Artisanal small-scale gold mining (ASGM) is a poverty-driven activity practiced in over 70 countries worldwide. Zimbabwe is amongst the top ten countries using large quantities of mercury to extract gold from ore. This analysis was performed to check data availability and derive a preliminary estimate of disability-adjusted life years (DALYs) due to mercury use in ASGM in Zimbabwe. Cases of chronic mercury intoxication were identified following an algorithm using mercury-related health effects and mercury in human specimens. The sample prevalence amongst miners and controls (surveyed by the United Nations Industrial Development Organization in 2004 and the University of Munich in 2006) was determined and extrapolated to the entire population of Zimbabwe. Further epidemiological and demographic data were taken from the literature and missing data modeled with DisMod II to quantify DALYs using the methods from the Global Burden of Disease (GBD) 2004 update published by the World Health Organization (WHO). While there was no disability weight (DW) available indicating the relative disease severity of chronic mercury intoxication, the DW of a comparable disease was assigned by following the criteria 1) chronic condition, 2) triggered by a substance, and 3) causing similar health symptoms. Miners showed a sample prevalence of 72% while controls showed no cases of chronic mercury intoxication. Data availability is very limited why it was necessary to model data and make assumptions about the number of exposed population, the definition of chronic mercury intoxication, DW, and epidemiology. If these assumptions hold, the extrapolation would result in around 95,400 DALYs in Zimbabwe’s total population in 2004. This analysis provides a preliminary quantification of the mercury-related health burden from ASGM based on the limited data available. If the determined assumptions hold, chronic mercury intoxication is likely to have been one of the top 20 hazards for population health in Zimbabwe in 2004 when comparing with more than 130 categories of diseases and injuries quantified in the WHO’s GBD 2004 update. Improving data quality would allow more accurate estimates. However, the results highlight the need to reduce a burden which could be entirely avoided.

Background: Burden of disease analyses quantify population health and provide comprehensive overviews of the health status of countries or specific population groups. The comparative risk assessment (CRA) methodology is commonly used to estimate the share of the burden attributable to risk factors. The aim of this paper is to identify and address some selected important challenges associated with CRA, illustrated by examples, and to discuss ways to handle them. Further, the main challenges are addressed and finally, similarities and differences between CRA and health impact assessments (HIA) are discussed, as these concepts are sometimes referred to synonymously but have distinctly different applications. Results: CRAs are very data demanding. One key element is the exposure-response relationship described e.g. by a mathematical function. Combining estimates to arrive at coherent functions is challenging due to the large variability in risk exposure definitions and data quality. Also, the uncertainty attached to this data is difficult to account for. Another key issue along the CRA-steps is to define a theoretical minimal risk exposure level for each risk factor. In some cases, this level is evident and self-explanatory (e.g., zero smoking), but often more difficult to define and justify (e.g., ideal consumption of whole grains). CRA combine all relevant information and allow to estimate population attributable fractions (PAFs) quantifying the proportion of disease burden attributable to exposure. Among many available formulae for PAFs, it is important to use the one that allows consistency between definitions, units of the exposure data, and the exposure response functions. When combined effects of different risk factors are of interest, the non-additive nature of PAFs and possible mediation effects need to be reflected. Further, as attributable burden is typically calculated based on current exposure and current health outcomes, the time dimensions of risk and outcomes may become inconsistent. Finally, the evidence of the association between exposure and outcome can be heterogeneous which needs to be considered when interpreting CRA results. Conclusions: The methodological challenges make transparent reporting of input and process data in CRA a necessary prerequisite. The evidence for causality between included risk-outcome pairs has to be well established to inform public health practice.

Traffic noise is nearly ubiquitous and thus can affect the health of many people. Using the German noise mapping data according to the Directive 2002/49/EC of 2017 and exposure-response functions for ischemic heart disease, noise annoyance and sleep disturbance assessed by the World Health Organization’s Environmental Noise Guidelines for the European Region the burden of disease due to traffic noise is quantified. The burden of disease is expressed in disability-adjusted life years (DALYs) and its components. The highest burden was found for road traffic noise, with 75,896 DALYs when only considering moderate evidence. When including all available evidence, 176,888 DALYs can be attributable to road traffic noise. The burden due to aircraft and railway noise is lower because fewer people are exposed. Comparing the burden by health outcomes, the biggest share is due to ischemic heart disease (90%) in regard to aircraft noise, however, the lowest evidence was expressed for the association between traffic noise and ischemic heart disease. Therefore, the results should be interpreted with caution. Using alternative input parameters (e.g., exposure data) can lead to a much higher burden. Nevertheless, environmental noise is an important risk factor which leads to considerable loss of healthy life years.

Ambient air pollution causes a considerable disease burden, particularly in South Asia. The objective of the study is to test the feasibility of applying the environmental burden of disease method at state level in India and to quantify a first set of disease burden estimates due to ambient air pollution in Kerala. Particulate Matter (PM) was used as an indicator for ambient air pollution. The disease burden was quantified in Years of Life Lost (YLL) for the population (30 + years) living in urban areas of Kerala. Scenario analyses were performed to account for uncertainties in the input parameters. 6108 (confidence interval (95% CI): 4150–7791) of 81,636 total natural deaths can be attributed to PM, resulting in 96,359 (95% CI: 65,479–122,917) YLLs due to premature mortality (base case scenario, average for 2008–2011). Depending on the underlying assumptions the results vary between 69,582 and 377,195 YLLs. Around half of the total burden is related to cardiovascular deaths. Scenario analyses show that a decrease of 10% in PM concentrations would save 15,904 (95% CI: 11,090–19,806) life years. The results can be used to raise awareness about air quality standards at a local level and to support decision-making processes aiming at cleaner and healthier environments.

The increased use of chemicals leads to a continuous deposition of chemicals in the environment and to a continuous increase in exposure of the global and the European population. Comprehensive burden of disease analyses are however still missing for many countries. Using the World Health Organization's Environmental Burden of Disease (EBD) approach and combining data from the European Human Biomonitoring (HBM) dashboard with disease and population data, we estimated the comprehensive attributable burden (AB) for the year 2021, in the best-case quantified by disability-adjusted life years (DALY). First analyses were performed for cadmium, per- and polyfluoroalkyl substances (PFAS) and phthalates. For cadmium, the highest AB for chronic kidney disease was calculated for Czechia with about 142 (95% confidence interval (CI): 3-409) years lived with disability (YLDs) per 1,000,000 persons. For osteoporosis related to cadmium, the highest AB was estimated for Poland with 2,024 (95% CI: 946-2,761) DALYs per 1,000,000 persons. For perfluorooctanoic acid (PFOA), the highest AB was associated with hypertension in Spain with about 2,591 (95% CI: 1,003-4,193) years of life lost (YLL) per 1,000,000 persons. For phthalates, the highest AB due to DiNP-exposure associated with obesity was 11,324 (95% CI: 1,929-22,391) cases per 1,000,000 persons. The highest AB for diabetes mellitus due to DEHP-exposure was estimated with 1,324 (95% CI: 49-2,476) YLDs per 1,000,000 women in Poland. Overall, we have identified many relevant challenges when estimating the burden attributable to the selected chemicals. Especially the missing data (e.g. HBM data for countries, age groups or sex) need to be tackled by more primary data or adequate gap filing techniques.