A Case-Crossover Analysis of Indoor Heat Exposure on Mortality and Hospitalizations among the Elderly in Houston, Texas

Abstract

Background: Despite the substantial role indoor exposure has played in heat wave-related mortality, few epidemiological studies have examined the health effects of exposure to indoor heat. As a result, knowledge gaps regarding indoor heat-health thresholds, vulnerability, and adaptive capacity persist.

Objective: We evaluated the role of indoor heat exposure on mortality and morbidity among the elderly ($\ge 65\text{years}$ of age) in Houston, Texas.

Methods: Mortality and emergency hospital admission data were obtained through the Texas Department of State Health Services. Summer indoor heat exposure was modeled at the U.S. Census block group (CBG) level using building energy models, outdoor weather data, and building characteristic data. Indoor heat-health associations were examined using time-stratified case-crossover models, controlling for temporal trends and meteorology, and matching on CBG of residence, year, month, and weekday of the adverse health event. Separate models were fitted for three indoor exposure metrics, for individual lag days 0-6, and for 3-d moving averages (lag 0-2). Effect measure modification was explored via stratification on individual- and area-level vulnerability factors.

Results: We estimated positive associations between short-term changes in indoor heat exposure and cause-specific mortality and morbidity [e.g., circulatory deaths, $\text{odds ratio per}5°C\text{increase}=1.16$ (95% CI: 1.03, 1.30)]. Associations were generally positive for earlier lag periods and weaker across later lag periods. Stratified analyses suggest stronger associations between indoor heat and emergency hospital admissions among African Americans compared with Whites.

Discussion: Findings suggest excess mortality among certain elderly populations in Houston who are likely exposed to high indoor heat. We developed a novel methodology to estimate indoor heat exposure that can be adapted to other U.S.

Locations: In locations with high air conditioning prevalence, simplified modeling approaches may adequately account for indoor heat exposure in vulnerable neighborhoods. Accounting for indoor heat exposure may improve the estimation of the total impact of heat on health. https://doi.org/10.1289/EHP6340.

Figure 1.

Overall map of our study area. Analyses were performed separately for all of Harris County and for City of Houston residents.

Figure 2.

Percentage of residential buildings in Harris County U.S. Census block groups (CBGs) from 2017 Harris County Appraisal District Real and Personal Property Database. Hatched areas represent CBGs excluded from analyses because all buildings within these CBGs were identified as nonresidential or because residential buildings could not be categorized into building archetypes used in our indoor modeling.

Figure 3.

Average daily minimum ambient temperature across U.S. Census block groups in Houston (June–September, 2000–2015). HRLDAS weather data were not available in the blank areas. HRLDAS, High-Resolution Land Data Assimilation System.

Figure 4.

Estimated ORs and 95% CIs per 5°C increase between 3-d moving averages of maximum indoor DI and health outcomes stratified by individual factors for City of Houston residents (June–September, 2000–2015). ORs were derived from single-exposure, time-stratified case-crossover models (conditional logistic regression), that matched on U.S. Census block group of subject residence, year, month, and weekday of the adverse health event. Models controlled for maximum ambient temperature and maximum ambient dew point temperature (°C) with cubic polynomials, federal holidays, day of the warm season, modeled as a smooth function with monthly knots across the summer season (June–September). An interaction term between year and day of the warm season was also included to capture between-year differences. Mortality data were available 2000–2015; EHA data were available 2004–2013. See Table 4 for corresponding numeric data. Note: CI, confidence interval; CIRC, circulatory diagnoses; DI, discomfort index; EHA, emergency hospital admissions; HEAT, heat-related diagnoses; OR, odds ratio.

Figure 5.

Estimated ORs and 95% CIs per 5°C increase between 3-d moving averages of maximum indoor DI and health outcomes stratified by U.S. Census block group (CBG) socio-demographic factors for City of Houston residents (June–September, 2000–2015). ORs were derived from single-exposure, time-stratified case-crossover models (conditional logistic regression) that matched on CBG of subject residence, year, month, and weekday of the adverse health event. Models controlled for maximum ambient temperature and maximum ambient dew point temperature (°C) with cubic polynomials, federal holidays, day of the warm season, modeled as a smooth function with monthly knots across the summer season (June–September). An interaction term between year and day of the warm season was also included to capture between-year differences. Mortality data were available 2000–2015; EHA data were available 2004–2013. See Table 4 for corresponding numeric data. Note: CI, confidence interval; CIRC, circulatory diagnoses; DI, discomfort index; EHA, emergency hospital admissions; HEAT, heat-related diagnoses; OR, odds ratio.

Figure 6.

Estimated number of heat-related deaths attributable to exposure to high indoor heat during the summer months (June–September) for each U.S. Census block group (CBG) in Harris County between 2000 and 2015. To map the estimated number of deaths across our study area, we used effect estimates between heat and mortality for City of Houston residents. Hatched areas represent CBGs excluded from analyses because all buildings within these CBGs were identified as nonresidential or because residential buildings could not be categorized into building archetypes used in our indoor modeling.