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Epidemiologists are attempting to address research questions of increasing complexity by developing novel methods for combining information from diverse sources. Cole et al. (Am J Epidemiol. 2023;192(3)467–474) provide 2 examples of the process of combining information to draw inferences about a population proportion. In this commentary, we consider combining information to learn about a target population as an epidemiologic activity and distinguish it from more conventional meta-analyses. We examine possible rationales for combining information and discuss broad methodological considerations, with an emphasis on study design, assumptions, and sources of uncertainty. [ABSTRACT FROM AUTHOR]

Suicide rates in the United States have increased over the past 15 years, with substantial geographic variation in these increases; yet there have been few attempts to cluster counties by the magnitude of suicide rate changes according to intercept and slope or to identify the economic precursors of increases. We used vital statistics data and growth mixture models to identify clusters of counties by their magnitude of suicide growth from 2008 to 2020 and examined associations with county economic and labor indices. Our models identified 5 clusters, each differentiated by intercept and slope magnitude, with the highest-rate cluster (4% of counties) being observed mainly in sparsely populated areas in the West and Alaska, starting the time series at 25.4 suicides per 100,000 population, and exhibiting the steepest increase in slope (0.69/100,000/year). There was no cluster for which the suicide rate was stable or declining. Counties in the highest-rate cluster were more likely to have agricultural and service economies and less likely to have urban professional economies. Given the increased burden of suicide, with no clusters of counties improving over time, additional policy and prevention efforts are needed, particularly targeted at rural areas in the West. [ABSTRACT FROM AUTHOR]

Understanding characteristics of patients with propensity scores in the tails of the propensity score (PS) distribution has relevance for inverse-probability-of-treatment–weighted and PS-based estimation in observational studies. Here we outline a method for identifying variables most responsible for extreme propensity scores. The approach is illustrated in 3 scenarios: 1) a plasmode simulation of adult patients in the National Ambulatory Medical Care Survey (2011–2015) and 2) timing of dexamethasone initiation and 3) timing of remdesivir initiation in patients hospitalized for coronavirus disease 2019 from February 2020 through January 2021. PS models were fitted using relevant baseline covariates, and tails of the PS distribution were defined using asymmetric first and 99th percentiles. After fitting of the PS model in each original data set, values of each key covariate were permuted and model-agnostic variable importance measures were examined. Visualization and variable importance techniques were helpful in identifying variables most responsible for extreme propensity scores and may help identify individual characteristics that might make patients inappropriate for inclusion in a study (e.g. off-label use). Subsetting or restricting the study sample based on variables identified using this approach may help investigators avoid the need for trimming or overlap weights in studies. [ABSTRACT FROM AUTHOR]

Propensity score analysis is a common approach to addressing confounding in nonrandomized studies. Its implementation, however, requires important assumptions (e.g. positivity). The disease risk score (DRS) is an alternative confounding score that can relax some of these assumptions. Like the propensity score, the DRS summarizes multiple confounders into a single score, on which conditioning by matching allows the estimation of causal effects. However, matching relies on arbitrary choices for pruning out data (e.g. matching ratio, algorithm, and caliper width) and may be computationally demanding. Alternatively, weighting methods, common in propensity score analysis, are easy to implement and may entail fewer choices, yet none have been developed for the DRS. Here we present 2 weighting approaches: One derives directly from inverse probability weighting; the other, named target distribution weighting , relates to importance sampling. We empirically show that inverse probability weighting and target distribution weighting display performance comparable to matching techniques in terms of bias but outperform them in terms of efficiency (mean squared error) and computational speed (up to >870 times faster in an illustrative study). We illustrate implementation of the methods in 2 case studies where we investigate placebo treatments for multiple sclerosis and administration of aspirin in stroke patients. [ABSTRACT FROM AUTHOR]

Variable selection in regression models is a particularly important issue in epidemiology, where one usually encounters observational studies. In contrast to randomized trials or experiments, confounding is often not controlled by the study design, but has to be accounted for by suitable statistical methods. For instance, when risk factors should be identified with unconfounded effect estimates, multivariable regression techniques can help to adjust for confounders. We investigated the current practice of variable selection in 4 major epidemiologic journals in 2019 and found that the majority of articles used subject-matter knowledge to determine a priori the set of included variables. In comparison with previous reviews from 2008 and 2015, fewer articles applied data-driven variable selection. Furthermore, for most articles the main aim of analysis was hypothesis-driven effect estimation in rather low-dimensional data situations (i.e. large sample size compared with the number of variables). Based on our results, we discuss the role of data-driven variable selection in epidemiology. [ABSTRACT FROM AUTHOR]

In this study, we compared location data from a dedicated Global Positioning System (GPS) device with location data from smartphones. Data from the Interventions, Equity, and Action in Cities Team (INTERACT) Study, a study examining the impact of urban-form changes on health in 4 Canadian cities (Victoria, Vancouver, Saskatoon, and Montreal), were used. A total of 337 participants contributed data collected for about 6 months from the Ethica Data smartphone application (Ethica Data Inc. Toronto, Ontario, Canada) and the SenseDoc dedicated GPS (MobySens Technologies Inc. Montreal, Quebec, Canada) during the period 2017–2019. Participants recorded an average total of 14,781 Ethica locations (standard deviation, 19,353) and 197,167 SenseDoc locations (standard deviation, 111,868). Dynamic time warping and cross-correlation were used to examine the spatial and temporal similarity of GPS points. Four activity-space measures derived from the smartphone app and the dedicated GPS device were compared. Analysis showed that cross-correlations were above 0.8 at the 125-m resolution for the survey and day levels and increased as cell size increased. At the day or survey level, there were only small differences between the activity-space measures. Based on our findings, we recommend dedicated GPS devices for studies where the exposure and the outcome are both measured at high frequency and when the analysis will not be aggregate. When the exposure and outcome are measured or will be aggregated to the day level, the dedicated GPS device and the smartphone app provide similar results. [ABSTRACT FROM AUTHOR]

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]

Background: Since 1997, research on Gulf War illness (GWI) has predominantly used 3 case definitions—the original Research definition, the CDC definition, and modifications of the Kansas definition—but they have not been compared against an objective standard. Methods: All 3 case definitions were measured in the U.S. Military Health Survey by a computer-assisted telephone interview in a random sample (n = 6,497) of the 1991 deployed U.S. military force. The interview asked whether participants had heard nerve agent alarms during the conflict. A random subsample (n = 1,698) provided DNA for genotyping the PON1 Q192R polymorphism. Results: The CDC and the Modified Kansas definition without exclusions were satisfied by 41.7% and 39.0% of the deployed force, respectively, and were highly overlapping. The Research definition, a subset of the others, was satisfied by 13.6%. The majority of veterans meeting CDC and Modified Kansas endorsed fewer and milder symptoms; whereas, those meeting Research endorsed more symptoms of greater severity. The group meeting Research was more highly enriched with the PON1 192R risk allele than those meeting CDC and Modified Kansas, and Research had twice the power to detect the previously described gene-environment interaction between hearing alarms and RR homozygosity (adjusted relative excess risk due to interaction [aRERI] = 7.69; 95% CI 2.71–19.13) than CDC (aRERI = 2.92; 95% CI 0.96–6.38) or Modified Kansas without exclusions (aRERI = 3.84; 95% CI 1.30–8.52) or with exclusions (aRERI = 3.42; 95% CI 1.20–7.56). The lower power of CDC and Modified Kansas relative to Research was due to greater false-positive disease misclassification from lower diagnostic specificity. Conclusions: The original Research case definition had greater statistical power to detect a genetic predisposition to GWI. Its greater specificity favors its use in hypothesis-driven research; whereas, the greater sensitivity of the others favor their use in clinical screening for application of future diagnostic biomarkers and clinical care. [ABSTRACT FROM AUTHOR]

Background: The need to call out and expose authors for their persistence in improperly using epidemiology has been previously noted. Tsuda et al. have done well to expose Schüz et al.'s arguments/assertions in their recent publication in Environmental Heath. In this Comment, I point out that, also warranting being called out, are the arguments/assertions of Cléro et al. who, in their recent response to an article by Tsuda et al., reiterated the conclusions and recommendations derived from their European project, which were published in Environment International in 2021. Tsuda et al. had critiqued the Cléro et al. 2021 publication in their 2022 review article. However, in their response to it, Cléro et al. deflected by not addressing any of the key points that Tsuda et al. had made in their review regarding the aftermath of the Chernobyl and Fukushima nuclear accidents. In this Comment, I critique Cléro et al.'s inadequate response. Publication of this Comment will help in routing out the improper use of epidemiology in the formulation of public health policy and thereby reduce the influence of misinformation on both science and public policy. My critique of Cléro et al. is not dissimilar from Tsuda et al.'s critique of Schüz et al.: in as much as Schüz et al. should withdraw their work, so should Cléro et al.'s article be retracted. Main body: The response by Cléro et al. consists of four paragraphs. First was their assertion that the purpose of the SHAMISEN project was to make recommendations based on scientific evidence and that it was not a systematic review of all related articles. I point out that the Cléro et al. recommendations were not based on objective scientific evidence, but on biased studies. In the second paragraph, Cléro et al. reaffirmed the SHAMISEN Consortium report, which claimed that the overdiagnosis observed in non-exposed adults was applicable to children because children are mirrors of adults. However, the authors of that report withheld statements about secondary examinations in Fukushima that provided evidence against overdiagnosis. In the third paragraph, Cléro et al. provided an explanation regarding their disclosure of conflicting interests, which was contrary to professional norms for transparency and thus was unacceptable. Finally, their insistence that the Tsuda et al. study was an ecological study susceptible to "the ecological fallacy" indicated their lack of epidemiological knowledge about ecological studies. Ironically, many of the papers cited by Cléro et al. regarding overdiagnosis were, in fact, ecological studies. Conclusion: Cléro et al. and the SHAMISEN Consortium should withdraw their recommendation "not to launch a mass thyroid cancer screening after a nuclear accident, but rather to make it available (with appropriate information counselling) to those who request it." Their recommendation is based on biased evidence and would cause confusion regarding public health measures following a nuclear accident. Those authors should, in my assessment, acquaint themselves with modern epidemiology and evidence-based public health. Like Tsuda et al. recommended of Schüz et al., Cléro et al. ought also to retract their article. [ABSTRACT FROM AUTHOR]

COVID-19 as a global pandemic has had an unprecedented impact on the entire world. Projecting the future spread of the virus in relation to its characteristics for a specific suite of countries against a temporal trend can provide public health guidance to governments and organizations. Therefore, this paper presented an epidemiological comparison of the traditional SEIR model with an extended and modified version of the same model by splitting the infected compartment into asymptomatic mild and symptomatic severe. We then exposed our derived layered model into two distinct case studies with variations in mitigation strategies and non-pharmaceutical interventions (NPIs) as a matter of benchmarking and comparison. We focused on exploring the United Arab Emirates (a small yet urban centre (where clear sequential stages NPIs were implemented). Further, we concentrated on extending the models by utilizing the effective reproductive number (Rt) estimated against time, a more realistic than the static R0, to assess the potential impact of NPIs within each case study. Compared to the traditional SEIR model, the results supported the modified model as being more sensitive in terms of peaks of simulated cases and flattening determinations. [ABSTRACT FROM AUTHOR]

Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces the fundamentals of deep learning from an epidemiologic perspective. Specifically, this article reviews core concepts in machine learning (e.g. overfitting, regularization, and hyperparameters); explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks); and summarizes training, evaluation, and deployment of models. Conceptual understanding of supervised learning algorithms is the focus of the article; instructions on the training of deep learning models and applications of deep learning to causal learning are out of this article's scope. We aim to provide an accessible first step towards enabling the reader to read and assess research on the medical applications of deep learning and to familiarize readers with deep learning terminology and concepts to facilitate communication with computer scientists and machine learning engineers. [ABSTRACT FROM AUTHOR]

Background: Online questionnaires are commonly used to collect information from participants in epidemiological studies. This requires building questionnaires using machine-readable formats that can be delivered to study participants using web-based technologies such as progressive web applications. However, the paucity of open-source markup standards with support for complex logic make collaborative development of web-based questionnaire modules difficult. This often prevents interoperability and reusability of questionnaire modules across epidemiological studies. Results: We developed an open-source markup language for presentation of questionnaire content and logic, Quest, within a real-time renderer that enables the user to test logic (e.g., skip patterns) and view the structure of data collection. We provide the Quest markup language, an in-browser markup rendering tool, questionnaire development tool and an example web application that embeds the renderer, developed for The Connect for Cancer Prevention Study. Conclusion: A markup language can specify both the content and logic of a questionnaire as plain text. Questionnaire markup, such as Quest, can become a standard format for storing questionnaires or sharing questionnaires across the web. Quest is a step towards generation of FAIR data in epidemiological studies by facilitating reusability of questionnaires and data interoperability using open-source tools. [ABSTRACT FROM AUTHOR]

Systematic error from selection bias, uncontrolled confounding, and misclassification is ubiquitous in epidemiologic research but is rarely quantified using quantitative bias analysis (QBA). This gap may in part be due to the lack of readily modifiable software to implement these methods. Our objective is to provide computing code that can be tailored to an analyst's dataset. We briefly describe the methods for implementing QBA for misclassification and uncontrolled confounding and present the reader with example code for how such bias analyses, using both summary-level data and individual record-level data, can be implemented in both SAS and R. Our examples show how adjustment for uncontrolled confounding and misclassification can be implemented. Resulting bias-adjusted point estimates can then be compared to conventional results to see the impact of this bias in terms of its direction and magnitude. Further, we show how 95% simulation intervals can be generated that can be compared to conventional 95% confidence intervals to see the impact of the bias on uncertainty. Having easy to implement code that users can apply to their own datasets will hopefully help spur more frequent use of these methods and prevent poor inferences drawn from studies that do not quantify the impact of systematic error on their results. [ABSTRACT FROM AUTHOR]

Background: Respondent-driven sampling (RDS) is a peer chain-recruitment method for populations without a sampling frame or that are hard-to-reach. Although RDS is usually done face-to-face, the online version (WebRDS) has drawn a lot of attention as it has many potential benefits, despite this, to date there is no clear framework for its implementation. This article aims to provide guidance for researchers who want to recruit through a WebRDS. Methods: Description of the development phase: guidance is provided addressing aspects related to the formative research, the design of the questionnaire, the implementation of the coupon system using a free software and the diffusion plan, using as an example a web-based cross-sectional study conducted in Spain between April and June 2022 describing the working conditions and health status of homecare workers for dependent people. Results: The application of the survey: we discuss about the monitoring strategies throughout the recruitment process and potential problems along with proposed solutions. Conclusions: Under certain conditions, it is possible to obtain a sample with recruitment performance similar to that of other RDS without the need for monetary incentives and using a free access software, considerably reducing costs and allowing its use to be extended to other research groups. [ABSTRACT FROM AUTHOR]

The aim of this study was to update and validate the Pregnancy Physical Activity Questionnaire (PPAQ), using novel and innovative accelerometer and wearable camera measures in a free-living setting, to improve the measurement performance of this method for self-reporting physical activity. A prospective cohort of 50 eligible pregnant women were enrolled in early pregnancy (mean = 14.9 weeks' gestation). In early, middle, and late pregnancy, participants completed the updated PPAQ and, for 7 days, wore an accelerometer (GT3X-BT; ActiGraph, Pensacola, Florida) on the nondominant wrist and a wearable camera (Autographer; OMG Life (defunct)). At the end of the 7-day period, participants repeated the PPAQ. Spearman correlations between the PPAQ and accelerometer data ranged from 0.37 to 0.44 for total activity, 0.17 to 0.53 for moderate- to vigorous-intensity activity, 0.19 to 0.42 for light-intensity activity, and 0.23 to 0.45 for sedentary behavior. Spearman correlations between the PPAQ and wearable camera data ranged from 0.52 to 0.70 for sports/exercise and from 0.26 to 0.30 for transportation activity. Reproducibility scores ranged from 0.70 to 0.92 for moderate- to vigorous-intensity activity and from 0.79 to 0.91 for sports/exercise, and were comparable across other domains of physical activity. The PPAQ is a reliable instrument and a valid measure of a broad range of physical activities during pregnancy. [ABSTRACT FROM AUTHOR]

Prior applications of machine learning to population health have relied on conventional model assessment criteria, limiting the utility of models as decision support tools for public health practitioners. To facilitate practitioners' use of machine learning as a decision support tool for area-level intervention, we developed and applied 4 practice-based predictive model evaluation criteria (implementation capacity, preventive potential, health equity, and jurisdictional practicalities). We used a case study of overdose prevention in Rhode Island to illustrate how these criteria could inform public health practice and health equity promotion. We used Rhode Island overdose mortality records from January 2016–June 2020 (n  = 1,408) and neighborhood-level US Census data. We employed 2 disparate machine learning models, Gaussian process and random forest, to illustrate the comparative utility of our criteria to guide interventions. Our models predicted 7.5%–36.4% of overdose deaths during the test period, illustrating the preventive potential of overdose interventions assuming 5%–20% statewide implementation capacities for neighborhood-level resource deployment. We describe the health equity implications of use of predictive modeling to guide interventions along the lines of urbanicity, racial/ethnic composition, and poverty. We then discuss considerations to complement predictive model evaluation criteria and inform the prevention and mitigation of spatially dynamic public health problems across the breadth of practice. This article is part of a Special Collection on Mental Health. [ABSTRACT FROM AUTHOR]

Epidemiologic studies of low-frequency exposures or outcomes using metabolomics analyses of neonatal dried blood spots (DBS) often require assembly of samples with substantial differences in duration of storage. Independent assessment of stability of metabolites in archived DBS will enable improved design and interpretation of epidemiologic research utilizing DBS. Neonatal DBS routinely collected and stored as part of the California Genetic Disease Screening Program between 1983 and 2011 were used. The study population included 899 children without cancer before age 6 years, born in California. High-resolution metabolomics with liquid-chromatography mass spectrometry was performed, and the relative ion intensities of common metabolites and selected xenobiotic metabolites of nicotine (cotinine and hydroxycotinine) were evaluated. In total, we detected 26,235 mass spectral features across 2 separate chromatography methods (C18 hydrophobic reversed-phase chromatography and hydrophilic-interaction liquid chromatography). For most of the 39 metabolites related to nutrition and health status, we found no statistically significant annual trends across the years of storage. Nicotine metabolites were captured in the DBS with relatively stable intensities. This study supports the usefulness of DBS stored long-term for epidemiologic studies of the metabolome. -Omics-based information gained from DBS may also provide a valuable tool for assessing prenatal environmental exposures in child health research. [ABSTRACT FROM AUTHOR]

Accurate forecasts can inform response to outbreaks. Most efforts in influenza forecasting have focused on predicting influenza-like activity, with fewer on influenza-related hospitalizations. We conducted a simulation study to evaluate a super learner's predictions of 3 seasonal measures of influenza hospitalizations in the United States: peak hospitalization rate, peak hospitalization week, and cumulative hospitalization rate. We trained an ensemble machine learning algorithm on 15,000 simulated hospitalization curves and generated weekly predictions. We compared the performance of the ensemble (weighted combination of predictions from multiple prediction algorithms), the best-performing individual prediction algorithm, and a naive prediction (median of a simulated outcome distribution). Ensemble predictions performed similarly to the naive predictions early in the season but consistently improved as the season progressed for all prediction targets. The best-performing prediction algorithm in each week typically had similar predictive accuracy compared with the ensemble, but the specific prediction algorithm selected varied by week. An ensemble super learner improved predictions of influenza-related hospitalizations, relative to a naive prediction. Future work should examine the super learner's performance using additional empirical data on influenza-related predictors (e.g. influenza-like illness). The algorithm should also be tailored to produce prospective probabilistic forecasts of selected prediction targets. [ABSTRACT FROM AUTHOR]

Background: Emulating randomized controlled trials (RCTs) by real-world evidence (RWE) studies would benefit future clinical and regulatory decision-making by balancing the limitations of RCT. We aimed to evaluate whether the findings from RWE studies can support regulatory decisions derived from RCTs of non-vitamin K antagonist oral anticoagulants (NOACs) in patients with venous thromboembolism (VTE). Methods: Five landmark trials (AMPLIFY, RE-COVER II, Hokusai-VTE, EINSTEIN-DVT, and EINSTEIN-PE) of NOACs were emulated using the South Korean nationwide claims database (January 2012 to August 2020). We applied an active comparator and new-user design to include patients who initiated oral anticoagulants within 28 days from their VTE diagnoses. The prespecified eligibility criteria, exposure (each NOAC, such as apixaban, rivaroxaban, dabigatran, and edoxaban), comparator (conventional therapy, defined as subcutaneous heparin followed by warfarin), and the definition of outcomes from RCTs were emulated as closely as possible in each separate emulation cohort. The primary outcome was identical to each trial, which was defined as recurrent VTE or VTE-related death. The safety outcome was major bleeding. Propensity score matching was conducted to balance 69 covariates between the exposure groups. Effect estimates for outcomes were estimated using the Mantel–Haenszel method and Cox proportional hazards model and subsequently compared with the corresponding RCT estimates. Results: Compared to trial populations, real-world study populations were older (range: 63–69 years [RWE] vs. 54–59 years [RCT]), with more females (55–60.5% vs. 39–48.3%) and had a higher prevalence of active cancer (4.2–15.4% vs. 2.5–9.5%). The emulated estimates for effectiveness outcomes showed superior effectiveness of NOAC (AMPLIFY: relative risk 0.81, 95% confidence interval 0.70–0.94; RE-COVER II: hazard ratio [HR] 0.60, 0.37–0.96; Hokusai-VTE: 0.49, 0.31–0.78; EINSTEIN-DVT: 0.54, 0.33–0.89; EINSTEIN-PE: 0.50, 0.34–0.74), when contrasted with trials that showed non-inferiority. For safety outcomes, all emulations except for AMPLIFY and EINSTEIN-DVT yielded results consistent with their corresponding RCTs. Conclusions: This study revealed the feasibility of complementing RCTs with RWE studies by using claims data in patients with VTE. Future studies to consider the different demographic characteristics between RCT and RWE populations are needed. [ABSTRACT FROM AUTHOR]

Background: After reviewing selected scientific evidence, Schüz et al. made two recommendations in the 2018 International Agency for Research on Cancer (IARC) Technical Publication No. 46. Their first recommendation was against population thyroid screening after a nuclear accident, and the second was that consideration be given to offering a long-term thyroid monitoring program for higher-risk individuals (100–500 mGy or more radiation) after a nuclear accident. However, their review of the scientific evidence was inadequate and misrepresented the information from both Chernobyl and Fukushima. We wrote a review article published in Environmental Health in 2022 using the "Toolkit for detecting misused epidemiological methods." Schüz et al. critiqued our 2022 review article in 2023; their critique, based also on their 2018 IARC Technical Publication No. 46, was so fraught with problems that we developed this response. Main body: Schüz et al. suggest that hundreds of thyroid cancer cases in children and adolescents, detected through population thyroid examinations using ultrasound echo and conducted since October 2011 in Fukushima, were not caused by the 2011 Fukushima Daiichi Nuclear Power Plant accident. Schüz et al. compared thyroid cancers in Fukushima directly with those in Chernobyl after April 1986 and listed up to five reasons to deny a causal relationship between radiation and thyroid cancers in Fukushima; however, those reasons we dismiss based on available evidence. No new scientific evidence was presented in their response to our commentary in which we pointed out that misinformation and biased scientific evidence had formed the basis of their arguments. Their published article provided erroneous information on Fukushima. The article implied overdiagnosis in adults and suggested that overdiagnosis would apply to current Fukushima cases. The IARC report did not validate the secondary confirmatory examination in the program which obscures the fact that overdiagnosis may not have occurred as much in Fukushima. The report consequently precluded the provision of important information and measures. Conclusion: Information provided in the IARC Technical Publication No. 46 was based on selected scientific evidence resulting in both public and policy-maker confusion regarding past and present nuclear accidents, especially in Japan. It should be withdrawn. [ABSTRACT FROM AUTHOR]

Aims: To estimate the effectiveness of metformin on glycaemic parameters among participants with incident prediabetes attending Australian general practices. Methods: This retrospective cohort study used electronic health records of regular participants (3+ visits in two consecutive years) attending 383 Australian general practices (MedicineInsight). Participants with 'incident' prediabetes (newly recorded diagnosis between 2012 and 2017) and their glycaemic parameters (haemoglobin A1c [HbA1c] or fasting blood glucose [FBG]) at 6‐, 12‐, and 18–24 months post diagnosis (unexposed) or post‐management with metformin (treatment) were identified from the database. We estimated the average treatment effect (ATE) of metformin management on glycaemic parameters using both linear regression and augmented inverse probability weighting. Results: Of the 4770 investigated participants with 'incident' prediabetes, 10.2% were managed with metformin. Participants on metformin had higher HbA1c levels at the baseline than those unexposed (mean 45 mmol/mol [6.2%] and 41 mmol/mol [5.9%], respectively), but no differences were observed at 6–12 months (mmol/mol ATE 0.0, 95% CI −0.4; 0.7) or 12–18 months (ATE −0.3, 95% CI −1.2; 0.3). However, participants on metformin had lower mean HbA1c mmol/mol at 18–24 months (ATE −1.1, 95% CI −2.0; 0.1) than those unexposed. Consistent results were observed for FBG (ATE at 6–12 months −0.14 [95% CI −0.25; −0.04], 12–18 months 0.02 [95% CI −0.08; 0.13] and 18–24 months −0.07 [95% CI −0.25; 0.12]). Conclusion: The higher HbA1c and FBG baseline levels among participants with 'incident' prediabetes managed with metformin improved after 6–12 months of starting pharmacological management, and the effect persisted for up to 24 months. Management with metformin could prevent further deterioration of glycaemic levels. [ABSTRACT FROM AUTHOR]

Unlike parametric regression, machine learning (ML) methods do not generally require precise knowledge of the true data-generating mechanisms. As such, numerous authors have advocated for ML methods to estimate causal effects. Unfortunately, ML algorithms can perform worse than parametric regression. We demonstrate the performance of ML-based singly and doubly robust estimators. We used 100 Monte Carlo samples with sample sizes of 200, 1,200, and 5,000 to investigate bias and confidence-interval coverage under several scenarios. In a simple confounding scenario, confounders were related to the treatment and the outcome via parametric models. In a complex confounding scenario, the simple confounders were transformed to induce complicated nonlinear relationships. In the simple scenario, when ML algorithms were used, double-robust estimators were superior to singly robust estimators. In the complex scenario, single-robust estimators with ML algorithms were at least as biased as estimators using misspecified parametric models. Doubly robust estimators were less biased, but coverage was well below nominal. The use of sample splitting, inclusion of confounder interactions, reliance on a richly specified ML algorithm, and use of doubly robust estimators was the only explored approach that yielded negligible bias and nominal coverage. Our results suggest that ML-based singly robust methods should be avoided. [ABSTRACT FROM AUTHOR]

Background Studies examining the potential impact of mothers' health during pregnancy on the health of their offspring often rely on self-reported information gathered several years later. To assess the validity of this approach, we analysed data from a national case–control study of childhood cancer (diagnosed <15 years) that collected health information from both interviews and medical records. Methods Mothers' interview reports of infections and medications in pregnancy were compared with primary care records. Taking clinical diagnoses and prescriptions as the reference, sensitivity and specificity of maternal recall along with kappa coefficients of agreement were calculated. Differences in the odd ratios estimated using logistic regression for each information source were assessed using the proportional change in the odds ratio (OR). Results Mothers of 1624 cases and 2524 controls were interviewed ∼6 years (range 0–18 years) after their child's birth. Most drugs and infections were underreported; in general practitioner records, antibiotic prescriptions were nearly three times higher and infections >40% higher. Decreasing with increasing time since pregnancy, sensitivity was ⩽40% for most infections and all drugs except 'anti-epileptics and barbiturates' (sensitivity 80% among controls). ORs associated with individual drug/disease categories that were based on self-reported data varied from 26% lower to 26% higher than those based on medical records; reporting differences between mothers of cases and controls were not systematically in the same direction. Conclusions The findings highlight the scale of under-reporting and poor validity of questionnaire-based studies conducted several years after pregnancy. Future research using prospectively collected data should be encouraged to minimize measurement errors. [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]

Inverse probability weighting (IPW), a well-established method of controlling for confounding in observational studies with binary exposures, has been extended to analyses with continuous exposures. Methods developed for continuous exposures may not apply when the exposure is quasicontinuous because of irregular exposure distributions that violate key assumptions. We used simulations and cluster-randomized clinical trial data to assess 4 approaches developed for continuous exposures—ordinary least squares (OLS), covariate balancing generalized propensity scores (CBGPS), nonparametric covariate balancing generalized propensity scores (npCBGPS), and quantile binning (QB)—and a novel method, a cumulative probability model (CPM), in quasicontinuous exposure settings. We compared IPW stability, covariate balance, bias, mean squared error, and standard error estimation across 3,000 simulations with 6 different quasicontinuous exposures, varying in skewness and granularity. In general, CBGPS and npCBGPS resulted in excellent covariate balance, and npCBGPS was the least biased but the most variable. The QB and CPM approaches had the lowest mean squared error, particularly with marginally skewed exposures. We then successfully applied the IPW approaches, together with missing-data techniques, to assess how session attendance (out of a possible 15) in a partners-based clustered intervention among pregnant couples living with human immunodeficiency virus in Mozambique (2017–2022) influenced postpartum contraceptive uptake. [ABSTRACT FROM AUTHOR]

Objectives: Persons with Parkinson's disease (PD) have a higher prevalence of oral diseases and orofacial dysfunction, but knowledge about the use of dental care and whether their dental care needs are met is sparse. This study aimed to investigate the dental attendance and usage of dental care services of the total PD population in Denmark and compare it with a control group. Methods: National registers were used to identify the total PD population in Denmark (n = 6874) and to obtain data on their dental care from 2015 to 2019. These data were compared with a five‐fold age‐, gender‐ and geographically matched control group without PD (n = 34 285). Register data on age, gender, civil status, educational level, income, nursing homes status and mortality were also collected and adjusted for in the analyses. The dental attendance was analysed using χ2‐test with Bonferroni correction, and the type of dental care services was analysed using negative binomial regression analysis. Results: A significantly higher proportion of persons with PD were irregular attenders of the dental care system (21.0%), compared with the control group (16.9%). Persons with PD had a significantly higher overall usage of dental cares services. Most prominent was the high usage of treatment services, where persons with PD had a 1.50 times higher incidence rate of tooth extractions and a 1.71 times higher incidence rate of tooth fillings in the five years compared with the control group. Conclusion: Persons with PD are more often irregular users of dental care and receive more treatment services than the control group. This indicates a need for high‐quality prophylactic initiatives to prevent high filling and tooth extraction rates. Furthermore, this knowledge can be used by clinicians and decision makers to ensure optimal dental care for persons with PD. [ABSTRACT FROM AUTHOR]

Dimitris et al. (Am J Epidemiol. 2022;191(6):980–986) outline how the coronavirus disease 2019 (COVID-19) pandemic has, with mixed results, put epidemiology under the spotlight. While epidemiologic theory and methods have been critical in many successes, the ongoing global death toll from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the sometimes chaotic public messaging underscore that epidemiology as a field has room for improvement. Here, we use examples from psychiatric epidemiologic studies conducted during the COVID-19 era to reflect on errors driven by overlooking specific major methodological advances of modern epidemiology. We focus on: 1) use of nonrepresentative sampling in online surveys, which limits the potential knowledge to be gained from descriptive studies and amplifies collider stratification bias in causal studies; and 2) failure to acknowledge multiple versions of exposures (e.g. lockdown, school closure) and differences in prevalence of effect measure modifiers across contexts, which causes violations of the consistency assumption and lack of effect transportability. We finish by highlighting: 1) the heterogeneity of psychiatric epidemiologic results during the pandemic across place and sociodemographic groups and over time; 2) the importance of following the foundational advancements of modern epidemiology even in emergency settings; and 3) the need to limit the role of political agendas in cherry-picking and reporting epidemiologic evidence. [ABSTRACT FROM AUTHOR]

Background: The incidence of diagnostic delays is unknown for many diseases and specific healthcare settings. Many existing methods to identify diagnostic delays are resource intensive or difficult to apply to different diseases or settings. Administrative and other real-world data sources may offer the ability to better identify and study diagnostic delays for a range of diseases. Methods: We propose a comprehensive framework to estimate the frequency of missed diagnostic opportunities for a given disease using real-world longitudinal data sources. We provide a conceptual model of the disease-diagnostic, data-generating process. We then propose a bootstrapping method to estimate measures of the frequency of missed diagnostic opportunities and duration of delays. This approach identifies diagnostic opportunities based on signs and symptoms occurring prior to an initial diagnosis, while accounting for expected patterns of healthcare that may appear as coincidental symptoms. Three different bootstrapping algorithms are described along with estimation procedures to implement the resampling. Finally, we apply our approach to the diseases of tuberculosis, acute myocardial infarction, and stroke to estimate the frequency and duration of diagnostic delays for these diseases. Results: Using the IBM MarketScan Research databases from 2001 to 2017, we identified 2,073 cases of tuberculosis, 359,625 cases of AMI, and 367,768 cases of stroke. Depending on the simulation approach that was used, we estimated that 6.9–8.3% of patients with stroke, 16.0-21.3% of patients with AMI and 63.9–82.3% of patients with tuberculosis experienced a missed diagnostic opportunity. Similarly, we estimated that, on average, diagnostic delays lasted 6.7–7.6 days for stroke, 6.7–8.2 days for AMI, and 34.3–44.5 days for tuberculosis. Estimates for each of these measures was consistent with prior literature; however, specific estimates varied across the different simulation algorithms considered. Conclusions: Our approach can be easily applied to study diagnostic delays using longitudinal administrative data sources. Moreover, this general approach can be customized to fit a range of diseases to account for specific clinical characteristics of a given disease. We summarize how the choice of simulation algorithm may impact the resulting estimates and provide guidance on the statistical considerations for applying our approach to future studies. [ABSTRACT FROM AUTHOR]

Background: Traditionally, heritability has been estimated using family-based methods such as twin studies. Advancements in molecular genomics have facilitated the development of methods that use large samples of (unrelated or related) genotyped individuals.Methods: Here, we provide an overview of common methods applied in genetic epidemiology to estimate heritability, i.e. the proportion of phenotypic variation explained by genetic variation. We provide a guide to key genetic concepts required to understand heritability estimation methods from family-based designs (twin and family studies), genomic designs based on unrelated individuals [linkage disequilibrium score regression, genomic relatedness restricted maximum-likelihood (GREML) estimation] and family-based genomic designs (sibling regression, GREML-kinship, trio-genome-wide complex trait analysis, maternal-genome-wide complex trait analysis, relatedness disequilibrium regression).Results: We describe how heritability is estimated for each method and the assumptions underlying its estimation, and discuss the implications when these assumptions are not met. We further discuss the benefits and limitations of estimating heritability within samples of unrelated individuals compared with samples of related individuals.Conclusions: Overall, this article is intended to help the reader determine the circumstances when each method would be appropriate and why. [ABSTRACT FROM AUTHOR]

In diagnostic medicine, the true disease status of a patient is often represented on an ordinal scale—for example, cancer stage (0, I, II, III, or IV) or coronary artery disease severity measured using the Coronary Artery Disease Reporting and Data System (CAD-RADS) scale (none, minimal, mild, moderate, severe, or occluded). With advances in quantitation of diagnostic images and in artificial intelligence (AI), both supervised and unsupervised algorithms are being developed to help physicians correctly grade disease. Most of the diagnostic accuracy literature deals with binary disease status (disease present or absent); however, tests diagnosing ordinal-scaled diseases should not be reduced to a binary status just to simplify diagnostic accuracy testing. In this paper, we propose different characterizations of ordinal-scale accuracy for different clinical use scenarios, along with methods for comparing tests. In the simplest scenario, just the proportion of correct grades is considered; other scenarios address the magnitude and direction of misgrading; and at the other extreme, a weighted accuracy measure with weights based on the relative costs of different types of misgrading is presented. The various scenarios are illustrated using a coronary artery disease example where the accuracy of AI algorithms in providing patients with the correct CAD-RADS grade is assessed. [ABSTRACT FROM AUTHOR]

Background: Direct estimates of rare disease prevalence from public health surveillance may only be available in a few catchment areas. Understanding variation among observed prevalence can inform estimates of prevalence in other locations. The Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) conducts population-based surveillance of major muscular dystrophies in selected areas of the United States. We identified sources of variation in prevalence estimates of Duchenne and Becker muscular dystrophy (DBMD) within MD STARnet from published literature and a survey of MD STARnet investigators, then developed a logic model of the relationships between the sources of variation and estimated prevalence. Results: The 17 identified sources of variability fell into four categories: (1) inherent in surveillance systems, (2) particular to rare diseases, (3) particular to medical-records-based surveillance, and (4) resulting from extrapolation. For the sources of uncertainty measured by MD STARnet, we estimated each source's contribution to the total variance in DBMD prevalence. Based on the logic model we fit a multivariable Poisson regression model to 96 age–site–race/ethnicity strata. Age accounted for 74% of the variation between strata, surveillance site for 6%, race/ethnicity for 3%, and 17% remained unexplained. Conclusion: Variation in estimates derived from a non-random sample of states or counties may not be explained by demographic differences alone. Applying these estimates to other populations requires caution. [ABSTRACT FROM AUTHOR]

Background: Measuring multiple and higher‐order interaction effects between multiple categorical variables proves challenging. Objectives: To illustrate a multilevel modelling approach to studying complex interactions. Methods: We apply a two‐level random‐intercept linear regression to a binary outcome for individuals (level‐1) nested within strata (level‐2) defined by all observed combinations of multiple categorical exposure variables. As a pedagogic application, we analyse 36 strata defined by five risk factors of preeclampsia (parity, previous preeclampsia, chronic hypertension, multiple pregnancies, body mass index category) among 652,603 women in the Swedish Medical Birth Registry between 2002 and 2010. Results: The absolute risk of preeclampsia was 4% but was predicted to vary from 1% to 44% across strata. The stratum discriminatory accuracy was 30% according to the variance partition coefficient (VPC) and 0.73 according to the area under the receiver operating characteristic curve (AUC). While the risk heterogeneity across strata was primarily due to the main effects of the categories defining the strata, 5% of the variation was attributable to their two‐ and higher‐way interaction effects. One stratum presented a positive interaction, and two strata presented negative interaction. Conclusions: Multilevel modelling is an innovative tool for identifying and analysing higher‐order interaction effects. Further work is needed to explore how this approach can best be applied to making causal inferences. [ABSTRACT FROM AUTHOR]

Oral conditions represent a critical public health challenge, and together with descriptive and predictive epidemiology, causal inference has a crucial role in developing and testing preventive oral health interventions. By identifying not just correlations but actual causes of disease, causal inference may quantify the average effect of interventions and guide policies. Although authors are not usually explicit about it, most oral health studies are guided by causal questions. However, methodological deficiencies limit their interpretability and the implementation of their findings. This manuscript is a call to action on the use of causal inference in oral research. Its application starts with asking theoretically sound questions and being explicit about causal relationships, defining the estimates to evaluate, and measuring them properly. Beyond promoting causal analytical approaches, we emphasize the need for more causal thinking to promote thoughtful research questions and the use of appropriate methods to answer them. Causal inference relies on the plausibility of assumptions underlying the data analysis and the quality of the data, and we argue that high‐quality observational studies can be used to estimate average causal effects. Although individual efforts to embrace causal inference in dentistry are essential, they will not yield substantial results if not led by a systematic and structural change in the field. We urge scientific societies, funding bodies, dental schools, and journals to promote transparency in research, causal thinking, and causal inference projects to move the field toward more meaningful studies. It is also time for researchers to move forward and connect with the community, co‐produce investigations and translate their findings, and engage in interventions that impact public health. We conclude by highlighting the importance of triangulating results from different data sources and methods to support causal inference and inform decision‐making on interventions to effectively improve population oral health. [ABSTRACT FROM AUTHOR]

Conceptual or theoretical models are crucial in developing causal hypotheses and interpreting study findings, but they have been underused and misused in aetiological research, particularly in dentistry and oral epidemiology. Good models should incorporate updated evidence and clarify knowledge gaps to derive logical hypotheses. Developing models and deriving testable hypotheses in operational models can be challenging, as seen in the four examples referred to in this commentary. One challenge concerns the theoretical validity of the model, while another relates to difficulties in operationalizing abstract concepts. A third challenge refers to the lack of sufficient information in the dataset to test partially or even the whole model. Finally, a common challenge is the application of a conceptual model to different contexts. Among the existing methodological approaches to operationalize conceptual models, causal graphs may be helpful, especially when combined with approaches from diverse disciplinary fields via triangulation. [ABSTRACT FROM AUTHOR]

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]

The All of Us Research Program, a health and genetics epidemiologic data collection program, has been substantially affected by the coronavirus disease 2019 (COVID-19) pandemic. Although the program is highly digital in nature, certain aspects of the data collection require in-person interaction between staff and participants. Before the pandemic, the program was enrolling approximately 12,500 participants per month at more than 400 clinical sites. In March 2020, because of the pandemic, all in-person activity at program sites and by engagement partners was paused to develop processes and procedures for in-person activities that incorporated strict safety protocols. In addition, the program adopted new data collection methodologies to reduce the need for in-person activities. Through February 2022, a total of 224 clinical sites had reactivated in-person activity, and all enrollment and engagement partners have adopted new data collection methods that can be used remotely. As the COVID-19 pandemic persists, the program continues to require safety procedures for in-person activity and continues to generate and pilot methodologies that reduce risk and make it easier for participants to provide information. [ABSTRACT FROM AUTHOR]

There have been calls for race to be denounced as a biological variable and for a greater focus on racism, instead of solely race, when studying racial health disparities in the United States. These calls are grounded in extensive scholarship and the rationale that race is not a biological variable, but instead socially constructed, and that structural/institutional racism is a root cause of race-related health disparities. However, there remains a lack of clear guidance for how best to incorporate these assertions about race and racism into tools, such as causal diagrams, that are commonly used by epidemiologists to study population health. We provide clear recommendations for using causal diagrams to study racial health disparities that were informed by these calls. These recommendations consider a health disparity to be a difference in a health outcome that is related to social, environmental, or economic disadvantage. We present simplified causal diagrams to illustrate how to implement our recommendations. These diagrams can be modified based on the health outcome and hypotheses, or for other group-based differences in health also rooted in disadvantage (e.g. gender). Implementing our recommendations may lead to the publication of more rigorous and informative studies of racial health disparities. [ABSTRACT FROM AUTHOR]

Background: Although treatment and control of diabetes can prevent complications and reduce morbidity, few data sources exist at the state level for surveillance of diabetes comorbidities and control. Surveys and electronic health records (EHRs) offer different strengths and weaknesses for surveillance of diabetes and major metabolic comorbidities. Data from self-report surveys suffer from cognitive and recall biases, and generally cannot be used for surveillance of undiagnosed cases. EHR data are becoming more readily available, but pose particular challenges for population estimation since patients are not randomly selected, not everyone has the relevant biomarker measurements, and those included tend to cluster geographically. Methods: We analyzed data from the National Health and Nutritional Examination Survey, the Health and Retirement Study, and EHR data from the DARTNet Institute to create state-level adjusted estimates of the prevalence and control of diabetes, and the prevalence and control of hypertension and high cholesterol in the diabetes population, age 50 and over for five states: Alabama, California, Florida, Louisiana, and Massachusetts. Results: The estimates from the two surveys generally aligned well. The EHR data were consistent with the surveys for many measures, but yielded consistently lower estimates of undiagnosed diabetes prevalence, and identified somewhat fewer comorbidities in most states. Conclusions: Despite these limitations, EHRs may be a promising source for diabetes surveillance and assessment of control as the datasets are large and created during the routine delivery of health care. Trial Registration: Not applicable. [ABSTRACT FROM AUTHOR]

The association between traumatic brain injury (TBI) and risk for Alzheimer disease and related dementias (ADRD) has been investigated in multiple studies, yet reported effect sizes have varied widely. Large differences in comorbid and demographic characteristics between individuals with and without TBI could result in spurious associations between TBI and poor outcomes, even when control for confounding is attempted. Yet, inadvertent control for post-TBI exposures (e.g., psychological and physical trauma) could result in an underestimate of the effect of TBI. Choice of the unexposed or comparison group is critical to estimating total associated risk. The objective of this study was to highlight how selection of the comparison group impacts estimates of the effect of TBI on risk for ADRD. Using data on Veterans aged ≥55 years obtained from the Veterans Health Administration (VA) for years 1999–2019, we compared risk of ADRD between Veterans with incident TBI (n = 9440) and (1) the general population of Veterans who receive care at the VA (All VA) (n = 119,003); (2) Veterans who received care at a VA emergency department (VA ED) (n = 111,342); and (3) Veterans who received care at a VA ED for non-TBI trauma (VA ED NTT) (n = 65,710). In inverse probability of treatment weighted models, TBI was associated with increased risk of ADRD compared with All VA (hazard ratio [HR] 1.94; 95% confidence interval [CI] 1.84, 2.04), VA ED (HR 1.42; 95% CI 1.35, 1.50), and VA ED NTT (HR 1.12; 95% CI 1.06, 1.18). The estimated effect of TBI on incident ADRD was strongly impacted by choice of the comparison group. [ABSTRACT FROM AUTHOR]

A covariate-adjusted estimate of an exposure-outcome association may be biased if the exposure variable suffers measurement error. We propose an approach to correct for exposure measurement error in a covariate-adjusted estimate of the association between a continuous exposure variable and outcome of interest. Our proposed approach requires data for a reference population in which the exposure was a priori set to some known level (e.g. 0, and is therefore unexposed); however, our approach does not require an exposure validation study or replicate measures of exposure, which are typically needed when addressing bias due to exposure measurement error. A key condition for this method, which we refer to as "partial population exchangeability," requires that the association between a measured covariate and outcome in the reference population equals the association between that covariate and outcome in the target population in the absence of exposure. We illustrate the approach using simulations and an example. [ABSTRACT FROM AUTHOR]

Background: Burden disease related to chronic HBV infection is increasing worldwide. Monitoring Hepatitis B occurrence is difficult due to intrinsic characteristics of the infection, nonetheless analyzing this information improves strategic planning towards reducing the burden related to chronic infection. In this line of thought, this study aims to analyze national and regional epidemiology of Hepatitis B and it's temporal trends based on Brazilian reported cases.Methods: Data obtained from the Brazilian National Notifiable Disease Reporting System (SINAN) from 2007 to 2018 were classified by infection status with an original classification algorithm, had their temporal trends analyzed by Joinpoint regression model and were correlated with gender, age and region.Results: Of the 487,180 hepatitis B cases notified to SINAN, 97.65% had it infection status correctly classified by the new algorithm. Hepatitis B detection rate, gender and age-distribution were different among Brazilian regions. Overall, detection rates remained stable from 2007 to 2018, achieving their maximal value (56.1 cases per 100,000 inhabitants) in North region. However, there were different temporal trends related to different hepatitis B status and age. Women mean age at notification were always inferior to those of men and the difference was higher in Central-West, North and Northeast regions.Conclusion: Hepatitis B affects heterogeneously different populations throughout Brazilian territory. The differences shown in its temporal trends, regional, gender and age-related distribution helps the planning and evaluation of control measures in Brazil. [ABSTRACT FROM AUTHOR]

Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we developed a model for predicting monthly dengue cases in Brazilian cities 1 month ahead, using data from 2007–2019. We compared different machine learning algorithms and feature selection methods using epidemiologic and meteorological variables. We found that different models worked best in different cities, and a random forests model trained on monthly dengue cases performed best overall. It produced lower errors than a seasonal naive baseline model, gradient boosting regression, a feed-forward neural network, or support vector regression. For each city, we computed the mean absolute error between predictions and true monthly numbers of dengue cases on the test data set. The median error across all cities was 12.2 cases. This error was reduced to 11.9 when selecting the optimal combination of algorithm and input features for each city individually. Machine learning and especially decision tree ensemble models may contribute to dengue surveillance in Brazil, as they produce low out-of-sample prediction errors for a geographically diverse set of cities. [ABSTRACT FROM AUTHOR]

Epidemiology has traditionally used quantitative approaches to characterizing disease prevalence and studying the effects of medical and public health interventions. Despite the power of such methods, they leave important gaps in understanding population health that can be addressed using qualitative and mixed methods. In this commentary, I describe philosophical differences in qualitative and quantitative approaches to research and explain how they can be used together to strengthen epidemiologic inquiry. [ABSTRACT FROM AUTHOR]

Ensuring that patients with opioid use disorder (OUD) have access to optimal medication therapies is a critical challenge in substance use epidemiology. Rudolph et al. (Am J Epidemiol. 2023;XXX(X):XXXX-XXXX) demonstrated that sophisticated data-adaptive statistical techniques can be used to learn optimal, individualized treatment rules that can aid providers in choosing a medication treatment modality for a particular patient with OUD. This important work also highlights the effects of the mathematization of epidemiologic research. Here, we define mathematization and demonstrate how it operates in the context of effectiveness research on medications for OUD using the paper by Rudolph et al. as a springboard. In particular, we address the normative dimension of mathematization and how it tends to resolve a fundamental tension in epidemiologic practice between technical sophistication and public health considerations in favor of more technical solutions. The process of mathematization is a fundamental part of epidemiology; we argue not for eliminating it but for balancing mathematization and technical demands equally with practical and community-centric public health needs. [ABSTRACT FROM AUTHOR]

Background: A large number of rectal cancer patients are cured after treatment. In such cases, cure models are used for survival analysis. This study aims to investigate factors that affect survival in rectal cancer using the Cox mixture cure model. Methods: Following a retrospective design, medical documents and pathological findings of newly diagnosed rectal cancer cases hospitalized at Imam Hossein Hospital, Tehran, Iran, from 2005 to 2013 were reviewed. The patients were followed up with until May 2018. The Cox mixture cure model was used. Data analysis was carried out using Statistical Analysis System (SAS) version 9.4. The statistical significance level was considered to be 0.05. Results: Four hundred nine patients were included in this study. The mean of disease-free survival was 87.08 ± 3.2 months. The hazard of the event for the patients who were drug abusers was 2.37 (95% CI: 1.30–4.31) times more than the other cases (P = 0.005). The odds ratio of the event for patients of stage III was 3.04 (95% CI: 1.51–6.12) times more than the cases of stage I (P = 0.002), and for the patients of stage IV, it was 12.42 (95% CI: 4.17–37.01) times more than patients of stage I (P < 0.001). Conclusions: The results of this cure model indicate that the tumor stage, tumor grade, and history of drug abuse are the risk factors for the survival of patients with rectal cancer. These results can attract the attention of doctors and patients who want to be aware of their physical status and prognosis. [ABSTRACT FROM AUTHOR]

There is growing acknowledgement of the legacy of White supremacy and racism in the discipline of epidemiology. Our department in the University of Washington School of Public Health undertook a systematic effort to begin addressing institutionalized racism and inclusive teaching in our courses. In July 2020, we introduced a new tool (the "Course Development Plan" (CDP)) to advance our curriculum. The CDP includes 2 components: 1) a guideline document that provides strategies on how to modify curricula and classroom teaching to incorporate antiracism and principles of equity, diversity, and inclusion (EDI); and 2) a structured worksheet for instructors to share EDI and antiracism practices they already incorporate and practices they plan to incorporate into their classes. Worksheets for each class are submitted prior to the beginning of the quarter and are reviewed by a peer faculty member and at least 1 epidemiology student; reviewers provide written feedback on the CDP worksheet. Further evaluation to assess the impact of the CDP process on classroom climate is ongoing. In this commentary, we discuss our department's efforts, the challenges we faced, and our hopes for next steps. [ABSTRACT FROM AUTHOR]

Measurement error is pervasive in epidemiologic research. Epidemiologists often assume that mismeasurement of study variables is nondifferential with respect to other analytical variables and then rely on the heuristic that "nondifferential misclassification will bias estimates towards the null." However, there are many exceptions to the heuristic for which bias towards the null cannot be assumed. In this paper, we compile and characterize 7 exceptions to this rule and encourage analysts to take a more critical and nuanced approach to evaluating and discussing bias from nondifferential mismeasurement. [ABSTRACT FROM AUTHOR]

Background: Malaria is transmitted by the bite of mosquitoes which belong to the genus Anopheles. The Tumbes region, located in the northern border of Peru, despite the permanent presence of the vector, was free from malaria transmission, and there are only sporadic imported cases. Material and Methods: A case series study of an outbreak of malaria caused by P. vivax associated with a conglomerate of imported cases was carried out in the context of massive Venezuelan migration to Peru. Results: The control activities implemented through the modified epidemiological fence methodology to control the reintroduction of the disease are described; transmission of the outbreak related to Venezuelan migrants in transit was detected. Conclusions: the reintroduction of malaria in the Tumbes region was related to cases imported from Venezuela. The modified epidemiological fences made it possible to improve the collection of cases in situ. [ABSTRACT FROM AUTHOR]

Simulation methods are a powerful set of tools that can allow researchers to better characterize phenomena from the real world. As such, the ability to simulate data represents a critical set of skills that epidemiologists should use to better understand epidemiologic concepts and ensure that they have the tools to continue to self-teach even when their formal instruction ends. Simulation methods are not always taught in epidemiology methods courses, whereas causal directed acyclic graphs (DAGs) often are. Therefore, this paper details an approach to building simulations from DAGs and provides examples and code for learning to perform simulations. We recommend using very simple DAGs to learn the procedures and code necessary to set up a simulation that builds on key concepts frequently of interest to epidemiologists (e.g. mediation, confounding bias, M bias). We believe that following this approach will allow epidemiologists to gain confidence with a critical skill set that may in turn have a positive impact on how they conduct future epidemiologic studies. [ABSTRACT FROM AUTHOR]