Social Determinants Of Health Measures Research Articles

The Association between Social Determinants of Health and Depressive Disorders: A 2017 Behavioral Risk Factor Surveillance System (BRFSS) Analysis

Background: Major Depressive Disorder is a leading cause of disability worldwide and one of the most common disorders in the United States. Contributors to an individual’s risk for experiencing depressive disorders include individual and social factors. Although the social determinants of health (SDOH) are conditions that contribute to healthy functioning, health outcomes, and quality of life, it is unclear to what extent adverse SDOH experiences are associated with self-reporting depressive disorder (DD). Methods: Data from the 2017 Behavioral Risk Factor Surveillance System (BRFSS), a population-based telephone survey of noninstitutionalized U.S. adults, was employed in this study. Lifetime diagnosis of DD was self-reported among survey participants who also completed the Social Determinants of Health optional module within 17 participating BRFSS states and entities. Rao-Scott chi-square analysis was used to determine the association of individual and SDOH measures with DD. Results: The prevalence of DD among participating states was 19.85%. Among respondents, significant proportional differences were observed for each SDOH indicator regarding DD status. However, significant differences in health insurance coverage were not observed. Conclusion: Survey respondents with a lifetime diagnosis of DD experienced adverse SDOH conditions in greater proportion than individuals not reporting DD. Individual and community-based approaches to address the contextual influences of depressive disorders should be aggressively implemented.

Impact of social determinants of health (SDOH) measures on prescribing advanced androgen blockade (AAB) in patients with metastatic hormone sensitive prostate cancer (mHSPC).

e17086 Background: Use of AAB (abiraterone acetate, apalutamide or enzalutamide) has been shown to improve survival in mHSPC, but many patients are not prescribed this treatment. There is substantial interest in the broader role of SDOH in cancer. We explored how SDOH influences prescribing patterns of AAB in patients with mHSPC. Methods: Patients diagnosed with mHSPC between 1/1/2017 and 12/31/2021 were identified using the iKnowMed electronic health record database from The US Oncology Network of community oncology practices. Records were searched for prescriptions for AAB (based on intent to treat). Individual level measures were age, diagnosis year, ECOG, race and type of health insurance. Area level measures were Area Deprivation Index (ADI) at national and state level, and rural status. ADI is a validated metric based on demographic variables from census block groups; high ADI scores for state ( > 8) and national ( > 80) level are markers of low socioeconomic status. Logistic regression models were run on each SDOH variable and adjusted for confounding variables, including a joint distribution model of African American (AA) and ADI (significance at p < 0.05). Results: There were 3,855 patients identified with mHSPC: 40% had a prescription for AAB. Summary measures overall were mean age: 71 years, AA: 10%, Medicaid: 5%, ECOG 0-1 and 2+: 60% and 14% respectively; and diagnosis year 2017-21: 15%, 20%, 21%, 22%, and 21% respectively. Area level scores: National ADI high: 8%, State ADI high: 18%, rural: 5%. Interaction variable: AA + State ADI (high): 2%. Statistical differences between those with/without AAB (not shown) were diagnosis year, ECOG and age. Multivariate logistic models were adjusted for these 3 variables with the single SDOH measure as the primary independent variable and the binary variable AAB as the dependent variable (Table). Conclusions: There was no significant association between AAB prescriptions and AA race, rural status, ADI (state or national), or Medicaid status. These data indicate that SDOH measures do not appear to influence prescribing of these treatments. We believe this study is among the first to examine these particular SDOH measures and their relationship to the prescribing of oncology treatments. Further research should be conducted into the impact of SDOH measures on the fulfillment and compliance of these drugs. [Table: see text]

Social determinants of health and utilization of neoadjuvant chemotherapy in patients with triple negative breast cancer (TNBC) in the community oncology setting.

610 Background: TNBC has a higher incidence in African American women compared to European American, often presents at a younger age or more advanced stage, and is associated with worse outcomes. The use of multi-agent neoadjuvant chemotherapy (NC) improves event-free survival and overall survival in patients with early-stage TNBC and is considered standard of care. Race and other social determinants of health (SDOH) have been shown to affect outcomes in patients with TNBC. Differences in use of NC associated with SDOH may be a contributing factor to inferior outcomes. The aim of this study is to determine if SDOH measures affect utilization of NC in patients with early-stage TNBC. Methods: This retrospective observational cross-sectional study examined patient profiles, treatment patterns and SDOH indicators among patients newly diagnosed with early-stage breast cancer in The US Oncology Network. Using iKnowMed EHR data, patients diagnosed with TNBC between 03/31/2017 and 09/30/2021 with stages II-IIIB disease or tumor size ≥2cm (T2 or higher) were included. The initial TNBC diagnosis date was used as the index date for each patient and records were assessed from 6 months pre- to 6 months post-diagnosis for NC initiation, baseline characteristics, and social determinants, including Area Deprivation Index (ADI), a validated measure of socioeconomic status. Results: 3321 patients with TNBC were identified, with a mean age of 58 years; 59% were White, 15% Black, 3% Asian, and 23% other/unknown; and 2139 (64%) received NC. Patients who received NC relative to those who did not were younger (mean 55.9 vs 61.8; p=0.001), had higher BMIs (70% with BMI>25 vs ≤25, p=0.045), and had commercial or Medicaid insurance (67.5% with commercial insurance or Medicaid vs. 52.5% with Medicare; p=0.001). Regional differences were observed, with proportionally fewer patients receiving neoadjuvant chemotherapy in the south US census regions and more in the west. There was no difference in NC use in Black vs White patients (66% vs 64%). No significant differences in receipt of NC were found for race, ethnicity, or ADI. Conclusions: In the community oncology setting, we did not observe inequities in the use of NC based on social determinants analyzed in this study, such as race, ethnicity, and ADI. Poorer outcomes observed in Black patients with TNBC may not be a result of underutilization of NC.

Geo-spatial risk factor analysis for drug overdose death in South Florida from 2014 to 2019, and the independent contribution of social determinants of health

PurposeThe physical environment and social determinants of health have been shown to influence health behaviors including drug use and fatal drug overdose. The current research examines the effects of the built environment, social determinants of health measures and aggregated risk from the built environment at neighborhood-level on drug overdose death locations in Miami-Dade County, Florida. MethodsRisk Terrain Modeling (RTM) was used to assess the place features risk factors that significantly increase the risk of drug overdose death spatially in Miami-Dade County ZIP Code Tabulation Areas, Florida from 2014 to 2019. An aggregated neighborhood risk of fatal drug overdose measure was developed by averaging the risk per grid cell from the RTM within census block groups each year. Six logistic and zero-inflated regression models were built to examine the effects of three indices of incident-specific social determinants of health (IS-SDH) measures and aggregated risk measures separately, and simultaneously on drug overdose death locations each year. ResultsSeven place features including parks, bus stops, restaurants and grocery stores were significantly related to the occurrence of fatal drug overdoses. When examined separately, one or more indices of the IS-SDH were significant covariates of drug overdose locations in some years. When examined simultaneously, the three indices of the IS-SDH and aggregated risk of fatal drug overdose measure could be all significant in certain years. ConclusionsThe patterns of high-risk areas and place features identified from the RTM related to drug overdose deaths may be used to inform the placement of treatment and prevention resources. A multi-factor approach that combines an aggregated neighborhood risk measure reflecting the risk from the built environment and the incident-specific social determinants of health measures can be used to identify the drug overdose death locations in certain years.

Social determinants of health among family caregiver centered outcomes in lung cancer: a systematic review.

Lung cancer is the leading cause of cancer death globally. Both lung cancer patients and family caregivers (FCGs) have unmet quality of life (QOL) needs. An understudied topic in lung cancer research is the role of social determinants of health (SDOH) on QOL outcomes for this population. The purpose of this review was to explore the state of research on SDOH FCGs centered outcomes in lung cancer. The databases PubMed/MEDLINE, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and American Psychological Association (APA) PsycInfo were searched for peer-reviewed manuscripts evaluating defined SDOH domains on FCGs published within the last ten years. The information extracted using Covidence included patients, FCGs and study characteristics. Level of evidence and quality of articles were assessed using the Johns Hopkins Nursing Evidence-Based Practice Rating Scale. Of the 344 full-text articles assessed, 19 were included in this review. The social and community context domain focused on caregiving stressors and interventions to reduce its effects. The health care access and quality domain showed barriers and underuse of psychosocial resources. The economic stability domain indicated marked economic burdens for FCGs. Four interconnected themes emerged among articles on the influence of SDOH on FCG-centered outcomes in lung cancer: (I) psychological well-being, (II) overall quality of life, (III) relationship quality, and (IV) economic hardship. Notably, most participants in the studies were White females. The tools used to measure SDOH factors included primarily demographic variables. Current studies provide evidence on the role of SDOH factors on lung cancer FCGs' QOL. Expanded utilization of validated SDOH measures in future studies would provide greater consistency in data, that could in turn inform interventions to improve QOL. Further research focusing on the domains of education quality and access and neighborhood and built environment should be carried out to bridge gaps in knowledge.

Social Determinants of Health and Adverse Health Outcomes in an Urban Community Hospital in Northwest Indiana

Background: Social determinants of health (SDOH) are underlying contributing factors ofhealth inequities. Facility-based SDOH screenings inform targeted care, community resource referrals, and actions. St. Mary Medical Center (SMMC), an urban hospital in Northwest Indiana, was first acute care hospital in the state to pilot comprehensive SDOH screenings and referrals using the Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences(PRAPARE), a nationally recognized and validated tool. This study is part of a three-phased Community Based Participatory Research (CBPR) partnership between SMMC and IUSM-NWto examine and address SDOH from January 2021-January 2025. This study’s research questions are: What is the distribution of adverse health outcomes by demographics and by SDOH in SMMC’s inpatients?
 Methods: This descriptive study analyzed a limited dataset generated from EPIC™ by SMMC. It included demographics, behavioral, health outcomes, and SDOH measures for adult inpatient visits from January 2021 to June 2022. Data analysis was conducted in SPSS 28.0 usingdescriptive statistics and tests of association (p<0.05) including One-way ANOVA, Kruskal Wallis H, Chi-square, and Fisher’s Exact. This study was exempted by Indiana UniversityHuman Research Protection Program (IRB #14040).
 Results: The sample comprised 4370 patients, majority non-Hispanic (90.4%), White (75.7%), older adults (median age 65, IQR = 24), and publicly insured (76.3%). 30-day readmissions accounted for 9.1%. The top three discharge diagnoses were diseases of the circulatory system (20%), digestive system (12.4%), and injury (7.5%). Discharge diagnoses differed significantly (p<.001) by family income, insurance type, stress presence, race, ethnicity, and sex. 30-day readmission was significantly associated with housing risk (p=.036) and insurance type (p<.001).
 Conclusions: This study underscores the role of SDOH in influencing health outcomes and the importance of continuous SDOH screenings. This study will inform subsequent advanced analysis, the program evaluation, and the translation-to-action phase of the CBPR project.

Area-level social determinants of health and individual-level social risks: Assessing predictive ability and biases in social risk screening.

Area-level social determinants of health (SDoH) and individual-level social risks are different, yet area-level measures are frequently used as proxies for individual-level social risks. This study assessed whether demographic factors were associated with patients being screened for individual-level social risks, the percentage who screened positive for social risks, and the association between SDoH and patient-reported social risks in a nationwide network of community-based health centers. Electronic health record data from 1,330,201 patients with health center visits in 2021 were analyzed using multilevel logistic regression. Associations between patient characteristics, screening receipt, and screening positive for social risks (e.g., food insecurity, housing instability, transportation insecurity) were assessed. The predictive ability of three commonly used SDoH measures (Area Deprivation Index, Social Deprivation Index, Material Community Deprivation Index) in identifying individual-level social risks was also evaluated. Of 244,155 (18%) patients screened for social risks, 61,414 (25.2%) screened positive. Sex, race/ethnicity, language preference, and payer were associated with both social risk screening and positivity. Significant health system-level variation in both screening and positivity was observed, with an intraclass correlation coefficient of 0.55 for social risk screening and 0.38 for positivity. The three area-level SDoH measures had low accuracy, sensitivity, and area under the curve when used to predict individual social needs. Area-level SDoH measures may provide valuable information about the communities where patients live. However, policymakers, healthcare administrators, and researchers should exercise caution when using area-level adverse SDoH measures to identify individual-level social risks.

Association of Social Determinants of Health Measures and Successful Community Discharge Across Rehabilitation Service Areas

<h3>Research Objectives</h3> Evaluate: 1) variation in successful community discharge across Rehabilitation Service Areas (RSAs) nationally, and 2) the relationship between social determinants of health (SDH) variables/indices and successful community discharge across RSAs. <h3>Design</h3> Retrospective cross-sectional analysis of Centers for Medicare & Medicaid Services (CMS) and AHRQ SDH dataset. We performed maximum likelihood factor analysis with varimax rotation and principled mean analysis to identify how many factors to extract from the AHRQ-SDH dataset and calculated a rehabilitation-specific SDH measure (SDH-R) based on these factors and variables. Next, we calculated the Social Deprivation Index (SDI) and the SDH-R for a random sample of 800 RSAs and examined the association between SDI and SDH-R and successful community discharge. <h3>Setting</h3> We examined discharges following post-acute care from long-term care hospitals, home health agencies, and skilled nursing and inpatient rehabilitation facilities. <h3>Participants</h3> The population included 2013–2015 Medicare records from fee-for-service beneficiaries >65 years. The cohort included 2,287,210 records across all diagnostic groups. <h3>Interventions</h3> N/A. <h3>Main Outcome Measures</h3> Successful community discharge across the 1711 RSAs nationally using CMS quality measure specifications. <h3>Results</h3> The national average for successful community discharge across RSAs was 55.1%. The pattern of successful community discharge indicates the majority of RSAs(1239, 72.4%) perform at the national average for successful community discharge with 253 RSAs (14.8%) significantly below and 219(12.8%) significantly above the national average. Factor analysis with AHRQ-SDH variables identified five factors with eigenvalues >1.0. The resultant model explained 23.1% of the variation in successful community discharge. The SDH-R model performed significantly better than the SDI (RMSE:SDH-R=0.88 vs SDI=0.99). <h3>Conclusions</h3> Successful community discharge varied across RSAs nationally. We identified SDH variables related to successful community discharge following rehabilitation. More research is needed to understand how SDH impacts community discharge across high- and low-performing RSAs and explore disparities and equity issues related to successful community discharge following post-acute care rehabilitation. <h3>Author(s) Disclosures</h3> This work was supported by the Agency for Healthcare Research and Quality (AHRQ) R01-HS024711 (TR,JP,AH,YK,KE), T32-HS026133 (YK,JD). All authors declare no conflicts.

Methods for development and application of data standards in an ontology-driven information model for measuring, managing, and computing social determinants of health for individuals, households, and communities evaluated through an example of asthma

ObjectiveTo describe methods to approach application of data standards to integrate social determinants of health (SDoH) into EHRs through evaluation of a case of clinical decision support for pediatric asthma. Materials and MethodsWe identified a list of environmental factors important for managing pediatric asthma. We identified and integrated data from local outdoor air quality monitors with elements available from the clinic’s EHR and self-reported indoor air quality questionnaire data. We assessed existing SDoH frameworks, assessment tools, and terminologies to identify representative data standards for these environmental SDoH measures. ResultsWe found many-to-many relationships between the multiple framework domains, the environmental exposure measures collected, and existing standards. The majority of concepts did not accurately align with environmental exposure measurements. We propose an ontology-driven information framework methodology to apply standards for SDoH measurements to support measuring, managing, and computing SDoH data. DiscussionTo support methods of integrating SDoH data in the EHR via an ontology-driven information framework, a common SDoH ecosystem should be developed descriptively and prescriptively integrating framework domains, assessment tools, and standard ontologies to support future data sharing, aggregation, and interoperability. A hierarchical object-oriented information model should be adopted to manage SDoH to extend beyond patient-centered orientation of EHRs to orient to households and communities. ConclusionSDoH data pose unique challenges and opportunities in collecting, measuring, and managing health information. Future work is needed to define data standards for implementing SDoH in a hierarchical, object-oriented information model representing multiple units of orientation including individuals, households, and communities.

Abstract 13552: Associations Between Food Security and Social Determinants of Health With Dietary Intakes- A Pilot Study

Introduction: Food insecurity remains prevalent in the United States and has disproportionately affected those living in poverty or experiencing financial hardships. Social determinants of health (SODH) influence food security; yet, little is known on which aspects of the built environment influence the most food insecurity and dietary intake, especially among minority groups living in urban food deserts. We conducted a pilot study to examine the association between individuals’ dietary intake, food insecurity and other SODH measures among individuals residing in an urban food desert. Methods: We recruited 45 adults, residing in the targeted study area. Following consent, participants completed surveys to measure sociodemographic and health status, dietary intake, social determinants of health (food insecurity, perceived neighborhood safety, healthy food environment, perceived discrimination). All demographic and survey data were examined graphically and summarized descriptively. Analysis of variance was used to examine associations between dietary data and SODH measures. Results: Overall, ~78% of the participants were African American, female (80%), 53 years old on average. More than 50% lived in a household with a yearly income below $12,000. Education level and neighborhood safety were the major SODH influencing dietary intake. Majority of participants were food insecure and 54% reported not feeling safe in their neighborhood. The effect of food insecurity was significant on the average intake of processed meat (F = 4.72, p = .036), fast food (F= 4.384, p= .042) and whole grains (F= 4.573, p= .038). The effect of education level on average intake of processed meat was significant, F = 3.59, p=.02. Lastly, neighborhood safety significantly influenced the average intake of processed meat (F= 3.593, p= 0.022). Conclusions: Findings from this pilot study confirm the importance of examining neighborhood contextual factors such as SODH in relation to food insecurity and diet. A better understanding of the relationships between dietary intake and SODH have important implications for clinical practice, behavioral interventions and policies aiming to decrease diet-related health disparities.

Abstract 13643: Examining the Association Between Social Determinants of Health and Health-Related Quality of Life Among Children With Cardiac Conditions

Introduction: Patient-reported outcomes like health-related quality of life (HRQOL) measure the health and well-being of patients, providing insights not communicated by traditional outcomes. The degree to which social determinants of health (SDOH) influence HRQOL is under-studied. Methods: A cross-sectional analysis of subjects aged 8-18 years referred for cardiac catheterization from 1/1/18-12/31/21 was performed, evaluating the association between HRQOL (measured through child and caregiver-proxy reports of cardiac-specific HRQOL (Pediatric Cardiac Quality of Life Inventory (PCQLI)) and general HRQOL (Pediatric Quality of Life Inventory (PedsQL))) and a panel of potential SDOH. Potential SDOH measures were calculated at subject’s neighborhood level by linking their addresses to block group/census tract data. SDOH measures included Childhood Opportunity Index, which measures neighborhood resources supporting child development, and Social Deprivation Index, which measures neighborhoods’ levels of disadvantage. Linear regression was used to evaluate the association between SDOH and HRQOL (PCQLI and PedsQL total scores), adjusting for presence of cardiac symptoms (New York Heart Association Class). Results: In total, 111 subjects (age 12.8±2.9 years, of which 89% identified as White, 5% Black, and 8% Hispanic, and 27% Medicaid-insured) were studied. Primary cardiac diagnoses included single ventricle heart disease (19%), complex biventricular repairs (20%), and orthotopic heart transplantation (12%). The majority (69%) denied having symptoms. Out of a potential 100, subject-reported scores were 67±18 on the PCQLI and 74±19 on the PedsQL. Caregiver-proxy reported scores were 70±17 on the PCQLI and 71±19 on the PedsQL. Worse SDOH was consistently associated with lower HRQOL adjusted for presence of symptoms (Table 1). Conclusions: Worse SDOH was associated with worse HRQOL in pediatric cardiac patients independent of symptoms severity

Are Detailed, Patient-level Social Determinant of Health Factors Associated With Physical Function and Mental Health at Presentation Among New Patients With Orthopaedic Conditions?

Are Detailed, Patient-level Social Determinant of Health Factors Associated With Physical Function and Mental Health at Presentation Among New Patients With Orthopaedic Conditions?

Exploring the role of discretionary income as a social determinant of health.

The object of this study is to explore the impact of discretionary income on the overall well-being of Americans. The data source used for this study was 2017 Behavioral Risk Factor Surveillance System data comprising 12 states that used the Social Determinants of Health (SDOH) module. Multivariable logistic regression models were used to analyze the relationship between discretionary income and self-reported health status after adjusting for common SDOH measures, sociodemographic factors, chronic conditions, and perspectives and experiences of respondents regarding specific aspects of their health. Average marginal effects (AME) were reported. Not applicable. At all income levels, those with discretionary income at the end of the month were 6-7 percentage points more likely to report better than average health than those with none, controlling for other factors (AME: 0.07, 95% CI: 0.02-0.12). Our study suggests that discretionary income has a role to play in overall health and well-being that goes beyond that of disposable income and may be an important resource for diverse communities.

Abstract P212: A Community Intervention For Hypertension Control Using Health Worker Outreach And Algorithmic Software-driven Blood Pressure Management

Background: Hypertension (HTN) in black and brown communities remains under-diagnosed and poorly controlled. Obstacles include access, lack of trust, social determinants of health (SDOH) and insufficient licensed providers. We hypothesized that a multi-faceted solution, combining screening, identification and support to address SDOH, and a remote software-driven HTN management program that shifts work to care navigators would be effective in controlling HTN. Methods: We launched a 500-person pilot in Detroit, MI, in March 2022. Community Health Workers (CHW) screened for patients with HTN in the community and surveyed them for SDOH. Patients received a cellular-enabled blood pressure (BP) cuff. Medications were initiated, titrated and side effects monitored according to a software-driven algorithm implementing latest HTN guidelines. Patients were contacted by a care navigator or community health worker using a combination of phone, text message, and email. Medication and testing were ordered by a licensed provider. Program success was evaluated based on proportion of patients who reached BP targets and patient satisfaction surveys. Results: During the initial deployment, 135 patients were screened and 86 (64%) were found to have Stage 1 HTN or greater. A total of 48 patients consented to join the program and 25 received a confirmatory BP measurement and cellular BP device. The average age of enrolled patients was 58±11 years with 52% females (N = 13), with 100% identified as Black. The median BP was 149/91mmHg, (IQR 144-159/87-98 mmHg). HTN severity on enrollment was 4% Stage 1, 59% Stage 2, and 37% Stage 3. Sixty-five % of patients were already taking one or more anti-HTN agents. A mean of 0.8 dose changes were made per patient per week. Results of the initial 250 managed patients will be presented including the percentage that reached BP goals, the number of medications required, time to reach goal, as well as impact of SDOH measures on BP control. Conclusions: Screening directly in an urban community resulted in a higher than expected rate of hypertension and included a high rate of stage 3 disease. A community-centered remote software-driven HTN program was feasible to implement.

67-OR: Measures of Social Determinants of Health and Risk of Incident Type 2 Diabetes

Introduction: Social determinants of health (SDOH) impact well-being. We tested the association between incident diabetes risk and SDOH using 2 measures, the Area Deprivation Index (ADI) and Social Vulnerability Index (SVI) . Methods: We examined 2015-2021 medical and claims data from the UC Data Warehouse for overweight/obese adults (age&amp;gt;18 yrs; BMI&amp;gt;25 kg/m2; &amp;gt;23 if Asian) . We ran 4 cox proportional hazards regression models with an outcome variable of incident diabetes; 1) base model with age, sex, race, ethnicity, income, BMI, prediabetes and comorbidities, 2) base model + ADI, 3) base model + SVI, and 4) base model + ADI and + SVI. Since SDOH indices were missing for ∼17%, we added them using an interaction term with an indicator variable. Results: Among 36,946 adults, 33,587 (91%) had &amp;gt;1 follow-up assessment. Among these, mean age was 43 yrs, BMI 28 kg/m2, ADI 3.9 (SD 2.4) , SVI 0.34 (SD 0.24) ; 14% had prediabetes. Over 4.6 (SD 3.3) mean follow-up yrs, 715 (2%) developed diabetes. In the base model, incident diabetes risk significantly increased with each additional yr of age (HR 1.03) , unit BMI (HR 1.09) , prediabetes (HR 2.2) , and in Asian (HR 1.5) or other/multi-racial groups (HR 1.6) compared to Whites; risk decreased with hyperlipidemia (HR 0.72) . These associations were significant in all SDOH models (base +ADI, +SVI or +both) . In our SDOH models, incident diabetes risk was not associated with the degree of area deprivation or social vulnerability. However, diabetes risk decreased if ADI estimates (HR 0.54 95%CI 0.38, 0.77) or SVI estimates (HR 0.48 95%CI 0.36, 0.63) were present (vs. undetermined/unknown) . In the SDOH model with both ADI/SVI, diabetes risk decreased if SVI was present (HR 0.49 95%CI 0.34, 0.70) . Conclusion: In our real-world cohort of overweight/obese adults, the degree of area deprivation or social vulnerability was not associated with risk of incident diabetes when traditional risk factors were available. More studies of SDOH measures are needed to inform national diabetes prevention efforts. Disclosure K.Ramm: None. C.Mangione: n/a. T.Moin: None. N.Jackson: None. U.Chung: None. O.Duru: None. K.Shedd: None. S.Soetenga: None. T.B.Loeb: None. D.Elashoff: None. A.B.Hamilton: None. Funding NIH/NIDDK 1R01DK124503

Assessment of Structural Barriers and Racial Group Disparities of COVID-19 Mortality With Spatial Analysis

Although social determinants of health (SDOH) are important factors in health inequities, they have not been explicitly associated with COVID-19 mortality rates across racial and ethnic groups and rural, suburban, and urban contexts. To explore the spatial and racial disparities in county-level COVID-19 mortality rates during the first year of the pandemic. This cross-sectional study analyzed data for all US counties in 50 states and the District of Columbia for the first full year of the COVID-19 pandemic (January 22, 2020, to February 28, 2021). Counties with a high concentration of a single racial and ethnic population and a high level of COVID-19 mortality rate were identified as concentrated longitudinal-impact counties. The SDOH that may be associated with mortality rate across these counties and in urban, suburban, and rural contexts were examined. The 3 largest racial and ethnic groups in the US were selected: Black or African American, Hispanic or Latinx, and non-Hispanic White populations. County-level characteristics and community health factors (eg, income inequality, uninsured rate, primary care physicians, preventable hospital stays, severe housing problems rate, and access to broadband internet) associated with COVID-19 mortality. Data on county-level COVID-19 mortality rates (deaths per 100 000 population) reported by the US Centers for Disease Control and Prevention were analyzed. Four indexes were used to measure multiple dimensions of SDOH: socioeconomic advantage index, limited mobility index, urban core opportunity index, and mixed immigrant cohesion and accessibility index. Spatial regression models were used to examine the associations between SDOH and county-level COVID-19 mortality rate. Of the 3142 counties included in the study, 531 were identified as concentrated longitudinal-impact counties. Of these counties, 347 (11.0%) had a large Black or African American population compared with other counties, 198 (6.3%) had a large Hispanic or Latinx population compared with other counties, and 33 (1.1%) had a large non-Hispanic White population compared with other counties. A total of 489 254 COVID-19-related deaths were reported. Most concentrated longitudinal-impact counties with a large Black or African American population compared with other counties were spread across urban, suburban, and rural areas and experienced numerous disadvantages, including higher income inequality (297 of 347 [85.6%]) and more preventable hospital stays (281 of 347 [81.0%]). Most concentrated longitudinal-impact counties with a large Hispanic or Latinx population compared with other counties were located in urban areas (114 of 198 [57.6%]), and 130 (65.7%) of these counties had a high percentage of people who lacked health insurance. Most concentrated longitudinal-impact counties with a large non-Hispanic White population compared with other counties were in rural areas (23 of 33 [69.7%]), included a large group of older adults (26 of 33 [78.8%]), and had limited access to quality health care (24 of 33 [72.7%]). In urban areas, the mixed immigrant cohesion and accessibility index was inversely associated with COVID-19 mortality (coefficient [SE], -23.38 [6.06]; P < .001), indicating that mortality rates in urban areas were associated with immigrant communities with traditional family structures, multiple accessibility stressors, and housing overcrowding. Higher COVID-19 mortality rates were also associated with preventable hospital stays in rural areas (coefficient [SE], 0.008 [0.002]; P < .001) and higher socioeconomic status vulnerability in suburban areas (coefficient [SE], -21.60 [3.55]; P < .001). Across all community types, places with limited internet access had higher mortality rates, especially in urban areas (coefficient [SE], 5.83 [0.81]; P < .001). This cross-sectional study found an association between different SDOH measures and COVID-19 mortality that varied across racial and ethnic groups and community types. Future research is needed that explores the different dimensions and regional patterns of SDOH to address health inequity and guide policies and programs.

Evaluating a Tobacco Treatment Program’s Transition to Telehealth Using a Social Determinants of Health Lens

IntroductionWhile strong associations exist between social determinants of health (SDOH), socioeconomic status, and smoking, these factors are not routinely assessed in tobacco treatment programs (TTP). This study addresses this gap by evaluating a composite metric of SDOH and a measure of access to care to determine program reach before and after the implementation of telehealth tobacco treatment delivery.Aims and MethodsWe examined inpatient data from a large TTP during two comparable time periods from April 1, 2019 to September 30, 2019 (pre-telehealth) and from April 1, 2020 to September 30, 2020 (telehealth). The populations were compared using point-of-care data, including 5-digit zip codes mapped to the CDC’s Social Vulnerability Index (SVI) and driving distance (in 60-min increments) to the study hospital. Chi-square tests for homogeneity were performed for SVI and driving distance comparisons.ResultsWhile distance distributions were significantly different between the pre-telehealth and telehealth populations (χ 2 = 13.5 (df = 3, N = 3234), p = .004, no significant differences existed in the proportion of SVI categories between the two populations (χ 2 = 5.8 (df = 3, N = 3234), p = .12). In the telehealth population, patients with the highest SVI vulnerability had the greatest proportions living >1 h from the hospital.ConclusionsThis study offers a novel evaluation of tobacco treatment in relation to an SDOH metric (SVI) and care access (distance to the hospital) for inpatient populations. Patient reach, including to those with high vulnerabilities, remained consistent in a transition to telehealth. These methods can inform future reach and engagement of patients who use tobacco products, including patients with high vulnerability or who reside at greater distances from treatment programs.ImplicationsThis study provides the first analysis of inpatient tobacco use treatment (TUT) transition to telehealth delivery of care during the COVID-19 pandemic using the CDC’s SVI metric and patient distance to the hospital. The transition resulted in consistent reach to patients at the highest vulnerability. These findings can inform efforts to evaluate SDOH measures and improve reach, engagement, and research on telehealth delivery of inpatient TUT.

Social determinants of health and pediatric cancer survival: A systematic review.

Despite treatment advancements and improved survival, approximately 1800 children in the United Stateswill die of cancer annually. Survival may depend on nonclinical factors, such as economic stability, neighborhood and built environment, health and health care, social and community context, and education, otherwise known as social determinants of health (SDoH). Extant literature reviews have linked socioeconomic status (SES) and race to disparate outcomes; however, these are not inclusive of all SDoH. Thus, we conducted a systematic review on associations between SDoH and survival in pediatric cancer patients. Of the 854 identified studies, 25 were included in this review. In addition to SES, poverty and insurance coverage were associated with survival. More studies that include other SDoH, such as social and community factors, utilize prospective designs, and conduct analyses with more precise SDoH measures are needed.

Abstract PO-075: COVID-19 and social determinants of health (SDOH): Impact on cancer prevention in GENZ population (Ages 18-25)

Abstract Purpose: Employing a social determinants of health (SDOH) framework including race/ethnicity, socioeconomic status (SES) (education, income) and other barriers we: 1) Describe the impact of COVID-19 on young adults (GENZ) who live in CT; 2) Determine if COVID-19 vaccine hesitancy is related to other vaccine utilization (HPV and Flu) and other cancer prevention behaviors Background: As is well recognized from previous pandemics and epidemics, the burden of disease falls disproportionately on those individuals with fewest resources. It is now clear that the COVID-19 associated death and disease burden in minority and low socioeconomic communities is disproportionate to their numbers in the general population. In addition to the disproportionate acute impact of the COVID-19 on vulnerable communities, the long-term impact may be lost ground with respect to cancer prevention due to disruption, distrust and misinformation. Little is known about how the pandemic has impacted young adults and whether this has influenced behaviors that are key to cancer prevention. Methods: In May, 2021, we conducted a Qualtrics survey assessing all aspects of COVID-19 impact, with extensive SDOH measures, including everyday racism and medical mistrust, cancer prevention and screening, access to health care, and intentions regarding future vaccination uptake, adherence to COVID-19 preventive practices, lifestyle behaviors associated with cancer prevention, and cancer screening. Participants (n=232) are 18-25 year olds with a permanent address in Connecticut. We used social networks and social media to recruit participants. Analysis includes descriptive and multivariate adjusted logistic regression findings predicting maintenance of healthy lifestyle (primary cancer prevention) and cancer screening and associations with COVID-19 vaccine hesitancy. Results: In this sample of young adults, the average age was 21.2 (range 18-25), with 52% reporting their race/ethnicity as Hispanic/Latinx (33%) or African American/Black (19%); 53.3% were female, 46.2% were male, and .4% reported other. Descriptive data demonstrate high levels of SDOH and the impact of COVID-19 on many aspects of life for this study population. COVID-19 vaccine hesitancy was relatively low at 12.2%, with 57% of GENZ participants reporting that they had already vaccinated. We present predictors of healthy lifestyle behaviors and intention to adhere to cancer screening guidelines going forward and the relationship to history of and intent to vaccinate against COVID-19. Conclusion: The unique challenges of young adults during COVID-19 are not well documented. Findings will inform community level interventions in the event of continued COVID-19 (or similar) public health challenges, while identifying opportunities to advance cancer prevention long-term for these young adults. Citation Format: Beth A. Jones, Sakinah C. Suttiratana, Shua Kim, William Eger. COVID-19 and social determinants of health (SDOH): Impact on cancer prevention in GENZ population (Ages 18-25) [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-075.

Abstract 12554: A Quality Improvement Program to Promote Assessment of Social Determinants of Health among Patients in an Urban Community Clinic

Introduction: Social determinants of health (SDoH) have been linked to poorer health outcomes and health disparities among patients (pts) with heart failure (HF). Attainment rates of selected SDoH indicators vary depending on the pt care environment. We sought to assess whether there are challenges in collecting SDoH indicators in an urban community clinic through a Plan-Do-Study-Act (PDSA) method of quality improvement (QI) using a physician-medical assistant team (PMAT). Methods: Using PDSA QI methodology, a trained PMAT tested use of an EHR-based survey to measure and record selected SDoH at each pt-visit over a span of four weeks. The survey includes questions about financial resource strain, housing stability, transportation needs, food insecurity and alcohol use. The target for improvement was input of SDoH indicators for 90% of consecutive patient visits. Successful completion of the survey required recording of responses for all SDoH measures. Results: Prior to the PDSA, SDoH indicators were collected in 51% of the pts seen between Jan 2019 and Dec 2020. Between April 26 and May 21, 2021, 114 pts were seen by the PMAT, with the SDoH indicators recorded in 88% of the pt-visits (n=101). Weekly rates ranged from 76.9%-100% (Week 1, 30/30 pts (100%); Week 2, 24/29 pts (82.7%); Week 3, 27/29 pts (93.1%); and Week 4, 20/26 pts (76.9%). Conclusion: The PMAT successfully increased measurement and recording of SDoH factors, although the target goal for improvement was not completely met 2 of the 4 weeks. Personnel substitutions and variable approaches to accessing the survey tool in the EHR resulted in smaller rates of improvement. Nonetheless the PDSA demonstrated the feasibility of increasing SDoH measurement. Expansion of the QI program will focus on clinic-wide staff training, standardization of SDoH assessment tool access in the EHR, and linking patients to resources with demonstrated social and economic needs identified by the SDoH survey tool.