The NYS HVI showed spatial variability in heat vulnerability across the state. Mapping the HVI allows quick identification of regions in NYS that could benefit from targeted interventions. The HVI will be used as a planning tool to help allocate appropriate adaptation measures like cooling centers and issue heat alerts to mitigate effects of heat in vulnerable areas.

While generally null results were found, long duration of unseasonable heat was associated with the increased risks for VSDs and ASDs, mainly in South and Northeast of the US. Further research to confirm our findings is needed.

The incidence of gestational diabetes mellitus (GDM) has increased significantly in the last few decades in the US. Understanding its risk factors is imperative for the prevention of GDM and its sequelae, but the roles of behavioural risk factors such as stressful events and smoking on GDM are generally not well understood. Using data obtained from the New York State (NYS) Pregnancy Risk Assessment Monitoring System survey for 2004-06 and the NYS birth certificates, we examined relationships between GDM, stressful events and smoking among 2690 women who had live singleton births and did not have pre-pregnancy diabetes. After adjustment for risk factors such as maternal age, race/ethnicity, pre-pregnancy body mass index, hypertension, as well as smoking exposure, education, parity, and gestation at first visit for prenatal care, we found that having five or more stressful events 12 months before the baby was born was significantly associated with GDM (OR = 2.49, [95% CI 1.49, 4.16]). In another model, having any stressful event(s) other than 'moved to a new address' 12 months before the baby was born was also moderately associated with GDM (OR = 1.38, [95% CI 1.04, 1.85]). Smoking exposure, assessed by combining maternal smoking and second-hand smoke exposure into six levels, had no significant association with GDM, and did not show a dose-response pattern. The present study suggests that stressful events during pregnancy may be an independent risk factor for GDM. Future studies of GDM should include this common, but potentially modifiable risk factor in analyses.

This study examined agreement (concordance or convergent validity) between self-report and birth certificate for gestational diabetes. Study population was 2,854 women who had live births 2-6 months earlier and responded to a questionnaire from the New York State Pregnancy Risk Assessment Monitoring System (PRAMS) survey, 2004-2006. Agreement between self-report and birth certificate was assessed for the study population overall, and for subgroups defined by race, age, education, marital status, number of previous live births, time of first prenatal care, and birth weight of the newborn. A total of 258 women self-reported gestational diabetes, while birth certificates indicated that 138 women had gestational diabetes. For the study population overall, percent agreement was 93.8% and Kappa was 0.53. Due to the moderate bias index (68.2% overall, ranged from 33.3 to 100% in subgroups) and the high skewed prevalence index (91.8% overall, ranged from 70.7 to 97.5% in subgroups), we determined Prevalence-Adjusted and Bias-Adjusted Kappa (PABAK) was a better measure of agreement. PABAK was 0.88 overall, indicating very good agreement. PABAK was uniformly high in all subgroups. The highest PABAK was found among women aged 25 years and younger (0.93), and the lowest PABAK was among Asian women (0.79). Although the absence of a gold standard for gestational diabetes hinders assessment of criterion validity, high PABAK measures suggest that self-reporting by PRAMS respondents is feasible for identifying cases of gestational diabetes for surveillance and population-based epidemiologic research.

Background 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. Methods We assessed the effect of multi-day lag exposure for maximum near-surface air temperature (T max) 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. Results 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 T max 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. Conclusion 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. Electronic supplementary material The online version of this article (10.1186/s12940-019-0467-5) contains supplementary material, which is available to authorized users.

Epidemiological analyses of air quality often estimate human exposure from ambient monitoring data, potentially leading to exposure misclassification and subsequent bias in estimated health risks. To investigate this, we conducted a case-crossover study of summertime ambient ozone and fine particulate matter (PM(2.5)) levels and daily respiratory hospitalizations in New York City during 2001-2005. Comparisons were made between associations estimated using two pollutant exposure metrics: observed concentrations and predicted exposures from the EPA's Stochastic Human Exposure and Dose Simulation (SHEDS) model. Small, positive associations between interquartile range mean ozone concentrations and hospitalizations were observed and were strongest for 0-day lags (hazard ratio (HR)=1.013, 95% confidence interval (CI): 0.998, 1.029) and 3-day lags (HR=1.006, 95% CI: 0.991, 1.021); applying mean predicted ozone exposures yielded similar results. PM(2.5) was also associated with admissions, strongest at 2- and 4-day lags, with few differences between exposure metrics. Subgroup analyses support recognized sociodemographic differences in concentration-related hospitalization risk, whereas few inter-stratum variations were observed in relation to SHEDS exposures. Predicted exposures for these spatially homogenous pollutants were similar across sociodemographic strata, therefore SHEDS predictions coupled with the case-crossover design may have masked observable heterogeneity in risks. However, significant effect modification was found for subjects in the top exposure-to-concentration ratio tertiles, suggesting risks may increase as a consequence of infiltration or greater exposure to outdoor air.

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