Vulnerability to extreme heat by socio-demographic characteristics and area green space among the elderly in Michigan, 1990–2007

Abstract

Objectives We examined how individual and area socio-demographic characteristics independently modified the extreme heat (EH)-mortality association among elderly residents of 8 Michigan cities, May-September, 1990-2007. Methods In a time-stratified case-crossover design, we regressed cause-specific mortality against EH (indicator for 4-day mean, minimum, maximum or apparent temperature above 97th or 99th percentiles). We examined effect modification with interactions between EH and personal marital status, age, race, sex and education and ZIP-code percent “non-green space” (National Land Cover Dataset), age, race, income, education, living alone, and housing age (U.S. Census). Results In models including multiple effect modifiers, the odds of cardiovascular mortality during EH (99th percentile threshold) vs. non-EH were higher among non-married individuals (1.21, 95% CI = 1.14-1.28 vs. 0.98, 95% CI = 0.90-1.07 among married individuals) and individuals in ZIP codes with high (91%) non-green space (1.17, 95% CI = 1.06-1.29 vs. 0.98, 95% CI = 0.89-1.07 among individuals in ZIP codes with low (39%) non-green space). Results suggested that housing age may also be an effect modifier. For the EH-respiratory mortality association, the results were inconsistent between temperature metrics and percentile thresholds of EH but largely insignificant. Conclusions Green space, housing and social isolation may independently enhance elderly peoples’ heat-related cardiovascular mortality vulnerability. Local adaptation efforts should target areas and populations at greater risk.

Vulnerability to renal, heat and respiratory hospitalizations during extreme heat among U.S. elderly

“…However, non-green space was not a modifier in any model at any lag in our study. This is inconsistent with other studies which found differential vulnerability to heat-associated mortality in Michigan, Phoenix, Arizona and Barcelona, Spain by perception of green space, vegetation or imperviousness, even with control for other socioeconomic characteristics (Gronlund et al 2015, Harlan et al 2013, Xu et al 2013), although in Barcelona, the measured, as opposed to perceived, tree cover in a census tract was not a significant modifier. Uejio and colleagues (2011) also found imperviousness in a census block group (smaller than a ZIP code) to predict heat distress ambulance calls in Phoenix, Arizona but not heat-related mortality in Philadelphia, Pennsylvania.…”

“…We included interactions between each of the potential modifiers and both of the EH time lags to provide for either potential differences in vulnerability by lag or potential short-term declines in hospitalizations in the 2–5 days following EH within each category of modifier. In addition to parameters for each matched set of case and controls which were conditioned out of the likelihood function, we modeled: $$\text{logit}false(\text{hospital}\text{admission}false)={\mathrm{\beta }}_1\text{MH}01+{\mathrm{\beta }}_2\text{MH}25+{\mathrm{\beta }}_3\text{EH}01+{\mathrm{\beta }}_4\text{EH}25+{\mathrm{\beta }}_5\text{EH}01\times {\text{EM}}_1+{\mathrm{\beta }}_6\text{EH}25\times {\text{EM}}_1+\dots +{\mathrm{\beta }}_{21}\text{EH}01\times {\text{EM}}_8+{\mathrm{\beta }}_{22}\text{EH}25\times {\text{EM}}_8$$ where EM 1 –EM 3 were indicator variables Male, Black and Aged 78+, respectively, and EM 4 –EM 8 were ZIP-code “% Non-Green,” “% No HS,” “% Black” and “% Built < 1940,” respectively, modeled simultaneously (Gronlund et al 2015). We also considered models with “% Poverty” instead of “% No HS” as the measure of socioeconomic status.…”

Delineation of Spatial Variability in the Temperature–Mortality Relationship on Extremely Hot Days in Greater Vancouver, Canada

“…These results are somewhat consistent with those of other, comparable studies (Gronlund et al 2015; Hondula et al 2015; Smargiassi et al 2009). For example, Hondula et al (2015) used baseline mortality at 10°C and observed mortality on hot days to report a 0–10% increase in risk for areas with temperatures > 30°C in multiple U.S. cities.…”

“…In comparison, locations between 20°C and 30°C had increases in risk ranging from –2.5% to 2.5%. In another spatially stratified case-crossover analysis of social vulnerability, Gronlund et al (2015) reported no significant differences in ORs for cardiovascular or respiratory mortality between all cases and those cases that occurred in areas of Michigan presumed to be vulnerable. The one exception was for respiratory mortality in areas with older housing.…”

“…The only way to accurately delineate areas of higher and lower risk is with data-driven methods designed to assess spatial differences in the temperature–mortality relationships across a city (Gabriel and Endlicher 2011; Greene et al 2011; Gronlund et al 2015; Hattis et al 2012; Hondula et al 2012, 2014, 2015; Petitti et al 2016; Schaeffer et al 2015; Schuster et al 2014; Smargiassi et al 2009). Herein, we report the development of an approach that combines data from a single representative weather station with exposure and vulnerability maps in a temporally and spatially stratified case-crossover design.…”