The impact of heat exposure on reduced gestational age in pregnant women in North Carolina, 2011-2015.

Research on the impact of heat on pregnant women has focused largely on outcomes following extreme temperature events, such as particular heat waves or spells of very cold weather on pregnant women. Consistently, the literature has shown a statistically significant relationship between heat with shortened gestational age with studies concentrated largely in the western states of the USA or other nations. The association between heat and shortened gestational age has not been examined in the Southeastern US where maternal outcomes are some of the most challenging in the nation. Unlike previous studies that focus on the impacts of a single heat wave event, this study seeks to understand the impact of high heat over a 5-year period during the annual warm season (May-September). To achieve this goal, a case-crossover study design is employed to understand the impact of heat on preterm labor across regions in North Carolina (NC). Temperature thresholds for impact and the underlying relationships between preterm labor and heat are investigated using generalized additive models (GAM). Gridded temperature data (PRISM) is used to establish exposure classifications. The results reveal significant impacts to pregnant women exposed to heat with regional variations. The exposure variable with the most stable and significant result was minimum temperature, indicating high overnight temperatures have the most impact on preterm birth. The magnitude of this impact varies across regions from a 1% increase in risk to 6% increase in risk per two-degree increment above established minimum temperature thresholds.

Impact of Hurricane Exposure on Reproductive Health Outcomes, Florida, 2004.

OBJECTIVE: Prenatal hurricane exposure may be an increasingly important contributor to poor reproductive health outcomes. In the current literature, mixed associations have been suggested between hurricane exposure and reproductive health outcomes. This may be due, in part, to residual confounding. We assessed the association between hurricane exposure and reproductive health outcomes by using a difference-in-difference analysis technique to control for confounding in a cohort of Florida pregnancies. METHODS: We implemented a difference-in-difference analysis to evaluate hurricane weather and reproductive health outcomes including low birth weight, fetal death, and birth rate. The study population for analysis included all Florida pregnancies conceived before or during the 2003 and 2004 hurricane season. Reproductive health data were extracted from vital statistics records from the Florida Department of Health. In 2004, 4 hurricanes (Charley, Frances, Ivan, and Jeanne) made landfall in rapid succession; whereas in 2003, no hurricanes made landfall in Florida. RESULTS: Overall models using the difference-in-difference analysis showed no association between exposure to hurricane weather and reproductive health. CONCLUSIONS: The inconsistency of the literature on hurricane exposure and reproductive health may be in part due to biases inherent in pre-post or regression-based county-level comparisons. We found no associations between hurricane exposure and reproductive health. (Disaster Med Public Health Preparedness. 2017;11:407-411).

Hurricane Charley Exposure and Hazard of Preterm Delivery, Florida 2004.

Objective Hurricanes are powerful tropical storm systems with high winds which influence many health effects. Few studies have examined whether hurricane exposure is associated with preterm delivery. We aimed to estimate associations between maternal hurricane exposure and hazard of preterm delivery. Methods We used data on 342,942 singleton births from Florida Vital Statistics Records 2004-2005 to capture pregnancies at risk of delivery during the 2004 hurricane season. Maternal exposure to Hurricane Charley was assigned based on maximum wind speed in maternal county of residence. We estimated hazards of overall preterm delivery (<37 gestational weeks) and extremely preterm delivery (<32 gestational weeks) in Cox regression models, adjusting for maternal/pregnancy characteristics. To evaluate heterogeneity among racial/ethnic subgroups, we performed analyses stratified by race/ethnicity. Additional models investigated whether exposure to multiples hurricanes increased hazard relative to exposure to one hurricane. Results Exposure to wind speeds ≥39 mph from Hurricane Charley was associated with a 9 % (95 % CI 3, 16 %) increase in hazard of extremely preterm delivery, while exposure to wind speed ≥74 mph was associated with a 21 % (95 % CI 6, 38 %) increase. Associations appeared greater for Hispanic mothers compared to non-Hispanic white mothers. Hurricane exposure did not appear to be associated with hazard of overall preterm delivery. Exposure to multiple hurricanes did not appear more harmful than exposure to a single hurricane. Conclusions Hurricane exposure may increase hazard of extremely preterm delivery. As US coastal populations and hurricane severity increase, the associations between hurricane and preterm delivery should be further studied.

Working with Climate Projections to Estimate Disease Burden: Perspectives from Public Health.

There is interest among agencies and public health practitioners in the United States (USA) to estimate the future burden of climate-related health outcomes. Calculating disease burden projections can be especially daunting, given the complexities of climate modeling and the multiple pathways by which climate influences public health. Interdisciplinary coordination between public health practitioners and climate scientists is necessary for scientifically derived estimates. We describe a unique partnership of state and regional climate scientists and public health practitioners assembled by the Florida Building Resilience Against Climate Effects (BRACE) program. We provide a background on climate modeling and projections that has been developed specifically for public health practitioners, describe methodologies for combining climate and health data to project disease burden, and demonstrate three examples of this process used in Florida.

Relationships between maximum temperature and heat-related illness across North Carolina, USA.

Heat kills more people than any other weather-related event in the USA, resulting in hundreds of fatalities each year. In North Carolina, heat-related illness accounts for over 2,000 yearly emergency department admissions. In this study, data on emergency department (ED) visits for heat-related illness (HRI) were obtained from the North Carolina Disease Event Tracking and Epidemiologic Collection Tool to identify spatiotemporal relationships between temperature and morbidity across six warm seasons (May-September) from 2007 to 2012. Spatiotemporal relationships are explored across different regions (e.g., coastal plain, rural) and demographics (e.g., gender, age) to determine the differential impact of heat stress on populations. This research reveals that most cases of HRI occur on days with climatologically normal temperatures (e.g., 31 to 35 °C); however, HRI rates increase substantially on days with abnormally high daily maximum temperatures (e.g., 31 to 38 °C). HRI ED visits decreased on days with extreme heat (e.g., greater than 38 °C), suggesting that populations are taking preventative measures during extreme heat and therefore mitigating heat-related illness.

Impact of Extreme Heat Events on Emergency Department Visits in North Carolina (2007-2011).

Extreme heat is the leading cause of weather-related mortality in the U.S. Extreme heat also affects human health through heat stress and can exacerbate underlying medical conditions that lead to increased morbidity and mortality. In this study, data on emergency department (ED) visits for heat-related illness (HRI) and other selected diseases were analyzed during three heat events across North Carolina from 2007 to 2011. These heat events were identified based on the issuance and verification of heat products from local National Weather Service forecast offices (i.e. Heat Advisory, Heat Watch, and Excessive Heat Warning). The observed number of ED visits during these events were compared to the expected number of ED visits during several control periods to determine excess morbidity resulting from extreme heat. All recorded diagnoses were analyzed for each ED visit, thereby providing insight into the specific pathophysiological mechanisms and underlying health conditions associated with exposure to extreme heat. The most common form of HRI was heat exhaustion, while the percentage of visits with heat stroke was relatively low (<10%). The elderly (>65 years of age) were at greatest risk for HRI during the early summer heat event (8.9 visits per 100,000), while young and middle age adults (18-44 years of age) were at greatest risk during the mid-summer event (6.3 visits per 100,000). Many of these visits were likely due to work-related exposure. The most vulnerable demographic during the late summer heat event was adolescents (15-17 years of age), which may relate to the timing of organized sports. This demographic also exhibited the highest visit rate for HRI among all three heat events (10.5 visits per 100,000). Significant increases (p < 0.05) in visits with cardiovascular and cerebrovascular diseases were noted during the three heat events (3-8%). The greatest increases were found in visits with hypotension during the late summer event (23%) and sequelae during the early summer event (30%), while decreases were noted for visits with hemorrhagic stroke during the middle and late summer events (13-24%) and for visits with aneurysm during the early summer event (15%). Significant increases were also noted in visits with respiratory diseases (5-7%). The greatest increases in this category were found in visits with pneumonia and influenza (16%), bronchitis and emphysema (12%), and COPD (14%) during the early summer event. Significant increases in visits with nervous system disorders were also found during the early summer event (16%), while increases in visits with diabetes were noted during the mid-summer event (10%).

County-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control.

BACKGROUND: Epidemiological analyses of aggregated data are often used to evaluate theoretical health effects of natural disasters. Such analyses are susceptible to confounding by unmeasured differences between the exposed and unexposed populations. To demonstrate the difference-in-difference method our population included all recorded Florida live births that reached 20 weeks gestation and conceived after the first hurricane of 2004 or in 2003 (when no hurricanes made landfall). Hurricane exposure was categorized using ≥74 mile per hour hurricane wind speed as well as a 60 km spatial buffer based on weather data from the National Oceanic and Atmospheric Administration. The effect of exposure was quantified as live birth rate differences and 95 % confidence intervals [RD (95 % CI)]. To illustrate sensitivity of the results, the difference-in-differences estimates were compared to general linear models adjusted for census-level covariates. This analysis demonstrates difference-in-differences as a method to control for time-invariant confounders investigating hurricane exposure on live birth rates. RESULTS: Difference-in-differences analysis yielded consistently null associations across exposure metrics and hurricanes for the post hurricane rate difference between exposed and unexposed areas (e.g., Hurricane Ivan for 60 km spatial buffer [-0.02 births/1000 individuals (-0.51, 0.47)]. In contrast, general linear models suggested a positive association between hurricane exposure and birth rate [Hurricane Ivan for 60 km spatial buffer (2.80 births/1000 individuals (1.94, 3.67)] but not all models. CONCLUSIONS: Ecological studies of associations between environmental exposures and health are susceptible to confounding due to unmeasured population attributes. Here we demonstrate an accessible method of control for time-invariant confounders for future research.