Exposure Enriched Case-Control (EECC) Design for the Assessment of Gene-Environment Interaction.

Genetic susceptibility and environmental exposure both play an important role in the aetiology of many diseases. Case-control studies are often the first choice to explore the joint influence of genetic and environmental factors on the risk of developing a rare disease. In practice, however, such studies may have limited power, especially when susceptibility genes are rare and exposure distributions are highly skewed. We propose a variant of the classical case-control study, the exposure enriched case-control (EECC) design, where not only cases, but also high (or low) exposed individuals are oversampled, depending on the skewness of the exposure distribution. Of course, a traditional logistic regression model is no longer valid and results in biased parameter estimation. We show that addition of a simple covariate to the regression model removes this bias and yields reliable estimates of main and interaction effects of interest. We also discuss optimal design, showing that judicious oversampling of high/low exposed individuals can boost study power considerably. We illustrate our results using data from a study involving arsenic exposure and detoxification genes in Bangladesh.

Spatial regression with covariate measurement error: A semiparametric approach.

Spatial data have become increasingly common in epidemiology and public health research thanks to advances in GIS (Geographic Information Systems) technology. In health research, for example, it is common for epidemiologists to incorporate geographically indexed data into their studies. In practice, however, the spatially defined covariates are often measured with error. Naive estimators of regression coefficients are attenuated if measurement error is ignored. Moreover, the classical measurement error theory is inapplicable in the context of spatial modeling because of the presence of spatial correlation among the observations. We propose a semiparametric regression approach to obtain bias-corrected estimates of regression parameters and derive their large sample properties. We evaluate the performance of the proposed method through simulation studies and illustrate using data on Ischemic Heart Disease (IHD). Both simulation and practical application demonstrate that the proposed method can be effective in practice.

Cumulative violence exposure and self-rated health: longitudinal study of adolescents in the United States.

OBJECTIVE: The goal was to determine whether cumulative exposure to violence in childhood and adolescence contributes to disparities in self-rated health among a nationally representative sample of US adolescents. METHODS: The National Longitudinal Survey of Youth 1997 is an ongoing, 8-year (1997-2004), longitudinal, cohort study of youths who were 12 to 18 years of age at baseline (N = 8224). Generalized estimating equations were constructed to investigate the relationship between cumulative exposure to violence and risk for poor health. RESULTS: At baseline, 75% of subjects reported excellent or very good health, 21.5% reported good health, and 4.5% reported fair or poor health. Cumulative violence exposures (witnessed gun violence, threat of violence, repeated bullying, perceived safety, and criminal victimization) were associated with a graded increase in risk for poor health and reduced the strength of the relationship between household income and poor health. In comparison with subjects with no violence exposure, risk for poor self-rated health was 4.6 times greater among subjects who reported >or=5 forms of cumulative exposure to violence, controlling for demographic features and household income. Trend analysis revealed that, for each additional violence exposure, the risk of poor health increased by 38%. Adjustment for alcohol use, drug use, smoking, depressive symptoms, and family and neighborhood environment reduced the strength of the relationships between household income and cumulative exposure to violence scores and poor self-rated health, which suggests partial mediation of the effects of socioeconomic status and cumulative exposure to violence by these factors. CONCLUSIONS: In this nationally representative sample, social inequality in risk for poor self-rated health during the transition from adolescence to adulthood was partially attributable to disparities in cumulative exposure to violence. A strong graded association was noted between cumulative exposure to violence and poor self-rated health in adolescence and young adulthood.

Using exposure prediction rules for exposure assessment: an example on whole-body vibration in taxi drivers.

BACKGROUND: It is often difficult and expensive to make direct measurements of an individual's occupational or environmental exposures in large epidemiologic studies. METHODS: In this study, we used information collected in validation studies to develop a prediction rule for assessing exposure in a study with no direct measurement. We established a prediction rule through mixed-effect modeling of direct measurement data and information on observable exposure predictors and their interactions. Specifically, we used 383 measures of whole-body vibration from 247 professional taxi drivers and attempted to quantify vibration exposures for individuals in a large study on low back pain. RESULTS: Using the "jackknife method," we found that our prediction rule had an acceptably low relative prediction error of 11% (95% confidence interval-10-12%). Implementing the prediction rule would result in measurement errors independent of low back pain and of all identified and observable predictors of whole-body vibration. We applied the predicted levels to compute each person's daily exposure, and found a strong association between the predicted daily whole-body vibration exposure and prevalence of low back pain. This supported the construct validity of the exposure prediction rule. CONCLUSIONS: The predictive and construct validity of our prediction rule suggests that this general statistical approach can be useful in other occupational settings to improve the quality of exposure assessment.