trends showing increases in heart failure, stroke, amputations, and end-stage renal disease, and hyperglycemic crisis hospitalizations, despite earlier declines in the 2000s (13).

Figure 1—

Established and emerging complications of diabetes. Blue text, microvascular complications; green text, macrovascular complications; red text, emerging complications. CKD, chronic kidney disease; ESRD, end-stage renal disease.

Disparities in diabetes and its complications are driven by the interplay of nonmedical (i.e., social, economic, and environmental) factors, alongside biological and behavioral influences. Nonmedical factors result from the unequal distribution of resources, opportunities, and care and create significant barriers to achieving optimal health (14,15). Among ethnic and racial minority populations rates are higher of diagnosed diabetes (i.e., non-Hispanic Black 12.7%, Asian 11.3%, Hispanic 11.1%) and undiagnosed diabetes (i.e., Asian 5.4%, non-Hispanic Black 4.7%, Hispanic 4.4%) in comparison with non-Hispanic Whites (11.0% and 2.7%, respectively) (1). Sex and age differences also exist: higher rates among males than females (16% vs. 13%) and higher rates among people aged ≥65 years than among those <65 years (13% vs. 4–12%) (1).

These disparities also translate to higher complication rates and worse health outcomes among racial and ethnic minority populations in the U.S. Black, Hispanic/Latino, American Indian/Alaska Native, and Asian adults with diabetes are more likely to experience cardiovascular disease, retinopathy, nephropathy, and lower-extremity amputations in comparison with non-Hispanic White adults with diabetes (13,16). Health care use for diabetic ketoacidosis and other complications, as well as hospital charges for these admissions, tends to be higher for Black and Hispanic/Latino patients than for non-Hispanic White patients with diabetes (16). Although there have been declines in racial and ethnic disparities in the prevalence of end-stage renal disease, this trend largely reflects increasing rates among non-Hispanic White adults rather than improvements among other racial and ethnic groups (17). Rural-urban disparities are also well-documented in the U.S., with 9%–17% higher complication rates seen among rural residents in comparison with urban residents (18,19). Age-related patterns have also shifted, and emerging evidence indicates that diabetes diagnosis at a younger age is associated with greater risk of mortality and cardiovascular disease (13). While myocardial infarction rates among people with diabetes have stabilized over recent years, myocardial infarctions are occurring at younger ages, affecting individuals as early as their 30s–40s (13).

Over the past decade, in diabetes research recognition has increased of a more comprehensive understanding of how nonmedical factors influence health outcomes (14,15,20,21), particularly through the socioecological lens (22-24)—a broad approach to health that includes consideration of behaviors and outcomes to be affected by the interaction among the individual, organization (health care system), community, and policies (25). The 2026 American Diabetes Association Standards of Care in Diabetes (26) outline evidence-based clinical strategies to achieve recommended targets for glycemic control, blood pressure, and lipid levels and provide therapeutic guidance on mitigating concurrent cardiometabolic risk conditions. However, these guidelines provide limited emphasis on the nonmedical factors that contribute to the progression of diabetes complications, particularly how to address these factors across socioecological levels to reduce complications. The Diabetes Care Symposium, Diabetes Care: Let’s Pick Up the Pace—Addressing Inequities in Diabetes Care, presented in 2025 at the 85th Scientific Sessions of the American Diabetes Association (27), focuses on accelerating progress toward solutions. In this article, we highlight how nonmedical factors operate across socioecological levels and exacerbate diabetes complications and summarize multilevel evidence-based interventions, real-world examples, and emerging opportunities that comprehensively address these nonmedical factors.

NONMEDICAL FACTORS EXACERBATING DIABETES COMPLICATIONS, THROUGH A SOCIOECOLOGICAL LENS

The term social drivers (also referred to as social determinants of health) refers to conditions in which individuals are born, live, learn, work, play, and age—factors that collectively shape a wide range of health, functioning, and quality-of-life outcomes (28). Adverse social drivers, often as specific social risks, such as food insecurity, housing instability, transportation barriers, economic insecurity, and limited access to health care services, create significant barriers to optimal diabetes management and complication prevention (14,15, 20,21). These risks are not evenly distributed across populations, and long-standing structural and systematic inequities across social, economic, and political domains disproportionately expose certain U.S. populations to adverse social drivers, contributing to higher complications rates and worse diabetes outcomes (29,30).

Social drivers interact dynamically across socioecological levels and exacerbate diabetes complications (Fig. 2). At the individual level, low income limits access to high-quality health care, while employment instability and limited health coverage further restrict care continuity and access to preventive care (20). A 2019 national survey indicated lower rates among uninsured individuals of hemoglobin A_1c (HbA_1c) screening (54.2% vs. 87.9%), foot examinations (39.9% vs. 71.4%), and dilated eye examinations (33.7% vs. 63.3%) in comparison with insured adults with diabetes, respectively (31). Moreover, significant racial and ethnic disparities in access to a usual source of care (e.g., primary care provider) have also been documented, with non-Hispanic White adults 7%–8% more likely than Black, 6%–12% more likely than Asian, and 10%–17% more likely than Hispanic/Latino adults to report having a usual care provider (32). These racial and ethnic disparities also vary by source of health insurance coverage, impacting access to treatment options including newer diabetes medications. In a cross-sectional analysis using the Medical Expenditure Panel Survey investigators found that racial and ethnic differences in SGLT2 inhibitors and glucagon-like peptide 1 (GLP-1) receptor agonist use were largest among privately insured patients (White vs. non-White: 16.1% vs. 8.3%; P < 0.001), with a smaller gap observed among Medicare beneficiaries (14.7% vs. 11.0%; P = 0.04) (33). The lowest overall rates of SGLT2 inhibitor and GLP-1 receptor agonist use were among patients with Medicaid, while no racial and ethnic differences were observed (10.0% for White vs. 9.0% for non-White patients; P = 0.74) (33). Low health literacy and language barriers may also limit understanding of self-management and undermine effective patient-provider communication (34). Mistrust in the health care system, shaped by historical and contemporary experiences of discrimination, can further diminish engagement and self-management among racial and ethnic minority populations (35). Limited access to, or familiarity with, digital health tools, including continuous glucose monitoring (CGM) and telehealth, also present increasingly important barriers, particularly as these technologies become central to prevention of complications.

Figure 2—

Socioecological model demonstrating how social drivers across levels influence diabetes management and risk of complications. Text inside the socioecological framework: social drivers. Text outside the socioecological framework: influence of adverse social drivers on diabetes management and risk of complications. Purple text, individual level; blue text, organization; green text, community; red, policy. AIDs, automated insulin delivery systems; GLP1-RAs, GLP-1 receptor agonists; RDs, registered dietitians; RNs, registered nurses; SGLT2i, sodium–glucose cotransporter 2 inhibitors.

At the organizational level, health system bias and structural racism contribute to disparities in diabetes care through less aggressive treatment, lower referral rates to specialists, and suboptimal communication (36,37). Inadequate training to recognize and address implicit bias, limited capacity to respond to identified social drivers, lack of standardized social drivers screening, and insufficient reimbursement for team-based care models (e.g., community health workers) further constrain effective intervention (38,39). Notably, disparities in complication rates persist even in settings with comparable health coverage, underscoring the influence of nonmedical factors beyond coverage alone.

At the community level, limited health care infrastructure, transportation barriers, and unhealthy food and physical activity environments compound individual- and organizational-level challenges (40). Disparities in access to primary care by geographic location have also been documented, with one large study indicating that while rural residents were more likely than urban residents to have a usual source of care (81% vs. 74%, respectively), they faced substantial barriers, including lower physician access (22% vs. 35%) and longer travel distances and reduced evening/weekend availability (27% vs. 39%), in accessing this care (41). Policy-level factors, including reduced investment in rural health care, zoning and food policies, and shifting health coverage regulations, further exacerbate these risks (42,43). Policy changes may also restrict access to newer, more effective, and safer medications (e.g., GLP-1 receptor agonists and SGLT2 inhibitors) and technologies (e.g., CGM and automated insulin delivery systems), while simultaneously increasing housing and financial instability that competes with daily diabetes self-care activities (44,45).

Taken together, the interaction of adverse social drivers across socioecological levels amplifies barriers to timely diagnosis, adequate monitoring, and access to preventive therapies, accelerating the development of diabetes complications and disparities therein. Coordinated, multilevel strategies are needed to address nonmedical factors underlying persistent disparities (Fig. 3).

Figure 3—

Adapted multilevel approach to reduction in risk of diabetes complications integrating social drivers across socioecological levels. Epic SDOH: EHR platform Social Determinants of Health (social drivers) screening module. Purple boxes, individual-level interventions; blue boxes, organization-level health care system interventions; green boxes, community-level interventions; red boxes, policy-level interventions. w/, with.

MULTILEVEL STRATEGIES TO ADDRESS NONMEDICAL FACTORS OF DIABETES COMPLICATIONS

This section synthesizes evidence-based, actionable strategies that span across the socioecological levels to comprehensively address social drivers contributing to diabetes complications and related disparities (Fig. 4).

Figure 4—

Multilevel framework for addressing social drivers across socioecological levels. Purple, individual-level interventions; blue, organization-level health care system interventions; green, community-level interventions; red, policy-level interventions.

Individual-Level Strategies

Implement Adapted Lifestyle Modification and Diabetes Self-management Education and Support Approaches

Addressing individual-level barriers can begin with effective approaches to education. Diabetes self-management education and support (DSMES) is a cornerstone of high-quality diabetes care and is associated with improved self-management, clinical, quality of life, and cost-effectiveness outcomes (46), yet use remains low, with only ~5% of Medicare beneficiaries and 7% of privately insured individuals accessing DSMES within the first year following diagnosis (47). Nonetheless, education delivered at an appropriate health literacy level, in an individual’s preferred language, and within community-based settings can lead to meaningful improvements in outcomes (48,49).

Scripps Health—a large health care system serving diverse populations in San Diego, CA—developed the American Diabetes Association–recognized Project Dulce through a collaboration with local Federally Qualified Health Centers and San Diego State University (50,51) to improve access to DSMES among medically underserved populations. Project Dulce uses a team-based approach that leverages nurses, community health worker (CHWs), and promotoras—or peer educators—both within health care systems and in the community. Peer educators serve as trusted bridges between patients and the health care system, helping individuals navigate adverse social drivers and connecting them to local resources. Project Dulce evaluation demonstrated improved clinical, behavioral, and cost-related outcomes, including reductions in emergency room visits and hospital use (50-52). Project Dulce also highlights the benefits of using a team-based approach, with peer educators delivering linguistically and culturally tailored DSMES and connecting individuals to social care services needed for optimal diabetes management and complication prevention.

In addition, the American Diabetes Association recommends offering DSMES via telehealth and/or digital interventions, as needed, to meet individual preferences, address access barriers, and improve satisfaction (53). For circumventing practical and social barriers (e.g., transportation, child care) inherent to traditional (face-to-face) DSMES, Scripps Health, together with academic partners at San Diego State University, developed the Dulce Digital program to extend the reach of the care team through mobile health technology (54). This low-cost text messaging program was based on the Project Dulce curriculum and offered in multiple languages (54) and demonstrated significant HbA1c reductions, improved care, and high acceptability (55). Other studies with use of text-based formats have also shown sustained engagement, reduced digital burden, and promoted DSMES access, particularly for older adults, Spanish speakers, and individuals with limited digital literacy or device access (54,56,57). However, some text-based interventions often do not fully address patient expectations, costs, and real-world integration into primary health care settings (58). A study of a low-cost, scalable messaging intervention demonstrated modest improvements in multiple cardiovascular risk factors among adults with type 2 diabetes. The small magnitude of net benefit suggests that further refinement is needed before such interventions can be meaningfully integrated into clinical practice (56,57). Additionally, in a recent systematic review and meta-analysis of 29 trials, investigators found that glycemic improvements from text-based interventions were most evident in patients with relatively low glycemic management at baseline (HbA_1c ≥8.6%), with reductions of 0.48% at 3 months and 0.36% at 6 months, diminishing by 1 year; greater benefit was also found among studies using multicomponent interventions (59). In future work text-based interventions should be enhanced through integrating them with social care support that addresses the broader nonmedical needs shaping diabetes outcomes.

Organizational-Level Strategies

Initiate Comprehensive Screening for Social Drivers Across Diverse Health Care Settings

Screening for social drivers is crucial for health care organizations to identify and address nonmedical factors impacting patient health outcomes, as it enables targeted referrals to community resources, improves individual outcomes, reduces health care costs (fewer emergency department visits), and informs broader population health strategies. The National Academies of Sciences, Engineering, and Medicine highlight tools for screening as essential for identifying and addressing social drivers, with various instruments available to capture risks across diverse populations (60,61).

In the U.S., health care organizations began implementing protocols for social drivers screening at the time of hospital admission to enhance the collection and use of individual-level demographic and social drivers data. At Scripps Health, the protocol includes nurse-administered screening at admission, with verbal guidance and resource referrals at discharge for patients screening positive for adverse social drivers. While >92% of hospitalized patients have been screened since implementation in 2023, screening of social drivers is not currently conducted systematically in the outpatient settings. National data reflect similar gaps, with reporting from a large study of 1,384 Federally Qualified Health Centers that while 71% collected data on social drivers, there was substantial interstate variation, driven in part by state-level policies and budgetary priorities (62). This gap is particularly critical as most routine diabetes care occurs in outpatient settings, where ongoing management and early intervention offer significant potential to prevent disease progression and complications. Participation in delivery models such as the Medicaid Accountable Care Organizations (63), which include quality metrics associated with social drivers screening and aim to integrate social services with physical and behavioral health services, has been linked with a higher likelihood of adopting social drivers screening in the outpatient setting (38). In future work these delivery models should be leveraged to expand screening and referrals for adverse social drivers in outpatient settings. While implementation at the organizational level is critically important, screening for social drivers illustrates how interventions and strategies can be addressed across socio-ecological levels (for example: policy level, mandates for screening; organizational level, electronic health record [EHR] data collection, provider training, and referral processes; community level, connections to needed resources; individual level, modifications of care plans to accommodate individual needs).

Patient-centered screening approaches for social drivers should also be considered. A systematic review on patients’ perceptions of social drivers screening reported positive attitudes more often than not in the 4 of the 22 studies reviewed, although some patients reported concerns about stigmatization and privacy (38). Health care organizations should consider using patient-centered approaches in screening social drivers to build trust and ensure data quality, completeness, and accuracy. These approaches include offering different screening modalities (e.g., interviews, paper forms, electronic submissions), creating a safe space for conducting screenings, getting patient buy-in, and identifying patient priorities (61).

Train Health Care Providers on How to Identify and Respond to Adverse Social Drivers

Training health care providers to identify and respond to adverse social drivers can improve patient engagement and facilitate timely referrals to social care services; however, many lack adequate training to effectively screen or respond when needs are identified (38). A systematic review highlighted gaps in training on screening tools, and limited awareness of available resources following a positive screen (38), although health care organizations are increasingly implementing formal training programs. For example, Scripps Health combined hospital-wide social driver screening with mandatory inpatient provider education, covering communication strategies, screening, and response protocols. Additionally, the American Hospital Association recommends cultural competency training, motivational interviewing, and active listening for health care providers conducting social drivers screening to enhance communication, reduce language barriers, and support patient-centered goal setting (64).

Other critical issues influence equitable diabetes care delivery, including inconsistent treatment and prescribing patterns, underscoring the need for providers to remain aware of internal biases when making clinical decisions (65-67). Several evidence-based approaches at the organizational level have been developed to address unconscious bias in health care (68), including the Bias Reduction in Internal Medicine intervention developed by Carnes et al. (69), with use of structured workshops to improve bias awareness. Adapting and implementing such initiatives within health care organizations can enhance provider awareness and ultimately improve diabetes outcomes.

Leverage Information Technology Systems

Benefits of large-scale data aggregation have been demonstrated—sharing EHR data for secondary research purposes and addressing the need for structured EHR data, including social drivers of health (70-72)—yet these data are often inconsistently collected. Leveraging information systems technology to systematically screen, track, and evaluate social drivers can enhance the integration of social care into clinical practice. EHR systems and other digital platforms enable standardized collection of social drivers data, facilitate timely identification of patient needs, and support long-term monitoring of outcomes (73,74). These systems can also automate referrals to social care services, streamline patient navigation, and generate population-level insights to inform resource allocation and program evaluation (74). By embedding social drivers screening within health information systems, health care organizations can more effectively align medical and social care interventions, ultimately improving both individual patient outcomes and broader population health management.

Major EHR vendors have incorporated structured fields for longitudinal collection and presentation of social drivers data, including the Epic Social Determinants of Health wheel (75). This wheel, configurable for institutional preferences regarding which social drivers are documented and questions are used, allows for more structured documentation of social drivers and behavioral health risk factors. The wheel could help care team members address adverse social drivers and promote care coordination, particularly with integration with additional Epic applications for population health management and care coordination (76).

It is important to note, however, that the very presence of structured fields within the EHR does not guarantee use or completeness of those fields in practice and does not account for social drivers captured in notes. Other challenges to systematic documentation and integration of social drivers within EHR include reliance on unstructured and nonstandardized data capture, gaps and overlap among available terminologies and codes, and patient privacy and security concerns (77,78). These limitations restrict the ability to normalize, exchange, and meaningfully aggregate data across clinical and nonclinical systems. The Gravity Project by the Social Interventions Research & Evaluation Network (79) addresses these barriers by developing consensus-based standards for social driver data exchange. Recent progress, driven by new grants, federal recognition, and real-world pilot use, has shifted the focus of the Gravity Project to operationalizing standardized terminology and Fast Healthcare Interoperability Resources (FHIR)-based technology for social drivers.

In addition to EHR systems, the integration of digital health technologies, including artificial intelligence (AI), provides a significant opportunity to optimize diabetes care through prediction and prevention of diabetes, screening and classification of the disease, and prediction, screening, and management of diabetes complications (80). A recent article outlined the development of AI using large language models to enhance diabetes self-management, where people with or without diabetes can use digital platforms to report patient-reported outcomes to their EHR and/or their clinicians. EHR and other diagnostic data are then integrated into a centralized, secure server environment. AI algorithms digest data to enable integration of all aspects of diabetes self-management, including between-visit communication for efficient visits, and better self-management through easier access to reliable and personalized health information. While integrating AI into clinical practice has the potential to shift diabetes care toward personalization, health care organizations investing in AI-enabled digital health approaches should consider critical challenges, such as data quality control, technology design, and privacy concerns (80).

Community-Level Strategies

Establish Community Mapping Strategies to Build Partnerships and Assess Assets and Barriers

Given the importance of community contexts of people living with diabetes, health care systems must collaborate with community constituents for understanding of the full landscape of available support and unmet social needs. This involves assessing community-level factors, such as social context and cohesion, as well as local assets (e.g., clinics, pharmacies, transportation, food banks), and barriers (e.g., transportation challenges, geographic gaps in care) that shape effective diabetes self-management. A central strategy for this work is the development of community and concept mapping approaches that visually and analytically document the socio-spatial circumstances shaping how people with diabetes navigate their daily lives (81,82) and reveal barriers and assets within their community. This can support the creation of community-informed interventions that address real-world challenges to diabetes care and targeted strategies to prevent or delay diabetes complications. Additionally, community mapping initiatives deepen collaboration among health care providers, public health agencies, and community organizations. Future work should include prioritization of hybrid interventions that integrate social driver screening, diabetes monitoring, and treatment navigation, including CGM and remote patient monitoring, in partnership with community organizations (80). In active engagement with community members and listening to their lived experiences, health care systems gain critical insights into both barriers and assets.

Develop Multisector Partnerships to Align and Leverage Social Care Resources

There has been growing recognition of the importance of multisector partnerships in addressing social drivers of health (83). Partners from community-based sectors (e.g., faith-based organizations, food banks) often collaborate with health care and academic institutions to codevelop strategies to address systemic problems influencing health outcomes (84). These partnerships strengthen community capacity to address adverse social drivers by developing or enhancing data systems, leveraging and aligning resources, and engaging community stakeholders around priority issues (83). In the context of diabetes, multisector partnerships offer a promising approach for addressing adverse social drivers to improve health outcomes (85). In a study conducted in collaboration between a primary care practice and a local food bank, investigators found greater improvements in HbA_1c (absolute change 3.1% vs. 1.7%) and diet quality (2.47-point improvement on a 14.00-point scale, P < 0.001) for participants who received twice-monthly food bank produce deliveries, brief education from a food bank dietitian, and home-based education, compared with the treatment as usual (86).

Other community-level initiatives, such as community health fairs, address social drivers in offering free health screenings and education and connecting underserved populations to vital social care services (87,88). For example, the Many Shades of Pink community-based organization (89), in collaboration with Scripps Health, other safety net health centers, and local food banks, provides health resources at monthly community health fairs in medically underserved South San Diego, CA, neighborhoods and offers resources including fresh produce and preventive diabetes screenings (e.g., HbA1c and blood pressure testing). This approach is particularly impactful for diabetes management as individuals with prediabetes or undiagnosed diabetes are identified and connected to care to prevent or delay the development of diabetes and/or its complications. The presence of local food banks also addresses food insecurity, which is a key social driver that undermines diabetes self-management (86). Through sustained partnerships with trusted community and health care organizations, community health fairs create pathways for continuous engagement rather than one-time interventions. As health care organizations develop or expand their partnerships, critical challenges, including administrative complexity, data sharing obstacles, unequal participation from imbalanced power dynamics, and historical mistrust of health care and research institutions, must be addressed for a more impactful and sustained collaboration (90).

Policy-Level Strategies

Integrate Social Care Interventions Within Clinical Guidelines and Practice

Despite growing recognition of the influence of nonmedical factors in the progression of diabetes complications, existing clinical guidelines offer limited guidance on mitigating the adverse social drivers that patients frequently encounter. In a review investigators found that 77% of clinical practice guidelines did not include any recommendations related to social care activities and only 23% referenced at least one social care activity (91). Among these, the most common recommendation was to adjust medical treatment based on the patient’s social drivers (94%) (91). While clinically relevant, this narrow focus underscores the need for integration of evidence-based social care interventions into clinical guidelines to support timely referrals to appropriate social care services and inform how treatment for diabetes is tailored.

Efforts to integrate social care into clinical practice as a strategy for improving diabetes outcomes are actively being explored. Bridging the Gap: Reducing Disparities in Diabetes Care—an initiative of the Merck Foundation—supported eight organizations in implementing integrated medical and social care models for patients with diabetes (39). This initiative underscored promising practices and emerging opportunities: 1) primary care transformation and workforce capacity involving social drivers stratification and the employment of CHWs to initiate and track referrals for both medical and social care needs, 2) addressing individual social drivers and structural changes via screening for social drivers (e.g., transportation barriers) and offering short-term resource (e.g., transportation vouchers, virtual visits, or home visits) to mitigate these challenges, and 3) payment reform such as engaging payors in collaborative funding models (39). While these approaches demonstrate promise, further research is needed to assess their impact on the prevention and management of diabetes complications. Moreover, for successful integration of social care into health care settings, key challenges must be considered, such as obtaining leadership support and staff buy-in, including new voices from those with expertise in social care alongside traditional health care professionals, and resolving logistical and operational issues (77).

Incentivize Care Models Integrating Medical and Social Care

Coverage for diabetes preventive services varies widely across states, contributing to inequitable access and care continuity (52), especially for populations with high diabetes burden. State-level Medicaid eligibility rules, coverage policies, and administrative requirements, such as work mandates and frequent reenrollment, are key policy determinants shaping diabetes outcomes. However, recent federal and state initiatives show potential for addressing social drivers adverse to diabetes care, including Medicaid expansion under the Affordable Care Act, which has been linked with improvements in diabetes screening, medication adherence, and glycemic control (92). Additionally, Medicaid-covered nonemergency medical transportation facilitates access to preventive and specialty care (93), while insulin cost caps under the Inflation Reduction Act and state-regulated private insurance in 29 states have reduced financial barriers (94). A recently established federal framework also reduced GLP-1 receptor agonist pricing (approximately $245 monthly for Medicare/Medicaid, $50 beneficiary out of pocket) and expanded Medicare Part D coverage for antiobesity medicines, indicating progress toward broader access to more effective therapies (95).

However, policy changes alone are insufficient without implementation support. CHWs and patient navigators are essential for translating coverage expansion into real-world benefit by assisting with enrollment, eligibility, and care navigation (96), yet sustainable reimbursement mechanisms for these roles remain limited, constraining scalability and long-term impact (39). Value-based and alternative payment models that support team-based care and incentivize reduction of health disparities are critical to sustaining medical-social care models.

Finally, equitable access must be combined with high-quality care delivery. Comprehensive quality measures that assess evidence-based diabetes care, care coordination, and outcomes are essential to identify and address disparities. Current metrics for social drivers, including Healthcare Effectiveness Data and Information Set (HEDIS) measures, remain limited and rarely evaluate intervention effectiveness (97). To achieve meaningful improvements in diabetes outcomes, both action on identified underlying social drivers and accountability through robust quality measures are warranted.

LIMITATIONS

This article has several limitations. Our focus was limited to nonmedical factors exacerbating diabetes complications, specifically within the context of social drivers of health. While we recognize that important psychological factors, such as depression and diabetes distress, significantly influence diabetes outcomes, these were not our primary focus. Future work should include examination of the interplay between psychological and social drivers to provide a more holistic understanding of nonmedical influences on diabetes care. Additionally, while we recognize that diabetes complications are worsening globally and many social drivers operate similarly across international contexts, we focus specifically on the U.S. context. Many of the multilevel interventions that we present, particularly at organizational and policy levels, are embedded within the U.S. health care infrastructure and regulatory environment, and we acknowledge that the global diabetes burden presents distinct social, cultural, and health system factors that require approaches tailored to local contexts. Lastly, while we focus on evidence-based strategies, some of our real-world examples are drawn from initiatives within Scripps Health and may not be generalizable to other health care organizations with different resources, organizational structures, community partnerships, and patient populations. This underscores the need for community collaboration to adapt strategies to local contexts, available resources, needs, and infrastructure.

CONCLUSIONS

The interaction of adverse social drivers across socioecological levels heightens barriers to diabetes care and accelerates progression of complications; coordinated multilevel strategies are required to address the underlying social drivers exacerbating complications. Individual-level strategies include DSMES approaches tailored to patients’ preferences and social context. Organizational-level approaches include comprehensive screening for social drivers across health care settings, training health care providers on key issues to inequitable diabetes care and leveraging health information technologies to systematically screen and document social drivers. Community-level strategies include using community mapping to assess assets and barriers and developing multisector partnerships to align social care resources. Policy-level strategies include integrating social care within clinical guidelines and incentivizing team-based care models that bridge medical and social care. By simultaneously addressing social drivers at these socioecological levels, we can move beyond traditional clinical approaches to create more equitable, effective, and sustainable interventions that reduce complications and narrow persistent disparities for the populations most affected by adverse social drivers of health.

ARTICLE HIGHLIGHTS.

• Why did we undertake this study?

• To describe nonmedical factors driving diabetes complications and provide evidence-based, actionable multilevel strategies that address these determinants across all socioecological levels to improve outcomes and advance health equity.

• What is the specific question(s) we wanted to answer?

• What evidence-based interventions and emerging opportunities across individual, organization (health care system), community, and policy levels can mitigate social drivers that increase diabetes complication risk?

• What did we find?

• Promising interventions exist at each socioecological level, but substantial barriers to widespread and sustained implementation persist.

• What are the implications of our findings?

• Systematically integrating multilevel social care interventions into diabetes clinical practice guidelines, health systems, and public policy can help reverse increasing complication trends and address disparities in diabetes care.