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Impact of a long-term air pollution exposure on the case fatality rate of COVID-19 patients-A multicity study.

ABSTRACT: Evidence in the literature suggests that air pollution exposure affects outcomes of patients with COVID-19. However, the extent of this effect requires further investigation. This study was designed to investigate the relationship between long-term exposure to air pollution and the case fatality rate (CFR) of patients with COVID-19. The data on air quality index (AQI), PM2.5, PM10, SO₂ , NO₂ , and O₃ from 14 major cities in China in the past 5 years (2015-2020) were collected, and the CRF of COVID-19 patients in these cities was calculated. First, we investigated the correlation between CFR and long-term air quality indicators. Second, we examined the air pollutants affecting CFR and evaluated their predictive values. We found a positive correlation between the CFR and AQI (1, 3, and 5 years), PM2.5 (1, 3, and 5 years), and PM10 (1, 3, and 5 years). Further analysis indicated the more significant correlation for both AQI (3 and 5 years) and PM2.5 (1, 3, and 5 years) with CFR, and moderate predictive values for air pollution indicators such as AQI (1, 3, and 5 years) and PM2.5 (1, 3, and 5 years) for CFR. Our results indicate that long-term exposure to severe air pollution is associated with higher CFR of COVID-19 patients. Air pollutants such as PM2.5 may assist with the prediction of CFR for COVID-19 patients.

SUBMITTER: Hou CK

PROVIDER: S-EPMC8014152 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Impact of a long-term air pollution exposure on the case fatality rate of COVID-19 patients-A multicity study.

Hou Chang-Kai CK Qin Ya-Fei YF Wang Grace G Liu Quan-Lei QL Yang Xin-Yu XY Wang Hao H

Journal of medical virology 20210215 5

Evidence in the literature suggests that air pollution exposure affects outcomes of patients with COVID-19. However, the extent of this effect requires further investigation. This study was designed to investigate the relationship between long-term exposure to air pollution and the case fatality rate (CFR) of patients with COVID-19. The data on air quality index (AQI), PM2.5, PM10, SO2 , NO2 , and O3 from 14 major cities in China in the past 5 years (2015-2020) w ...[more]

PMID: 33470428

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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.

Project description:BackgroundThe negative effect of air pollution on human health is widely reported in recent literature. It typically involves urbanized areas where the population is concentrated and where most primary air pollutants are produced. A comprehensive health risk assessment is therefore of strategic importance for health authorities.MethodsIn this study we propose a methodology to perform an indirect and retrospective health risk assessment of all-cause mortality associated with long-term exposure to particulate matter less than 2.5 microns (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in a typical Monday to Friday working week. A combination of satellite-based settlement data, model-based air pollution data, land use, demographics and regional scale mobility, allowed to examine the effect of population mobility and pollutants daily variations on the health risk. A Health Risk Increase (HRI) metric was derived on the basis of three components: hazard, exposure and vulnerability, utilizing the relative risk values from the World Health Organization. An additional metric, the Health Burden (HB) was formulated, which accounts for the total number of people exposed to a certain risk level.ResultsThe effect of regional mobility patterns on the HRI metric was assessed, resulting in an increased HRI associated with all three stressors when considering a dynamic population compared to a static one. The effect of diurnal variation of pollutants was only observed for NO2 and O3. For both, the HRI metric resulted in significantly higher values during night. Concerning the HB parameter, we identified the commuting flows of the population as the main driver in the resulting metric.ConclusionsThis indirect exposure assessment methodology provides tools to support policy makers and health authorities in planning intervention and mitigation measures. The study was carried out in Lombardy, Italy, one of the most polluted regions in Europe, but the incorporation of satellite data makes our approach valuable for studying global health.

Project description:BackgroundEpidemiological 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.MethodsWe 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.ResultsWe 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).ConclusionOur 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.

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.

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Impact of a long-term air pollution exposure on the case fatality rate of COVID-19 patients-A multicity study.

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Impact of a long-term air pollution exposure on the case fatality rate of COVID-19 patients-A multicity study.

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