Subsidized Housing and Adult Asthma in Boston, 2010-2015.

OBJECTIVES: To examine whether subsidized housing, specifically public housing and rental assistance, is associated with asthma in the Boston, Massachusetts, adult population. METHODS: We analyzed a pooled cross-sectional sample of 9554 adults taking part in 3 Boston Behavioral Risk Factor Surveillance System surveys from 2010 to 2015. We estimated odds ratios for current asthma in association with housing status (public housing development [PHD] resident, rental assistance [RA] renter, non-RA renter, nonrenter nonowner, homeowner as reference) in logistic regression analyses adjusting for year, age, sex, race/ethnicity, education, and income. RESULTS: The odds of current asthma were 2.02 (95% confidence interval [CI] = 1.35, 3.03) and 2.34 (95% CI = 1.60, 3.44) times higher among PHD residents and RA renters, respectively, than among homeowners. We observed smoking-related effect modification (interaction P = .04); elevated associations for PHD residents and RA renters remained statistically significant (P < .05) only among ever smokers. Associations for PHD residents and RA renters remained consistent in magnitude in comparison with non-RA renters who were eligible for subsidized housing according to income. CONCLUSIONS: Public housing and rental assistance were strongly associated with asthma in this large cross-sectional sample of adult Boston residents.

Using 3 Health Surveys to Compare Multilevel Models for Small Area Estimation for Chronic Diseases and Health Behaviors.

BACKGROUND: We used a multilevel regression and poststratification approach to generate estimates of health-related outcomes using Behavioral Risk Factor Surveillance System 2013 (BRFSS) data for the 500 US cities. We conducted an empirical study to investigate whether the approach is robust using different health surveys. METHODS: We constructed a multilevel logistic model with individual-level age, sex, and race/ethnicity as predictors (Model I), and sequentially added educational attainment (Model II) and area-level poverty (Model III) for 5 health-related outcomes using the nationwide BRFSS, the Massachusetts BRFSS 2013 (a state subset of nationwide BRFSS), and the Boston BRFSS 2010/2013 (an independent survey), respectively. We applied each model to the Boston population (2010 Census) to predict each outcome in Boston and compared each with corresponding Boston BRFSS direct estimates. RESULTS: Using Model I for the nationwide BRFSS, estimates of diabetes, high blood pressure, physical inactivity, and binge drinking fell within the 95% confidence interval of corresponding Boston BRFSS direct estimates. Adding educational attainment and county-level poverty (Models II and III) further improved their accuracy, particularly for current smoking (the model-based estimate was 15.2% by Model I and 18.1% by Model II). The estimates based on state BRFSS and Boston BRFSS models were similar to those based on the nationwide BRFSS, but area-level poverty did not improve the estimates significantly. CONCLUSION: The estimates of health-related outcomes were similar using different health surveys. Model specification could vary by surveys with different geographic coverage.

Measures of Local Segregation for Monitoring Health Inequities by Local Health Departments.

OBJECTIVES: To assess the use of local measures of segregation for monitoring health inequities by local health departments. METHODS: We analyzed preterm birth and premature mortality (death before the age of 65 years) rates for Boston, Massachusetts, for 2010 to 2012, using the Index of Concentration at the Extremes (ICE) and the poverty rate at both the census tract and neighborhood level. RESULTS: For premature mortality at the census tract level, the rate ratios comparing the worst-off and best-off terciles were 1.58 (95% confidence interval [CI] = 1.36, 1.83) for the ICE for income, 1.66 (95% CI = 1.43, 1.93) for the ICE for race/ethnicity, and 1.63 (95% CI = 1.40, 1.90) for the ICE combining income and race/ethnicity, as compared with 1.47 (95% CI = 1.27, 1.71) for the poverty measure. Results for the ICE and poverty measures were more similar for preterm births than for premature mortality. CONCLUSIONS: The ICE, a measure of social spatial polarization, may be useful for analyzing health inequities at the local level. Public Health Implications. Local health departments in US cities can meaningfully use the ICE to monitor health inequities associated with racialized economic segregation.

Comparison of Methods for Estimating Prevalence of Chronic Diseases and Health Behaviors for Small Geographic Areas: Boston Validation Study, 2013.

INTRODUCTION: Local health authorities need small-area estimates for prevalence of chronic diseases and health behaviors for multiple purposes. We generated city-level and census-tract-level prevalence estimates of 27 measures for the 500 largest US cities. METHODS: To validate the methodology, we constructed multilevel logistic regressions to predict 10 selected health indicators among adults aged 18 years or older by using 2013 Behavioral Risk Factor Surveillance System (BRFSS) data; we applied their predicted probabilities to census population data to generate city-level, neighborhood-level, and zip-code-level estimates for the city of Boston, Massachusetts. RESULTS: By comparing the predicted estimates with their corresponding direct estimates from a locally administered survey (Boston BRFSS 2010 and 2013), we found that our model-based estimates for most of the selected health indicators at the city level were close to the direct estimates from the local survey. We also found strong correlation between the model-based estimates and direct survey estimates at neighborhood and zip code levels for most indicators. CONCLUSION: Findings suggest that our model-based estimates are reliable and valid at the city level for certain health outcomes. Local health authorities can use the neighborhood-level estimates if high quality local health survey data are not otherwise available.

Racial/ethnic health disparities among children with special health care needs in Boston, Massachusetts.

BACKGROUND: Little is known about the factors that contribute to racial/ethnic disparities among children with special health care needs (CSHCN). OBJECTIVE: To quantify the contributions of determinants of racial/ethnic disparities in health and health care among CSHCN in Boston, Massachusetts. METHODS: A sample of 326 Black, Latino, and white CSHCN was drawn from the Boston Survey of Children's Health, a city-wide representative sample of children. The study implemented Oaxaca-Blinder-style decomposition techniques to examine the relative contributions of health resources and child-, family-, and neighborhood-level factors to disparities in four outcomes: health status, barriers to medical care, oral health status, and utilization of preventive dental care. RESULTS: White CSHCN had a greater likelihood of having very good/excellent health and oral health and were less likely to experience barriers to care than Black CSHCN. Compositional differences on predictors explained 63%, 98%, and 80% of these gradients, respectively. Group variation in household income, family structure, neighborhood support, and exposure to adverse childhood experiences accounted for significant portions of the Black-white gaps in health and access. White CSHCN were also more likely to have very good/excellent health and oral health compared to Latino CSHCN. Differences on predictors accounted for about 86% and 80% of these gaps, respectively. Household income, adverse childhood experiences, and household language emerged as significant determinants of Latino-white disparities. CONCLUSIONS: Racial/ethnic health disparities among CSHCN are explained by relatively few determinants. Several of the contributing factors that emerged from the analysis and could be targeted by public health and policy interventions.