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The authors wish to make the following corrections to this paper [...].

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BACKGROUND: The disability weight is an essential factor to estimate the healthy time that is lost due to living with a certain state of illness. A 2014 review showed a considerable variation in methods used to derive disability weights. Since then, several sets of disability weights have been developed. This systematic review aimed to provide an updated and comparative overview of the methodological design choices and surveying techniques that have been used in disability weights measurement studies and how they evolved over time. METHODS: A literature search was conducted in multiple international databases (early-1990 to mid-2021). Records were screened according to pre-defined eligibility criteria. The quality of the included disability weights measurement studies was assessed using the Checklist for Reporting Valuation Studies (CREATE) instrument. Studies were collated by characteristics and methodological design approaches. Data extraction was performed by one reviewer and discussed with a second. RESULTS: Forty-six unique disability weights measurement studies met our eligibility criteria. More than half (n = 27; 59%) of the identified studies assessed disability weights for multiple ill-health outcomes. Thirty studies (65%) described the health states using disease-specific descriptions or a combination of a disease-specific descriptions and generic-preference instruments. The percentage of studies obtaining health preferences from a population-based panel increased from 14% (2004-2011) to 32% (2012-2021). None of the disability weight studies published in the past 10 years used the annual profile approach. Most studies performed panel-meetings to obtain disability weights data. CONCLUSIONS: Our review reveals that a methodological uniformity between national and GBD disability weights studies increased, especially from 2010 onwards. Over years, more studies used disease-specific health state descriptions in line with those of the GBD study, panel from general populations, and data from web-based surveys and/or household surveys. There is, however, a wide variation in valuation techniques that were used to derive disability weights at national-level and that persisted over time.

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Noise from neighbours has been shown to be one of the most noise annoying sources in Germany, but research on the influencing factors for the annoyance ratings is scarce. Therefore, we investigated whether different personal and contextual (social, physical) factors contribute to neighbour noise annoyance to better understand the neighbour noise annoyance situation. A population-representative survey in four areas in Germany was conducted, with each area further stratified according to their density of agglomeration (inner city, urban outskirt, rural area). Randomly selected residents from each area were invited by mail to participate in the study, either online or via a paper-pencil mode. Noise annoyance was assessed for different noise sources (e.g., neighbourhood, road, railway, aircrafts, different types of industry). In total, 1973 questionnaires were completed. We identified several factors to be predictive of neighbour noise annoyance: satisfaction with the neighbourhood, relationship with neighbours, residential satisfaction, noise sensitivity, and density of agglomeration for people living in the inner city in comparison to rural areas. Particularly, social aspects such as the relationship with neighbours and satisfaction with the neighbourhood have been shown to affect neighbour noise annoyance.

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Environmental noise is a great burden for the population in Germany, especially in urban areas. People are often exposed not only to one but several noise sources. Long-term exposure to environmental noise can have several and severe adverse effects on human health, such as noise annoyance, sleep disturbances, ischaemic heart disease and depression. Additional burdens arise from multiple exposure to particulate matter or air pollutants. The current article describes the relevant adverse health effects due to long-term environmental noise exposure, discusses the challenges of environmental noise in urban areas different and introduces measures and instruments to abate environmental noise.

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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.

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Following the Parma Declaration on Environment and Health adopted at the Fifth Ministerial Conference (2010), the Ministers and representatives of Member States in the WHO European Region requested the World Health Organization (WHO) to develop updated guidelines on environmental noise, and called upon all stakeholders to reduce children's exposure to noise, including that from personal electronic devices. The WHO Environmental Noise Guidelines for the European Region will provide evidence-based policy guidance to Member States on protecting human health from noise originating from transportation (road traffic, railway and aircraft), wind turbine noise, and leisure noise in settings where people spend the majority of their time. Compared to previous WHO guidelines on noise, the most significant developments include: consideration of new evidence associating environmental noise exposure with health outcomes, such as annoyance, cardiovascular effects, obesity and metabolic effects (such as diabetes), cognitive impairment, sleep disturbance, hearing impairment and tinnitus, adverse birth outcomes, quality of life, mental health, and wellbeing; inclusion of new noise sources to reflect the current noise environment; and the use of a standardized framework (grading of recommendations, assessment, development, and evaluations: GRADE) to assess evidence and develop recommendations. The recommendations in the guidelines are underpinned by systematic reviews of evidence on several health outcomes related to environmental noise as well as evidence on interventions to reduce noise exposure and/or health outcomes. The overall body of evidence is published in this Special Issue.

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The Noise Related Annoyance Cognition and Health (NORAH) research initiative is one of the most extensive studies on the physiological and psychological long-term effects of transportation noise in Europe. It includes research on the quality of life and annoyance as well as cardiovascular effects, sleep disturbance, breast cancer, blood pressure, depression and the cognitive development of children. Within the realm of the annoyance module of the study approximately 10,000 residents of the Rhine-Main district were surveyed on the combined effects of transportation noise. This included combined noise from aircraft and road traffic noise (N = 4905), or aircraft and railway noise (N = 4777). Results show that judgment of the total noise annoyance of participants was strongly determined by the sound source which was judged as more annoying (in this case aircraft noise). To a lesser extent, the average sound pressure level of the two present sources was also of relevance.

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BACKGROUND AND AIMS: This is the first study to examine the effect of alcohol marketing exposure on adolescents' drinking in a cross-national context. The aim was to examine reciprocal processes between exposure to a wide range of alcohol marketing types and adolescent drinking, controlled for non-alcohol branded media exposure. DESIGN: Prospective observational study (11-12- and 14-17-month intervals), using a three-wave autoregressive cross-lagged model. SETTING: School-based sample in 181 state-funded schools in Germany, Italy, Netherlands, Poland. PARTICIPANTS: A total of 9075 eligible respondents participated in the survey (mean age 14 years, 49.5% male. MEASUREMENTS: Adolescents reported their frequency of past-month drinking and binge drinking. Alcohol marketing exposure was measured by a latent variable with 13 items measuring exposure to online alcohol marketing, televised alcohol advertising, alcohol sport sponsorship, music event/festival sponsorship, ownership alcohol-branded promotional items, reception of free samples and exposure to price offers. Confounders were age, gender, education, country, internet use, exposure to non-alcohol sponsored football championships and television programmes without alcohol commercials. FINDINGS: The analyses showed one-directional long-term effects of alcohol marketing exposure on drinking (exposure T1 on drinking T2: ß = 0.420 (0.058), P < 0.001, 95% confidence interval (CI) = 0.324-0.515; exposure T2 on drinking T3: ß = 0.200 (0.044), P < 0.001, 95% CI = 0.127-0.272; drinking T1 and drinking T2 on exposure: P > 0.05). Similar results were found in the binge drinking model (exposure T1 on binge T2: ß = 0.409 (0.054), P < 0.001, 95% CI = 0.320-0.499; exposure T2 on binge T3: ß = 0.168 (0.050), P = 0.001, 95% CI = 0.086-0.250; binge T1 and binge T2 on exposure: P > 0.05). CONCLUSIONS: There appears to be a one-way effect of alcohol marketing exposure on adolescents' alcohol use over time, which cannot be explained by either previous drinking or exposure to non-alcohol-branded marketing.

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