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The World Health Organization specifies that sexual health requires the potential for pleasurable and safe sexual experiences. Yet epidemiologic research into sexual pleasure and other positive sexual outcomes has been scant. In this commentary, we aim to support the development and adoption of sex-positive epidemiology, which we define as epidemiology that incorporates the study of pleasure and other positive features alongside sexually transmitted infections and other familiar negative outcomes. We first call epidemiologists' attention to the potential role that stigma plays in the suppression of sex-positive research. We further describe existing measures of sex-positive constructs that may be useful in epidemiologic research. Finally, the study of sex-positive constructs is vulnerable to biases that are well-known to epidemiologists, especially selection bias, information bias, and confounding. We outline how these biases influence existing research and identify opportunities for future research. Epidemiologists have the potential to contribute a great deal to the study of sexuality by bringing their considerable methodological expertise to long-standing challenges in the field. We hope to encourage epidemiologists to broaden their sexual health research to encompass positive outcomes and pleasure. [ABSTRACT FROM AUTHOR]

Administrative claims databases often do not capture date or fact of death, so studies using these data may inappropriately treat death as a censoring event—equivalent to other withdrawal reasons—rather than a competing event. We examined 1-, 3-, and 5-year inverse-probability-of-treatment weighted cumulative risks of a composite cardiovascular outcome among 34 527 initiators of telmisartan (exposure) and ramipril (referent), who were aged ≥55 years, in Optum (United States) claims data from 2003 to 2020. Differences in cumulative risks of the cardiovascular endpoint due to censoring of death (cause-specific), as compared with treating death as a competing event (subdistribution), increased with greater follow-up time and older age, where event and mortality risks were higher. Among ramipril users, 5-year cause-specific and subdistribution cumulative risk estimates per 100, respectively, were 16.4 (95% CI, 15.3-17.5) and 16.2 (95% CI, 15.1-17.3) among ages 55-64 (difference = 0.2) and were 43.2 (95% CI, 41.3-45.2) and 39.7 (95% CI, 37.9-41.4) among ages ≥75 (difference = 3.6). Plasmode simulation results demonstrated the differences in cause-specific versus subdistribution cumulative risks to increase with increasing mortality rate. We suggest researchers consider the cohort's baseline mortality risk when deciding whether real-world data with incomplete death information can be used without concern. This article is part of a Special Collection on Pharmacoepidemiology. [ABSTRACT FROM AUTHOR]

Calculation of sample size is an essential part of research study design since it affects the reliability and feasibility of the research study. In this article, we look at the principles of sample size calculation for different types of research studies. [ABSTRACT FROM AUTHOR]

Background: Spatial epidemiology has emerged as an important subfield of epidemiology over the past quarter century. We trace the origins of spatial epidemiology and note that its emergence coincided with technological developments in spatial statistics and geography. We hypothesize that spatial epidemiology makes important contributions to descriptive epidemiology and analytic risk-factor studies but is not yet aligned with epidemiology’s current focus on causal inference and intervention. Methods: We conducted a systematic review of studies indexed in PubMed that used the term “spatial epidemiolog*” in the title, abstract, or keywords. Excluded articles were not written in English, examined disease in animals, or reported biologic pathogen distribution only. We coded the included papers into five categories (review, demonstration of method, descriptive, analytic, and intervention) and recorded the unit of analysis (i.e., individual vs. ecological). We additionally examined articles coded as analytic ecologic studies using scales for lexical content. Results: A total of 482 articles met the inclusion criteria, including 76 reviews, 117 demonstrations of methods, 122 descriptive studies, 167 analytic studies, and 0 intervention studies. Demonstration studies were most common from 2006 to 2014, and analytic studies were most common after 2015. Among the analytic ecologic studies, those published in later years used more terms relevant to spatial statistics (incidence rate ratio =1.3; 95% confidence interval [CI] = 1.1, 1.5) and causal inference (incidence rate ratio =1.1; 95% CI = 1.1, 1.2). Conclusions: Spatial epidemiology is an important and growing subfield of epidemiology. We suggest a re-orientation to help align its practice with the goals of contemporary epidemiology. [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.

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).

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]

ObjectivesThe Simplified Oral Hygiene Index for Maxillary Incisors (OHI-MIS) is a novel plaque scoring system adapted for young children. This study describes calibration training and testing used to establish the inter- and intra-rater reliability for OHI-MIS measured from clinical photographs.MethodsTwo raters from the Coordinated Oral Health Promotion Chicago (CO-OP) and one from the Behavioral EConomics for Oral health iNnovation (BEECON) randomized controlled trials (RCTs) underwent calibration with gold standard raters, followed by annual re-calibration. Raters from CO-OP also completed inter-rater reliability testing; all three raters completed intra-rater reliability testing rounds. Photographs were obtained from children aged 9-39 months.ResultsAll three raters achieved greater than 0.77 Lin's Concordance Correlation (LCC) versus gold standard consensus during calibration. All three raters had LCC ≥0.83 at recalibration 1 year later. CO-OP trial raters scored 604 photos (151 sets of 4 photographs); mostly both raters were somewhat/very confident in their scoring (≥89%), describing the most photos as "clear" (90% and 81%). The CO-OP inter-rater LCC for total OHI-MIS score was 0.86, changing little when low quality or confidence photos were removed. All three raters demonstrated high intra-rater reliability (≥0.83).ConclusionsThe OHI-MIS plaque scoring system on photos had good reliability within and between trials following protocol training and calibration. OHI-MIS provides a novel asynchronous plaque scoring system for use in young children. Non-clinicians in field or clinical settings can obtain photographs, offering new opportunities for research and clinical care.

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]

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]

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]

The Rotterdam Study is a population-based cohort study, started in 1990 in the district of Ommoord in the city of Rotterdam, the Netherlands, with the aim to describe the prevalence and incidence, unravel the etiology, and identify targets for prediction, prevention or intervention of multifactorial diseases in mid-life and elderly. The study currently includes 17,931 participants (overall response rate 65%), aged 40 years and over, who are examined in-person every 3 to 5 years in a dedicated research facility, and who are followed-up continuously through automated linkage with health care providers, both regionally and nationally. Research within the Rotterdam Study is carried out along two axes. First, research lines are oriented around diseases and clinical conditions, which are reflective of medical specializations. Second, cross-cutting research lines transverse these clinical demarcations allowing for inter- and multidisciplinary research. These research lines generally reflect subdomains within epidemiology. This paper describes recent methodological updates and main findings from each of these research lines. Also, future perspective for coming years highlighted. [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]

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]

Background: Non-adherence to the drug treatment of type 2 diabetes mellitus is a problem that generates significant clinical, health, economic, and social damage. Objective: The objective of this study was to describe the research methods on adherence to the drug treatment of type 2 diabetes mellitus in the elderly people of the Family Health Strategy in Ribeirão Preto, São Paulo. Method: This is a cross-sectional study with random sampling by clusters conducted from March to October 2018. The data collection instrument had sociodemographic variables, health care, access and use of services, emotional distress related to diabetes, and use of medication, with adherence estimated by the Brief Medication Questionnaire. Results: A total of 16 conglomerates were drawn from the 45 groups. Then, 338 participants were obtained in proportion to the frequency of elderly people with diabetes. Most of the participants were female (66.3%), aged between 60 and 69 years (49.4%), self-reported skin color/ race being white (60.9%), studied from 4 to 11 years (53.0%), had a partner (55.6%), and belonged to economic class C (51.8%). Conclusions: The methods used in this research proved feasible for the context of Primary Health Care, as the cost was accessible and made it possible to investigate the variables that influenced treatment adherence. [ABSTRACT FROM AUTHOR]

While frameworks to systematically assess bias in systematic reviews and meta-analyses (SRMAs) and frameworks on causal inference are well established, they are less frequently integrated beyond the data analysis stages. This paper proposes the use of Directed Acyclic Graphs (DAGs) in the design stage of SRMAs. We hypothesize that DAGs created and registered a priori can offer a useful approach to more effective and efficient evidence synthesis. DAGs provide a visual representation of the complex assumed relationships between variables within and beyond individual studies prior to data analysis, facilitating discussion among researchers, guiding data analysis, and may lead to more targeted inclusion criteria or set of data extraction items. We illustrate this argument through both experimental and observational case examples. [ABSTRACT FROM AUTHOR]

Purpose: For observational cohort studies that employ matching by propensity scores (PS), preliminary stratification by consequential predictors of outcome better emulates stratified randomization and potentially reduces variance and bias through relaxed dependence on modeling assumptions. We assessed the impact of pre‐stratification in two real‐life examples. For both, prior evidence from placebo‐controlled randomized clinical trials (RCTs) suggested small or no risk reduction, but observational analysis suggested protection, presumably the result of confounding bias. Study Design and Setting: The study populations consisted of Medicare beneficiaries (2014‐18) with type 2 diabetes initiating either (i) empagliflozin versus dipeptidyl peptidase‐4 inhibitors (DPP‐4i) or (ii) empagliflozin versus glucagon‐like peptide‐1 receptor agonists (GLP‐1RA). The outcome was myocardial infarction or stroke. We estimated hazard ratios (HR) and rate differences (RD) after controlling for 143 pre‐exposure covariates via 1:1 PS matching after (1) PS estimation in the total cohort (total‐cohort PS‐matching) and (2) PS estimation separately by baseline cardiovascular disease (stratified PS matching). Results: Stratified PS matching resulted in HRs that exceeded those from total‐cohort PS‐matching by 13% and 9%, respectively, for the comparisons of empagliflozin to DPP‐4i and GLP‐1RA. Against both comparators, HRs and RDs after stratified PS matching were closer to the null, with slightly higher variances (2%–3%) than those after total‐cohort PS matching. Conclusion: Stratified PS matching produced effect estimates closer to the expected trial findings than total‐cohort PS matching. The price paid in increased variance was minimal. [ABSTRACT FROM AUTHOR]

Abstract 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 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 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 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.

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]

The analysis of time series studies linking daily counts of a health indicator with environmental variables (e.g., mortality or hospital admissions with air pollution concentrations or temperature; or motor vehicle crashes with temperature) is usually conducted with Poisson regression models controlling for long-term and seasonal trends using temporal strata. When the study includes multiple zones, analysts usually apply a two-stage approach: first, each zone is analyzed separately, and the resulting zone-specific estimates are then combined using meta-analysis. This approach allows zone-specific control for trends. A one-stage approach uses spatio-temporal strata and could be seen as a particular case of the case-time series framework recently proposed. However, the number of strata can escalate very rapidly in a long time series with many zones. A computationally efficient alternative is to fit a conditional Poisson regression model, avoiding the estimation of the nuisance strata. To allow for zone-specific effects, we propose a conditional Poisson regression model with a random slope, although available frequentist software does not implement this model. Here, we implement our approach in the Bayesian paradigm, which also facilitates the inclusion of spatial patterns in the effect of interest. We also provide a possible extension to deal with overdispersed data. We first introduce the equations of the framework and then illustrate their application to data from a previously published study on the effects of temperature on the risk of motor vehicle crashes. We provide R code and a semi-synthetic dataset to reproduce all analyses presented. [ABSTRACT FROM AUTHOR]

We propose a novel definition of selection bias in analytic epidemiology using potential outcomes. This definition captures selection bias under both the structural approach (where conditioning on selection into the study opens a noncausal path from exposure to disease in a directed acyclic graph) and the traditional definition (where a given measure of association differs between the study sample and the population eligible for inclusion). This approach is nonparametric, and selection bias under the approach can be analyzed using single-world intervention graphs both under and away from the null hypothesis. It allows the simultaneous analysis of confounding and selection bias, it explicitly links the selection of study participants to the estimation of causal effects using study data, and it can be adapted to handle selection bias in descriptive epidemiology. Through examples, we show that this approach provides a novel perspective on the variety of mechanisms that can generate selection bias and simplifies the analysis of selection bias in matched studies and case-cohort studies. [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]

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]

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]

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]

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]

Racial residential segregation is associated with multiple adverse health outcomes in Black individuals. Yet, the influence of structural racism and racial residential segregation on brain aging is less understood. In this study, we investigated the association between cumulative exposure to racial residential segregation over 25 years (1985-2010) in young adulthood, as measured by the Getis-Ord Gi* statistic, and year 25 measures of brain volume (cerebral, gray matter, white matter, and hippocampal volumes) in midlife. We studied 290 Black participants with available brain imaging data who were enrolled in the Coronary Artery Risk Development in Young Adults (CARDIA) Study, a prospective cohort study. CARDIA investigators originally recruited 2,637 Black participants aged 18-30 years from 4 field centers across the United States. We conducted analyses using marginal structural models, incorporating inverse probability of treatment weighting and inverse probability of censoring weighting. We found that compared with low/medium segregation, greater cumulative exposure to a high level of racial residential segregation throughout young adulthood was associated with smaller brain volumes in general (e.g., for cerebral volume, β = -0.08, 95% confidence interval: -0.15, -0.02) and with a more pronounced reduction in hippocampal volume, though results were not statistically significant. Our findings suggest that exposure to segregated neighborhoods may be associated with worse brain aging.

In many perinatal pharmacoepidemiologic studies, exposure to a medication is classified as "ever exposed" versus "never exposed" within each trimester or even over the entire pregnancy. This approach is often far from real-world exposure patterns, may lead to exposure misclassification, and does not to incorporate important aspects such as dosage, timing of exposure, and treatment duration. Alternative exposure modeling methods can better summarize complex, individual-level medication use trajectories or time-varying exposures from information on medication dosage, gestational timing of use, and frequency of use. We provide an overview of commonly used methods for more refined definitions of real-world exposure to medication use during pregnancy, focusing on the major strengths and limitations of the techniques, including the potential for method-specific biases. Unsupervised clustering methods, including k-means clustering, group-based trajectory models, and hierarchical cluster analysis, are of interest because they enable visual examination of medication use trajectories over time in pregnancy and complex individual-level exposures, as well as providing insight into comedication and drug-switching patterns. Analytical techniques for time-varying exposure methods, such as extended Cox models and Robins' generalized methods, are useful tools when medication exposure is not static during pregnancy. We propose that where appropriate, combining unsupervised clustering techniques with causal modeling approaches may be a powerful approach to understanding medication safety in pregnancy, and this framework can also be applied in other areas of epidemiology.

Abstract 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 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.

The timely identification of expected surges of cases during infectious disease epidemics is essential for allocating resources and preparing interventions. Failing to detect critical phases in time may lead to delayed implementation of interventions and have serious consequences. This study describes a simple way to evaluate whether an epidemic wave is likely to be present based solely on daily new case count data. The proposed measure compares two models that assume exponential or linear dynamics, respectively. The most important assumption of this approach is that epidemic waves are characterized rather by exponential than linear growth in the daily number of new cases. Technically, the coefficient of determination of two regression analyses is used to approximate a Bayes factor, which quantifies the support for the exponential over the linear model and can be used for epidemic wave detection. The trajectory of the coronavirus epidemic in three countries is analyzed and discussed for illustration. The proposed measure detects epidemic waves at an early stage, which are otherwise visible only by inspecting the development of case count data retrospectively. Major limitations include missing evidence on generalizability and performance compared to other methods. Nevertheless, the outlined approach may inform public health decision-making and serve as a starting point for scientific discussions on epidemic waves.

Extensive empirical health research leverages variation in the timing and location of policy changes as quasi-experiments. Multiple social policies may be adopted simultaneously in the same locations, creating co-occurrence that must be addressed analytically for valid inferences. The pervasiveness and consequences of co-occurring policies have received limited attention. We analyzed a systematic sample of 13 social policy databases covering diverse domains including poverty, paid family leave, and tobacco use. We quantified policy co-occurrence in each database as the fraction of variation in each policy measure across different jurisdictions and times that could be explained by covariation with other policies. We used simulations to estimate the ratio of the variance of effect estimates under the observed policy co-occurrence to variance if policies were independent. Policy co-occurrence ranged from very high for state-level cannabis policies to low for country-level sexual minority-rights policies. For 65% of policies, greater than 90% of the place-time variation was explained by other policies. Policy co-occurrence increased the variance of effect estimates by a median of 57-fold. Co-occurring policies are common and pose a major methodological challenge to rigorously evaluating health effects of individual social policies. When uncontrolled, co-occurring policies confound one another, and when controlled, resulting positivity violations may substantially inflate the variance of estimated effects. Tools to enhance validity and precision for evaluating co-occurring policies are needed.

Social policies have great potential to improve population health and reduce health disparities. Increasingly, those doing empirical research have sought to quantify the health effects of social policies by exploiting variation in the timing of policy changes across places. Multiple social policies are often adopted simultaneously or in close succession in the same locations, creating co-occurrence that must be handled analytically for valid inferences. Although this is a substantial methodological challenge for researchers aiming to isolate social policy effects, only in a limited number of studies have researchers systematically considered analytic solutions within a causal framework or assessed whether these solutions are being adopted. We designated 7 analytic solutions to policy co-occurrence, including efforts to disentangle individual policy effects and efforts to estimate the combined effects of co-occurring policies. We used an existing systematic review of social policies and health to evaluate how often policy co-occurrence is identified as a threat to validity and how often each analytic solution is applied in practice. Of the 55 studies, only in 17 (31%) did authors report checking for any co-occurring policies, although in 36 studies (67%), at least 1 approach was used that helps address policy co-occurrence. The most common approaches were adjusting for measures of co-occurring policies; defining the outcome on subpopulations likely to be affected by the policy of interest (but not other co-occurring policies); and selecting a less-correlated measure of policy exposure. As health research increasingly focuses on policy changes, we must systematically assess policy co-occurrence and apply analytic solutions to strengthen studies on the health effects of social policies.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.

Abstract 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.

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]

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: 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]