To date, few studies have investigated the causal relationship between mortality and long-term exposure to a low level of fine particulate matter (PM2.5) concentrations.
We studied 242,320 registered ...deaths in Queensland between January 1, 1998, and December 31, 2013, with satellite-retrieved annual average PM2.5 concentrations to each postcode. A variant of difference-in-differences (DID) approach was used to investigate the association of long-term PM2.5 exposure with total mortality and cause-specific (cardiovascular, respiratory, and non-accidental) mortality. We observed 217,510 non-accidental deaths, 133,661 cardiovascular deaths, and 30,748 respiratory deaths in Queensland during the study period. The annual average PM2.5 concentrations ranged from 1.6 to 9.0 μg/m3, which were well below the current World Health Organization (WHO) annual standard (10 μg/m3). Long-term exposure to PM2.5 was associated with increased total mortality and cause-specific mortality. For each 1 μg/m3 increase in annual PM2.5, we found a 2.02% (95% CI 1.41%-2.63%; p < 0.01) increase in total mortality. Higher effect estimates were observed in Brisbane than those in Queensland for all types of mortality. A major limitation of our study is that the DID design is under the assumption that no predictors other than seasonal temperature exhibit different spatial-temporal variations in relation to PM2.5 exposure. However, if this assumption is violated (e.g., socioeconomic status SES and outdoor physical activities), the DID design is still subject to confounding.
Long-term exposure to PM2.5 was associated with total, non-accidental, cardiovascular, and respiratory mortality in Queensland, Australia, where PM2.5 levels were measured well below the WHO air quality standard.
Air pollution may increase risk of Alzheimer's disease and related dementias (ADRD) in the U.S., but the extent of this relationship is unclear. Here, we constructed two national U.S. ...population-based cohorts of those aged ≥65 from the Medicare Chronic Conditions Warehouse (2000-2018), combined with high-resolution air pollution datasets, to investigate the association of long-term exposure to ambient fine particulate matter (PM
), nitrogen dioxide (NO
), and ozone (O
) with dementia and AD incidence, respectively. We identified ~2.0 million incident dementia cases (N = 12,233,371; dementia cohort) and ~0.8 million incident AD cases (N = 12,456,447; AD cohort). Per interquartile range (IQR) increase in the 5-year average PM
(3.2 µg/m
), NO
(11.6 ppb), and warm-season O
(5.3 ppb) over the past 5 years prior to diagnosis, the hazard ratios (HRs) were 1.060 (95% confidence interval CI: 1.054, 1.066), 1.019 (95% CI: 1.012, 1.026), and 0.990 (95% CI: 0.987, 0.993) for incident dementias, and 1.078 (95% CI: 1.070, 1.086), 1.031 (95% CI: 1.023, 1.039), and 0.982 (95%CI: 0.977, 0.986) for incident AD, respectively, for the three pollutants. For both outcomes, concentration-response relationships for PM
and NO
were approximately linear. Our study suggests that exposures to PM
and NO
are associated with incidence of dementia and AD.
Various approaches have been proposed to model PM2.5 in the recent decade, with satellite-derived aerosol optical depth, land-use variables, chemical transport model predictions, and several ...meteorological variables as major predictor variables. Our study used an ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM2.5 at a resolution of 1 km × 1 km across the contiguous United States. We used a generalized additive model that accounted for geographic difference to combine PM2.5 estimates from neural network, random forest, and gradient boosting. The three machine learning algorithms were based on multiple predictor variables, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis datasets, and others. The model training results from 2000 to 2015 indicated good model performance with a 10-fold cross-validated R2 of 0.86 for daily PM2.5 predictions. For annual PM2.5 estimates, the cross-validated R2 was 0.89. Our model demonstrated good performance up to 60 μg/m3. Using trained PM2.5 model and predictor variables, we predicted daily PM2.5 from 2000 to 2015 at every 1 km × 1 km grid cell in the contiguous United States. We also used localized land-use variables within 1 km × 1 km grids to downscale PM2.5 predictions to 100 m × 100 m grid cells. To characterize uncertainty, we used meteorological variables, land-use variables, and elevation to model the monthly standard deviation of the difference between daily monitored and predicted PM2.5 for every 1 km × 1 km grid cell. This PM2.5 prediction dataset, including the downscaled and uncertainty predictions, allows epidemiologists to accurately estimate the adverse health effect of PM2.5. Compared with model performance of individual base learners, an ensemble model would achieve a better overall estimation. It is worth exploring other ensemble model formats to synthesize estimations from different models or from different groups to improve overall performance.
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•An ensemble model integrates three machine learning algorithms and estimates PM2.5.•Satellite measurements, land-use terms, and many variables were predictors.•Model predicts daily PM2.5 at 1 km × 1 km grid cells in the entire United States.•Model predictions were downscaled to 100 m × 100 m level.•Monthly uncertainty level of prediction was also estimated.
BACKGROUND:Little is known about what factors modify the effect of long-term exposure to PM2.5 on mortality, in part because in most previous studies certain groups such as rural residents and ...individuals with lower socioeconomic status (SES) are under-represented.
METHODS:We studied 13.1 million Medicare beneficiaries (age ≥65) residing in seven southeastern US states during 2000–2013 with 95 million person-years of follow-up. We predicted annual average of PM2.5 in each zip code tabulation area (ZCTA) using a hybrid spatiotemporal model. We fit Cox proportional hazards models to estimate the association between long-term PM2.5 and mortality. We tested effect modification by individual-level covariates (race, sex, eligibility for both Medicare and Medicaid, and medical history), neighborhood-level covariates (urbanicity, percentage below poverty level, lower education, median income, and median home value), mean summer temperature, and mass fraction of 11 PM2.5 components.
RESULTS:The hazard ratio (HR) for death was 1.021 (95% confidence interval1.019, 1.022) per 1 μg m increase in annual PM2.5. The HR decreased with age. It was higher among males, non-whites, dual-eligible individuals, and beneficiaries with previous hospital admissions. It was higher in neighborhoods with lower SES or higher urbanicity. The HR increased with mean summer temperature. The risk associated with PM2.5 increased with relative concentration of elemental carbon, vanadium, copper, calcium, and iron and decreased with nitrate, organic carbon, and sulfate.
CONCLUSIONS:Associations between long-term PM2.5 exposure and death were modified by individual-level, neighborhood-level variables, temperature, and chemical compositions.
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•A random forest machine learning and a mixed effect models were used.•PM2.5 exposures from 2001 to 2018 for Kuwait and Iraq were assessed.•The study region had very high PM2.5 ...concentrations.
Iraq and Kuwait are in a region of the world known to be impacted by high levels of fine particulate matter (PM2.5) attributable to sources that include desert dust and ambient pollution, but historically have had limited pollution monitoring networks. The inability to assess PM2.5 concentrations have limited the assessment of the health impact of these exposures, both in the native populations and previously deployed military personnel. As part of a Department of Veterans Affairs Cooperative Studies Program health study of land-based U.S. military personnel who were previously deployed to these countries, we developed a novel approach to estimate spatially and temporarily resolved daily PM2.5 exposures 2001–2018. Since visibility is proportional to ground-level particulate matter concentrations, we were able to take advantage of extensive airport visibility data that became available as a result of regional military operations over this time period. First, we combined a random forest machine learning and a generalized additive mixed model to estimate daily high resolution (1 km × 1 km) visibility over the region using satellite-based aerosol optical depth (AOD) and airport visibility data. The spatially and temporarily resolved visibility data were then used to estimate PM2.5 concentrations from 2001 to 2018 by converting visibility to PM2.5 using empirical relationships derived from available regional PM2.5 monitoring stations. We adjusted for spatially resolved meteorological parameters, land use variables, including the Normalized Difference Vegetation Index, and satellite-derived estimates of surface dust as a measure of sandstorm activity. Cross validation indicated good model predictive ability (R2 = 0.71), and there were considerable spatial and temporal differences in PM2.5 across the region. Annual average PM2.5 predictions for Iraq and Kuwait were 37 and 41 μg/m3, respectively, which are greater than current U.S. and WHO standards. PM2.5 concentrations in many U.S. bases and large cities (e.g. Bagdad, Balad, Kuwait city, Karbala, Najaf, and Diwaniya) had annual average PM2.5 concentrations above 45 μg/m3 with weekly averages as high as 150 μg/m3 depending on calendar year. The highest annual PM2.5 concentration for both Kuwait and Iraq were observed in 2008, followed by 2009, which was associated with extreme drought in these years. The lowest PM2.5 values were observed in 2014. On average, July had the highest concentrations, and November had the lowest values, consistent with seasonal patterns of air pollution in this region. This is the first study that estimates long-term PM2.5 exposures in Iraq and Kuwait at a high resolution based on measurements data that will allow the study of health effects and contribute to the development of regional environmental policies. The novel approach demonstrated may be used in other parts of the world with limited monitoring networks.
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•This study examined ozone impacts on cognitive function in Chinese older adults.•Long-term ozone exposure was associated with increased cognitive impairment risk.•The association was ...observed at concentrations above the WHO guideline (60 μg/m3).•Older adults from Eastern, Central, and Southern China were especially susceptible.
Ambient particulate matter pollution has been linked to impaired cognitive performance, but the effect of ambient ozone exposure on cognitive function remains largely unknown. We examined the association of long-term ozone exposure with the risk of cognitive impairment among a national representative cohort of 9,544 Chinese older adults (aged 65 years and over) with baseline normal cognition from the Chinese Longitudinal Healthy Longevity Survey (2005–2018). The ozone exposure of each participant was measured by annual mean ozone concentrations for the county of residence. Cognitive function was assessed by the Chinese version of the Mini-Mental State Examination (MMSE). We defined cognitive impairment as an MMSE score below 18 points accompanied by an MMSE score that declined ≥ 4 points from baseline. Cox proportional hazards models were applied to explore the association of ozone exposure with cognitive impairment. During the mean follow-up time of 6.5 years, 2,601 older adults developed cognitive impairment. Each 10-μg/m3 increase in annual mean ozone exposure was associated with a 10.4% increased risk of cognitive impairment. The exposure–response relationship between ozone exposure and risk of cognitive impairment showed a linear trend. Sensitivity analyses revealed the association to be robust. We found that older adults from Eastern, Central, and Southern China were particularly susceptible. Our results show that ozone is a risk factor for late-life cognitive decline. Reducing ambient ozone pollution may help delay the onset of cognitive impairment among older adults.
Introduction
Alzheimer's disease (AD) incidence is thought to be higher among Black than White individuals.
Methods
We studied the US Medicare population from 2000 to 2018. Cox regression was used to ...determine the roles of race and co‐morbidities for AD incidence.
Results
We studied 11,880,906 Medicare beneficiaries, with 774,548 AD cases. Hazard ratios (HRs) by increasing numbers of co‐morbidities (1–7) were 1.51, 2.00, 2.55, 3.16, 2.89, 4.77, and 5.65. Among those with no co‐morbidities, Black individuals had a lower rate than those who are White (HR = 0.69), while among those with one more co‐morbidities, Black individuals had a higher rate (HR = 1.19). The presence of hypertension increased AD rates by 14% for White individuals, but 69% for those who are Black.
Discussion
More co‐morbidities was strongly associated with higher AD rates. The higher rates for Black versus White individuals was apparent only for those with co‐morbidities and appears driven both by more co‐morbidities, and the greater effect of hypertension.
Highlights
Black individuals have been shown to have higher Alzheimer's disease (AD) rates than those who are White.
Some co‐morbidities are known to increase AD risk.
Among those In Medicare data with no co‐morbidities, Black individuals have less risk than those who are White.
Among those with co‐morbidities, Black individuals have higher rates than those who are White.
Hypertension results in a much stronger increase in AD risk for Black versus White individuals.
The novel human coronavirus disease 2019 (COVID-19) pandemic has claimed more than 600,000 lives worldwide, causing tremendous public health, social, and economic damages. Although the risk factors ...of COVID-19 are still under investigation, environmental factors, such as urban air pollution, may play an important role in increasing population susceptibility to COVID-19 pathogenesis.
We conducted a cross-sectional nationwide study using zero-inflated negative binomial models to estimate the association between long-term (2010–2016) county-level exposures to NO2, PM2.5, and O3 and county-level COVID-19 case-fatality and mortality rates in the United States. We used both single- and multi-pollutant models and controlled for spatial trends and a comprehensive set of potential confounders, including state-level test positive rate, county-level health care capacity, phase of epidemic, population mobility, population density, sociodemographics, socioeconomic status, race and ethnicity, behavioral risk factors, and meteorology.
From January 22, 2020, to July 17, 2020, 3,659,828 COVID-19 cases and 138,552 deaths were reported in 3,076 US counties, with an overall observed case-fatality rate of 3.8%. County-level average NO2 concentrations were positively associated with both COVID-19 case-fatality rate and mortality rate in single-, bi-, and tri-pollutant models. When adjusted for co-pollutants, per interquartile-range (IQR) increase in NO2 (4.6 ppb), COVID-19 case-fatality rate and mortality rate were associated with an increase of 11.3% (95% CI 4.9%–18.2%) and 16.2% (95% CI 8.7%–24.0%), respectively. We did not observe significant associations between COVID-19 case-fatality rate and long-term exposure to PM2.5 or O3, although per IQR increase in PM2.5 (2.6 μg/m3) was marginally associated, with a 14.9% (95% CI 0.0%–31.9%) increase in COVID-19 mortality rate when adjusted for co-pollutants.
Long-term exposure to NO2, which largely arises from urban combustion sources such as traffic, may enhance susceptibility to severe COVID-19 outcomes, independent of long-term PM2.5 and O3 exposure. The results support targeted public health actions to protect residents from COVID-19 in heavily polluted regions with historically high NO2 levels. Continuation of current efforts to lower traffic emissions and ambient air pollution may be an important component of reducing population-level risk of COVID-19 case fatality and mortality.
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•One of the first US studies on air pollution exposures and COVID-19 death outcomes•Urban air pollutants, especially NO2, may enhance population susceptibility to death fromCOVID-19•Reduction in air pollution would have avoided over 14,000 COVID-19 deaths in the US as of July 17, 2020•Public health actions needed to protect populations from COVID-19 in areas with historically high NO2 exposure•Expansion of efforts to lower air pollution may reduce population-level risk of COVID-19
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