Temilayo Adeyeye

New York State (NYS) has experienced warming outpacing the national average, and heat events are increasing. This case-crossover study uses conditional logistic regression to estimate how maximum heat index affects heat-related illness across temporal, demographic, and spatial groups in NYS, from May to September, 2008–2019. The highest risks were in May (Relative Risk (RR) = 1.81, CI: 1.72, 1.90) and August (RR = 1.86, CI: 1.79, 1.94). Older adults, especially those aged 85 and above, are at greatest risk (RR = 1.83, CI: 1.71, 1.96). The Southern Tier climate region had a higher risk (RR = 1.93, CI: 1.80, 2.07) than several other regions. Overall, similar risk between rural and urban NYS was observed. Rural non-Hispanic Black (RR = 2.38, CI: 1.78, 3.19) populations had a higher risk than their urban counterparts. This study was innovative for NYS, providing a deeper understanding of heat–health risks to vulnerable sub-groups. This can assist with facilitating targeted interventions and public health messaging during the periods of highest risk, such as promoting awareness of cooling centers and air-conditioning benefits.

The Earth's precipitation patterns are changing, and regional precipitation is expected to continue to increase in New York State (NYS). Heavy precipitation may negatively affect asthma prevalence through its effect on seasonally varying allergens. We employed a threshold analysis using a time‐stratified semi‐symmetric bi‐directional case‐crossover study design to assess the effect of increase in precipitation on asthma (ICD‐9 code 493.xx, N = 970,903) emergency department (ED) visits between 2005 and 2014 during non‐winter months in NYS. Spatially contiguous gridded meteorological data from North American Land Data Assimilation System (NLDAS) were utilized. We used conditional logistic regression models and stratified the analyses by seasons. During non‐winter months, we found a small, statistically significant risk of asthma ED visits for precipitation levels above 50 mm, with differences by season. These results suggest that heavy precipitation may be related to an increased risk of asthma ED visits. Gridded meteorological estimates provide a means of addressing the gaps in exposure classification, and these findings provide opportunities for further research on interactions with aeroallergens and meteorological conditions in the context of climate and health.

We examined the association between variation in COVID-19 deaths and spatial differences in the racial, ethnic, and nativity-status composition of New York City neighborhoods, which has received little scholarly attention. Using COVID-19 mortality data (through 31 May 2021) and socioeconomic and demographic data from the American Community Survey at the Zip Code Tabulation Area level as well as United-Hospital-Fund-level neighborhood data from the Community Health Survey of the New York City Department of Health and Mental Hygiene, we employed multivariable Poisson generalized estimating equation models and assessed the association between COVID-19 mortality, racial/ethnic/nativity-status composition, and other ecological factors. Our results showed an association between neighborhood-level racial and ethnic composition and COVID-19 mortality rates that is contingent upon the neighborhood-level nativity-status composition. After multivariable adjustment, ZCTAs with large shares of native-born Blacks and foreign-born Hispanics and Asians were more likely to have higher COVID-19 mortality rates than areas with large shares of native-born Whites. Areas with more older adults and essential workers, higher levels of household crowding, and population with diabetes were also at high risk. Small-area analyses of COVID-19 mortality can inform health policy responses to neighborhood inequalities on the basis of race, ethnicity, and immigration status.

Extreme temperature events are linked to increased emergency department visits, hospitalizations, and mortality for individuals with behavioral health disorders (BHD). This study aims to characterize risk factors for concurrent temperature-related illness among BHD hospitalizations in New York State. Using data from the NYS Statewide and Planning Research and Cooperative System between 2005–2019, multivariate log binomial regression models were used in a population of BHD hospitalizations to estimate risk ratios (RR) for a concurrent heat-related (HRI) or cold-related illness (CRI). Dementia (RR 1.65; 95% CI:1.49, 1.83) and schizophrenia (RR 1.38; 95% CI:1.19, 1.60) were associated with an increased risk for HRI among BHD hospitalizations, while alcohol dependence (RR 2.10; 95% CI:1.99, 2.22), dementia (RR 1.52; 95% CI:1.44, 1.60), schizophrenia (RR 1.41; 95% CI:1.31, 1.52), and non-dependent drug/alcohol use (RR 1.20; 95% CI:1.15, 1.26) were associated with an increased risk of CRI among BHD hospitalizations. Risk factors for concurrent HRI among BHD hospitalizations include increasing age, male gender, non-Hispanic Black race, and medium hospital size. Risk factors for concurrent CRI among BHD hospitalizations include increasing age, male gender, non-Hispanic Black race, insurance payor, the presence of respiratory disease, and rural hospital location. This study adds to the literature by identifying dementia, schizophrenia, substance-use disorders, including alcohol dependence and non-dependent substance-use, and other sociodemographic factors as risk factors for a concurrent CRI in BHD hospitalizations.

Regional National Weather Service (NWS) heat advisory criteria in New York State (NYS) were based on frequency of heat events estimated by sparse monitoring data. These may not accurately reflect temperatures at which specific health risks occur in large geographic regions. The objectives of the study were to use spatially resolved temperature data to characterize health risks related to summertime heat exposure and estimate the temperatures at which excessive risk of heat-related adverse health occurs in NYS. We also evaluated the need to adjust current heat advisory threshold and messaging based on threshold temperatures of multiple health outcomes. We assessed the effect of multi-day lag exposure for maximum near-surface air temperature (Tmax) and maximum Heat Index derived from the gridded National Land Data Assimilation System (NLDAS) reanalysis dataset on emergency department (ED) visits/ hospitalizations for heat stress, dehydration, acute kidney failure (AKF) and cardiovascular diseases (CVD) using a case-crossover analysis during summers of 2008–2012. We assessed effect modification using interaction terms and stratified analysis. Thresholds were estimated using piecewise spline regression. We observed an increased risk of heat stress (Risk ratio (RR) = 1.366, 95% confidence interval (CI): 1.347, 1.386) and dehydration (RR = 1.024, 95% CI: 1.021, 1.028) for every 1 °C increase in Tmax on the day of exposure. The highest risk for AKF (RR = 1.017, 95% CI: 1.014, 1.021) and CVD (RR = 1.001, 95% CI: 1.000, 1.002) were at lag 1 and 4 respectively. The increased risk of heat-health effects persists up to 6 days. Rural areas of NYS are at as high a risk of heat-health effects as urban areas. Heat-health risks start increasing at temperatures much lower than the current NWS criteria. Reanalysis data provide refined exposure-response functions for health research, in areas with sparse monitor observations. Based on this research, rural areas in NYS had similar risk for health effects of heat. Heat advisories in New York City (NYC) had been reviewed and lowered previously. As such, the current NWS heat advisory threshold was lowered for the upstate region of New York and surrounding areas. Enhanced outreach materials were also developed and disseminated to local health departments and the public.