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Purpose: To describe the methodological aspects and characteristics of the participants of the EPOCA survey.Methods: The study was conducted with schoolchildren aged between seven to 14 years old from 30 schools in Florianópolis, Southern Brazil. Body mass, height, girths, and skinfold thicknesses were measured. Food consumption and physical activity from the previous day were self-reported using the validated Web-CAAFE questionnaire. Adolescents completed a specific questionnaire about physical activity, meal consumption, and weight control behaviors. Parents/guardians responded to a sociodemographic and habits questionnaire.Results: A total of 1671 schoolchildren participated in the study (response rate: 27.2%). About 63% of schoolchildren were enrolled in public schools. Most studied in the morning shift (54.2%), were female (53.1%) and aged between seven and 10 years (58.1%). The prevalence of overweight was 33.7% and obesity was 11.3%.Conclusions: The data obtained will allow us to assess the trend in the prevalence of overweight and obesity and associated factors when compared to other surveys performed. Descriptions of the logistics and protocols can help in the development and improvement of similar studies. It is hoped that the results of EPOCA 2018/2019 may help in the design of obesity prevention policies and programs for this population. [ABSTRACT FROM AUTHOR]

Purpose: Passively generated cell-phone location ("mobility") data originally intended for commercial use has become frequently used in epidemiologic research, notably during the COVID-19 pandemic to study the impact of physical-distancing recommendations on aggregate population behavior (e.g., average daily mobility). Given the opaque nature of how individuals are selected into these datasets, researchers have cautioned that their use may give rise to selection bias, yet little guidance exists for assessing this potential threat to validity in mobility-data research. Through an example analysis of cell-phone-derived mobility data, we present a set of conditions to guide the assessment of selection bias in measures comparing aggregate mobility patterns over time and between groups.Methods: We specifically consider bias in measures comparing group-level mobility in the same group (difference, ratio, percent difference) and between groups (difference in differences, ratio of ratios, ratio of percent differences). We illustrate no-bias conditions in these measures through an example comparing block-group-level mobility between income groups in United States metro areas before (January 1st-March 10, 2020) and after (March 11th-April 19th, 2020) the day COVID-19 was declared a pandemic.Results: Within-group contrasts describing mobility over time, especially for the higher-income decile, were expected to be most resistant to bias during the example study period.Conclusions: The presented conditions can be used to assess the susceptibility to selection bias of group-level measures comparing mobility. Importantly, they can be used even without knowledge of the degree of bias in each group at each time point. We further highlight links between no-bias principles originating in epidemiology and economics, showing that certain assumptions (e.g., parallel trends) can apply to biases beyond their original application. [ABSTRACT FROM AUTHOR]

Purpose: A growing area of research in epidemiology is the identification of health-related sibling spillover effects, or the effect of one individual's exposure on their sibling's outcome. The health within families may be confounded by unobserved factors, rendering identification of sibling spillovers challenging.Methods: We demonstrate a gain-score (fixed effects) regression method for identifying exposure-to-outcome spillover effects within sibling pairs in linear models. The method identifies the exposure-to-outcome spillover effect if only one sibling's exposure affects the other's outcome, and it identifies the difference between the spillover effects if both siblings' exposures affect the others' outcomes. The method fails with outcome-to-exposure spillover or with outcome-to-outcome spillover. Analytic results, Monte Carlo simulations, and a brief application demonstrate the method and its limitations.Results: We estimate the spillover effect of a child's preterm birth on an older sibling's literacy skills, measured by the Phonological Awareness Literacy Screening-Kindergarten test. We analyze 20,010 sibling pairs from a population-wide, Wisconsin-based (United States) birth cohort. Without covariate adjustment, we estimate that preterm birth modestly decreases an older sibling's test score.Conclusions: Gain-scores are a promising strategy for identifying exposure-to-outcome spillover effects in sibling pairs while controlling for sibling-invariant unobserved confounding. [ABSTRACT FROM AUTHOR]

Purpose: Coefficients of determination (R2) for continuous longitudinal data are typically reported as time constant, if they are reported at all. The widely used mixed model with random intercepts and slopes yields the total outcome variance as a time-varying function. We propose a generalized and intuitive approach based on this variance function to estimate the time-varying predictive power (R2) of a variable on outcome levels and changes.Methods: Using longitudinal estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease in Children Study, linear mixed models characterized the R2 for two chronic kidney disease (CKD) risk factors measured at baseline: a traditional marker (proteinuria) and a novel marker (fibroblast growth factor 23 [FGF23]).Results: Time-varying R2 divulged different disease processes by risk factor and diagnoses. Among children with glomerular CKD, time-varying R2 for proteinuria had significant upward trends, suggesting increasing power to predict eGFR change, but crossed with FGF23, which was higher up to 2.5 years from baseline. In contrast, among those with nonglomerular CKD, proteinuria explained more than FGF23 at all times, and time-varying R2 for each risk factor was not substantially different from time-constant estimates.Conclusions: Proteinuria and FGF23 explained substantial eGFR variability over time. Time-varying R2 can characterize predictive roles of risk factors on disease progression, overcome limitations of time-constant estimates, and are easily derived from mixed effects models. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to describe time trends in response rates in case-control studies of cancer and identify study design factors that influence response rate.Methods: We reviewed 370 case-control studies of cancer published in 12 journals during indicator years in each of the last four decades. We estimated time trends of response rates and reasons for nonresponse in each of the following types of study subjects: cases, medical source controls, and population controls. We also estimated response rates according to characteristics of study context.Results: Median response rates among cases and population controls were between 75% and 80% in the 1970s. Between 1971 and 2010, study response rates declined by 0.31% per year for cases and 0.78% for population controls. Only a minority of studies reported reasons for nonparticipation; subject refusal was the most common reported reason. Studies conducted in North America had lower median response rates than studies conducted in Europe. In-person and telephone interviews elicited higher response rates than mail questionnaires.Conclusions: Response rates from case-control studies of cancer have declined, and this could threaten the validity of results derived from these studies. [ABSTRACT FROM AUTHOR]

Because public health agencies usually monitor health outcomes over time for surveillance, program evaluation, and policy decisions, a correct health outcome trend analysis is vital. If the analysis is done incorrectly and/or results are misinterpreted, a faulty trend analysis could jeopardize key aspects of public health initiatives such as program planning, implementations, policy development, and clinical decision making. It is essential then that accurate health outcome trend analysis be implemented in any data-driven decision-making process. Unfortunately, there continues to be common statistical mistakes in prevalence trend analysis. In this article, using recently published results from the Pediatric Nutrition Surveillance System, we will show the effect that an incorrect trend analysis and subsequent interpretation of results can have. We will also propose more appropriate statistical processes, such as the log-binomial model, for these situations. [ABSTRACT FROM AUTHOR]

Purpose Obesity is a highly prevalent condition in the United States and elsewhere and is associated with increased mortality and morbidity. Here, we discuss some issues involved in quantifying the health burden of obesity using population attributable fraction (PAF) estimates and provide examples. Methods We searched PubMed for articles reporting attributable fraction estimates for obesity. We reviewed eligible articles to identify methodological concerns and tabulated illustrative examples of PAF estimates for obesity relative to cancer, diabetes, cardiovascular disease, and all-cause mortality. Results There is considerable variability among studies regarding the methods used for PAF calculation and the selection of appropriate counterfactuals. The reported estimates ranged from 5% to 15% for all-cause mortality, −0.2% to 8% for all-cancer incidence, 7% to 44% for cardiovascular disease incidence, and 3% to 83% for diabetes incidence. Conclusions To evaluate a given estimate, it is important to consider whether the exposure and outcome were defined similarly for the PAF and for the relative risks, whether the relative risks were suitable for the population at hand, and whether PAF was calculated using correct methods. Strong causal assumptions are not necessarily warranted. In general, PAFs for obesity may be best considered as indicators of association. [ABSTRACT FROM AUTHOR]

Purpose: To examine the extent to which commonly ordered laboratory values obtained from large health care databases are representative of the distribution of laboratory values from the general population as reflected in the National Health and Nutrition Examination Survey.Methods: Means of test values from commercial insurance laboratory data and National Health and Nutrition Examination Survey data were compared. Inverse probability of selection weighting was used to account for possible selection bias and to create comparability between the two data sources.Results: The average values of most of the laboratory results from routine care were very close to their population means as estimated from NHANES. Tests that were more selectively ordered tended to differ. The inverse probability of selection weighting approach generally had a small effect on the estimated means but did improve estimation of some of the more selected tests.Conclusions: Commonly ordered laboratory tests appear to be representative of values from the underlying population. This suggests that trends and other patterns in biomarker levels in the population may be reasonably studied using data collected during the routine delivery of medical care. [ABSTRACT FROM AUTHOR]

Purpose: Selection bias is a form of systematic error that can be severe in compromised study designs such as case-control studies with inappropriate selection mechanisms or follow-up studies that suffer from extensive attrition. External adjustment for selection bias is commonly undertaken when such bias is suspected, but the methods used can be overly simplistic, if not unrealistic, and fail to allow for simultaneous adjustment of associations of the exposure and covariates with the outcome, when of interest. Internal adjustment for selection bias via inverse probability weighting allows bias parameters to vary with the levels of covariates but has only been formalized for longitudinal studies with covariate data on patients up until loss to follow-up.Methods: We demonstrate the use of inverse probability weighting and externally obtained bias parameters to perform internal adjustment of selection bias in studies lacking covariate data on unobserved participants.Results: The "true" or selection-adjusted odds ratio for the association between exposure and outcome was successfully obtained by analyzing only data on those in the selected stratum (i.e., responders) weighted by the inverse probability of their being selected as function of their observed covariate data.Conclusions: This internal adjustment technique using user-supplied bias parameters and inverse probability weighting for selection bias can be applied to any type of observational study. [ABSTRACT FROM AUTHOR]

Purpose: Epidemiologists often think about the balance between study error and cost-efficiency in terms of study design and strategies to reduce random error. We less often consider cost-efficiencies in terms of dealing with systematic errors that arise within a study, such as in deciding how to measure study variables and misclassification implications., Methods: Given the information used to inform a study size calculation, the expected study data can be simulated during study planning, and the impact of anticipated biases can be estimated using quantitative bias analysis. This would allow investigators and stakeholders to identify areas where better data collection through more valid instruments is critical and where additional investment will not yield strong validity benefits. This could promote better use of study resources and help increase investigators' chances of funding by demonstrating they have thought through biases and have a plan for mitigating the impact., Results: We demonstrate how this would work with a practical example using the relationship between smoking during pregnancy as measured on birth certificates and incident breast cancer., Conclusions: We show that although exposure sensitivity would likely be poor, spending more money to get a better smoking measure is unlikely to yield more valid estimates., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Purpose: Measurement error discussions often assume classification errors of key variables are independent. Yet, small amounts of dependent error can create large biases in effect estimates. The purpose of this review was to evaluate frequency of measurement error discussions and potential for dependent error in the observational literature.Methods: Two samples of articles analyzing exposure-outcome contrasts were collected: a random sample (n = 100) from high-impact epidemiology and medical journals (June 2015-July 2016), and a citation-based sample (n = 39) of studies citing one of two prominent dependent misclassification articles (through July 2016). We extracted study details, recorded measurement error mentions, and qualitatively assessed dependent error potential.Results: Measurement error was often discussed. No random sample articles explicitly mentioned dependent error, compared with 59% of the citation-based sample. The random sample was found to be at low risk of exposure-outcome (15% plausible/probable) but increased risk for exposure-confounder (38% plausible/probable) dependency. The citation-based sample was at higher risk for dependent error (exposure-outcome: 46% plausible/probable; exposure-confounder: 61% plausible/probable).Conclusions: Although measurement error was frequently mentioned, potential impact on observed results was rarely discussed in-depth or quantified. Dependent error mentions were rare, even among studies deemed susceptible. Further education and steps to avoid dependent error are needed. [ABSTRACT FROM AUTHOR]

Purpose: Many cohort studies in the United States link with the National Death Index to detect deaths. Although linkage with National Death Index is relatively sensitive, some participant deaths will be missed. These participants continue to contribute person-time to the data set after their death, resulting in bias, which we refer to as ghost-time bias. We sought to evaluate the influence of ghost-time bias on mortality relative risk (RR) estimates.Methods: Simulations were performed to determine the magnitude of ghost-time bias under a variety of plausible conditions.Results: Our simulations demonstrate that ghost-time bias can be substantial, particularly among the elderly, where it can reverse the direction of the RR. For example, we conducted a simulation of a cohort of men beginning follow-up at age of 70 years, assuming 5% missed deaths and a true RR of 2.0. In this simulation, observed RRs were 1.89 during the year the cohort was aged 85 years, 1.60 during the year the cohort was aged 90 years, and 0.61 during the year the cohort was aged 95 years. We also provide results from actual cohort data that are consistent with ghost-time bias.Conclusions: Ghost-time bias may meaningfully affect mortality RR estimates under conditions that can plausibly occur in aging cohorts. [ABSTRACT FROM AUTHOR]

Purpose: To show conditions of covariate balance for no confounding in the sufficient-cause model and discuss its relationship with exchangeability conditions.Methods: We consider the link between the sufficient-cause model and the counterfactual model, emphasizing that the target population plays a key role when discussing these conditions. Furthermore, we incorporate sufficient causes within the directed acyclic graph framework. We propose to use each of the background factors in sufficient causes as representing a set of covariates of interest and discuss the presence of covariate balance by comparing joint distributions of the relevant background factors between the exposed and the unexposed groups.Results: We show conditions for partial covariate balance, covariate balance, and full covariate balance, each of which is stronger than partial exchangeability, exchangeability, and full exchangeability, respectively. This is consistent with the fact that the sufficient-cause model is a "finer" model than the counterfactual model.Conclusions: Covariate balance is a sufficient, but not a necessary, condition for no confounding irrespective of the target population. Although our conceptualization of covariate imbalance is closely related to the recently proposed counterfactual-based definition of a confounder, the concepts of covariate balance and confounder should be clearly distinguished. [ABSTRACT FROM AUTHOR]

Purpose: With observational epidemiologic studies, there is often concern that an unmeasured variable might confound an observed association. Investigators can assess the impact from such unmeasured variables on an observed relative risk (RR) by utilizing externally sourced information and applying an indirect adjustment procedure, for example, the "Axelson adjustment." Although simple and easy to use, this approach applies to exposure and confounder variables that are binary. Other approaches eschew specific values and provide only bounds on the potential bias., Methods: For both multiplicative and additive RR models, we present formulae for indirect adjustment of observed RRs for unmeasured potential confounding variables when there are multiple categories. In addition, we suggest an alternative strategy to identify the characteristics that the confounder must have to explain fully the observed association., Results and Conclusions: We provide examples involving studies of pediatric computer tomography scanning and leukemia and nuclear radiation workers and smoking to demonstrate that with externally sourced information, an investigator can assess whether confounding from unmeasured factors is likely to occur., (Published by Elsevier Inc.)

Purpose: To provide an organizational schema for systematic error and random error in estimating causal measures, aimed at clarifying the concept of errors from the perspective of causal inference.Methods: We propose to divide systematic error into structural error and analytic error. With regard to random error, our schema shows its four major sources: nondeterministic counterfactuals, sampling variability, a mechanism that generates exposure events and measurement variability.Results: Structural error is defined from the perspective of counterfactual reasoning and divided into nonexchangeability bias (which comprises confounding bias and selection bias) and measurement bias. Directed acyclic graphs are useful to illustrate this kind of error. Nonexchangeability bias implies a lack of "exchangeability" between the selected exposed and unexposed groups. A lack of exchangeability is not a primary concern of measurement bias, justifying its separation from confounding bias and selection bias. Many forms of analytic errors result from the small-sample properties of the estimator used and vanish asymptotically. Analytic error also results from wrong (misspecified) statistical models and inappropriate statistical methods.Conclusions: Our organizational schema is helpful for understanding the relationship between systematic error and random error from a previously less investigated aspect, enabling us to better understand the relationship between accuracy, validity, and precision. [ABSTRACT FROM AUTHOR]

Purpose: Receiver operating characteristic (ROC) curve analysis is a popular method for evaluating the performance of (bio)markers. However, the standard ROC curve does not directly connect marker performance to patient-related outcomes. Our aim was to fill this gap by proposing a conceptually similar graphical tool that carries information about the clinical uitility of markers., Methods: We propose a novel graphical tool, the relative impact characteristic (RIC) curve, that depicts the trade-off between the population-level impact of treatment as a function of the size of the treated population for a given marker positivity rule (e.g., a threshold). We establish analogies between the ROC and the RIC curves around the interpretations of shape, slopes, and area under the curve and discuss parametric inference on RIC., Results: As a case study, we used data from a clinical trial on preventive therapy for exacerbations of chronic obstructive pulmonary disease. We illustrate how the RIC curve can be constructed for a predication score and be interpreted in terms of a marker's ability toward concentrating treatment benefit in the population. We discuss hoe the RIC curve can be used to identify a threshold on the risk score based on the maximal acceptable number-needed-to-treat., Conclusions: The RIC curve enables evaluation of markers in terms of their treatment-related clinical utility. Its analogies with the standard ROC analysis can facilitate its interpretation, bringing a population-based perspective to the activities of diverse marker development and evaluation teams., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To describe self-reported herpes zoster (HZ) and explore factors that could impact interpretation of results from health care-based HZ studies.Methods: We performed logistic regression using data from the 2008 Health and Retirement Study (HRS) to evaluate risk factors for having a history of HZ and experiencing severe HZ pain, and predictors for seeking health care for HZ.Results: Among 14,564 respondents aged ≥55 years, women were more likely than men to report a history of HZ (15.7% vs. 11.6%, P < .01). Blacks (6.4% vs. 14.7% in whites, P < .01) and respondents with less than a high school diploma (12.2% vs.14.2% in respondents with at least a high school diploma, P = .01) were less likely to report a history of HZ. Women, blacks, Hispanics, and those with less than a high school diploma were more likely to report severe HZ pain. Most (91.1%) respondents sought health care for HZ; Hispanics (64.2% vs. 92.1% in whites, P < .001) and those with recurrent HZ were less likely to seek health care for HZ, whereas those with severe pain were more likely (95.4% vs. 87.9% in those without severe pain, P < .01).Conclusions: HRS provides a new platform for studies of HZ, one which allowed us to uncover issues that warrant particular attention when interpreting results of health care-based studies. [ABSTRACT FROM AUTHOR]

Purpose: The health consequences of obesity are often assessed using categorical, self-reported data on body mass index (BMI). This article investigates the combined effects of categorization and self-report bias on the estimated association between obesity and mortality.Methods: We used the National Health and Nutrition Examination Survey (1988-2008) linked to death records through 2011. Cox models and age-standardized death rates were used to evaluate the effects of categorization and self-report bias on the mortality risks and percent of deaths attributable to obesity.Results: Despite a correlation between measured and self-reported BMI of 0.96, self-reports miscategorized 20% of adults. Hazard ratios using self-reports were overstated for the obese 1 (BMI, 30-35 kg/m(2)) and obese 2 (BMI ≥ 35 kg/m(2)) categories. The bias was much smaller using a continuous measure of BMI. In contrast, the percent of deaths attributable to excess weight was lower using self-reported versus measured data because self-reports led to systematic downward bias in the BMI distribution.Conclusions: Categorization of BMI and self-report bias combine to produce substantial error in the estimated hazard ratios and percent of deaths attributable to obesity. Future studies should use caution when estimating the association between obesity and mortality using categorical self-reported data. [ABSTRACT FROM AUTHOR]

Purpose: Network analysis has become increasingly popular in epidemiologic research, but the accuracy of data key to constructing risk networks is largely unknown. Using network data from people who use drugs (PWUDs), the study examined how accurately PWUD reported their network members' (i.e., alters') names and ages., Methods: Data were collected from 2008 to 2010 from 503 PWUD residing in rural Appalachia. Network ties (n = 897) involved recent (past 6 months) sex, drug cousage, and/or social support. Participants provided alters' names, ages, and relationship-level characteristics; these data were cross-referenced to that of other participants to identify participant-participant relationships and to determine the accuracy of reported ages (years) and names (binary)., Results: Participants gave alters' exact names and ages within two years in 75% and 79% of relationships, respectively. Accurate name was more common in relationships that were reciprocally reported and those involving social support and male alters. Age was more accurate in reciprocal ties and those characterized by kinship, sexual partnership, recruitment referral, and financial support, and less accurate for ties with older alters., Conclusions: Most participants reported alters' characteristics accurately, and name accuracy was not significantly different in relationships involving drug-related and/or sexual behavior compared to those not involving these behaviors., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose The objective is to modify the longitudinal extension of the average attributable fraction (LE-AAF) for recurrent outcomes with time-varying exposures and control for covariates. Methods We included Medicare Current Beneficiary Survey participants with two or more chronic conditions enrolled from 2005 to 2009 with follow-up through 2011. Nine time-varying medications indicated for nine time-varying common chronic conditions and 14 of 18 forward-selected participant characteristics were used as control variables in the generalized estimating equations step of the LE-AAF to estimate associations with the recurrent universal health outcome self-rated health (SRH). Modifications of the LE-AAF were made to accommodate these indicated medication-condition interactions and covariates. Variability was empirically estimated by bias-corrected and accelerated bootstrapping. Results In the adjusted LE-AAF, thiazide, warfarin, and clopidogrel had significant contributions of 1.2%, 0.4%, 0.2%, respectively, to low (poor or fair) SRH; whereas there were no significant contributions of the other medications to SRH. Hyperlipidemia significantly contributed 4.6% to high SRH. All the other conditions except atrial fibrillation contributed significantly to low SRH. Conclusions Our modifications to the LE-AAF method apply to a recurrent binary outcome with time-varying factors accounting for covariates. [ABSTRACT FROM AUTHOR]

The requirement for framing all causal questions as well-defined interventions is being promoted in the causal inference literature within epidemiology. One can consider this perspective as an intervention on the field which requires a refocusing of epidemiologic questions and retooling of epidemiologic methods. Although this intervention has produced many positive results, we think that its underlying assumptions and the possibilities of unintended consequences warrant examination. In so doing, we argue that this approach can lead to the neglect of causal identification as a useful link between associations and the estimation of intervention effects., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: Few studies have delineated body mass index (BMI) trajectories that associate with premature mortality, which is defined as death occurring before age 75 years., Methods: We used generalized additive mixed model to identify BMI trajectories of individuals dying before or after age 75 years among 14,172 U.S. men. We used logistic regression to validate whether the BMI trajectory developed predicted mortality in an independent cohort of 7000 participants., Results: Comparing to participants with age at death less than 75 years, the BMI among participants with age at death 75 years or more was lower throughout adulthood, and the mean BMI was 23.98, 24.63, 25.33, and 25.29 kg/m 2 at age 40, 50, 60, and 70 years. In the validation cohort, participants following the BMI trajectory with age at death less than 75 years had higher risks of total mortality (odds ratio: 1.35; 95% confidence interval: 1.01-1.78) and cardiovascular disease mortality (1.92; 1.10-3.35) compared to participants following the trajectory with age at death 75 years or more. Participants whose BMI trajectories diverged the most from the trajectory with age at death 75 years or more had highest risks of total mortality (1.72; 1.23-2.40) and cardiovascular disease mortality (3.06; 1.49-6.30)., Conclusions: Individual is suggested to maintain a normal BMI throughout adulthood to have greater longevity., (Copyright © 2016 Elsevier Inc. All rights reserved.)

In this commentary I review the fundamentals of counterfactual theory and its role in causal reasoning in epidemiology. I consider if counterfactual theory dictates that causal questions must be framed in terms of well-defined interventions. I conclude that it does not. I hypothesize that the interventionist approach to causal inference in epidemiology stems from elevating the randomized trial design to the gold standard for thinking about causal inference. I suggest that instead the gold standard we should use for thinking about causal inference in epidemiology is the thought experiment that, for example, compares an actual disease frequency under one exposure level with a counterfactual disease frequency under a different exposure level (as discussed in Greenland and Robins (1986) and Maldonado and Greenland (2002)). I also remind us that no method should be termed "causal" unless it addresses the effect of other biases in addition to the problem of confounding., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: Observational studies are prone to (unmeasured) confounding. Sensitivity analysis of unmeasured confounding typically focuses on a single unmeasured confounder. The purpose of this study was to assess the impact of multiple (possibly weak) unmeasured confounders., Methods: Simulation studies were performed based on parameters estimated from the British Women's Heart and Health Study, including 28 measured confounders and assuming no effect of ascorbic acid intake on mortality. In addition, 25, 50, or 100 unmeasured confounders were simulated, with various mutual correlations and correlations with measured confounders., Results: The correlated unmeasured confounders did not need to be strongly associated with exposure and outcome to substantially bias the exposure-outcome association at interest, provided that there are sufficiently many unmeasured confounders. Correlations between unmeasured confounders, in addition to the strength of their relationship with exposure and outcome, are key drivers of the magnitude of unmeasured confounding and should be considered in sensitivity analyses. However, if the unmeasured confounders are correlated with measured confounders, the bias yielded by unmeasured confounders is partly removed through adjustment for the measured confounders., Conclusions: Discussions of the potential impact of unmeasured confounding in observational studies, and sensitivity analyses to examine this, should focus on the potential for the joint effect of multiple unmeasured confounders to bias results., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: In randomized controlled trials reducing high hematocrit (Hct) in patients with polycythemia vera protects against cardiovascular disease (CVD) events, whereas increasing Hct in anemia patients causes CVD events. Hct is influenced by environmental and lifestyle factors. Given limited knowledge concerning the drivers of Hct, we took an agnostic approach to identify drivers of Hct., Methods: We used an environment-wide association study to identify environmental and lifestyle factors associated with Hct in 20443 older Chinese adults (mean age = 62.7 years) from the Guangzhou Biobank Cohort Study. We evaluated the role of 25 nutrients, 40 environmental contaminants, two metals (only available for 10405 participants), and six lifestyle factors in relation to Hct, adjusted for sex, age, recruitment phase, and socioeconomic position., Results: In a mutually adjusted model vitamin A, serum calcium, serum magnesium, and alcohol use were associated with higher Hct, whereas physical activity was associated with lower Hct., Conclusions: Despite the difficulty of ascertaining causality, finding both expected (vitamin A and physical inactivity) and novel factors (serum calcium, serum magnesium and alcohol use) strongly associated with Hct illustrates the utility of environment-wide association study to generate hypotheses regarding the potential contribution of modifiable exposures to CVD., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: When individuals underreport risk behaviors, data gathered from public health research and practice will underestimate risk. To date, there is little guidance on if or how reports can be adjusted to better reflect true levels of a risk behavior in a given cohort, sample or, by extension, population., Methods: We develop the underreporting correction factor (UCF), which can be used to correct estimates of the prevalence of a risk behavior using self-report of the behavior and a specific (but not necessarily sensitive) biomarker. The UCF rests on three assumptions: (1) there is no overreporting of the behavior, (2) the biomarker can only be acquired if the person engages in the behavior, and (3) the presence of the biomarker does not affect reporting of the behavior. We investigate the sensitivity of the UCF to violation of these assumptions and develop confidence intervals for the UCF and the corrected prevalence of the behavior., Results: The UCF is most sensitive to the second assumption (biomarker perfectly specific). We apply the UCF to estimates of sexual risk behaviors in various settings using a variety of biomarkers., Conclusions: Implementation of the UCF corrects for underreporting and more accurately quantifies risk in cohorts., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: Causal effects in epidemiology are almost invariably studied by considering disease incidence even when prevalence data are used to estimate the causal effect. For example, if certain conditions are met, a prevalence odds ratio can provide a valid estimate of an incidence rate ratio. Our purpose and main result are conditions that assure causal effects on prevalence can be estimated in cross-sectional studies, even when the prevalence odds ratio does not estimate incidence., Methods: Using a general causal effect definition in a multivariate counterfactual framework, we define causal contrasts that compare prevalences among survivors from a target population had all been exposed at baseline with that prevalence had all been unexposed. Although prevalence is a measure reflecting a moment in time, we consider the time sequence to study causal effects., Results: Effects defined using a contrast of counterfactual prevalences can be estimated in an experiment and, with conditions provided, in cross-sectional studies. Proper interpretation of the effect includes recognition that the target is the baseline population, defined at the age or time of exposure., Conclusions: Prevalences are widely reported, readily available measures for assessing disabilities and disease burden. Effects on prevalence are estimable in cross-sectional studies but only if appropriate conditions hold., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: In case-control studies of the human microbiome, the goal is to evaluate whether cases differ from controls in the microbiome composition of a particular body habitat and which taxa are responsible for the differences. These studies leverage sequencing technology and spectroscopy that provide new measurements of the microbiome., Methods: Three challenges in conducting reproducible microbiome research using a case-control design are compensating for differences in observed and actual microbial community composition, detecting "rare" taxa in microbial communities, and choosing properly powered analysis methods. The significance of each challenge, evaluation of commonly held views, analysis of unanswered questions, and suggestions of strategies for solutions are discussed., Results: Understanding the effects of these choices on case-control analyses has been underappreciated, with an implicit assumption that further advances in technology will address all the current shortcomings., Conclusions: It is recommended that research on the human microbiome include positive and negative control experiments to provide insight into bias, contamination, and technical variation. Research protocols such as these may afford a better opportunity to make quantitative and qualitative adjustments to data, thereby reducing the risk of falsely positive results, increasing power to discover true disease determinants, and enhancing interpretation across studies., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: Network diffusion depends on both the pattern and timing of relations, but the relative effects of timing and structure remain unclear. Here, we first show that concurrency (relations that overlap in time) increases epidemic potential by opening new routes in the network. Because this is substantively similar to adding contact paths, we next compare the effects of concurrency by observed levels of path redundancy (structural cohesion) to determine how the features interact., Methods: We establish that concurrency increases exposure analytically and then use simulation methods to manipulate concurrency over observed networks that vary naturally on structural cohesion. This design allows us to compare networks across a wide concurrency range holding constant features that might otherwise conflate concurrency and cohesion. We summarize the simulation results with general linear models., Results: Our results indicate interdependent effects of concurrency and structural cohesion: although both increase epidemic potential, concurrency matters most when the graph structure is sparse, because the exposure created by concurrency is redundant to observed paths within structurally cohesive networks., Conclusions: Concurrency works by opening new paths in temporally ordered networks. Because this is substantively similar to having additional observed paths, concurrency in sparse networks has the same effect as adding relations and will have the greatest effect on epidemic potential in sparse networks., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: To explore whether contextual variables attenuate disparities in weight among 18,639 US children and adolescents aged 2 to 18 years participating in the National Health and Nutrition Examination Survey, 2001 to 2010.Methods: Disparities were assessed using the Symmetrized Rényi Index, a new measure that summarizes disparities in the severity of a disease, as well as the prevalence, across multiple population groups. Propensity score subclassification was used to ensure covariate balance between racial and ethnic subgroups and account for individual-level and contextual covariates.Results: Before propensity score subclassification, significant disparities were evident in the prevalence of overweight and/or obesity and the degree of excess weight among overweight/obese children and adolescents. After propensity score subclassification, racial/ethnic disparities in the prevalence and severity of excess weight were completely attenuated within matched groups, indicating that racial and ethnic differences were explained by social determinants such as neighborhood socioeconomic and demographic factors.Conclusions: The limited overlap in covariate distributions between various racial/ethnic subgroups warrants further attention in disparities research. The attenuation of disparities within matched groups suggests that social determinants such as neighborhood socioeconomic factors may engender disparities in weight among US children and adolescents. [ABSTRACT FROM AUTHOR]

Purpose: Despite significant improvements in treatment for ovarian cancer, survival is poorer for non-Hispanic black (NHB) women compared to non-Hispanic white (NHW) women. Neighborhood socioeconomic status (SES) has been implicated in racial disparities across a variety of health outcomes and may similarly contribute to racial disparities in ovarian cancer survival. The purpose of this analysis is to assess the influence of neighborhood SES on NHB-NHW survival differences after accounting for differences in tumor characteristics and in treatment., Methods: Data were obtained from 2432 women (443 NHB and 1989 NHW) diagnosed with epithelial ovarian cancer in Cook County, Illinois between 1998 and 2007. Neighborhood (i.e., census tract) SES at the time of diagnosis was calculated for each woman using two well-established composite measures of affluence and disadvantage. Cox proportional hazard models measured the association between NHB race and survival after adjusting for age, tumor characteristics, treatment, year of diagnosis, and neighborhood SES., Results: There was a strong association between ovarian cancer survival and both measures of neighborhood SES (P < .0001 for both affluence and disadvantage). After adjusting for age, tumor characteristics, treatment, and year of diagnosis, NHB were more likely than NHW to die of ovarian cancer (hazard ratio [HR] = 1.47, 95% confidence interval [CI]: 1.28-1.68). The inclusion of neighborhood affluence and disadvantage into models separately and together attenuated this risk (HRaffluence = 1.37, 95% CI: 1.18-1.58; HRdisadvantage = 1.28, 95% CI: 1.08-1.52; and HRaffluence + disadvantage = 1.28, 95% CI: 1.08-1.52., Conclusions: Neighborhood SES, as measured by composite measures of affluence and disadvantage, is a predictor of survival in women diagnosed with ovarian cancer in Cook County, Illinois and may contribute to the racial disparity in survival., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Purpose: To demonstrate the implications of choosing analytical methods for quantifying spatiotemporal trends, we compare the assumptions, implementation, and outcomes of popular methods using county-level heart disease mortality in the United States between 1973 and 2010., Methods: We applied four regression-based approaches (joinpoint regression, both aspatial and spatial generalized linear mixed models, and Bayesian space-time model) and compared resulting inferences for geographic patterns of local estimates of annual percent change and associated uncertainty., Results: The average local percent change in heart disease mortality from each method was -4.5%, with the Bayesian model having the smallest range of values. The associated uncertainty in percent change differed markedly across the methods, with the Bayesian space-time model producing the narrowest range of variance (0.0-0.8). The geographic pattern of percent change was consistent across methods with smaller declines in the South Central United States and larger declines in the Northeast and Midwest. However, the geographic patterns of uncertainty differed markedly between methods., Conclusions: The similarity of results, including geographic patterns, for magnitude of percent change across these methods validates the underlying spatial pattern of declines in heart disease mortality. However, marked differences in degree of uncertainty indicate that Bayesian modeling offers substantially more precise estimates., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Risk factor epidemiology has contributed to substantial public health success. In this essay, we argue, however, that the focus on risk factor epidemiology has led epidemiology to ever increasing focus on the estimation of precise causal effects of exposures on an outcome at the expense of engagement with the broader causal architecture that produces population health. To conduct an epidemiology of consequence, a systematic effort is needed to engage our science in a critical reflection both about how well and under what conditions or assumptions we can assess causal effects and also on what will truly matter most for changing population health. Such an approach changes the priorities and values of the discipline and requires reorientation of how we structure the questions we ask and the methods we use, as well as how we teach epidemiology to our emerging scholars., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Measures of causal attribution and preventive potential appear deceptively simple to define, yet have many subtle variations and are subject to numerous pitfalls in conceptualization, interpretation, and application. This article reviews basic concepts, measures, and problems to serve as an introduction to more detailed literature. Allowing for validity and generalization (projection) issues, epidemiologic attribution measures can serve as useful policy inputs for contrasting expected caseloads or survival times under different well-defined interventions. Nonetheless, their application in these settings requires attention to effects of the interventions besides those on the study outcome. Their use as estimates of etiologic attribution requires assumptions beyond the usual validity and statistical assumptions; these further assumptions will usually have little support or plausibility when the mechanisms of action are unknown., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Purpose: A review of methods for the estimation of attributable fraction (AF) statistics from case-control, cross-sectional, or cohort data collected under a complex sample design. Provide guidance on practical methods of complex sample AF estimation and inference using contemporary software tools., Methods: Statistical literature on AF estimation from complex samples for the period 1980 to 2014 is reviewed. A general approach based on weighted sum estimators of the AF and application of Jackknife repeated replication and Bootstrap resampling methods for estimating the variance of AF estimates is outlined and applied to an example analysis of risk factors for alcohol dependency., Results: The literature lays the theoretical foundation to address the problem of AF estimation and inference from complex samples. To date, major statistical software packages do not provide a complete program but the approach is easily implemented using the modeling software and macro/function language capabilities available in major statistical analysis packages. In an example application, weighted sum estimation and inference for the population AF showed stable and consistent results under both Jackknife repeated replication and Bootstrap methods of variance estimation., Conclusions: Future work on AF estimation for complex samples should focus on simulation studies and empirical testing to investigate the properties of the resampling variance estimation methods across a range of complex study design features and populations., (Copyright © 2015 Elsevier Inc. All rights reserved.)