Hepatitis C virus in Mexican Americans: a population-based study reveals relatively high prevalence and negative association with diabetes.

This study aimed to estimate the prevalence and risk factors for hepatitis C virus (HCV) infection in Mexican Americans living in South Texas. We tested plasma for the presence of HCV antibody from the Cameron County Hispanic Cohort (CCHC), a randomized, population-based cohort in an economically disadvantaged Mexican American community on the United States/Mexico border with high rates of chronic disease. A weighted prevalence of HCV antibody of 2·3% [n = 1131, 95% confidence interval (CI) 1·2-3·4] was found. Participants with diabetes had low rates of HCV antibody (0·4%, 95% CI 0·0-0·9) and logistic regression revealed a statistically significant negative association between HCV and diabetes (OR 0·20, 95% CI 0·05-0·77) after adjusting for sociodemographic and clinical factors. This conflicts with reported positive associations of diabetes and HCV infection. No classic risk factors were identified, but important differences between genders emerged in analysis. This population-based study of HCV in Mexican Americans suggests that national studies do not adequately describe the epidemiology of HCV in this border community and that unique risk factors may be involved.

Depression in Mexican Americans with diagnosed and undiagnosed diabetes.

BACKGROUND: Depression and diabetes commonly co-occur; however, the strength of the physiological effects of diabetes as mediating factors towards depression is uncertain. METHOD: We analyzed extensive clinical, epidemiological and laboratory data from n = 2081 Mexican Americans aged 35-64 years, recruited from the community as part of the Cameron County Hispanic Cohort (CCHC) divided into three groups: Diagnosed (self-reported) diabetes (DD, n = 335), Undiagnosed diabetes (UD, n = 227) and No diabetes (ND, n = 1519). UD participants denied being diagnosed with diabetes, but on testing met the 2010 American Diabetes Association and World Health Organization definitions of diabetes. Depression was measured using the Center for Epidemiological Studies - Depression (CES-D) scale. Weighted data were analyzed using dimensional and categorical outcomes using univariate and multivariate models. RESULTS: The DD group had significantly higher CES-D scores than both the ND and UD (p ⩽ 0.001) groups, whereas the ND and UD groups did not significantly differ from each other. The DD subjects were more likely to meet the CES-D cut-off score for depression compared to both the ND and UD groups (p = 0.001), respectively. The UD group was also less likely to meet the cut-off score for depression than the ND group (p = 0.003). Our main findings remained significant in models that controlled for socio-demographic and clinical confounders. CONCLUSIONS: Meeting clinical criteria for diabetes was not sufficient for increased depressive symptoms. Our findings suggest that the 'knowing that one is ill' is associated with depressive symptoms in diabetic subjects.

The Effect of Ignoring Statistical Interactions in Regression Analyses Conducted in Epidemiologic Studies: An Example with Survival Analysis Using Cox Proportional Hazards Regression Model.

OBJECTIVE: To demonstrate the adverse impact of ignoring statistical interactions in regression models used in epidemiologic studies. STUDY DESIGN AND SETTING: Based on different scenarios that involved known values for coefficient of the interaction term in Cox regression models we generated 1000 samples of size 600 each. The simulated samples and a real life data set from the Cameron County Hispanic Cohort were used to evaluate the effect of ignoring statistical interactions in these models. RESULTS: Compared to correctly specified Cox regression models with interaction terms, misspecified models without interaction terms resulted in up to 8.95 fold bias in estimated regression coefficients. Whereas when data were generated from a perfect additive Cox proportional hazards regression model the inclusion of the interaction between the two covariates resulted in only 2% estimated bias in main effect regression coefficients estimates, but did not alter the main findings of no significant interactions. CONCLUSIONS: When the effects are synergic, the failure to account for an interaction effect could lead to bias and misinterpretation of the results, and in some instances to incorrect policy decisions. Best practices in regression analysis must include identification of interactions, including for analysis of data from epidemiologic studies.