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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 studied a cohort of 30,157 male pulp and paper workers in British Columbia, Canada. Of these, 20,373 worked in kraft mills only, 5,249 in sulfite mills only, and 4,535 in both kraft and sulfite mills. All workers with at least 1 year of employment on January 1, 1950, or thereafter until December 31, 1992, were studied. Standardized mortality ratios (SMRs) were used to compare the mortality rates of the cohort with those of the Canadian male population. Ninety percent confidence intervals (CIs) for the SMRs were obtained. Cancer risks significantly associated with work duration and time from first employment of 15 years or more were observed: 1) total cohort: pleura (SMR = 3.61, 90% CI 1.42-7.58); kidney (SMR = 1.69, 90% CI 1.13-2.43); brain (SMR = 1.51, 90% CI 1.03-2.16); 2) workers in kraft mills only: kidney (SMR = 1.92, 90% CI 1.04-3.26); 3) workers in sulfite mills only: Hodgkin's disease (SMR = 4.79, 90% CI 1.29-12.37); 4) workers ever employed in both kraft and sulfite mills: esophagus (SMR = 1.91, 90% CI 1.00-3.33). These malignancies have been associated with the following known or suspected carcinogens to which pulp and paper workers may have been exposed: asbestos (pleura), biocides (kidney), formaldehyde (kidney, brain, Hodgkin's disease), hypochlorite (esophagus). A nested case-control study with detailed exposure assessment is under way to help determine whether excess risks for specific cancers reflect exposure among subsets of workers.

In the article, the authors present their analysis of the characteristics of article submissions to the journal, including the number of foreign submissions and the topics tackled like social epidemiology, methodology or statistics, and psychiatric epidemiology.

In this article, I present a brief summary of landmark events in the American Journal of Epidemiology , including its founding, the first few decades, the change in name, the increasing focus on nontransmissible disease, and selected key manuscripts. A list of developments that will likely result in new papers submitted to epidemiology journals are also described, and they include themes such as consequential epidemiology and the use of artificial intelligence methods in epidemiologic data analyses. [ABSTRACT FROM AUTHOR]

Peer-reviewed journals provide an invaluable but inadequate vehicle for scientific communication. Preprints are now an essential complement to peer-reviewed publications. Eschewing preprints will slow scientific progress and reduce the public health impact of epidemiologic research. The coronavirus disease 2019 (COVID-19) pandemic highlighted long-standing limitations of the peer-review process. Preprint servers, such as bioRxiv and medRxiv, served as crucial venues to rapidly disseminate research and provide detailed backup to sound-bite science that is often communicated through the popular press or social media. The major criticisms of preprints arise from an unjustified optimism about peer review. Peer review provides highly imperfect sorting and curation of research and only modest improvements in research conduct or presentation for most individual papers. The advantages of peer review come at the expense of months to years of delay in sharing research methods or results. For time-sensitive evidence, these delays can lead to important missteps and ill-advised policies. Even with research that is not intrinsically urgent, preprints expedite debate, expand engagement, and accelerate progress. The risk that poor-quality papers will have undue influence because they are posted on a preprint server is low. If epidemiology aims to deliver evidence relevant for public health, we need to embrace strategic uses of preprint servers. [ABSTRACT FROM AUTHOR]

In their seminal 2002 paper, "Causal Knowledge as a Prerequisite for Confounding Evaluation: An Application to Birth Defects Epidemiology," Hernán et al. (Am J Epidemiol. 2002;155(2):176–184) emphasized the importance of using theory rather than data to guide confounding control, focusing on colliders as variables that share characteristics with confounders but whose control may actually introduce bias into analyses. In this commentary, we propose that the importance of this paper stems from the connection the authors made between nonexchangeability as the ultimate source of bias and structural representations of bias using directed acyclic graphs. This provided both a unified approach to conceptualizing bias and a means of distinguishing between different sources of bias, particularly confounding and selection bias. Drawing on examples from the paper, we also highlight unresolved questions about the relationship between collider bias, selection bias, and generalizability and argue that causal knowledge is a prerequisite not only for identifying confounders but also for developing any hypothesis about potential sources of bias. [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]

The American Journal of Epidemiology has been a platform for findings from the Black Women's Health Study (BWHS) that are relevant to health disparities. Topics addressed have included methods of follow-up of a large cohort of Black women, disparities in health-care delivery, modifiable risk factors for health conditions that disproportionately affect Black women, associations with exposures that are highly prevalent in Black women, and methods for genetic research. BWHS papers have also highlighted the importance of considering social context, including perceived experiences of racism, in understanding health disparities. In the future, BWHS investigators will contribute to documentation of the role that structural racism plays in health disparities. [ABSTRACT FROM AUTHOR]

In this commentary, I reflect on the paper "Poverty and Health: Prospective Evidence From the Alameda County Study" by Haan et al. (Am J Epidemiol. 1987;125(6):989-998), which together with supporting work ushered in a surge of epidemiologic interest in the impact of social contexts on health. I discuss why the paper was influential and how the premise of this type of work influenced theories and methods in epidemiology. The paper is placed in the broader context of the nature of evidence in epidemiology and the ultimate purpose of epidemiologic inquiry. [ABSTRACT FROM AUTHOR]

The perverseness of racial and ethnic inequities in the United States continues to implore the investigation of their causes. While there have been improvements in the health of the US population, these improvements have not been equally distributed. To commemorate the 100th anniversary of the American Journal of Epidemiology , in this commentary, we aim to highlight the Journal 's contributions to: 1) the definition and use of race and ethnicity in research, and 2) understanding racial and ethnic inequities, both empirically and methodologically, over the past decade. We commend the Journal for its contributions and for spearheading many of the challenges related to measuring and interpreting racial and ethnic data for the past 20 years. We identify 3 additional areas in which the Journal could make further impact to address racial and ethnic inequities: 1) devote a section in every issue of the Journal to scientific papers that make substantive epidemiologic or methodological contributions to racial and ethnic inequities in health; 2) update the Journal 's guidelines for authors to include justifying the use of race and ethnicity; and 3) diversify the field of epidemiology by bringing a new cadre of scholars from minoritized racial and ethnic groups who represent the most affected communities into the research process. [ABSTRACT FROM AUTHOR]

Previous papers have mentioned that conditioning on a binary collider would introduce an association between its causes in at least 1 stratum. In this paper, we prove this statement and, along with intuitions, formally examine the direction and magnitude of the associations between 2 risk factors of a binary collider using interaction contrasts. Among level one of the collider, 2 variables are independent, positively associated, and negatively associated if multiplicative risk interaction contrast is equal to, more than, and less than 0, respectively; the same results hold for the other level of the collider if the multiplicative survival interaction contrast, equal to multiplicative risk interaction contrast minus the additive risk interaction contrast, is compared with 0. The strength of the association depends on the magnitude of the interaction contrast: The stronger the interaction is, the larger the magnitude of the association will be. However, the common conditional odds ratio under the homogeneity assumption will be bounded. A figure is presented that succinctly illustrates our results and helps researchers to better visualize the associations introduced upon conditioning on a collider. [ABSTRACT FROM AUTHOR]

Contemporary searches for new risk factors frequently involve genome-wide explorations of very large numbers of candidate risk variants. Given that diseases can often be classified into subtypes that possess evidence of etiologic heterogeneity, the question arises as to whether or not a search for new risk factors would be improved by looking separately within subtypes. Etiologic risk heterogeneity inevitably increases the signal in at least one of the subtypes, but this advantage may be offset by smaller sample sizes and the increased chances of false discovery. In this article, the authors show that only a relatively modest degree of etiologic heterogeneity is necessary for the subtyping strategies to have improved statistical power. In practice, effective exploitation of etiologic heterogeneity requires strong evidence that the subtypes selected are likely to exhibit substantial heterogeneity. Further, defining the subtypes that demonstrate the most heterogeneous profiles is important for optimizing the search for new risk factors. The concepts are illustrated by using data from a breast cancer study in which results are available separately for estrogen receptor-positive (ER+) and -negative (ER−) tumors. [ABSTRACT FROM AUTHOR]

Almost 60% of American households were connected to the Internet in 2001, when the Millennium Cohort Study, the largest longitudinal study ever undertaken by the Department of Defense, was launched. To facilitate survey completion, increase data integrity, and encourage cohort retention while maintaining the highest standards of participant privacy, an online questionnaire was made available on the World Wide Web in addition to a traditional paper questionnaire sent via US mail. Over 50% of 77,047 participants chose to enroll in the study via the Web, affording substantial cost savings to the project. Using multivariable logistic regression, the authors compared the demographic and health characteristics of Web responders with those of paper responders. Web responders were slightly more likely to be male, to be younger, to have a high school diploma or college degree, and to work in information technology or another technical occupation. Web responders were more likely to be obese and to smoke more cigarettes and were less likely to be problem alcohol drinkers and to report occupational exposures. Question completion rates were 98.3%, on average, for both Web and paper responders. Web responders provided more complete contact information, including their e-mail addresses. These results demonstrate the value of survey research conducted over the Internet in concert with traditional mail survey strategies. [ABSTRACT FROM AUTHOR]

Self-collection of buccal cells provides a noninvasive method for obtaining biologic samples for genetic analyses in pediatric studies. Nevertheless, low yields, microbial contamination, and degradation of buccal samples present challenges for epidemiologic studies incorporating genetic investigations. The aims of this study were to compare the quality and yield of DNA extracted from buccal specimens with BuccalAmp swabs (Epicenter BioTechnologies, Madison, Wisconsin) or FTA cards (Whatman, Inc., Clifton, New Jersey) and to investigate the use of whole-genome amplification (WGA) for increasing DNA yields for single nucleotide polymorphism analyses. Buccal specimens were collected from 55 children with acute lymphoblastic leukemia and 52 control children without acute lymphoblastic leukemia in New South Wales, Australia, in 2003-2004. Real-time polymerase chain reaction was used to evaluate polymorphisms in the genes encoding the cytochrome p450 enzyme CYP3A4 (CYP3A4 A392G, also known as CYP3A4*1B) and the steroid xenobiotic receptor (SXR C25385T). Results showed that DNA could be isolated from buccal specimens collected by use of both methods and that yields could be substantially improved with WGA without introducing genotyping error. However, DNA quality was poorer in samples collected by BuccalAmp swabs, and the presence of polymerase chain reaction inhibitors in these samples reduced the sensitivity of quantitative real-time PCR analysis. These findings show that different methods for collecting buccal samples impact on the downstream success of genetic investigations and influence DNA quality after WGA.

In their accompanying article, Samoilenko and Lefebvre (Am J Epidemiol. 2019;188(7):1203–1205) correctly note 2 typographical errors in the formulas presented in a 2011 paper on placental abruption by Ananth and VanderWeele (Am J Epidemiol. 2011;174(1):99–108). Fortunately, to the best of our knowledge, researchers are using our methods papers and Dr. VanderWeele's 2015 book on mediation analysis (Explanation in Causal Inference ; Oxford University Press, New York, New York), rather than the paper on placental abruption, to carry out their direct and indirect effect analyses; and in our methods papers and the book, the formulas are correct. The formulas discussed by Samoilenko and Lefebvre and in our work make reference to a "rare outcome assumption." In evaluating this assumption, it is important to note that the outcome is to be relatively rare across all strata defined by the exposure and the mediator —a point that is often neglected. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Office Equipment and Supplies: A Modern Occupational Health Concern?" in a previous issue, as well as a response by the authors to the comments.

In this paper, we advocate and expand upon a previously described monitoring strategy for efficient and robust estimation of disease prevalence and case numbers within closed and enumerated populations such as schools, workplaces, or retirement communities. The proposed design relies largely on voluntary testing, which is notoriously biased (e.g. in the case of coronavirus disease 2019) due to nonrepresentative sampling. The approach yields unbiased and comparatively precise estimates with no assumptions about factors underlying selection of individuals for voluntary testing, building on the strength of what can be a small random sampling component. This component enables the use of a recently proposed "anchor stream" estimator, a well-calibrated alternative to classical capture-recapture (CRC) estimators based on 2 data streams. We show that this estimator is equivalent to a direct standardization based on "capture," that is, selection (or not) by the voluntary testing program, made possible by means of a key parameter identified by design. This equivalency simultaneously allows for novel 2-stream CRC-like estimation of general mean values (e.g. means of continuous variables like antibody levels or biomarkers). For inference, we propose adaptations of Bayesian credible intervals when estimating case counts and bootstrapping when estimating means of continuous variables. We use simulations to demonstrate significant precision benefits relative to random sampling alone. [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]

Diverse manifestations of biological aging often reflect disparities in socioeconomic status (SES). In this paper, we examine associations between indicators of SES and an mRNA-based aging signature during young adulthood, before clinical indications of aging are common. We use data from wave V (2016–2018) of the National Longitudinal Study of Adolescent to Adult Health, a nationally representative study of adults aged 33–43 years, with transcriptomic data from a subset of 2,491 participants. Biological aging is measured using 1) a composite transcriptomic aging signature previously identified by Peters et al.'s out-of-sample meta-analysis (Nat Commun. 2015;6:8570) and 2) 9 subsets that represent functional pathways of coexpressed genes. SES refers to income, education, occupation, subjective social status, and a composite measure combining these 4 dimensions. We examine hypothesized mechanisms through which SES could affect aging: body mass index, smoking, health insurance status, difficulty paying bills, and psychosocial stress. We find that SES—especially the composite measure and income—is associated with transcriptomic aging and immune, mitochondrial, ribosomal, lysosomal, and proteomal pathways. Counterfactual mediational models suggest that the mediators partially account for these associations. The results thus reveal that numerous biological pathways associated with aging are already linked to SES in young adulthood. [ABSTRACT FROM AUTHOR]

The use of longitudinal finite mixture models such as group-based trajectory modeling has seen a sharp increase during the last few decades in the medical literature. However, these methods have been criticized, especially because of the data-driven modeling process, which involves statistical decision-making. In this paper, we propose an approach that uses the bootstrap to sample observations with replacement from the original data to validate the number of groups identified and to quantify the uncertainty in the number of groups. The method allows investigation of the statistical validity and uncertainty of the groups identified in the original data by checking to see whether the same solution is also found across the bootstrap samples. In a simulation study, we examined whether the bootstrap-estimated variability in the number of groups reflected the replicationwise variability. We evaluated the ability of 3 commonly used adequacy criteria (average posterior probability, odds of correct classification, and relative entropy) to identify uncertainty in the number of groups. Finally, we illustrate the proposed approach using data from the Quebec Integrated Chronic Disease Surveillance System to identify longitudinal medication patterns between 2015 and 2018 in older adults with diabetes. [ABSTRACT FROM AUTHOR]