The association between different timeframes of air pollution exposure and COVID-19 incidence, morbidity and mortality in German counties in 2020 – Environmental Health

The Effects of Air Pollution on COVID-19 Disease Burden in Germany

The aim of this study was to analyze the effects of both long- and short-term exposure to nitrogen dioxide (NO₂) and particulate matter with a diameter of 2.5 μm or less (PM_2.5) on the burden of COVID-19 at the county level in Germany during the first outbreak of the pandemic in spring 2020.

Introduction

This study is an observational county-based study that builds on the methods and data sources utilized in a previous study by Koch et al. (2022) [13]. The analysis focuses on the effects of long-term exposure (10 years and 2 years) and short-term exposure (28 days and 7 days) to air pollutants on COVID-19 incidence, mortality, admission to intensive care units, and the need for mechanical ventilation. The study also considers the time window in which the SARS-CoV-2 virus might survive on particulate matter.

Ethical approval was obtained from the ethical commission of the Charité (EA2/038/21; head: Prof. Dr. Kaschina). Patient consent was waived, as no individual patient data were collected and data analysis was performed anonymously.

Setting and Design

The unit of analysis in this study is German counties, which corresponds to the Nomenclature of Territorial Unit for Statistics level 3 (NUTS-3). The analysis is limited to the first COVID-19 outbreak period (March 4th to May 16th) when social distancing rules were implemented consistently throughout the country.

During this period, schools, national borders, restaurants, shops, and churches were closed. Social distancing rules were imposed by federal states, limiting meetings between different households and restricting residents’ movement outside their homes. These rules started to be lifted by April 15th, with schools reopening on May 4th and borders gradually reopening from May 15th [15]. The first wave of the pandemic affected different regions of Germany differently, with high incidence in southern states and large cities, as well as cluster events during the February carnival festivities in the Rhine region. Many counties in the north and east were comparatively less affected during the first wave.

Data Sources

COVID-19 Data

The German Interdisciplinary Association for Intensive Care and Emergency Medicine (DIVI) register tracks intensive care capacities and COVID-19 patient numbers in German hospitals [16]. Daily reporting to the register became mandatory for all hospitals on April 16, 2020. Data on COVID-19 patient-days on intensive care units and mechanical ventilation were extracted for the period between April 16 and May 16, 2020. The Robert-Koch-Institute (RKI) provides a public-access database of COVID-19 cases and deaths reported for each county by local public health offices [14]. The analysis is limited to the counties that reported to the DIVI register and the period from April 16th to May 16th.

Air Pollution Data

The APExpose dataset (version 2.0) was used to analyze the association between long-term exposure to air pollution and COVID-19 outcomes [17]. The dataset combines observed data from the European Environmental Agency’s Airbase database with modelled global reanalysis data from the Copernicus Atmospheric Monitoring Service (CAMS) to create a complete dataset for all German counties for the period 2010–2019. The dataset includes parameters for nitrogen dioxide (NO₂), nitrogen oxide (NO), ozone (O₃), and particulate matter with diameters smaller than 2.5μm and 10μm (PM_2.5 and PM₁₀). The analysis calculates the means of each pollutant in each county over the ten-year period (2010–2019) and the two-year period (2018–2019) prior to the COVID-19 outbreak.

A new dataset was created for the analysis of short-term air pollution exposure. The dataset contains daily observations for the period from March 4th to May 16th, 2020, with values for NO₂, O₃, and PM_2.5, averaged over the preceding 48 hours, 7 days, and 4 weeks.

Temperature time series for the German counties were obtained from the CAMS reanalysis.

Demographic Data and German Index of Social Deprivation

Demographic data for each county, including population size, area, and population distribution by age group and sex, were obtained from the Federal Statistical Office of Germany. Data from 2019 was used to calculate population density, the share of the population aged over 64 years, and the fraction of the population that is female. The German Index of Social Deprivation (GISD), developed by the RKI, was used as a measure of relative regional socio-economic disadvantage. The index score is on a scale from 0 to 1, with a higher score indicating more deprivation. The mean GISD score between 2010 and 2019 was calculated for each county.

Statistics

The analysis includes four outcome variables: new cases (incidence), new deaths (mortality), patient days on ICUs, and patient days on mechanical ventilation. All outcomes were calculated as rates per 100,000 residents.

Separate models were fitted for long-term exposures (10 years and 2 years) and short-term exposures (48 hours, 7 days, and 4 weeks). The models were adjusted for confounders such as age distribution, sex distribution, days between the first reported COVID-19 case and March 1st, population density, and the social deprivation index score. Sensitivity analyses were conducted for tri-pollutant models and additional adjustments for temperature and weekdays. Statistical analysis was conducted using R Statistical Software.

SDGs, Targets, and Indicators

SDGs, Targets, and Indicators Identified in the Article:

1. SDG 3: Good Health and Well-being

• Target 3.4: By 2030, reduce by one-third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being
• Indicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease
• Indicator 3.4.2: Suicide mortality rate
• SDG 11: Sustainable Cities and Communities

• Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management
• Indicator 11.6.2: Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted)
• SDG 13: Climate Action

• Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries
• Indicator 13.1.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population

Explanation:

1. SDG 3: Good Health and Well-being is addressed in the article as it focuses on the effects of air pollution on COVID-19 disease burden, including incidence, mortality, and the need for intensive care and mechanical ventilation. This aligns with the goal of promoting good health and well-being for all.

2. Target 3.4 under SDG 3 can be identified based on the article’s content. The target aims to reduce premature mortality from non-communicable diseases, including chronic respiratory diseases. The article analyzes the effects of long- and short-term exposure to air pollutants on COVID-19 outcomes, which can contribute to the prevention and treatment of respiratory diseases.

3. The article mentions indicators that can be used to measure progress towards the identified targets. Indicator 3.4.1, which measures the mortality rate attributed to cardiovascular disease, cancer, diabetes, or chronic respiratory disease, is relevant to the analysis of the effects of air pollution on COVID-19 disease burden. Additionally, Indicator 3.4.2, which measures the suicide mortality rate, is mentioned in the article.

4. The table below presents the findings from analyzing the article, listing the relevant SDGs, targets, and indicators:

Source: ehjournal.biomedcentral.com