Heatwaves are projected to increase in frequency, intensity, and duration as the climate warms. However, it is unclear whether human mortality from heatwaves is changing in frequency with time. We used Quasi-Poisson regression with a distributed non-linear model (DLNM) to examined associations of heatwaves and their char-acteristics (intensity, duration, and timing) with human mortality due to different diseases and total non-accidental diseases (TND) for different sociodemographic subgroups between 2002 and 2004 and 2012-2014 in Shanghai, China. We found that heatwaves showed a significant association with cause-specific mortality and TND for socio-demographic subgroups during the two study periods. Relative risks (RR) of mortality decreased for most demographic subgroups from 2002 to 2004 to 2012-2014, while RR of respiratory diseases (RD) increased over time. The association between heatwave characteristics and human mortality changed over time. RRs of heatwaves on mortality were higher for females, the elderly, and low-and middle-educational level populations than for males, younger and highly educated counterparts, respectively. Overall, heatwaves had a stronger association with the mortality of RD from 2002 to 2004 to 2012-2014. Heatwave duration also had an enhanced association with all subgroups over time. Our research findings could provide insights into the design of sustainable cities and society.

BACKGROUND AND AIM: Climate change and extreme weather are predicted to have a significant impact on diarrheal diseases. However, rainfall depends on local conditions and studies use varied measurements (e.g., absolute rainfall, heavy rainfall, or antecedent conditions) to define exposures. We systematically explored the influence of different rainfall measures on the association between acute gastrointestinal illness (AGI) and rainfall in North Carolina. METHODS: Common measures of rainfall were derived from recent studies (e.g., absolute rainfall (mm); heavy rainfall (90th, 95th, 99th percentile) and constructed from PRISM gridded daily weather data spatially aggregated to the ZIP code level. Rate ratios between rainfall (lagged 0-7 days) and AGI were estimated using quasi-Poisson time series models. All-cause AGI was defined by the daily emergency department (ED) visits per ZIP code using ICD-9 diagnosis codes from North Carolina's syndromic surveillance system (2008-2015). Unadjusted and adjusted (for mean temperature and relative humidity) model estimates and goodness-of-fit measures were compared across rainfall measures. RESULTS: Between 2008-2015, there were 1.07M ZIP code-days with at least one ED visits for all-cause AGI per ZIP code per day in North Carolina. Adjusting for lagged mean temperature and relative humidity, we observed a 4.2% increase (RR=1.035; 95% CI: 1.029-1.040) and 5.1% (1.051; 1.035-1.067) in AGI ED visits respectively following lagged 90th and 99th percentile rainfall (1 day lag), with similar patterns for lags 2-7. By contrast, a 0.1% change in AGI ED visits was associated with a 1 mm increase in rainfall depending on the lag (e.g., lag 1: 1.001-1.001, 1.002 vs. lag 7: 0.999; 0.999-1.000). CONCLUSIONS: Heavy rainfall was associated with an increase in AGI rates, but absolute rainfall had a much smaller and ambiguous effect. These results suggest the importance of rainfall measurement and adjustment for other weather variables. KEYWORDS: diarrhea, rainfall, weather, climate, time series, exposure measurement

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.

Background Climate change, which is shifting weather patterns and modifying weather extremes, will likely influence public health via multiple pathways. Calculating future disease burden estimates can be daunting, given the complexities of climate modeling and the multiple pathways by which climate influences health. Interdisciplinary coordination between public health and climatological experts is necessary for scientifically-derived estimates. A partnership of state and regional climate scientists and public health experts assembled by the Florida Building Resilience Against Climate Effects (BRACE) program projected future heat-related illness. Methods We provide a brief background on climate modeling and projections, including selection criteria for climate projections and addressing climate model uncertainty. Downscaled climate projection data from ten global climate models are used to project the future health burden for 2040-2069 for six National Weather Service regions. Future disease burden is estimated using an attributable fraction approach. Attributable fractions and attributable numbers are calculated for each value of maximum temperature above a reference range of 88°F (average maximum temperature for Florida during historical period 2005-2012). Results Disease burden projection results are presented by six National Weather Service regions for the average number of heat-related illness cases per year for 2040-2069, averaged across ten global climate models. The average projected additional heat-related illness cases per year range from 36 to 421 across regions. Results are shown by region. Conclusions Projecting future disease of climate-sensitive outcomes from within a state or local health department is novel given resource limitations. The methodology and results presented here highlight the challenges in using climate projection data, calculating future health burden and the importance of partnering with interdisciplinary subject matter experts.

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.

Upslope-enhanced snowfall events during periods of northwesterly flow in the southern Appalachians have been recognized as a significant winter forecasting problem for some time. However, only in recent years has this problem received noteworthy attention by both the academic and operational communities. The complex meteorology of these events includes significant topographic influences, as well as a linkage between the upstream Great Lakes and resultant southern Appalachian snowfall. A unique collaborative team has recently formed, working toward the goals of improving the physical understanding of the mechanisms at work in these events and developing more accurate forecasts and more detailed climatologies. The literature shows only limited attention to this problem through the 1990s. However, with modernization of the National Weather Service (NWS) in the mid-1990s came opportunities to bring more attention to new or poorly understood forecast problems. These opportunities included the establishment of new forecast offices, often collocated with universities, the deployment of the Weather Surveillance Radar-1988 Doppler (WSR-88D) network, expansion of the surface observational network in both space and time, improved access to sophisticated numerical models, and growth of the spotter and cooperative observer networks. A collaborative team, consisting of faculty from five universities and meteorologists from six NWS forecast offices, has established an ongoing, structured dialogue to help advance the understanding and improve the forecasting of these events. The team utilizes a variety of communication strategies to discuss emerging research findings, review recent events, and share data and ideas. The ultimate goal is to continue fostering working relationships among research and operational meteorologists, climatologists, and students, all with a common motivation of continually improving forecasts and understanding of this important phenomenon. This group may serve as a model for other collaborative efforts between the research and operational communities interested in a common forecast problem.

A synoptic Climatology of warm season heavy rainfall is developed from patterns of 850 mb thermal advection over the Appalachian region. Heavy rain events are categorized according to the position and orientation of a warm air advection (WAA) ridge, a feature found in nearly two-thirds of the events. Numerous study events occur within the conditionally unstable region of the WAA ridge. In fact, numerous occurrences of heavy rainfall are tied to a superpositioning of a WAA and air mass instability ridge in the vicinity or upstream of the heavy rain area.