Air pollution exposure and COVID-19: A look at mortality in Mexico City using individual-level data.
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ABSTRACT:
SUBMITTER: Lopez-Feldman A
PROVIDER: S-EPMC7688431 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
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Air pollution exposure and COVID-19: A look at mortality in Mexico City using individual-level data.
López-Feldman Alejandro A Heres David D Marquez-Padilla Fernanda F
The Science of the total environment 20201126
We use individual-level data to estimate the effects of long- and short-term exposure to air pollution (PM<sub>2.5</sub>) on the probability of dying from COVID-19. To the best of our knowledge, our study is the first to look at this relationship using individual-level data. We find that for Mexico City there is evidence of a positive relationship between pollution and mortality that significantly grows with age and that appears to be mostly driven by long- rather than short-term exposure. By us ...[more]
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Project description:BACKGROUND:Postpartum depression (PPD), which affects up to 1 in 5 mothers globally, negatively impacts the health of both mothers and children. Exposure to ambient air pollution has been linked to depressive symptoms in animal models and human studies, but the relationship between air pollution and PPD has not been widely studied. METHODS:In a birth cohort in Mexico City (509 mothers with available data), we examined the association between exposure to particulate matter ≤2.5 μm in diameter (PM2.5) with symptoms of psychological dysfunction at 1 and 6 months postpartum. Daily PM2.5 estimates were derived from a hybrid satellite-based spatio-temporally resolved model and averaged over pregnancy and the first year postpartum. Edinburgh Postnatal Depression Scale (EPDS) scores at 1 and 6 months were used to assess the relationship between PM2.5 exposure and probable PPD (EPDS score ≥13) using relative risk regression and symptoms of anhedonia, depression, and anxiety (derived from EPDS subscales) using negative binomial regression. RESULTS:A 5-μg/m3 increase in average PM2.5 exposure during pregnancy was associated with an increased risk of PPD at 6 months (RR = 1.59; 95% CI: 1.11 to 2.28) and of late-onset PPD (no PPD at 1 month, PPD at 6 months) (RR = 2.58; 95% CI: 1.40 to 4.73) in covariate-adjusted models. No association was observed between PM2.5 exposure in the first year postpartum and PPD. Average PM2.5 exposure during pregnancy was also associated with increased 6-month EPDS subscale symptom scores for anhedonia (p = 0.03) and depression (p = 0.04). CONCLUSION:Our results suggest that in women in Mexico City, particulate matter exposure during pregnancy is positively associated with PPD and symptoms of anhedonia and depression at 6 months postpartum. Future studies should examine mechanisms linking air pollution and other environmental exposures during pregnancy with postpartum psychological functioning.
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Project description:Imposing stricter regulations for PM2.5 has the potential to mitigate damaging health and climate change effects. Recent evidence establishing a link between exposure to air pollution and COVID-19 outcomes is one of many arguments for the need to reduce the National Ambient Air Quality Standards (NAAQS) for PM2.5. However, many studies reporting a relationship between COVID-19 outcomes and PM2.5 have been criticized because they are based on ecological regression analyses, where area-level counts of COVID-19 outcomes are regressed on area-level exposure to air pollution and other covariates. It is well known that regression models solely based on area-level data are subject to ecological bias, i.e., they may provide a biased estimate of the association at the individual-level, due to within-area variability of the data. In this paper, we augment county-level COVID-19 mortality data with a nationally representative sample of individual-level covariate information from the American Community Survey along with high-resolution estimates of PM2.5 concentrations obtained from a validated model and aggregated to the census tract for the contiguous United States. We apply a Bayesian hierarchical modeling approach to combine county-, census tract-, and individual-level data to ultimately draw inference about individual-level associations between long-term exposure to PM2.5 and mortality for COVID-19. By analyzing data prior to the Emergency Use Authorization for the COVID-19 vaccines we found that an increase of 1 μg/m3 in long-term PM2.5 exposure, averaged over the 17-year period 2000-2016, is associated with a 3.3% (95% credible interval, 2.8 to 3.8%) increase in an individual's odds of COVID-19 mortality. Code to reproduce our study is publicly available at https://github.com/NSAPH/PM_COVID_ecoinference. The results confirm previous evidence of an association between long-term exposure to PM2.5 and COVID-19 mortality and strengthen the case for tighter regulations on harmful air pollution and greenhouse gas emissions.
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Project description:Several studies are pointing out that exposure to elevated air pollutants could contribute to increased COVID-19 mortality. However, literature on the associations between air pollution exposure and COVID-19 severe morbidity is rather sparse. In addition, the majority of the studies used an ecological study design and were applied in regions with rather high air pollution levels. Here, we study the differential effects of long-term exposure to air pollution on severe morbidity and mortality risks from COVID-19 in various population subgroups in Switzerland, a country known for clean air. We perform individual-level analyses using data covering the first two major waves of COVID-19 between February 2020 and May 2021. High-resolution maps of particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations were produced for the 6 years preceding the pandemic using Bayesian geostatistical models. Air pollution exposure for each patient was measured by the long-term average concentration across the municipality of residence. The models were adjusted for the effects of individual characteristics, socio-economic, health-system, and climatic factors. The variables with an important association to COVID-19 case-severity were identified using Bayesian spatial variable selection. The results have shown that the individual-level characteristics are important factors related to COVID-19 morbidity and mortality in all the models. Long-term exposure to air pollution appears to influence the severity of the disease only when analyzing data during the first wave; this effect is attenuated upon adjustment for health-system related factors during the entire study period. Our findings suggest that the burden of air pollution increased the risks of COVID-19 in Switzerland during the first wave of the pandemic, but not during the second wave, when the national health system was better prepared.
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Project description:BackgroundSeveral studies have been focusing on the potential role of atmospheric pollutants in the diffusion and impact on health of Covid-19. This study's objective was to estimate the association between ≤10 μm diameter particulate matter (PM10) exposure and the likelihood of experiencing pneumonia due to Covid-19 using individual-level data in Italy.MethodsInformation on Covid-19 patients was retrieved from the Italian IQVIA® Longitudinal Patient Database (LPD), a computerized network of general practitioners (GPs) including anonymous data on patients' consultations and treatments. All patients with a Covid-19 diagnosis during March 18th, 2020 - June 30th, 2020 were included in the study. The date of first Covid-19 registration was the starting point of the 3-month follow-up (Index Date). Patients were classified based on Covid-19-related pneumonia registrations on the Index date and/or during follow-up presence/absence. Each patient was assigned individual exposure by calculating average PM10 during the 30-day period preceding the Index Date, and according to GP's office province. A multiple generalized linear mixed model, mixed-effects logistic regression, was used to assess the association between PM10 exposure tertiles and the likelihood of experiencing pneumonia.ResultsAmong 6483 Covid-19 patients included, 1079 (16.6%) had a diagnosis of pneumonia. Pneumonia patients were older, more frequently men, more health-impaired, and had a higher individual-level exposure to PM10 during the month preceding Covid-19 diagnosis. The mixed-effects model showed that patients whose PM10 exposure level fell in the second tertile had a 30% higher likelihood of having pneumonia than that of first tertile patients, and the risk for those who were in the third tertile was almost doubled.ConclusionThe consistent findings toward a positive association between PM10 levels and the likelihood of experiencing pneumonia due to Covid-19 make the implementation of new strategies to reduce air pollution more and more urgent.
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Project description:Background The association between fine particulate matter and cardiovascular disease has been convincingly demonstrated. The role of traffic-related air pollutants is less clear. To better understand the role of traffic-related air pollutants in cardiovascular disease development, we examined associations between NO2, carotid atherosclerotic plaque, and cardiometabolic disorders associated with cardiovascular disease. Methods and Results Cross-sectional analyses were conducted among 2227 patients (62.9±13.8 years; 49.5% women) from the Stroke Prevention and Atherosclerosis Research Centre (SPARC) in London, Ontario, Canada. Total carotid plaque area measured by ultrasound, cardiometabolic disorders, and residential locations were provided by SPARC medical records. Long-term outdoor residential NO2 concentrations were generated by a land use regression model. Associations between NO2, total carotid plaque area, and cardiometabolic disorders were examined using multiple regression models adjusted for age, sex, smoking, and socioeconomic status. Mean NO2 was 5.4±1.6 ppb in London, Ontario. NO2 was associated with a significant increase in plaque (3.4 mm2 total carotid plaque area per 1 ppb NO2), exhibiting a linear dose-response. NO2 was also positively associated with triglycerides, total cholesterol, and the ratio of low- to high-density lipoprotein cholesterol (P<0.05). Diabetes mellitus mediated the relationship between NO2 and total carotid plaque area (P<0.05). Conclusions Our results demonstrate that even low levels of traffic-related air pollutants are linked to atherosclerotic plaque burden, an association that may be partially attributable to pollution-induced diabetes mellitus. Our findings suggest that reducing ambient concentrations in cities with NO2 below current standards would result in additional health benefits. Given the billions of people exposed to traffic emissions, our study supports the global public health significance of reducing air pollution.
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Project description:Ambient air pollution was known to cause central nervous system diseases and depressive symptoms. In this study, we examined the associations between air pollution exposure and the prevalence of insomnia in Taipei City of Taiwan. We applied the health information system of electrical medical records of Taipei City Hospital to collect a total of 5108 study subjects (insomniacs N = 912 and non-insomniacs N = 4196) over 18 years old from the family medicine and internal medicine outpatients of six branches of Taipei City Hospital. These patients were grouped into insomniacs and non-insomniacs following the primary insomnia diagnosis (ICD9:780.52, 780.54, 307.41, 307.42, ICD10: G47.00, G47.01, G47.09, F51.01, F51.09) and the prescription times of anxiolytics and hypnotics. We estimated one-year average concentrations of PM2.5, ozone, and NOx before the first date of insomnia diagnosis and the last date of outpatient visit for insomniacs and non-insomniacs, respectively, by using the data of nearest air quality monitoring stations relative to study subjects' residential addresses. Logistic regression analysis was employed to examine the independent effects of air pollution concentrations on the risk of insomnia. One-year average PM2.5, ozone, and NOx levels for insomniacs was significantly higher than those of non-insomniacs. After adjusting for confounding factors, increase each 1(μg/m3) in one-year average PM2.5 showed a statistically significant association with insomnia (the odds ratio 1.610, 95% CI [1.562,1.660]). As to multi pollutants, one-year average PM2.5 (1.624, [1.570, 1.681] and ozone (1.198, [1.094, 1.311]) exposure showed a significant association with insomnia. Subgroup analysis revealed that the influence of PM2.5 and ozone on insomnia have significant risks in people with major chronic disease. This study demonstrated a positive association between PM2.5 and ozone exposure and the prevalence of hypnotic-treated insomnia. Especially, the people with major chronic diseases were with obvious effect of PM2.5 and ozone on risk of insomnia.
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Project description:Air pollution's mortality effects may differ by subpopulation; however, few studies have investigated this issue in Asia. We investigated susceptibility to air pollutants on total, cardiovascular, and respiratory mortality in Seoul, Korea for the period 2000-2007. We applied time-stratified case-crossover analysis, which allows direct modeling of interaction terms, to estimate susceptibility based on sex, age, education, marital status, and occupation. An interquartile range increase in pollution was associated with odds ratios of 0.94 (95% confidence interval, 0.25-1.62), 2.27 (1.03-3.53), 1.94 (0.80-3.09), and 2.21 (1.00-3.43) for total mortality and 1.95 (0.64-3.27), 4.82 (2.18-7.54), 3.64 (1.46-5.87), and 4.32 (1.77-6.92) for cardiovascular mortality for PM(10), nitrogen dioxide (NO(2)), sulfur dioxide (SO(2)), and carbon monoxide (CO), respectively. Ozone effect estimates were positive, but not statistically significant. Results indicate that some populations are more susceptible than others. For total or cardiovascular mortality, associations were higher for males, those 65-74 years, and those with no education or manual occupation for some pollutants. For example, the odds ratio for SO(2) and cardiovascular mortality was 1.19 (1.03-1.37) times higher for those with manual occupations than professional occupations. Our findings provide evidence that some populations are more susceptible to the effects of air pollution than others, which has implications for public policy and risk assessment for susceptible subpopulations.
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Project description:BackgroundInfluenza imposes a heavy burden on public health. Little is known, however, of the associations between detailed measures of exposure to ambient air pollution and influenza at an individual level.ObjectiveWe examined individual-level associations between six criteria air pollutants and influenza using case-crossover design.MethodsIn this individual-level time-stratified case-crossover study, we linked influenza cases collected by the Guangzhou Center for Disease Control and Prevention from 1 January 2013 to 31 December 2019 with individual residence-level exposure to particulate matter (PM2.5 and PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and carbon monoxide (CO). The exposures were estimated for the day of onset of influenza symptoms (lag 0), 1-7 d before the onset (lags 1-7), as well as an 8-d moving average (lag07), using a random forest model and linked to study participants' home addresses. Conditional logistic regression was developed to investigate the associations between short-term exposure to air pollution and influenza, adjusting for mean temperature, relative humidity, public holidays, population mobility, and community influenza susceptibility.ResultsN=108,479 eligible cases were identified in our study. Every 10-μg/m3 increase in exposure to PM2.5, PM10, NO2, and CO and every 5-μg/m3 increase in SO2 over 8-d moving average (lag07) was associated with higher risk of influenza with a relative risk (RR) of 1.028 (95% CI: 1.018, 1.038), 1.041 (95% CI: 1.032, 1.049), 1.169 (95% CI: 1.151, 1.188), 1.004 (95% CI: 1.003, 1.006), and 1.134 (95% CI: 1.107, 1.163), respectively. There was a negative association between O3 and influenza with a RR of 0.878 (95% CI: 0.866, 0.890).ConclusionsOur findings suggest that short-term exposure to air pollution, except for O3, is associated with greater risk for influenza. Further studies are necessary to decipher underlying mechanisms and design preventive interventions and policies. https://doi.org/10.1289/EHP12145.
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