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

Recent Patterns of Care with BTK Inhibitors and Distribution of Social Determinants of Health Among Patients with CLL/SLL in the US Community Setting

Rationale: Social determinants of health (SDOH) are infrequently captured by electronic health records (EHRs) yet are associated with many health conditions, including asthma. Some research studies attempt to overcome this limitation by assigning to individual people area-level SDOH measures such as the Area Deprivation Index (ADI), a metric reported by the Neighborhood Atlas. However, neighborhood characteristics do not necessarily reflect the characteristics of people living within them, and individual-level and neighborhood-level SDOH may have distinct and independent associations with asthma which are not well-characterized. Methods: We invited asthma patients from a large academic health system to complete the PADES survey, a 52-question IRB-approved online questionnaire which assessed participants’ demographics, asthma history, and SDOH. Individual-level SDOH were summarized using 9 variables: health insurance type, education, exposure to violence, housing insecurity, food insecurity, transportation insecurity, energy insecurity (i.e. lack of access to electricity/gas), and communication insecurity (i.e. lack of access to telephone/cellphone service). Neighborhood-level SDOH were captured using ADI in the Census block corresponding to participants’ residential address. Logistic regression models were fit with self-reported asthma-related emergency department (ED) visits in the last year as a binary outcome. We checked for multicollinearity by computing Pearson's correlation coefficients and variance inflation factors (VIF) for all independent variables. Results and Conclusions: 26.6% of all participants considered in analysis (N = 862) reported an asthma-related ED visit within the last year. Except for some college and Medicare health insurance, most individual-level and neighborhood-level SDOH were significantly (p < 10-4) positively associated with ED visits in bivariate models (bachelor's and advanced degree showed a significant negative association). In a multivariable model containing individual-level variables only, Medicaid health insurance, housing insecurity, and food insecurity were the strongest positive predictors, and the strongest negative predictor was bachelor's degree (p < 0.01). Additionally adjusting for ADI further reduced the effect size (p < 0.05) compared with the individual SDOH-adjusted multivariable model for these variables and compared with bivariate models for ADI. This attenuation may be attributable to moderate correlation (0.3<ρ<0.5) between several variables, although the VIF suggested that all variables could be included in multivariable models. Our results suggest that individual-level and neighborhood-level SDOH measures contribute to asthma exacerbation risk and demonstrate the importance of collecting SDOH data at multiple scales to monitor complex conditions such as asthma.

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.

We suggest using the ADI as the geographically based SDoH index in orthopaedic surgery in the United States. Further, we caution against comparing findings in one study that use one geographically based SDoH index to another study's findings that incorporates another geographically based SDoH index. Although the general findings may be the same, the strength of association and clinical relevance could differ and have policy ramifications that are not otherwise appreciated; however, the degree to which this may be true is an area for future inquiry.

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

Neighborhood-Level Social Determinants of Health and Cardiovascular Burden in Adolescent and Young Adult Patients Treated for Hodgkin Lymphoma

IntroductionIndividual‐ and neighborhood‐level social determinants of health (SDOH) have been assessed separately in pregnancy, but their relationship to one another remains uncertain. We investigated the intersectionality of three neighborhood‐level SDOH measures with three individual‐level SDOH measures. This was done to examine the concomitant experiences of multiple SDOH in pregnancy.MethodsA secondary analysis of data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers‐To‐Be. We assessed three neighborhood‐level SDOH measures using geocoded participant home addresses in the first trimester at the census‐tract level: (1) high socioeconomic disadvantage (in tertiles) by the 2015 Area Deprivation Index, (2) inadequate food access by the USDA Food Access Research Atlas, and (3) low walkability by the EPA National Walkability Score. We assessed three individual‐level SDOH measures: low household income, lower educational attainment, and Medicaid insurance. We examined the combinations of these three neighborhood SDOH and three individual SDOH measures by graphical visualization and using statistical tests to assess overall differences in the distribution of these measures.ResultsOf 9588 nulliparous individuals, adverse neighborhood‐level SDOH [high socioeconomic disadvantage (28%), inadequate food access (24%), and low walkability (66%)] and adverse individual‐level SDOH [low household income (19%), lower educational attainment (23%), and Medicaid insurance (33%)] were common in early pregnancy. Six percent of individuals lived in a community with all three adverse neighborhood‐level SDOH measures. Of those living in a community with at least two neighborhood‐level SDOH measures, 23% lived in areas with inadequate food access and low walkability, 19% with high socioeconomic disadvantage and low walkability, and 1% with high socioeconomic disadvantage and inadequate food access. Overall, 23% lived in a community with no adverse neighborhood‐level SDOH, and among this group, 88% had no adverse individual‐level SDOH. There were significant differences in adverse individual‐level SDOH based on whether individuals lived in a community with all three adverse neighborhood‐level measures [low household income (39%), lower educational attainment (44%), Medicaid (55%)], any two measures [low household income (22%), lower educational attainment (27%), Medicaid (37%)], or only one measure [low household income (14%), lower educational attainment (17%), Medicaid (27%)] (p < 0.001 for all).ConclusionAmong nulliparous individuals in early pregnancy, the frequency of adverse individual‐level SDOH was generally higher when they lived in communities with more adverse neighborhood‐level SDOH. Future approaches that identify and classify the multifaceted and multilevel nature of structural determinants as they relate to pregnancy outcomes are needed.

Introduction: Stark disparities exist in the prevalence of Alzheimer’s disease (AD) and related dementias (ADRD) by social determinants of health (SDoH). Limited research is available on associations of SDoH with AD/ADRD biomarkers. This study aims to assess the impact of SDoH measures on ADRD-associated neuroimaging and plasma biomarkers. Hypothesis: SDoH measures of area deprivation index (ADI), social vulnerability index (SVI), and environmental justice index (EJI) will be positively associated with poorer ADRD-associated neuroimaging and plasma biomarkers, and cardiometabolic health will partially account for this association. Methods: This study includes participant data from the Wake Forest Alzheimer’s Disease Research Center from baseline visits. Independent SDoH variables include ADI, SVI, and EJI. Dependent variables include cortical thickness and white matter hyperintensity (WMH) volume, adjusted for intracranial volume, from MRI, and plasma glial fibrillary acidic protein (GFAP) and pTau-181. We conducted bivariate and hierarchical multivariable linear regressions, with demographics (age, sex, education,&cognitive diagnosis) [Model 1], additional adjustment for cardiometabolic health with cardiometabolic index (CMI) [Model 2], and estimated glomerular filtration rate (eGFR) [Model 3; for plasma biomarkers] as covariates. Results: In bivariate analyses, a significant negative association was observed between ADI and cortical thickness (β=-0.08, p=0.047). No other significant associations were observed. In multivariable analyses (Table 1) , EJI was negatively associated with cortical thickness (β=-0.04, p=0.04), and both ADI and SVI were negatively associated with GFAP (β=-0.36, p<0.01&β=-0.52, p<0.01, respectively), in Model 1. Additionally adjusting for CMI in Model 2, the negative associations of ADI and SVI with GFAP were preserved (β=-0.33, p=0.02&β=-0.50, p<0.01, respectively). In Model 3, further adjusting for eGFR, ADI remained negatively associated with GFAP (β=-0.37, p=0.03). Conclusions: Place-based environmental injustice was associated with lower cortical thickness, while neighborhood disadvantage and social vulnerability were associated with higher GFAP levels in adjusted models, suggesting place-based SDoH may be associated with structural brain changes and neuroinflammation. The impact of place-based SDoH needs to be further studied in association with ADRD biomarkers to better understand how we may tackle SDoH among older adults.

BackgroundDisparities in non-medical health factors, such as social determinants of health (SDoH), are associated with increased risk of negative health outcomes. Leveraging contextual (or area-based) measures of SDoH is essential for uncovering broader factors influencing disparities in critical care-related outcomes. Our objective is to review evidence analyzing the association between contextual SDoH obtained from publicly available databases and critical care-related outcomes in the United States (US).MethodsWe conducted searches in the Web of Science, PubMed, Cochrane, and Embase electronic database to obtain clinical studies utilizing SDoH datasets from publicly available data sources and analyzed these studies for associations between critical care-related outcomes and SDoH (search date June 8th, 2025). We excluded non-English articles, reviews, editorial commentaries, letters to editors, studies without intensive care unit (ICU) patients or SDoH variables, studies based on countries outside of the US and studies that lacked full text or contained only the abstract. We extracted cohort characteristics, SDoH measures and domains, SDOH database characteristics, ICU admissions and outcomes, analytical method used for determining the association between SDoH and ICU variables, and significant SDoH variables.ResultsWe identified 87 publications (44 with pediatric patients, 40 with adult patients, and 3 with a mixture of both) and study population characteristics (e.g., surgical or specific disease-diagnosed patients). Child Opportunity Index and American Community Survey were the top platforms utilized for acquiring SDoH in pediatric and adult cohorts, respectively, followed by Area Deprivation Index and Social Vulnerability Index in both cohort types. Area-level granularity included boundaries determined by counties, ZIP codes, census block groups and census tracts.ConclusionsAmong five SDoH domains, economic stability was found to be the top investigated SDoH category for critical care-related outcomes. Contextual SDoH variables, indicating more vulnerable and adverse conditions, were associated with higher ICU admissions, greater need for ICU resource utilization, longer ICU duration, higher likelihood of developing critical illnesses, worsened life quality following ICU discharge, and higher mortality. Social determinants of health offer a broad area for modifiable intervention targets. Public databases serve as facilitators towards SDoH integration into electronic health records, promoting value-based care and mitigating health inequities.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13054-025-05622-1.

Social determinants of health (SDOH) are associated with disparities in disease severity and in-hospital outcomes among critically ill children. It is unknown whether SDOH are associated with later outcomes. We evaluated associations between SDOH measures and mortality, new functional morbidity, and health-related quality of life (HRQL) decline among children surviving septic shock. Secondary analysis of the Life After Pediatric Sepsis Evaluation (LAPSE) prospective cohort study was conducted between 2014 and 2017. Twelve academic U.S. PICUs were involved in the study. Children younger than 18 years with community-acquired septic shock were involved in the study. None. We assessed associations between race, ethnicity, income, education, marital status, insurance, language, and home U.S. postal code with day 28 mortality, new functional morbidity at discharge per day 28, and HRQL decline using logistic regression. Of 389 patients, 32% ( n = 98) of families had household income less than $50,000 per year. Median Pediatric Risk of Mortality (PRISM) score was 11 (interquartile range 6, 17). We found that English language and Area Deprivation Index less than 50th percentile were associated with higher PRISM scores. Mortality was 6.7% ( n = 26), new functional morbidity occurred in 21.8% ( n = 78) of patients, and HRQL decline by greater than 10% occurred in 31.0% of patients ( n = 63). We failed to identify any association between SDOH measures and mortality, new functional morbidity, or HRQL decline. We are unable to exclude the possibility that annual household income greater than or equal to $50,000 was associated with up to 81% lesser odds of mortality and, in survivors, more than three-fold greater odds of HRQL decline by greater than 10%. In this secondary analysis of the 2014-2017 LAPSE dataset, we failed to identify any association between SDOH measures and in-hospital or postdischarge outcomes following pediatric septic shock. This finding may be reflective of the high illness severity and single disease (sepsis) of the cohort, with contribution of clinical factors to functional and HRQL outcomes predominating over prehospital and posthospital SDOH factors.

To compare patient-reported social determinants of health (SDOH) to the Brokamp Area Deprivation Index (ADI), and evaluate the association of patient-reported SDOH and ADI with mortality in patients with cardiovascular disease (CVD). Prospective cohort. Academic medical center. Adults with acute coronary syndrome (ACS) and/or acute exacerbation of heart failure (HF) hospitalized between 2011 and 2015. Patient-reported SDOH included: income range, education, health insurance, and household size. ADI was calculated using census tract level variables of poverty, median income, high school completion, lack of health insurance, assisted income, and vacant housing. All-cause mortality, up to 5 years follow-up. The sample was 60% male, 84% White, and 93% insured; mean patient-reported household income was $48,000 (SD $34,000). ADI components were significantly associated with corresponding patient-reported variables. In age, sex, and race adjusted Cox regression models, ADI was associated with mortality for ACS (HR 1.23, 95% CI 1.06, 1.42), but not HF (HR 1.09, 95% CI .99, 1.21). Mortality models for ACS improved with consideration of social determinants data (C-statistics: base demographic model=.612; ADI added=.644; patient-reported SDOH added=.675; both ADI and patient-reported SDOH added=.689). HF mortality models improved only slightly (C-statistics: .600, .602, .617, .620, respectively). The Brokamp ADI is associated with mortality in hospitalized patients with CVD. In the absence of available patient-reported data, hospitals could implement the Brokamp ADI as an approximation for patient-reported data to enhance risk stratification of patients with CVD.

BACKGROUNDPediatric brain tumors and their treatment impact cognition including intellectual functioning (IQ), attention, working memory, and processing speed. Few studies have examined the role of social determinants of health (SDoH) on cognition in pediatric brain tumors. To date, studies have primarily focused on family-related SDoH factors such as insurance status or parental education as predictors of cognition. To our knowledge, no study has examined multiple indicators of SDoH to determine which family-based and community-based predict cognition. METHODSParticipants included 151 pediatric brain tumor patients (M diagnosis age=8.82) seen for a clinical neuropsychological evaluation (M evaluation age=13.71). IQ, brief attention (WISC/DAS-II Digits Forward), working memory (WISC/DAS-II Digits Backwards), and processing speed (WISC Coding) were obtained from age-appropriate measures (WISC>6 years, DAS-II<6 years). Family-based SDoH indicators include insurance type (public, private) and maternal education (<high school degree, some college, ≥college degree). Community-based SDoH include the Area Deprivation Index (ADI) and Childhood Opportunity Index (COI). Multiple regressions examined variance in IQ, attention, working memory, and processing speed accounted for by the SDoH measures, after controlling for treatment exposures using the Neurological Predictor Scale. RESULTSInsurance accounted for 14.9% of the IQ variance [β=-.316, t(151)=-4.18; p<.001]; COI accounted for 18.3% of the IQ variance [β=.202, t(151)=2.70; p=.008]. Insurance accounted for 12% of the variance in attention [β=-.324, t(152)=-4.22; p<.001], and 17.5% of the variance in processing speed [F(2,134)=15.399, p<.001]. In the working memory model, insurance [β=-.179, t(148)=-2.19; p=.03] and ADI [β=-.171, t(148)=-2.11; p=.04], accounted for 5.3% and 7.5% of the variance, respectively. CONCLUSIONSFindings highlight the important contribution of both family and community factors on neuropsychological outcomes in pediatric brain tumors. Nuances of the data will be discussed as they relate to clinical care and research.

1507 Background: Social determinants of health (SDoH) are key factors contributing to breast cancer disparities but are difficult to measure. We surveyed a diverse population of women with breast cancer to identify SDoH and demographic measures associated with avoiding information, treatment receipt, and physician mistrust. Methods: During 2018-2020, we interviewed 297 women (60% White, 25% Black, 15% Hispanic) with a history of breast cancer treated at three academic institutions. We used multivariable regression to assess the associations of SDoH, demographics, and related factors (social support, discrimination in daily life and within healthcare, education, financial strain, faith, age, birth country, race and ethnicity, marital status) with three outcomes: (1) avoiding information about cancer, (2) lack of initiation of recommended treatments, and (3) physician mistrust. For each model, we included factors statistically significant (p&lt;0.05) in unadjusted analyses. Results: Overall, 79 participants (26.6%) reported avoiding information about cancer. Age &lt;40, discrimination (treated with less courtesy or as if dishonest), and financial strain (worry about medical bills, uninsured status) were all significantly associated with avoiding information (p&lt;0.05 for all). In multivariable analyses, discrimination in daily life (OR 3.37, 95% CI 1.40-8.10 for being treated as if dishonest) and age (OR 3.36 for age &lt;40 compared to &gt;60) were associated with avoiding information. Twenty-two participants (7.4%) reported not initiating at least one recommended treatment. Discrimination in daily life and in healthcare were both significantly associated with treatment receipt, as was financial strain (all p&lt;0.01). In multivariable analyses, discrimination (OR 3.51, 95% CI 1.05-11.76 for being treated as if dishonest) and worrying about medical bills (OR 3.53, 95% CI 1.40-8.93) were associated with lack of treatment initiation. Twelve participants (4.0%) reported not trusting physician’s judgements about medical care. Discrimination in daily life and in health care and insurance status were significantly associated with physician mistrust (both p&lt;0.05). Multivariable analysis found that discrimination in both daily life and in health care (OR 5.95, 95% CI 1.40-25.3 for being treated with less courtesy in daily life, and OR 12.79, 95% CI 2.49-65.6 for reporting being treated as dishonest in health care setting) were associated with mistrust. Race and ethnicity were not significantly associated with any of the outcome measures in unadjusted or adjusted analyses (all p&gt;0.05). Conclusions: SDoH measures and not race were associated with lack of engagement and trust within the health care system, with experiences of discrimination and financial strain associated with avoiding information, not initiating treatments, and physician mistrust.

Social determinants of health (SDOH) are an important contributor to health status and health outcomes. In this analysis, we compare SDOH measured both at the individual and population levels in patients with high comorbidity who receive primary care at Federally Qualified Health Centers in New York and Chicago and enrolled in the Tipping Points trial. We analyzed individual- and population-level measures of SDOH in 1,488 patients with high comorbidity (Charlson Comorbidity Index ≥ 4) enrolled in Tipping Points. At the individual level, we used a standardized patient-reported questionnaire. At the population level, we employed patient addresses to calculate the Social Deprivation Index (SDI) and Area Deprivation Index. Multivariable regressions were conducted in addition to qualitative feedback from stakeholders. Individual-level SDOH are distinct from population-level measures. Significant component predictors of population SDI are being unhoused, unable to pay for utilities, and difficulty accessing medical transportation. Qualitative findings mirrored these results. High comorbidity patients report significant SDOH challenges at the individual level. Fitting a binomial generalized linear model, the comorbidity score is significantly predicted by the composite individual SDOH index (p < 0.0001) controlling for age and race/ethnicity. Individual- and population-level SDOH measures provide different risk assessments. The use of community-level SDI data is informative in the aggregate but should not be used to identify patients with individual unmet social needs. Health systems should implement a standardized individualized assessment of unmet SDOH needs and build strong, enduring partnerships with community-based organizations that can provide those services.

Individual- and neighborhood-level social determinants of health (SDOH) measures have been associated with higher incidence of acute kidney injury (AKI), lower likelihood of recovery, and higher risk of mortality following AKI. The association of SDOH measures with post-hospitalization AKI follow-up care is unknown. Using a retrospective cohort design, we evaluated the association of individual- (insurance status, race, ethnicity) and neighborhood-level (socioeconomic deprivation, rurality, residential segregation, and social vulnerability to natural or human-caused disasters) SDOH measures with receipt of post-hospitalization follow-up for AKI within three months of hospital discharge among intensive care unit (ICU) survivors with AKI stage 2 or 3 hospitalized between 2012 and 2023 at a major academic medical center. The primary outcome, post-hospitalization AKI follow-up, was defined as the occurrence of at least one of the following within three months of hospital discharge: a nephrology outpatient visit, serum creatinine measurement, or urine protein measurement. We utilized pooled logistic regression models with inverse probability of censoring weighting to adjust for demographics, comorbidities, and hospitalization characteristics and to account for the competing risks of death, re-hospitalization, or dialysis initiation. Among 13,392 adult ICU survivors with AKI stages 2 or 3, 5,970 (45%) were female, 4,488 (34%) were of Black race, and 1,561 (12%) were uninsured. A total of 7,316 (61%) received post-hospitalization follow-up for AKI within three months of hospital discharge. Uninsured individuals (adjusted odds ratio (aOR) 0.77, 95% confidence interval (CI) 0.70-0.84), individuals residing in a neighborhood with greater socioeconomic deprivation (aOR 0.86, 95% CI 0.81-0.92), greater rurality (aOR 0.86, 95% CI 0.81-0.92), greater segregation (aOR 0.92, 95% CI 0.87-0.98), and greater social vulnerability (aOR 0.83, 95% CI 0.77-0.89) all experienced significantly lower odds of post-hospitalization AKI care. Both individual- and neighborhood-level SDOH were associated with lower odds of post-AKI follow-up among ICU survivors with severe AKI.