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Lung cancer is the most common malignancy in the United States and is ranked second only to bladder cancer in the proportion of cases thought to be due to occupational exposures. We review the epidemiology of occupational lung cancer, focusing on agents identified as pulmonary carcinogens by the International Agency for Research on Cancer. We derive estimates of overall relative risks from the major studies of these lung carcinogens, and we also provide estimates of the number of exposed workers. Using our data as well as estimates from other authors, we estimate that approximately 9,000–10,000 men and 900–1,900 women develop lung cancer annually in the United States due to past exposure to occupational carcinogens. More than half of these lung cancers are due to asbestos. This estimate is likely conservative, in that we have restricted our analysis to confirmed lung carcinogens and have ignored occupations with documented excess risk but for which the specific agents are unknown. Also, our estimate of the proportion of workers exposed in the past is probably too low. Our estimate should be viewed only as a broad approximation. Nevertheless, it is in line with other estimates by authors using different methods. The current number of cases estimated to be due to occupational exposures reflects past high exposures and is likely to drop in the future, unless other occupational lung carcinogens are confirmed or new carcinogens are introduced into the workplace. (This article is a US Government work and, as such, is in the public domain in the United States of America.) © 1996 Wiley‐Liss, Inc.

Abstract. Many previous studies have suggested a link between alcohol outlets and assaultive violence. In this paper, we evaluate the impact of the "1992 Civil Unrest" in Los Angeles (which followed the "Rodney King incident"), in which many alcohol outlets were damaged leading to a decrease in alcohol outlet density, on crime. We leverage the natural experiment created by the closure of alcohol outlets in certain areas and not others to explore the effects of alcohol availability on assault crimes at the census tract level. We develop a hierarchical model that controls for important covariates (such as race and socio-economic status) and accounts for unexplained spatial and temporal variability. While our model is somewhat complex, its hierarchical Bayesian analysis is accessible via the WinBUGS software. Our results show that, keeping other effects fixed, the reduction in alcohol availability within a census tract was associated with a drop in the assaultive violence rate at the census tract level. Comparing several dual candidate changepoint models using the Deviance Information Criterion, the drop in assaultive violence rate is best seen as having occurred one year after the reduction in alcohol availability, with the effect lasting roughly five years. We also create maps of the fitted assault rates in Los Angeles, as well as spatial residual maps that suggest various spatially-varying covariates are still missing from our model.

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Background Given the growing availability of multilevel data from national surveys, researchers interested in contextual effects may find themselves with a small number of individuals per group. Although there is a growing body of literature on sample size in multilevel modeling, few have explored the impact of group size < 5. Methods In a simulated analysis of real data, we examined the impact of group size < 5 on both a continuous and dichotomous outcome in a simple two-level multilevel model. Models with group sizes 1 to 5 were compared to models with complete data. Four different linear and logistic models were examined: empty models, models with a group-level covariate, models with an individual-level covariate, and models with an aggregated group-level covariate. We further evaluated whether the impact of small group size differed depending on the total number of groups. Results When the number of groups was large (N=459), neither fixed nor random components were affected by small group size, even when 90% of tracts had only 1 individual per tract and even when an aggregated group -level covariate was examined. As the number of groups decreased, the standard error estimates of both fixed and random effects were inflated. Furthermore, group-level variance estimates were more affected than were fixed components. Conclusions Datasets where there are a small to moderate number of groups with the majority very small group size (n < 5) size may fail to find or even consider a group-level effect when one may exist and also may be under-powered to detect fixed effects.