The Association of Air Pollution Exposure With Glucose and Lipid Levels: The Role of an Extreme Air Pollution Event Alongside 2 Decades of Moderate Exposure.

Extreme air pollution events and moderate exposure to fine particulate matter (PM2.5) are associated with increased cardiometabolic risk. The World Trade Center (WTC) Health Program general responder cohort includes responders to the WTC disaster. We investigated whether their exposure to this extreme air pollution event (2001) was associated with long-term metabolic outcomes, independently from the associations of intermediate-term PM2.5 exposure later in life (2004-2019). We included 22,447 cohort members with cholesterol (n = 96,155) and glucose (n = 81,599) laboratory results. Self-reported WTC exposure was derived from a questionnaire. PM2.5 exposure was derived from a satellite-based model. We observed an increase of 0.78 mg/dL (95% confidence interval (CI): 0.30, 1.26) in glucose and 0.67 mg/dL (95% CI: 1.00, 2.35) in cholesterol levels associated with an interquartile range increase in PM2.5 averaged 6 months before the study visit. Higher WTC-exposure categories were also associated with higher cholesterol (0.99 mg/dL, 95% CI: 0.30, 1.67, for intermediate exposure) and glucose (0.82 mg/dL, 95% CI: 0.22, 1.43, for high exposure) levels. Most associations were larger among people with diabetes. Extreme air pollution events and intermediate PM2.5 exposure have independent metabolic consequences. These exposures contributed to higher glucose and lipids levels among WTC responders, which may be translated into increased cardiovascular risk.

Critical Windows of Maternal Exposure to Biothermal Stress and Birth Weight for Gestational Age in Western Australia.

BACKGROUND: There is limited and inconsistent evidence on the risk of ambient temperature on small for gestational age (SGA) and there are no known related studies for large for gestational age (LGA). In addition, previous studies used temperature rather than a biothermal metric. OBJECTIVES: Our aim was to examine the associations and critical susceptible windows of maternal exposure to a biothermal metric [Universal Thermal Climate Index (UTCI)] and the hazards of SGA and LGA. METHODS: We linked 385,337 singleton term births between 1 January 2000 and 31 December 2015 in Western Australia to daily spatiotemporal UTCI. Distributed lag nonlinear models with Cox regression and multiple models were used to investigate maternal exposure to UTCI from 12 weeks preconception to birth and the adjusted hazard ratios (HRs) of SGA and LGA. RESULTS: Relative to the median exposure, weekly and monthly specific exposures showed potential critical windows of susceptibility for SGA and LGA at extreme exposures, especially during late gestational periods. Monthly exposure showed strong positive associations from the 6th to the 10th gestational months with the highest hazard of 13% for SGA (HR=1.13; 95% CI: 1.10, 1.14) and 7% for LGA (HR=1.07; 95% CI: 1.03, 1.11) at the 10th month for the 1st UTCI centile. Entire pregnancy exposures showed the strongest hazards of 11% for SGA (HR=1.11; 95% CI: 1.04, 1.18) and 3% for LGA (HR=1.03; 95% CI: 0.95, 1.11) at the 99th UTCI centile. By trimesters, the highest hazards were found during the second and first trimesters for SGA and LGA, respectively, at the 99th UTCI centile. Based on estimated interaction effects, male births, mothers who were non-Caucasian, smokers, ≥35 years of age, and rural residents were most vulnerable. CONCLUSIONS: Both weekly and monthly specific extreme biothermal stress exposures showed potential critical susceptible windows of SGA and LGA during late gestational periods with disproportionate sociodemographic vulnerabilities. https://doi.org/10.1289/EHP12660.

Predicting fine-scale daily NO2 over Mexico City using an ensemble modeling approach.

In recent years, there has been growing interest in developing air pollution prediction models to reduce exposure measurement error in epidemiologic studies. However, efforts for localized, fine-scale prediction models have been predominantly focused in the United States and Europe. Furthermore, the availability of new satellite instruments such as the TROPOsopheric Monitoring Instrument (TROPOMI) provides novel opportunities for modeling efforts. We estimated daily ground-level nitrogen dioxide (NO2) concentrations in the Mexico City Metropolitan Area at 1-km2 grids from 2005 to 2019 using a four-stage approach. In stage 1 (imputation stage), we imputed missing satellite NO2 column measurements from the Ozone Monitoring Instrument (OMI) and TROPOMI using the random forest (RF) approach. In stage 2 (calibration stage), we calibrated the association of column NO2 to ground-level NO2 using ground monitors and meteorological features using RF and extreme gradient boosting (XGBoost) models. In stage 3 (prediction stage), we predicted the stage 2 model over each 1-km2 grid in our study area, then ensembled the results using a generalized additive model (GAM). In stage 4 (residual stage), we used XGBoost to model the local component at the 200-m2 scale. The cross-validated R2 of the RF and XGBoost models in stage 2 were 0.75 and 0.86 respectively, and 0.87 for the ensembled GAM. Cross-validated rootmean-squared error (RMSE) of the GAM was 3.95 µg/m3. Using novel approaches and newly available remote sensing data, our multi-stage model presented high cross-validated fits and reconstructs fine-scale NO2 estimates for further epidemiologic studies in Mexico City.

National Cohort Study of Long-Term Exposure to PM2.5 Components and Mortality in Medicare American Older Adults.

There is increasing evidence linking long-term fine particulate matter (PM2.5) exposure to negative health effects. However, the relative influence of each component of PM2.5 on health risk is poorly understood. In a cohort study in the contiguous United States between 2000 and 2017, we examined the effect of long-term exposure to PM2.5 main components and all-cause mortality in older adults who had to be at least 65 years old and enrolled in Medicare. We estimated the yearly mean concentrations of six key PM2.5 compounds, including black carbon (BC), organic matter (OM), soil dust (DUST), nitrate (NO3-), sulfate (SO42-), and ammonium (NH4+), using two independently sourced well-validated prediction models. We applied Cox proportional hazard models to evaluate the hazard ratios for mortality and penalized splines for assessing potential nonlinear concentration-response associations. Results suggested that increased exposure to PM2.5 mass and its six main constituents were significantly linked to elevated all-cause mortality. All components showed linear concentration-response relationships in the low exposure concentration ranges. Our research indicates that long-term exposure to PM2.5 mass and its essential compounds are strongly connected to increased mortality risk. Reductions of fossil fuel burning may yield significant air quality and public health benefit.

Long-term air pollution exposure and diabetes risk in American older adults: A national secondary data-based cohort study.

Type 2 diabetes is a major public health concern. Several studies have found an increased diabetes risk associated with long-term air pollution exposure. However, most current studies are limited in their generalizability, exposure assessment, or the ability to differentiate incidence and prevalence cases. We assessed the association between air pollution and first documented diabetes occurrence in a national U.S. cohort of older adults to estimate diabetes risk. We included all Medicare enrollees 65 years and older in the fee-for-service program, part A and part B, in the contiguous United States (2000-2016). Participants were followed annually until the first recorded diabetes diagnosis, end of enrollment, or death (264, 869, 458 person-years). We obtained annual estimates of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and warm-months ozone (O3) exposures from highly spatiotemporally resolved prediction models. We assessed the simultaneous effects of the pollutants on diabetes risk using survival analyses. We repeated the models in cohorts restricted to ZIP codes with air pollution levels not exceeding the national ambient air quality standards (NAAQS) during the study period. We identified 10, 024, 879 diabetes cases of 41, 780, 637 people (3.8% of person-years). The hazard ratio (HR) for first diabetes occurrence was 1.074 (95% CI 1.058; 1.089) for 5 µg/m3 increase in PM2.5, 1.055 (95% CI 1.050; 1.060) for 5 ppb increase in NO2, and 0.999 (95% CI 0.993; 1.004) for 5 ppb increase in O3. Both for NO2 and PM2.5 there was evidence of non-linear exposure-response curves with stronger associations at lower levels (NO2 ≤ 36 ppb, PM2.5 ≤ 8.2 µg/m3). Furthermore, associations remained in the restricted low-level cohorts. The O3-diabetes exposure-response relationship differed greatly between models and require further investigation. In conclusion, exposures to PM2.5 and NO2 are associated with increased diabetes risk, even when restricting the exposure to levels below the NAAQS set by the U.S. EPA.

Particulate Air Pollution Exposure and Stroke among Adults in Israel.

Stroke is the second most common cause of death and disability in the world. Many studies have found fine particulate matter (PM2.5) exposure to be associated with an increased risk of atherosclerotic cardiovascular disease, mostly focusing on ischemic heart disease and acute myocardial infarction. In a national analysis conducted in Israel-an area with unique climate conditions and high air pollution levels, we estimated the association between short-term PM2.5 exposure and ischemic stroke, intracerebral hemorrhage (ICH), or transient ischemic attacks (TIA). Using the Israeli National Stroke Registry, we obtained information on all stroke cases across Israel in 2014-2018. We obtained daily PM2.5 exposures from spatiotemporally resolved exposure models. We restricted the analytical data to days in which PM2.5 levels did not exceed the Israeli 24 h standard (37.5 µg/m3). We repeated the analysis with a stratification by sociodemographic characteristics and comorbidities. For all outcomes, the exposure-response curves were nonlinear. PM2.5 exposure was associated with a higher ischemic stroke risk, with larger effect estimates at higher exposure levels. Although nonsignificant, the exposure-response curve for TIA was similar. The associations with ICH were nonsignificant throughout the PM2.5 exposure distribution. The associations with ischemic stroke/TIA were larger among women, non-Jewish individuals, older adults, and individuals with diabetes, hypertension, and ischemic heart disease. In conclusion, short-term PM2.5 exposure is associated with a higher risk for ischemic stroke and possibly TIA, even when PM2.5 concentrations do not exceed the Israeli air quality guideline threshold. Vulnerability to the air pollution effects differed by age, sex, ethnicity, and comorbidities.

Air Pollution and Maximum Temperature Are Associated with Neurodevelopmental Regressive Events in Autism Spectrum Disorder.

Neurodevelopmental regression (NDR) is an enigmatic event associated with autism spectrum disorder (ASD) during which a child loses previously acquired skills and develops ASD symptoms. In some, a trigger which precedes the NDR event, such as a fever, can be identified, but in many cases no trigger is obvious. We hypothesize that air pollution (PM2.5) may trigger NDR, especially in those children without an identified trigger. Average daily PM2.5, ozone, precipitation and maximum temperature (Tmax) were derived from Environmental Protection Agency models and National Oceanic and Atmospheric Administration monitors based on zip-code information from 83 ASD participants during the six-weeks following the onset month of an NDR event and a reference period defined as one year before and one year after the event. Seasonally adjusted logistic regression (LR) and linear mixed models (LMM) compared cases (with a history of NDR) and matched controls (without a history of NDR). LR models found that the risk of NDR was related to higher PM2.5 during 3 to 6 weeks of the NDR event period, particularly in those without a trigger. Overall, both models converged on NDR being related to a higher PM2.5 and lower Tmax both during the NDR event period as well as the reference period, particularly in those without a known trigger. This temporal pattern suggests that environmental triggers, particularly PM2.5, could be related to NDR, especially in those without an identifiable trigger. Further studies to determine the underlying biological mechanism of this observation could help better understand NDR and provide opportunities to prevent NDR.

Intermediate- and long-term associations between air pollution and ambient temperature and glycated hemoglobin levels in women of child bearing age.

BACKGROUND: Air pollution has been linked to obesity while higher ambient temperatures typically reduce metabolic demand in a compensatory manner. Both relationships may impact glucose metabolism, thus we examined the association between intermediate- and long-term exposure to fine particulate matter (PM2.5) and ambient temperature and glycated hemoglobin(HbA1c), a longer-term marker of glucose control. METHODS: We assessed 3-month, 6-month, and 12-month average air pollution and ambient temperature at 1-km2 spatial resolution via satellite remote sensing models (2013-2019), and assessed HbA1c at four, six, and eight years postpartum in women enrolled in the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) cohort based in Mexico City. PM2.5 and ambient temperature were matched to participants' addresses and confirmed by GPS tracker. Using linear mixed-effects models, we examined the association between 3-month, 6-month, and 12-month average PM2.5 and ambient temperature with repeated log-transformed HbA1c values. All models included a random intercept for each woman and were adjusted for calendar year, season, and individual-level confounders (age, marital status, smoking, alcohol consumption level, and education level). RESULTS: We analyzed 1,265 HbA1c measurements of 484 women. Per 1 µg/m3 increase in 3-month and 6-month PM2.5, HbA1c levels increased by 0.28% (95% confidence interval (95 %CI): 0.14, 0.42%) and 0.28% (95 %CI: 0.04, 0.52%) respectively. No association was seen for 12-month average PM2.5. Per 1 °C increase in ambient temperature, HbA1c levels decreased by 0.63% (95 %CI: -1.06, -0.21%) and 0.61% (95 %CI: -1.08, -0.13%), while the 12-month average again is not associated with HbA1c. CONCLUSIONS: Intermediate-term exposure to PM2.5 and ambient temperature are associated with opposing changes in HbA1c levels, in this region of high PM2.5 and moderate temperature fluctuation. These effects, measurable in mid-adult life, may portend future risk of type 2 diabetes and possible heart disease.

A self-controlled approach to survival analysis, with application to air pollution and mortality.

BACKGROUND: Many studies have reported that long-term air pollution exposure is associated with increased mortality rates. These investigations have been criticized for failure to control for omitted, generally personal, confounders. Study designs that are robust to such confounders can address this issue. METHODS: We used a self-controlled design for survival analysis. We stratified on each person in the Medicare cohort between 2000 and 2015 who died, and examined whether PM2.5, O3 and NO2 exposures predicted in which follow-up period the death occurred. We used conditional logistic regression stratified on person and controlled for nonlinear terms in calendar year and age. By design slowly varying covariates such as smoking history, BMI, diabetes and other pre-existing conditions, usual alcohol consumption, sex, race, socioeconomic status, and green space were controlled by matching each person to themselves. RESULTS: There were 6,452,618 deaths in the study population in the study period. We observed a 5.37% increase in the mortality rate (95% CI 4.67%, 6.08%) for every 5 µg/m3 increase in PM2.5, a 1.98% (95% CI 1.61%, 2.36%) increase for 5 ppb increment in O3, and a 2.10% decrease (95% CI 1.88%, 2.33%) for a 5 ppb increase in NO2. When restricted to persons whose PM2.5 exposure never exceeded 12 µg/m3 in any year between 2000 and 2015, the effect size increased for PM2.5 (12.71% (11.30, 14.15)), and the signs of O3 and NO2 reversed (-0.26% (-0.88, 0.35) for O3 and 1.77% increase (1.40, 2.13) for NO2). Effect sizes were larger for Blacks (e.g. 7.71% (5.46, 10.02) for PM2.5). CONCLUSION: There is strong evidence that the association between annual exposure to PM2.5 and mortality is not confounded by individual or neighborhood covariates, and continues below the standard. The effects of O3 and NO2 are difficult to disentangle.

Exposure to metals and morbidity at eight years follow-up in women of childbearing age.

This exploratory study was aimed to investigate the link between toxic metal content in women's urine and their morbidity 2 years before and 6 years after the test. Concentrations of 25 metals in urine were analyzed for 111 pregnant women collected prior to delivery. All women were of Arab-Bedouin origin. Information on primary care and hospital visits during the study period was obtained. In a Poisson regression model, a health outcome was regressed over metal exposure and other factors. A Weighted Quantile Sum Regression (WQS) approach was used to indicate metals dominating in their possible impact on women's morbidity. Obesity was the most frequently diagnosed condition in this population (27.9%). Diagnoses in a neurological category accounted for 36.0%, asthma or respiratory-25.2%, psychiatric-12.6%, cardiovascular-14.4% and cancer or benign growth-for 13.5%. Based on WQS analysis, cancer and benign growth were mostly attributed to the increased levels of cadmium, cardiovascular outcomes were linked with lead, and obesity was found associated with elevated levels of nickel. Hematological, neurological and respiratory outcomes were attributed to multiple non-essential metals. The health and exposure profile of women in the study warrants a periodic biomonitoring in attempt to identify and reduce exposure to potentially dangerous elements.

The effect of prenatal temperature and PM2.5 exposure on birthweight: Weekly windows of exposure throughout the pregnancy.

BACKGROUND: Birthweight is a strong predictor of normal growth, healthy development, and survival. Several studies have found associations between temperature, fine particulate matter (PM2.5), and birth weight. However, the relevant timing of exposures varies between studies and is yet unclear. Therefore, we assessed the difference in term birthweight (TBW) associated with weekly exposure to temperature and PM2.5 throughout 37 weeks of gestation. METHODS: We included all singleton live term births in Massachusetts, U.S between 2004 and 2015 (n = 712,438). Weekly PM2.5 and temperature predictions were estimated on a 1 km grid from satellite-based models. We utilized a distributed lag nonlinear model (DLNM) to estimate the difference in TBW associated with weekly exposures from the last menstrual period to 37 weeks of gestation. RESULTS: We found a nonlinear association with prenatal temperature exposure. Larger effects were observed in warmer temperatures, where higher temperatures were negatively associated with TBW. Temperature effects were larger in the first and final weeks of gestation. We observed a negative difference in TBW associated with PM2.5 exposure. Overall, a 1 µg/m3 increase in prenatal exposure was associated with 3.9 g lower TBW (95% CI -5.0 g; -2.9 g). PM2.5 effects were larger in the final weeks of gestation. CONCLUSION: We found heat and PM2.5 exposure to be related to lower TBW. Our findings suggest that women are more susceptible to both exposures towards the end of pregnancy. Susceptibility to heat was higher in the initial weeks of pregnancy as well. These critical windows of susceptibility can be communicated to pregnant women during routine prenatal visits to increase awareness and target interventions to reduce exposures.

Modeling the impact of exposure reductions using multi-stressor epidemiology, exposure models, and synthetic microdata: an application to birthweight in two environmental justice communities.

BACKGROUND: Many vulnerable populations experience elevated exposures to environmental and social stressors, with deleterious effects on health. Multi-stressor epidemiological models can be used to assess benefits of exposure reductions. However, requisite individual-level risk factor data are often unavailable at adequate spatial resolution. OBJECTIVE: To leverage public data and novel simulation methods to estimate birthweight changes following simulated environmental interventions in two environmental justice communities in Massachusetts, USA. METHODS: We gathered risk factor data from public sources (US Census, Behavioral Risk Factor Surveillance System, and Massachusetts Department of Health). We then created synthetic individual-level data sets using combinatorial optimization, and probabilistic and logistic modeling. Finally, we used coefficients from a multi-stressor epidemiological model to estimate birthweight and birthweight improvement associated with simulated environmental interventions. RESULTS: We created geographically resolved synthetic microdata. Mothers with the lowest predicted birthweight were those identifying as Black or Hispanic, with parity > 1, utilization of government prenatal support, and lower educational attainment. Birthweight improvements following greenness and temperature improvements were similar for all high-risk groups and were larger than benefits from smoking cessation. SIGNIFICANCE: Absent private health data, this methodology allows for assessment of cumulative risk and health inequities, and comparison of individual-level impacts of localized health interventions.

Maternal metal concentration during gestation and pediatric morbidity in children: an exploratory analysis.

BACKGROUND: The majority of studies linking exposure to metals with certain health outcomes focus on known toxic metals. Alternatively, this study assesses the extent to which exposure to a wider range of metals during gestation is associated with childhood morbidity. METHODS: We analyzed the concentrations of 25 metals found in urine samples of 111 pregnant women of Arab-Bedouin origin collected prior to birth. In addition, we collected medical records on their offspring for six years following birth, including every interaction with HMOs, local hospitals, and pharmacies. RESULTS: The main types of morbidities diagnosed and treated during this period were preterm births, malformations, asthma-like morbidity, cardiovascular and behavioral problems, and obesity. Multivariable analysis showed that offspring born before term were more likely to have been exposed to elevated maternal concentrations of zinc, thallium, aluminum, manganese, and uranium, all with adjusted relative risk above 1.40 for an increase by each quintile. Likewise, children with asthma had been exposed to higher levels of magnesium, strontium, and barium at gestation, while behavioral outcomes were associated with elevated biometals, i.e., sodium, magnesium, calcium, selenium, and zinc, as well as higher levels of lithium, cobalt, nickel, strontium, cadmium, vanadium, arsenic, and molybdenum. A heatmap of adjusted relative risk estimates indicates the considerable implications that exposure to metals may have for preterm birth and developmental outcomes. CONCLUSIONS: The current study shows that perinatal exposure to metals is adversely associated with pediatric morbidity. Further such analyses on additional samples are warranted.

Differences in environmental factors contributing to preterm labor and PPROM - Population based study.

BACKGROUND: Previous reports indicate an association between ambient temperature (Ta) and air pollution exposure during pregnancy and preterm birth (PTB). Nevertheless, information regarding the association between environmental factors and specific precursors of spontaneous preterm birth is lacking. We aimed to determine the association between Ta and air pollution during gestation and the precursors of spontaneous preterm parturition, i.e. preterm labor (PTL) and preterm prelabor rupture of membranes (PPROM). METHODS: From 2003 to 2013 there were 84,476 deliveries of singleton gestation that comprised the study cohort. Exposure data during pregnancy included daily measurements of temperature and particulate matter <2.5 µm and <10 µm, PM2.5 and PM10, respectively. Deliveries were grouped into PPROM, PTL and non-spontaneous preterm and term deliveries. Exposure effect was tested in windows of a week and two days prior to admission for delivery and adjusted to gestational age and socio-economic status. Poisson regression models were used for analyses. RESULTS: There is an association of environmental exposure with the precursors of spontaneous preterm parturition; PPROM was more sensitive to Ta fluctuations than PTL. This effect was modified by the ethnicity, Bedouin-Arabs were susceptible to elevated Ta, especially within the last day prior to admission with PPROM (Relative Risk (RR) =1.19 [95% CI, 1.03; 1.37]). Jews, on the other hand, were susceptible to ambient pollutants, two (RR=1.025 [1.010; 1.040]) and one (RR= 1.017 [1.002; 1.033]) days prior to spontaneous PTL with intact membranes resulting in preterm birth. CONCLUSION: High temperature is an independent risk factor for PPROM among Bedouin-Arabs; ambient pollution is an independent risk factor for spontaneous PTL resulting in preterm birth. Thus, the precursors of spontaneous preterm parturition differ in their association with environmental factors.

A national difference in differences analysis of the effect of PM2.5 on annual death rates.

Many studies have reported that PM2.5 was associated with mortality, but these were criticized for unmeasured confounding, not using causal modeling, and not focusing on changes in exposure and mortality rates. Recent studies have used propensity scores, a causal modeling approach that requires the assumption of no unmeasured confounders. We used differences in differences, a causal modeling approach that focuses on exposure changes, and controls for unmeasured confounders by design to analyze PM2.5 and mortality in the U.S. Medicare population, with 623, 036, 820 person-years of follow-up, and 29, 481, 444 deaths. We expanded the approach by clustering ZIP codes into 32 groups based on racial, behavioral and socioeconomic characteristics, and analyzing each cluster separately. We controlled for multiple time varying confounders within each cluster. A separate analysis examined participants whose exposure was always below 12 µg/m3. We found an increase of 1 µg/m3 in PM2.5 produced an increased risk of dying in that year of 3.85 × 10-4 (95% CI 1.95 × 10-4, 5.76 × 10-4). This corresponds to 14,000 early deaths per year per 1 µg/m3. When restricted to exposures below 12 µg/m3, the increased mortality risk was 4.26 × 10-4 (95% CI 1.43 × 10-4, 7.09 × 10-4). Using a causal modeling approach robust to omitted confounders, we found associations of PM2.5 with increased death rates, including below U.S. and E.U. standards.

PM2.5 and hospital admissions among Medicare enrollees with chronic debilitating brain disorders.

BACKGROUND: Although long-term exposure to particulate matter<2.5 µm (PM2.5) has been linked to chronic debilitating brain disorders (CDBD), the role of short-term exposure in health care demand, and increased susceptibility for PM2.5-related health conditions, among Medicare enrollees with CDBD has received little attention. We used a causal modeling approach to assess the effect of short-term high PM2.5 exposure on all-cause admissions, and prevalent cause-specific admissions among Medicare enrollees with CDBD (Parkinson's disease-PD, Alzheimer's disease-AD and other dementia). METHODS: We constructed daily zipcode counts of hospital admissions of Medicare beneficiaries older than 65 across the United-States (2000-2014). We obtained daily PM2.5 estimates from a satellite-based model. A propensity score matching approach was applied to match high-pollution (PM2.5 > 17.4 µg/m3) to low-pollution zip code-days with similar background characteristics. Then, we estimated the percent change in admissions attributable to high pollution. We repeated the models restricting the analysis to zipcode-days with PM2.5 below of 35 µg/m3. RESULTS: We observed significant increases in all-cause hospital admissions (2.53% in PD and 2.49% in AD/dementia) attributable to high PM2.5 exposure. The largest observed effect for common causes was for pneumonia and urinary tract infection. All the effects were larger in CDBD compared to the general Medicare population, and similarly strong at levels of exposure considered safe by the EPA. CONCLUSION: We found Medicare beneficiaries with CDBD to be at higher risk of being admitted to the hospital following acute exposure to PM2.5 levels well below the National Ambient Air Quality Standard defined as safe by the EPA.

The association between exposure to radiation and the incidence of cataract.

OBJECTIVE: To examine the association between exposure to radiation from computed tomography (CT) studies and the incidence of cataract. METHODS: In a nested case-control study, all cataract cases and their matched controls were sampled from a retrospective cohort of Israeli residents who underwent CT scans or ultrasonic tests in Soroka Medical Center, Beer-Sheva, Israel, between the years 1996 and 2014. The risk of cataract associated with head, neck or the rest of the body CT was assessed using Poisson survival analysis. RESULTS: The nested matched sample included 3841 cataract cases and their age- and sex-matched controls (n = 228,743). CT radiation exposure was more frequent in the cataract group, with 9.7% head CT, 1.2% neck CT and 6.6% other CT, compared to 5%, 0.7% and 3.7% among person-years without cataract (p < 0.001). In a multivariate analysis, a similar increased risk of cataract associated with head (hazard ratio (HR): 1.24, 95% confidence interval (CI): 1.11; 1.38) and other CT (HR: 1.25, 95% CI: 1.10; 1.43) was found. No association with neck CT (HR: 1.07, 95% CI: 0.80; 1.43) was observed. CONCLUSION: In our study population, a similar risk of cataract with head, neck or the rest of the body CT was detected.

Estimating the Combined Effects of Natural and Built Environmental Exposures on Birthweight among Urban Residents in Massachusetts.

Intrauterine growth has health implications both in childhood and adulthood. Birthweight is partially determined by prenatal environmental exposures. We aim to identify important predictors of birthweight out of a set of environmental, built environment exposures, and socioeconomic environment variables during pregnancy (i.e., fine particulate matter (PM2.5), temperature, greenness, walkability, noise, and economic indices). We included all singleton live births of mothers who resided in urban census block-groups and delivered in Massachusetts between 2001 and 2011 (n = 640,659). We used an elastic-net model to select important predictors of birthweight and constructed a multivariate model including the selected predictors, with adjustment for confounders. We additionally used a weighted quantile sum regression to assess the contribution of each exposure to differences in birthweight. All exposures were selected as important predictors of birthweight. In the multivariate model, lower birthweight was significantly associated with lower greenness and with higher temperature, walkability, noise, and segregation of the "high income" group. Treating the exposures individually, nighttime noise had the highest weight in its contribution to lower birthweight. In conclusion, after accounting for individual confounders, maternal environmental exposures, built environment exposures, and socioeconomic environment during pregnancy were important predictors of birthweight, emphasizing the role of these exposures in fetal growth and development.

The effect of exposure to particulate matter during pregnancy on lower respiratory tract infection hospitalizations during first year of life.

BACKGROUND: Lower respiratory tract infections (LRTI) in early life, including pneumonia, bronchitis and bronchiolitis, can lead to decreased lung function, persistent lung damage and increased susceptibility to various respiratory diseases such as asthma. In-utero exposure to particulate matter (PM) during pregnancy may disrupt biological mechanisms that regulate fetal growth, maturation and development. We aimed to estimate the association between intrauterine exposure to PM of size < 2.5 µm in diameter (PM2.5) and incidence of LRTIs during the first year of life. METHODS: A retrospective population-based cohort study in a population of mothers and infants born in Soroka University Medical Center (SUMC) in the years 2004-2012. All infants < 1 year old that were hospitalized due to LRTIs were included. The main exposure assessment was based on a hybrid model incorporating daily satellite-based predictions at 1 km2 spatial resolution. Data from monitoring stations was used for imputation of main exposure and other pollutants. Levels of environmental exposures were assigned to subjects based on their residential addresses and averaged for each trimester. Analysis was conducted by a multivariable generalized estimating equation (GEE) Poisson regression. Data was analyzed separately for the two main ethnic groups in the region, Jewish and Arab-Bedouin. RESULTS: The study cohort included 57,331 deliveries that met the inclusion criteria. Overall, 1871 hospitalizations of infants < 1 year old due to pneumonia or bronchiolitis were documented. In a multivariable analysis, intrauterine exposure to high levels of PM2.5 (> 24 µg/m3) in the first and second trimesters was found to be adversely associated with LRTIs in the Arab-Bedouin population (1st trimester, RR = 1.31, CI 95% 1.08-1.60; 2nd trimester: RR = 1.34, CI 95% 1.09-1.66). CONCLUSION: Intrauterine exposure to high levels of PM2.5 is associated with a higher risk of hospitalizations due to lower respiratory tract infections in Arab-Bedouin infants.