BACKGROUND:Air pollution is a major public health threat globally. Health studies, regulatory actions, and policy evaluations typically rely on air pollutant concentrations from single exposure models, assuming accurate estimations and ignoring related uncertainty. We developed a modeling framework, bneR, to apply the Bayesian Nonparametric Ensemble (BNE) prediction model that combines existing exposure models as inputs to provide air pollution estimates and their spatio-temporal uncertainty. METHODS:The bneR modeling framework (1) harmonizes air pollutant datasets to use standardized inputs for the BNE algorithm; (2) applies the BNE algorithm to obtain the posterior predictive distribution of pollutant concentrations; and (3) generates visualizations. We applied bneR to estimate NO2 concentrations and characterize uncertainty levels at high spatio-temporal resolution (daily, 1 km2) over New York State (NYS) for 2015. We met with stakeholders and modelers to discuss bneR user-friendliness and interpretation of its estimates. RESULTS:Using bneR, we harmonized the spatial scale of four input NO2 models (using the finer resolution, 1 km2 for BNE estimations), applied BNE to obtain the NO2 daily posterior predictive distribution, and visualized the results. Over NYS, the daily average NO2 concentration was 6.0 (interquartile range, IQR: 4.6-6.8) pbb with daily average uncertainty (as SD) of 1.2 (IQR: 1.0-1.3) ppb. BNE performed well with cross-validated RMSE=2.84 ppb and R2=0.80. CONCLUSION:Meeting stakeholders and modelers allowed us to understand that efficient communication on how uncertainty is estimated and interpreted is a key feature for these communities to engage in using bneR and its data products.

Journal Article Standards in responsibly sharing cohort data for transparency and reproducibility: response to the Young Lives Study Get access Ilan Cerna-Turoff, Ilan Cerna-Turoff Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA Corresponding author. Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York City, NY 10032, USA. E-mail: it2208@caa.columbia.edu https://orcid.org/0000-0002-0787-9068 Search for other works by this author on: Oxford Academic PubMed Google Scholar Lawrence G Chillrud, Lawrence G Chillrud Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Kara E Rudolph, Kara E Rudolph Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Joan A Casey Joan A Casey Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USADepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA https://orcid.org/0000-0002-9809-4695 Search for other works by this author on: Oxford Academic PubMed Google Scholar International Journal of Epidemiology, Volume 52, Issue 5, October 2023, Pages 1666–1669, https://doi.org/10.1093/ije/dyad066 Published: 18 May 2023 Article history Received: 29 June 2022 Editorial decision: 01 May 2023 Accepted: 04 May 2023 Published: 18 May 2023

Background: The association between fine particulate matter (PM2.5) and cardiovascular outcomes is well established. To evaluate whether source-specific PM2.5 is differentially associated with cardiovascular disease in New York City (NYC), we identified PM2.5 sources and examined the association between source-specific PM2.5 exposure and risk of hospitalization for myocardial infarction (MI). Methods: We adapted principal component pursuit (PCP), a dimensionality-reduction technique previously used in computer vision, as a novel pattern recognition method for environmental mixtures to apportion speciated PM2.5 to its sources. We used data from the NY Department of Health Statewide Planning and Research Cooperative System of daily city-wide counts of MI admissions (2007–2015). We examined associations between same-day, lag 1, and lag 2 source-specific PM2.5 exposure and MI admissions in a time-series analysis, using a quasi-Poisson regression model adjusting for potential confounders. Results: We identified four sources of PM2.5 pollution: crustal, salt, traffic, and regional and detected three single-species factors: cadmium, chromium, and barium. In adjusted models, we observed a 0.40% (95% confidence interval [CI]: –0.21, 1.01%) increase in MI admission rates per 1 μg/m3 increase in traffic PM2.5, a 0.44% (95% CI: –0.04, 0.93%) increase per 1 μg/m3 increase in crustal PM2.5, and a 1.34% (95% CI: –0.46, 3.17%) increase per 1 μg/m3 increase in chromium-related PM2.5, on average. Conclusions: In our NYC study, we identified traffic, crustal dust, and chromium PM2.5 as potentially relevant sources for cardiovascular disease. We also demonstrated the potential utility of PCP as a pattern recognition method for environmental mixtures.

BACKGROUND While evidence suggests that daily ambient temperature exposure influences stroke risk, little is known about the potential triggering role of ultra short-term temperature. METHODS We examined the association between hourly temperature and ischemic and hemorrhagic stroke, separately, and identified any relevant lags of exposure among adult New York State residents from 2000 to 2015. Cases were identified via ICD-9 codes from the New York Department of Health Statewide Planning and Reearch Cooperative System. We estimated ambient temperature up to 36 h prior to estimated stroke onset based on patient residential ZIP Code. We applied a time-stratified case-crossover study design; control periods were matched to case periods by year, month, day of week, and hour of day. Additionally, we assessed effect modification by leading stroke risk factors hypertension and atrial fibrillation. RESULTS We observed 578,181 ischemic and 164,755 hemorrhagic strokes. Among ischemic and hemorrhagic strokes respectively, the mean (standard deviation; SD) patient age was 71.8 (14.6) and 66.8 (17.4) years, with 55% and 49% female. Temperature ranged from -29.5 °C to 39.2 °C, with mean (SD) 10.9 °C (10.3 °C). We found linear relationships for both stroke types. Higher temperature was associated with ischemic stroke over the 7 h following exposure; a 10 °C increase over 7 h was associated with 5.1% (95% Confidence Interval [CI]: 3.8, 6.4%) increase in hourly stroke rate. In contrast, temperature was negatively associated with hemorrhagic stroke over 5 h, with a 5-h cumulative association of -6.2% (95% CI: 8.6, -3.7%). We observed suggestive evidence of a larger association with hemorrhagic stroke among patients with hypertension and a smaller association with ischemic stroke among those with atrial fibrillation. CONCLUSION Hourly temperature was positively associated with ischemic stroke and negatively associated with hemorrhagic stroke. Our results suggest that ultra short-term weather influences stroke risk and hypertension may confer vulnerability.

BACKGROUND AND AIM: While evidence suggests that daily ambient temperature exposure influences stroke risk, little is known about the potential triggering role of hourly temperature exposure. We examined the association between hourly temperature and ischemic and hemorrhagic stroke, separately, and assessed effect modification by hypertension in a secondary analysis. METHODS: We identified primary hospitalizations for ischemic and hemorrhagic stroke among New York State adults, 2000—2015, from the NY Department of Health Statewide Planning and Research Cooperative System, via ICD-9-CM codes. We estimated hourly temperature exposure from North American Land Data Assimilation System-2 estimates based on subject's residential ZIP Code. We conducted a case-crossover study with time-stratified matching. The association between temperature up to 36 hours prior to stroke and stroke risk was estimated via conditional logistic regression adjusted for relative humidity, with a distributed-lag non-linear term for temperature. We assessed effect modification via z-tests. RESULTS:Among ischemic and hemorrhagic strokes respectively, we observed 578,572 and 164,041 cases; the mean (standard deviation; SD) age was 71.7 (14.6) and 66.7 (17.5) years, with 55% and 49% female. The mean (SD) temperature was 10.9°C (10.3°C), with a range of -29.5–39.2°C. We found evidence of linear exposure-response relationships. Higher temperature was associated with ischemic stroke up to seven hours before the stroke; a 10°C temperature increase over seven hours was associated with 4.9% (95% Confidence Interval [CI]: 3.2, 6.2%) increase in hourly stroke rate. Temperature up to five hours prior was negatively associated with hemorrhagic stroke, with a five-hour cumulative association of -6.0% (95% CI: -8.4, -3.5%). For hemorrhagic stroke we observed suggestive evidence of a larger association among subjects with hypertension. CONCLUSIONS:In this study, hourly temperature was positively associated with ischemic stroke and negatively associated with hemorrhagic stroke. Our results suggest that ultra short-term weather may trigger stroke, and that hypertension may confer vulnerability. KEYWORDS: Climate, Temperature, Cardiovascular Disease,

Background: While evidence suggests that daily ambient temperature exposure influences stroke risk, little is known about the potential triggering role of ultra short-term temperature. Methods: We examined the association between hourly temperature and ischemic and hemorrhagic stroke, separately, and identified any relevant lags of exposure among adult New York State residents from 2000 to 2015. Cases were identified via ICD-9 codes from the New York Department of Health Statewide Planning and Reearch Cooperative System. We estimated ambient temperature up to 36 h prior to estimated stroke onset based on patient residential ZIP Code. We applied a time-stratified case-crossover study design; control periods were matched to case periods by year, month, day of week, and hour of day. Additionally, we assessed effect modification by leading stroke risk factors hypertension and atrial fibrillation. Results: We observed 578,181 ischemic and 164,755 hemorrhagic strokes. Among ischemic and hemorrhagic strokes respectively, the mean (standard deviation; SD) patient age was 71.8 (14.6) and 66.8 (17.4) years, with 55% and 49% female. Temperature ranged from 29.5 degrees C to 39.2 degrees C, with mean (SD) 10.9 degrees C (10.3 degrees C). We found linear relationships for both stroke types. Higher temperature was associated with ischemic stroke over the 7 h following exposure; a 10 degrees C increase over 7 h was associated with 5.1% (95% Confidence Interval [CI]: 3.8, 6.4%) increase in hourly stroke rate. In contrast, temperature was negatively associated with hemorrhagic stroke over 5 h, with a 5-h cumulative association of -6.2% (95% CI: 8.6, -3.7%). We observed suggestive evidence of a larger association with hemorrhagic stroke among patients with hypertension and a smaller association with ischemic stroke among those with atrial fibrillation. Conclusion: Hourly temperature was positively associated with ischemic stroke and negatively associated with hemorrhagic stroke. Our results suggest that ultra short-term weather influences stroke risk and hypertension may confer vulnerability.

BACKGROUND AND AIM: Accurate PM2.5 exposure assessment, often performed using statistical prediction models, is a critical component of health studies and regulatory action. Ensemble modeling is becoming increasingly popular as it improves accuracy by combining the unique strengths of different models. Identifying where prediction uncertainty is greatest can inform monitor deployment and model development. We fit an ensemble model integrating multiple existing PM2.5 prediction models, estimated location-specific uncertainty in predictions, and then identified factors contributing to uncertainty. METHODS: We predicted 2015 annual PM.5 concentrations at 0.01°×0.01° resolution across the contiguous US by combining three well-validated prediction models with the Bayesian Non-parametric Ensemble (BNE). Training data came from the US Environmental Protection Agency's Air Quality System database. We estimated model uncertainty, which captures disagreement between models, uncertainty of weights, and random error, as the standard deviations of predictions' location-specific posterior predictive distribution. We analyzed how predicted PM2.5, AQS monitor count within a 50-km radius, summer- and winter-mean temperature, and population density vary with uncertainty via a generalized additive mixed model, with penalized splines and a random intercept for state. RESULTS:Mean (standard deviation; SD) predicted PM2.5 was 6.37 μg/m3 (1.78), with a spatial RMSE of 0.71 μg/m^3, and mean (SD) uncertainty was 0.47 (0.20) μg/m^3. We observed greater uncertainty in the Midwest and Great Lakes areas. Predicted concentration had a complex relationship with uncertainty, with a generally positive association above ~8 μg/m^3. Monitor density had a negative association. Winter temperature below 0°C was positively associated with uncertainty, and summer temperature was positively associated below 19°C and negatively above 19°C. Very high population density was associated with lower uncertainty. CONCLUSIONS:PM2.5 prediction uncertainty varied across space; uncertainty was greater in areas with more pollution, fewer monitors, colder winters, more moderate summers, and lower population density. Subsequent monitoring and prediction model development should consider prioritizing these areas. KEYWORDS: Exposure assessment, particulate matter, spatial statistics

BACKGROUND AND AIM: The association between fine particulate matter (PM2.5) air pollution and cardiovascular outcomes is well-established. PM2.5 is a heterogeneous mixture of chemical constituents and its composition can vary by air pollution source. To evaluate whether PM2.5 from certain sources may be differentially associated with cardiovascular disease, we examined the association between same-day exposure to source-specific PM2.5 and risk of hospital admission for myocardial infarction (MI) in New York City (NYC). METHODS: We applied Absolute Principal Components Analysis to identify sources of PM2.5 pollution using data from three NYC monitors. We used data from the New York Department of Health Statewide Planning and Research Cooperative System on daily city-wide counts of MI admissions (2007–2015). We examined associations between same-day exposure to source-specific PM2.5 and MI admissions in a time-series analysis, using a quasi-Poisson regression model and adjusting for temperature, relative humidity, day of week, and seasonal and long-term time trends. RESULTS:We identified six sources of PM2.5 pollution: 1) traffic emissions, 2) salt, 3) crustal dust, 4) secondary/regional sulfate and nitrate, 5) road dust, and 6) industrial emissions. In adjusted models, an interquartile range (IQR) increase in PM2.5 from crustal dust was associated with a 0.68% increase in the rate of hospitalization for MI, on average (95% CI: 0.12, 1.25%). We observed a 1.01% (95% CI: -0.11, 2.16%) and a 0.62% (95%CI: -0.13, 1.36%) increase in MI admission rates per one IQR increase in traffic-related and regional PM2.5, respectively. We observed no association with PM2.5 from other sources. CONCLUSIONS:Identifying particularly toxic sources of PM2.5 can lead to maximally efficient policies. In our NYC study we identified crustal dust and traffic-related PM2.5 as potentially toxic sources for cardiovascular disease. KEYWORDS: Air pollution, Particle components, Particulate matter, Cardiovascular diseases, Mixtures analysis, Short-term exposure

BACKGROUND While evidence suggests that daily ambient temperature exposure influences stroke risk, little is known about the potential triggering role of ultra short-term temperature. METHODS We examined the association between hourly temperature and ischemic and hemorrhagic stroke, separately, and identified any relevant lags of exposure among adult New York State residents from 2000 to 2015. Cases were identified via ICD-9 codes from the New York Department of Health Statewide Planning and Reearch Cooperative System. We estimated ambient temperature up to 36 h prior to estimated stroke onset based on patient residential ZIP Code. We applied a time-stratified case-crossover study design; control periods were matched to case periods by year, month, day of week, and hour of day. Additionally, we assessed effect modification by leading stroke risk factors hypertension and atrial fibrillation. RESULTS We observed 578,181 ischemic and 164,755 hemorrhagic strokes. Among ischemic and hemorrhagic strokes respectively, the mean (standard deviation; SD) patient age was 71.8 (14.6) and 66.8 (17.4) years, with 55% and 49% female. Temperature ranged from -29.5 °C to 39.2 °C, with mean (SD) 10.9 °C (10.3 °C). We found linear relationships for both stroke types. Higher temperature was associated with ischemic stroke over the 7 h following exposure; a 10 °C increase over 7 h was associated with 5.1% (95% Confidence Interval [CI]: 3.8, 6.4%) increase in hourly stroke rate. In contrast, temperature was negatively associated with hemorrhagic stroke over 5 h, with a 5-h cumulative association of -6.2% (95% CI: 8.6, -3.7%). We observed suggestive evidence of a larger association with hemorrhagic stroke among patients with hypertension and a smaller association with ischemic stroke among those with atrial fibrillation. CONCLUSION Hourly temperature was positively associated with ischemic stroke and negatively associated with hemorrhagic stroke. Our results suggest that ultra short-term weather influences stroke risk and hypertension may confer vulnerability.