Heat illness data strengthens vulnerability maps

The frequency of heat waves has increased in Europe, Asia, and Australia [1, 2]. Meehl and Tebaldi (2004) and Beniston et al. (2007) also projected that future heat waves will be more intense, more frequent, and longer lasting in Europe and North America [3, 4]. Multiple studies reported that extreme heat elevates the mortality and morbidity risk from cardiovascular [5‐7], respiratory [8, 9], heat-related [10, 11], and renal disease [12, 13]. With an increase in the total number of older adults worldwide, the impact of heat waves is predicted to get worse in the future.

Heat stress is not only influenced by heat exposure, but also by demographic factors and social determinants of health (SDOH). Demographic factors, such as age [14‐17], sex [18‐20], and race/ethnicity [21, 22], influence the adaptive capacity of individuals to extreme heat exposure through physiological processes or socioeconomic status. SDOH also affect the impact of heat waves on human health. Financial assets (e.g. money, vehicles, housing), human capital (e.g. education), and social capital (e.g. churches, neighborhood associations, friend groups, etc.) readily influence people’s well-being and vulnerability [23‐25]. For example, employment status (e.g. unemployment rate, labor force) and occupation type (e.g. agriculture and construction) are highly associated with vulnerability to heat waves since it could represent the amount of human and financial capital and the magnitude of heat exposures during hazards [26].

Historically, almost all extreme heat and human health studies have examined associations either over time (e.g. case-crossover or time series designs) or across geographic areas (e.g. spatial analysis). This analytical choice sometimes narrowed research scopes and areas due to limited health outcomes and patient information restricted by health information privacy. In case-crossover designs, each case serves as his or her own control, which strengthens causal inferences [27]. Although the case-control study design implicitly controls for time-invariant individual confounders (e.g. age, sex), it provides limited information about heat vulnerability between geographic areas. In addition, only a very limited number of variables such as age, sex, and race/ethnicity, excluding most socioeconomic variables (e.g. occupation, income, education), were studied due to privacy issues. Instead of focusing on time, geographic studies often map extreme heat vulnerability using a combination of pre-selected SDOH. In general, this technique can identify areas with disproportionately high heat-related mortality and morbidity rates [28, 29]. However, validation studies suggest some of the pre-selected SDOH are not consistently associated with health outcomes [30, 31].

Our study examines the relative importance of SDOH for five different heat-related health outcomes (cardiovascular disease, dehydration, heat-related illness, acute renal disease, and respiratory disease) over 2008–2012. The study sequentially combines a case-crossover design and spatial analysis to identify the counties in Florida most vulnerable to extreme heat and the strongest and most consistent SDOH related to heat-sensitive health outcomes. This methodology attempts to address research gaps by providing more insight into SDOH heat sensitivities and increasing the accuracy of vulnerability mapping.

We first calculate the increase in likelihood of health outcomes when temperature rises through a case-crossover design for 67 counties in the state of Florida. Many case-crossover studies in the past averaged results over an entire state or multi-county region. Then, the study regresses SDOH on the case-crossover odds ratios (ORs) to identify the most consistent and strongest SDOH metrics. This alternative method of identifying place-based risk factors may be more accurate than vulnerability indices created with pre-selected risk factors. This research may increase the accuracy of vulnerability mapping and help identify high risk groups who can further benefit from public health interventions.

This study illustrates the benefits of spatially analyzing heat health sensitivities derived from a case-crossover study design. Our identification of the strongest and most consistent heat risk factors produced different results than “all hazards” vulnerability mapping using pre-selected risk factors. In our study, southwest Florida displays the highest risks of cardiovascular disease, dehydration, acute renal disease, and respiratory disease. The higher proportion of adults over the age of 65, 24.7% according to the U.S. Decennial Census (2010), was notably higher than other regions (13.7 to 19.2%) and may be partially responsible for increased risk. This spatial pattern is notably different from Emrich et al. (2014) which suggested southeast Florida was the most vulnerable areas to natural hazards [44]. Our study used empirical morbidity data to calculate the health risks, whereas Emrich et al. (2014)‘s vulnerability index is based on pre-selected risk factors and not health outcomes [44]. Our results suggest that a priori vulnerability assessments can be improved by more explicitly considering climate sensitive health outcomes. Social vulnerability assessments frequently give equal weight to individual or synthetic (e.g. principal component) risk factors. In contrast, the regression beta coefficients derived from the present study could be used as heat risk factor weights.

In this study, we identified evidence to suggest that demographic and socioeconomic variables influence the magnitude of heat-related health risk. The percent of adults over age 65 in nursing homes was one of the most influential factors for cardiovascular disease, acute renal disease, and respiratory disease. This result is consistent with previous papers [45, 46]. These papers indicate that institutionalized older adults are at higher risk than those not living in institutions during heat waves. For example, Stafoggia et al. (2006) found a higher OR from those living in nursing homes (OR: 1.61, 95% CI: 1.41–1.84) than any other age groups over 65 (65–74: 1.25, 1.12–1.38; 75–84: 1.36, 1.28–1.44; 85–94: 1.49, 1.37–1.63; 95+: 1.58, 1.34–1.85) [45].

Our study also clearly showed differential heat-related health impacts depend on occupation. When temperature increases, the counties having more people working in farming, fishing, mining, forestry, construction, and extraction tended to have higher risks of dehydration and acute renal disease. This is consistent with other research. Moyce (2016) reported that agricultural workers are at high risk of acute kidney disease related to work in hot conditions (adjusted OR 1.34, 95% CI: 1.04–1.74) [47]. High rates of chronic kidney disease were also found from sugarcane workers in Central America, and repeated exposure to heat stress has been implicated as a potential cause [48]. Moreover, temperature raises the risk of dehydration for farm workers [49]. In contrast, the counties having larger populations working in installation, maintenance, and repair tended to have lower risks of cardiovascular, dehydration, acute renal, and respiratory disease. According to the U.S. Bureau of Labor Statistics, those employed in installation, maintenance, and repair occupations (43.0 h/week) had relatively shorter work hours than mining (49.2) and construction (42.6) in 2019 [50]. The Central Statistics Office also reported that workers in the agriculture, forestry, and fishing industries worked 50.4 h/week compared with the national average of 35.7 h/week in 2015 [51]. Those employed in farming, fishing, mining, forestry, construction, and extraction spend a large proportion of time doing heavy physical work outside (e.g. harvest, roofing), which can substantially increase heat exposure. Furthermore, these jobs are more physically demanding than other occupations, and workers may not have control over their work conditions to allow for adequate breaks and cooling.

Our result exhibited that high income counties consistently have lower health risk of dehydration, heat-related illness, acute renal disease, and respiratory disease. Hondula and Barnett (2014) also showed that a 1% increase in the proportion of high-income residents (weekly income > $1600) was associated with 8.5 fewer heat-related hospitalizations in Brisbane, Australia [52]. Other papers support the negative relationships between income and heat-related health risks [53‐55]. The relation may exist because people with low incomes are more likely to reside in poor indoor environments with limited cooling [56], have limited access to healthy food [57, 58], and not have health insurance [59, 60].

In addition, we consistently observed negative associations between health outcomes and unemployment rate (cardiovascular disease and acute renal disease) and the percent of housing units with no automobile (cardiovascular disease and respiratory disease). We suspect these factors may impact the number of health care visits through limited health insurance coverage or limited accessibility to health care facilities. In the U.S., where health insurance is frequently connected to employment, healthcare usage is negatively associated with unemployment rate [61‐63]. Comber et al. (2011) also presents that car ownership is highly associated with assesses to health care facilities, particularly in rural areas [64]. Furthermore, we found the median building age may not be a significant extreme heat risk factor in Florida for most heat-related health outcomes. Kovach et al. (2015) and Boroushaki (2017) also support that building year is not a main factor after adjusting for population density, age, poverty, and tree canopy [65, 66].

Finally, our novel methodology showed a couple of advantages over traditional methods. First, the approach expands upon current research topics by providing a way to connect heat-health associations with a wide range of neighborhood demographic or socioeconomic variables collected through various surveys such as American Community Survey (ACS). Traditional stratified analysis or models with interaction terms often only examine a limited number of sociodemographic characteristics, such as age, sex, race/ethnicity, due to limited individual sociodemographic data availability. In addition, our study provides the relative importance of each sociodemographic data for different health outcomes. Previous case-crossover analyses typically considered the interaction between heat and a composite vulnerability index comprised of multiple sociodemographic variables. The vulnerability index provides less guidance to public health departments for interventions due to less information on specific sociodemographic risk factors. Furthermore, our approach can explicitly examine the local and neighboring heat impact effect modification by SDOH, which could benefit public health departments in understanding the spatial processes of heat impacts. Traditional case-crossover study designs, which control residual autocorrelation using conditional Poisson regression, would provide limited information on spatial processes.

Our study design suffers from the same limitations as other spatial ecologic studies. First, our study’s county level correlations do not necessarily translate to individual-level associations. For example, even though we found a strong correlation between those over age 65 in nursing homes and cardiovascular disease, this does not mean every individual over 65 in nursing homes has a higher heat cardiovascular risk. Second, the results may be contingent on the county analysis level. The modifiable areal unit problem is commonly generated when point data are aggregated into areal units. We would anticipate somewhat different associations if the same series of analyses were repeated at regional or ZIP-code analysis levels. Finally, our demographic/socioeconomic variables were from two different data sources. Even though most of our data were from ACS, we still included two variables from U.S. Decennial Census data. Since there are differences regarding data collection methods, some uncertainties and errors could be introduced in the analysis.

Our study has several key policy implications for public health practitioners and policy makers. First, our results show that each health outcome has a different relationship with various demographic and socioeconomic variables. Current warning systems and occupational health standards for heat waves (e.g. California, Washington, Oregon State rules for heat waves) are mostly based on a one-size-fits-all criteria (e.g. 100 °F threshold). This study provides strong evidence that we may need to devise an individual vulnerability or risk index for each health outcome. Second, we provide the relative importance of demographic and socioeconomic variables for each health outcome. This information provides important background for determining the weights of each variable when making vulnerability maps or indexes. Given that previous maps and indexes typically assumed equal weights without an objective way to assign a weight to each variable, our results may improve current vulnerability products. In addition, these results could prioritize prevention efforts, prepare occupational health and safety guidelines, and plan health care resource (e.g., emergency department and hospital access and capacity planning [67]). Third, our method allows investigators to expand the scope of their research, and test other possible mechanisms connecting health outcomes and sociodemographic variables. Most previous studies only investigated basic demographic variables such as age, sex, race/ethnicity, due to limited individual sociodemographic information contained in electronic medical records. With our method, demographic and socioeconomic data at various regional levels (e.g. county, ZIP code) can be connected with health outcomes. Finally, our method provides more in-depth information about the spatial processes of each demographic and socioeconomic variable. Since each variable has a different level of direct or indirect impacts with neighbors, different strategies are needed for each variable. Our results will improve health equity and reduce the overall public health burden of negative health effects from heat exposures.