Exposure-response Functions Research Articles

Estimating health impacts and economic costs of air pollution in the Republic of Macedonia

Aim: This paper assesses the magnitude of health impacts and economic costs of fine particulate matter (PM) air pollution in the Republic of Macedonia. Methods: Ambient PM10 and PM2.5 monitoring data were combined with population characteristics and exposure-response functions to calculate the incidence of several health end-points known to be highly influenced by air pollution. Health impacts were converted to Disability-Adjusted Life Years (DALYs) and then translated into economic terms using three valuation approaches to form lower and higher bounds: the (adjusted) Human Capital Approach (HCA), Value of a Statistical Life (VSL) and the COI (cost of illness). Results: Fine particulate matter frequently exceeds daily and annual limit values andinfluences a person‟s day-to-day health and their ability to work. Converting lost years of life and disabilities into DALYs - these health effects represent an annual economic cost of approximately €253 million or 3.2% of GDP (midpoint estimate). Premature death accounts for over 90% of the total health burden since this represents a loss of total life-long income. A reduction of even 1μg/m3 in ambient PM10 or PM 2.5 would imply 195 fewer deaths and represent an economic savings of €34 million per year in reduced health costs. Conclusion: Interventions that reduce ambient PM10 or PM2.5 have significant economic savings in both the short and long run. Currently, these benefits (costs) are „hidden‟ due to the lack of information linking air quality and health outcomes and translating this into economic terms. Policymakers seeking ways to improve the public‟s health and lessen the burden on the health system could focus on a narrow set of air pollution sources to achieve these goals

Ozone exposure and health risks of different age structures in major urban agglomerations in People's Republic of China from 2013 to 2018.

High concentration of surface ozone (O3) will cause health risks to people. In order to analyze the spatiotemporal characteristics of O3 and assess O3 exposure and health risks for different age groups in China, we applied multiple methods including standard deviation ellipse, spatial autocorrelation, and exposure-response functions. Results show that O3 concentrations increased in 64.5% of areas in China from 2013 to 2018. The central plain urban agglomeration (CPU), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) witnessed the greatest incremental rates of O3 by 16.7%, 14.3%, and 13.1%. Spatially, the trend of O3 shows a significant positive autocorrelation, and high trend values primarily in central and east China. The proportion of the total population exposed to high O3 (above 160μg/m3) increased annually. Compared to 2013, the proportion of the young, adult, and old populations exposed to high O3 increased to different extents in 2018 by 26.8%, 29.6%, and 27.2%, respectively. The extent of population exposure risk areas in China expanded in size, particularly in north and east China. The total premature respiratory mortalities attributable to long-term O3 exposure in six urban agglomerations were about 177,000 in 2018 which has increased by 16.4% compared to that in 2013. Among different age groups, old people are more vulnerable to O3 pollution, so we need to strengthen their relevant health protection of them.

Source sectors underlying PM2.5-related deaths among children under 5 years of age in 17 low- and middle-income countries

BackgroundFine particulate matter (PM2.5) from different source sectors might differ in toxicity. However, data from large-scale studies on vulnerable children in low- and middle-income countries (LMICs) are insufficient. ObjectiveTo analyze the association of under-five death (U5D) with long-term exposure to PM2.5 from different sources. MethodWe evaluated demographic and health survey data for 79,995 babies born in 2017 in 16 Asian and African LMICs (AA-LMICs) and a Latin America low-income country (i.e., Haiti). Long-term exposure to PM2.5 was assessed by a well-established product that attributed the annual concentration to 20 source sectors in 2017. The associations of survival during < 5-year periods with each source-specific concentration of PM2.5 were analyzed by Cox regression with multiple adjustments. We derived a multiple-pollutant ridge regression model to estimate the joint exposure–response function (JERF) between U5D and PM2.5 mixtures. To evaluate how sources affected PM2.5 toxicity, we evaluated the number of U5Ds attributable to PM2.5 based on the source profiles for 88 AA-LMICs. ResultsAccording to the single-pollutant model, the risk of U5D increased by 7% (95% confidence interval [CI]: 5%, 9%) for each 10 μg/m3 increment in total PM2.5 concentration. The model performance was lower than that of the multiple-pollutant ridge regression model. For each 10 μg/m3 increment in PM2.5, the excess risk of U5D ranged from 6% (95% CI: 4%, 9%) in Nepal to 10% (95% CI: 6%, 14%) in Mauritania. Based on the JERF, PM2.5 contributed to 817,647 (95% CI: 585,729, 1,050,439), i.e., 28.0% (95% CI: 20.1%, 35.8%), of all U5Ds across the 88 AA-LMICs. The PM2.5-related U5Ds were mostly attributable to PM2.5 produced by desert dust, followed by solid biofuel combustion and open fires. ConclusionThe average toxicity of PM2.5 varied by source profile, which should be taken into consideration when planning public health interventions. For some AA LMICs, natural sources of PM2.5 had the most significant health effects, and should not be ignored to ensure the protection of child health.

Premature mortality attributable to NO2 exposure in cities and the role of built environment: A global analysis

BackgroundEnvironmental risks accumulate in cities, including polluted air and health disparities, but these risks can be reduced through scientific city planning. The purpose of this study was to investigate the global burden of premature mortality attributable to NO2 exposure in urban areas and the role of the built environment in this regard. MethodsAn approach based on health impact assessment was used to estimate the premature mortality burdens associated with NO2 exposure in 13,169 urban areas around the world using globally gridded NO2 and population estimates, baseline mortality, and epidemiologically derived exposure-response functions. We used the most recent WHO recommended value (i.e.,10 μg/m3) as a counterfactual concentration. Finally, the relationship between the characteristics of the built environment at the city level and the burden of NO2-related mortality was evaluated. ResultsWorldwide, 549,715(95%CI: 276204–815,023) cases of death attributable to NO2 exposure in urban areas could be prevented if compliance with the latest WHO guideline, accounting for 2.7 % (95%CI:1.4 %–4.0 %) of total mortalities in 2019. Across cities around the world, the age-standardized mortality rate (per 100,000 people) attributable to NO2 exposure ranged from 51.3 (95%CI:25.8–76.0) in Central Asia to 3.4(95%CI: 1.7–5.1) in Oceania. Although there was a significant decrease in premature mortality attributable to NO2 exposure globally, considerable regional heterogeneity exists, with cities in Central Asia and Andean Latin America in particular exhibiting an upward trend. Further, we discovered a positive association between population density and street connectivity with mortality attributable to NO2. While the increase in green and blue space were significantly associated with a lower NO2-associated mortality. ConclusionThe findings of this study provided a comprehensive understanding of the premature mortality burden due to NO2 in cities throughout the world and the role that urban planning policies can play in reducing the health burden associated with air pollution.

Correction to: Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality

Correction to: Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality

Selenium attenuates the association of co-exposure to arsenic, cadmium, and lead with cognitive function among Chinese community-dwelling older adults.

The effects of interactions between the toxic and essential metal mixtures on cognitive function are poorly understood. This study aims to identify the joint association of arsenic (As), cadmium (Cd), and lead (Pb) with cognitive function in older adults and the moderating role of selenium (Se), zinc (Zn), and copper (Cu) in this association. This study included 1000 community-dwelling older adults. Cognitive function was assessed by the Mini-Mental State Examination (MMSE). Blood concentrations of As, Cd, Pb, Se, Zn, and Cu were measured using inductively coupled plasma mass spectrometry. Linear regression and Bayesian kernel machine regression (BKMR) models were applied to assess the individual and joint associations of As, Cd, and Pb with cognitive function and to examine whether Se, Zn, and Cu (individually and as a mixture) modified these associations. In the adjusted single-metal models, both Cd (β = - 0.37, 95% CI: - 0.73 to - 0.01) and Pb (β = - 0.44, 95% CI: - 0.86 to - 0.02) were associated with MMSE scores, while Se (β = 0.71, 95% CI: 0.30 to 1.13) exhibited a positive relationship with MMSE scores. Univariate exposure-response functions from BKMR models showed similar results. Moreover, the toxic metal mixture (As, Cd, and Pb) exhibited a significant negative association with MMSE scores in a dose-response pattern, with Pb being the greatest contributor within the mixture. The negative association of Pb alone or the toxic metal mixture with MMSE scores became weaker at higher concentrations of Se within its normal range, especially when Se levels were greater than the median (89.18μg/L). Our findings support that Se can attenuate the negative associations of exposure to single Pb or the As, Cd, and Pb mixtures with cognitive function. Future prospective studies are needed to replicate our findings.

Labour productivity and economic impacts of carbon mitigation: a modelling study and benefit–cost analysis

Despite the emerging carbon neutrality pledges from different countries, it is still unclear how much these pledges would cost and how the costs would compare with the economic benefits. Comparisons at the country level are important for tightening country-specific emissions trajectories to keep the temperature limit targets outlined in the Paris Agreement within reach. We aimed to systematically estimate avoided heat-related labour productivity losses against the costs of climate change mitigation at country and regional levels. In this modelling study, to address the above-mentioned research gaps, we first selected two representative climate change scenarios (Representative Concentration Pathway 6.0 [RCP6.0] scenario, a higher warming scenario representing limited mitigation pledges before the Paris Agreement with around 3°C warming by the end of this century; and RCP2.6 scenario, a lower warming scenario assuming global temperature rise is limited to 2°C) and estimated heat-related labour productivity loss using the exposure-response function at country and regional levels. By representing the direct heat-related labour productivity losses in a multiregional global computable general equilibrium model, we then did a benefit-cost analysis to quantify the economic benefits of avoided heat-related labour productivity losses as well as the estimated reduction in gross domestic product (GDP) related to carbon reduction. By 2100, the overall economic losses due to heat-related labour productivity loss could range from about 1·5% of global GDP under the RCP6.0 scenario to about 0·1% of global GDP under the RCP2.6 scenario. The productivity losses will be highly concentrated in low-latitude regions, especially in southeast Asia, India, and the Middle East, implying the necessity of additional adaptation measures. By 2100, about 51·8% of global climate change mitigation costs could be offset by economic benefits from reduced labour productivity losses. Cumulatively, about 17·0% of climate change mitigation costs could be offset by the economic benefits between 2020 and 2100, when using a 2% social discounting rate. The costs and benefits of climate change mitigation will be distributed highly unevenly across regions due to their varying climate zones and economic structures. Regions with benefits from reduced productivity losses higher than mitigation costs are mainly low-latitude and tropical regions with lower income and lower emissions, such as southeast Asia, Brazil, and Mexico. More than half the climate change mitigation costs could be offset by the economic benefits by 2100 for the world's largest emitters, including the USA, China, the EU, and India. Low benefit-cost ratios are expected in economies that rely on fossil fuels, such as Canada, Russia, and the Middle East. Although pledging carbon neutrality implies radical changes to most economies, substantial health and economic gains can be achieved by reduced heat-related labour productivity loss, even without accounting for other benefits. The benefit-cost analysis in this study shows the potential for choosing more stringent climate change mitigation pathways in some regions. Regions with low benefit-cost ratios need to restructure their economies to reduce mitigation costs as well as losses from declined fossil fuel exports. National Natural Science Foundation of China, Tsinghua-Toyota Joint Research Fund, the Wellcome Trust, Tsinghua University-China Three Gorges Corporation Joint Research Center for Climate Governance Mechanism and Green Low-carbon Transformation Strategy, the National Research Foundation, Prime Minister's Office, Singapore (Campus for Research Excellence and Technological Enterprise [CREATE] programme), and the Global Energy Interconnection Development and Coorperation Organization.

An alternate approach to regulatory analyses of the findings of a 20-airport social survey

Miller, Czech, Hellauer, Nicholas, Lohr, Jodts, Broene, Morganstein, Kali, Zhu, Cantor, Hudnall, and Melia [(2021). HMMH Report 308520.004.001] reported the findings of a 20-airport social survey sponsored by the U.S. Federal Aviation Administration that had been designed and conducted six years earlier. Their primary analyses yielded FICON (1992) style (national average and airport-specific bivariate logistic regression) functions relating aircraft noise exposure to the prevalence of high annoyance [FICON (1992). "Federal agency review of selected airport noise analysis issues" (Department of Defense, Washington, DC)]. Alternate analyses of these field observations offer pragmatic benefits for regulatory purposes. Community Tolerance Level (CTL) analysis yields a national average exposure-response predictive function that is nearly indistinguishable from that of a bivariate logistic regression function. Since it natively distinguishes dose-related from collective, non-dose influences on annoyance, it also permits a more parsimonious and readily understood account of the association between aircraft noise exposure and annoyance. Within the exposure range of primary contemporary regulatory concern, CTL analysis accounts for as much variance as FICON-style analysis in the association between noise exposure and predicted annoyance; provides a causal basis for regulation; and can be more effectively implemented and defended than a purely correlation-based analysis. In addition, CTL analysis helps to address the substantial disagreement between the findings of the literature and those of national and international technical consensus standards.

Estimation of stillbirths attributable to ambient fine particles in 137 countries

Gestational exposure to ambient fine particles (PM2.5) increases the risk of stillbirth, but the related disease burden is unknown, particularly in low- and middle-income countries (LMICs). We combine state-of-the-art estimates on stillbirths, and multiple exposure–response functions obtained from previous meta-analyses or derived by a self-matched case-control study in 54 LMICs. 13,870 stillbirths and 32,449 livebirths are extracted from 113 geocoded surveys from the Demographic and Health Surveys. Each stillbirth is compared to livebirth(s) of the same mother using a conditional logit regression. We find that 10-µg/m3 increase of PM2.5 is associated with an 11.0% (95% confidence interval [CI] 6.4, 15.7) increase in the risk of stillbirth, and the association is significantly enhanced by maternal age. Based on age-specific nonlinear PM2.5–stillbirth curves, we evaluate the PM2.5-related stillbirths in 137 countries. In 2015, of 2.09 (95% CI: 1.98, 2.20) million stillbirths, 0.83 (0.54, 1.08) million or 39.7% (26.1, 50.8) are attributable to PM2.5 exposure exceeding the reference level of 10 μg/m3. In LMICs, preventing pregnant women from being exposed to PM2.5 can improve maternal health.

Burden of Disease (BoD) Assessment to Estimate Risk Factors Impact in a Real Nanomanufacturing Scenario.

An industrial nanocoating process air emissions impact on public health was quantified by using the burden of disease (BoD) concept. The health loss was calculated in Disability Adjusted Life Years (DALYs), which is an absolute metric that enables comparisons of the health impacts of different causes. Here, the health loss was compared with generally accepted risk levels for air pollution. Exposure response functions were not available for Ag nanoform. The health loss for TiO2 nanoform emissions were 0.0006 DALYs per 100,000 persons per year. Moreover, the exposure risk characterization was performed by comparing the ground level air concentrations with framework values. The exposure levels were ca. 3 and 18 times lower than the derived limit values of 0.1 μg-TiO2/m3 and 0.01 μg-Ag/m3 for the general population. The accumulations of TiO2 and Ag nanoforms on the soil top layer were estimated to be up to 85 μg-TiO2/kg and 1.4 μg-Ag/kg which was considered low as compared to measured elemental TiO2 and Ag concentrations. This assessment reveals that the spray coating process air emissions are adequately controlled. This study demonstrated how the BoD concept can be applied to quantify health impacts of nanoform outdoor air emissions from an industrial site.

Matching on Generalized Propensity Scores with Continuous Exposures

In the context of a binary treatment, matching is a well-established approach in causal inference. However, in the context of a continuous treatment or exposure, matching is still underdeveloped. We propose an innovative matching approach to estimate an average causal exposure-response function under the setting of continuous exposures that relies on the generalized propensity score (GPS). Our approach maintains the following attractive features of matching: (a) clear separation between the design and the analysis; (b) robustness to model misspecification or to the presence of extreme values of the estimated GPS; (c) straightforward assessments of covariate balance. We first introduce an assumption of identifiability, called local weak unconfoundedness. Under this assumption and mild smoothness conditions, we provide theoretical guarantees that our proposed matching estimator attains point-wise consistency and asymptotic normality. In simulations, our proposed matching approach outperforms existing methods under settings with model misspecification or in the presence of extreme values of the estimated GPS. We apply our proposed method to estimate the average causal exposure-response function between long-term PM 2.5 exposure and all-cause mortality among 68.5 million Medicare enrollees, 2000–2016. We found strong evidence of a harmful effect of long-term PM 2.5 exposure on mortality. Code for the proposed matching approach is provided in the CausalGPS R package, which is available on CRAN and provides a computationally efficient implementation. Supplementary materials for this article are available online.

Climate change and human health in the Eastern Mediterranean and Middle East: Literature review, research priorities and policy suggestions

Human health is linked to climatic factors in complex ways, and climate change can have profound direct and indirect impacts on the health status of any given region. Susceptibility to climate change is modulated by biological, ecological and socio-political factors such as age, gender, geographic location, socio-economic status, occupation, health status and housing conditions, among other.In the Eastern Mediterranean and Middle East (EMME), climatic factors known to affect human health include extreme heat, water shortages and air pollution. Furthermore, the epidemiology of vector-borne diseases (VBDs) and the health consequences of population displacement are also influenced by climate change in this region.To inform future policies for adaptation and mitigation measures, and based on an extensive review of the available knowledge, we recommend several research priorities for the region. These include the generation of more empirical evidence on exposure-response functions involving climate change and specific health outcomes, the development of appropriate methodologies to evaluate the physical and psychological effects of climate change on vulnerable populations, determining how climate change alters the ecological determinants of human health, improving our understanding of the effects of long-term exposure to heat stress and air pollution, and evaluating the interactions between adaptation and mitigation strategies.Because national boundaries do not limit most climate-related factors expected to impact human health, we propose that adaptation/mitigation policies must have a regional scope, and therefore require collaborative efforts among EMME nations. Policy suggestions include a decisive region-wide decarbonisation, the integration of environmentally driven morbidity and mortality data throughout the region, advancing the development and widespread use of affordable technologies for the production and management of drinking water by non-traditional means, the development of comprehensive strategies to improve the health status of displaced populations, and fostering regional networks for monitoring and controlling the spread of infectious diseases and disease vectors.

Impact of long-term air pollution exposure on incidence of neurodegenerative diseases: A protocol for a systematic review and exposure-response meta-analysis

BackgroundAmbient air pollution is a pervasive and ubiquitous hazard, which has been linked to premature morbidity and a growing number of morbidity endpoints. Air pollution may be linked to neurodegeneration, and via this or other pathways, to neurodegenerative diseases. Emerging evidence suggests that air pollution may contribute to neurodegenerative diseases such as dementia, Parkinson’s Disease (PD), Multiple Sclerosis (MS) and Motor Neuron Diseases (MND), although this evidence remains inconsistent and very limited for MS and MND. In addition, this evidence base is rapidly emerging and would benefit from a wide and critical synthesis, including a better understanding of heterogeneity. ObjectivesIn this paper, we present a protocol for a systematic review and meta-analysis and specify our methods a priori. The main aim of the planned systematic review is to answer the question of whether long-term exposure (>1 year) to ambient (outdoor) air pollution (exposure, compared to lower exposure) increases the risk of adult (population) incidence of neurodegenerative diseases (outcomes) in epidemiological observational studies (study design). Another aim is to meta-analyze the associations between long-term exposure to ambient air pollutants and the risk of the selected outcomes and assess the shape of exposure–response functions. To set the stage for the proposed work, we also overview the existing epidemiological evidence in this protocol, but do not critically evaluate it, as these results will be fully presented in the planned systematic review. Search and study eligibilityWe will search the electronic databases Medline (via Ovid), Embase (via Ovid), Cochrane Library, Cinahl (via Ebscohost), Global Health (via Ebscohost), PsycINFO (via Ebscohost), Scopus, Web of Science (Core Collection), from inception to October 2022. Eligible studies must contain primary research investigating the link between 1-year + exposure to any outdoor air pollutant, from any source, and dementia, PD, MS, and MND, or dementia subtypes: Alzheimer’s Disease, vascular dementia, and mixed dementia. The search strategy and eligibility criteria are pre-determined and described in full in this protocol. Study appraisal and synthesis methodsArticles will be stored and screened using Rayyan QCRI. Title and abstract screening, full text review, data extraction, risk of bias assessment and data preparation for statistical analysis will be conducted independently by two reviewers using pre-defined forms and criteria, described in this protocol. All these steps will also be piloted and the forms and/or methods adapted if issues arise. Meta-analysis and assessment of the shape of the exposure–response functions will be conducted if four independent exposure-outcomes pairs are available, and the remainder of results will be synthesized in the forms of tables and via a narrative summary. Certainty in the body of evidence will be assessed using the OHAT approach. This protocol describes the planned analysis and synthesis a priori and serves to increase transparency and impact of this systematic review and meta-analysis.

Future temperature-related mortality considering physiological and socioeconomic adaptation: a modelling framework

As the climate changes, it is crucial to focus not only on mitigation measures but also on building climate change resilience by developing efficient adaptation strategies. Although population adaptation is a major determinant of future climate-related health burden, it is not well accounted for in studies that project the health impact of climate change. We propose a methodological framework for temperature-related mortality that incorporates two simultaneous adaptation-sensitivity pathways: the physiological pathway, considering both heat adaptation and cold sensitivity, and the socioeconomic pathway, which is influenced by changes in future adaptive capacities. To demonstrate its utility we apply the framework to a case study mortality time-series dataset from Bavaria, Germany. In this modelling framework, we used extrapolated location-specific and age-specific baseline exposure-response functions and propose different future scenarios of cold sensitivity and heat adaptation on the basis of varying slopes of these exposure-response functions. We also incorporated future socioeconomic adaptation in the exposure-response functions using projections of gross domestic product under the respective shared socioeconomic pathways. Future adaptable fractions, representing the deaths avoided under each of the future scenarios, are projected under combinations of two climate change scenarios (shared socioeconomic pathway [SSP]1-2.6 and SSP3-7.0) and the respective plausible population projection scenarios (SSP1 and SSP3), also incorporating the future changes in demographic age structure and mortality. The case study for this framework was done for five districts in Bavaria, for both total non-accidental mortality and cardiovascular disease mortality. The baseline data was obtained for the period 1990-2006, and the future period was defined as 2083-99. In our Bavaria case study, average temperature was projected to increase by 2099 by an average of 1·1°C under SSP1-2.6 and by 4·1°C under SSP3-7.0. We observed the adaptable fraction to be largely influenced by socioeconomic adaptation for both total mortality and cardiovascular disease mortality, and for both climate change scenarios. For example, for total mortality, the highest adaptable fraction of 18·56% (95% empirical CI 10·77-23·67) was observed under the SSP1-2.6 future scenario, in the presence of socioeconomic adaptation and under the highest heat adaptation (10%) provided the cold sensitivity remains 0%. The cold adaptable fraction is lower than the heat adaptable fraction under all scenarios. In the absence of socioeconomic adaptation, population ageing will lead to higher temperature-related mortality. Our developed framework helps to systematically understand the effectiveness of adaptation mechanisms. In the future, socioeconomic adaptation is estimated to play a major role in determining temperature-related excess mortality. Furthermore, cold sensitivity might outweigh heat adaptation in the majority of locations worldwide. Similarly, population ageing is projected to continue to determine future temperature-related mortality. EU Horizon 2020 (EXHAUSTION).

Synergies of carbon neutrality, air pollution control, and health improvement — a case study of China energy interconnection scenario

Climate change and air pollution are primarily caused by the combustion and utilization of fossil fuels. Both climate change and air pollution cause health problems. Based on the development of China, it is extremely important to explore the synergies of the energy transition, CO2 reduction, air pollution control, and health improvement under the target of carbon peaking before 2030 and carbon neutrality before 2060. This study introduces the policy evolution and research progress related to energy, climate change, and the environment in China and proposes a complete energy-climate-air- health mechanism framework. Based on the MESSAGE-GLOBIOM integrated assessment model, emission inventory and chemical transport model, and exposure-response function, a comprehensive assessment method of energy-climate-air- health synergies was established and applied to quantify the impacts of Chinese Energy Interconnection Carbon Neutrality (CEICN) scenario. The results demonstrate that, by 2060, the SO2, NOx and PM2.5 emissions are estimated to be reduced by 91 %, 85 %, and 90 % respectively compared to the business-as-usual (BAU) scenario. The direct health impacts brought by achieving the goal of carbon neutrality will drive the proactive implementation of more emission reduction measures and bring greater benefits to human health.

Benefits of future clean air policies in Europe: Proposed analyses of the mortality impacts of PM2.5 and NO2.

Benefits of future clean air policies in Europe: Proposed analyses of the mortality impacts of PM2.5 and NO2.

The effects of night-time aviation noise exposure on sleep disturbance and annoyance

This paper will report on the methodology for a new UK study of aviation night-noise exposure on health. Funded by the United Kingdom Department of Transport (DfT), the study is examining the effects of aviation night-noise exposure for a range of night-time periods on sleep disturbance and annoyance. The study involves a cross-sectional survey of 4000 participants living near eight UK airports to assess associations of aircraft noise exposure at night and subjective sleep disturbance and annoyance, as well as an objective sleep disturbance study of 170 participants, where physiological assessments of sleep disturbance will be linked to aircraft noise exposure at the participant’s home. The study will deliver exposure-response functions showing how time-averaged metrics such as LAeq,8h, LAeq,1h, N60 relate to subjective and objective sleep disturbance, and annoyance, which could be used to inform updates to the DfT’s Transport Analysis Guidance (TAG) and subsequently aviation night-noise policy in the UK. It will also explore if a relationship for objective sleep disturbance can be estimated for event-related metrics such as LAmax and sound exposure level. Effect modification will be examined; quantifying whether some population groups may be more vulnerable to the effects of aviation night-noise on sleep disturbance and annoyance.

The burden of premature mortality from coal-fired power plants in India is high and inequitable

Prior mortality estimates of air pollution from coal-fired power plants in India use PM2.5 exposure-response functions from settings that may not be representative, and do not include other potentially harmful effects of these plants, such as fly ash pollution and heavy freshwater consumption. We use a national, district level dataset to assess the impact of coal-fired power plants on all-cause mortality (15–69 years) in 2014. We compare districts with coal-fired power plants (total capacity >1000 MW) to districts without a coal-fired power plant, estimating the effect of these power plants on all-cause mortality within districts that have these plants. Out of 597 districts in India in 2014, 60 districts had a coal-fired power plant. When compared to districts without a coal-fired power plant, districts with a coal-fired power plant (>1000 MW) had higher rates of age-standardized mortality in both women (0.38, 95% CI: −0.14–0.90) and men (0.55, −0.17–1.27). Similarly, these districts had higher rates of conditional probability of premature death in both women (2.22, −0.13–4.56) and men (2.12, −0.54–4.77). The point estimates for total excess deaths were 19 320 for women and 27 727 for men. In affected districts, the proportion of premature adult deaths attributable to coal-fired power plants was 5.8% (−0.3%–11.9%) in women and 4.3% (−1.1%–9.6%) in men. We estimate that ∼47 000 premature adult deaths can be attributed to large coal-fired power plants in India in 2014. These deaths are concentrated in the ∼10% of districts that have the nation’s power plants, where they are associated with 1 out of 20 premature adult deaths. Effective regulation of emissions from these plants, coupled with a phaseout of coal-fired power plants, can help decrease this burden of inequitable and premature adult mortality.

Traffic noise in relation to markers of obesity in nine Nordic cohorts

Background and aims: Evidence suggests that transportation noise may increase the risk of obesity. However, there are uncertainties regarding e.g. exposure-response relations, potential threshold effects and interactions, in particular with air pollution. In this study, we aim to assess the exposure-response associations between residential exposure to road traffic noise and Body Mass Index (BMI), overweight and obesity, as well as to investigate interaction with air pollution (PM₂.₅). Methods: The study was based on pooled analyses of nine cohorts from Denmark and Sweden, including 128 000 subjects, recruited 1990–2004. Road traffic noise was calculated as time-weighted average Lden exposure five years prior to the baseline of each cohort. Linear and logistic regression models were used to assess associations with BMI and overweight (BMI &amp;#x2265;25 kg/m²) and obesity (BMI &amp;#x2265;30 kg/m²), respectively, adjusting for individual and area-based demographic, socio-economic, lifestyle and environmental factors. Exposure-response relationships were assessed using restricted cubic splines. Results: In the fully adjusted model, exposure to road traffic noise was associated with an increased BMI of 0.07 kg/m² (95% confidence interval, 95% CI, 0.03–0.10) per 10 dB Lden, as well as with an excess risk of being overweight, odds ratio (OR) 1.03 (95% CI 1.01–1.05), or obese, OR 1.06 (95% CI 1.03–1.09). There appeared to be a threshold around 50–55 dB Lden in the exposure-response relationship for BMI. Furthermore, stronger associations were suggested at levels above the mean PM2.5 (15 µg/m³): 3rd quartile 0.11 kg/m² (95% CI 0.04–0.17) and 4th quartile 0.22 kg/m² (95% CI 0.16–0.28). Conclusions: Preliminary findings from this study indicate positive associations between road traffic noise and BMI as well as with overweight and obesity. Stronger associations appeared at noise levels above 50–55 dB Lden and at higher PM₂.₅ levels. Keywords: Road traffic noise, BMI, overweight, obesity, exposure-response function, interaction

Age standardized heat and cold related mortality impacts across European cities

Background and aim. Heat and cold are important environmental risk factors for mortality with apparent geographical differences in vulnerability. Factors such as the local climate, topology, demography or the socio-economic situation can all affect the mortality burden of heat and cold. This study aims to comprehensively assess of the heat- and cold-related mortality in European cities, including less represented areas such as Scandinavia and Eastern Europe while accounting for the different demographic and vulnerability factors. Methods. We performed a mortality impact assessment related to heat and cold in 801 European cities through a three-stage analysis. First, in a selection of 191 cities, we assessed the exposure-response functions of temperature on mortality for different age groups. Second, these functions were pooled in a meta-analysis model with meta-predictors, including a regional background, age, and 22 vulnerability factors, reduced into a handful of composite indices. Third, this model was used to infer the exposure-response function to the whole 801 city sample, and to compute age-standardized excess mortality rates. Results. Across all examined European cities, we estimated an overall annual age-standardize excess mortality of 116 deaths (95%CI: 104-132) per 100,000 persons due to cold and 15 deaths (95%CI: 13-17) per 100,000 persons due to heat. The burden widely varied across Europe, being lower in western countries and higher in Eastern Europe for both heat and cold. At the same time, southern countries also experienced an important heat-related burden. Conclusions. This study provides a high-resolution assessment of the heat and cold mortality burden across Europe. Results indicate important geographical differences with high vulnerability in the easternmost locations of the continent. These results might give valuable input for designing adaptation policies across Europe. Keywords. Temperature; Mortality; Europe; Meta-analysis; Time series