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Few biomarker-based validation studies have examined error in online self-report dietary assessment instruments, and food records (FRs) have been considered less than food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). We investigated measurement error in online and paper-based FFQs, online 24HRs, and paper-based FRs in 3 samples drawn primarily from 3 cohorts, comprising 1,393 women and 1,455 men aged 45–86 years. Data collection occurred from January 2011 to October 2013. Attenuation factors and correlation coefficients between reported and true usual intake for energy, protein, sodium, potassium, and respective densities were estimated using recovery biomarkers. Across studies, average attenuation factors for energy were 0.07, 0.07, and 0.19 for a single FFQ, 24HR, and FR, respectively. Correlation coefficients for energy were 0.24, 0.23, and 0.40, respectively. Excluding energy, the average attenuation factors across nutrients and studies were 0.22 for a single FFQ, 0.22 for a single 24HR, and 0.51 for a single FR. Corresponding correlation coefficients were 0.31, 0.34, and 0.53, respectively. For densities (nutrient expressed relative to energy), the average attenuation factors across studies were 0.37, 0.17, and 0.50, respectively. The findings support prior research suggesting different instruments have unique strengths that should be leveraged in epidemiologic research. [ABSTRACT FROM AUTHOR]

The authors assessed the utility and cost-effectiveness of using a World Wide Web-based questionnaire in a large prospective cohort study, the Black Women's Health Study (BWHS). In 1995, 59,000 African-American women were recruited into the BWHS through a paper questionnaire. Follow-up paper questionnaires have been mailed every 2 years since then. During the 2003, 2005, and 2007 questionnaire cycles, participants were given the option of completing a Web-based questionnaire. The cost of developing and processing a returned paper questionnaire was 4 times that of a returned Web questionnaire, primarily because of return postage costs and greater processing time for paper questionnaires. The proportion of respondents who completed a Web questionnaire doubled from 2003 to 2007, from 10.1% to 19.9%, but the characteristics of those completing the Web questionnaire remained the same. Web response was greatest at younger ages (20.9% of those aged <30 years) and declined with age to 3.6% among women aged 60 years or more. Web questionnaires were filled out more completely than paper questionnaires, regardless of the sensitivity of a question. The use of a Web questionnaire in the BWHS resulted in cost savings and more complete responses. Although there are advantages to using a Web questionnaire, the use of multiple means of soliciting questionnaire responses is still needed. [ABSTRACT FROM PUBLISHER]

Qualitative research methods, while rising in popularity, are still a relatively underutilized tool in public health research. Usually reserved for small samples, qualitative research techniques have the potential to enhance insights gained from large questionnaires and cohort studies, both deepening the interpretation of quantitative data and generating novel hypotheses that might otherwise be missed by standard approaches; this is especially true where exposures and outcomes are new, understudied, or rapidly changing, as in a pandemic. However, methods for the conduct of qualitative research within large samples are underdeveloped. Here, we describe a novel method of applying qualitative research methods to free-text comments collected in a large epidemiologic questionnaire. Specifically, this method includes: 1) a hierarchical system of coding through content analysis; 2) a qualitative data management application; and 3) an adaptation of Cohen's κ and percent agreement statistics for use by a team of coders, applying multiple codes per record from a large codebook. The methods outlined in this paper may help direct future applications of qualitative and mixed methods within large cohort studies. [ABSTRACT FROM AUTHOR]

The Preconception Period Analysis of Risks and Exposures Influencing Health and Development (PrePARED) Consortium creates a novel resource for addressing preconception health by merging data from numerous cohort studies. In this paper, we describe our data harmonization methods and results. Individual-level data from 12 prospective studies were pooled. The crosswalk-cataloging-harmonization procedure was used. The index pregnancy was defined as the first postbaseline pregnancy lasting more than 20 weeks. We assessed heterogeneity across studies by comparing preconception characteristics in different types of studies. The pooled data set included 114,762 women, and 25,531 (22%) reported at least 1 pregnancy of more than 20 weeks' gestation during the study period. Babies from the index pregnancies were delivered between 1976 and 2021 (median, 2008), at a mean maternal age of 29.7 (standard deviation, 4.6) years. Before the index pregnancy, 60% of women were nulligravid, 58% had a college degree or more, and 37% were overweight or obese. Other harmonized variables included race/ethnicity, household income, substance use, chronic conditions, and perinatal outcomes. Participants from pregnancy-planning studies had more education and were healthier. The prevalence of preexisting medical conditions did not vary substantially based on whether studies relied on self-reported data. Use of harmonized data presents opportunities to study uncommon preconception risk factors and pregnancy-related events. This harmonization effort laid the groundwork for future analyses and additional data harmonization. [ABSTRACT FROM AUTHOR]

The disease risk score (DRS) is a summary score that is a function of a potentially large set of covariates. The DRS can be used to control for confounding by the covariates that went into estimation of the DRS and obtain a standardized estimate of an exposure's effect on disease. However, to date, literature on the DRS has not addressed analyses that focus on estimation of survival or hazard functions, which are common in epidemiologic analyses of cohort data. Here, we propose a method for standardization of hazard ratios using the DRS in longitudinal analyses of the association between a binary exposure and an outcome. This approach to handling a potentially large set of covariates through a model-based approach to standardization may provide a useful tool for cohort analyses of hazard ratios and may be particularly well-suited to settings where an exposure propensity score is difficult to model. Simulations are used in this paper to illustrate the approach, and an empirical example is provided. [ABSTRACT FROM AUTHOR]

The acquired immune deficiency syndrome (AIDS) epidemic was first recognized in 1981, and it quickly became a public health emergency. In a 1987 paper in the American Journal of Epidemiology (Am J Epidemiol. 1987;126(2): 310-318), Richard Kaslow et al. described the launch of the Multicenter AIDS Cohort Study (MACS), a cohort study of homosexual men in 4 US cities, the purpose of which was to better understand the natural history of AIDS and its determinants. The MACS enrolled participants through a range of community contacts. These efforts facilitated rapid recruitment, but given the targeted approaches, participants tended to comprise high-risk social networks. At baseline, 4%-26% of participants at the 4 sites reported having a sexual partner who had developed AIDS. Kaslow et al. also described baseline testing for the causative agent of AIDS, the human immunodeficiency virus (HIV). HIV sero- prevalence was remarkably high, ranging from 11 %-26% across age groups in Pittsburgh to 38%-53% in Los Angeles. The major turning point in the epidemic occurred in 1995-1996 when combination antiretroviral therapy was introduced, effectively blocking HIV replication and markedly reducing AIDS morbidity and mortality. The MACS cohort continues to be followed actively 3 decades after its launch and has proven to be an important resource for information on HIV infection and AIDS. [ABSTRACT FROM AUTHOR]

In 2019, the National Institutes of Health combined the Multicenter AIDS Cohort Study (MACS) and the Women's Interagency HIV Study (WIHS) into the MACS/WIHS Combined Cohort Study (MWCCS). In this paper, participants who made a study visit during October 2018–September 2019 (targeted for MWCCS enrollment) are described by human immunodeficiency virus (HIV) serostatus and compared with people living with HIV (PLWH) in the United States. Participants include 2,115 women and 1,901 men with a median age of 56 years (interquartile range, 48–63); 62% are PLWH. Study sites encompass the South (18%), the Mid-Atlantic/Northeast (45%), the West Coast (22%), and the Midwest (15%). Participant race/ethnicity approximates that of PLWH throughout the United States. Longitudinal data and specimens collected for 35 years (men) and 25 years (women) were combined. Differences in data collection and coding were reviewed, and key risk factor and comorbidity data were harmonized. For example, recent use of alcohol (62%) and tobacco (28%) are common, as are dyslipidemia (64%), hypertension (56%), obesity (42%), mildly or severely impaired daily activities (31%), depressive symptoms (28%), and diabetes (22%). The MWCCS repository includes serum, plasma, peripheral blood mononuclear cells, cell pellets, urine, cervicovaginal lavage samples, oral samples, B-cell lines, stool, and semen specimens. Demographic differences between the MACS and WIHS can confound analyses by sex. The merged MWCCS is both an ongoing observational cohort study and a valuable resource for harmonized longitudinal data and specimens for HIV-related research. [ABSTRACT FROM AUTHOR]

Researchers increasingly wish to test hypotheses concerning the impact of environmental or disease exposures on telomere length (TL), and they use longitudinal study designs to do so. In population studies, TL is usually measured with a quantitative polymerase chain reaction (qPCR)-based method. This method has been validated by calculating its correlation with a gold standard method such as Southern blotting (SB) in cross-sectional data sets. However, in a cross-section, the range of true variation in TL is large, and measurement error is introduced only once. In a longitudinal study, the target variation of interest is small, and measurement error is introduced at both baseline and follow-up. In this paper, we present results from a small data set (n  = 20) in which leukocyte TL was measured twice 6.6 years apart by means of both qPCR and SB. The cross-sectional correlations between qPCR and SB were high at both baseline (r  = 0.90) and follow-up (r  = 0.85), yet their correlation for TL change was poor (r  = 0.48). Moreover, the qPCR data but not the SB data showed strong signatures of measurement error. Through simulation, we show that the statistical power gain from performing a longitudinal analysis is much greater for SB than for qPCR. We discuss implications for optimal study design and analysis. [ABSTRACT FROM AUTHOR]

Medication use is often underreported in paper-based questionnaires or interviews.Web-based questionnaires may improve recall ofmedication use, but data on their validity are currently lacking. Participants in the Pregnancy and Infant Development (PRIDE) Study (2014-2016; n = 557) and the Pregnancy Drug Registry (pREGnant) (2015-2016; n = 169) completed a 6-week paper-based medication diary during gestational weeks 19-24 or 26-31. In week 34, they completed a Web-based questionnaire with questions on medication names, time period and frequency of use, and quantity taken. To assess the degree of underreporting, we calculated the questionnaire's sensitivity using the medication diary as the reference standard. Sensitivity was high formany medication groups, including antiepilepticmedication (sensitivity (Sn) = 0.96, 95% confidence interval (CI): 0.89, 1.00), antacids (Sn = 0.89, 95% CI: 0.86, 0.93), and iron preparations (Sn = 0.81, 95% CI: 0.64, 0.98). However, medications for short-term use were underreported more frequently, with sensitivities of 0.54 (95% CI: 0.35, 0.72) for antihistamines, 0.63 (95% CI: 0.57, 0.69) for analgesic and antipyretic agents, and 0.57 (95% CI: 0.51, 0.64) for acetaminophen. Shortening the period of time between exposure and questionnaire administration increased sensitivity substantially. In conclusion, underreporting in Web-based questionnaires is limited for many medication groups. In prospective studies, underreporting of medications for short-term usemay be reduced by decreasing the interval between consecutive questionnaires. medication use; pregnancy; Pregnancy Drug Registry (pREGnant); Pregnancy and Infant Development (PRIDE) [ABSTRACT FROM AUTHOR]

Microvascular dysfunction (MVD) is a common pathophysiological change that occurs in various diseases, such as type 2 diabetes mellitus (T2DM), heart failure, dementia, and depression. Recent technical advances have enabled noninvasive measurement and quantification of microvascular changes in humans. In this paper, we describe the protocols of the microvascular measurements applied in the Maastricht Study, an ongoing prospective, population-based cohort study of persons aged 40–75 years being carried out in the southern part of the Netherlands (baseline data assessment, November 2010–January 2020). The study includes a variety of noninvasive measurements in skin, retina, brain, and sublingual tissue, as well as plasma and urine biomarker assessments. Following this, we summarize our main findings involving these microvascular measurements through the end of 2018. Finally, we provide a brief perspective on future microvascular investigations within the framework of the Maastricht Study. [ABSTRACT FROM AUTHOR]

Epidemiologists often compare the observed number of deaths in a cohort with the expected number of deaths, obtained by multiplying person-time accrued in the cohort by mortality rates for a reference population (ideally, a reference that represents the mortality rate in the cohort in the absence of exposure). However, if exposure is hazardous (or salutary), this calculation will not consistently estimate the number of deaths expected in the absence of exposure because exposure will have affected the distribution of person-time observed in the study cohort. While problems with interpretation of this standard calculation of expected counts were discussed more than 2 decades ago, these discussions had little impact on epidemiologic practice. The logic of counterfactuals may help clarify this topic as we revisit these issues. In this paper, we describe a simple way to consistently estimate the expected number of deaths in such settings, and we illustrate the approach using data from a cohort study of mortality among underground miners. [ABSTRACT FROM AUTHOR]

The case-control study design has evolved substantially over the past half century. The design has long been recognized as a way to increase efficiency by studying fewer subjects than would be required for a full cohort study. Historically, it was thought that case-control studies required a rare disease assumption for valid risk ratio estimation, but it was later realized that rare disease was not necessary. Over time, the design and analysis methods were further modified to allow estimation of rate ratios or to allow each person to serve as his/her own control (as we see with case-cohort and case-crossover studies, for example). We now understand that efficiency can be increased through the use of outcome-dependent sampling not only for dichotomous outcomes but also for continuous outcomes in longitudinal studies with repeated outcome measurement during follow-up. In their accompanying paper, Schildcrout et al. (Am J Epidemiol. 2019;000(00):000–000) contribute to our understanding, clearly summarizing many recent advances in study design and analyses that allow more general and efficient use of case-control studies. Their simulations demonstrate that improved efficiency is achieved with these methods when the goal is to estimate associations of exposure with trajectories and patterns of change over time. Here we comment on application of some of these generalized case-control methods to causal inference. [ABSTRACT FROM AUTHOR]

Cohort or period components of trends can provide a rationale for new research or point to clues on the effectiveness of control strategies. Graphical display of trends guides models that quantify the experience of a population. In this paper, a method for smoothing rates by single year of age and year is developed and displayed to show the contributions of period and cohort to trends. The magnitude of the contribution of period and/or cohort in a model for trends may be assessed by the percentage of deviance explained and the relative contributions of cohort (C) and period (P) individually, known as the C-P score. The method is illustrated using Surveillance, Epidemiology, and End Results data (1975–2014) on lung and bronchial cancer mortality in females and prostate and colorectal cancer incidence in males. Smoothed age-period and age-cohort rates provide a useful first step in studies of etiology and the impact of disease control without imposing a restrictive model. We found that, in this data set, cohort predominates for female lung and bronchial cancer and period predominates for male prostate cancer. However, the effects change with age for male colorectal cancer incidence, indicating an age shift in relevant exposures. These methods are applied on an interactive website for both incidence and mortality at over 20 cancer sites in the United States. [ABSTRACT FROM AUTHOR]

Estimation of causal effects of time-varying exposures using longitudinal data is a common problem in epidemiology. When there are time-varying confounders, which may include past outcomes, affected by prior exposure, standard regression methods can lead to bias. Methods such as inverse probability weighted estimation of marginal structural models have been developed to address this problem. However, in this paper we show how standard regression methods can be used, even in the presence of time-dependent confounding, to estimate the total effect of an exposure on a subsequent outcome by controlling appropriately for prior exposures, outcomes, and time-varying covariates. We refer to the resulting estimation approach as sequential conditional mean models (SCMMs), which can be fitted using generalized estimating equations. We outline this approach and describe how including propensity score adjustment is advantageous. We compare the causal effects being estimated using SCMMs and marginal structural models, and we compare the two approaches using simulations. SCMMs enable more precise inferences, with greater robustness against model misspecification via propensity score adjustment, and easily accommodate continuous exposures and interactions. A new test for direct effects of past exposures on a subsequent outcome is described. [ABSTRACT FROM AUTHOR]

In modern society, we are increasingly disconnected from natural light/dark cycles and beset by round-the-clock exposure to artificial light. Light has powerful effects on physical and mental health, in part via the circadian system, and thus the timing of light exposure dictates whether it is helpful or harmful. In their compelling paper, Obayashi et al. (Am J Epidemiol. 2018;187(3):427-434.) offer evidence that light at night can prospectively predict an elevated incidence of depressive symptoms in older adults. Strengths of the study include the longitudinal design and direct, objective assessment of light levels, as well as accounting for multiple plausible confounders during analyses. Follow-up studies should address the study's limitations, including reliance on a global self-report of sleep quality and a 2-night assessment of light exposure that may not reliably represent typical light exposure. In addition, experimental studies including physiological circadian measures will be necessary to determine whether the light effects on depression are mediated through the circadian system or are so-called "direct" effects of light. In any case, these exciting findings could inform novel approaches to preventing depressive disorders in older adults. [ABSTRACT FROM AUTHOR]

Propensity score methods are a popular tool with which to control for confounding in observational data, but their bias-reduction properties--as well as internal validity, generally--are threatened by covariate measurement error. There are few easy-to-implement methods of correcting for such bias. In this paper, we describe and demonstrate how existing sensitivity analyses for unobserved confounding--propensity score calibration, VanderWeele and Arah's bias formulas, and Rosenbaum's sensitivity analysis--can be adapted to address this problem. In a simulation study, we examine the extent to which these sensitivity analyses can correct for several measurement error structures: classical, systematic differential, and heteroscedastic covariate measurement error. We then apply these approaches to address covariate measurement error in estimating the association between depression and weight gain in a cohort of adults in Baltimore, Maryland. We recommend the use of VanderWeele and Arah's bias formulas and propensity score calibration (assuming it is adapted appropriately for the measurement error structure), as both approaches perform well for a variety of propensity score estimators and measurement error structures. [ABSTRACT FROM AUTHOR]

Longitudinal data sources offer new opportunities for the evaluation of sequential interventions. To adjust for time-dependent confounding in these settings, longitudinal targeted maximum likelihood based estimation (TMLE), a doubly robust method that can be coupled with machine learning, has been proposed. This paper provides a tutorial in applying longitudinal TMLE, in contrast to inverse probability of treatment weighting and g-computation based on iterative conditional expectations. We apply these methods to estimate the causal effect of nutritional interventions on clinical outcomes among critically ill children in a United Kingdom study (Control of Hyperglycemia in Paediatric Intensive Care, 2008-2011). We estimate the probability of a child's being discharged alive from the pediatric intensive care unit by a given day, under a range of static and dynamic feeding regimes. We find that before adjustment, patients who follow the static regime "never feed" are discharged by the end of the fifth day with a probability of 0.88 (95% confidence interval: 0.87, 0.90), while for the patients who follow the regime "feed from day 3," the probability of discharge is 0.64 (95% confidence interval: 0.62, 0.66). After adjustment for time-dependent confounding, most of this difference disappears, and the statistical methods produce similar results. TMLE offers a flexible estimation approach; hence, we provide practical guidance on implementation to encourage its wider use. [ABSTRACT FROM AUTHOR]

Changes in women’s menstrual bleeding patterns precede the onset of menopause. In this paper, the authors identify population subgroups based on menstrual characteristics of the menopausal transition experience. Using the TREMIN data set (1943–1979), the authors apply a Bayesian change-point model with 8 parameters for each woman that summarize change in menstrual bleeding patterns during the menopausal transition. The authors then use estimates from this model to classify menstrual patterns into subgroups using a K-medoids algorithm. They identify 6 subgroups of women whose transition experience can be distinguished by age at onset, variability of the menstrual cycle, and duration of the early transition. These results suggest that for most women, mean and variance change points are well aligned with proposed bleeding markers of the menopausal transition, but for some women they are not clearly associated. Increasing understanding of population differences in the transition experience may lead to new insights into ovarian aging. Because of age inclusion criteria, most longitudinal studies of the menopausal transition probably include only a subset of the 6 subgroups of women identified in this paper, suggesting a potential bias in the understanding of both the menopausal transition and the linkage between the transition and chronic disease. [ABSTRACT FROM PUBLISHER]

Two frequently encountered but underrecognized challenges for causal inference in studying the long-term health effects of disasters among survivors include 1) time-varying effects of disasters on a time-to-event outcome and 2) selection bias due to selective attrition. In this paper, we review approaches for overcoming these challenges and demonstrate application of the approaches to a real-world longitudinal data set of older adults who were directly affected by the 2011 Great East Japan Earthquake and Tsunami (n  = 4,857). To illustrate the problem of time-varying effects of disasters, we examined the association between degree of damage due to the tsunami and all-cause mortality. We compared results from Cox regression analysis assuming proportional hazards with those derived using adjusted parametric survival curves allowing for time-varying hazard ratios. To illustrate the problem of selection bias, we examined the association between proximity to the coast (a proxy for housing damage from the tsunami) and depressive symptoms. We corrected for selection bias due to attrition in the 2 postdisaster follow-up surveys (conducted in 2013 and 2016) using multivariable adjustment, inverse probability of censoring weighting, and survivor average causal effect estimation. Our results demonstrate that analytical approaches which ignore time-varying effects on mortality and selection bias due to selective attrition may underestimate the long-term health effects of disasters. [ABSTRACT FROM AUTHOR]

Q-learning is a method of reinforcement learning that employs backwards stagewise estimation to identify sequences of actions that maximize some long-term reward. The method can be applied to sequential multipleassignment randomized trials to develop personalized adaptive treatment strategies (ATSs)--longitudinal practice guidelines highly tailored to time-varying attributes of individual patients. Sometimes, the basis for choosing which ATSs to include in a sequential multiple-assignment randomized trial (or randomized controlled trial) may be inadequate. Nonrandomized data sources may inform the initial design of ATSs, which could later be prospectively validated. In this paper, we illustrate challenges involved in using nonrandomized data for this purpose with a case study from the Center for International Blood and Marrow Transplant Research registry (1995--2007) aimed at 1) determining whether the sequence of therapeutic classes used in graft-versus-host disease prophylaxis and in refractory graft-versus-host disease is associated with improved survival and 2) identifying donor and patient factors with which to guide individualized immunosuppressant selections over time. We discuss how to communicate the potential benefit derived from following an ATS at the population and subgroup levels and how to evaluate its robustness to modeling assumptions. This worked example may serve as a model for developing ATSs from registries and cohorts in oncology and other fields requiring sequential treatment decisions. [ABSTRACT FROM AUTHOR]

Bias due to selective mortality is a potential concern in many studies and is especially relevant in cognitive aging research because cognitive impairment strongly predicts subsequent mortality. Biased estimation of the effect of an exposure on rate of cognitive decline can occur when mortality is a common effect of exposure and an unmeasured determinant of cognitive decline and in similar settings. This potential is often represented as collider-stratification bias in directed acyclic graphs, but it is difficult to anticipate the magnitude of bias. In this paper, we present a flexible simulation platform with which to quantify the expected bias in longitudinal studies of determinants of cognitive decline. We evaluated potential survival bias in naive analyses under several selective survival scenarios, assuming that exposure had no effect on cognitive decline for anyone in the population. Compared with the situation with no collider bias, the magnitude of bias was higher when exposure and an unmeasured determinant of cognitive decline interacted on the hazard ratio scale to influence mortality or when both exposure and rate of cognitive decline influenced mortality. Bias was, as expected, larger in high-mortality situations. This simulation platform provides a flexible tool for evaluating biases in studies with high mortality, as is common in cognitive aging research. [ABSTRACT FROM AUTHOR]

Missing data are a commonly occurring threat to the validity and efficiency of epidemiologic studies. Perhaps the most common approach to handling missing data is to simply drop those records with 1 or more missing values, in so-called "complete records" or "complete case" analysis. In this paper, we bring together earlier-derived yet perhaps now somewhat neglected results which show that a logistic regression complete records analysis can provide asymptotically unbiased estimates of the association of an exposure of interest with an outcome, adjusted for a number of confounders, under a surprisingly wide range of missing-data assumptions. We give detailed guidance describing how the observed data can be used to judge the plausibility of these assumptions. The results mean that in large epidemiologic studies which are affected by missing data and analyzed by logistic regression, exposure associations may be estimated without bias in a number of settings where researchers might otherwise assume that bias would occur. [ABSTRACT FROM AUTHOR]

In cohort mortality studies, there often is interest in associations between an exposure of primary interest and mortality due to a range of different causes. A standard approach to such analyses involves fitting a separate regression model for each type of outcome. However, the statistical precision of some estimated associations may be poor because of sparse data. In this paper, we describe a hierarchical regression model for estimation of parameters describing outcome-specific relative rate functions and associated credible intervals. The proposed model uses background stratification to provide flexible control for the outcome-specific associations of potential confounders, and it employs a hierarchical “shrinkage” approach to stabilize estimates of an exposure’s associations with mortality due to different causes of death. The approach is illustrated in analyses of cancer mortality in 2 cohorts: a cohort of dioxin-exposed US chemical workers and a cohort of radiation-exposed Japanese atomic bomb survivors. Compared with standard regression estimates of associations, hierarchical regression yielded estimates with improved precision that tended to have less extreme values. The hierarchical regression approach also allowed the fitting of models with effect-measure modification. The proposed hierarchical approach can yield estimates of association that are more precise than conventional estimates when one wishes to estimate associations with multiple outcomes. [ABSTRACT FROM AUTHOR]

Increasingly often in epidemiologic research, associations between survival time and predictors of interest are measured by differences between distribution functions rather than hazard functions. For example, differences in percentiles of survival time, expressed in absolute time units (e.g., weeks), may complement the popular risk ratios, which are unitless measures. When analyzing time to an event of interest (e.g., death) in prospective cohort studies, the time scale can be set to start at birth or at study entry. The advantages of one time origin over the other have been thoroughly explored for the estimation of risks but not for the estimation of survival percentiles. In this paper, we analyze the use of different time scales in the estimation of survival percentiles with Laplace regression. Using this regression method, investigators can estimate percentiles of survival time over levels of an exposure of interest while adjusting for potential confounders. Our findings may help to improve modeling strategies and ease interpretation in the estimation of survival percentiles in prospective cohort studies. [ABSTRACT FROM AUTHOR]

In this issue of the Journal, Isanaka et al. (Am J Epidemiol. 2016;184(12):861-869) set out to update an incidence correction factor used for estimating numbers of cases of severe acute malnutrition (SAM) in children aged 6-59 months. The total number of current SAM cases (prevalent cases) increases by the number of new (incident) cases and decreases as a result of recovery or death. Prevalence estimates are obtained from cross-sectional surveys. Calculation of incidence typically requires longitudinal data, which evidently are rarely collected for SAM, and so a correction factor is applied instead. Isanaka et al. pool and meta-analyze data from longitudinal and community programs in 3 West African countries (Mali, Niger, and Burkina Faso), covering the period 2009-2012. Heterogeneity and the ongoing lack of data undermine the use of a single incidence correction factor for SAM estimates. Routine data collection is recommended as a way forward and aligns with recommendations of the World Health Organization. This commentary helps to outline a context for the use of such data and provide some perspective on the inadequacy of data, relative to the importance of the issue. [ABSTRACT FROM AUTHOR]

Relationships between some risk factors and breast cancer incidence are known to vary by tumor subtype. However, breast tumors can be classified according to a number of markers, which may be correlated, making it difficult to identify heterogeneity of risk factors with specific tumor markers when using standard competing-risk survival analysis. In this paper, we propose a constrained competing-risk survival model that allows for assessment of heterogeneity of risk factor associations according to specific tumor markers while controlling for other markers. These methods are applied to Nurses’ Health Study data from 1980–2006, during which 3,398 incident invasive breast cancers occurred over 1.4 million person-years of follow-up. Results suggested that when estrogen receptor (ER) and progesterone receptor (PR) status are mutually considered, some risk factors thought to be characteristic of “estrogen-positive tumors,” such as high body mass index during postmenopause and increased height, are actually significantly associated with PR-positive tumors but not ER-positive tumors, while other risk factors thought to be characteristic of “estrogen-negative tumors,” such as late age at first birth, are actually significantly associated with PR-negative rather than ER-negative breast cancer. This approach provides a strategy for evaluating heterogeneity of risk factor associations by tumor marker levels while controlling for additional tumor markers. [ABSTRACT FROM PUBLISHER]

Multiple imputation is a common technique for dealing with missing values and is mostly applied in regression settings. Its application in cluster analysis problems, where the main objective is to classify individuals into homogenous groups, involves several difficulties which are not well characterized in the current literature. In this paper, we propose a framework for applying multiple imputation to cluster analysis when the original data contain missing values. The proposed framework incorporates the selection of the final number of clusters and a variable reduction procedure, which may be needed in data sets where the ratio of the number of persons to the number of variables is small. We suggest some ways to report how the uncertainty due to multiple imputation of missing data affects the cluster analysis outcomes—namely the final number of clusters, the results of a variable selection procedure (if applied), and the assignment of individuals to clusters. The proposed framework is illustrated with data from the Phenotype and Course of Chronic Obstructive Pulmonary Disease (PAC-COPD) Study (Spain, 2004–2008), which aimed to classify patients with chronic obstructive pulmonary disease into different disease subtypes. [ABSTRACT FROM PUBLISHER]

To date, Web-based 24-hour recalls have not been validated using objective biomarkers. From 2006 to 2009, the validity of 6 Web-based DietDay 24-hour recalls was tested among 115 black and 118 white healthy adults from Los Angeles, California, by using the doubly labeled water method, and the results were compared with the results of the Diet History Questionnaire, a food frequency questionnaire developed by the National Cancer Institute. The authors performed repeated measurements in a subset of 53 subjects approximately 6 months later to estimate the stability of the doubly labeled water measurement. The attenuation factors for the DietDay recall were 0.30 for blacks and 0.26 for whites. For the Diet History Questionnaire, the attenuation factors were 0.15 and 0.17 for blacks and whites, respectively. Adjusted correlations between true energy intake and the recalls were 0.50 and 0.47 for blacks and whites, respectively, for the DietDay recall. For the Diet History Questionnaire, they were 0.34 and 0.36 for blacks and whites, respectively. The rate of underreporting of more than 30% of calories was lower with the recalls than with the questionnaire (25% and 41% vs. 34% and 52% for blacks and whites, respectively). These findings suggest that Web-based DietDay dietary recalls offer an inexpensive and widely accessible dietary assessment alternative, the validity of which is equally strong among black and white adults. The validity of the Web-administered recall was superior to that of the paper food frequency questionnaire. [ABSTRACT FROM PUBLISHER]

Dialysis has been used in the treatment of patients with end-stage renal disease since the 1960s. Recently, several large observational studies have been conducted to assess whether early initiation of dialysis prolongs survival, as compared with late initiation. However, these studies have used analytic approaches which are likely to suffer from either lead-time bias or immortal-time bias. In this paper, the authors demonstrate that recently developed methods in the causal inference literature can be used to avoid both types of bias and accurately estimate the ideal time for dialysis initiation from observational data. This is illustrated using data from a nationwide population-based cohort of patients with chronic kidney disease in Sweden (1996–2003). [ABSTRACT FROM PUBLISHER]

The Life Journeys of Young Women Project is the first population-based study to examine the role of economic uncertainty throughout early adulthood on age at first childbirth. A retrospective cross-sectional component was added to an existing cohort study that is based on a birth cohort of women born during 1973–1975 in Adelaide, South Australia (n ˜ 1,000). An event history calendar instrument was used to obtain data regarding a range of life domains including partnering, educational attainment, home ownership, higher education debt, employment, and pregnancies over a 20-year period (sometimes as detailed as at monthly intervals). Interviews were conducted between 2007 and 2009. An analysis framework applying time-varying and time-constant survival analysis techniques within a life-course framework was developed that will guide analyses to examine the role of duration and life-course timing of economic uncertainty on age at first childbirth. This paper discusses study objectives and design, fieldwork procedures, planned statistical analyses, and recruitment outcomes, focusing on novel features that would facilitate analogous epidemiologic research. [ABSTRACT FROM AUTHOR]

Life-course epidemiology seeks to better understand the mechanisms that lead to the development of chronic diseases. An example is the mechanism leading from body size to coronary heart disease (CHD); one way to acquire a better understanding of this mechanism is to investigate to what extent it works through other risk factors. In this paper, the dynamic path analysis model is presented as a tool to analyze these dynamic mechanisms in life-course epidemiology. A key feature of dynamic path analysis is its ability to decompose the total effect of a risk factor into a direct effect (not mediated by other variables) and indirect effects (mediated through other variables). This is illustrated by examining the associations between repeated measurements of body mass index (BMI) and systolic blood pressure (SBP) and the risk of CHD in a sample of Danish men between 1976 and 2006. The effect of baseline BMI on the risk of CHD is decomposed into a direct effect and indirect effects going through later BMI, concurrent SBP, or later SBP. In conclusion, dynamic path analysis is a flexible tool that by the decomposition of effects can be used to increase the understanding of mechanisms that underlie the etiology of chronic disease. [ABSTRACT FROM AUTHOR]

In observational studies on cancer patients, progression of performance status over time can be described by using a multistate model in which state-to-state transitions represent changes in a patient's health condition. Although a patient experiences transitions in continuous time, assessments on the patient are often made at irregularly spaced time points. In this paper, the authors formulate a Markov 4-state model for examining longitudinal data on performance status collected under intermittent observation. The cohort consisted of 11,342 patients diagnosed with cancer in Ontario, Canada, from 2007 to 2009. The authors extend the model to estimate the predicted probability of reaching the absorbing state, death, over various time intervals. The authors also illustrate what happens to the estimated transition intensities if the true observational scheme is overlooked. Methods for multistate analysis should be used by epidemiologists, since they prove particularly useful for examining the complexities of disease processes. [ABSTRACT FROM PUBLISHER]

Arsenic in drinking water causes increased mortality from several cancers, ischemic heart disease, bronchiectasis, and other diseases. This paper presents the first evidence relating arsenic exposure to pulmonary tuberculosis, by estimating mortality rate ratios for Region II of Chile compared with Region V for the years 1958–2000. The authors compared mortality rate ratios with time patterns of arsenic exposure, which increased abruptly in 1958 in Region II and then declined starting in 1971. Tuberculosis mortality rate ratios in men started increasing in 1968, 10 years after high arsenic exposure commenced. The peak male 5-year mortality rate ratio occurred during 1982–1986 (rate ratio = 2.1, 95% confidence interval: 1.7, 2.6; P < 0.001) and subsequently declined. Mortality rates in women were also elevated but with fewer excess pulmonary tuberculosis deaths (359 among men and 95 among women). The clear rise and fall of tuberculosis mortality rate ratios in men following high arsenic exposure are consistent with a causal relation. The findings are biologically plausible in view of evidence that arsenic is an immunosuppressant and also a cause of chronic lung disease. Finding weaker associations in women is unsurprising, because this is true of most arsenic-caused health effects. Confirmatory evidence is needed from other arsenic-exposed populations. [ABSTRACT FROM PUBLISHER]

Longitudinal epidemiologic studies with irregularly observed categorical outcomes present considerable analytical challenges. Generalized linear models (GLMs) tolerate without bias only values missing completely at random and assume that all observations contribute equally. A triggered sampling study design and an analysis using inverse intensity weights in a GLM offer promise of effectively addressing both shortcomings. A triggered sampling design generates irregularly spaced outcomes because, in addition to regularly scheduled follow-up interviews, it specifies that data be collected after a “trigger” (a decline in health status during follow-up) occurs. It is intended to mitigate bias introduced by study participant loss to follow-up. For each observation, an inverse intensity weight is calculated from an Anderson-Gill recurrent-event regression model whose events of interest are observed interviews; the weights help to equalize observation contributions. Investigators in the Longitudinal Examination of Attitudes and Preferences (LEAP) Study (1999–2002), a Connecticut study of seriously ill older adults at the end of life, used a triggered sampling design. In this paper, the authors analyze data from the LEAP Study to illustrate the methods and benefits of inverse intensity weighting in GLMs. An additional benefit of the analytical approach presented is that it allows for assessment of the utility of triggered sampling in longitudinal studies. [ABSTRACT FROM PUBLISHER]

In research on the determinants of change in health status, a crucial analytic decision is whether to adjust for baseline health status. In this paper, the authors examine the consequences of baseline adjustment, using for illustration the question of the effect of educational attainment on change in cognitive function in old age. With data from the US-based Assets and Health Dynamics Among the Oldest Old survey (n = 5,726; born before 1924), they show that adjustment for baseline cognitive test score substantially inflates regression coefficient estimates for the effect of schooling on change in cognitive test scores compared with models without baseline adjustment. To explain this finding, they consider various plausible assumptions about relations among variables. Each set of assumptions is represented by a causal diagram. The authors apply simple rules for assessing causal diagrams to demonstrate that, in many plausible situations, baseline adjustment induces a spurious statistical association between education and change in cognitive score. More generally, when exposures are associated with baseline health status, this bias can arise if change in health status preceded baseline assessment or if the dependent variable measurement is unreliable or unstable. In some cases, change-score analyses without baseline adjustment provide unbiased causal effect estimates when baseline-adjusted estimates are biased. [ABSTRACT FROM AUTHOR]

Time-series studies have linked daily variations in nonaccidental deaths with daily variations in ambient particulate matter air pollution, while controlling for qualitatively larger influences of weather and season. Although time-series analyses typically include nonlinear terms for weather and season, questions remain as to whether models to date have completely controlled for these important predictors. In this paper, the authors use two flexible versions of distributed lag models to control extensively for the confounding effects of weather and season. One version builds on the current approach to controlling for weather, while the other version offers a new approach. The authors conduct a comprehensive sensitivity analysis of the particulate matter–mortality relation by applying these methods to the recently updated National Morbidity, Mortality, and Air Pollution Study database that comprises air pollution, weather, and mortality time series from 1987 to 2000 for 100 US cities. They combine city-specific estimates of the short-term effects of particulate matter on mortality using a Bayesian hierarchical model. They conclude that, within the broad classes of models considered, national average estimates of particulate matter relative risk are consistent with previous estimates from this study and are robust to model specification for weather and seasonal confounding. [ABSTRACT FROM AUTHOR]

Marginal structural models (MSMs) are causal models designed to adjust for time-dependent confounding in observational studies of time-varying treatments. MSMs are powerful tools for assessing causality with complicated, longitudinal data sets but have not been widely used by practitioners. The objective of this paper is to illustrate the fitting of an MSM for the causal effect of iron supplement use during pregnancy (time-varying treatment) on odds of anemia at delivery in the presence of time-dependent confounding. Data from pregnant women enrolled in the Iron Supplementation Study (Raleigh, North Carolina, 1997–1999) were used. The authors highlight complexities of MSMs and key issues epidemiologists should recognize before and while undertaking an analysis with these methods and show how such methods can be readily interpreted in existing software packages, including SAS and Stata. The authors emphasize that if a data set with rich information on confounders is available, MSMs can be used straightforwardly to make robust inferences about causal effects of time-dependent treatments/exposures in epidemiologic research. [ABSTRACT FROM AUTHOR]

Care for type 1 diabetes mellitus should both alleviate the physical complications of the disease and improve overall quality of life. The Wisconsin Diabetes Registry, comprising a population-based cohort that is followed longitudinally from diagnosis of type 1 diabetes, provided the authors with a unique opportunity to examine quality of life in children, adolescents, and young adults with type 1 diabetes and its relation to both sociodemographic and clinical risk factors. The authors analyzed data from 1987–2002 with a mean of 10.2 years’ duration of diabetes (n = 569). They used ordinal-scaled self-rated global health as a measure of quality of life. In this paper, they propose a random-effects model for drawing inferences on individuals regarding the relation of longitudinally measured quality of life to multiple risk factors. Results showed that male gender, higher parental socioeconomic level, younger age at diabetes diagnosis, shorter diabetes duration, no hospitalization in the preceding 6 months, lower glycosylated hemoglobin level, and questionnaire responses by a person other than the subject were independently associated with better reported health. The authors found that individuals varied in their reported health even after adjustment for all identified risk factors. This could imply either that there is variability in perception even with the same health status or that there are independent unmeasured risk factors for poor health in persons with type 1 diabetes. [ABSTRACT FROM AUTHOR]

The method of generalized estimating equations (GEE) is often used to analyze longitudinal and other correlated response data, particularly if responses are binary. However, few descriptions of the method are accessible to epidemiologists. In this paper, the authors use small worked examples and one real data set, involving both binary and quantitative response data, to help end-users appreciate the essence of the method. The examples are simple enough to see the behind-the-scenes calculations and the essential role of weighted observations, and they allow nonstatisticians to imagine the calculations involved when the GEE method is applied to more complex multivariate data. [ABSTRACT FROM PUBLISHER]

Measurement error is a major issue in self-reported diet that can distort diet-disease relationships. Use of blood concentration biomarkers has the potential to mitigate the subjective bias inherent in self-reporting. As part of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) baseline visit (2008–2011), self-reported information on diet was collected from all participants (n  = 16,415). The HCHS/SOL also included annual telephone follow-up, as well as a second (2014–2017) and third (2020–2023) clinic visit. Blood concentration biomarkers for carotenoids, tocopherols, retinol, vitamin B12, and folate were measured in a subset of participants (n  = 476) as part of the Study of Latinos: Nutrition and Physical Activity Assessment Study (SOLNAS) (2010–2012). We examined the relationships among biomarker levels, self-reported intake, Hispanic/Latino background (Central American, Cuban, Dominican, Mexican, Puerto Rican, or South American), and other participant characteristics in this diverse cohort. We built regression calibration–based prediction equations for 10 nutritional biomarkers and used a simulation to study the power of detecting a diet-disease association in a multivariable Cox model using a predicted concentration level. Good statistical power was observed for some nutrients with high prediction model R 2 values, but further research is needed to understand how best to realize the potential of these dietary biomarkers. This study provides a comprehensive examination of several nutritional biomarkers within the HCHS/SOL, characterizing their associations with subject characteristics and the influence of the measurement characteristics on the power to detect associations with health outcomes. [ABSTRACT FROM AUTHOR]

The Environmental Influences on Child Health Outcomes (ECHO)-Wide Cohort Study (EWC), a collaborative research design comprising 69 cohorts in 31 consortia, was funded by the National Institutes of Health (NIH) in 2016 to improve children's health in the United States. The EWC harmonizes extant data and collects new data using a standardized protocol, the ECHO-Wide Cohort Data Collection Protocol (EWCP). EWCP visits occur at least once per life stage, but the frequency and timing of the visits vary across cohorts. As of March 4, 2022, the EWC cohorts contributed data from 60,553 children and consented 29,622 children for new EWCP data and biospecimen collection. The median (interquartile range) age of EWCP-enrolled children was 7.5 years (3.7–11.1). Surveys, interviews, standardized examinations, laboratory analyses, and medical record abstraction are used to obtain information in 5 main outcome areas: pre-, peri-, and postnatal outcomes; neurodevelopment; obesity; airways; and positive health. Exposures include factors at the level of place (e.g. air pollution, neighborhood socioeconomic status), family (e.g. parental mental health), and individuals (e.g. diet, genomics). [ABSTRACT FROM AUTHOR]

The Mini-Mental State Examination (MMSE) measures global cognitive performance and is often used as a screening test for dementia. This paper presents a 5-year longitudinal study of the MMSE score in a sample of 2, 537 non-demented French community residents aged 65 years and older who were participants in the Paquid Study in 1988–1992. Subjects were evaluated at the baseline visit (TO) and 1 year (T1), 3 years (T3), and 5 years (T5) later. Analyses performed with a random effects linear model showed that the score rose between TO and T1 (by 0.60 points for subjects aged 65 years at TO to 0.83 points for subjects aged 85 years at TO), then it decreased very slightly between T1 and T5 (by 0.02 points for subjects aged 65 years to 0.57 points for subjects aged 85 years). The improvement during the first year, which was larger for less educated subjects, may be explained by the stress due to the test situation at TO or by a learning effect at T1. The decline during the last 4 years was more pronounced for older and less well educated subjects. The cross-sectional measure of age effect was larger than the longitudinal measure of time effect. This difference may be explained by a cohort effect or by a practice effect induced by repetition of the test. The authors conclude that the MMSE score declines very slightly in non-demented subjects, thus suggesting that the cognitive processes involved are spared by the aging process. These results may have implications for dementia screening. Am J Epidemiol 1997;145:49–506. [ABSTRACT FROM AUTHOR]

We consider the problem of designing a prospective randomized trial in which the outcome data will be self-reported and will involve sensitive topics. Our interest is in how a researcher can adequately power her study when some respondents misreport the binary outcome of interest. To correct the power calculations, we first obtain expressions for the bias and variance induced by misreporting. We model the problem by assuming each individual in our study is a member of one "reporting class": a true-reporter, false-reporter, never-reporter, or always-reporter. We show that the joint distribution of reporting classes and "response classes" (characterizing individuals' response to the treatment) will exactly define the error terms for our causal estimate. We propose a novel procedure for determining adequate sample sizes under the worst-case power corresponding to a given level of misreporting. Our problem is motivated by prior experience implementing a randomized controlled trial of a sexual-violence prevention program among adolescent girls in Kenya. [ABSTRACT FROM AUTHOR]

Epidemiologists face a unique challenge in measuring risk relationships involving time-varying exposures in early pregnancy. Each week in early pregnancy is distinct in its contribution to fetal development, and this period is commonly characterized by shifts in maternal behavior and, consequently, exposures. In this simulation study, we used alcohol as an example of an exposure that often changes during early pregnancy and miscarriage as an outcome affected by early exposures. Data on alcohol consumption patterns from more than 5,000 women in the Right From the Start cohort study (United States, 2000–2012) informed measures of the prevalence of alcohol exposure, the distribution of gestational age at cessation of alcohol use, and the likelihood of miscarriage by week of gestation. We then compared the bias and precision of effect estimates and statistical power from 5 different modeling approaches in distinct simulated relationships. We demonstrate how the accuracy and precision of effect estimates depended on alignment between model assumptions and the underlying simulated relationship. Approaches that incorporated data about patterns of exposure were more powerful and less biased than simpler models when risk depended on timing or duration of exposure. To uncover risk relationships in early pregnancy, it is critical to carefully define the role of exposure timing in the underlying causal hypothesis. [ABSTRACT FROM AUTHOR]

Recurrent events can occur more than once in the same individual; such events may be of different types, known as multitype recurrent events. They are very common in longitudinal studies. Often there is a terminating event, after which no further events can occur. The risk of any event, including terminating events such as death or cure, is typically affected by prior events. We propose a flexible joint multitype recurrent-events model that explicitly provides estimates of the change in risk for each event due to subject characteristics, including number and type of prior events and the absolute risk for every event type (terminating and nonterminating), and predicts event-free survival probability over a desired time period. The model is fully parametric, and therefore a standard likelihood function and robust standard errors can be constructed. We illustrate the model with applications to the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (1994–2002) and provide discussion of the results and model features. [ABSTRACT FROM AUTHOR]

Racism is embedded in society, and higher education is an important structure for patterning economic and health outcomes. Historically Black Colleges and Universities (HBCUs) were founded on antiracism while predominantly White institutions (PWIs) were often founded on white supremacy. This contrast provides an opportunity to study the association between structural racism and health among Black Americans. We used the National Longitudinal Study of Adolescent to Adult Health (Add Health) to estimate the long-term causal effect of attending an HBCU (vs. PWI) on depressive symptoms among Black students in the United States from 1994–2018. While we found no overall association with attending an HBCU (vs. PWI) on depressive symptoms, we found that this association varied by baseline mental health and region, and across time. For example, among those who attended high school outside of the South, HBCU attendance was protective against depressive symptoms 7 years later, and the association was strongest for those with higher baseline depressive symptoms. We recommend equitable state and federal funding for HBCUs, and that PWIs implement and evaluate antiracist policies to improve mental health of Black students. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) incidence rates have decreased among adults aged 50 years or older while increasing in adults under age 50 years. Understanding these trends is challenging because of the multiple related time scales of age, diagnosis period, and birth cohort. We analyzed incidence rates of rectal, distal colon, and proximal colon cancer for individuals aged 20 years or more from the Surveillance, Epidemiology, and End Results Program for diagnosis years 1978–2017. We used a 2-stage generalized linear model to determine age, period, and cohort effects for CRC incidence. We first estimated birth cohort effects among people under age 45 years. We used these results to specify prior distributions for cohort effects in a Bayesian model to estimate period effects among people aged 45 years or more. There was no evidence of period effects for people under age 45 years. Risks of rectal and distal colon cancer increased for later birth cohorts. Compared with the 1943–1952 birth cohort, the 1983–1992 birth cohort had 2.2 times the risk of rectal cancer, 1.9 times the risk of distal colon cancer, and 1.3 times the risk of proximal colon cancer. For people aged ≥45 years, period effects showed declines in CRC risk that were attributable to screening. [ABSTRACT FROM AUTHOR]

Multimorbidity (≥2 chronic conditions) is a common and important marker of aging. To better understand racial differences in multimorbidity burden and associations with important health-related outcomes, we assessed differences in the contribution of chronic conditions to hospitalization, skilled nursing facility admission, and mortality among non-Hispanic Black and non-Hispanic White older adults in the United States. We used data from a nationally representative study, the National Health and Aging Trends Study, linked to Medicare claims from 2011–2015 (n  = 4,871 respondents). This analysis improved upon prior research by identifying the absolute contributions of chronic conditions using a longitudinal extension of the average attributable fraction for Black and White Medicare beneficiaries. We found that cardiovascular conditions were the greatest contributors to outcomes among White respondents, while the greatest contributor to outcomes for Black respondents was renal morbidity. This study provides important insights into racial differences in the contributions of chronic conditions to costly health-care utilization and mortality, and it prompts policy-makers to champion delivery reforms that will expand access to preventive and ongoing care for diverse Medicare beneficiaries. [ABSTRACT FROM AUTHOR]

Follow-up of US cohort members for incident cancer is time-consuming, is costly, and often results in underascertainment when the traditional methods of self-reporting and/or medical record validation are used. We conducted one of the first large-scale investigations to assess the feasibility, methods, and benefits of linking participants in the US Radiologic Technologists (USRT) Study (n  = 146,022) with the majority of US state or regional cancer registries. Follow-up of this cohort has relied primarily on questionnaires (mailed approximately every 10 years) and linkage with the National Death Index. We compared the level of agreement and completeness of questionnaire/death-certificate–based information with that of registry-based (43 registries) incident cancer follow-up in the USRT cohort. Using registry-identified first primary cancers from 1999–2012 as the gold standard, the overall sensitivity was 46.5% for self-reports only and 63.0% for both self-reports and death certificates. Among the 37.0% false-negative reports, 27.8% were due to dropout, while 9.2% were due to misreporting. The USRT cancer reporting patterns differed by cancer type. Our study indicates that linkage to state cancer registries would greatly improve completeness and accuracy of cancer follow-up in comparison with questionnaire self-reporting. These findings support ongoing development of a national US virtual pooled registry with which to streamline cohort linkages. [ABSTRACT FROM AUTHOR]

We compared the performance of prognostic tools for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using parameters fitted either at the time of hospital admission or across all time points of an admission. This cohort study used clinical data to model the dynamic change in prognosis of SARS-CoV-2 at a single hospital center in the United Kingdom, including all patients admitted from February 1, 2020, to December 31, 2020, and then followed up for 60 days for intensive care unit (ICU) admission, death, or discharge from the hospital. We incorporated clinical observations and blood tests into 2 time-varying Cox proportional hazards models predicting daily 24- to 48-hour risk of admission to the ICU for those eligible for escalation of care or death for those ineligible for escalation. In developing the model, 491 patients were eligible for ICU escalation and 769 were ineligible for escalation. Our model had good discrimination of daily risk of ICU admission in the validation cohort (n  = 1,141; C statistic: C  = 0.91, 95% confidence interval: 0.89, 0.94) and our score performed better than other scores (National Early Warning Score 2, International Severe Acute Respiratory and Emerging Infection Comprehensive Clinical Characterisation Collaboration score) calculated using only parameters measured on admission, but it overestimated the risk of escalation (calibration slope = 0.7). A bespoke daily SARS-CoV-2 escalation risk prediction score can predict the need for clinical escalation better than a generic early warning score or a single estimation of risk calculated at admission. [ABSTRACT FROM AUTHOR]