Short-Term total and wildfire fine particulate matter exposure and work loss in California.

BACKGROUND: Few studies investigated the impact of particulate matter (PM2.5) on some symptom exacerbations that are not perceived as severe enough to search for medical assistance. We aimed to study the association of short-term daily total PM2.5 exposure with work loss due to sickness among adults living in California. METHODS: We included 44,544 adult respondents in the workforce from 2015 to 2018 California Health Interview Survey data. Daily total PM2.5 concentrations were linked to respondents' home addresses from continuous spatial surfaces of PM2.5 generated by a geostatistical surfacing algorithm. We estimated the effect of a 2-week average of daily total PM2.5 exposure on work loss using logistic regression models. RESULTS: About 1.69% (weighted percentage) of adult respondents reported work loss in the week before the survey interview. The odds ratio of work loss was 1.45 (odds ratio [OR] = 1.45, 95% confidence interval [CI]: 1.03, 2.03) when a 2-week average of daily total PM2.5 exposure was higher than 12 µg/m3. The OR for work loss was 1.05 (95% CI: 0.98, 1.13) for each 2.56ug/m3 increase in the 2-week average of daily total PM2.5 exposure, and became stronger among those who were highly exposed to wildfire smoke (OR = 1.06, 95% CI: 1.00, 1.13), compared to those with lower wildfire smoke exposure (OR = 1.04, 95% CI: 0.79, 1.39). CONCLUSIONS: Our findings suggest that short-term ambient PM2.5 exposure is positively associated with work loss due to sickness and the association was stronger among those with higher wildfire smoke exposure. It also indicated that the current federal and state PM2.5 standards (annual average of 12 µg/m3) could be further strengthened to protect the health of the citizens of California.

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires.

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8-20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke.Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health.

Tropical Cyclone Exposures and Risks of Emergency Medicare Hospital Admission for Cardiorespiratory Diseases in 175 Urban United States Counties, 1999-2010.

BACKGROUND: Although injuries experienced during hurricanes and other tropical cyclones have been relatively well-characterized through traditional surveillance, less is known about tropical cyclones' impacts on noninjury morbidity, which can be triggered through pathways that include psychosocial stress or interruption in medical treatment. METHODS: We investigated daily emergency Medicare hospitalizations (1999-2010) in 180 US counties, drawing on an existing cohort of high-population counties. We classified counties as exposed to tropical cyclones when storm-associated peak sustained winds were ≥21 m/s at the county center; secondary analyses considered other wind thresholds and hazards. We matched storm-exposed days to unexposed days by county and seasonality. We estimated change in tropical cyclone-associated hospitalizations over a storm period from 2 days before to 7 days after the storm's closest approach, compared to unexposed days, using generalized linear mixed-effect models. RESULTS: For 1999-2010, 175 study counties had at least one tropical cyclone exposure. Cardiovascular hospitalizations decreased on the storm day, then increased following the storm, while respiratory hospitalizations were elevated throughout the storm period. Over the 10-day storm period, cardiovascular hospitalizations increased 3% (95% confidence interval = 2%, 5%) and respiratory hospitalizations increased 16% (95% confidence interval = 13%, 20%) compared to matched unexposed periods. Relative risks varied across tropical cyclone exposures, with strongest association for the most restrictive wind-based exposure metric. CONCLUSIONS: In this study, tropical cyclone exposures were associated with a short-term increase in cardiorespiratory hospitalization risk among the elderly, based on a multi-year/multi-site investigation of US Medicare beneficiaries ≥65 years.

Assessing United States County-Level Exposure for Research on Tropical Cyclones and Human Health.

BACKGROUND: Tropical cyclone epidemiology can be advanced through exposure assessment methods that are comprehensive and consistent across space and time, as these facilitate multiyear, multistorm studies. Further, an understanding of patterns in and between exposure metrics that are based on specific hazards of the storm can help in designing tropical cyclone epidemiological research. OBJECTIVES: a) Provide an open-source data set for tropical cyclone exposure assessment for epidemiological research; and b) investigate patterns and agreement between county-level assessments of tropical cyclone exposure based on different storm hazards. METHODS: We created an open-source data set with data at the county level on exposure to four tropical cyclone hazards: peak sustained wind, rainfall, flooding, and tornadoes. The data cover all eastern U.S. counties for all land-falling or near-land Atlantic basin storms, covering 1996-2011 for all metrics and up to 1988-2018 for specific metrics. We validated measurements against other data sources and investigated patterns and agreement among binary exposure classifications based on these metrics, as well as compared them to use of distance from the storm's track, which has been used as a proxy for exposure in some epidemiological studies. RESULTS: Our open-source data set was typically consistent with data from other sources, and we present and discuss areas of disagreement and other caveats. Over the study period and area, tropical cyclones typically brought different hazards to different counties. Therefore, when comparing exposure assessment between different hazard-specific metrics, agreement was usually low, as it also was when comparing exposure assessment based on a distance-based proxy measurement and any of the hazard-specific metrics. DISCUSSION: Our results provide a multihazard data set that can be leveraged for epidemiological research on tropical cyclones, as well as insights that can inform the design and analysis for tropical cyclone epidemiological research. https://doi.org/10.1289/EHP6976.

The Effects of Social, Personal, and Behavioral Risk Factors and PM2.5 on Cardio-Metabolic Disparities in a Cohort of Community Health Center Patients.

(1) Background: Cardio-metabolic diseases (CMD), including cardiovascular disease, stroke, and diabetes, have numerous common individual and environmental risk factors. Yet, few studies to date have considered how these multiple risk factors together affect CMD disparities between Blacks and Whites. (2) Methods: We linked daily fine particulate matter (PM2.5) measures with survey responses of participants in the Southern Community Cohort Study (SCCS). Generalized linear mixed modeling (GLMM) was used to estimate the relationship between CMD risk and social-demographic characteristics, behavioral and personal risk factors, and exposure levels of PM2.5. (3) Results: The study resulted in four key findings: (1) PM2.5 concentration level was significantly associated with reported CMD, with risk rising by 2.6% for each µg/m3 increase in PM2.5; (2) race did not predict CMD risk when clinical, lifestyle, and environmental risk factors were accounted for; (3) a significant variation of CMD risk was found among participants across states; and (4) multiple personal, clinical, and social-demographic and environmental risk factors played a role in predicting CMD occurrence. (4) Conclusions: Disparities in CMD risk among low social status populations reflect the complex interactions of exposures and cumulative risks for CMD contributed by different personal and environmental factors from natural, built, and social environments.

Downscaling NLDAS-2 daily maximum air temperatures using MODIS land surface temperatures.

We have developed and applied a relatively simple disaggregation scheme that uses spatial patterns of Land Surface Temperature (LST) from MODIS warm-season composites to improve the spatial characterization of daily maximum and minimum air temperatures. This down-scaling model produces qualitatively reasonable 1 km daily maximum and minimum air temperature estimates that reflect urban and coastal features. In a 5-city validation, the model was shown to provide improved daily maximum air temperature estimates in the three coastal cities, compared to 12 km NLDAS-2 (North American Land Data Assimilation System). Down-scaled maximum temperature estimates for the other two (non-coastal) cities were marginally worse than the original NLDAS-2 temperatures. For daily minimum temperatures, the scheme produces spatial fields that qualitatively capture geographic features, but quantitative validation shows the down-scaling model performance to be very similar to the original NLDAS-2 minimum temperatures. Thus, we limit the discussion in this paper to daily maximum temperatures. Overall, errors in the down-scaled maximum air temperatures are comparable to errors in down-scaled LST obtained in previous studies. The advantage of this approach is that it produces estimates of daily maximum air temperatures, which is more relevant than LST in applications such as public health. The resulting 1 km daily maximum air temperatures have great potential utility for applications such as public health, energy demand, and surface energy balance analyses. The method may not perform as well in conditions of strong temperature advection. Application of the model also may be problematic in areas having extreme changes in elevation.

Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA.

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM2.5 concentrations at a 1-km spatial resolution using three different PM2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with an increase of cross-validation (CV) R2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact.

Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models.

Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.Implications: Fine particulate matter (PM2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.

Compilation and spatio-temporal analysis of publicly available total solar and UV irradiance data in the contiguous United States.

Skin cancer is the most common type of cancer in the United States, the majority of which is caused by overexposure to ultraviolet (UV) irradiance, which is one component of sunlight. National Environmental Public Health Tracking Program at CDC has collaborated with partners to develop and disseminate county-level daily UV irradiance (2005-2015) and total solar irradiance (1991-2012) data for the contiguous United States. UV irradiance dataset was derived from the Ozone Monitoring Instrument (OMI), and solar irradiance was extracted from National Solar Radiation Data Base (NSRDB) and SolarAnywhere data. Firstly, we produced daily population-weighted UV and solar irradiance datasets at the county level. Then the spatial distributions and long-term trends of UV irradiance, solar irradiance and the ratio of UV irradiance to solar irradiance were analyzed. The national average values across all years are 4300 Wh/m2, 2700 J/m2 and 130 mW/m2 for global horizontal irradiance (GHI), erythemally weighted daily dose of UV irradiance (EDD) and erythemally weighted UV irradiance at local solar noon time (EDR), respectively. Solar, UV irradiances and the ratio of UV to solar irradiance all increased toward the South and in some areas with high altitude, suggesting that using solar irradiance as indicator of UV irradiance in studies covering large geographic regions may bias the true pattern of UV exposure. National annual average daily solar and UV irradiances increased significantly over the years by about 0.3% and 0.5% per year, respectively. Both datasets are available to the public through CDC's Tracking network. The UV irradiance dataset is currently the only publicly-available, spatially-resolved, and long-term UV irradiance dataset covering the contiguous United States. These datasets help us understand the spatial distributions and temporal trends of solar and UV irradiances, and allow for improved characterization of UV and sunlight exposure in future studies.

Development and validation of improved PM2.5 models for public health applications using remotely sensed aerosol and meteorological data.

In this study, Moderate Resolution Imaging Spectrometer (MODIS) satellite measurements of aerosol optical depth (AOD) from different retrieval algorithms have been correlated with ground measurements of fine particulate matter less than 2.5 µm (PM2.5). Several MODIS AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target vs. Deep Blue), collections (5.1 vs. 6), and spatial resolutions (10 km vs. 3 km) for cities in the Western, Midwestern, and Southeastern USA have been evaluated. We developed and validated PM2.5 prediction models using remotely sensed AOD data. These models were further improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind gust, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the simulation quality of all the PM2.5 models, especially in the Western USA. Temperature, relative humidity, and wind gust were significant meteorological variables throughout the year in the Western USA. Wind speed was the most significant meteorological variable for the cold season while for the warm season, temperature was the most prominent one in the Midwestern and Southeastern USA. Using this satellite-derived PM2.5 data can improve the spatial coverage, especially in areas where PM2.5 ground monitors are lacking, and studying the connections between PM2.5 and public health concerns including respiratory and cardiovascular diseases in the USA can be further advanced.

Estimating policy-relevant health effects of ambient heat exposures using spatially contiguous reanalysis data.

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 (Tmax) 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 Tmax 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.

Fine Particulate Matter Pollution and Risk of Community-Acquired Sepsis.

While air pollution has been associated with health complications, its effect on sepsis risk is unknown. We examined the association between fine particulate matter (PM2.5) air pollution and risk of sepsis hospitalization. We analyzed data from the 30,239 community-dwelling adults in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort linked with satellite-derived measures of PM2.5 data. We defined sepsis as a hospital admission for a serious infection with ≥2 systemic inflammatory response (SIRS) criteria. We performed incidence density sampling to match sepsis cases with 4 controls by age (±5 years), sex, and race. For each matched group we calculated mean daily PM2.5 exposures for short-term (30-day) and long-term (one-year) periods preceding the sepsis event. We used conditional logistic regression to evaluate the association between PM2.5 exposure and sepsis, adjusting for education, income, region, temperature, urbanicity, tobacco and alcohol use, and medical conditions. We matched 1386 sepsis cases with 5544 non-sepsis controls. Mean 30-day PM2.5 exposure levels (Cases 12.44 vs. Controls 12.34 µg/m³; p = 0.28) and mean one-year PM2.5 exposure levels (Cases 12.53 vs. Controls 12.50 µg/m³; p = 0.66) were similar between cases and controls. In adjusted models, there were no associations between 30-day PM2.5 exposure levels and sepsis (4th vs. 1st quartiles OR: 1.06, 95% CI: 0.85⁻1.32). Similarly, there were no associations between one-year PM2.5 exposure levels and sepsis risk (4th vs. 1st quartiles OR: 0.96, 95% CI: 0.78⁻1.18). In the REGARDS cohort, PM2.5 air pollution exposure was not associated with risk of sepsis.

Reconnoitering the linkage between cardiovascular disease mortality and long-term exposures to outdoor environmental factors in the USA using remotely-sensed data.

This ecological study aimed to assess the association between long-term exposures to outdoor environmental factors and mortality rate from cardiovascular disease (CVD) in a diverse and spatially distributed population from 3,094 counties within the U.S. (n > 3,780,000 CVD deaths) using satellite-derived data of PM2.5 concentrations, sunlight, and maximum heat index. Multivariable logistic regression analyses were conducted to determine whether PM2.5, sunlight and maximum heat index were related to the odds of the total CVD death rate based on gender, race, and age taking into consideration the confounding risk factors of diabetes, obesity, leisure- time physical inactivity, smoking and socioeconomic status. The study has shown that elevated levels of PM2.5, sunlight and heat long-term exposures are significantly associated with an increase in the odds ratio of the total CVD mortality. The results suggest a 9.8% (95% CI = 6.3% - 13.4%), 0.9% (95% CI = 0.5% - 1.2%), and 0.7% (95% CI = 0.5% - 11.2%) increase in total CVD mortality associated with 10 µg/m3 increase in PM2.5 concentrations, 1,000 kJ/m2 increases in sunlight, and 1 oF increase in heat index, respectively. The odds ratios for the CVD death rate due to long-term exposures of PM2.5, sunlight, and heat index were significantly greater than 1.0 for all categories except for Asians, Hispanics, and American Indians, indicating that the effect of long-term exposures to particulate matter, sunlight radiation, and maximum heat on CVD mortality is trivial for Asians, Hispanics, and American Indians. Among the categories of age, the group of 65 years and older had the highest odds ratios, suggesting that the age group of 65 years and older are the most vulnerable group to the environmental exposures of PM2.5 (OR = 1.179, 95% CI = 1.124 - 1.237), sunlight (OR = 1.047, 95% CI = 1.041 - 1.053), and maximum heat (OR = 1.014, 95% CI = 1.011 - 1.016). The odds ratios of CVD mortality due to the environmental exposures were higher for Blacks than those for Whites. The odds ratios for all categories were attenuated with the inclusion of diabetes, obesity, leisure-time physical inactivity, smoking, and income covariates, reflecting the effect of other medical conditions, lifestyle, behavioral and socioeconomic factors on the CVD death rate besides the environmental factors.

Fine particulate matter and incident coronary heart disease in the REGARDS cohort.

Chronic exposure to fine particulate matter (PM2.5) is accepted as a causal risk factor for coronary heart disease (CHD). However, most of the evidence for this hypothesis is based upon cohort studies in whites, comprised of either only males or females who live in urban areas. It is possible that many estimates of the effect of chronic exposure to PM2.5 on risk for CHD do not generalize to more diverse samples. METHODS: Therefore, we estimated the relationship between chronic exposure to PM2.5 and risk for CHD in among participants in the REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort who were free from CHD at baseline (n=17,126). REGARDS is a sample of whites and blacks of both genders living across the continental United States. We fit Cox proportional hazards models for time to CHD to estimate the hazard ratio for baseline 1-year mean PM2.5 exposure, adjusting for environmental variables, demographics, and other risk factors for CHD including the Framingham Risk Score. RESULTS: The hazard ratio (95% CI) for a 2.7-µg/m3 increase (interquartile range) 1-year mean concentration of PM2.5 was 0.94 (0.83-1.06) for combined CHD death and nonfatal MI, 1.13 (0.92-1.40) for CHD death, and 0.85 (0.73-0.99) for nonfatal MI. We also did not find evidence that these associations depended upon overall CHD risk factor burden. CONCLUSIONS: Our results do not provide strong evidence for an association between PM2.5 and incident CHD in a heterogeneous cohort, and we conclude that the effects of chronic exposure to fine particulate matter on CHD require further evaluation.

Investigating the effects of environmental factors on autism spectrum disorder in the USA using remotely sensed data.

This study aimed to assess the association between exposures to outdoor environmental factors and autism spectrum disorder (ASD) prevalence in a diverse and spatially distributed population of 8-year-old children from the USA (n = 2,097,188) using the air quality index (AQI) of the US Environmental Protection Agency as well as satellite-derived data of PM2.5 concentrations, sunlight, and maximum heat index. Multivariable logistic regression analyses were performed to determine whether the unhealthy AQI, PM2.5, sunlight, and maximum heat index were related to the odds of ASD prevalence based on gender and race and taking into consideration the confounding factors of smoking and socioeconomic status. The logistic regression odds ratios for ASD per 10% increase in the unhealthy AQI were greater than 1 for all categories, indicating that unhealthy AQI is related to the odds of ASD prevalence. The odds ratio of ASD due to the exposure to the unhealthy AQI was higher for Asians (OR = 2.96, 95% CI = 1.11-7.88) than that for Hispanics (OR = 1.308, 95% CI = 0.607-2.820), and it was higher for Blacks (OR = 1.398, 95% CI = 0.827-2.364) than that for Whites (OR = 1.219, 95% CI = 0.760-1.954). The odds ratio of ASD due to the unhealthy AQI was slightly higher for males (OR = 1.123, 95% CI = 0.771-1.635) than that for females (OR = 1.117, 95% CI = 0.789-1.581). The effects of the unhealthy environmental exposures on the odds ratios of ASD of this study were inconclusive (i.e., statically insignificant; p value > 0.05) for all categories except for Asians. The odds ratios of ASD for Asians were increased by 5, 12, and 14% with increased levels of the environmental exposures of 10 µg/m3 of PM2.5, 1000 kJ/m2 of sunlight, and 1 °F of maximum heat index, respectively. The odds ratios of ASD prevalence for all categories, except for Asians, were increased with the inclusion of the smoking covariate, reflecting the effect of smoking on ASD prevalence besides the unhealthy environmental factors.

Fine Particulate Matter (PM2.5) and the Risk of Stroke in the REGARDS Cohort.

BACKGROUND: Ambient particulate matter has been shown to be associated with declining human health, although the association between fine particulate matter (PM2.5) and stroke is uncertain. METHODS: We utilized satellite-derived measures of PM2.5 to examine the association between exposure and stroke in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. We used a time-stratified case-crossover design, with exposure lags of 1 day, 2 days, and 3 days. We examined all strokes, as well as ischemic and hemorrhagic strokes separately. RESULTS: Among 30,239 participants in the REGARDS study, 746 incident events were observed: 72 hemorrhagic, 617 ischemic, and 57 of unknown type. Participants exposed to higher levels of PM2.5 more often resided in urban areas compared to rural, and in the southeastern United States. After adjustment for temperature and relative humidity, no association was observed between PM2.5 exposure and stroke, regardless of the lag (1-day lag OR = .99, 95% CI: .83-1.19; 2-day lag OR = .95, 95% CI: .80-1.14; 3-day lag OR = .95, 95% CI = .79-1.13). Similar results were observed for the stroke subtypes. CONCLUSIONS: In this large cohort of African-Americans and whites, no association was observed between PM2.5 and stroke. The ability to examine this association with a large number of outcomes and by stroke subtype helps fill a gap in the literature examining the association between PM2.5 and stroke.

The association of remotely sensed outdoor fine particulate matter with cancer incidence of respiratory system in the USA.

This study aimed to assess the association between exposure to fine particulate matter (PM2.5) and respiratory system cancer incidence in the US population (n = 295,404,580) using a satellite-derived estimate of PM2.5 concentrations. Linear and logistic regression analyses were performed to determine whether PM2.5 was related to the odds of respiratory system cancer (RSC) incidence based on gender and race. Positive linear regressions were found between PM2.5 concentrations and the age-adjusted RSC incidence rates for all groups (Males, Females, Whites, and Blacks) except for Asians and American Indians. The linear relationships between PM2.5 and RSC incidence rate per 1 µg/m3 PM2.5 increase for Males, Females, Whites, Blacks, and all categories combined had slopes of, respectively, 7.02 (R2 = 0.36), 2.14 (R2 = 0.14), 3.92 (R2 = 0.23), 5.02 (R2 = 0.21), and 4.15 (R2 = 0.28). Similarly, the logistic regression odds ratios per 10 µg/m3 increase of PM2.5 were greater than one for all categories except for Asians and American Indians, indicating that PM2.5 is related to the odds of RSC incidence. The age-adjusted odds ratio for males (OR = 2.16, 95% CI = 1.56-3.01) was higher than that for females (OR = 1.50, 95% CI = 1.09-2.06), and it was higher for Blacks (OR = 2.12, 95% CI = 1.43-3.14) than for Whites (OR = 1.72, 95% CI = 1.23-2.42). The odds ratios for all categories were attenuated with the inclusion of the smoking covariate, reflecting the effect of smoking on RSC incidence besides PM2.5.

Heat Maps of Hypertension, Diabetes Mellitus, and Smoking in the Continental United States.

BACKGROUND: Geographic variations in cardiovascular mortality are substantial, but descriptions of geographic variations in major cardiovascular risk factors have relied on data aggregated to counties. Herein, we provide the first description of geographic variation in the prevalence of hypertension, diabetes mellitus, and smoking within and across US counties. METHODS AND RESULTS: We conducted a cross-sectional analysis of baseline risk factor measurements and latitude/longitude of participant residence collected from 2003 to 2007 in the REGARDS study (Reasons for Geographic and Racial Differences in Stroke). Of the 30 239 participants, all risk factor measurements and location data were available for 28 887 (96%). The mean (±SD) age of these participants was 64.8(±9.4) years; 41% were black; 55% were female; 59% were hypertensive; 22% were diabetic; and 15% were current smokers. In logistic regression models stratified by race, the median(range) predicted prevalence of the risk factors were as follows: for hypertension, 49% (45%-58%) among whites and 72% (68%-78%) among blacks; for diabetes mellitus, 14% (10%-20%) among whites and 31% (28%-41%) among blacks; and for current smoking, 12% (7%-16%) among whites and 18% (11%-22%) among blacks. Hypertension was most prevalent in the central Southeast among whites, but in the west Southeast among blacks. Diabetes mellitus was most prevalent in the west and central Southeast among whites but in south Florida among blacks. Current smoking was most prevalent in the west Southeast and Midwest among whites and in the north among blacks. CONCLUSIONS: Geographic disparities in prevalent hypertension, diabetes mellitus, and smoking exist within states and within counties in the continental United States, and the patterns differ by race.

Application of Citizen Science Risk Communication Tools in a Vulnerable Urban Community.

A public participatory geographical information systems (PPGIS) demographic, environmental, socioeconomic, health status portal was developed for the Stambaugh-Elwood (SE) community in Columbus, OH. We hypothesized that soil at SE residences would have metal concentrations above natural background levels. Three aims were developed that allowed testing of this hypothesis. Aim 1 focused on establishing partnerships between academia, state agencies and communities to assist in the development of a community voice. Aim 2 was to design and conduct soil sampling for residents of the SE community. Aim 3 was to utilize our interactive, customized portal as a risk communication tool by allowing residents to educate themselves as to the potential risks from industrial sources in close proximity to their community. Multiple comparisons of means were used to determine differences in soil element concentration by sampling location at p < 0.05. The results demonstrated that eight metals (As, Cd, Cu, Pb, Mo, Se, Tl, Zn) occurred at statistically-significantly greater levels than natural background levels, but most were below risk-based residential soil screening levels. Results were conveyed to residents via an educational, risk-communication informational card. This study demonstrates that community-led coalitions in collaboration with academic teams and state agencies can effectively address environmental concerns.