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Background On-road vehicles are an important source of fine particulate matter (PM 2.5 ) in cities, but spatially varying traffic emissions and vulnerable populations make it difficult to assess impacts to inform policy and the public. Methods We estimated PM 2.5 -attributable mortality and morbidity from on-road vehicle generated air pollution in the New York City (NYC) region using high-spatial-resolution emissions estimates, air quality modeling, and local health incidence data to evaluate variations in impacts by vehicle class, neighborhood, and area socioeconomic status. We developed multiple ‘zero-out’ emission scenarios focused on regional and local cars, trucks, and buses in the NYC region. We simulated PM 2.5 concentrations using the Community Multi-scale Air Quality Model at a 1-km spatial resolution over NYC and combined modeled estimates with monitored data from 2010 to 2012. We applied health impact functions and local health data to quantify the PM 2.5 -attributable health burden on NYC residents within 42 city neighborhoods. Results We estimate that all on-road mobile sources in the NYC region contribute to 320 (95 % Confidence Interval (CI): 220–420) deaths and 870 (95 % CI: 440–1280) hospitalizations and emergency department visits annually within NYC due to PM 2.5 exposures, accounting for 5850 (95 % CI: 4020–7620) years of life lost. Trucks and buses within NYC accounted for the largest share of on-road mobile-attributable ambient PM 2.5 , contributing up to 14.9 % of annual average levels across 1-km grid cells, and were associated with 170 (95 % CI: 110–220) PM 2.5 -attributable deaths each year. These contributions were not evenly distributed, with high poverty neighborhoods experiencing a larger share of the exposure and health burden than low poverty neighborhoods. Conclusion Reducing motor vehicle emissions, especially from trucks and buses, could produce significant health benefits and reduce disparities in impacts. Our high-spatial-resolution modeling approach could improve assessment of on-road vehicle health impacts in other cities.

The impact of heat on mortality is well documented but deaths tend to occur after (or lag) extreme heat events, and mortality data is generally not available for timely surveillance during extreme heat events. Recently, systems for near-real time surveillance of heat illness have been reported but have not been validated as predictors of non-external cause of deaths associated with extreme heat events. We analyzed associations between daily weather conditions, emergency medical system (EMS) calls flagged as heat-related by EMS dispatchers, emergency department (ED) visits classified as heat-related based on chief complaint text, and excess non-external cause mortality in New York City. EMS and ED data were obtained from data reported daily to the city health department for syndromic surveillance. We fit generalized linear models to assess the relationships of daily counts of heat related EMS and ED visits to non-external cause deaths after adjustment for weather conditions during the months of May-September between 1999 and 2013. Controlling for temporal trends, a 7% (95% confidence interval (CI): 2-12) and 6% (95% CI: 3-10) increase in non-external cause mortality was associated with an increase from the 50th percentile to 99th percentile of same-day and one-day lagged heat-related EMS calls and ED visits, respectively. After controlling for both temporal trends and weather, we observed a 7% (95% CI: 3-12) increase in non-external cause mortality associated with one-day lagged heat-related EMS calls and a 5% mortality increase with one-day lagged ED visits (95% CI: 2-8). Heat-related illness can be tracked during extreme heat events using EMS and ED data which are indicators of heat associated excess non-external cause mortality during the warm weather season.

Routine air monitoring provides data to assess urban scale temporal variation in pollution concentrations in relation to regulatory standards, but is not well suited to characterizing intraurban spatial variation in pollutant concentrations from local sources. To address these limitations and inform local control strategies, New York City developed a program to track spatial patterns of multiple air pollutants in each season of the year. Monitor locations include 150 distributed street-level sites chosen to represent a range of traffic, land-use and other characteristics. Integrated samples are collected at each distributed site for one 2-week session each season and in every 2-week period at five reference locations to track city-wide temporal variation. Pollutants sampled include PM 2.5 and constituents, nitrogen oxides, black carbon, ozone (summer only) and sulfur dioxide (winter only). During the first full year of monitoring more than 95% of designed samples were completed. Agreement between colocated samples was good (absolute mean % difference 3.2–8.9%). Street-level pollutant concentrations spanned a much greater range than did concentrations at regulatory monitors, especially for oxides of nitrogen and sulfur dioxide. Monitoring to characterize intraurban spatial gradients in ambient pollution usefully complements regulatory monitoring data to inform local air quality management.

Preventing heat-related illness and death requires an understanding of who is at risk and why, and options for intervention. We sought to understand the drivers of socioeconomic disparities in heat-related vulnerability in New York City (NYC), the perceived risk of heat exposure and climate change, and barriers to protective behaviors. A random digit dial telephone survey of 801 NYC adults aged 18 and older was conducted from 22 September–1 October, 2015. Thirteen percent of the population did not possess an air conditioner (AC), and another 15% used AC never/infrequently. In adjusted models, odds of not possessing AC were greater for non-Hispanic blacks compared with other races/ethnicities, odds ratio (OR) = 2.0 (95% CI: 1.1, 3.5), and for those with low annual household income, OR = 3.1 (95% CI: 1.8, 5.5). Only 12% reported going to a public place with AC if they could not keep cool at home. While low-income individuals were less likely to be aware of heat warnings, they were more likely to be concerned that heat could make them ill and that climate change would affect their health than participants with a higher household income, OR = 1.6 (95% CI: 1.0, 2.3). In NYC, lack of access to AC partially explains disparities in heat-related health outcomes. Our results point to opportunities for knowledge building and engagement on heat-health awareness and climate change adaptation that can be applied in NYC and other metropolitan areas to improve and target public health prevention efforts.

Background: To assess the public health risk of heat waves and to set criteria for alerts for excessive heat, various meteorologic metrics and models are used in different jurisdictions, generally without systematic comparisons of alternatives. We report such an analysis for New York City that compared maximum heat index with alternative metrics in models to predict daily variation in warm-season natural-cause mortality from 1997 through 2006. Materials and Methods: We used Poisson time-series generalized linear models and generalized additive models to estimate weather-mortality relationships using various metrics, lag and averaging times, and functional forms and compared model fit. Results: A model that included cubic functions of maximum heat index on the same and each of the previous 3 days provided the best fit, better than models using maximum, minimum, or average temperature, or spatial synoptic classification (SSC) of weather type. We found that goodness of fit and maximum heat index-mortality functions were similar using parametric and nonparametric models. Same-day maximum heat index was linearly related to mortality risk across its range. The slopes at lags of 1, 2, and 3 days were flat across moderate values but increased sharply between maximum heat index of 95°F and 100°F (35-38°C). SSC or other meteorologic variables added to the maximum heat index model moderately improved goodness of fit, with slightly attenuated maximum heat index-mortality functions. Conclusions: In New York City, maximum heat index performed similarly to alternative and more complex metrics in estimating mortality risk during hot weather. The linear relationship supports issuing heat alerts in New York City when the heat index is forecast to exceed approximately 95-100°F. Periodic city-specific analyses using recent data are recommended to evaluate public health risks from extreme heat.

Objective To assess the impact of fast food restaurants adding calorie labelling to menu items on the energy content of individual purchases.Design Cross sectional surveys in spring 2007 and spring 2009 (one year before and nine months after full implementation of regulation requiring chain restaurants’ menus to contain details of the energy content of all menu items). Setting 168 randomly selected locations of the top 11 fast food chains in New York City during lunchtime hours.Participants 7309 adult customers interviewed in 2007 and 8489 in 2009.Main outcome measures Energy content of individual purchases, based on customers’ register receipts and on calorie information provided for all items in menus.Results For the full sample, mean calories purchased did not change from before to after regulation (828 v 846 kcal, P=0.22), though a modest decrease was shown in a regression model adjusted for restaurant chain, poverty level for the store location, sex of customers, type of purchase, and inflation adjusted cost (847 v 827 kcal, P=0.01). Three major chains, which accounted for 42% of customers surveyed, showed significant reductions in mean energy per purchase (McDonald’s 829 v 785 kcal, P=0.02; Au Bon Pain 555 v 475 kcal, P<0.001; KFC 927 v 868 kcal, P<0.01), while mean energy content increased for one chain (Subway 749 v 882 kcal, P<0.001). In the 2009 survey, 15% (1288/8489) of customers reported using the calorie information, and these customers purchased 106 fewer kilocalories than customers who did not see or use the calorie information (757 v 863 kcal, P<0.001).Conclusion Although no overall decline in calories purchased was observed for the full sample, several major chains saw significant reductions. After regulation, one in six lunchtime customers used the calorie information provided, and these customers made lower calorie choices.

We surveyed 7318 customers from 275 randomly selected restaurants of 11 fast food chains. Participants purchased a mean of 827 calories, with 34% purchasing 1000 calories or more. Unlike other chains, Subway posted calorie information at point of purchase and its patrons more often reported seeing calorie infomation than patrons of other chains (32% vs 4%; P<.001); Subway patrons who saw calorie information purchased 52 fewer calories than did other Subway patrons (P<.01). Fast-food chains should display calorie information prominently at point of purchase, where it can be seen and used to inform purchases.

Coastal storms can take a devastating toll on the public's health. Urban areas like New York City (NYC) may be particularly at risk, given their dense population, reliance on transportation, energy infrastructure that is vulnerable to flood damage, and high-rise residential housing, which may be hard-hit by power and utility outages. Climate change will exacerbate these risks in the coming decades. Sea levels are rising due to global warming, which will intensify storm surge. These projections make preparing for the health impacts of storms even more important. We conducted a broad review of the health impacts of US coastal storms to inform climate adaptation planning efforts, with a focus on outcomes relevant to NYC and urban coastal areas, and incorporated some lessons learned from recent experience with Superstorm Sandy. Based on the literature, indicators of health vulnerability were selected and mapped within NYC neighborhoods. Preparing for the broad range of anticipated effects of coastal storms and floods may help reduce the public health burden from these events.

Background Many types of tree pollen trigger seasonal allergic illness, but their population-level impacts on allergy and asthma morbidity are not well established, likely due to the paucity of long records of daily pollen data that allow analysis of multi-day effects. Our objective in this study was therefore to determine the impacts of individual spring tree pollen types on over-the-counter allergy medication sales and asthma emergency department (ED) visits. Methods Nine clinically-relevant spring tree pollen genera (elm, poplar, maple, birch, beech, ash, sycamore/London planetree, oak, and hickory) measured in Armonk, NY, were analyzed for their associations with over-the-counter allergy medication sales and daily asthma syndrome ED visits from patients’ chief complaints or diagnosis codes in New York City during March 1 st through June 10 th , 2002-2012. Multi-day impacts of pollen on the outcomes (0-3 days and 0-7 days for the medication sales and ED visits, respectively) were estimated using a distributed lag Poisson time-series model adjusting for temporal trends, day-of-week, weather, and air pollution. For asthma syndrome ED visits, age groups were also analyzed. Year-to-year variation in the average peak dates and the 10 th -to-90 th percentile duration between pollen and the outcomes were also examined with Spearman’s rank correlation. Results Mid-spring pollen types (maple, birch, beech, ash, oak, and sycamore/London planetree) showed the strongest significant associations with both outcomes, with cumulative rate ratios up to 2.0 per 0-to-98 th percentile pollen increase (e.g., 1.9 [95 % CI: 1.7, 2.1] and 1.7 [95 % CI: 1.5, 1.9] for the medication sales and ED visits, respectively, for ash). Lagged associations were longer for asthma syndrome ED visits than for the medication sales. Associations were strongest in children (ages 5-17; e.g., a cumulative rate ratio of 2.6 [95 % CI: 2.1, 3.1] per 0-to-98 th percentile increase in ash). The average peak dates and durations of some of these mid-spring pollen types were also associated with those of the outcomes. Conclusions Tree pollen peaking in mid-spring exhibit substantive impacts on allergy, and asthma exacerbations, particularly in children. Given the narrow time window of these pollen peak occurrences, public health and clinical approaches to anticipate and reduce allergy/asthma exacerbation should be developed.

BACKGROUND:Previous studies suggested a possible association between fine particulate matter air pollution (PM2.5) and nitrogen dioxide (NO2) and the development of hypertensive disorders of pregnancy, but effect sizes have been small and methodologic weaknesses preclude firm conclusions. METHODS:We linked birth certificates in New York City in 2008–2010 to hospital discharge diagnoses and estimated air pollution exposure based on maternal address. The New York City Community Air Survey provided refined estimates of PM2.5 and NO2 at the maternal residence. We estimated the association between exposures to PM2.5 and NO2 in the first and second trimester and risk of gestational hypertension, mild preeclampsia, and severe preeclampsia among 268,601 births. RESULTS:In unadjusted analyses, we found evidence of a positive association between both pollutants and gestational hypertension. However, after adjustment for individual covariates, socioeconomic deprivation, and delivery hospital, we did not find evidence of an association between PM2.5 or NO2 in the first or second trimester and any of the outcomes. CONCLUSIONS:Our data did not provide clear evidence of an effect of ambient air pollution on hypertensive disorders of pregnancy. Results need to be interpreted with caution considering the quality of the available exposure and health outcome measures and the uncertain impact of adjusting for hospital. Relative to previous studies, which have tended to identify positive associations with PM2.5 and NO2, our large study size, refined air pollution exposure estimates, hospital-based disease ascertainment, and little risk of confounding by socioeconomic deprivation, does not provide evidence for an association.