Gradients in Depressive Symptoms by Socioeconomic Position Among Men Who Have Sex With Men in the EXPLORE Study

ABSTRACT

This study examines gradients in depressive symptoms by socioeconomic position (SEP; i.e., income, education, employment) in a sample of men who have sex with men (MSM). Data were used from EXPLORE, a randomized, controlled behavioral HIV prevention trial for HIV-uninfected MSM in six U.S. cities (n = 4,277). Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression scale (short form). Multiple linear regressions were fitted with interaction terms to assess additive and multiplicative relationships between SEP and depressive symptoms. Depressive symptoms were more prevalent among MSM with lower income, lower educational attainment, and those in the unemployed/other employment category. Income, education, and employment made significant contributions in additive models after adjustment. The employment-income interaction was statistically significant, indicating a multiplicative effect. This study revealed gradients in depressive symptoms across SEP of MSM, pointing to income and employment status and, to a lesser extent, education as key factors for understanding heterogeneity of depressive symptoms.     

Bayesian estimation of log odds ratios over two-way contingency tables with intraclass correlated cells

In this article, a Bayesian approach is proposed for the estimation of log odds ratios and intraclass correlations over a two-way contingency table, including intraclass correlated cells. Required likelihood functions of log odds ratios are obtained, and determination of prior structures is discussed. Hypothesis testing for log odds ratios and intraclass correlations by using the posterior simulations is outlined. Because the proposed approach includes no asymptotic theory, it is useful for the estimation and hypothesis testing of log odds ratios in the presence of certain intraclass correlation patterns. A family health status and limitations data set is analyzed by using the proposed approach in order to figure out the impact of intraclass correlations on the estimates and hypothesis tests of log odds ratios. Although intraclass correlations are small in the data set, we obtain that even small intraclass correlations can significantly affect the estimates and test results, and our approach is useful for the estimation and testing of log odds ratios in the presence of intraclass correlations.     

Does Diesel Exhaust Cause Human Lung Cancer?

Recent reviews of epidemiological evidence on the relation between exposure to diesel exhaust (DE) and lung cancer risk have reached conflicting conclusions, ranging from belief that there is sufficient evidence to conclude that DE is a human lung carcinogen (California EPA, 1994) to conclusions that there is inadequate evidence to support a causal association between DE and human lung cancer (Muscat and Wynder, 1995). Individual studies also conflict, with both increases and decreases in relative risks of lung cancer mortality being cited with 95% statistical confidence. On balance, reports of elevated risk outnumber reports of reduced risk. This paper reexamines the evidence linking DE exposures to lung cancer risk. After briefly reviewing animal data and biological mechanisms, it surveys the relevant epidemiological literature and examines possible explanations for the discrepancies. These explanations emphasize the distinction between statistical associations, which have been found in many studies, and causal associations, which appear not to have been established. Methodological threats to valid causal inference are identified and new approaches for controlling them are proposed using recent techniques from artificial intelligence (AI) and computational statistics. These threats have not been adequately controlled for in previous epidemiological studies. They provide plausible noncausal explanations for the reported increases in relative risks, making it impossible to infer causality between DE exposure and lung cancer risk from these studies. A key contribution is to show how recent techniques developed in the AI-and-statistics literature can help clarify the causal interpretation of complex multivariate data sets used in epidemiological risk assessments. Applied to the key study of Garshick et al. (1988), these methods show that DE concentration has no positive causal association with occupational lung cancer mortality risk.     

Correlation Between Carcinogenic Potency of Chemicals in Animals and Humans

Twenty-three chemicals were selected for comparison of the carcinogenic potencies estimated from epidemiological data to those estimated from animal carcinogenesis bioassays. The chemicals were all those for which reasonably strong evidence of carcinogenicity could be found in humans or animals and for which suitable data could be obtained for quantifying carcinogenic potencies in both humans and animals. Many alternative methods of analyzing the bioassay data were investigated. Almost all of the methods yielded potency estimates that were highly correlated with potencies estimated from epidemiological data; correlations were highly statistically significant (p less than 0.001), with the corresponding correlation coefficients ranging as high as 0.9. These findings provide support for the general use of animal data to evaluate carcinogenic potential in humans and also for the use of animal data to quantify human risk.     

Spatio-temporal modelling using B-spline for disease mapping: analysis of childhood cancer trends

To examine childhood cancer diagnoses in the province of Alberta, Canada during 1983–2004, we construct a generalized additive mixed model for the analysis of geographic and temporal variability of cancer ratios. In this model, spatially correlated random effects and temporal components are adopted. The interaction between space and time is also accommodated. Spatio-temporal models that use conditional autoregressive smoothing across the spatial dimension and B-spline over the temporal dimension are considered. We study the patterns of incidence ratios over time and identify areas with consistently high ratio estimates as areas for potential further investigation. We apply the method of penalized quasi-likelihood to estimate the model parameters. We illustrate this approach using a yearly data set of childhood cancer diagnoses in the province of Alberta, Canada during 1983–2004.     

Gambling,geographical variations and deprivation: findings from the adult psychiatric morbidity survey

Gambling problems are prevalent in the UK, especially in the most deprived boroughs of the country. Individual-level characteristics may exist alongside a social and geographical gradient. The study aimed to establish whether living in specific geographic areas increases problem gambling likelihood. It used data from the 2007 Adult Psychiatric Morbidity Survey. It adopted a twofold categorization distinguishing recreational from problem/pathological gambling. It used the 2004 Overall Index of Multiple Deprivation to measure deprivation of the district of residence, and primary sampling units, based on postcode sectors, to take into account area characteristics in multi-level mixed-effects regression models. The determinants of recreational gambling operated solely at the individual level. These included male sex, stable relationship and employment, though a number of clinical variables were also important: impulsivity, hazardous use or dependency on alcohol, and current smoking. In contrast, an appreciable proportion of the variance in problem/pathological gambling was explained by area-level clustering. Unlike recreational gamblers, problem/pathological gamblers appear to cluster in specific areas. Thus, there are grounds for restricting the location and density of gambling opportunities and for providing selective prevention programmes targeting geographic areas characterized by contextual determinants.     

Estimation of the Leukemia Risk in Human Populations Exposed to Benzene from Tobacco Smoke Using Epidemiological Data

Several epidemiological studies have demonstrated an association between occupational benzene exposure and increased leukemia risk, in particular acute myeloid leukemia (AML). However, there is still uncertainty as to the risk to the general population from exposure to lower environmental levels of benzene. To estimate the excess risk of leukemia from low‐dose benzene exposure, various methods for incorporating epidemiological data in quantitative risk assessment were utilized. Tobacco smoke was identified as one of the main potential sources of benzene exposure and was the focus of this exposure assessment, allowing further investigation of the role of benzene in smoking‐induced leukemia. Potency estimates for benzene were generated from individual occupational studies and meta‐analysis data, and an exposure assessment for two smoking subgroups (light and heavy smokers) carried out. Subsequently, various techniques, including life‐table analysis, were then used to evaluate both the excess lifetime risk and the contribution of benzene to smoking‐induced leukemia and AML. The excess lifetime risk for smokers was estimated at between two and six additional leukemia deaths in 10,000 and one to three additional AML deaths in 10,000. The contribution of benzene to smoking‐induced leukemia was estimated at between 9% and 24% (U_pperCL 14–31%). For AML this contribution was estimated as 11–30% (U_pperCL 22–60%). From the assessments carried out here, it appears there is an increased risk of leukemia from low‐level exposure to benzene and that benzene may contribute up to a third of smoking‐induced leukemia. Comparable results from using methods with varying degrees of complexity were generated.     

The relationship between environmental exposures and hormonal abnormalities in pregnant women: An epidemiological study in Yazd,Iran

Problem

The process of industrialization and lifestyle changes have gradually exposed human ?societies to a larger number of environmental risk factors, which may cause hormonal ?abnormalities and congenital anomalies.

Health and living conditions of Samis compared with other citizens based on representative surveys in three Swedish regions

This is the first general health survey of Samis compared with other Swedes to be based on randomised samples. In three regions, Samis were compared with respondents to the Public Health Investigation (n = 613 Samis and 6,309 respondents). Samis were also compared as to gender and membership in reindeer‐herding Sami villages (SVs). The survey shows that Samis of today have better education, work situation and health, and a healthier lifestyle than other Swedish citizens living in the same regions. There are, however, great differences among the Samis themselves. Members of SVs have weaker finances, and they report having less societal trust and worse health than non‐members do. Male members have lower education, are less involved in social activities and report worse overall health, but do not have a higher incidence of psychiatric problems, than other Samis. Samis, in general, have similar or better health and social situation than non‐Samis, but male SV‐members face greater problems and higher risks than other Samis. Key Practitioner Message: ? Health and welfare authorities in cooperation with the Sami parliament and the Sami villages should develop special strategies to assist the herding communities in culturally sensitive ways.     

Bayesian analysis of seasonal variation when the sample size and the amplitude are small

We propose what appears to be the first Bayesian procedure for the analysis of seasonal variation when the sample size and the amplitude are small. Such data occur often in the medical sciences, where seasonality analyses and environmental considerations can help clarify disease etiologies. The method is explained in terms of a simple physico-geometric setting. We present the Bayesian version of a frequentist test that performs well. Two examples of real data illustrate the procedure's application.