County-Level Enrollment in Medicare Advantage Plans Offering Expanded Supplemental Benefits.

Importance: Since 2019 and 2020, Medicare Advantage (MA) plans have been able to offer supplemental benefits that address long-term services and supports (LTSS) and social determinants of health (SDOH). Objective: To examine the temporal trends and geographic variation in enrollment in MA plans offering LTSS and SDOH benefits. Design, Setting, and Participants: This cross-sectional study used publicly available data to examine changes in beneficiary enrollment and plan offerings of LTSS and SDOH benefits from the benefits data from the second quarter of each year and other data from April of each year except 2024, for which the first quarter was the latest for benefits data and January the latest for other data at the time of analysis. Multivariable linear regression models for each type of benefit were used to investigate associations between county characteristics and enrollment in 2024. Analyses were stratified for (1) Dual Eligible Special Needs Plans (D-SNPs) that exclusively enroll dual-eligible beneficiaries and (2) non-D-SNPs. Main Outcomes and Measures: The percentage of MA enrollees in plans offering LTSS or SDOH benefits at the county level. Results: This study included 2 631 697 D-SNP and 20 114 506 non-D-SNP enrollees in 2020, which increased to 5 494 426 and 25 561 455, respectively, in 2024. From 2020 to 2024, the percentage of D-SNP enrollees in plans offering SDOH benefits increased from 9% to 46%, whereas the percentage fluctuated between 23% and 39% for LTSS benefits. There was an increase in non-D-SNP enrollees with LTSS (from 9% to 22%) and SDOH (from 4% to 20%) benefits from 2020 to 2023, which decreased in 2024. In 2024, the most offered LTSS benefit was in-home support services, and the most offered SDOH benefit was food and produce. The percentage of enrollees with these benefits varied across counties in 2024. In multivariable linear regression models, among D-SNPs, enrollment in plans offering any SDOH benefits was higher in counties with greater MA penetration (coefficient, 5.0 percentage points [pp] per 10-pp change; 95% CI, 2.1-7.9 pp), in urban counties (coefficient, 7.2 pp vs rural counties; 95% CI, 3.8-10.6 pp), in counties with greater enrollment in fully integrated D-SNPs (coefficient, 3.0 pp per 10-pp change; 95% CI, 2.2-3.9 pp), and in counties in states with approved Medicaid home- and community-based services waivers for individuals 65 years or older or those with disabilities (coefficient, 10.8 pp; 95% CI, 4.0-17.6 pp). Enrollment in D-SNPs offering LTSS benefits was also higher in counties with greater MA penetration (coefficient, 5.9 pp per 10-pp change; 95% CI, 2.4-9.5 pp), urban vs rural counties (coefficient, 4.6 pp; 95% CI, 1.1-8.1 pp), and counties with greater enrollment in fully integrated D-SNPs (coefficient, 3.0 pp per 10-pp change; 95% CI, 2.1-3.9 pp) in addition to counties with greater social vulnerability scores (coefficient, 1.4 pp per 10-pp change; 95% CI, 0.3-2.5 pp). Conclusions and Relevance: In this cross-sectional study of MA plans and enrollees, an increase in enrollment was most consistent in D-SNPs offering SDOH benefits compared with LTSS benefits and in D-SNPs compared with non-D-SNPs. Geographic variation in enrollment patterns highlights potential gaps in access to LTSS and SDOH benefits for rural MA beneficiaries and dual-eligible enrollees living in counties with lower enrollment in fully integrated D-SNPs and states with more limited Medicaid home- and community-based services coverage.

Health inequalities in a middle-income country: a systematic review of the Costa Rican case.

Objective: This study systematically reviews evidence of socioeconomic health disparities in Costa Rica, a middle-income country, to elucidate the relationship between socioeconomic status and health outcomes. Methods: Published studies were identified through a systematic review of PubMed (English) and Scielo (Spanish) databases from December 2023 to January 2024, following PRISMA guidelines. Search terms included socioeconomic status, social determinants, social gradient in health, and health inequalities. Results: Of 236 identified references, 55 met the inclusion criteria. Findings were categorized into health inequalities in mortality (among the general population, infants, and older adults), life expectancy, cause-specific mortality, and health determinants or risk factors mediating the association between the social environment and health. The studies indicate higher mortality among the most disadvantaged groups, including deaths from respiratory diseases, violence, and infections. Higher socioeconomic status was associated with lower mortality rates in the 1990s, indicating a positive social gradient in health (RII = 1.3, CI [1.1-1.5]). Disparities were less pronounced among older adults. Urban areas exhibited concentrated wealth and increased risky behaviors, while rural areas, despite greater socioeconomic deprivation, showed a lower prevalence of risky behaviors. Regarding smoking, people living in rural areas smoked significantly less than those in urban areas (7% vs. 10%). Despite the relatively equitable distribution of public primary healthcare, disparities persisted in the timely diagnosis and treatment of chronic diseases. Cancer survival rates post-diagnosis were positively correlated with the wealth of districts (1.23 [1.12-1.35] for all cancers combined). Conclusion: The study highlights the existence of social health inequalities in Costa Rica. However, despite being one of the most unequal OECD countries, Costa Rica shows relatively modest social gradients in health compared to other middle and high-income nations. This phenomenon can be attributed to distinctive social patterns in health behaviors and the equalizing influence of the universal healthcare system.

Social Risk and Clinical Outcomes Among Adults With Type 2 Diabetes.

Importance: No clear process exists for categorizing social risks in a way that informs effective social risk screening and intervention development. Objective: To investigate social risk profiles and associations of those profiles with clinical outcomes in adults with diabetes using latent profile analysis. Design, Setting, and Participants: For this cross-sectional study, a latent profile analysis was conducted using data for adults with type 2 diabetes collected at 2 primary care clinics in the Southeastern US from 2013 to 2014. Data were analyzed from November to December 2023. Main Outcomes and Measures: Participants completed validated questionnaires for 26 social risk factors within 5 domains of social determinants of health: socioeconomic, neighborhood, education, food, and social and community context. In addition, participants completed questions that assessed psychological risk and behavioral risk. A 3-step latent profile analysis was used to identify different subgroups within the sample. Profiles were then regressed on outcomes of hemoglobin A1c (HbA1c), blood pressure, and quality of life. Results: A total of 615 participants (mean [SD] age, 61.3 [10.9] years; 379 men [61.6%]) were included. Five latent class profiles were identified. The lowest risk group had significantly higher mental health-related quality of life compared with a group with higher neighborhood risk (ß, 1.11; 95% CI, 0.67 to 1.55). The second group had low economic risk but high neighborhood risk and served as the reference group. The third group had high economic and neighborhood risk and had significantly higher blood pressure (ß, 8.08; 95% CI, 2.16 to 14.01) compared with the reference. The fourth group had high psychological and behavioral risks but low socioeconomic and neighborhood risks. This group had significantly higher HbA1c (ß, 0.47; 95% CI, 0.01 to 0.92) and lower mental health-related quality of life (ß, -1.83; 95% CI, -2.41 to -1.24) compared with the reference. The highest risk group indicated high risk in all domains, had significantly higher HbA1c (ß, 1.07; 95% CI, 0.50 to 1.63), and had lower mental health-related quality of life (ß, -2.15; 95% CI, -2.87 to -1.42) compared with the reference. Conclusions and Relevance: These findings suggest that social risk profiles can be identified according to social, psychological, and behavioral risk domains and the health outcome of concern among adults with diabetes. Future work should consider the use of social risk profiles in intervention development and testing.

Examining rurality and social determinants of health among women with GDM: a 15-year comprehensive population analysis.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common pregnancy complication with long-term health consequences for mothers and their children. The escalating trends of GDM coupled with the growing prevalence of maternal obesity, a significant GDM risk factor projected to approach nearly 60% by 2030 in Kansas, has emerged as a pressing public health issue. METHODS: The aim of this study was to compare GDM and maternal obesity trends in rural and urban areas and investigate maternal demographic characteristics influencing the risk of GDM development over a 15-year period. Trend analyses and a binary logistic regression were employed utilizing 2005 to 2019 de-identified birth record vital statistics from the Kansas Department of Health and Environment (N = 589,605). RESULTS: Over the cumulative 15-year period, a higher prevalence of GDM was observed across age, race/ethnicity, education, and insurance source. Throughout this period, there was an increasing trend in both GDM and obese pre-pregnancy BMI age-adjusted prevalence, with noticeable rural-urban disparities. From 2005 to 2019, women, including Asians (OR: 2.73, 95% CI 2.58%-2.88%), American Indian or Alaskan Natives (OR: 1.58, 95%, CI 1.44-1.73%), Hispanics (OR: 1.42, 95% CI 1.37%-1.48%), women residing in rural areas (OR: 1.09, 95%, CI 1.06-1.12%), with advanced maternal age (35-39 years, OR: 4.83 95% CI 4.47%-5.22%; ≥40 years, OR: 6.36 95%, CI 5.80-6.98%), with lower educational status (less than high school, OR: 1.15, 95% CI 1.10%-1.20%; high school graduate, OR: 1.10, 95% CI 1.06%-1.13%), Medicaid users (OR: 1.10, 95% CI 1.06%-1.13%), or with an overweight (OR: 1.78, 95% CI 1.72%-1.84%) or obese (OR: 3.61, 95% CI 3.50%-3.72%) pre-pregnancy BMI were found to be at an increased risk of developing GDM. CONCLUSIONS: There are persistent rural-urban and racial/ethnic disparities present from 2005 to 2019 among pregnant women in Kansas with or at-risk of GDM. There are several socioeconomic factors that contribute to these health disparities affecting GDM development. These findings, alongside with prominent rising maternal obesity trends, highlight the need to expand GDM services in a predominantly rural state, and implement culturally-responsive interventions for at-risk women.

Racial Composition of Past and Current Social Environments and Health Literacy.

BACKGROUND: Research is needed to understand the impact of social determinants of health on health literacy throughout the life course. This study examined how racial composition of multiple past and current social environments was related to adults' health literacy. METHODS: In this study, 546 adult patients at a primary care clinic in St. Louis, Missouri, completed a self-administered written questionnaire that assessed demographic characteristics and a verbally administered component that assessed health literacy with the Rapid Estimate of Adult Literacy in Medicine - Revised (REALM-R) and Newest Vital Sign (NVS), and self-reported racial composition of six past and four current social environments. Multilevel logistic regression models were built to examine the relationships between racial composition of past and current social environments and health literacy. RESULTS: Most participants identified as Black or multiracial (61%), had a high school diploma or less (54%), and household income <$20,000 (72%). About 56% had adequate health literacy based on REALM-R and 38% based on NVS. In regression models, participants with multiple past white environments (e.g., locations/conditions in which most of the people who live, go to school, work, and have leisure time are White) and (vs. 0 or 1) were more likely to have adequate health literacy based on REALM-R (adjusted odds ratio [aOR] = 1.79; 95% confidence interval [CI]: 1.04-3.07). Similarly, participants who had multiple past white social environments were more likely (aOR = 1.94, 95% CI: 1.15-3.27) to have adequate health literacy based on NVS than those who had not. The racial composition of current social environments was not significantly associated with health literacy in either model. CONCLUSIONS: Racial composition of past, but not current, educational and residential social environments was significantly associated with adult health literacy. The results highlight the importance of examining the impact of social determinants over the life course on health literacy. The findings suggest that policies ensuring equitable access to educational resources in school and community contexts is critical to improving equitable health literacy. [HLRP: Health Literacy Research and Practice. 2024;8(3):e130-e139.].

PLAIN LANGUAGE SUMMARY: We studied how the racial make-up of past and current places where people live, work, and go to school were related to their health literacy as adults. We found that the racial make-up of past places, but not current places, was related to health literacy. Our results show the need to study the impact of childhood places on health literacy.

Examining Diabetes Status by the Social Determinants of Health Among Adults in Hawai'i.

The social determinants of health (SDoH) influence health outcomes based on conditions from birth, growth, living, and age factors. Diabetes is a chronic condition, impacted by race, education, and income, which may lead to serious health consequences. In Hawai'i, approximately 11.2% of adults have been diagnosed with diabetes. The objective of this secondary cross-sectional study is to assess the relationship between the prevalence of diabetes and the social determinants of health among Hawai'i adults who participated in the Behavioral Risk Factor Surveillance System between 2018-2020. The prevalence of diabetes among adults was 11.0% (CI: 10.4-11.5%). Filipino, Japanese and Native Hawaiian adults had the highest prevalence of diabetes at 14.4% (CI: 12.7-16.2%), 14.2% (CI: 12.7-15.7%), and 13.2% (CI: 12.0-14.4%), respectively. Poverty level and education were significantly associated with diabetes status. Within employment categories, the adjusted odds ratio (AOR) for retired and unable to work adults were large at AOR: 1.51 (CI: 1.26-1.81) and AOR: 2.91 (CI: 2.28-3.72), respectively. SDoH can impact the development and management of diabetes. Understanding the role SDoH plays on diabetes status is crucial for promoting health equity, building community capacity, and improving diabetes management.

Social determinants and pre-arrest care patterns associated with cardiac arrest and mortality.

BACKGROUND: Understanding the impact of social determinants of health (SDOH) on CA, including access to care pre-cardiac arrest (CA) can improve outcomes. Large databases, such as Epic Cosmos, can help identify trends in patient demographics and SDOH that identify gaps in care. The purpose of this study was to determine the incidence of CA and subsequent mortality in a large national database across patient demographics and social determinants and characterize pre-arrest care patterns. METHODS: This was a retrospective cohort study using a large national deidentified electronic health database (Epic Cosmos) with 227 million patients. Inclusion criteria was ED encounter for CA (ICD-10-CM: I46). Patient demographics and social determinants included age, sex, race, ethnicity, social vulnerability index (SVI, a composite measure with greater SVI representing more vulnerability). The primary outcome was difference in CA incidence between groups, reported as odds ratios (ORs). The secondary outcomes were 1) incidence of pre-arrest care within 30 days and 2) post-arrest mortality at 7,30, and 180 days. Statistical analysis was performed using Chi-squared analysis (unadjusted OR) and aggregated logistic procedure (adjusted OR). RESULTS: There were 201,846 ED visits for CA between April 20, 2020, and April 19, 2023 (0.11% incidence). For all ages, males had a higher incidence of CA (OR 1.76, p < 0.0001). Black, Native Hawaiian or Pacific Islander, and American Indian or Alaska Native had a higher OR of CA while Asian patients were less likely than White patients (adjusted OR 1.85, 1.44,1.51, and 0.81 respectively, all p < 0.0001). Hispanic/Latino patients had a lower OR of CA (adjusted OR 0.72, p < 0.0001). CA was more common in the highest SVI quartile compared to the lowest (adjusted OR 1.71, p < 0.0001). Significant heterogeneities were identified in pre-arrest care across patient demographics and social determinants, where ED visits were more common than office visits among male patients, patients in the highest SVI, Hispanic/Latino, and minority patients, except for Asian patients. Post-arrest mortality after 30 days was highest in females, Black patients, and patients in the highest SVI. CONCLUSIONS: SDOH have a significant impact on the risk of CA, pre-arrest care patterns, and post-arrest mortality. Determining the impact that SDOH have on the CA care continuum provides can provide actionable targets to prevent CA and subsequent mortality.

Race and Ethnicity, Socioeconomic Factors, and Epigenetic Age Acceleration in Survivors of Childhood Cancer.

Importance: Current research in epigenetic age acceleration (EAA) is limited to non-Hispanic White individuals. It is imperative to improve inclusivity by considering racial and ethnic minorities in EAA research. Objective: To compare non-Hispanic Black with non-Hispanic White survivors of childhood cancer by examining the associations of EAA with cancer treatment exposures, potential racial and ethnic disparity in EAA, and mediating roles of social determinants of health (SDOH). Design, Setting, and Participants: In this cross-sectional study, participants were from the St Jude Lifetime Cohort, which was initiated in 2007 with ongoing follow-up. Eligible participants included non-Hispanic Black and non-Hispanic White survivors of childhood cancer treated at St Jude Children's Research Hospital between 1962 and 2012 who had DNA methylation data. Data analysis was conducted from February 2023 to May 2024. Exposure: Three treatment exposures for childhood cancer (chest radiotherapy, alkylating agents, and epipodophyllotoxin). Main Outcomes and Measures: DNA methylation was generated from peripheral blood mononuclear cell-derived DNA. EAA was calculated as residuals from regressing Levine or Horvath epigenetic age on chronological age. SDOH included educational attainment, annual personal income, and the socioeconomic area deprivation index (ADI). General linear models evaluated cross-sectional associations of EAA with race and ethnicity (non-Hispanic Black and non-Hispanic White) and/or SDOH, adjusting for sex, body mass index, smoking, and cancer treatments. Adjusted least square means (ALSM) of EAA were calculated for group comparisons. Mediation analysis treated SDOH as mediators with average causal mediation effect (ACME) calculated for the association of EAA with race and ethnicity. Results: Among a total of 1706 survivors including 230 non-Hispanic Black survivors (median [IQR] age at diagnosis, 9.5 [4.3-14.3] years; 103 male [44.8%] and 127 female [55.2%]) and 1476 non-Hispanic White survivors (median [IQR] age at diagnosis, 9.3 [3.9-14.6] years; 766 male [51.9%] and 710 female [48.1%]), EAA was significantly greater among non-Hispanic Black survivors (ALSM = 1.41; 95% CI, 0.66 to 2.16) than non-Hispanic White survivors (ALSM = 0.47; 95% CI, 0.12 to 0.81). Among non-Hispanic Black survivors, EAA was significantly increased among those exposed to chest radiotherapy (ALSM = 2.82; 95% CI, 1.37 to 4.26) vs those unexposed (ALSM = 0.46; 95% CI, -0.60 to 1.51), among those exposed to alkylating agents (ALSM = 2.33; 95% CI, 1.21 to 3.45) vs those unexposed (ALSM = 0.95; 95% CI, -0.38 to 2.27), and among those exposed to epipodophyllotoxins (ALSM = 2.83; 95% CI, 1.27 to 4.40) vs those unexposed (ALSM = 0.44; 95% CI, -0.52 to 1.40). The association of EAA with epipodophyllotoxins differed by race and ethnicity (ß for non-Hispanic Black survivors, 2.39 years; 95% CI, 0.74 to 4.04 years; ß for non-Hispanic White survivors, 0.68; 95% CI, 0.05 to 1.31 years) and the difference was significant (1.77 years; 95% CI, 0.01 to 3.53 years; P for interaction = .049). Racial and ethnic disparities in EAA were mediated by educational attainment (

Preparing to Address Social Determinants of Health (SDOH): Approaches to Clinic Transformation.

CONTEXT: Pennsylvanians' health is influenced by numerous social determinants of health (SDOH). Integrating SDOH data into electronic health records (EHRs) is critical to identifying health disparities, informing public health policies, and devising interventions. Nevertheless, challenges remain in its implementation within clinical settings. In 2018, the Pennsylvania Department of Health (PADOH) received the Centers for Disease Control and Prevention's DP18-1815 "Improving the Health of Americans Through Prevention and Management of Diabetes and Heart Disease and Stroke" grant to strengthen SDOH data integration in Pennsylvania practices. IMPLEMENTATION: Quality Insights was contracted by PADOH to provide training tailored to each practice's readiness, an International Classification of Diseases, Tenth Revision (ICD-10) guide for SDOH, Continuing Medical Education on SDOH topics, and introduced the PRAPARE toolkit to streamline SDOH data integration and address disparities. Dissemination efforts included a podcast highlighting success stories and lessons learned from practices. From 2019 to 2022, Quality Insights and the University of Pittsburgh Evaluation Institute for Public Health (Pitt evaluation team) executed a mixed-methods evaluation. FINDINGS: During 2019-2022, Quality Insights supported 100 Pennsylvania practices in integrating SDOH data into EHR systems. Before COVID-19, 82.8% actively collected SDOH data, predominantly using PRAPARE tool (62.7%) and SDOH ICD-10 codes (80.4%). Amidst COVID-19, these statistics shifted to 65.1%, 45.2%, and 42.7%, respectively. Notably, the pandemic highlighted the importance of SDOH assessment and catalyzed some practices' utilization of SDOH data. Progress was evident among practices, with additional contribution to other DP18-1815 objectives. The main challenge was the variable understanding, utilization, and capability of handling SDOH data across practices. Effective strategies involved adaptable EHR systems, persistent efforts by Quality Insights, and the presence of change champions within practices. DISCUSSION: The COVID-19 pandemic strained staffing in many practices, impeding SDOH data integration into EHRs. Addressing the diverse understanding and use of SDOH data requires standardized training and procedures. Customized support and sustained engagement by facilitating organizations are paramount in ensuring practices' efficient SDOH data collection and integration.

Social transition, socioeconomic status and self-rated health in China: evidence from a national cross-sectional survey (CGSS).

Background: Social transition is one of the multi-level mechanisms that influence health disparities. However, it has received less attention as one of the non-traditional social determinants of health. A few studies have examined China's social transition and its impact on health inequality in self-rated health (SRH). Therefore, this study explores the impact of China's market-oriented reforms-social transition and socioeconomic status (SES)-on residents' SRH. Methods: Using the cross-sectional data from the Chinese General Social Survey (CGSS) in 2017, we analyzed the effects of social transition and SES on the SRH of Chinese residents using the RIF (Recentered influence function) method. The RIF decomposition method investigated health differences among different populations and their determinants. Results: Social transition and SES have significant positive effects on the SRH of Chinese residents. The correlation between SES and the SRH of Chinese residents is moderated by social transition, implying that social transition can weaken the correlation between SES and the SRH of Chinese residents. The impacts of SES and social transition on SRH vary across populations. Conclusion: Promoting social transition and favoring disadvantaged groups with more resources are urgently needed to promote equitable health outcomes.

Social Determinants of Health and Psychological Capital Among Youth Experiencing Homelessness.

BACKGROUND: Social determinants of health affect health behaviors and outcomes. Youth experiencing homelessness suffer significant deprivation of resources such as inadequate housing, reduced education, poor health care, and decreased economic stability. Inner resources, such as psychological capital, may also be related to health behaviors and health outcomes. OBJECTIVE: In this study, we sought to describe and explore associations among selected determinants of health and self-reported scores on indicators of psychological capital among youth experiencing homelessness. METHODS: This cross-sectional secondary analysis was conducted with a randomized subsample of 148 youth. We calculated chi-square frequencies to describe the data, classical item analyses to evaluate responses, and correlation tests to examine significance of associations. RESULTS: Youth in this sample demonstrated that they possess inner resources associated with determinants of health. Education, health care, and social support were significantly associated with attributes of psychological capital (hope, efficacy, resilience, optimism). Sexual minority groups had high representation in this subsample (25.7%), indicating a need for more study and equitable services for this population. CONCLUSION: More research should be conducted to better understand the associations between determinants of health, psychological capital, and health behaviors among disadvantaged youth to advance health equity initiatives.

Impacts of social determinants of health on chronic opioid therapy for chronic non-cancer pain.

Aim: We aimed to investigate the association between social determinants of health and chronic opioid therapy.Materials & methods: We conducted a retrospective analysis of electronic health records from five family medicine and internal medicine clinics in Oregon in 2020 and 2021. Our outcome variable was whether a patient was receiving chronic opioid therapy for chronic non-cancer pain. Our variables of interest included financial difficulty, insurance types, transportation barriers, currently married or living with a partner and organizations participation.Results: Our results showed that patients with financial difficulty were more likely to have chronic opioid therapy (OR: 2.69; 95% CI: 1.14, 6.33).Conclusion: Addressing patients' social determinants of health disadvantages is important for optimizing pain management.

What is this article about? Addressing the opioid crisis is a national priority in the USA. Our objective was to focus on a broad set of social determinants of health (SDOH) and examine whether patients with SDOH disadvantages were more likely to receive chronic opioid therapy for chronic non-cancer pain. Current literature has not assessed some important SDOH characteristics. We aimed to address this limitation by using electronic health records that incorporated SDOH data.What were the results? Patients with financial difficulty in this study had approximately two-times higher odds of receiving chronic opioid therapy.What do the results of the study mean? Our study has important clinical and policy implications. Clinicians should screen for patient SDOH disadvantages and provide support as an integral part of patient-centered pain management. Payers and policymakers should also consider expanding coverage and reimbursement for multimodal treatments for pain.

Syphilis Has Surged for Reasons That Go Beyond the Pathogen That Causes It.

This Medical News story discusses the multiple factors contributing to the resurgence of syphilis in the US.

Prediabetes Prevalence by Adverse Social Determinants of Health in Adolescents.

Importance: Several clinical practice guidelines advise race- and ethnicity-based screening for youth-onset type 2 diabetes (T2D) due to a higher prevalence among American Indian and Alaska Native, Asian, Black, and Hispanic youths compared with White youths. However, rather than a biological risk, this disparity likely reflects the inequitable distribution of adverse social determinants of health (SDOH), a product of interpersonal and structural racism. Objective: To evaluate prediabetes prevalence by presence or absence of adverse SDOH in adolescents eligible for T2D screening based on weight status. Design, Setting, and Participants: This cross-sectional study and analysis used data from the 2011 to 2018 cycles of the National Health and Nutrition Examination Survey. Data were analyzed from June 1, 2023, to April 5, 2024. Participants included youths aged 12 to 18 years with body mass index (BMI) at or above the 85th percentile without known diabetes. Main Outcomes and Measures: The main outcome consisted of an elevated hemoglobin A1c (HbA1c) level greater than or equal to 5.7% (prediabetes or undiagnosed presumed T2D). Independent variables included race, ethnicity, and adverse SDOH (food insecurity, nonprivate health insurance, and household income <130% of federal poverty level). Survey-weighted logistic regression was used to adjust for confounders of age, sex, and BMI z score and to determine adjusted marginal prediabetes prevalence by race, ethnicity, and adverse SDOH. Results: The sample included 1563 individuals representing 10 178 400 US youths aged 12 to 18 years (mean age, 15.5 [95% CI, 15.3-15.6] years; 50.5% [95% CI, 47.1%-53.9%] female; Asian, 3.0% [95% CI, 2.2%-3.9%]; Black, 14.9% [95% CI, 11.6%-19.1%]; Mexican American, 18.8% [95% CI, 15.4%-22.9%]; Other Hispanic, 8.1% [95% CI, 6.5%-10.1%]; White, 49.1% [95% CI, 43.2%-55.0%]; and >1 or other race, 6.1% [95% CI, 4.6%-8.0%]). Food insecurity (4.1% [95% CI, 0.7%-7.5%]), public insurance (5.3% [95% CI, 1.6%-9.1%]), and low income (5.7% [95% CI, 3.0%-8.3%]) were each independently associated with higher prediabetes prevalence after adjustment for race, ethnicity, and BMI z score. While Asian, Black, and Hispanic youths had higher prediabetes prevalence overall, increasing number of adverse SDOH was associated with higher prevalence among White youths (8.3% [95% CI, 4.9%-11.8%] for 3 vs 0.6% [95% CI, -0.7% to 2.0%] for 0 adverse SDOH). Conclusions and Relevance: Adverse SDOH were associated with higher prediabetes prevalence, across and within racial and ethnic categories. Consideration of adverse SDOH may offer a more actionable alternative to race- and ethnicity-based screening to evaluate T2D risk in youth.

Incidence of Traumatic Brain Injury in a Longitudinal Cohort of Older Adults.

Importance: Traumatic brain injury (TBI) occurs at the highest rate in older adulthood and increases risk for cognitive impairment and dementia. Objectives: To update existing TBI surveillance data to capture nonhospital settings and to explore how social determinants of health (SDOH) are associated with TBI incidence among older adults. Design, Setting, and Participants: This nationally representative longitudinal cohort study assessed participants for 18 years, from August 2000 through December 2018, using data from the Health and Retirement Study (HRS) and linked Medicare claims dates. Analyses were completed August 9 through December 12, 2022. Participants were 65 years of age or older in the HRS with survey data linked to Medicare without a TBI prior to HRS enrollment. They were community dwelling at enrollment but were retained in HRS if they were later institutionalized. Exposures: Baseline demographic, cognitive, medical, and SDOH information from HRS. Main Outcomes and Measures: Incident TBI was defined using inpatient and outpatient International Classification of Diseases, Ninth or Tenth Revision, diagnosis codes received the same day or within 1 day as the emergency department (ED) visit code and the computed tomography (CT) or magnetic resonance imaging (MRI) code, after baseline HRS interview. A cohort with TBI codes but no ED visit or CT or MRI scan was derived to capture diagnoses in nonhospital settings. Descriptive statistics and bivariate associations of TBI with demographic and SDOH characteristics used sample weights. Fine-Gray regression models estimated associations between covariates and TBI, with death as a competing risk. Imputation considering outcome and complex survey design was performed by race and ethnicity, sex, education level, and Area Deprivation Index percentiles 1, 50, and 100. Other exposure variables were fixed at their weighted means. Results: Among 9239 eligible respondents, 5258 (57.7%) were female and 1210 (9.1%) were Black, 574 (4.7%) were Hispanic, and 7297 (84.4%) were White. Mean (SD) baseline age was 75.2 (8.0) years. During follow-up (18 years), 797 (8.9%) of respondents received an incident TBI diagnosis with an ED visit and a CT code within 1 day, 964 (10.2%) received an incident TBI diagnosis and an ED code, and 1148 (12.9%) received a TBI code with or without an ED visit and CT scan code. Compared with respondents without incident TBI, respondents with TBI were more likely to be female (absolute difference, 7.0 [95% CI, 3.3-10.8]; P < .001) and White (absolute difference, 5.1 [95% CI, 2.8-7.4]; P < .001), have normal cognition (vs cognitive impairment or dementia; absolute difference, 6.1 [95% CI, 2.8-9.3]; P = .001), higher education (absolute difference, 3.8 [95% CI, 0.9-6.7]; P < .001), and wealth (absolute difference, 6.5 [95% CI, 2.3-10.7]; P = .01), and be without baseline lung disease (absolute difference, 5.1 [95% CI, 3.0-7.2]; P < .001) or functional impairment (absolute difference, 3.3 [95% CI, 0.4-6.1]; P = .03). In adjusted multivariate models, lower education (subdistribution hazard ratio [SHR], 0.73 [95% CI, 0.57-0.94]; P = .01), Black race (SHR, 0.61 [95% CI, 0.46-0.80]; P < .001), area deprivation index national rank (SHR 1.00 [95% CI 0.99-1.00]; P = .009), and male sex (SHR, 0.73 [95% CI, 0.56-0.94]; P = .02) were associated with membership in the group without TBI. Sensitivity analyses using a broader definition of TBI yielded similar results. Conclusions and Relevance: In this longitudinal cohort study of older adults, almost 13% experienced incident TBI during the 18-year study period. For older adults who seek care for TBI, race and ethnicity, sex, and SDOH factors may be associated with incidence of TBI, seeking medical attention for TBI in older adulthood, or both.

Cumulative lifetime violence, social determinants of health, and cannabis use disorder post-cannabis legalization in a community sample of men: An intersectional perspective.

Despite Canada having the highest disease burden globally for cannabis use disorder (CUD) and violence being ubiquitous in men's lives, little is known about how intersections among social determinants of health (SDOH) and cumulative lifetime violence severity (CLVS) influence CUD in men post-cannabis legalization. Using data collected in a survey with a national community sample of 597 men who self-identified as having experienced violence, we conducted a latent profile analysis using 11 subscales of the CLVS-44 scale and explored differential associations between CLVS profiles and CUD considering SDOH covariates. Four profiles were distinguished by intersections among CLVS-44 subscale severity and roles as target and perpetrator. CLVS profiles were significantly associated with CUD in the unadjusted model and in the adjusted model where age, adverse housing, and education were significant covariate controls. In the adjusted model, CUD was differentially associated with CLVS profiles and significantly higher in Profile 4 (highest severity target and perpetrator) than in Profile 1 (lowest severity target, no perpetration). Chi-square tests showed significant intersection between adverse housing, younger age, Profile 4 CLVS, and moderate to severe CUD among cannabis users. These results reveal the importance of understanding simultaneous intersections among indicators of CLVS in determining profiles of lifetime violence. Also critical are intersections among CLVS profiles and significant covariates as a basis for trauma- and violence-informed care for CUD that prioritizes men most disadvantaged by this convergence and attends to individual and structural health disparities at practice and policy levels.

Assessing the Impact of Social Determinants of Health on HIV Care Engagement in the Southern United States: A Cross-Sectional Study.

Recent studies have shown social determinants of health (SDOH) to impact HIV care engagement. This cross-sectional study (Oct 20-Apr 21) assessed the impact of a range of SDOH on HIV care engagement using data from HIV Care Connect, a consortium of three HIV care facility-led programs (Alabama, Florida, Mississippi). The exposures were captured using the PRAPARE (Protocol for Responding to and Assessing Patient Assets, Risks, and Experiences) scale. The outcome was captured using the Index of Engagement in HIV Care scale. Participants (n = 132) were predominantly non-White (87%) and male (52%) with a median age of 41 years. Multivariable logistic regression adjusted for various sociodemographics showed lower HIV care engagement to be associated with being uninsured/publicly insured, having 1-3 unmet needs, socially integrating ≤five times/week, and having stable housing. Factors such as unmet needs, un-/underinsurance, and social integration may be addressed by healthcare and community organizations.

Assessing How Social Drivers of Health Affect Engagement in HIV Care in the Southern United StatesIt has been found that social factors that have a direct impact on health affect engagement in HIV Care among people living with HIV. We included various social drivers of health to see how they affect engagement in HIV Care. We used data between October 2020 and April 2021 from a project titled HIV Care Connect, which is a group of three facilities providing HIV care in Alabama, Florida, and Mississippi. We used social drivers of health as risk factors from a scale called PRAPARE (Protocol for Responding to and Assessing Patient Assets, Risks, and Experiences). Engagement in HIV care was measured by using a scale called Index of Engagement in HIV Care. A total of 132 participants were included. Majority of the participants were of races other than white (87%), male (52%) and were aged 41 years on average. Statistical analysis showed that participants without insurance or with public insurance, participants with 1-3 unsatisfied needs, participants that met with other people less than or equal to five times a week, and participants that had reliable housing had lower engagement in HIV care. These factors have a potential to be addressed by healthcare and community organizations.

Analysis of Social Determinants of Health in the Pediatric General Surgical Population of a Northeastern Urban Health System.

BACKGROUND: Social determinants of health (SDOH) have been identified as factors that influence patient health outcomes. These are well described in adult and pediatric general populations, however, there is a paucity of data for surgical patients. This study compares the prevalence of health-related social needs (HRSN) among pediatric surgery and general pediatric patients. METHODS: We retrospectively assessed electronic health record data to identify patients who completed a standardized HRSN screener within our health system and underwent surgery by a pediatric surgeon between January 2019 and December 2021. We compared this population to non-surgical pediatric patients during this time. Bivariate and multivariate logistic regressions were conducted to estimate the likelihood of having 1 or more HRSN given a patient's surgery status. Logistic and linear regressions were conducted to estimate healthcare utilization in pediatric surgery patients given their HRSN status. RESULTS: 33,989 general pediatric and pediatric surgery patients (age <21 years) were screened for HRSNs, and 2112 operations were performed during the study period. 343 (20%) of the surgical patients operated on by pediatric surgeons were screened for HRSNs. Surgical patients were more likely to be younger, Latinx, Spanish-speaking, and non-commercially insured (p < 0.0001). Surgical patients were 50% more likely to report one or more HRSN, when adjusting for demographic characteristics (aOR 1.50, 95% CI 1.16, 1.94). CONCLUSION: Pediatric surgery patients are more likely to report HRSNs compared to the general pediatric population. Surgical patients may represent an at-risk group, and universal HRSN screening and support should be considered to improve outcomes. LEVEL OF EVIDENCE: Level III.

Communication and Social Determinants of Cancer Preventive Behaviors in Adolescents From Low Socio-Economic Backgrounds in India: A Cross-Sectional Study.

PURPOSE: Promoting cancer preventive behaviors among adolescents, especially those from lower socioeconomic backgrounds, is crucial due to the significant impact of health behaviors in adolescence on disease risk in adulthood. With India witnessing a rise in cancer incidence and mortality, adolescence becomes a pivotal stage for establishing healthy habits, emphasizing the need for early cancer prevention efforts. METHODS: This cross-sectional study used survey data from 2242 adolescents attending public schools of Mumbai, India. Multiple logistic regression was conducted to determine the associations between cancer preventive behaviors and: (1) the individual and social determinants of health, and (2) media exposure. FINDINGS: Merely 21.5% of the adolescents ate fruits and vegetables daily, 50% of the adolescents exercised 3 or more times a week, and 20% of the adolescents admitted having used tobacco and/or supari. Girls were found to have lower odds of exercising, as well as using tobacco and/or supari. Wealth and father's education were positively associated with all 3 cancer preventive behaviors. Media exposure was negatively associated, with television exposure linked to reduced fruits and vegetables consumption, while movies and social media exposure were associated with increased tobacco and/or supari use. INTERPRETATION: Our findings suggest that individual and social determinants of health and media exposure can influence cancer preventive health behaviors in low socio-economic status (SES) adolescents. Efforts to increase awareness to promote cancer preventive behaviors among the adolescents, particularly low SES adolescents, a population more vulnerable to poor health outcomes, is critical.

This study investigates factors that can influence cancer preventive behaviors among low socioeconomic status (SES) adolescents, focusing on dietary habits, physical activity, and avoidance of tobacco and areca nut. Our study gathered data from an underrepresented population of India, which is more vulnerable to poor health outcomes and have less access to health care. Our findings can alert public health officials, policy makers and non-governmental organizations to target this population and customize their intervention strategies to promote health and prevent cancer.

Multidimensional Approach to Exploring Neighborhood Determinants and Symptom Severity Among Individuals With Psychosis.

Importance: The impact of cumulative exposure to neighborhood factors on psychosis, depression, and anxiety symptom severity prior to specialized services for psychosis is unknown. Objective: To identify latent neighborhood profiles based on unique combinations of social, economic, and environmental factors, and validate profiles by examining differences in symptom severity among individuals with first episode psychosis (FEP). Design, Setting, and Participants: This cohort study used neighborhood demographic data and health outcome data for US individuals with FEP receiving services between January 2017 and August 2022. Eligible participants were between ages 14 and 40 years and enrolled in a state-level coordinated specialty care network. A 2-step approach was used to characterize neighborhood profiles using census-tract data and link profiles to mental health outcomes. Data were analyzed March 2023 through October 2023. Exposures: Economic and social determinants of health; housing conditions; land use; urbanization; walkability; access to transportation, outdoor space, groceries, and health care; health outcomes; and environmental exposure. Main Outcomes and Measures: Outcomes were Community Assessment of Psychic Experiences 15-item, Patient Health Questionnaire 9-item, and Generalized Anxiety Disorder 7-item scale. Results: The total sample included 225 individuals aged 14 to 36 years (mean [SD] age, 20.7 [4.0] years; 152 men [69.1%]; 9 American Indian or Alaska Native [4.2%], 13 Asian or Pacific Islander [6.0%], 19 Black [8.9%], 118 White [55.1%]; 55 Hispanic ethnicity [26.2%]). Of the 3 distinct profiles identified, nearly half of participants (112 residents [49.8%]) lived in urban high-risk neighborhoods, 56 (24.9%) in urban low-risk neighborhoods, and 57 (25.3%) in rural neighborhoods. After controlling for individual characteristics, compared with individuals residing in rural neighborhoods, individuals residing in urban high-risk (mean estimate [SE], 0.17 [0.07]; P = .01) and urban low-risk neighborhoods (mean estimate [SE], 0.25 [0.12]; P = .04) presented with more severe psychotic symptoms. Individuals in urban high-risk neighborhoods reported more severe depression (mean estimate [SE], 1.97 [0.79]; P = .01) and anxiety (mean estimate [SE], 1.12 [0.53]; P = .04) than those in rural neighborhoods. Conclusions and Relevance: This study found that in a cohort of individuals with FEP, baseline psychosis, depression, and anxiety symptom severity differed by distinct multidimensional neighborhood profiles that were associated with where individuals reside. Exploring the cumulative effect of neighborhood factors improves our understanding of social, economic, and environmental impacts on symptoms and psychosis risk which could potentially impact treatment outcomes.