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COVID-19 incidence and mortality in Lombardy, Italy: an ecological study on the role of air pollution, meteorological factors, demographic and socioeconomic variables.

ABSTRACT: Lombardy, the most populated and industrialized Italian region, was the epicentre of the first wave (March and April, 2020) of COVID-19 in Italy and it is among the most air polluted areas of Europe. We carried out an ecological study to assess the association between long-term exposure to particulate matter (PM) and nitrogen dioxide (NO₂) on COVID-19 incidence and all-cause mortality after accounting for demographic, socioeconomic and meteorological variables. The study was based on publicly available data. Multivariable negative binomial mixed regression models were fitted, and results were reported in terms of incidence rate ratios (IRRs) and standardized mortality ratios (SMR). The effect of winter temperature and humidity was modelled through restricted cubic spline. Data from 1,439 municipalities out of 1,507 (95%) were included in the analyses, leading to a total of 61,377 COVID-19 cases and 40,401 deaths from all-causes collected from February 20^th to April 16^th and from March 1^st to April 30^th, 2020, respectively. Several demographic and socioeconomic variables resulted significantly associated with COVID-19 incidence and all-cause mortality in a multivariable fashion. An increase in average winter temperature was associated with a nonlinear decrease in COVID-19 incidence and all-cause mortality, while an opposite trend emerged for the absolute humidity. An increase of 10 ?g/m³ in the mean annual concentrations of PM2.5 and PM10 over the previous years was associated with a 58% and 34% increase in COVID-19 incidence rate, respectively. Similarly, a 10 ?g/m³ increase of annual mean PM2.5 concentration was associated with a 23% increase in all-cause mortality. An inverse association was found between NO₂ levels and COVID-19 incidence and all-cause mortality. Our ecological study showed that exposure to PM was significantly associated with the COVID-19 incidence and excess mortality during the first wave of the outbreak in Lombardy, Italy.

SUBMITTER: de Angelis E

PROVIDER: S-EPMC7826113 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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COVID-19 incidence and mortality in Lombardy, Italy: An ecological study on the role of air pollution, meteorological factors, demographic and socioeconomic variables.

De Angelis Elena E Renzetti Stefano S Volta Marialuisa M Donato Francesco F Calza Stefano S Placidi Donatella D Lucchini Roberto G RG Rota Matteo M

Environmental research 20210122

Lombardy, the most populated and industrialized Italian region, was the epicentre of the first wave (March and April 2020) of COVID-19 in Italy and it is among the most air polluted areas of Europe. We carried out an ecological study to assess the association between long-term exposure to particulate matter (PM) and nitrogen dioxide (NO<sub>2</sub>) on COVID-19 incidence and all-cause mortality after accounting for demographic, socioeconomic and meteorological variables. The study was based on p ...[more]

PMID: 33485909

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Project description:The 2019 novel coronavirus disease (COVID-19) has become a severe public health and social problem worldwide. A limitation of the existing literature is that multiple environmental variables have not been frequently elaborated, which is why the overall effect of the environment on COVID-19 has not been conclusive. In this study, we used generalized additive model (GAM) to detect the relationship between meteorological and air pollution variables and COVID-19 in four urban agglomerations in China and made comparisons among the urban agglomerations. The four urban agglomerations are Beijing-Tianjin-Hebei (BTH), middle reaches of the Yangtze River (MYR), Yangtze River Delta (YRD), and the Pearl River Delta (PRD). The daily rates of average precipitation, temperature, relative humidity, sunshine duration, and atmospheric pressure were selected as meteorological variables. The PM2.5, PM10, sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) contents were selected as air pollution variables. The results indicated that meteorological and air pollution variables tended to be significantly correlated. Moreover, the nature of the relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and meteorological and air pollution variables (i.e., linear or nonlinear) varied with urban agglomerations. Among the variance explained by GAMs, BTH had the highest value (75.4%), while MYR had the lowest value (35.2%). The values of the YRD and PRD were between the above two, namely 45.6% and 62.2%, respectively. The findings showed that the association between SARS-CoV-2 and meteorological and air pollution variables varied in regions, making it difficult to obtain a relationship that is applicable to every region. Moreover, this study enriches our understanding of SARS-CoV-2. It is required to create awareness within the government that anti-COVID-19 measures should be adapted to the local meteorological and air pollution conditions.

Project description:Various countries across the globe have been affected by different COVID-19 waves at different points in time and with varying levels of virulence. With the backdrop of the two COVID-19 waves that broke out in Delhi, this study examines the variations in the concentrations of criteria pollutants, air quality, and meteorological variables across the waves and their influence on COVID-19 morbidity/mortality. Descriptive statistics, violin plots, and Spearman rank correlation tests were employed to assess the variations in environmental parameters and investigate their associations with COVID-19 incidence under the two waves. The susceptible-infected-recovered model and multiple linear regression were used to assess the wave-wise basic reproduction number (R0) and infection spreading trajectory of the virus. Our results show that the first wave in Delhi had three successive peaks and valleys, and the first peak of the second wave was the tallest, indicating the severity of per-day infection cases. During the analysed period (April 2020 and April 2021), concentrations of criteria pollutants varied across the waves, and air pollution was substantially higher during the second wave. In addition, the results revealed that during the second wave, NO2 maintained a significant negative relationship with COVID-19 (cases per day), while SO2 had a negative relationship with COVID-19 (cumulative cases) during the first wave. Our results also show a significant positive association of O3 with COVID-19 deaths during the first wave and cumulative cases and deaths during the second wave. The study indicates that a higher relative humidity in Delhi had a negative relation with COVID-19 cumulative cases and mortality during the first wave. The study confirms that the estimated R0 was marginally different during the two waves, and the spread of COVID-19 new cases followed a cubic growth trajectory. The findings of this study provide valuable information for policymakers in handling COVID-19 waves in various cities.

Project description:PurposeVernal keratoconjunctivitis (VKC) is a severe chronic allergic inflammation of the ocular surface with episodes of acute exacerbations, that primarily affects children and young adults. Although the etiology and pathogenesis of VKC remain unclear, studies have suggested that environmental factors may be involved. This study aims to investigate the association between exposure to meteorological and environmental factors and the incidence of VKC exacerbations.MethodsThis study was conducted in southern Israel, which is a semi-arid, hot, and dry climate with frequent dust storms. Patients diagnosed with VKC were recruited for the study. VKC exacerbations were identified as the need for medical intervention. Pollutants measured included nitrogen dioxide (NO2), ozone (O3), particulate matter (PM10 and PM2.5), sulfur dioxide (SO2), relative humidity (RH), temperature, and solar radiation (SR). To assess the association between VKC exacerbations and exposure to different pollutants, a case-crossover analysis was conducted. We also stratified the analysis by sex, age, ethnicity, immigration status, and social state score.ResultsOur results demonstrated that the pollutants NO2, O3, and PM10 were associated with VKC exacerbations with odds ratio (OR) = 2.17 (95% confidence interval [CI] =1.40 to 3.04), OR = 2.28 (95% CI = 1.30 to 3.39), and OR = 1.89 (95% CI = 1.06 to 2.74). Other pollutants PM2.5, temperature, and solar radiation were also independently associated with incidence of exacerbations with OR = 1.15 (95% CI = 0.87 to 1.50), OR = 1.75 (95% CI = 1.16 to 2.65), and OR = 1.37 (95% CI = 1.01 to 1.63) and had varying effects in different demographic strata.ConclusionsThe environmental parameters, NO2, O3, PM10, PM2.5, temperature, and solar radiation were found to be significantly associated with VKC exacerbations, with NO2, O3, and PM10 showing the strongest associations. Our findings suggest that environmental factors should be considered when developing strategies to prevent and manage VKC exacerbations.

Project description:BackgroundResearch investigating the effect of air pollution on diabetes incidence is mostly conducted in Europe and the United States and often produces conflicting results. The link between meteorological factors and diabetes incidence remains to be explored. We aimed to explore associations between air pollution and diabetes incidence and to estimate the nonlinear and lag effects of meteorological factors on diabetes incidence.MethodsOur study included 19,000 people aged ≥60 years from the Binhai New District without diabetes at baseline. The generalized additive model (GAM) and the distributed lag nonlinear model (DLNM) were used to explore the effect of air pollutants and meteorological factors on the incidence of diabetes. In the model combining the GAM and DLNM, the impact of each factor (delayed by 30 days) was first observed separately to select statistically significant factors, which were then incorporated into the final multivariate model. The association between air pollution and the incidence of diabetes was assessed in subgroups based on age, sex, and body mass index (BMI).ResultsWe found that cumulative RRs for diabetes incidence were 1.026 (1.011-1.040), 1.019 (1.012-1.026), and 1.051 (1.019-1.083) per 10 μg/m3 increase in PM2.5, PM10, and NO2, respectively, as well as 1.156 (1.058-1.264) per 1 mg/m3 increase in CO in a single-pollutant model. Increased temperature, excessive humidity or dryness, and shortened sunshine duration were positively correlated with the incidence of diabetes in single-factor models. After adjusting for temperature, humidity, and sunshine, the risk of diabetes increased by 9.2% (95% confidence interval (CI):2.1%-16.8%) per 10 μg/m3 increase in PM2.5. We also found that women, the elderly (≥75 years), and obese subjects were more susceptible to the effect of PM2.5.ConclusionOur data suggest that PM2.5 is positively correlated with the incidence of diabetes in the elderly, and the relationship between various meteorological factors and diabetes in the elderly is nonlinear.

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Dataset Information

COVID-19 incidence and mortality in Lombardy, Italy: an ecological study on the role of air pollution, meteorological factors, demographic and socioeconomic variables.

Publications

COVID-19 incidence and mortality in Lombardy, Italy: An ecological study on the role of air pollution, meteorological factors, demographic and socioeconomic variables.

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