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BackgroundExtreme heat poses current and future risks to human health. Heat vulnerability indices (HVIs), commonly developed using principal components analysis (PCA), are mapped to identify populations vulnerable to extreme heat. Few studies critically assess implications of analytic choices made when employing this methodology for fine-scale vulnerability mapping.ObjectiveWe investigated sensitivity of HVIs created by applying PCA to input variables and whether training input variables on heat-health data produced HVIs with similar spatial vulnerability patterns for Detroit, Michigan, USA.MethodsWe acquired 2010 Census tract and block group level data, land cover data, daily ambient apparent temperature, and all-cause mortality during May-September, 2000-2009. We used PCA to construct HVIs using: a) "unsupervised"-PCA applied to variables selected a priori as risk factors for heat-related health outcomes; b) "supervised"-PCA applied only to variables significantly correlated with proportion of all-cause mortality occurring on extreme heat days (i.e., days with 2-d mean apparent temperature above month-specific 95th percentiles).ResultsUnsupervised and supervised HVIs yielded differing spatial vulnerability patterns, depending on selected land cover input variables. Supervised PCA explained 62% of variance in the input variables and was applied on half the variables used in the unsupervised method. Census tract-level supervised HVI values were positively associated with increased proportion of mortality occurring on extreme heat days; supervised PCA could not be applied to block group data. Unsupervised HVI values were not associated with extreme heat mortality for either tracts or block groups.DiscussionHVIs calculated using PCA are sensitive to input data and scale. Supervised HVIs may provide marginally more specific indicators of heat vulnerability than unsupervised HVIs. PCA-derived HVIs address correlation among vulnerability indicators, although the resulting output requires careful contextual interpretation beyond generating epidemiological research questions. Methods with reliably stable outputs should be leveraged for prioritizing heat interventions. https://doi.org/10.1289/EHP4030.

Household-level information on central air conditioning (cenAC) and room air conditioning (rmAC) air conditioning and cold-weather thermal comfort are often missing from publicly available housing databases hindering research and action on climate adaptation and air pollution exposure reduction. We modeled these using information from the American Housing Survey for 2003-2013 and 140 US core-based statistical areas employing variables that would be present in publicly available parcel records. We present random-intercept logistic regression models with either cenAC, rmAC or "home was uncomfortably cold for 24 h or more" (tooCold) as outcome variables and housing value, rented vs. owned, age, and multi- vs. single-family, each interacted with cooling- or heating-degree days as predictors. The out-of-sample predicted probabilities for years 2015-2017 were compared with corresponding American Housing Survey values (0 or 1). Using a 0.5 probability threshold, the model had 63% specificity (true negative rate), and 91% sensitivity (true positive rate) for cenAC, while specificity and sensitivity for rmAC were 94% and 34%, respectively. Area-specific sensitivities and specificities varied widely. For tooCold, the overall sensitivity was effectively 0%. Future epidemiologic studies, heat vulnerability maps, and intervention screenings may reliably use these or similar AC models with parcel-level data to improve understanding of health risk and the spatial patterning of homes without AC.

ObjectivesPreterm births (PTBs) represent significant health risks, and several studies have found associations between high outdoor temperatures and PTB. We estimated both the total and natural direct effects (independent of particulate matter, ozone and nitrogen dioxide air pollutants) of the prior 2-day mean apparent temperature (AT) on PTB. We evaluated effect modification by maternal age, race, education, smoking status and prenatal care.Design and settingWe obtained birth records and meteorological data for the Detroit, Michigan, USA area, for the warm months (May to September), 1991 to 2001. We used a time series Poisson regression with splines of AT, wind speed, solar radiation and citywide average precipitation to estimate total effects. To accommodate multiple mediators and exposure-mediator interactions, AT inverse odds weights, predicted by meteorological and air pollutant covariates, were added in a subsequent model to estimate direct effects.ResultsAt 24.9°C relative to 18.6°C, 10.6% (95% CI: 3.8% to 17.2%) of PTBs were attributable to the total effects of AT, and 10.4% (95% CI: 2.2% to 17.5%) to direct effects. Relative excess risks of interaction indicated that the risk of PTB with increasing temperature above 18.6°C was significantly lower among black mothers and higher among mothers less than 19, older than 30, with late or no prenatal care and who smoked.ConclusionThis additional evidence of a direct association between high temperature and PTB may motivate public health interventions to reduce extreme heat exposures among pregnant women, particularly among those who may have enhanced vulnerability.

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971-2000--a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections.

ObjectivesWe 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.MethodsIn 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 99 th 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).ResultsIn models including multiple effect modifiers, the odds of cardiovascular mortality during EH (99 th 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.ConclusionsGreen 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.

Background: Research has not yet examined how hot weather moderates the relationship between the built environment and outdoor physical activity levels. The authors posited that hot days will increase the magnitude of the expected directional effect of built environment features on physical activity. Methods: This longitudinal study included 134 US adults from the Three city Heat and Electrical failure AdapTation study. Adults self-reported physical activity for multiple summer days (nstudy-days = 742) in 2016. Hot days were defined as ≥90th percentile of daily maximum heat index. Built environment features included density, safety, trees, hilliness, connectivity, access to parks, and access to shops + services. Separate growth curve models with interaction terms (ie, hot day x built environment feature) were run for daily minutes of outdoor physical activity (ie, any activity and recommended activity). Results: Neither hot days nor built environment features impacted outdoor physical activity significantly, and hot days did not moderate the relationship between built environment features and physical activity (P > .05). Conclusions:With adults failing to modify behavior on hot days, cities may be placing adults at increased risk of exertional heat illness. The authors recommend incorporating the risk of exertional heat illness in health impact assessments and deploying heat management strategies. [ABSTRACT FROM AUTHOR]

Indoor residential environments will be an increasing source of dangerous heat exposure as summer temperatures rise. Although air conditioning is a recognized preventative measure, many vulnerable residents either lack air conditioning or cannot always afford the electricity. Understanding the relative influence of different building characteristics and percentages of tree canopy coverage on indoor air temperatures during extreme heat events can help prioritize intervention strategies. We measured indoor and outdoor temperatures at 140 homes in Detroit (MI), Atlanta (GA), and Phoenix (AZ) and surveyed residents to determine the presence of a working central air-conditioning system and their ability to afford utilities. For each home, we collected information on construction year, interior size, single versus multifamily structure, façade type, and percentage of tree canopy. After air conditioning, the importance of building characteristics versus tree canopy measures varied by city. In Detroit, masonry façades worsened the influence of outdoor temperatures, whereas increased tree canopy coverage moderated the influence. In Atlanta, building characteristics were not significant, but tree canopy moderated indoor temperatures in late afternoon. In Phoenix, some building characteristics moderated the influence of outdoor temperatures in late afternoon. In Detroit, only 35% of respondents had and used central air conditioning as needed compared with 57% in Atlanta and 95% in Phoenix. The influence of tree canopy and building characteristics on indoor temperatures varied by city depending on its background climate. We recommend adding questions about air conditioning and utility poverty to the U.S. Census. For rental properties, municipalities should establish and enforce maximum indoor temperature thresholds. States should include cooling in their Low Income Home Energy Assistance (LIHEAP) programs. Public cooling centers need to anticipate electricity outages. Urban planners must ensure efforts to increase mechanical cooling are powered by renewable energy sources. [ABSTRACT FROM AUTHOR]

Research is presented on reproductive isolation, occurring after mating and before the zygote stage is reached, in three hybridizations of Drosophila simulans. Topics include retention and storage of sperm, number and viability of eggs produced, number of resulting progeny, heterospecific fertilization, and mated female longevity.

The burden of temperature‐associated mortality and hospital visits is significant, but temperature's effects on non‐emergency health outcomes is less clear. This burden is potentially greater in low‐income households unable to afford efficient heating and cooling. We examined short‐term associations between indoor temperatures and cognitive function and daytime sleepiness in low‐income residents of Detroit, Michigan. Apparent temperature (AT, based on temperature and humidity) was recorded hourly in 34 participant homes between July 2019‐March 2020. Between July‐October 2019, 18 participants were administered word list immediate (WLL) and delayed (WLD) recall tests (10‐point scales) and the Epworth Sleepiness Scale (24‐point scale) 2–4 times. We applied longitudinal models with nonlinear distributed lags of temperature up to 7 days prior to testing. Indoor temperatures ranged 8–34°C overall and 15–34°C on survey days. We observed a 0.4 (95% CI: 0.0, 0.7) point increase in WLL and 0.4 (95% CI: 0.0, 0.9) point increase in WLD scores per 2°C increase in AT. Results suggested decreasing sleepiness scores with decreasing nighttime AT below 22°C. Low‐income Detroit residents experience uncomfortably high and low indoor temperatures. Indoor temperature may influence cognitive function and sleepiness, although we did not observe deleterious effects of higher temperatures. [ABSTRACT FROM AUTHOR]

Background: Heat-wave frequency, intensity, and duration are increasing with global climate change. The association between heat and mortality in the elderly is well documented, but less is known regarding associations with hospital admissions. OBJECTIVES: Our goal was to determine associations between moderate and extreme heat, heat waves, and hospital admissions for nonaccidental causes among Medicare beneficiaries ≥ 65 years of age in 114 cities across five U.S. climate zones. METHODS: We used Medicare inpatient billing records and city-specific data on temperature, humidity, and ozone from 1992 through 2006 in a time-stratified case-crossover design to estimate the association between hospitalization and moderate [90th percentile of apparent temperature (AT)] and extreme (99th percentile of AT) heat and heat waves (AT above the 95th percentile over 2-8 days). In sensitivity analyses, we additionally considered confounding by ozone and holidays, different temperature metrics, and alternate models of the exposure-response relationship. RESULTS: Associations between moderate heat and hospital admissions were minimal, but extreme heat was associated with a 3% (95% CI: 2%, 4%) increase in all-cause hospital admissions over the subsequent 8 days. In cause-specific analyses, extreme heat was associated with increased hospitalizations for renal (15%; 95% CI: 9%, 21%) and respiratory (4%; 95% CI: 2%, 7%) diseases, but not for cardiovascular diseases. An added heat-wave effect was observed for renal and respiratory admissions. CONCLUSION: Extreme heat is associated with increased hospital admissions, particularly for renal causes, among the elderly in the United States. Environ Health Perspect 122:1187-1192; http://dx.doi.org/10.1289/ehp.1206132, Introduction Heat waves, or extreme heat events, are predicted to increase in frequency, intensity, and duration with climate change (Intergovernmental Panel on Climate Change 2007). The elderly are more vulnerable [...]