Invited Commentary: Raising a High-Pressure Alarm About Pediatric Hypertension.

Hypertension is a common "silent killer" in adult medicine, but epidemiologic estimates of elevated blood pressure in children and adolescents are challenged by under-diagnosis and resultant low utilization of relevant administrative or billing codes. In the article by Horgan et al (Am J Epidemiol 2024), children and adolescents with hypertension and elevated blood pressure were identified using direct assessment of blood pressure measurements available in the electronic health record from both inpatient and outpatient visits ("clinical cohort") in comparison to diagnosis codes ("claims-based cohort"). The study population included 3.75 million pediatric healthcare visits available in the US Food and Drug Administration's Sentinel System. While the study applied a relatively novel methodology to interrogate available clinical data within the EHR to better understand the prevalence of pediatric hypertension and raised concern for a higher occurrence of hypertension among children and adolescents than previously realized using claims codes, the utility of the prevalence estimates may be limited by the potential for misclassification bias inherent in EHR data. However, these data raise important concerns about relaying solely on ICD-9-CM/ICD-10-CM codes to quantify the epidemiology of pediatric hypertension and highlight opportunities to address elevated blood pressure in children that could improve long-term cardiovascular health.

The role and limitations of electronic medical records versus patient interviews for determining symptoms, underlying comorbidities, and medication usage for patients with COVID-19.

Electronic medical records (EMR) are important for rapidly compiling information to determine disease characteristics (e.g., symptoms) and risk factors (e.g., underlying comorbidities, medications) for disease-related outcomes. To assess EMR data accuracy, agreement between EMR abstractions and patient interviews was evaluated. Symptoms, medical history, and medication usage among COVID-19 patients collected from EMR and patient interviews were compared using overall agreement (same answer in EMR and interview), reported agreement (yes answer in both EMR and interview among those who reported yes in either), and Kappa statistics. Overall, patients reported more symptoms in interviews than in EMR abstractions. Overall agreement was high (≥50% for 20/23 symptoms), but only subjective fever and dyspnea had reported agreement of ≥50%. Kappa statistics for symptoms were generally low. Reported medical conditions had greater agreement with all condition categories (10/10) having ≥50% overall agreement and half (5/10) having ≥50% reported agreement. More non-prescription medications were reported in interviews than in EMR abstractions leading to low reported agreement (28%). Discordance was observed for symptoms, medical history, and medication usage between EMR abstractions and patient interviews. Investigations utilizing EMR to describe clinical characteristics and identify risk factors should consider the potential for incomplete data, particularly for symptoms and medications.

Influence of Incomplete Death Information on Cumulative Risk Estimates in United States Claims Data.

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) ages ≥55 in Optum claims from 2003 to 2020. Differences in cumulative risks of the cardiovascular endpoint due to censoring of death (cause-specific), as compared to treating death as a competing event (sub-distribution), increased with greater follow-up time and older age, where event and mortality risks were higher. Among ramipril users (selected results), 5-year cause-specific and sub-distribution 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 sub-distribution 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.

Comparing the Effect of Early Versus Delayed Metformin Treatment on Glycaemic Parameters Among Australian Adults With Incident Diabetes: Evidence Using a National General Practice Database.

PURPOSE: To compare the effect of early vs delayed metformin treatment for glycaemic management among patients with incident diabetes. METHODS: Cohort study using electronic health records of regular patients (1+ visits per year in 3 consecutive years) aged 40+ years with 'incident' diabetes attending Australian general practices (MedicineInsight, 2011-2018). Patients with incident diabetes were defined as those who had a) 12+ months of medical data before the first recording of a diabetes diagnosis AND b) a diagnosis of 'diabetes' recorded at least twice in their electronic medical records or a diagnosis of 'diabetes' recorded only once combined with at least 1 abnormal glycaemic result (i.e., HbA1c ≥6.5%, fasting blood glucose [FBG] ≥7.0 mmol/L, or oral glucose tolerance test ≥11.1mmol/L) in the preceding 3 months. The effect of early (<3 months), timely (3-6 months), or delayed (6-12 months) initiation of metformin treatment vs no metformin treatment within 12 months of diagnosis on HbA1c and FBG levels 3 to 24 months after diagnosis was compared using linear regression and augmented inverse probability weighted models. Patients initially managed with other antidiabetic medications (alone or combined with metformin) were excluded. FINDINGS: Of 18,856 patients with incident diabetes, 38.8% were prescribed metformin within 3 months, 3.9% between 3 and 6 months, and 6.2% between 6 and 12 months after diagnosis. The untreated group had the lowest baseline parameters (mean HbA1c 6.4%; FBG 6.9mmol/L) and maintained steady levels throughout follow-up. Baseline glycaemic parameters for those on early treatment with metformin (<3 months since diagnosis) were the highest among all groups (mean HbA1c 7.6%; FBG 8.8mmol/L), reaching controlled levels at 3 to 6 months (mean HbA1c 6.5%; FBG 6.9mmol/L) with sustained improvement until the end of follow-up (mean HbA1c 6.4%; FBG 6.9mmol/L at 18-24 months). Patients with timely and delayed treatment also improved their glycaemic parameters after initiating treatment (timely treatment: mean HbA1c 7.3% and FBG 8.3mmol/L at 3-6 months; 6.6% and 6.9mmol/L at 6-12 months; delayed treatment: mean HbA1c 7.2% and FBG 8.4mmol/L at 6-12 months; 6.7% and 7.1mmol/L at 12-18 months). Compared to those not managed with metformin, the corresponding average treatment effect for HbA1c at 18-24 months was +0.04% (95%CI -0.05;0.10) for early, +0.24% (95%CI 0.11;0.37) for timely, and +0.29% (95%CI 0.20;0.39) for delayed treatment. IMPLICATIONS: Early metformin therapy (<3 months) for patients recently diagnosed with diabetes consistently improved HbA1c and FBG levels in the first 24 months of diagnosis.

Invited Commentary: Combining Information to Answer Epidemiologic Questions About a Target Population.

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.

The Rotterdam Study. Design update and major findings between 2020 and 2024.

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.

Data Distribution: Normal or Abnormal?

Determining if the frequency distribution of a given data set follows a normal distribution or not is among the first steps of data analysis. Visual examination of the data, commonly by Q-Q plot, although is acceptable by many scientists, is considered subjective and not acceptable by other researchers. One-sample Kolmogorov-Smirnov test with Lilliefors correction (for a sample size ≥ 50) and Shapiro-Wilk test (for a sample size < 50) are common statistical tests for checking the normality of a data set quantitatively. As parametric tests, which assume that the data distribution is normal (Gaussian, bell-shaped), are more robust compared to their non-parametric counterparts, we commonly use transformations (e.g., log-transformation, Box-Cox transformation, etc.) to make the frequency distribution of non-normally distributed data close to a normal distribution. Herein, I wish to reflect on presenting how to practically work with these statistical methods through examining of real data sets.

A Design and Analytical Strategy for Monitoring Disease Positivity and Biomarker Levels in Accessible Closed Populations.

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.

Comparing Location Data From Smartphone and Dedicated Global Positioning System Devices: Implications for Epidemiologic Research.

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.

From complexity to clarity: how directed acyclic graphs enhance the study design of systematic reviews and meta-analyses.

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.

Stratified analysis in comparative effectiveness studies that emulate randomized trials.

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.

Confounder Adjustment Using the Disease Risk Score: A Proposal for Weighting Methods.

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.

Geographic Variation, Economic Activity, and Labor Market Characteristics in Trajectories of Suicide in the United States, 2008-2020.

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.

Characterizing Imbalance in the Tails of the Propensity Score Distribution.

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.

Recent Methodological Trends in Epidemiology: No Need for Data-Driven Variable Selection?

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.

Monitoria de metodologia científica: relato de experiência em um componente curricular de saúde coletiva / Scientific methodologic mentoring: experience report in a public health academic subject

RESUMO Introdução: A monitoria acadêmica é uma ferramenta complementar à construção do conhecimento dos discentes do ensino superior. Apresenta-se como mecanismo de auxílio e de orientação para os monitorados ao mesmo tempo que desenvolve as habilidades comunicacionais para além dos saberes essenciais do monitor. O presente trabalho tem como objetivo relatar uma experiência de monitoria com diferentes estratégias de ensino e de avaliação por meio de metodologias ativas, aplicando atividades teóricas e práticas interdisciplinares para estudantes de Medicina. Relato de experiência: Este relato de monitoria, realizado em um componente curricular de saúde coletiva da graduação em Medicina de uma universidade pública federal do Rio Grande do Sul, apresenta dados quantitativos e discute os conhecimentos adquiridos com a aplicação das atividades do projeto de ensino. Discussão: Analisaram-se quatro semestres, em que se compararam registros realizados em formulários on-line sem identificação dos respondentes referentes à participação das monitorias de caráter teórico e prático, sendo a maior adesão de participantes nas atividades práticas (42,31% versus 25,55%). Depreenderam-se as principais motivações dos discentes para buscar a monitoria, sendo a elucidação de uma etapa da simulação de pesquisa a mais requisitada. Houve predominância dos valores próximos ao máximo em monitorias práticas em relação às teóricas na avaliação geral delas, sendo considerados três aspectos: importância da atividade; clareza e didática da monitora; esclarecimento de dúvidas. Percebeu-se que o tempo destinado aos encontros variou de forma considerável, sendo mais frequente destinar 60 minutos às atividades teóricas e 30 minutos às atividades práticas. A comparação da proposta de atividades práticas e teóricas aplicadas por meio de metodologias ativas no projeto de monitoria evidenciou o quanto o discente, monitor ou monitorando, beneficia-se do processo de ensino-aprendizagem quando inserido como responsável principal pela própria educação. Conclusão: Na perspectiva da monitora, houve um fortalecimento das próprias competências relacionadas ao saber científico, assim como se observaram um nivelamento da turma em relação aos conteúdos de base e a progressão gradual da aquisição de um raciocínio crítico do estudante da área da saúde.

ABSTRACT Introduction: Academic monitoring is used as a complementary tool for knowledge construction among higher education students. It works as a mechanism to aid and guide the students, as well as to develop the communication skills beyond the essential knowledge of the monitor. This study aims to report a monitoring experience using different teaching and evaluation strategies through active forms of learning, applying interdisciplinary theoretical and practical activities for medical students. Experience report: This experience report, developed in a public health curricular component of a medical undergraduate course at a federal public university in Rio Grande do Sul, Brazil, presents quantitative data and debates the knowledge acquired through the implementation of education project activities. Discussion: Four semesters were analyzed, comparing records obtained from online forms without identification of the respondents related to the participation of the theoretical and practical monitoring sessions, with a higher participation rate in practical activities (42.31% vs. 25.55%). The main motivations for students looking for monitoring meetings were identified, with explanation of a research simulation stage being the most requested one. Practical monitoring sessions received higher evaluations compared to theoretical ones, considering three aspects: activity importance, clarity and didactics of the monitor, and elucidation of doubts. It was observed a relevant variation in the time assigned to the meetings, with a more frequent duration session of 60 minutes for theoretical activities and 30 minutes for practical activities. The comparison of the proposed practical and theoretical activities implemented through active forms of learning in the monitoring project evidenced the knowledge addition for both students, monitor or monitored, in the teaching-learning process when they are actively engaged in their own education. Conclusion: From the monitor's perspective, there was a strengthening of their own competencies related to scientific knowledge. Furthermore, a leveling of the class was observed regarding the basic content and a gradual progression towards critical thinking among health area students.

Exposing additional authors who suppress evidence about radiation-induced thyroid cancer in children: a Comment adding to Tsuda et al.'s response to Schüz et al. (2023).

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.

The effect of disease misclassification on the ability to detect a gene-environment interaction: implications of the specificity of case definitions for research on Gulf War illness.

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.

Quest markup for developing FAIR questionnaire modules for epidemiologic studies.

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.