Structural road network redundancy contributes to reliable mobility by providing alternative routes in caseof disruptions. Existing structural redundancy metrics tend to emphasize the criticality of links (infrastructure-focused) or the pairwise redundancy of origin-destination (OD) connections under equal weighting (demand-agnostic), overlooking place- and population-specific differences in redundancy. This study introduces an alternative population-based, demand-aware redundancy metric to evaluate structural redundancy at the census block group level by integrating origin-specific demand distributions derived from mobility data with a path-penalized Dijkstra's algorithm to evaluate redundancy separately for each census block group. This population-centered approach aligns with transportation justice principles and provides an accessible tool for assessing heterogeneities in local access to road network redundancy. Applied to North Carolina, the metric highlights geographic variation in redundancy across the state, especially considering urban-rural and regional divides, and identifies populations in western North Carolina as having comparatively lower access to redundancy. The findings of the case study underscore the necessity of considering road networks' unique structural characteristics in planning for equitable and resilient transportation systems.

Heat is the leading cause of weather -related death in the United States. Wet bulb globe temperature (WBGT) is a heat stress index commonly used among active populations for activity modification, such as outdoor workers and athletes. Despite widespread use globally, WBGT forecasts have been uncommon in the United States until recent years. This research assesses the accuracy of WBGT forecasts developed by NOAA's Southeast Regional Climate Center (SERCC) and the Carolinas Integrated Sciences and Assessments (CISA). It also details efforts to refine the forecast by accounting for the impact of surface roughness on wind using satellite imagery. Comparisons are made between the SERCC/CISA WBGT forecast and a WBGT forecast modeled after NWS methods. Additionally, both of these forecasts are compared with in situ WBGT measurements (during the summers of 2019-21) and estimates from weather stations to assess forecast accuracy. The SERCC/CISA WBGT forecast was within 0.6 degrees C of observations on average and showed less bias than the forecast based on NWS methods across North Carolina. Importantly, the SERCC/CISA WBGT forecast was more accurate for the most dangerous conditions (WBGT . 31 degrees C), although this resulted in higher false alarms for these extreme conditions compared to the NWS method. In particular, this work improved the forecast for sites more sheltered from wind by better accounting for the influences of land cover on 2-m wind speed. Accurate forecasts are more challenging for sites with complex microclimates. Thus, appropriate caution is necessary when interpreting forecasts and onsite, real-time WBGT measurements remain critical.

Weather-related road closures have the potential to cause serious impacts to society by disrupting road network function. Impacts to the population are variable based on the temporal and spatial extent of the closures, as well as the ability of the road network to absorb the impacts of closures by offering suitable alternative routes. In general, analyses of the impacts of weather-related road closures have focused on short-term, major events, such as hurricanes. There has been little focus on the ability for weather-related disruptions of varying size and severity (from localized tree fall to major hurricanes) to cause cumulative impacts to the population over longer time scales. This analysis considers daily impacts to free-flow travel time by employing an adjusted graph theory approach that also considers demand to more effectively analyze travel time impacts. In particular, this study uses mobility data to determine "habitual travel" for each census block group in the state, which allows for consideration of weather-related travel time impacts based on regularly occurring trips. We conduct a case study in North Carolina over the period of 2016-2023. Results indicate that although major events (such as Hurricane Matthew and Florence) represent the days with the most intense travel time disruptions, much of the state has experienced more than 30 days of travel-time disruption due to weather-related closures. Ultimately, rural areas of the state, especially the southeast coastal plain and the far western area of the state, emerge as the most impacted regions, which exposes potential vulnerabilities, especially considering the expected increase of weather-related road closures due to climate change.

Since its inception in 1983, NOAA's Regional Climate Center (RCC) Program has been providing timely, customized climate services for decision making across all climate-sensitive sectors. Through this 40-yr period, the RCC Program has not only seen but also has played an active role in, the evolution of climate services from the days of climate data libraries-where books of data were consulted to fulfill simple data requests-to coproduced tools that can calculate sectoral-specific, on-the-fly climate analyses in a matter of seconds. With new technologies emerging, the RCC Program is poised to build on its reputation as a trusted climate service provider by incorporating advanced methods for climate service delivery to continue to meet the needs of the nation. This publication will provide a look back at the evolution of regional climate services over the past 40 years, along with a vision for the future.

Wet -bulb globe temperature (WBGT) is used to assess environmental heat stress and accounts for the influ- ences of air temperature, humidity, wind speed, and radiation on heat stress. Measurements of WBGT are highly sensitive to slight changes in environmental conditions and can vary several degrees Celsius across small distances (tens to hundreds of meters). Relative to observations with an International Organization for Standardization (ISO) -compliant WBGT meter, this work assesses the accuracy of WBGT measurements made with a popular handheld meter (the Kestrel 5400 Heat Stress Tracker) and WBGT estimates. Measurements were made during the summers of 2019-21 in a variety of suburban and urban environments in North Carolina, including three high school campuses. WBGT can be estimated from standard weather station variables, and many of these stations report cloud cover in lieu of solar radiation. Therefore, this work also evaluates the accuracy of clear -sky radiation estimates and adjustments to those estimates based on cloud cover. WBGT estimated with the method from Liljegren et al. from a weather station were on average 0.2 degrees C warmer than Observed WBGT, while the Kestrel 5400 WBGT was 0.7 degrees C warmer. Large variations in WBGT were observed across surfaces and shade conditions, with differences of 0.9 degrees C (0.3 degrees-1.4 degrees C) between a tennis court and a neighboring grass field. The method for estimating clear -sky radiation in Ryan and Stolzenbach was most accurate and the clear -sky radiation modified by percentage cloud cover was found to be within 75 W m22of observations on average.

The intensity of extreme events like hurricanes is predicted to increase. As such, the role of federal disaster aid programmes in assisting community recovery will also grow, and potential inequities in these programmes could compound in an increasing disaster landscape. This study analyzes recovery efforts after Hurricane Florence (2018) to identify trends in areas that were targeted for recovery aid. Using a series of Ordinary Least Square (OLS) and spatial lag models, divergences in aid are investigated after controlling for physical damage and the study suggests that these divergences can be partially predicted by social and community factors, including characteristics that are typically associated with increased social vulnerability (such as high renter population, older housing stock, and high population of non-white residents). In addition, because North Carolina experienced two major hurricanes in the period of just two years (Hurricane Matthew in 2016), this study also analyzes the role of successive extremes in the outcomes of aid concentration and finds that communities successful in achieving aid after Hurricane Matthew were similarly successful after Hurricane Florence. Finally, the paper emphasises the importance of monitoring potential inequities in federal recovery aid payout, which can provide actionable opportunities for potential improvements to these programmes.

The Painted Bunting: A Songbird Facing Multiple Threats Charles E. Konrad The Painted Bunting is unquestionably the most colorful songbird in the southern United States (cover photo, Figure 1). The male exhibits dazzling splashes of blue, green, yellow, and red plumage, and the reserved female possesses a solid coat of bright green, which blends in with the brush and trees where the nest is located. Because of its limited geographical range and stealthy nature, relatively few birders have experienced the delight of seeing this bird, and its dramatically declining population (Dybas 2018) has further reduced the chances of spotting it. Click for larger view View full resolution Figure 1. Painted Bunting taken at Huntington Beach State Park in South Carolina on May 16, 2022. [End Page 293] The Painted Bunting is categorized into two subspecies that are nearly identical in appearance but display distinct geographical distributions separated by a distance on the order of 500 km (Yirka et al. 2021). The eastern subspecies, Passerina ciris, breeds in a region that stretches from coastal North Carolina to northern Florida and includes an inland area in southern South Carolina and southeastern Georgia. The western subspecies, Passerina pallidior, breeds in a region extending from coastal Louisiana and Texas northward to southeastern Kansas and southern Missouri (Yirka et al. 2021). During the fall, the eastern population migrates southward to southern Florida and portions of the Bahamas and Cuba, while the western population migrates to portions of coastal Mexico and Central America. Given their geographic separation, these two populations no longer interact or breed, suggesting that the Painted Bunting will eventually divide into two different bird species (Dybas 2018). Within its breeding region, the Painted Bunting is typically present in bushy areas and woodland edges, often staying hidden within the dense cover of the vegetation. The males may be spotted by looking in the direction of their warbling songs during the breeding season. They often appear on an exposed perch higher up in the brush or trees. While the male sings out to announce its territory, the female typically remains hidden in the brush near the nest. In suitable habitats, both the male and female may be seen frequenting bird feeders. There has been a concerning decline in the number of Painted Buntings over the last fifty-plus years. According to the Breeding Bird Survey, the bird’s combined eastern and western populations have declined about 55 percent over the last thirty years (Dybas 2018). Springborn and Meyers (2005) report that the estimated number of individuals in the eastern population, Passerina ciris, decreased by about 75 percent between 1966 and 1996. Since 1996, this population has become so sparse in places that it is difficult to get a handle on the population trends across the southeastern US (Meyers 2011). Concern for the decline in the eastern and western populations of the bird species prompted the U.S. Fish and Wildlife Service and Partners in Flight (Springborn and Meyers 2005) to identify the Painted Bunting as a “species of concern” and a “near threatened” species. Several factors have been tied to the decline in Painted Bunting populations. Habitat loss in the summer breeding areas is unquestionably a major factor in the rapid reduction of the eastern population (Meyers 2011, Springborn and Meyers 2005). Painted Buntings are most common in dense marine shrubs, particularly live oak, which occur along the coast, largely on the barrier and sea islands from the Carolinas southward to Georgia and northern Florida (Meyers 2011, Dybas 2018). Many of these islands have seen rapid residential and commercial development over the last several decades, and this has greatly diminished the acreage of the Painted Bunting’s breeding habitat. In addition, many of the individuals in this eastern population winter in South Florida, which has seen a similar pattern of development and consequent habitat loss. Also, the reduction in riparian habitats near the southern US and Mexican coastlines, which are used during migration by the western population, have further contributed to the population decline (Lowther et al. 1999, Sykes and Holzman 2005). Given its extraordinary colors and beauty, the Painted Bunting has unfortunately been trapped in portions of its wintering grounds and sold as a...

As a significant detriment to physical and mental health, millions of motor vehicle crashes occur in the United States each year, with approximately 23% of these crashes linked to adverse weather conditions. This study builds upon a strong knowledge base to provide a deeper understanding of how rainfall intensity influences relative crash risk. Gridded precipitation and temperature data were aggregated to the county level and analyzed alongside motor vehicle crash data for all 146 counties in the Carolinas (North Carolina and South Carolina) for the period 2003-19. A matched-pair analysis routine linked unique time steps of rainfall (daily, 6-h, and hourly) to corresponding dry periods to evaluate relative crash risk across each state. Risk estimates were calculated on the basis of precipitation thresholds (light, moderate, heavy, and very heavy). Results indicate a statistically significant increase in crash risk during periods of rainfall in the Carolinas. As a baseline, the relative risk of experiencing a crash increases by 11.6% during days with accumulating rainfall and as much as 81.0% during heavy rainfall events over a 6-h period. In general, estimates of risk increase relative to the intensity of the rainfall event and the temporal delineation of the matched-pair routine. However, these relationships have unique spatiotemporal patterns indicating that, although hourly risk estimates may be beneficial for urban counties, daily relative risk estimates may be the only way to accurately capture risk in rural areas.

This study investigates the spatiotemporal relationships between growing season precipitation, maximum temperature, and minimum temperature anomalies on yield for corn (Zea mays), soybean (Glycine max), cotton (Gossypium), peanut (arachis hypogaea), and sweet potato (Ipomoea batatas) crops in the southeastern United States (SEUS). Detrended county-level yield data (1981-2018) were analyzed alongside spatially derived growing season (May-Oct) climate anomalies. Results reveal that the relationships between climate anomalies and crop yield differ considerably across the SEUS based on the crop type and timing of meteorological extremes. Aligning with previous findings, surface crops in the region suffer considerable declines as a result of higher than normal maximum temperatures during the growing season, with the most significant losses occurring during the months of July and August when daytime temperatures frequently exceed ideal growing conditions. While the association is weaker, higher than normal minimum temperatures during critical crop development stages were also found to lead to significant declines in crop productivity. Notably, although drought conditions result in negative departures from expected yield, the findings of this study highlight that excess moisture in the latter part of the growing season (Sep-Oct) can be equally damaging for certain regional crops, including peanuts and sweet potatoes. The results of this study underscore the need for further research on the impact of climatic variability on regional and specialty crops in the era of anthropogenic climate change.

This paper addresses warm season hydroclimatic variability in the southern Appalachian region of the southeastern U.S., where precipitation can vary as much as 127 mm or more, with maximum seasonal totals exceeding 736 mm in extreme cases. Despite the occurrence of droughts, floods, and their socioecological impacts, hydroclimate variability is still poorly understood. This study characterizes the regional scale variations in the hydroclimate by examining the daily distribution of precipitation patterns in different topographic environments. Parameter-elevation relationships on independent slopes model (PRISM) gridded precipitation estimates are used to identify the location and frequency of different types of rainfall events. Several types of clustering algorithms are used as a regionalization approach to define areas where the precipitation regime exhibits similarities in its frequency of occurrence. The results are compared with internal validation statistics and a visualization is used to assess how well the resulting hydroclimatic regions align with different topographic environments. This study reveals the intricate spatial footprint of dry and wet regimes and demonstrates how clustering applications can be used with gridded climate data to determine where extremes are most likely to develop across mountain catchments.

Hydroclimatic variability has increased in recent decades across the southeastern US, with more frequent droughts and heavy precipitation events. Among these extremes, past research reveals that there is much variety in the synoptic-scale circulation that controls warm-season precipitation. However, research has yet to examine the subtle variation between these circulation patterns and their influence on hydroclimate variability. This is particularly the case in the southern Appalachian Mountains, where topographic complexity mediates the relationship between large-scale circulation and precipitation characteristics. In this study, we use a self-organizing map to classify and spatially visualize synoptic-scale circulation patterns over the southeastern US from 1979 to 2014. The patterns identified in the self-organizing map are linked with daily precipitation characteristics in the region. Our results demonstrate that underlying topographic features have a marked influence on the hydroclimate, and this influence varies according to the configuration of circulation. Greater frequencies of light precipitation are observed across broad-scale regions of high elevation, to varying degrees, no matter which circulation pattern is present. In contrast, precipitation frequencies along interior valleys and leeward slopes are lower and largely limited to a subset of circulation patterns. This study demonstrates how shifts in the large-scale circulation are likely to alter the occurrence of different types of warm-season precipitation events across mountain catchments.

Snowfall in the Southern Appalachian Mountain region of the eastern US is characterized by much spatiotemporal variability. Annual snowfall totals vary by up to 75 cm, and variations in snowfall intensity can lead to large differences in the local snowfall distribution. Research has shown that the synoptic pattern associated with the snowfall strongly influences the regional-scale distribution of snow cover. However, topographic variability results in locally complex snow cover patterns that are not well understood or documented. In this study, we characterize the snow covered area (SCA) and fractional snow cover associated with different synoptic patterns in 14 individual sub-regions. We analyze 63 snow events using Moderate-resolution Imaging Spectroradiometer standard snow cover products to ascertain both qualitative and quantitative differences in snow cover across sub-regions. Among sub-regions, there is significant variation in the snow cover pattern from individual synoptic classes. Furthermore, the percent SCA follows the regional snowfall climatology, and sub-regions with the highest elevations and northerly latitudes exhibit the greatest variability. Results of the sub-regional analysis provide valuable guidance to forecasters by contributing a deeper understanding of local snow cover patterns and their relationship to synoptic-scale circulation features.

Spatio-temporal patterns in mean and extreme rainfall are examined around the city of Atlanta, Georgia using the Multi-sensor Precipitation Estimates (MPE) and ERA-Interim reanalysis datasets. The analysis spans the period 2002 to 2015 and employs a 9-cell gridded framework centered on downtown Atlanta. Statistically significant anomalies in daily precipitation were found over and downwind (predominately east to northeast) of Atlanta. The pattern of rainfall anomalies is most evident in the early evening hours of the day and is hypothesized to be related to the evolution of the skin or surface urban heat island (UHI), rather than the canopy layer UHI. The study formally proposes the term “flow regime dependent” downwind anomaly regions. Like previous results, the study reveals that downwind anomaly regions can vary as a function of prevailing wind regime. Using a metric called the Wet Millimeter Day (WMD), the study also finds that there is a tendency for extreme rainfall to cluster in the climatological downwind area of Atlanta. The work builds upon previous findings while employing different datasets to provide novel additional contributions related to the temporal evolution of the “urban rainfall effect” and the patterns of extreme rainfall.

AbstractObjectivePrenatal 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.MethodsWe 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.ResultsOverall models using the difference-in-difference analysis showed no association between exposure to hurricane weather and reproductive health.ConclusionsThe 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)

The incidence of allergic diseases has been increasing in recent decades, in part due to increased exposure to aeroallergens, particularly pollen. Allergic diseases have a major burden on the health care system, with annual costs in the USA alone exceeding $30 billion. There is evidence that the production of aeroallergens, including pollen, is increasing in response to environmental and climatic change, which has important implications for the treatment of allergy sufferers. In this study, pollen data from a Rotorod sampler in Raleigh, North Carolina, was used to characterize and examine trends in the atmospheric pollen seasons for trees, grasses, and weeds over the period 1999–2012. The influence of mean monthly antecedent and concurrent temperature and precipitation on the timing, duration, and severity of the pollen seasons was assessed using Pearson’s product-moment correlation coefficients and multiple linear regression models. An increasing trend was noted in seasonal tree pollen concentrations, while seasonal and peak weed pollen concentrations declined over time. The atmospheric pollen seasons for grasses and weeds trended toward earlier start dates and longer durations, while the tree pollen season trended toward an earlier end date. Peak daily tree pollen concentrations were strongly associated with antecedent temperature and precipitation, while peak daily grass pollen concentrations were strongly associated with concurrent precipitation. The strongest relationships between climate and weed pollen were associated with the timing and duration of the pollen season, with drier antecedent and warmer concurrent conditions tied to longer weed pollen seasons.

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.

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%).

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.

Increasing coastal populations and storm intensity may lead to more adverse health effects from tropical storms and hurricanes. Exposure during pregnancy can influence birth outcomes through mechanisms related to healthcare, infrastructure disruption, stress, nutrition, and injury. However, accurate estimation of health effects may be limited by nonspecific exposure definitions that create potential misclassification. The two predominant hurricane exposure assignments are (1) the county of a FEMA presidential disaster declaration; and (2) the specified area within a storm track. The authors propose a third method: meteorological severity of wind speed. Based on the Saffir-Simpson categories, wind speed was examined through binary and quartile comparisons. All three methods of exposure classification were compared by examining the associations with county-level preterm birth and low-birth-weight rates among Florida women who were pregnant during the 2004 hurricane season. The county-level environmental quality index developed by the EPA was used to control for county-level environmental factors. Although the models yielded unexpected negative results and insignificant rate differences, a descriptive and mapping analysis of the exposure methods showed clear heterogeneity of county exposure. (C) 2015 American Society of Civil Engineers.

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.