Jobs International,13197

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) pollution is an important threat to human health. The aim of this study is to estimate the environmental burden of disease (EBD) for the German population associated with PM

Exposure in Germany for the years 2010 until 2018. The EBD method was used to quantify relevant indicators, e.g., disability-adjusted life years (DALYs), and the life table approach was used to estimate the reduction in life expectancy caused by long-term PM

Attributable DALYs for all disease outcomes showed a downward trend. In 2018, the highest EBD was estimated for ischemic heart disease (101.776; 95% uncertainty interval (UI) 62, 713–145, 644), followed by lung cancer (60, 843; 95% UI 43, 380–79, 379). The estimates for Germany differ from those provided by other institutions. This is mainly related to considerable differences in the input data, the use of a specific German national life expectancy and the selected relative risks. A transparent description of input data, computational steps, and assumptions is essential to explain differing results of EBD studies to improve methodological credibility and trust in the results. Furthermore, the different calculated indicators should be explained and interpreted with caution.

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Ambient particulate matter pollution is a serious threat to human health worldwide [1, 2]. Especially for the small fraction of particles with an aerodynamic diameter less than 2.5 µm (PM

), the evidence base for long-term health effects is well-established. Studies continuously show associations with health outcomes even at very low concentration levels that are well below existing European limit values [3].

Despite the predominant use of the comparative risk assessment representing the state-of-the-art method in this area, the resulting estimates often vary widely because of different input data and selected assumptions. For Germany, as an example, the EEA reports 63, 100 deaths and 710, 900 years of life lost because of mortality (YLLs) attributable to PM

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In 2018 [5]. For the same year, the IHME’s Global Burden of Disease (GBD) study presents 26, 592 (uncertainty interval, 95% UI 19, 743–33, 900) deaths and 430, 491 (95% UI 423, 932–562, 365) YLLs attributable to PM

Decisionmakers show an increased interest not only to have information on the overall number of YLLs or attributable deaths but also on the average loss in life expectancy (LE) that is due to PM

. Global assessments of life expectancy reductions indicated a loss of about one year on a global average and of around 0.4 years in Germany for 2016 [8]. Lelieveld and Pozzer [9] estimated a loss of 2.41 years; however, their estimate included the effects of ozone as well and they used a different exposure-response function (ERF).

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The increasing number of available environmental burden of disease (EBD) estimates raises the question about which estimates are most reliable, valid, or simply most suitable for the intended purpose [4, 10]. For such a comparison, it is important to also compare the input data and underlying assumptions used in the respective assessments.

The aim of this study is to compare different available EBD estimates using Germany as an example. Accordingly, for our calculation of estimates in the main analysis, we use input data that were specific for the situation in Germany. We present a time series of disease burden attributable to PM

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For the years 2010 to 2018. Furthermore, we use selected methodological variations for estimating the attributable burden and, in addition, calculate estimates for the reduction in life expectancy for comparisons with recent estimates from other institutions. Finally, we present and discuss differences of the estimates and reflect on the main factors causing these differences.

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Related population health effects for five different health outcomes and the years 2010 to 2018 [11]. Thus, YLLs, years lived with disability (YLDs), disability-adjusted life years (DALYs), and the number of attributable deaths were quantified. Furthermore, we calculated the EBD using different calculation approaches to compare the main analysis results (see Table 1).

For our main analysis we have chosen health outcomes with the strongest scientific evidence base regarding the health effects due to PM

: chronic obstructive pulmonary disease (COPD), stroke, ischemic heart disease (IHD), tracheal, bronchus and lung cancer (LC), and type 2 diabetes mellitus (T2DM) [12], as done in the GBD-2019 study. All indicators were calculated for the years 2010 to 2018 using a common counterfactual value of 4.2 µg/m

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, which was based on the same epidemiological studies from which the relative risks (RR) were taken. This counterfactual value was also used in the GBD-2019 study. We applied the same RR for mortality and morbidity effects, as done in the GBD-2019 study. The UI of the RR were used to estimate the UI for the burden of disease estimates. Uniform age weights and no time discounts were applied. The estimation processes were performed stratified by sex and five-year age groups. We used Microsoft Excel (version 2013) for all calculations. A sample spreadsheet can be found in the Supplementary Materials. These results were compared with estimates from the GBD-2019 study [12].

Within method A, we used the AirQ+ software (version 2.07, WHO Regional Office for Europe, European Centre for Environment and Health (ECEH), Bonn, Germany) as provided by the WHO [13] to estimate the burden caused by the outcomes available in the software (COPD, stroke, IHD, and LC) attributable to PM

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For 2018. We fed the same input data into AirQ+ to allow for comparability to the results of the main analysis. Regarding the ERF, we selected ‘GBD2015/2016 integrated function 2016′ as the most recent one, which included a fixed counterfactual value of 2.4 µg/m

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. Regarding the exposure data, AirQ+ only offers to insert one concentration value per area under investigation—in this case, one value for Germany. Therefore, we estimated a population-weighted exposure mean value of 10.8 µg/m

For the year 2018. The formulas from AirQ+ are explained in the manuals of the software, which can be found on t WHO’s website [13].

Within method B, we calculated the EBD based on natural all-cause mortality rates for the year 2018 to compare the results with corresponding estimates from the EEA [5]. For this calculation, we assumed a counterfactual value of 0 µg/m

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Be comparable with the EEA estimates [5]. We used the RR for natural all-cause mortality as reported by Hoek et al. [14] and the according confidence interval (CI) for the RR to estimate the EBD.

Exposure for the year 2017, based on the life table approach. The year 2017 was chosen because this was the most recent year with detailed mortality data available. In this approach, life expectancy is re-estimated by assuming that reducing PM

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—the counterfactual value used in the main analysis). Life expectancy is then re-estimated with a natural all-cause mortality rate reduced by the effect of PM

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. In this case, we used a RR for natural all-cause mortality reported by Hoek et al. (2013). Spreadsheets provided by the Institute of Occupational Medicine (IOM) were used as a basis for the calculations [15, 16]. The models were run for both sexes separately.

Rural and urban background concentrations using a fixed conversion factor of 0.7 [19], which was validated based on measurement data from Germany.

Apart from population health measures such as DALYs, sustainability strategies such as the Global Sustainable Development Goals or the German Sustainable Development Strategy defined further indicators to represent the impacts of air pollutants. These are, for example, the population-weighted annual mean PM

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Population data: population data and data on life expectancy for Germany were obtained from the Federal Statistical Office (Wiesbaden, Germany) [24, 25]. Both datasets were stratified by five-year age groups and sex.

Mortality data: we used mortality data from the cause-of-death statistics for selected ICD-10 codes as published by the Federal Statistical Office in the German Federal Health Monitoring [26]. The data are stratified by five-year age groups and sex. No garbage-code correction was conducted. Neonatal deaths, as needed for the life table approach, were gathered from the Federal Statistical Office [27].

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Morbidity data: there is no official registry for morbidity data in Germany. Therefore, prevalence rates for COPD, IHD, T2DM, and stroke were obtained from the German representative study “German Health Update” (GEDA) [28,

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