Carina J. Gronlund

Mapping community determinants of heat vulnerability.

Background The evidence that heat waves can result in both increased deaths and illness is substantial, and concern over this issue is rising because of climate change. Adverse health impacts from heat waves can be avoided, and epidemiologic studies have identified specific population and community characteristics that mark vulnerability to heat waves. Objectives We situated vulnerability to heat in geographic space and identified potential areas for intervention and further research. Methods We mapped and analyzed 10 vulnerability factors for heat-related morbidity/mortality in the United States: six demographic characteristics and two household air conditioning variables from the U.S. Census Bureau, vegetation cover from satellite images, and diabetes prevalence from a national survey. We performed a factor analysis of these 10 variables and assigned values of increasing vulnerability for the four resulting factors to each of 39,794 census tracts. We added the four factor scores to obtain a cumulative heat vulnerability index value. Results Four factors explained > 75% of the total variance in the original 10 vulnerability variables: a) social/environmental vulnerability (combined education/poverty/race/green space), b) social isolation, c) air conditioning prevalence, and d) proportion elderly/diabetes. We found substantial spatial variability of heat vulnerability nationally, with generally higher vulnerability in the Northeast and Pacific Coast and the lowest in the Southeast. In urban areas, inner cities showed the highest vulnerability to heat. Conclusions These methods provide a template for making local and regional heat vulnerability maps. After validation using health outcome data, interventions can be targeted at the most vulnerable populations.

Summer temperature variability and long-term survival among elderly people with chronic disease

Time series studies show that hot temperatures are associated with increased death rates in the short term. In light of evidence of adaptation to usual temperature but higher deaths at unusual temperatures, a long-term exposure relevant to mortality might be summertime temperature variability, which is expected to increase with climate change. We investigated whether the standard deviation (SD) of summer (June–August) temperatures was associated with survival in four cohorts of persons over age 65 y with predisposing diseases in 135 US cities. Using Medicare data (1985–2006), we constructed cohorts of persons hospitalized with chronic obstructive pulmonary disease, diabetes, congestive heart failure, and myocardial infarction. City-specific yearly summer temperature variance was linked to the individuals during follow-up in each city and was treated as a time-varying exposure. We applied a Cox proportional hazard model for each cohort within each city, adjusting for individual risk factors, wintertime temperature variance, yearly ozone levels, and long-term trends, to estimate the chronic effects on mortality of long-term exposure to summer temperature SD, and then pooled results across cities. Mortality hazard ratios ranged from 1.028 (95% confidence interval, 1.013– 1.042) per 1 °C increase in summer temperature SD for persons with congestive heart failure to 1.040 (95% confidence interval, 1.022–1.059) per 1 °C increase for those with diabetes. Associations were higher in elderly persons and lower in cities with a higher percentage of land with green surface. Our data suggest that long-term increases in temperature variability may increase the risk of mortality in different subgroups of susceptible older populations.

Heat, Heat Waves, and Hospital Admissions among the Elderly in the United States, 1992–2006

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. Citation: Gronlund CJ, Zanobetti A, Schwartz JD, Wellenius GA, O’Neill MS. 2014. Heat, heat waves, and hospital admissions among the elderly in the United States, 1992–2006. Environ Health Perspect 122:1187–1192; http://dx.doi.org/10.1289/ehp.1206132

Susceptibility to mortality in weather extremes: effect modification by personal and small-area characteristics.

Background: Extremes of temperature have been associated with short-term increases in daily mortality. We identified subpopulations with increased susceptibility to dying during temperature extremes, based on personal demographics, small-area characteristics, and preexisting medical conditions. Methods: We examined Medicare participants in 135 US cities and identified preexisting conditions based on hospitalization records before their deaths, from 1985 to 2006. Personal characteristics were obtained from the Medicare records, and area characteristics were assigned based on zip code of residence. We conducted a case-only analysis of over 11 million deaths and evaluated modification of the risk of dying associated with extremely hot days and extremely cold days, continuous temperatures, and water vapor pressure. Modifiers included preexisting conditions, personal characteristics, zip code–level population characteristics, and land cover characteristics. For each effect modifier, a city-specific logistic regression model was fitted and then an overall national estimate was calculated using meta-analysis. Results: People with certain preexisting conditions were more susceptible to extreme heat, with an additional 6% (95% confidence interval = 4%–8%) increase in the risk of dying on an extremely hot day in subjects with previous admission for atrial fibrillation, an additional 8% (4%–12%) in subjects with Alzheimer disease, and an additional 6% (3%–9%) in subjects with dementia. Zip code level and personal characteristics were also associated with increased susceptibility to temperature. Conclusions: We identified several subgroups of the population who are particularly susceptible to temperature extremes, including persons with atrial fibrillation.

Racial and Socioeconomic Disparities in Heat-Related Health Effects and Their Mechanisms: a Review

Adaptation to increasing extreme heat in a changing climate requires a precise understanding of who is most vulnerable to the health effects of extreme heat. The evidence for race, ethnicity, income, education, and occupation at both the individual and area levels as indicators of vulnerability is reviewed. The evidence for the social, behavioral, and technological mechanisms by which racial and socioeconomic disparities in vulnerability exist is also reviewed. These characteristics include cardiorespiratory, renal and endocrine comorbidities; cognitive, mental, or physical disabilities; medication use; housing characteristics; neighborhood characteristics such as urban heat islands, crime, and safety; social isolation; and individual behaviors such as air conditioning use, opening windows, using fans, and use of cooler public spaces. Pre-existing and future research identifying these more proximal indicators of vulnerability will provide information that is more generalizable across locations and time to aid in identifying who to target for prevention of heat-associated morbidity and mortality.

Impacts of Climate Change on Public Health in India: Future Research Directions

Background Climate change and associated increases in climate variability will likely further exacerbate global health disparities. More research is needed, particularly in developing countries, to accurately predict the anticipated impacts and inform effective interventions. Objectives Building on the information presented at the 2009 Joint Indo–U.S. Workshop on Climate Change and Health in Goa, India, we reviewed relevant literature and data, addressed gaps in knowledge, and identified priorities and strategies for future research in India. Discussion The scope of the problem in India is enormous, based on the potential for climate change and variability to exacerbate endemic malaria, dengue, yellow fever, cholera, and chikungunya, as well as chronic diseases, particularly among the millions of people who already experience poor sanitation, pollution, malnutrition, and a shortage of drinking water. Ongoing efforts to study these risks were discussed but remain scant. A universal theme of the recommendations developed was the importance of improving the surveillance, monitoring, and integration of meteorological, environmental, geospatial, and health data while working in parallel to implement adaptation strategies. Conclusions It will be critical for India to invest in improvements in information infrastructure that are innovative and that promote interdisciplinary collaborations while embarking on adaptation strategies. This will require unprecedented levels of collaboration across diverse institutions in India and abroad. The data can be used in research on the likely impacts of climate change on health that reflect India’s diverse climates and populations. Local human and technical capacities for risk communication and promoting adaptive behavior must also be enhanced.

US local action on heat and health: are we prepared for climate change?

ObjectivesGlobal climate change is increasing the frequency of heat waves, hot weather, and temperature variability, which contribute to mortality and illness. Baseline information on local efforts to reduce heat vulnerability, including public advisories; minimizing greenhouse gas emissions; and mitigating urban heat islands, is lacking.MethodsWe designed a survey about local government programs to prevent health problems and reduce heat exposure during heatwaves and administered it to 285 US communities.ResultsOf 70 respondents, 26 indicated that excessive heat events are a significant issue for the local government; 30 had established preventive programs. Local government leadership and public health impacts of heat were cited most frequently as extremely important determinants of preventive programs, followed by implementation costs, economic impacts of hot weather, and greenhouse gas emissions mitigation. Cool paving materials and vegetated roofs were common heat mitigation strategies. Fact sheets and case studies were desired guidance for protecting communities during hot weather.ConclusionsNew partnerships and financial resources are needed to support more widespread local action to prevent adverse health consequences of climate change and promote environmental sustainability.

Geostatistical exploration of spatial variation of summertime temperatures in the Detroit metropolitan region

BACKGROUND Because of the warming climate urban temperature patterns have been receiving increased attention. Temperature within urban areas can vary depending on land cover, meteorological and other factors. High resolution satellite data can be used to understand this intra-urban variability, although they have been primarily studied to characterize urban heat islands at a larger spatial scale. OBJECTIVE This study examined whether satellite-derived impervious surface and meteorological conditions from multiple sites can improve characterization of spatial variability of temperature within an urban area. METHODS Temperature was measured at 17 outdoor sites throughout the Detroit metropolitan area during the summer of 2008. Kriging and linear regression were applied to daily temperatures and secondary information, including impervious surface and distance-to-water. Performance of models in predicting measured temperatures was evaluated by cross-validation. Variograms derived from several scenarios were compared to determine whether high-resolution impervious surface information could capture fine-scale spatial structure of temperature in the study area. RESULTS Temperatures measured at the sites were significantly different from each other, and all kriging techniques generally performed better than the two linear regression models. Impervious surface values and distance-to-water generally improved predictions slightly. Restricting models to days with lake breezes and with less cloud cover also somewhat improved the predictions. In addition, incorporating high-resolution impervious surface information into cokriging or universal kriging enhanced the ability to characterize fine-scale spatial structure of temperature. CONCLUSIONS Meteorological and satellite-derived data can better characterize spatial variability in temperature across a metropolitan region. The data sources and methods we used can be applied in epidemiological studies and public health interventions to protect vulnerable populations from extreme heat events.

Validating Satellite-Derived Land Surface Temperature with in Situ Measurements: A Public Health Perspective

Background: Land surface temperature (LST) and percent surface imperviousness (SI), both derived from satellite imagery, have been used to characterize the urban heat island effect, a phenomenon in which urban areas are warmer than non-urban areas. Objectives: We aimed to assess the correlations between LSTs and SI images with actual temperature readings from a ground-based network of outdoor monitors. Methods: We evaluated the relationships among a) LST calculated from a 2009 summertime satellite image of the Detroit metropolitan region, Michigan; b) SI from the 2006 National Land Cover Data Set; and c) ground-based temperature measurements monitored during the same time period at 19 residences throughout the Detroit metropolitan region. Associations between these ground-based temperatures and the average LSTs and SI at different radii around the point of the ground-based temperature measurement were evaluated at different time intervals. Spearman correlation coefficients and corresponding p-values were calculated. Results: Satellite-derived LST and SI values were significantly correlated with 24-hr average and August monthly average ground temperatures at all but two of the radii examined (100 m for LST and 0 m for SI). Correlations were also significant for temperatures measured between 0400 and 0500 hours for SI, except at 0 m, but not LST. Statistically significant correlations ranging from 0.49 to 0.91 were observed between LST and SI. Conclusions: Both SI and LST could be used to better understand spatial variation in heat exposures over longer time frames but are less useful for estimating shorter-term, actual temperature exposures, which can be useful for public health preparedness during extreme heat events.

Characterizing the burden of disease of particulate matter for life cycle impact assessment

Fine particulate air pollution (PM2.5) is a major environmental contributor to human burden of disease and therefore an important component of life cycle impact assessments. An accurate PM2.5 characterization factor, i.e., the impact per kilogram of PM2.5 emitted, is critical to estimating “cradle-to-grave” human health impacts of products and processes. We developed and assessed new characterization factors (disability-adjusted life years (DALY)/kgPM2.5 emitted), or the products of dose-response factors (deaths/kgPM2.5 inhaled), severity factors (DALY/death), and intake fractions (kgPM2.5 inhaled/kgPM2.5 emitted). In contrast to previous health burden estimates, we calculated age-specific concentration- and dose-response factors using baseline data, from 63 US metropolitan areas, consistent with the US study population used to derive the relative risk. We also calculated severity factors using 2010 Global Burden of Disease data. Multiplying the revised PM2.5 dose responses, severity factors, and intake fractions yielded new PM2.5 characterization factors that are higher than previous factors for primary PM2.5 but lower for secondary PM2.5 due to NOx. Multiplying the concentration-response and severity factors by 2005 ambient PM2.5 concentrations yielded an annual US burden of 2,000,000 DALY, slightly lower than previous US estimates. The annual US health burden estimated from PM emissions and characterization factors was 2.2 times higher.