Epidemiology
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Propensity scores are useful for confounding adjustment in the commonly observed setting of many potential confounders, frequent exposure, and rare events. However, with few exposed outcomes to inform covariate selection and many candidate confounders, optimal approaches to construct and implement propensity-score-based confounding adjustment remain unclear. ⋯ The high-dimensional propensity-score algorithm complements expert knowledge for confounding adjustment, but in settings with few exposed outcomes, its performance without investigator-specified covariates is less clear and may be associated with an increased likelihood of bias. In our example, investigator specification of variables combined with high-dimensional propensity-score empirical selection and the use of trimmed propensity-score-stratified analysis seem to improve effect estimation. Plotting the relation of effect estimates to the increasing number of empirical covariates is a useful diagnostic.
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Health behaviors may contribute to socioeconomic inequalities in mortality, although the extent of such contribution remains unclear. We assessed the extent to which smoking, alcohol consumption, and physical inactivity have mediated the association between socioeconomic status (SES) and all-cause mortality in a representative sample of US adults. ⋯ The distribution of health-damaging behaviors may explain a substantial proportion of excess mortality associated with low SES in the United States, suggesting the importance of social inequalities in unhealthy behaviors.
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There is concern about potential effects of radiofrequency fields generated by mobile phones on cancer risk. Most previous studies have found no association between mobile phone use and acoustic neuroma, although information about long-term use is limited. ⋯ The findings do not support the hypothesis that long-term mobile phone use increases the risk of acoustic neuroma. The study suggests that phone use might increase the likelihood that an acoustic neuroma case is detected and that there could be bias in the laterality analyses performed in previous studies.