A better understanding of links between mental illness and risk of bloodborne infectious disease could inform preventive and therapeutic strategies in individuals with mental illness.
We performed a ...cross-sectional study using the National Health and Nutrition Examination Survey (NHANES) to estimate the seroprevalence of hepatitis B and C in individuals with and without a prior prescription for antipsychotic medications, and to determine whether differences in seroprevalence could be explained by differential distribution in known infection risk factors. Multivariable logistic regression models were used to examine the association between receipt of antipsychotic medication and HBV and HCV seropositivity.
Those who had HBV core antibody had 1.64 (95% CI: 0.89, 3.02) times the odds and those with HCV antibody (anti-HCV) had 3.48 (95% CI: 1.71, 7.09) times the odds of having a prescription for at least one antipsychotic medication compared to those who did not have HBV core antibody or HCV antibody, respectively. While prior antipsychotic receipt was a potent risk marker for HCV seropositivity, risk was explained by adjusting for known bloodborne infection risk factors (adjusted ORs 1.01 95% CI: 0.50, 2.02 and 1.38 95% CI: 0.44, 4.36 for HBV and HCV, respectively).
Prior receipt of antipsychotic medications is a strong predictor of HCV (and to a lesser extent HBV) seropositivity. Treatment with antipsychotic medications should be considered as additional risk markers for individuals who may benefit from targeted prevention, screening, and harm reduction interventions for HCV.
Abstract
Authors aiming to estimate causal effects from observational data frequently discuss 3 fundamental identifiability assumptions for causal inference: exchangeability, consistency, and ...positivity. However, too often, studies fail to acknowledge the importance of measurement bias in causal inference. In the presence of measurement bias, the aforementioned identifiability conditions are not sufficient to estimate a causal effect. The most fundamental requirement for estimating a causal effect is knowing who is truly exposed and unexposed. In this issue of the Journal, Caniglia et al. (Am J Epidemiol. 2019;000(00):000–000) present a thorough discussion of methodological challenges when estimating causal effects in the context of research on distance to obstetrical care. Their article highlights empirical strategies for examining nonexchangeability due to unmeasured confounding and selection bias and potential violations of the consistency assumption. In addition to the important considerations outlined by Caniglia et al., authors interested in estimating causal effects from observational data should also consider implementing quantitative strategies to examine the impact of misclassification. The objective of this commentary is to emphasize that you can’t drive a car with only three wheels, and you also cannot estimate a causal effect in the presence of exposure misclassification bias.
Abstract Objective To discuss possible explanations for the obesity paradox and explore whether the paradox can be attributed to a form of selection bias known as collider stratification bias. Method ...The paper is divided into three parts. First, possible explanations for the obesity paradox are reviewed. Second, a simulated example is provided to describe collider stratification bias and how it could generate the obesity paradox. Finally, an example is provided using data from 17,636 participants in the US National and Nutrition Examination Survey (NHANES III). Generalized linear models were fit to assess the effect of obesity on mortality both in the general population and among individuals with diagnosed cardiovascular disease (CVD). Additionally, results from a bias analysis are presented. Results In the general population, the adjusted risk ratio relating obesity and all-cause mortality was 1.24 (95% CI 1.11, 1.39). Adjusted risk ratios comparing obese and non-obese among individuals with and without CVD were 0.79 (95% CI 0.68, 0.91) and 1.30 (95% CI = 1.12, 1.50), indicating that obesity has a protective association among individuals with CVD. Conclusion Results demonstrate that collider stratification bias is one plausible explanation for the obesity paradox. After conditioning on CVD status in the design or analysis, obesity can appear protective among individuals with CVD.
Abstract Objectives The objectives of this article are to demonstrate that the obesity paradox may be explained by collider stratification bias and to estimate the biasing effects of unmeasured ...common causes of cardiovascular disease (CVD) and mortality on the observed obesity-mortality relationship. Methods We use directed acyclic graphs, regression modeling, and sensitivity analyses to explore whether the observed protective effect of obesity among individuals with CVD can be plausibly attributed to selection bias. Data from the third National Health and Examination Survey was used for the analyses. Results The adjusted total effect of obesity on mortality was a risk difference (RD) of 0.03 (95% confidence interval CI: 0.02, 0.05). However, the controlled direct effect of obesity on mortality among individuals without CVD was RD = 0.03 (95% CI: 0.01, 0.05) and RD = −0.12 (95% CI: −0.20, −0.04) among individuals with CVD. The adjusted total effect estimate demonstrates an increased number of deaths among obese individuals relative to nonobese counterparts, whereas the controlled direct effect shows a paradoxical decrease in morality among obese individuals with CVD. Conclusions Sensitivity analysis demonstrates unmeasured confounding of the mediator-outcome relationship provides a sufficient explanation for the observed protective effect of obesity on mortality among individuals with CVD.
Abstract
Quantitative bias analysis can be used to empirically assess how far study estimates are from the truth (i.e., an estimate that is free of bias). These methods can be used to explore the ...potential impact of confounding bias, selection bias (collider stratification bias), and information bias. Quantitative bias analysis includes methods that can be used to check the robustness of study findings to multiple types of bias and methods that use simulation studies to generate data and understand the hypothetical impact of specific types of bias in a simulated data set. In this article, we review 2 strategies for quantitative bias analysis: 1) traditional probabilistic quantitative bias analysis and 2) quantitative bias analysis with generated data. An important difference between the 2 strategies relates to the type of data (real vs. generated data) used in the analysis. Monte Carlo simulations are used in both approaches, but the simulation process is used for different purposes in each. For both approaches, we outline and describe the steps required to carry out the quantitative bias analysis and also present a bias-analysis tutorial demonstrating how both approaches can be applied in the context of an analysis for selection bias. Our goal is to highlight the utility of quantitative bias analysis for practicing epidemiologists and increase the use of these methods in the epidemiologic literature.
Abstract
Aims
Central adiposity is associated with increased cardiovascular disease (CVD) risk, even among people with normal body mass index (BMI). We tested the hypothesis that regional body fat ...deposits (trunk or leg fat) are associated with altered risk of CVD among postmenopausal women with normal BMI.
Methods and results
We included 2683 postmenopausal women with normal BMI (18.5 to <25 kg/m2) who participated in the Women’s Health Initiative and had no known CVD at baseline. Body composition was determined by dual energy X-ray absorptiometry. Incident CVD events including coronary heart disease and stroke were ascertained through February 2017. During a median 17.9 years of follow-up, 291 incident CVD cases occurred. After adjustment for demographic, lifestyle, and clinical risk factors, neither whole-body fat mass nor fat percentage was associated with CVD risk. Higher percent trunk fat was associated with increased risk of CVD highest vs. lowest quartile hazard ratio (HR) = 1.91, 95% confidence interval (CI) 1.33–2.74; P-trend <0.001, whereas higher percent leg fat was associated with decreased risk of CVD (highest vs. lowest quartile HR = 0.62, 95% CI 0.43–0.89; P-trend = 0.008). The association for trunk fat was attenuated yet remained significant after further adjustment for waist circumference or waist-to-hip ratio. Higher percent trunk fat combined with lower percent leg fat was associated with particularly high risk of CVD (HR comparing extreme groups = 3.33, 95% CI 1.46–7.62).
Conclusion
Among postmenopausal women with normal BMI, both elevated trunk fat and reduced leg fat are associated with increased risk of CVD.
We describe the use of Apisensr, a web-based application that can be used to implement quantitative bias analysis for misclassification, selection bias, and unmeasured confounding. We apply Apisensr ...using an example of exposure misclassification bias due to use of self-reported body mass index (BMI) to define obesity status in an analysis of the relationship between obesity and diabetes.
We used publicly available data from the National Health and Nutrition Examination Survey. The analysis consisted of: (1) estimating bias parameter values (sensitivity, specificity, negative predictive value, and positive predictive value) for self-reported obesity by sex, age, and race-ethnicity compared to obesity defined by measured BMI, and (2) using Apisensr to adjust for exposure misclassification.
The discrepancy between self-reported and measured obesity varied by demographic group (sensitivity range: 75%-89%; specificity range: 91%-99%). Using Apisensr for quantitative bias analysis, there was a clear pattern in the results: the relationship between obesity and diabetes was underestimated using self-report in all age, sex, and race-ethnicity categories compared to measured obesity. For example, in non-Hispanic White men aged 40-59 years, prevalence odds ratios for diabetes were 3.06 (95% confidence inerval = 1.78, 5.30) using self-reported BMI and 4.11 (95% confidence interval = 2.56, 6.75) after bias analysis adjusting for misclassification.
Apisensr is an easy-to-use, web-based Shiny app designed to facilitate quantitative bias analysis. Our results also provide estimates of bias parameter values that can be used by other researchers interested in examining obesity defined by self-reported BMI.
Body mass index (BMI) is a widely used indicator of obesity status in clinical settings and population health research. However, there are concerns about the validity of BMI as a measure of obesity ...in postmenopausal women. Unlike BMI, which is an indirect measure of obesity and does not distinguish lean from fat mass, dual-energy x-ray absorptiometry (DXA) provides a direct measure of body fat and is considered a gold standard of adiposity measurement. The goal of this study is to examine the validity of using BMI to identify obesity in postmenopausal women relative to total body fat percent measured by DXA scan.
Data from 1,329 postmenopausal women participating in the Buffalo OsteoPerio Study were used in this analysis. At baseline, women ranged in age from 53 to 85 years. Obesity was defined as BMI ≥ 30 kg/m and body fat percent (BF%) greater than 35%, 38%, or 40%. We calculated sensitivity, specificity, positive predictive value, and negative predictive value to evaluate the validity of BMI-defined obesity relative BF%. We further explored the validity of BMI relative to BF% using graphical tools, such as scatterplots and receiver-operating characteristic curves. Youden's J index was used to determine the empirical optimal BMI cut-point for each level of BF% defined obesity.
The sensitivity of BMI-defined obesity was 32.4% for 35% body fat, 44.6% for 38% body fat, and 55.2% for 40% body fat. Corresponding specificity values were 99.3%, 97.1%, and 94.6%, respectively. The empirical optimal BMI cut-point to define obesity is 24.9 kg/m for 35% BF, 26.49 kg/m for 38% BF, and 27.05 kg/m for 40% BF according to the Youden's index.
Results demonstrate that a BMI cut-point of 30 kg/m does not appear to be an appropriate indicator of true obesity status in postmenopausal women. Empirical estimates of the validity of BMI from this study may be used by other investigators to account for BMI-related misclassification in older women.
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