A systematic review of the epidemiology evidence on talc and cancer.

Over the past several decades, there have been many epidemiology studies on talc and cancer published in the scientific literature, and several reviews and meta-analyses of talc and respiratory, female reproductive, and stomach cancers, specifically. To help provide a resource for the evaluation of talc as a potential human carcinogen, we applied a consistent set of examination methods and criteria for all epidemiology studies that examined the association between talc exposure (by various routes) and cancers (of various types). We identified 30 cohort, 35 case-control, and 12 pooled studies that evaluated occupational, medicinal, and personal-care product talc exposure and cancers of the respiratory system, the female reproductive tract, the gastrointestinal tract, the urinary system, the lymphohematopoietic system, the prostate, male genital organs, and the central nervous system, as well as skin, eye, bone, connective tissue, peritoneal, and breast cancers. We tabulated study characteristics, quality, and results in a systematic manner, and evaluated all cancer types for which studies of at least three unique populations were available in a narrative review. We focused on study quality aspects most likely to impact the interpretation of results. We found that only one study, of medicinal talc use, evaluated direct exposure measurements for any individuals, though some used semi-quantitative exposure metrics, and few studies adequately assessed potential confounders. The only consistent associations were with ovarian cancer in case-control studies and these associations were likely impacted by recall and potentially other biases. This systematic review indicates that epidemiology studies do not support a causal association between occupational, medicinal, or personal talc exposure and any cancer in humans.

Acute exposure to total and source-specific ambient fine particulate matter and risk of respiratory disease hospitalization in Kuwait.

Many epidemiologic studies concerned with acute exposure to ambient PM2.5 have reported positive associations for respiratory disease hospitalization. However, few studies have investigated this relationship in Kuwait and extrapolating results from other regions may involve considerable uncertainty due to variations in concentration levels, particle sources and composition, and population characteristics. Local studies can provide evidence for strategies to reduce risks from episodic exposures to high levels of ambient PM2.5 and generating hypotheses for evaluating health risks from chronic exposures. Therefore, using speciated PM2.5 data from local samplers, we analyzed the impact of daily total and source-specific PM2.5 exposure on respiratory hospitalizations in Kuwait using a case-crossover design with conditional quasi-Poisson regression. Total and source-specific ambient PM2.5 were modeled using 0-5-day cumulative distributed lags. For total PM2.5, we observed a 0.16% (95% confidence interval [CI] = 0.05, 0.27%) increase in risk for respiratory hospitalization per 1 µg/m3 increase in concentration. Of the source factors assessed, dust demonstrated a statistically significant increase in risk (0.16%, 95% CI = 0.04, 0.29%), and the central estimate for regional PM2.5 was positive (0.11%) but not statistically significant (95% CI = -0.11, 0.33%). No effect was observed from traffic emissions and 'other' source factors. When hospitalizations were stratified by sex, nationality, and age, we found that female, Kuwaiti national, and adult groups had higher effect estimates. These results suggest that exposure to ambient PM2.5 is harmful in Kuwait and provide some evidence of differential toxicity and effect modification depending on the PM2.5 source and population affected.

Mortality Attributable to Long-Term Exposure to Ambient Fine Particulate Matter: Insights from the Epidemiologic Evidence for Understudied Locations.

Epidemiologic cohort studies have consistently demonstrated that long-term exposure to ambient fine particles (PM2.5) is associated with mortality. Nevertheless, extrapolating results to understudied locations may involve considerable uncertainty. To explore this issue, this review discusses the evidence for (i) the associated risk of mortality, (ii) the shape of the concentration-response function, (iii) a causal interpretation, and (iv) how the source mix/composition of PM2.5 and population characteristics may alter the effect. The accumulated evidence suggests the following: (i) In the United States, the change in all-cause mortality risk per µg/m3 is about 0.8%. (ii) The concentration-response function appears nonlinear. (iii) Causation is overwhelmingly supported. (iv) Fossil fuel combustion-related sources are likely more toxic than others, and age, race, and income may modify the effect. To illustrate the use of our findings in support of a risk assessment in an understudied setting, we consider Kuwait. However, given the complexity of this relationship and the heterogeneity in reported effects, it is unreasonable to think that, in such circumstances, point estimates can be meaningful. Consequently, quantitative probabilistic estimates, which cannot be derived objectively, become essential. Formally elicited expert judgment can provide such estimates, and this review provides the evidence to support an elicitation.

A retrospective assessment of COVID-19 model performance in the USA.

Coronavirus disease 2019 (COVID-19) forecasts from over 100 models are readily available. However, little published information exists regarding the performance of their uncertainty estimates (i.e. probabilistic performance). To evaluate their probabilistic performance, we employ the classical model (CM), an established method typically used to validate expert opinion. In this analysis, we assess both the predictive and probabilistic performance of COVID-19 forecasting models during 2021. We also compare the performance of aggregated forecasts (i.e. ensembles) based on equal and CM performance-based weights to an established ensemble from the Centers for Disease Control and Prevention (CDC). Our analysis of forecasts of COVID-19 mortality from 22 individual models and three ensembles across 49 states indicates that-(i) good predictive performance does not imply good probabilistic performance, and vice versa; (ii) models often provide tight but inaccurate uncertainty estimates; (iii) most models perform worse than a naive baseline model; (iv) both the CDC and CM performance-weighted ensembles perform well; but (v) while the CDC ensemble was more informative, the CM ensemble was more statistically accurate across states. This study presents a worthwhile method for appropriately assessing the performance of probabilistic forecasts and can potentially improve both public health decision-making and COVID-19 modelling.