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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: 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: 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: 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 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]

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]