Social Vulnerability and Safe Building Recertification Violations in Miami, Florida, 2013-2018.

Objectives. To determine whether an association exists between Social Vulnerability Index (SVI) scores and 40-year recertification violation within the City of Miami, Florida. Methods. A cross-sectional, observational secondary data analysis of social and housing vulnerability, including the Centers for Disease Control and Prevention's SVI overall themes, estimated median year a housing unit was built, and 40-year recertification code violation data. We conducted the study using data sets from 2013 to 2018 at the census tract level in response to the collapse of Champlain Tower South in Surfside, Florida. Results. Every 1-unit increase in a census tract's SVI score yielded a 21-fold increase in the odds of being a census tract with high 40-year recertification violations. Census tracts within the third quartile for SVI scores had approximately 9 times the odds, and tracts within the fourth quartile had 11 times the odds of being tracts with high 40-year recertification violations. Conclusions. Findings demonstrate that inequitable conditions exist among the City of Miami's most socially vulnerable residents, through greater exposure to risky housing environments. (Am J Public Health. 2022;112(8):1217-1220. https://doi.org/10.2105/AJPH.2022.306890).

The Association Between the Social Determinants of Health and HIV Control in Miami-Dade County ZIP Codes, 2017.

BACKGROUND: There were 28,055 people living with HIV (PLWH) in Miami-Dade County (MDC) in 2017; 40.1% was either out of care or was not virally suppressed (uncontrolled HIV). The purpose of this study was to determine the association between the social determinants of health (SDOH) and the number of persons with uncontrolled HIV in MDC. SETTING: This cross-sectional study included PLWH 15 and older with uncontrolled HIV in MDC, 2017. Data on PLWH's viral load, age, gender, mode of HIV transmission, and race/ethnicity were aggregated to the ZIP code level. All five SDOH per HealthyPeople 2020 were represented: economic stability, education, social and community context, health and healthcare, and neighborhood and built environment. METHODS: Descriptive analyses on all study variables and a principal component analysis on the SDOH variables were performed. To account for overdispersion, multivariate negative binomial regressions were run while controlling for confounders and testing for significant interactions. RESULTS: The results of the regression analysis indicated that an increase in Factor 1 (economic stability, education, and health and healthcare determinants) was associated with a statistically significant increase in the number of PLWH with uncontrolled HIV. Additionally, we found a significant interaction between Factor 1 and White race. Among persons of low socioeconomic status, White race is associated with a reduction in PLWH with uncontrolled HIV. CONCLUSIONS: These results suggest that reducing poverty and increasing education and rates of health insurance should result in significant reductions in PLWH with uncontrolled HIV. These results have the potential to influence future policy, interventions for retention, adherence, and continuity of care to improve suppression rates in MDC.