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This paper focuses on clarifying the relationship between noise exposure and the prevalence of highly annoyed people due to transportation noise in Japan. The authors accumulated 34 datasets, which were provided by Socio-Acoustic Survey Data Archive and derived from the other surveys conducted in Japan. All the datasets include the following micro-data: demographic factors, exposure, and annoyance data associated with specific noise sources. We performed secondary analyses using micro-data and established the relationships between noise exposure (Lden) and the percentage of highly annoyed people (%HA) for the following noise source: road traffic, conventional railway, Shinkansen railway, civil aircraft, and military aircraft noises. Among the five transportation noises, %HA for the military aircraft noise is the highest, followed by civil aircraft noise and Shinkansen railway noise. The %HA for conventional railway noise was higher than that for road traffic noise. To validate the representativeness of the exposure–response curves, we have discussed factors affecting the difference in annoyance. In addition, comparing the Japanese relationship with that shown in the “Environmental Noise Guidelines for the European Region,” we revealed that Japanese annoyance is higher than the WHO-reported annoyance.

Background Estimating the health effects of multi-pollutant mixtures is of increasing interest in environmental epidemiology. Recently, a new approach for estimating the health effects of mixtures, Bayesian kernel machine regression (BKMR), has been developed. This method estimates the multivariable exposure-response function in a flexible and parsimonious way, conducts variable selection on the (potentially high-dimensional) vector of exposures, and allows for a grouped variable selection approach that can accommodate highly correlated exposures. However, the application of this novel method has been limited by a lack of available software, the need to derive interpretable output in a computationally efficient manner, and the inability to apply the method to non-continuous outcome variables. Methods This paper addresses these limitations by (i) introducing an open-source software package in the R programming language, the bkmr R package, (ii) demonstrating methods for visualizing high-dimensional exposure-response functions, and for estimating scientifically relevant summaries, (iii) illustrating a probit regression implementation of BKMR for binary outcomes, and (iv) describing a fast version of BKMR that utilizes a Gaussian predictive process approach. All of the methods are illustrated using fully reproducible examples with the provided R code. Results Applying the methods to a continuous outcome example illustrated the ability of the BKMR implementation to estimate the health effects of multi-pollutant mixtures in the context of a highly nonlinear, biologically-based dose-response function, and to estimate overall, single-exposure, and interactive health effects. The Gaussian predictive process method led to a substantial reduction in the runtime, without a major decrease in accuracy. In the setting of a larger number of exposures and a dichotomous outcome, the probit BKMR implementation was able to correctly identify the variables included in the exposure-response function and yielded interpretable quantities on the scale of a latent continuous outcome or on the scale of the outcome probability. Conclusions This newly developed software, integrated suite of tools, and extended methodology makes BKMR accessible for use across a broad range of epidemiological applications in which multiple risk factors have complex effects on health.

The recent proliferation of cohort studies of long-term exposure to outdoor fine particulate air pollution and mortality has led to a significant increase in knowledge about this important global health risk factor. As scientific knowledge has grown, mortality relative risk estimators for fine particulate matter have evolved from simple risk models based on a single study to complex, computationally intensive, integration of multiple independent particulate sources based on nearly one hundred studies. Since its introduction nearly 10 years ago, the integrated exposure-response (IER) model has become the state-of-the art model for such estimates, now used by the Global Burden of Disease Study (GBD), the World Health Organization, the World Bank, the United States Environmental Protection Agency’s benefits assessment software, and scientists worldwide to estimate the burden of disease and examine strategies to improve air quality at global, national, and sub-national scales for outdoor fine particulate air pollution, secondhand smoke, and household pollution from heating and cooking. With each yearly update of the GBD, estimates of the IER continue to evolve, changing with the incorporation of new data and fitting methods. As the number of outdoor fine particulate air pollution cohort studies has grown, including recent estimates of high levels of fine particulate pollution in China, new estimators based solely on outdoor fine particulate air pollution evidence have been proposed which require fewer assumptions than the IER and yield larger relative risk estimates. This paper will discuss the scientific and technical issues analysts should consider regarding the use of these methods to estimate the burden of disease attributable to outdoor fine particulate pollution in their own settings.

Regression models have been in use for decades to explore and quantify the association between a dependent response and several independent variables in environmental sciences, epidemiology and public health. However, researchers often encounter situations in which some independent variables exhibit high bivariate correlation, or may even be collinear. Improper statistical handling of this situation will most certainly generate models of little or no practical use and misleading interpretations. By means of two example studies, we demonstrate how diagnostic tools for collinearity or near-collinearity may fail in guiding the analyst. Instead, the most appropriate way of handling collinearity should be driven by the research question at hand and, in particular, by the distinction between predictive or explanatory aims.

Background: The impact of environmental pollution (such as air pollution) on health costs has received a great deal of global attention in the last 20 years. Methods: This review aims to summarize the theoretical analysis model of air pollution affecting health costs, and further explore the actual characteristics of the impact of air pollution on health costs. The following main databases were taken into account: Web of Science Core Collection, Medline, SCOPUS, PubMed, and CNKI (China). As of 30 March 2021, we retrieved a total of 445 papers and ended up with 52 articles. Results: This review mainly expounds clarification of the concept of air pollution and health costs, the theoretical model and the actual characteristics of air pollution affecting health costs. In addition, it also discusses other related factors affecting health costs. Conclusion: Our conclusion is that, while academic research on the relationship between air pollution and health costs has made some progress, there are still some shortcomings, such as insufficient consideration of individual avoidance behavior and rural–urban and international mobility. Therefore, the simple use of the original data obtained in the statistical yearbook of the health cost caused by air pollution is also the reason for the errors in the empirical results. In addition, the choice of proxy variables of environmental pollution by scholars is relatively simple, mainly focusing on air pollutants, while the impact of water quality or soil pollution safety on health costs is becoming increasingly prominent, and will become the focus of future research.

BackgroundPost hoc analyses from the GENERATION HD1 study (NCT03761849) suggested that 120 mg of tominersen every 16 weeks (Q16W) may benefit younger adult patients with lower disease burden.AimTo use population pharmacokinetics (popPK), population pharmacokinetic/pharmacodynamic (popPKPD) and exposure-response analyses to support tominersen Phase II dose selection.Methods/TechniquesPopPK and popPKPD models were developed based on pooled data from 750 patients from five clinical studies, with doses ranging from 10–120 mg administered every 4 or 8 weeks or Q16W. The models were used to predict individual average cerebrospinal fluid (CSF) tominersen concentrations (Cav,SS) and average mutant Huntingtin protein (mHTT) reduction (mHTTav,SS) at steady state. A principal stratum analysis (PSA) investigated the relationship between model-predicted individual parameters and clinical benefit for GENERATION HD1 patients receiving 120 mg Q16W.Results/OutcomePSA analyses suggested that low tominersen exposure (Cav,SS<2.51 µg/mL) may benefit younger adult patients with lower disease burden. Based on Cav,SS simulations using the popPK model, patients aged 25–50 years with a CAG age product score of 400–500 receiving 100 mg and 60 mg Q16W are expected to achieve a median Cav,SS of 2.00 µg/mL and 1.20 µg/mL, respectively.ConclusionsQ16W 100 mg and 60 mg dosing regimens are proposed to explore the potential benefits of tominersen in a new Phase II study in younger adult patients with less disease burden, by targeting anticipated efficacious CSF exposure and mHTT lowering ranges, and will further characterise the lower limit of therapeutic range of mHTT reduction.

Background: This paper describes a systematic review and meta-analyses on effects of environmental noise on annoyance. The noise sources include aircraft, road, and rail transportation noise as well as wind turbines and noise source combinations. Objectives: Update knowledge about effects of environmental noise on people living in the vicinity of noise sources. Methods: Eligible were published studies (2000–2014) providing comparable acoustical and social survey data including exposure-response functions between standard indicators of noise exposure and standard annoyance responses. The systematic literature search in 20 data bases resulted in 62 studies, of which 57 were used for quantitative meta-analyses. By means of questionnaires sent to the study authors, additional study data were obtained. Risk of bias was assessed by means of study characteristics for individual studies and by funnel plots to assess the risk of publication bias. Main Results: Tentative exposure-response relations for percent highly annoyed residents (%HA) in relation to noise levels for aircraft, road, rail, wind turbine and noise source combinations are presented as well as meta-analyses of correlations between noise levels and annoyance raw scores, and the OR for increase of %HA with increasing noise levels. Quality of evidence was assessed using the GRADE terminology. The evidence of exposure-response relations between noise levels and %HA is moderate (aircraft and railway) or low (road traffic and wind turbines). The evidence of correlations between noise levels and annoyance raw scores is high (aircraft and railway) or moderate (road traffic and wind turbines). The evidence of ORs representing the %HA increase by a certain noise level increase is moderate (aircraft noise), moderate/high (road and railway traffic), and low (wind turbines). Strengths and Limitations: The strength of the evidence is seen in the large total sample size encompassing the included studies (e.g., 18,947 participants in aircraft noise studies). Main limitations are due to the variance in the definition of noise levels and %HA. Interpretation: The increase of %HA in newer studies of aircraft, road and railway noise at comparable Lden levels of earlier studies point to the necessity of adjusting noise limit recommendations. Funding: The review was funded by WHO Europe.

Dose–response relationships reflect the effects of a substance on organisms, and are widely used in broad research areas, from medicine and physiology, to vector control and pest management in agronomy. Furthermore, reporting on the response of organisms to stressors is an essential component of many public policies (e.g. public health, environment), and assessment of xenobiotic responses is an integral part of World Health Organization recommendations. Building upon an R script that we previously made available, and considering its popularity, we have now developed a software package in the R environment, BioRssay , to efficiently analyze dose–response relationships. It has more user-friendly functions and more flexibility, and proposes an easy interpretation of the results. The functions in the BioRssay package are built on robust statistical analyses to compare the dose/exposure–response of various bioassays and effectively visualize them in probit-graphs. Graphical Abstract

We used advanced model-based methods to characterize the relationship between individual rifampicin exposure and antituberculosis treatment response. With data from a trial investigating high-dose rifampicin, a significant relation could be derived, and the clinical impact of increased doses was predicted. Abstract Background Tuberculosis remains a huge public health problem and the prolonged treatment duration obstructs effective tuberculosis control. Higher rifampicin doses have been associated with better bactericidal activity, but optimal dosing is uncertain. This analysis aimed to characterize the relationship between rifampicin plasma exposure and treatment response over 6 months in a recent study investigating the potential for treatment shortening with high-dose rifampicin. Methods Data were analyzed from 336 patients with pulmonary tuberculosis (97 with pharmacokinetic data) treated with rifampicin doses of 10, 20, or 35 mg/kg. The response measure was time to stable sputum culture conversion (TSCC). We derived individual exposure metrics with a previously developed population pharmacokinetic model of rifampicin. TSCC was modeled using a parametric time-to-event approach, and a sequential exposure-response analysis was performed. Results Higher rifampicin exposures increased the probability of early culture conversion. No maximal limit of the effect was detected within the observed range. The expected proportion of patients with stable culture conversion on liquid medium at week 8 was predicted to increase from 39% (95% confidence interval, 37%-41%) to 55% (49%-61%), with the rifampicin area under the curve increasing from 20 to 175 mg/L·h (representative for 10 and 35 mg/kg, respectively). Other predictors of TSCC were baseline bacterial load, proportion of culture results unavailable, and substitution of ethambutol for either moxifloxacin or SQ109. Conclusions Increasing rifampicin exposure shortened TSCC, and the effect did not plateau, indicating that doses >35 mg/kg could be yet more effective. Optimizing rifampicin dosage while preventing toxicity is a clinical priority.