The association of indoor heat exposure with diabetes and respiratory 9-1-1 calls through emergency medical dispatch and services documentation.

BACKGROUND: People with pre-existing medical conditions, who spend a large proportion of their time indoors, are at risk of emergent morbidities from elevated indoor heat exposures. In this study, indoor heat of structures wherein exposed people received Grady Emergency Services based care in Atlanta, GA, U.S., was measured from May to September 2016. METHOD: ology: In this case-control study, analyses were conducted to investigate the effect of indoor heat on the odds of 9-1-1 calls for diabetic (n = 90 cases) and separately, for respiratory (n = 126 cases), conditions versus heat-insensitive emergencies (n = 698 controls). Generalized Additive Models considered both linear and non-linear indoor heat and health outcome associations using thin-plate regression splines. RESULTS: Hotter and more humid indoor conditions were non-linearly associated with an increasing likelihood of receiving emergency care for complications of diabetes and severe respiratory distress. Higher heat indices were associated with increased odds of a diabetes (odds ratio for change from 30 to 31 °C: 1.12, 95% CI: 1.08-1.16) or respiratory 9-1-1 medical call versus control (odds ratio for change from 34 to 35 °C: 1.18, 95% CI: 1.09-1.28) call. Both diabetic and respiratory distress patients were more likely to be African-American and/or have comorbidities. CONCLUSIONS: In this study, the statistical association of indoor heat exposure with emergency morbidities (diabetic, respiratory) was demonstrated. The study also showcased the value and utility of data gathered by emergency medical dispatch and services from inaccessible private indoor sources (i.e., domiciles) for environmental health.

Environmental predictors of seasonal influenza epidemics across temperate and tropical climates.

Human influenza infections exhibit a strong seasonal cycle in temperate regions. Recent laboratory and epidemiological evidence suggests that low specific humidity conditions facilitate the airborne survival and transmission of the influenza virus in temperate regions, resulting in annual winter epidemics. However, this relationship is unlikely to account for the epidemiology of influenza in tropical and subtropical regions where epidemics often occur during the rainy season or transmit year-round without a well-defined season. We assessed the role of specific humidity and other local climatic variables on influenza virus seasonality by modeling epidemiological and climatic information from 78 study sites sampled globally. We substantiated that there are two types of environmental conditions associated with seasonal influenza epidemics: "cold-dry" and "humid-rainy". For sites where monthly average specific humidity or temperature decreases below thresholds of approximately 11-12 g/kg and 18-21°C during the year, influenza activity peaks during the cold-dry season (i.e., winter) when specific humidity and temperature are at minimal levels. For sites where specific humidity and temperature do not decrease below these thresholds, seasonal influenza activity is more likely to peak in months when average precipitation totals are maximal and greater than 150 mm per month. These findings provide a simple climate-based model rooted in empirical data that accounts for the diversity of seasonal influenza patterns observed across temperate, subtropical and tropical climates.

Predicting indoor heat exposure risk during extreme heat events.

Increased heat-related morbidity and mortality are expected direct consequences of global warming. In the developed world, most fatal heat exposures occur in the indoor home environment, yet little is known of the correspondence between outdoor and indoor heat. Here we show how summertime indoor heat and humidity measurements from 285 low- and middle-income New York City homes vary as a function of concurrent local outdoor conditions. Indoor temperatures and heat index levels were both found to have strong positive linear associations with their outdoor counterparts; however, among the sampled homes a broad range of indoor conditions manifested for the same outdoor conditions. Using these models, we simulated indoor conditions for two extreme events: the 10-day 2006 NYC heat wave and a 9-day event analogous to the more extreme 2003 Paris heat wave. These simulations indicate that many homes in New York City would experience dangerously high indoor heat index levels during extreme heat events. These findings also suggest that increasing numbers of NYC low- and middle-income households will be exposed to heat index conditions above important thresholds should the severity of heat waves increase with global climate change. The study highlights the urgent need for improved indoor temperature and humidity management.

Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation.

The environmental mechanisms that determine the inter-annual and seasonal variability in incidence of coccidioidomycosis are unclear. In this study, we use Arizona coccidioidomycosis case data for 1995-2006 to generate a timeseries of monthly estimates of exposure rates in Maricopa County, AZ and Pima County, AZ. We reveal a seasonal autocorrelation structure for exposure rates in both Maricopa County and Pima County which indicates that exposure rates are strongly related from the fall to the spring. An abrupt end to this autocorrelation relationship occurs near the the onset of the summer precipitation season and increasing exposure rates related to the subsequent season. The identification of the autocorrelation structure enabled us to construct a "primary" exposure season that spans August-March and a "secondary" season that spans April-June which are then used in subsequent analyses. We show that October-December precipitation is positively associated with rates of exposure for the primary exposure season in both Maricopa County (R = 0.72, p = 0.012) and Pima County (R = 0.69, p = 0.019). In addition, exposure rates during the primary exposure seasons are negatively associated with concurrent precipitation in Maricopa (R = -0.79, p = 0.004) and Pima (R = -0.64, p = 0.019), possibly due to reduced spore dispersion. These associations enabled the generation of models to estimate exposure rates for the primary exposure season. The models explain 69% (p = 0.009) and 54% (p = 0.045) of the variance in the study period for Maricopa and Pima counties, respectively. We did not find any significant predictors for exposure rates during the secondary season. This study builds on previous studies examining the causes of temporal fluctuations in coccidioidomycosis, and corroborates the "grow and blow" hypothesis.