The effect of heterogeneous severe drought on all-cause and cardiovascular mortality in the Northern Rockies and Plains of the United States.

Drought is a distinct and complicated climate hazard that regularly leads to severe economic impacts. Changes in the frequency and occurrence of drought due to anthropogenic climate change can lead to new and unanticipated outcomes. To better prepare for health outcomes, more research is needed to develop methodologies to understand potential consequences. This study suggests a new methodology for assessing the impact of monthly severe drought exposure on mortality in the Northern Rockies and Plains of the United States from 2000 to 2018. A two-stage model with the power prior approach was applied to integrate heterogeneous severe drought pattern and estimate overall risk ratios of all-cause and cardiovascular mortality related to multiple drought indices (the US Drought Monitor, 6- and 12-month Standardized Precipitation-Evapotranspiration Index, 6- and 12 month Evaporative Demand Drought Index). Under severe drought, the risk ratios of all-cause mortality are 1.050 (95 % Cr: 1.031 to 1.071, USDM), 1.041 (95 % Cr: 1.022 to 1.060, 6-SPEI), 1.009 (95 % Cr: 0.989 to 1.031, 12SPEI), 1.045 (95 % Cr: 1.022 to 1.067, 6-EDDI), and 1.035 (95 % Cr: 1.009 to 1.062, 12-EDDI); cardiovascular mortality are 1.057 (95 % Cr: 1.023 to 1.091, USDM), 1.028 (95 % Cr: 0.998 to 1.059, 6-SPEI), 1.005 (95 % Cr: 0.973 to 1.040, 12-SPEI), 1.042 (95 % Cr: 1.005 to 1.080, 6-EDDI), and 1.004 (95 % Cr: 0.959 to 1.049, 12-EDDI). Our results showed that (i) a model with properly accounted for heterogeneous exposure pattern had greater risk ratios if statistically significant; (ii) a mid-term (6-month) severe drought had higher risk ratios compared to longer-term (12-month) drought; and (iii) different severe droughts affect populations in a different way. These results expand the existing knowledge of drought relationship to increasing mortality in the United States. The findings from this study highlight the need for communities and policymakers to establish effective drought-prevention initiatives in this region.

The Association between Drought Exposure and Respiratory-Related Mortality in the United States from 2000 to 2018.

Climate change has brought increasing attention to the assessment of health risks associated with climate and extreme events. Drought is a complex climate phenomenon that has been increasing in frequency and severity both locally and globally due to climate change. However, the health risks of drought are often overlooked, especially in places such as the United States, as the pathways to health impacts are complex and indirect. This study aims to conduct a comprehensive assessment of the effects of monthly drought exposure on respiratory mortality for NOAA climate regions in the United States from 2000 to 2018. A two-stage model was applied to estimate the location-specific and overall effects of respiratory risk associated with two different drought indices over two timescales (the US Drought Monitor and the 6-month and 12-month Evaporative Demand Drought Index). During moderate and severe drought exposure, respiratory mortality risk ratio in the general population increased up to 6.0% (95% Cr: 4.8 to 7.2) in the Northeast, 9.0% (95% Cr: 4.9 to 13.3) in the Northern Rockies and Plains, 5.2% (95% Cr: 3.9 to 6.5) in the Ohio Valley, 3.5% (95% Cr: 1.9 to 5.0) in the Southeast, and 15.9% (95% Cr: 10.8 to 20.4) in the Upper Midwest. Our results showed that age, ethnicity, sex (both male and female), and urbanicity (both metro and non-metro) resulted in more affected population subgroups in certain climate regions. The magnitude and direction of respiratory risk ratio differed across NOAA climate regions. These results demonstrate a need for policymakers and communities to develop more effective strategies to mitigate the effects of drought across regions.

Estimating the Burden of Heat-Related Illness Morbidity Attributable to Anthropogenic Climate Change in North Carolina.

Climate change is known to increase the frequency and intensity of hot days (daily maximum temperature ≥30°C), both globally and locally. Exposure to extreme heat is associated with numerous adverse human health outcomes. This study estimated the burden of heat-related illness (HRI) attributable to anthropogenic climate change in North Carolina physiographic divisions (Coastal and Piedmont) during the summer months from 2011 to 2016. Additionally, assuming intermediate and high greenhouse gas emission scenarios, future HRI morbidity burden attributable to climate change was estimated. The association between daily maximum temperature and the rate of HRI was evaluated using the Generalized Additive Model. The rate of HRI assuming natural simulations (i.e., absence of greenhouse gas emissions) and future greenhouse gas emission scenarios were predicted to estimate the HRI attributable to climate change. Over 4 years (2011, 2012, 2014, and 2015), we observed a significant decrease in the rate of HRI assuming natural simulations compared to the observed. About 3 out of 20 HRI visits are attributable to anthropogenic climate change in Coastal (13.40% [IQR: -34.90,95.52]) and Piedmont (16.39% [IQR: -35.18,148.26]) regions. During the future periods, the median rate of HRI was significantly higher (78.65%: Coastal and 65.85%: Piedmont), assuming a higher emission scenario than the intermediate emission scenario. We observed significant associations between anthropogenic climate change and adverse human health outcomes. Our findings indicate the need for evidence-based public health interventions to protect human health from climate-related exposures, like extreme heat, while minimizing greenhouse gas emissions.

Evaluating the Sensitivity of Heat Wave Definitions among North Carolina Physiographic Regions.

Exposure to extreme heat is a known risk factor that is associated with increased heat-related illness (HRI) outcomes. The relevance of heat wave definitions (HWDs) could change across health conditions and geographies due to the heterogenous climate profile. This study compared the sensitivity of 28 HWDs associated with HRI emergency department visits over five summer seasons (2011−2016), stratified by two physiographic regions (Coastal and Piedmont) in North Carolina. The HRI rate ratios associated with heat waves were estimated using the generalized linear regression framework assuming a negative binomial distribution. We compared the Akaike Information Criterion (AIC) values across the HWDs to identify an optimal HWD. In the Coastal region, HWDs based on daily maximum temperature with a threshold > 90th percentile for two or more consecutive days had the optimal model fit. In the Piedmont region, HWD based on the daily minimum temperature with a threshold value > 90th percentile for two or more consecutive days was optimal. The HWDs with optimal model performance included in this study captured moderate and frequent heat episodes compared to the National Weather Service (NWS) heat products. This study compared the HRI morbidity risk associated with epidemiologic-based HWDs and with NWS heat products. Our findings could be used for public health education and suggest recalibrating NWS heat products.

Verifying Experimental Wet Bulb Globe Temperature Hindcasts Across the United States.

Hot and humid heat exposures challenge the health of outdoor workers engaged in occupations such as construction, agriculture, first response, manufacturing, military, or resource extraction. Therefore, government institutes developed guidelines to prevent heat-related illnesses and death during high heat exposures. The guidelines use Wet Bulb Globe Temperature (WBGT), which integrates temperature, humidity, solar radiation, and wind speed. However, occupational heat exposure guidelines cannot be readily applied to outdoor work places due to limited WBGT validation studies. In recent years, institutions have started providing experimental WBGT forecasts. These experimental products are continually being refined and have been minimally validated with ground-based observations. This study evaluated a modified WBGT hindcast using the historical National Digital Forecast Database and the European Centre for Medium-Range Weather Forecasts Reanalysis v5. We verified the hindcasts with hourly WBGT estimated from ground-based weather observations. After controlling for geographic attributes and temporal trends, the average difference between the hindcast and in situ data varied from -0.64°C to 1.46°C for different Köppen-Geiger climate regions, and the average differences are reliable for decision making. However, the results showed statistically significant variances according to geographical features such as aspect, coastal proximity, land use, topographic position index, and Köppen-Geiger climate categories. The largest absolute difference was observed in the arid desert climates (1.46: 95% CI: 1.45, 1.47), including some parts of Nevada, Arizona, Colorado, and New Mexico. This research investigates geographic factors associated with systematic WBGT differences and points toward ways future forecasts may be statistically adjusted to improve accuracy.

Short-term effects of specific humidity and temperature on COVID-19 morbidity in select US cities.

Little is known about the environmental conditions that drive the spatiotemporal patterns of SARS-CoV-2. Preliminary research suggests an association with meteorological parameters. However, the relationship with temperature and humidity is not yet apparent for COVID-19 cases in US cities first impacted. The objective of this study is to evaluate the association between COVID-19 cases and meteorological parameters in select US cities. A case-crossover design with a distributed lag nonlinear model was used to evaluate the contribution of ambient temperature and specific humidity on COVID-19 cases in select US cities. The case-crossover examines each COVID case as its own control at different time periods (before and after transmission occurred). We modeled the effect of temperature and humidity on COVID-19 transmission using a lag period of 7 days. A subset of 8 cities were evaluated for the relationship with meteorological parameters and 5 cities were evaluated in detail. Short-term exposure to humidity was positively associated with COVID-19 transmission in 4 cities. The associations were small with 3 out of 4 cities exhibiting higher COVID19 transmission with specific humidity that ranged from 6 to 9 g/kg. Our results suggest that weather should be considered in infectious disease modeling efforts. Future work is needed over a longer time period and across different locations to clearly establish the weather-COVID19 relationship.