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Estimating health benefits from improvements in ambient air quality requires the characterization of the magnitude and shape of the association between marginal changes in exposure and marginal changes in risk, and its uncertainty. Several attempts have been made to do this, each requiring different assumptions. These include the L o g − L i n e a r (L L) , I n t e g r a t e d E x p o s u r e − Re s p o n s e (I E R) , and G l o b a l E x p o s u r e M o r t a l i t y M o d e l (G E M M). In this paper we develop an improved relative risk model suitable for use in health benefits analysis that incorporates features of existing models while addressing limitations in each model. We model the derivative of the relative risk function within a meta-analytic framework; a quantity directly applicable to benefits analysis, incorporating a F u s i o n of algebraic functions used in previous models. We assume a constant derivative in concentration over low exposures, like the L L model, a declining derivative over moderate exposures observed in cohort studies, and a derivative declining as the inverse of concentration over high global exposures in a similar manner to the G E M M. The model properties are illustrated with examples of fitting it to data for the six specific causes of death previously examined by the G l o b a l B u r d e n o f D i s e a s e program with ambient fine particulate matter (P M 2.5). In a test case analysis assuming a 1% (benefits analysis) or 100% (burden analysis), reduction in country-specific fine particulate matter concentrations, corresponding estimated global attributable deaths using the Fusion model were found to lie between those of the I E R and L L models, with the G E M M estimates similar to those based on the L L model. ∙ Fine particulate air pollution is one of the leading causes of death globally. ∙ Risk models relating exposure and disease are required to estimate health burden. ∙ Current risk models (IER, Log-Linear, GEMM) have limitations extrapolating risk either above or below observed health bases studies. ∙ A new risk model, based on the fusion of existing models, is proposed to address these limitations. ∙ The magnitude of predicted deaths using the Fusion model is in the middle of estimates from the Log-Linear and IER models. [ABSTRACT FROM AUTHOR]

The questioned link between air pollution and coronavirus disease 2019 (COVID-19) spreading or related mortality represents a hot topic that has immediately been regarded in the light of divergent views. A first "school of thought" advocates that what matters are only standard epidemiological variables (i.e. frequency of interactions in proportion of the viral charge). A second school of thought argues that co-factors such as quality of air play an important role too. We analyzed available literature concerning the link between air quality, as measured by different pollutants and a number of COVID-19 outcomes, such as number of positive cases, deaths, and excess mortality rates. We reviewed several studies conducted worldwide and discussing many different methodological approaches aimed at investigating causality associations. Our paper reviewed the most recent empirical researches documenting the existence of a huge evidence produced worldwide concerning the role played by air pollution on health in general and on COVID-19 outcomes in particular. These results support both research hypotheses, i.e. long-term exposure effects and short-term consequences (including the hypothesis of particulate matter acting as viral "carrier") according to the two schools of thought, respectively. The link between air pollution and COVID-19 outcomes is strong and robust as resulting from many different research methodologies. Policy implications should be drawn from a "rational" assessment of these findings as "not taking any action" represents an action itself. [ABSTRACT FROM AUTHOR]

Much of the key epidemiological evidence that long-term exposure to fine particulate matter air pollution (PM 2.5) contributes to increased risk of mortality comes from survival studies of cohorts of individuals. Although the first two of these studies, published in the mid-1990s, were highly controversial, much has changed in the last 25 + years. The objectives of this paper are to succinctly compile and summarize the findings of these cohort studies using meta-analytic tools and to address several of the key controversies. Independent reanalysis and substantial extended analysis of the original cohort studies have been conducted and many additional studies using a wide variety of cohorts, including cohorts constructed from public data and leveraging natural experiments have been published. Meta-analytic estimates of the mean of the distribution of effects from cohort studies that are currently available, provide substantial evidence of adverse air pollution associations with all-cause, cardiopulmonary, and lung cancer mortality. • Cohort studies link PM 2.5 air pollution with excess mortality risk. • 25+ years of re-analyses, extended analyses, many replicative analyses. • Evidence from many studies from North America, Europe, and Asia. • Meta-estimates indicate robust PM 2.5 -mortality associations with heterogeneity. • PM 2.5 associated with all-cause, cardiopulmonary and lung-cancer mortality. [ABSTRACT FROM AUTHOR]

Air pollutants are suggested to be related to type 2 diabetes (T2D). Since several high quality papers on air pollutants and T2D have been published beyond the last reviews, an extended systematic review is highly warranted. We review epidemiological studies to quantify the association between air pollutants and T2D, and to answer if diabetes patients are more vulnerable to air pollutants. We systematically reviewed the databases of PubMed and Web of Science based on the guidelines of the Preferred Reporting Items for Systematic review and Meta-analysis (PRISMA). We calculated odds ratios (OR) or hazard ratios (HR) and their 95% confidence intervals (CI) to assess the strength of the associations between air pollutants [e.g., particulate matter with diameter ≤ 2.5 μm (PM 2.5), particulate matter with diameter ≤ 10 μm (PM 10), and nitrogen dioxide (NO 2)] and T2D. We evaluated the quality and risk of bias of the included studies and graded the credibility of the pooled evidence using several recommended tools. We also performed sensitivity analysis, meta-regression analysis, and publication bias test. Out of 716 articles identified, 86 were used for this review and meta-analysis. Meta-analyses showed significant associations of PM 2.5 with T2D incidence (11 studies; HR = 1.10, 95% CI = 1.04–1.17 per 10 μg/m3 increment; I2 = 74.4%) and prevalence (11 studies; OR = 1.08; 95% CI = 1.04–1.12 per 10 μg/m3 increment; I2 = 84.3%), of PM 10 with T2D prevalence (6 studies; OR = 1.10; 95% CI = 1.03–1.17 per 10 μg/m3 increment; I2 = 89.5%) and incidence (6 studies; HR = 1.11; 95% CI = 1.00–1.22 per μg/m3 increment; I2 = 70.6%), and of NO 2 with T2D prevalence (11 studies; OR = 1.07; 95% CI = 1.04–1.11 per 10 μg/m3 increment; I2 = 91.1%). The majority of studies on glucose-homoeostasis markers also showed increased risks with higher air pollutants levels, but the studies were too heterogeneous for meta-analysis. Overall, patients with diabetes might be more vulnerable to PM. Recent publications strengthened the evidence for adverse effects of ambient air pollutants exposure (especially for PM) on T2D and that diabetic patients might be more vulnerable to air pollutants exposure. • Evidence on ambient air pollution and diabetes is still inconsistent. • We reviewed 86 studies on air pollution and diabetes and its traits. • We meta-analyzed 29 studies for PM and NO2 with diabetes. • Air pollutants (e.g., PM and NO2) are associated with higher odds of diabetes. • Air pollution is associated with dysregulation of glucose metabolism. [ABSTRACT FROM AUTHOR]