Impact of a long-term air pollution exposure on the case fatality rate of COVID-19 patients-A multicity study.
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ABSTRACT:
SUBMITTER: Hou CK
PROVIDER: S-EPMC8014152 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAir pollution is a major risk factor for planetary health and has long been suspected of predisposing humans to respiratory diseases induced by pathogens like influenza viruses. However, epidemiological evidence remains elusive due to lack of longitudinal data from large cohorts.ObjectiveOur aim is to quantify the short-term association of influenza incidence with exposure to ambient air pollutants in Chinese cities.MethodsBased on air pollutant data and influenza surveillance data from 82 cities in China over a period of 5 years, we applied a two-stage time series analysis to assess the association of daily incidence of reported influenza cases with six common air pollutants [particulate matter with aerodynamic diameter ≤2.5μm (PM2.5), particulate matter with aerodynamic diameter ≤10μm (PM10), NO2, SO2, CO, and O3], while adjusting for potential confounders including temperature, relative humidity, seasonality, and holiday effects. We built a distributed lag Poisson model for one or multiple pollutants in each individual city in the first stage and conducted a meta-analysis to pool city-specific estimates in the second stage.ResultsA total of 3,735,934 influenza cases were reported in 82 cities from 2015 to 2019, accounting for 72.71% of the overall case number reported in the mainland of China. The time series models for each pollutant alone showed that the daily incidence of reported influenza cases was positively associated with almost all air pollutants except for ozone. The most prominent short-term associations were found for SO2 and NO2 with cumulative risk ratios of 1.094 [95% confidence interval (CI): 1.054, 1.136] and 1.093 (95% CI: 1.067, 1.119), respectively, for each 10 μg/m3 increase in the concentration at each of the lags of 1-7 d. Only NO2 showed a significant association with the daily incidence of influenza cases in the multipollutant model that adjusts all six air pollutants together. The impact of air pollutants on influenza was generally found to be greater in children, in subtropical cities, and during cold months.DiscussionIncreased exposure to ambient air pollutants, particularly NO2, is associated with a higher risk of influenza-associated illness. Policies on reducing air pollution levels may help alleviate the disease burden due to influenza infection. https://doi.org/10.1289/EHP12146.
| S-EPMC10711743 | biostudies-literature
Project description:The coronavirus disease 2019 (COVID-19) pandemic is still rapidly spreading globally. To probe high-risk cities and the impacts of air pollution on public health, this study explores the relationship between the long-term average concentration of air pollution and the city-level case fatality rate (CFR) of COVID-19 globally. Then, geographically weighted regression (GWR) is applied to examine the spatial variability of the relationships. Six air pollution factors, including nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), PM2.5 (particles with diameter ≤2.5 μm), PM10 (particles with diameter ≤10 μm), and air quality index (AQI), are positively associated with the city-level COVID-19 CFR. Our results indicate that a 1-unit increase in NO2 (part per billion, PPB), SO2 (PPB), O3 (PPB), PM2.5 (microgram per cubic meter, μg/m3), PM10 (μg/m3), AQI (score), is related to a 1.450%, 1.005%, 0.992%, 0.860%, 0.568%, and 0.776% increase in the city-level COVID-19 CFR, respectively. Additionally, the effects of NO2, O3, PM2.5, AQI, and probability of living with poor AQI on COVID-19 spatially vary in view of the estimation of the GWR. In other words, the adverse impacts of air pollution on health are different among the cities. In summary, long-term exposure to air pollution is negatively related to the COVID-19 health outcome, and the relationship is spatially non-stationary. Our research sheds light on the impacts of slashing air pollution on public health in the COVID-19 pandemic to help governments formulate air pollution policies in light of the local situations.
| S-EPMC8724597 | biostudies-literature
Project description:Previous studies revealed that chronic exposure to air pollution can significantly increase the risk of the development of Parkinson's disease (PD), but this relationship is inconclusive as large-scale prospective studies are limited and the results are inconsistent. Therefore, the purpose of this study was to ascertain the adverse health effects of air pollution exposure in a nationwide population using a longitudinal approach.We conducted a nested case-control study using the National Health Insurance Research Dataset (NHIRD), which consisted of 1,000,000 beneficiaries in the National Health Insurance Program (NHI) in the year 2000 and their medical records from 1995 to 2013 and using public data on air pollution concentrations from monitoring stations across Taiwan released from the Environmental Protection Administration to identify people with ages ≥ 40 years living in areas with monitoring stations during 1995-1999 as study subjects. Then, we excluded subjects with PD, dementia, stroke and diabetes diagnosed before Jan. 1, 2000 and obtained 54,524 subjects to follow until Dec. 31, 2013. In this observational period, 1060 newly diagnosed PD cases were identified. 4240 controls were randomly selected from those without PD using a matching strategy for age, sex, the year of PD diagnosis and the year of entering the NHI program at a ratio of 1:4. Ten elements of air pollution were examined, and multiple logistic regression models were used to measure their risks in subsequent PD development.The incidence of PD in adults aged ≥ 40 years was 1.9%, and the median duration for disease onset was 8.45 years. None of the chemical compounds (SO2, O3, CO, NOx, NO, NO2, THC, CH4, or NMHC) significantly affected the incidence of PD except for particulate matter. PM10 exposure showed significant effects on the likelihood of PD development (T3 level: > 65μg/m3 versus T1 level: ≤ 54μg/m3; OR = 1.35, 95% CI = 1.12-1.62, 0.001 ≤ P < 0.01). In addition, comorbid conditions such as dementia (ORs = 3.53-3.93, Ps < 0.001), stroke (ORs = 2.99-3.01, Ps < 0.001), depression (ORs = 2.51-2.64, Ps < 0.001), head injury (ORs = 1.24-1.29, 0.001 ≤ Ps < 0.01 or 0.01 ≤ Ps < 0.05), sleep disorder (OR = 1.23-1.26, 0.001 ≤ Ps < 0.01), and hypertension (ORs = 1.18-1.19, 0.01 ≤ Ps < 0.05) also significantly increased the risk for PD development.Although PM10 plays a significant role in PD development, the associated chemical/metal compounds that are capable of inducing adverse biological mechanisms still warrant further exploration. Because of a link between comorbid conditions and PM exposure, research on the causal relationship between long-term exposure to PM and the development of PD should be considered with caution because other possible modifiers or mediators, comorbid diseases in particular, may be involved.
| S-EPMC5557354 | biostudies-other
Project description:Background and Purpose- China bears a heavy burden of stroke because of its large population of elderly people and the propensity for stroke. Previous studies have examined the association between air pollution and stroke mortality or hospital admission. However, the global evidence for adverse effects of air pollution on survival after stroke is scarce. Methods- We used the first national hospital-based prospective registry cohort of stroke in China, which included 12?291 ischemic stroke patients who visited hospitals during 2007 to 2008. All patients were followed for 1-year poststroke. Deaths during the follow-up period were recorded. Participants' 3-year prestroke exposures to ambient PM1, PM2.5, PM10 (particulate matter with aerodynamic diameters ?1, ?2.5, and ?10 ?m, respectively) and NO2 (nitrogen dioxide) were estimated by machine learning algorithms with satellite remote sensing, land use information, and meteorological data. Cox proportional hazards models were used to examine the association between air pollution and survival after ischemic stroke. Results- In total, 1649 deaths were identified during the 1-year follow-up period. After controlling for potential confounders, significant associations were observed between exposure to PM1 and PM2.5 and incident fatal ischemic stroke. The corresponding hazard ratios and 95% CIs associated with 10 µg/m3 increase in PM1 and PM2.5 were 1.05 (1.02-1.09) and 1.03 (1.00-1.06), respectively. No significant association was observed for PM10 or NO2 (hazard ratios and 95% CIs, 1.01 [1.00-1.03] and 1.03 [0.99-1.06], respectively). Higher hazard ratios (and 95% CIs) were observed for male, elderly and obese individuals. Conclusions- Prestroke exposure to PM1 and PM2.5 was associated with increased incident fatal ischemic stroke in the year following an ischemic stroke in China. Improved air quality may be beneficial for people to recover from stroke.
| S-EPMC6389419 | biostudies-literature
Project description:Long-term exposure to air pollution is associated with age-related diseases. We explored the association between accelerated biological aging and air pollution, a potential mechanism linking air pollution and health. We estimated long-term exposure to PM10, PM2.5, PM2.5 absorbance/black carbon (BC), and NOx via land-use regression models in individuals from the KORA F4 cohort. Accelerated biological aging was assessed using telomere length (TeloAA) and three epigenetic measures: DNA methylation age acceleration (DNAmAA), extrinsic epigenetic age acceleration (correlated with immune cell counts, EEAA), and intrinsic epigenetic age acceleration (independent of immune cell counts, IEAA). We also investigated sex-specific associations between air pollution and biological aging, given the published association between sex and aging measures. In KORA an interquartile range (0.97 µg/m3) increase in PM2.5 was associated with a 0.33 y increase in EEAA (CI = 0.01, 0.64; P = 0.04). BC and NOx (indicators or traffic exposure) were associated with DNAmAA and IEAA in women, while TeloAA was inversely associated with BC in men. We replicated this inverse BC-TeloAA association in the Normative Aging Study, a male cohort based in the USA. A multiple phenotype analysis in KORA F4 combining all aging measures showed that BC and PM10 were broadly associated with biological aging in men. Thus, we conclude that long-term exposure to air pollution is associated with biological aging measures, potentially in a sex-specific manner. However, many of the associations were relatively weak and further replication of overall and sex-specific associations is warranted.
| S-EPMC5342683 | biostudies-literature
Project description:BackgroundRecently, there has been increasing evidence that exposure to air pollution is linked to neurodegenerative diseases, but little is known about the association with amyotrophic lateral sclerosis (ALS).ObjectivesWe investigated the association between long-term exposure to air pollution and risk of developing ALS.MethodsA population-based case-control study was conducted in Netherlands from 1 January 2006 to 1 January 2013. Data from 917 ALS patients and 2,662 controls were analyzed. Annual mean air pollution concentrations were assessed by land use regression (LUR) models developed as part of the European Study of Cohorts for Air Pollution Effects (ESCAPE). Exposure estimates included nitrogen oxides (NO2, NOx), particulate matter (PM) with diameters of <2.5 ?m (PM2.5), <10 ?m (PM10), between 10 ?m and 2.5 ?m (PMcoarse), and PM2.5 absorbance. We performed conditional logistic regression analysis using two different multivariate models (model 1 adjusted for age, gender, education, smoking status, alcohol use, body mass index, and socioeconomic status; model 2 additionally adjusted for urbanization degree).ResultsRisk of ALS was significantly increased for individuals in the upper exposure quartile of PM2.5 absorbance [OR=1.67; 95% confidence interval (CI): 1.27, 2.18], NO2 (OR=1.74; 95% CI: 1.32, 2.30), and NOx concentrations (OR=1.38; 95% CI: 1.07, 1.77). These results, except for NOx, remained significant after adjusting additionally for urbanization degree.ConclusionsBased on a large population-based case-control study, we report evidence for the association between long-term exposure to traffic-related air pollution and increased susceptibility to ALS. Our findings further support the necessity for regulatory public health interventions to combat air pollution levels and provide additional insight into the potential pathophysiology of ALS. https://doi.org/10.1289/EHP1115.
| S-EPMC5915195 | biostudies-literature
Project description:BACKGROUND: Errors in address geocodes may affect estimates of the effects of air pollution on health. OBJECTIVE: We investigated the impact of four geocoding techniques on the association between urban air pollution estimated with a fine-scale (10 m × 10 m) dispersion model and lung function in adults. METHODS: We measured forced expiratory volume in 1 sec (FEV1) and forced vital capacity (FVC) in 354 adult residents of Grenoble, France, who were participants in two well-characterized studies, the Epidemiological Study on the Genetics and Environment on Asthma (EGEA) and the European Community Respiratory Health Survey (ECRHS). Home addresses were geocoded using individual building matching as the reference approach and three spatial interpolation approaches. We used a dispersion model to estimate mean PM10 and nitrogen dioxide concentrations at each participant's address during the 12 months preceding their lung function measurements. Associations between exposures and lung function parameters were adjusted for individual confounders and same-day exposure to air pollutants. The geocoding techniques were compared with regard to geographical distances between coordinates, exposure estimates, and associations between the estimated exposures and health effects. RESULTS: Median distances between coordinates estimated using the building matching and the three interpolation techniques were 26.4, 27.9, and 35.6 m. Compared with exposure estimates based on building matching, PM10 concentrations based on the three interpolation techniques tended to be overestimated. When building matching was used to estimate exposures, a one-interquartile range increase in PM10 (3.0 ?g/m3) was associated with a 3.72-point decrease in FVC% predicted (95% CI: -0.56, -6.88) and a 3.86-point decrease in FEV1% predicted (95% CI: -0.14, -3.24). The magnitude of associations decreased when other geocoding approaches were used [e.g., for FVC% predicted -2.81 (95% CI: -0.26, -5.35) using NavTEQ, or 2.08 (95% CI -4.63, 0.47, p = 0.11) using Google Maps]. CONCLUSIONS: Our findings suggest that the choice of geocoding technique may influence estimated health effects when air pollution exposures are estimated using a fine-scale exposure model.
| S-EPMC3764075 | biostudies-literature
Project description:Background Epidemiological studies have investigated the short-term effects of meteorological factors and air pollution on the incidence of hand, foot, and mouth disease (HFMD). Several meteorological indicators, such as relative humidity and the diurnal temperature range (DTR), significantly modify the relationship between short-term exposure to temperature and HFMD incidence. However, it remains unclear whether (and how) long-term air pollution levels modify the short-term relationships of HFMD incidence with meteorological factors and air pollution. Methods We obtained daily data on meteorological factors, air pollutants, and HFMD counts in children from 21 prefecture-level cities in Sichuan Province in Southwest China from 2015 to 2017. First, we constructed a distributed lag nonlinear model (DLNM) at each prefecture-level site to evaluate the short-term impacts of meteorological variables and air pollutants on HFMD incidence. Then, we assessed the pooled effects of the exposures and incorporated long-term city-specific air pollutant indicators as meta-predictors to examine their potential modification effects by performing multivariate meta-regression models. Results We found that long-term SO2 and CO concentrations significantly modified the short-term relationships between climatic variables and HFMD incidence. Specifically, high concentrations of CO (P = 0.027) and SO2 (P = 0.039) reduced the risk of HFMD at low temperatures. The relationship between relative humidity and HFMD incidence was weakened at high SO2 concentrations (P = 0.024), especially when the relative humidity was below the median level. When the minimum relative humidity (32%) was compared to the median relative humidity (77%), the risk ratio (RR) was 0.77 (95% CI: 0.51–1.17) in the 90th percentile of SO2 (19.6 μg/m3) and 0.41 (95% CI: 0.27–0.64) in the 10th percentile of SO2 (10.6 μg/m3). Conclusion Our results indicated that long-term SO2 and CO levels modified the short-term associations between HFMD incidence in children and meteorological variables. These findings may inform health authorities to optimize targeted public health policies including reducing ambient air pollution and reinforcing self-protective actions to weaken the adverse health impacts of environmental factors on HFMD incidence. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-13890-7.
| S-EPMC9351082 | biostudies-literature
Project description:BackgroundCOVID-19 is a lung disease, and there is medical evidence that air pollution is one of the external causes of lung diseases. Fine particulate matter is one of the air pollutants that damages pulmonary tissue. The combination of the coronavirus and fine particulate matter air pollution may exacerbate the coronavirus' effect on human health.Research questionThis paper considers whether the long-term concentration of fine particulate matter of different sizes changes the number of detected coronavirus infections and the number of COVID-19 fatalities in Germany.Study designData from 400 German counties for fine particulate air pollution from 2002 to 2020 are used to measure the long-term impact of air pollution. Kriging interpolation is applied to complement data gaps. With an ecological study, the correlation between average particulate matter air pollution and COVID-19 cases, as well as fatalities, are estimated with OLS regressions. Thereby, socioeconomic and demographic covariates are included.Main findingsAn increase in the average long-term air pollution of 1 μg/m3 particulate matter PM2.5 is correlated with 199.46 (SD = 29.66) more COVID-19 cases per 100,000 inhabitants in Germany. For PM10 the respective increase is 52.38 (SD = 12.99) more cases per 100,000 inhabitants. The number of COVID-19 deaths were also positively correlated with PM2.5 and PM10 (6.18, SD = 1.44, respectively 2.11, SD = 0.71, additional COVID-19 deaths per 100,000 inhabitants).ConclusionLong-term fine particulate air pollution is suspected as causing higher numbers of COVID-19 cases. Higher long-term air pollution may even increase COVID-19 death rates. We find that the results of the correlation analysis without controls are retained in a regression analysis with controls for relevant confounding factors. Nevertheless, additional epidemiological investigations are required to test the causality of particulate matter air pollution for COVID-19 cases and the severity.
| S-EPMC8400616 | biostudies-literature
Project description:BackgroundLong-term exposures to air pollution has been reported to be associated with inflammation and oxidative stress. However, the underlying metabolic mechanisms remain poorly understood.ObjectivesWe aimed to determine the changes in the blood metabolome and thus the metabolic pathways associated with long-term exposure to outdoor air pollution and ambient temperature.MethodsWe quantified metabolites using mass-spectrometry based global untargeted metabolomic profiling of plasma samples among men from the Normative Aging Study (NAS). We estimated the association between long-term exposure to PM2.5, NO2, O3, and temperature (annual average of central site monitors) with metabolites and their associated metabolic pathways. We used multivariable linear mixed-effect regression models (LMEM) while simultaneously adjusting for the four exposures and potential confounding and correcting for multiple testing. As a reduction method for the intercorrelated metabolites (outcome), we further used an independent component analysis (ICA) and conducted LMEM with the same exposures.ResultsMen (N?=?456) provided 648 blood samples between 2000 and 2016 in which 1158 metabolites were quantified. On average, men were 75.0?years and had an average body mass index of 27.7?kg/m2. Almost all men (97%) were not current smokers. The adjusted analysis showed statistically significant associations with several metabolites (58 metabolites with PM2.5, 15 metabolites with NO2, and 6 metabolites with temperature) while no metabolites were associated with O3. One out of five ICA factors (factor 2) was significantly associated with PM2.5. We identified eight perturbed metabolic pathways with long-term exposure to PM2.5 and temperature: glycerophospholipid, sphingolipid, glutathione, beta-alanine, propanoate, and purine metabolism, biosynthesis of unsaturated fatty acids, and taurine and hypotaurine metabolism. These pathways are related to inflammation, oxidative stress, immunity, and nucleic acid damage and repair.ConclusionsUsing a global untargeted metabolomic approach, we identified several significant metabolites and metabolic pathways associated with long-term exposure to PM2.5, NO2 and temperature. This study is the largest metabolomics study of long-term air pollution, to date, the first study to report a metabolomic signature of long-term temperature exposure, and the first to use ICA in the analysis of both.
| S-EPMC7788989 | biostudies-literature