UNASSIGNED: Few studies have examined how multi-level social factors interact and affect developmental patterns of sexual risk among middle-to-late adolescents who are at risk of experiencing sexual risk behaviors. We examined developmental trajectories of sexual risk behaviors of boys and girls in middle-to-late adolescence and the effects of exposure to three social risk factors (poor parental monitoring, peer risk, and neighborhood risk).
UNASSIGNED: We followed 2,332 Bahamian adolescents every six months from Grades 10-12. We used group-based trajectory modeling to identify distinct trajectories of sexual risk behaviors for boys and girls.
UNASSIGNED: We identified three trajectories each for boys and girls. Peer risk and neighborhood risk predicted a high sexual-risk trajectory for boys, and peer risk (alone or combined with other risk factors) had the greatest impact on the membership of moderate-to-high-risk trajectory for girls. Parental monitoring had a relatively small effect on adolescents\' sexual risk behavior.
UNASSIGNED: Our results underscore the importance of early identification of adolescents with sexual risk behavior and development of targeted prevention interventions to improve adolescent health outcomes.

The study examines effects of the CMS State Innovation Models(SIM) on capturing social risk factors in adults hospitalized with Atherosclerotic Cardiovascular Disease (ASCVD). Using a difference-in-differences(DID) approach with propensity score weights, the study compared documentation of secondary diagnosis of SDOH/social factors using ICD-9 V codes (\"SDOH codes\") in adults hospitalized with ASCVD as a primary diagnosis (N= 1,485,354). Data were gathered from January 1, 2010, to September 30, 2015, covering the period before and after the SIM implementation in October 2013. From January 2010 to September 2015, SDOH codes were infrequently utilized among adults with ASCVD(0.55%, 95% CI: 0.43%-0.67%). SDOH codes with ASCVD increased from pre- to post-period in SIM states(0.56% to 0.93%) and comparison states (0.46% to 0.56%). SIM implementation was associated with greater improvement in SDOH codes utilization (adjusted OR 1.30, 95%CI: 1.18-1.43) during ASCVD hospitalizations. The odds of SDOH codes utilization were 86% higher in ED admissions(AOR 1.86, 95%CI: 1.76-1.97) than in routine admissions with ASCVD. Findings were similar when limiting population to older adults(>=65 years) enrolled in Medicare(AOR 1.50, 95%CI 1.31-1.71), whereas not significant for Medicaid beneficiaries. The study points to challenges for healthcare providers in documenting SDOH in adults with ASCVD.

BACKGROUND: The standard of care for congestive heart failure (CHF) aims to slow disease progression and maximize patient function, however there is an increase in emergency department (ED) revisits and readmissions. Social risk factors play a role in the disease management and prognosis of CHF. There is a gap in the identification of low-risk CHF patient who would be safely discharged using an initial social risk factor stratification.
OBJECTIVE: To generate a social risk profile for patients presenting to the ED with acute CHF exacerbation and identify variables that may increase the risk of 7-day and overall mortality, 30-day ED revisit, and readmission.
METHODS: We conducted a pilot prospective survey-based study among adult patients presenting to the ED with acute CHF exacerbation. The combination of a self-report questionnaire and retrospective chart review was used to generate a CHF risk profile.
RESULTS: A total of 62 patients were recruited in the pilot study with a mean age of 69.5 years. The preliminary data indicated that prior to this ED visit, 21% of patients were not aware of a previous CHF diagnosis; 64.5% of patients rated their sleep quality as poor or very poor; 72.6% reported orthopnea; and 43.5% reported recent weight gain. 37.1% of patients did not adhere to dietary recommendations and some patients did not adhere to their medication regime 100%.
CONCLUSIONS: This study suggests establishing a social risk profile for patients presenting to the ED with CHF can help formulate a CHF-specific care plan and optimize multidisciplinary management to reduce ED revisits and readmissions.

OBJECTIVE: (1) To estimate the association of social risk factors with unplanned readmission and emergency care after a hospital stay. (2) To create a social risk scoring index.
METHODS: We analyzed administrative data from the Department of Veterans Affairs (VA) Corporate Data Warehouse. Settings were VA medical centers that participated in a national social work staffing program.
METHODS: We grouped socially relevant diagnoses, screenings, assessments, and procedure codes into nine social risk domains. We used logistic regression to examine the extent to which domains predicted unplanned hospital readmission and emergency department (ED) use in 30 days after hospital discharge. Covariates were age, sex, and medical readmission risk score. We used model estimates to create a percentile score signaling Veterans\' health-related social risk.
METHODS: We included 156,690 Veterans\' admissions to a VA hospital with discharged to home from 1 October, 2016 to 30 September, 2022.
RESULTS: The 30-day rate of unplanned readmission was 0.074 and of ED use was 0.240. After adjustment, the social risks with greatest probability of readmission were food insecurity (adjusted probability = 0.091 [95% confidence interval: 0.082, 0.101]), legal need (0.090 [0.079, 0.102]), and neighborhood deprivation (0.081 [0.081, 0.108]); versus no social risk (0.052). The greatest adjusted probabilities of ED use were among those who had experienced food insecurity (adjusted probability 0.28 [0.26, 0.30]), legal problems (0.28 [0.26, 0.30]), and violence (0.27 [0.25, 0.29]), versus no social risk (0.21). Veterans with social risk scores in the 95th percentile had greater rates of unplanned care than those with 95th percentile Care Assessment Needs score, a clinical prediction tool used in the VA.
CONCLUSIONS: Veterans with social risks may need specialized interventions and targeted resources after a hospital stay. We propose a scoring method to rate social risk for use in clinical practice and future research.

OBJECTIVE: To evaluate whether geocoded social risk factor data predict the development of severe visual impairment or blindness due to glaucoma during follow-up using a large electronic health record (EHR) database.
METHODS: Cohort study.
METHODS: Patients diagnosed with open-angle glaucoma (OAG) at a tertiary care institution. All eyes had glaucomatous visual field defects at baseline. Sociodemographic and ocular data were extracted from the EHR, including age, gender, self-reported race and ethnicity, insurance status, OAG type, prior glaucoma laser or surgery, baseline disease severity using Hodapp-Anderson-Parrish criteria, mean intraocular pressure (IOP) during follow-up, and central corneal thickness. Social vulnerability index (SVIndex) data at the census tract level were obtained using geocoded patient residences. Mixed-effects Cox proportional hazard models were completed to assess for the development of severe visual impairment or blindness during follow-up, defined as BCVA ≤ 20/200 at least at the last two clinic visits or standard automated perimetry (SAP) mean deviation (MD) ≤ -22dB confirmed on two tests.
RESULTS: A total of 4,046 eyes from 2,826 patients met inclusion criteria and were followed for an average of 4.3 ± 2.2 years. Severe visual impairment or blindness developed in 79 eyes (2.0%) from 76 patients (2.7%) after an average of 3.4 ± 1.8 years, leading to an incidence rate of severe visual impairment or blindness of 0.5% per year. Older age (adjusted hazards ratio [HR] 1.36 per decade, P = .007), residence in areas with higher SVIndex (HR 1.56 per 25% increase, P < .001), higher IOP during follow-up (HR 3.01 per 5 mmHg increase, P < .001), and moderate or severe glaucoma at baseline (HR 7.31 and 26.87, P < .001) were risk factors for developing severe visual impairment or blindness. Concordance index of the model was 0.88. Socioeconomic, minority status/language, and housing type/transportation SVIndex themes were key contributors to developing severe visual impairment or blindness.
CONCLUSIONS: Risk factors for developing glaucoma-related severe visual impairment or blindness included older age, elevated IOP during follow-up, moderate or severe disease at baseline, and residence in areas associated with greater social vulnerability. In addition to ocular risk factors, geocoded EHR data regarding social risk factors could help identify patients at high risk of developing glaucoma-related visual impairment.

BACKGROUND: Social risk data collection is expanding in community health centers (CHCs). We explored clinicians\' practices of adjusting medical care based on their awareness of patients\' social risk factors-that is, changes they make to care plans to mitigate the potential impacts of social risk factors on their patients\' care and health outcomes-in a set of Texas CHCs.
METHODS: Convergent mixed methods. Surveys/interviews explored clinician perspectives on adjusting medical care based on patient social risk factors. Survey data were analyzed with descriptive statistics; interviews were analyzed using thematic analysis and inductive coding.
RESULTS: Across 4 CHCs, we conducted 15 clinician interviews and collected 97 surveys. Interviews and surveys overall indicated support for adjustment activities. Two main themes emerged: 1) clinicians reported making frequent adjustments to patient care plans based on their awareness of patients\' social contexts, while simultaneously expressing concerns about adjustment; and 2) awareness of patients\' social risk factors, and clinician time, training, and experience all influenced clinician adjustments.
CONCLUSIONS: Clinicians at participating CHCs described routinely adjusting patient care plans based on their patients\' social contexts. These adjustments were being made without specific guidelines or training. Standardization of adjustments may facilitate the contextualization of patient care through shared decision making to improve outcomes.

BACKGROUND: Interest is growing in clinic-based programs that screen for and intervene on patients\' social risk factors, including housing, food, and transportation. Though several studies suggest these programs can positively impact health, few examine the mechanisms underlying these effects. This study explores pathways through which identifying and intervening on social risks can impact families\' health.
METHODS: This qualitative study was embedded in a randomized clinical trial that examined the health impacts of participation in a social services navigation program. We conducted semi-structured interviews with 27 English or Spanish-speaking caregivers of pediatric patients who had participated in the navigation program. Interviews were analyzed using thematic analysis.
RESULTS: Caregivers described 3 pathways through which the navigation program affected overall child and/or caregiver health: 1) increasing families\' knowledge of and access to social services; 2) helping families connect with health care services; and 3) providing emotional support that reduced caregiver isolation and anxiety. Participants suggested that navigation programs can influence health even when they do not directly impact resource access.
CONCLUSIONS: Social care programs may impact health through multiple potential pathways. Program impacts seem to be mediated by the extent to which programs increase knowledge of and access to social and health care services and support positive relationships between families and program personnel.

This study reviews the impact of eligibility policies in the early rollout of the COVID-19 vaccine on coverage and probable outcomes, with a focus on New York City. We conducted a retrospective ecological study assessing age  65+, area-level income, vaccination coverage, and COVID-19 mortality rates, using linked Census Bureau data and New York City Health administrative data aggregated at the level of modified zip code tabulation areas (MODZCTA). The population for this study was all individuals in 177 MODZCTA in New York City. Population data were obtained from Census Bureau and New York City Health administrative data. The total mortality rate was examined through an ordinary least squares (OLS) regression model, using area-level wealth, the proportion of the population aged 65 and above, and the vaccination rate among this age group as predictors. Low-income areas with high proportions of older people demonstrated lower coverage rates (mean vaccination rate 52.8%; maximum coverage 67.9%) than wealthier areas (mean vaccination rate 74.6%; maximum coverage 99% in the wealthiest quintile) in the first 3 months of vaccine rollout and higher mortality over the year. Despite vaccine shortages, many younger people accessed vaccines ahead of schedule, particularly in high-income areas (mean coverage rate 60% among those 45-64 years in the wealthiest quintile). A vaccine program that prioritized those at greatest risk of COVID-19-associated morbidity and mortality would have prevented more deaths than the strategy that was implemented. When rolling out a new vaccine, policymakers must account for local contexts and conditions of high-risk population groups. If New York had focused limited vaccine supply on low-income areas with high proportions of residents 65 or older, overall mortality might have been lower.

BACKGROUND: The COVID-19 pandemic posed numerous obstacles to psychosocial wellbeing for children. We conducted a longitudinal study to evaluate child mental health and social risks during the pandemic.
METHODS: Participants were 172 caregivers of children aged 6-11 years old who attended well child visits within 6 months before pandemic onset at an urban safety net hospital in the US. Prepandemic data was extracted from the electronic medical record, and surveys were administered at three time points between August 2020 and July 2021. We measured mental health symptoms with the Pediatric Symptom Checklist-17, social risks (e.g., food and housing insecurity) with the THRIVE questionnaire, and school modality (in-person, hybrid, remote).
RESULTS: Compared to pre-pandemic, children had significantly higher PSC-17 total scores (overall mental health symptoms) and THRIVE total scores (total burden of social risks) at all three mid-pandemic waves. Using longitudinal mixed models accounting for time, social risks, and school modality, both social risks (B = 0.37, SE = 0.14, p < 0.01) and school modality were significantly associated with PSC-17 scores (B = - 1.95, SE = 0.63, p < 0.01). Children attending in-person school had fewer mental health symptoms than those attending remote or hybrid school.
CONCLUSIONS: Mental health symptoms and social risks remained significantly higher fifteen months after the onset of the COVID-19 pandemic compared to prepandemic. In-person attendance at school appeared protective against persistently elevated mental health symptoms.

BACKGROUND: Social risk factors are key drivers of the geographic variation in spending in the United States but little is known how community-level social risk factors are associated with hospital prices. Our objective was to describe the relationship between regional hospital-reported prices and social risk factors by price type (chargemaster, cash, commercial, Medicare, and Medicaid).
METHODS: This cross-sectional analysis used newly available hospital-reported prices from acute general hospitals in 2022. The prices were for 14 common services. Prices were winsorized at 98%, wage index-adjusted, standardized by service, and aggregated to hospital service areas (HSAs). For social risk, we used 23 measures across 5 domains of social risk (socioeconomic position; race, ethnicity, and culture; gender; social relationships; and residential and community context). Spearman\'s correlation was used to estimate associations between median prices and social risk by price type.
RESULTS: Prices were reported from 2,386 acute general hospitals in 45% (1,502 of 3,436) HSAs. Correlations between regional prices and other social risk factors varied by price type (range: -0.19 to 0.31). Chargemaster and cash prices were significantly correlated with the most community characteristics (10 of 23, 43%) followed by commercial prices (8, 35%). Medicare and Medicaid prices were only significantly correlated with 1 measure (all p < 0.01). All price types were significantly correlated with the percentage of uninsured (all p < 0.01). Chargemaster, cash, and commercial prices were positively correlated with percentage of Hispanic residents, residents with limited English proficiency, and non-citizens (all p < 0.05).
CONCLUSIONS: While regional correlations between prices and social risk factors were weak across all prices, chargemaster, cash, and commercial prices were more like closely aligned with community-level social risk factors than the two public payers (Medicare and Medicaid). Chargemaster, cash, and commercial hospital prices appeared to be higher in socially disadvantaged communities. Further research is needed to clarify the relationship between prices and community social risk factors.