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AbstractBackground and purposeMethodsResultsConclusionPublic medical insurance data are increasingly used to study myasthenia gravis (MG); however, a validated case definition is lacking. We assessed the clinical characteristics of patients identified according to previously used case definitions.Patients with diagnosis code G70.0 (myasthenia gravis) who visited Severance Hospital between January 2019 and December 2020 were retrospectively analyzed. Three case definitions used in previous epidemiologic studies were applied to the included patients, and their performance was evaluated based on sensitivity, specificity, and positive predictive value (PPV).A total of 843 patients with diagnosis code G70.0 were identified, and 796 (96.7%) met the clinical diagnostic criteria for MG. All three previously-used case definitions showed a PPV of >98% in identifying clinically diagnosed MG, with sensitivities ranging from 68.7 to 71.1%. Case definition #1 was unable to identify 230 patients with MG: 42.6% were in remission, 23.5% were lost to follow-up, and 25.2% were receiving prescriptions elsewhere. A similar pattern was observed for definitions #2 and #3: Patients with thymoma-associated MG who were followed-up by thoracic surgeons were frequently missed based on the previously used case definitions. A proposed case definition that additionally includes a history of thymoma enhanced the sensitivity to 77.9%, while maintaining a PPV of 99.4%.The previously used case definitions showed a high PPV but were unable to identify patients in remission or followed-up elsewhere. These limitations can be overcome by utilizing a new case definition and expanding the study duration. [ABSTRACT FROM AUTHOR]

Objective: The Mental and Substance Use Disorders Prevalence Study (MDPS) builds upon previous epidemiologic studies to provide estimates of prevalence and treatment rates of mental and substance use disorders among adults aged 18–65 in the U.S. The study background and methods are described. Method: The MDPS employed novel techniques such as the inclusion of household, prison, homeless and state psychiatric hospital populations, a semi‐structured clinical interview administered by trained clinical interviewers to assess disorders, the assessment of both past year and lifetime schizophrenia spectrum disorder (SSD) using full Diagnostic and Statistical Manual 5 criteria, as well as other mental and substance use disorders, and video‐based interviewing. Population specific and combined sample weights were developed to estimate nationally representative prevalence and treatment rates. Results: Data collection was conducted between October 2020 and October 2022 resulting in 5679 clinical interviews. The statistical weighting and analytic plan are described. Weighted response rates and reasons for non‐response are provided for each study population. Conclusions: The MDPS successfully developed and employed novel techniques to estimate the prevalence and treatment rates of mental and substance use disorders in both household and non‐household populations, including some of the most impairing disorders such as SSD. [ABSTRACT FROM AUTHOR]

Abstract Objective The Mental and Substance Use Disorders Prevalence Study (MDPS) builds upon previous epidemiologic studies to provide estimates of prevalence and treatment rates of mental and substance use disorders among adults aged 18–65 in the U.S. The study background and methods are described. Method The MDPS employed novel techniques such as the inclusion of household, prison, homeless and state psychiatric hospital populations, a semi‐structured clinical interview administered by trained clinical interviewers to assess disorders, the assessment of both past year and lifetime schizophrenia spectrum disorder (SSD) using full Diagnostic and Statistical Manual 5 criteria, as well as other mental and substance use disorders, and video‐based interviewing. Population specific and combined sample weights were developed to estimate nationally representative prevalence and treatment rates. Results Data collection was conducted between October 2020 and October 2022 resulting in 5679 clinical interviews. The statistical weighting and analytic plan are described. Weighted response rates and reasons for non‐response are provided for each study population. Conclusions The MDPS successfully developed and employed novel techniques to estimate the prevalence and treatment rates of mental and substance use disorders in both household and non‐household populations, including some of the most impairing disorders such as SSD.

PurposeWe examine whether the race and armed status interact to modify the risk of being fatally shot by police within categories of civilian age and mental illness status, and U.S. region.MethodsData are from The Washington Post online public-use database of all U.S. police-involved shooting deaths. The sample includes black and white males with known armed status who were killed from 1/1/2015 through 12/31/2019 (n = 3090). A case-only design is used to assess multiplicative interaction using adjusted logistic regression.ResultsThe fully adjusted interaction estimate is null (SOR = 0.75; 95% confidence interval [CI] = 0.55-1.04). However, adjusted estimates within strata show that the risk of being armed versus unarmed when fatally shot is smaller for black than white males older than 54 years (SOR = 0.18; 95% CI = 0.06-0.65), those showing mental illness signs (SOR = 0.50; 95% CI = 0.26-0.98), and those killed in the South (SOR = 0.52; 95% CI = 0.33-0.83), and that the risk is greater in the Midwest (SOR = 2.42; 95% CI = 1.11-5.26). Notably, there is no black-white difference in armed status among younger age groups (SOR≈0.89).ConclusionThe race and armed status may interact leaving black males at a higher risk of being unarmed than white males when fatally shot by police among those older than 54 years, mentally impaired, and residing in the South. Causal interaction suggests a lower risk for unarmed blacks in the Midwest. Researchers should further explore the utility of the case-only design to study social-environmental interaction.

Traditional active vaccine safety monitoring involves pre-specifying health outcomes and biologically plausible outcome-specific time windows of concern, limiting the adverse events that can be evaluated. In this study, we used tree-based scan statistics to look broadly for >60,000 possible adverse events after bivalent COVID-19 vaccination. Vaccine Safety Datalink enrollees aged ≥5 years receiving Moderna or Pfizer-BioNTech bivalent COVID-19 vaccine through November 2022 were followed for 56 days post-vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within the hierarchical ICD-10-CM diagnosis code "tree" and temporally within post-vaccination follow-up. The conditional self-controlled tree-temporal scan statistic was used, conditioning on total number of cases of each diagnosis and total number of cases of any diagnosis occurring during the scanning risk window across the entire tree. P = 0.01 was the pre-specified cut-off for statistical significance. Analysis included 352,509 doses of Moderna and 979,189 doses of Pfizer-BioNTech bivalent vaccines. After Moderna vaccination, no statistically significant clusters were found. After Pfizer-BioNTech, there were clusters of unspecified adverse events (Days 1–3, p = 0.0001–0.0007), influenza (Days 35–56, p = 0.0001), cough (Days 44–55, p = 0.0002), and COVID-19 (Days 52–56, p = 0.0004). For Pfizer-BioNTech only, we detected clusters of: (1) unspecified adverse effects, as have been observed in other vaccine studies using this method, and (2) respiratory disease toward the end of follow-up. The respiratory clusters were likely due to overlap of follow-up with the spread of respiratory syncytial virus, influenza, and COVID-19, i.e., confounding by seasonality. The untargeted nature of the method and its inherent adjustment for the many diagnoses and risk intervals evaluated are unique advantages. Limitations include susceptibility to time-varying confounding, lower statistical power for assessing risks of specific outcomes than in traditional studies targeting fewer outcomes, and the possibility of missing adverse events not strongly clustered in time or within the "tree." [ABSTRACT FROM AUTHOR]

Serologic studies are crucial for clarifying dynamics of the coronavirus disease pandemic. Past work on serologic studies (e.g., during influenza pandemics) has made relevant contributions, but specific conditions of the current situation require adaptation. Although detection of antibodies to measure exposure, immunity, or both seems straightforward conceptually, numerous challenges exist in terms of sample collection, what the presence of antibodies actually means, and appropriate analysis and interpretation to account for test accuracy and sampling biases. Successful deployment of serologic studies depends on type and performance of serologic tests, population studied, use of adequate study designs, and appropriate analysis and interpretation of data. We highlight key questions that serologic studies can help answer at different times, review strengths and limitations of different assay types and study designs, and discuss methods for rapid sharing and analysis of serologic data to determine global transmission of severe acute respiratory syndrome coronavirus 2.

Purpose of reviewThis review considers statistical issues in the design and analysis of the studies used to develop long-acting formulations of antiretrovirals for pre-exposure prophylaxis (PrEP).Recent findingAn abundant pipeline of products is maturing. Accelerating their evaluation as clinical products requires abandonment of noninferiority standards. Randomized trials should be based on the comparison of principled but innovative estimates of background HIV risk and enrich enrollment for those who do not desire current PrEP products. At every stage of testing, innovative analyses can be applied to help inform and accelerate later studies.SummaryThe development of new long-acting PrEP regimens can be accelerated by innovations in design, ingenuity in synthesizing data sources, and application of causal inference methods.

Test-negative designs are commonplace in assessments of influenza vaccination effectiveness, estimating this value from the exposure odds ratio of vaccination among individuals treated for acute respiratory illness who test positive for influenza virus infection. This approach is widely believed to recover the vaccine direct effect by correcting for differential health-care-seeking behavior among vaccinated and unvaccinated persons. However, the relationship of the measured odds ratio to true vaccine effectiveness is poorly understood. We derived the odds ratio under circumstances of real-world test-negative studies. The odds ratio recovers the vaccine direct effect when 2 conditions are met: 1) Individuals' vaccination decisions are uncorrelated with exposure or susceptibility to the test-positive or test-negative conditions, and 2) vaccination confers "all-or-nothing" protection (whereby certain individuals have no protection while others are perfectly protected). Biased effect-size estimates arise if either condition is unmet. Such bias might suggest misleading associations of vaccine effectiveness with time since vaccination or the force of infection of influenza. The test-negative design could also fail to correct for differential health-care-seeking behavior among vaccinated and unvaccinated persons without stringent criteria for enrollment and testing. Our findings demonstrate a need to reassess how data from test-negative studies can inform policy decisions.

Tree-based scan statistics have been successfully used to study the safety of several vaccines without prespecifying health outcomes of concern. In this study, the binomial tree-based scan statistic was applied sequentially to detect adverse events in days 1–28 compared with days 29–56 after recombinant herpes zoster (RZV) vaccination, with 5 looks at the data and formal adjustment for the repeated analyses over time. IBM MarketScan data on commercially insured persons ≥50 years of age receiving RZV during January 1, 2018, to May 5, 2020, were used. With 999,876 doses of RZV included, statistically significant signals were detected only for unspecified adverse effects/complications following immunization, with attributable risks as low as 2 excess cases per 100,000 vaccinations. Ninety percent of cases in the signals occurred in the week after vaccination and, based on previous studies, likely represent nonserious events like fever, fatigue, and headache. Strengths of our study include its untargeted nature, self-controlled design, and formal adjustment for repeated testing. Although the method requires prespecification of the risk window of interest and may miss some true signals detectable using the tree-temporal variant of the method, it allows for early detection of potential safety problems through early initiation of ongoing monitoring. [ABSTRACT FROM AUTHOR]

The Centers for Disease Control and Prevention's Vaccine Safety Datalink (VSD) has been performing safety surveillance for COVID-19 vaccines since their earliest authorization in the United States. Complementing its real-time surveillance for pre-specified health outcomes using pre-specified risk intervals, the VSD conducts tree-based data-mining to look for clustering of a broad range of health outcomes after COVID-19 vaccination. This study's objective was to use this untargeted, hypothesis-generating approach to assess the safety of first booster doses of Pfizer-BioNTech (BNT162b2), Moderna (mRNA-1273), and Janssen (Ad26.COV2.S) COVID-19 vaccines. VSD enrollees receiving a first booster of COVID-19 vaccine through April 2, 2022 were followed for 56 days. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within both the hierarchical ICD-10-CM code structure and the follow-up period. The self-controlled tree-temporal scan statistic was used, conditioning on the total number of cases for each diagnosis. P-values were estimated by Monte Carlo simulation; p = 0.01 was pre-specified as the cut-off for statistical significance of clusters. More than 2.4 and 1.8 million subjects received Pfizer-BioNTech and Moderna boosters after an mRNA primary series, respectively. Clusters of urticaria/allergy/rash were found during Days 10–15 after the Moderna booster (p = 0.0001). Other outcomes that clustered after mRNA boosters, mostly with p = 0.0001, included unspecified adverse effects, common vaccine-associated reactions like fever and myalgia, and COVID-19. COVID-19 clusters were in Days 1–10 after booster receipt, before boosters would have become effective. There were no noteworthy clusters after boosters following primary Janssen vaccination. In this untargeted data-mining study of COVID-19 booster vaccination, a cluster of delayed-onset urticaria/allergy/rash was detected after the Moderna booster, as has been reported after Moderna vaccination previously. Other clusters after mRNA boosters were of unspecified or common adverse effects and COVID-19, the latter evidently reflecting immunity to COVID-19 after 10 days. [ABSTRACT FROM AUTHOR]

The purpose of this study was to identify and compare the research priorities of Australian practicing chiropractors and academics across listed research domains and to seek their views on existing chiropractic research strategies. Concurrent objectives were to gain insight into the perspectives on characteristics of research and solicit ideas and suggestions for future research from both groups. This study used a mixed-method research design to collect data using an online survey portal. Australian chiropractic academics (n = 220) and practicing chiropractors who were also members of a nationally representative, practice-based research network database (n = 1680) were invited to participate. Data were collected (February 19, 2019, to May 24, 2019). The free-text data were analyzed primarily via semantic coding and verbatim referential units in cases where the category was an exact match for the textual data. Content analyses of the qualitative data were presented in a tabulated and narrative manner as identified domains. Selected representative examples were provided verbatim. The response rate for the survey was 44% for full-time equivalent academics, 8% for casuals and part-time chiropractic academics, and 21.5% for Australian Chiropractic Research Network database chiropractic practitioners. Open-text data comprised a narrower focus on musculoskeletal (MSK) conditions and opposition or reservations by academics and some practitioners toward the research agenda of those espousing traditional concepts and terminology. Comments from both groups illustrate the strongly held views that characterize divergent factions of the chiropractic profession. Some practitioners were highly critical of the narrow focus and epistemological paradigm of Australian university-based research, while others were strongly supportive of the traditional focus of the Australian Spinal Research Foundation. Australian academics at the 4 university-based programs held the view that MSK and spinal pain, for which some evidence already exists, should be the priority of future research, building on what is known. Practitioners believed that future research should be directed toward expanded areas such as basic science, younger populations, and non-MSK conditions. Respondents were sharply divided on attitudes toward traditional chiropractic terminology, concepts, and philosophy and the utility of future research on these topics. Our qualitative findings suggest there is a division in the Australian chiropractic profession regarding research direction and priorities. This divide exists between academics and researchers and within field practitioners. This study highlights the attitudes, opinions, and perceptions of important stakeholder groups, which should be considered by decision-makers when formulating research policy, strategy, and prioritization of funding. [ABSTRACT FROM AUTHOR]

We evaluated the effectiveness of a community-based intervention for dengue vector control in Ouagadougou, the capital city of Burkina Faso. Households in the intervention (n = 287) and control (n = 289) neighborhoods were randomly sampled and the outcomes collected before the intervention (October 2015) and after the intervention (October 2016). The intervention reduced residents' exposure to dengue vector bites (vector saliva biomarker difference -0.08 [95% CI -0.11 to -0.04]). The pupae index declined in the intervention neighborhood (from 162.14 to 99.03) and increased in the control neighborhood (from 218.72 to 255.67). Residents in the intervention neighborhood were less likely to associate dengue with malaria (risk ratio 0.70 [95% CI 0.58-0.84]) and had increased knowledge about dengue symptoms (risk ratio 1.44 [95% CI 1.22-1.69]). Our study showed that well-planned, evidence/community-based interventions that control exposure to dengue vectors are feasible and effective in urban settings in Africa that have limited resources.

BackgroundPrevious studies have found that overestimating treatment effects (i.e. 'optimism bias') leads to underpowered clinical trials. The prevalence of 'optimism bias' in contemporary National Clinical Trials Network (NCTN) cancer clinical trials is unknown.MethodsWe conducted a systematic review of NCTN phase III randomized trials published from January 2007 to January 2017. We compared the hypothesized versus observed treatment effects in each trial, and examined whether trial-related factors were correlated with the study results. We also reviewed the methods of each protocol to assess whether a rationale for the hypothesized effect size was provided.ResultsWe identified 161 clinical trials, of which 130 were eligible for analysis. Original protocols could not be located for 8 trials (5.0%). Twenty-eight trials (21.5%) observed a statistically significant difference in the primary end point favoring the experimental treatment. The median ratio of observed-to-expected hazard ratios among trials that observed a statistically significant effect on the primary end point was 1.07 (range: 0.33-1.28) versus 1.32 (range: 0.86-2.02) for trials that did not, compared with 1.34 and 1.86, respectively, for National Cancer Institute (NCI) trials published between 1955 and 2006. An effect size at least as large as the one projected in the protocol trials was observed in 9.8% of trials, compared with 17% of NCI trials published from 1955 to 2006. Most trials (64.6%) provided no rationale to support the magnitude of the proposed treatment effect in the protocol.ConclusionsDespite a reduction in 'optimism bias' compared with previous eras, most contemporary NCTN phase III trials failed to establish statistically significant benefits of new cancer therapies. Better rationalization of proposed effect sizes in research protocols is needed.

The epidemiological evidence demonstrating the effect of disinfection by-products (DBPs) from drinking water on colon and rectal cancers is well documented. However, no systematic assessment has been conducted to assess the potential effect measure modification (EMM) in the relationship between DBPs and cancer. The objective of this paper is to conduct a systematic literature review to determine the extent to which EMM has been assessed in the relationship between DBPs in drinking water in past epidemiological studies. Selected articles (n = 19) were reviewed, and effect estimates and covariates that could have been used in an EMM assessment were gathered. Approximately half of the studies assess EMM (n = 10), but the majority of studies only estimate it relative to sex subgroups (n = 6 for bladder cancer and n = 2 both for rectal and colon cancers). Although EMM is rarely assessed, several variables that could have a potential modification effect are routinely collected in these studies, such as socioeconomic status or age. The role of environmental exposures through drinking water can play an important role and contribute to cancer disparities. We encourage a systematic use of subgroup analysis to understand which populations or territories are more vulnerable to the health impacts of DBPs.

There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.

Previous laboratory studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed that the stability of the virus in the air or on surfaces is sensitive to seasonally relevant environmental conditions. However, the seasonality of the virus in the real world remains unclear because each country adopted various infection control policies. Therefore, we investigated peak dates with regard to new confirmed cases of coronavirus disease 2019 (COVID-19) and an association of these dates with the timing of the lockdown among G20 countries that have four seasons from 1 June 2020 to 18 February 2021. As a result, countries in both hemispheres experienced seasonal peaks in the number of COVID-19 cases both in the middle of warm and cold seasons. In addition, there were no apparent relationships between the peak date and periods with stringent measures. Our study demonstrated that SARS-CoV-2 causes seasonal outbreaks in the winter and possibly summer and thus, countries might need to consider measures to prepare for resurgence of the virus in the middle of 2021. [ABSTRACT FROM AUTHOR]

Objective: This study investigated the role of oral health self‐perception as mediator of association between dental caries and depression among pregnant women from the 2015 Pelotas Birth Cohort Study, Brazil. Materials and Methods: Overall, 2,496 pregnant women participated of this oral health sub‐study. Data related to demographic and socioeconomic conditions, lifetime and current depression, and oral health self‐perception were collected. Participants were also assessed for dental caries (DMF‐T index). Depression was assessed using the Edinburgh Postnatal Depression Scale. For analysis, six outcomes related to dental caries experience were considered. Causal mediation analysis was performed using parametric regression models. Results: When it was assumed that all subjects had poor perception about oral health, it was observed that the presence of moderate/severe depressive signals and symptoms was higher in subjects with dental caries experience (OR 1.13; CI 95% 1.06–1.20), with severity of untreated dental caries (OR 2.08; CI 95% 1.16–3.78), untreated dental caries (OR 1.18; 95% CI 1.07–1.29), tooth loss (OR 1.08; 95% CI 1.02–1.14), and filled tooth (OR 1.07; 95% CI 1.02–1.16). Conclusions: Our findings show the effect of dental caries on depression is mediated by self‐perception about oral health in pregnant women. [ABSTRACT FROM AUTHOR]

Epidemiologists spend a great deal of time on confounding in our teaching, in our methods development, and in our assessment of study results. This may give the impression that uncontrolled confounding is the biggest problem observational epidemiology faces, when in fact, other sources of bias such as selection bias, measurement error, missing data, and misalignment of zero time may often (especially if they are all present in a single study) lead to a stronger deviation from the truth. Compared with the amount of time we spend teaching how to address confounding in data analysis, we spend relatively little time teaching methods for simulating confounding (and other sources of bias) to learn their impact and develop plans to mitigate or quantify the bias. Here we review the accompanying paper by Desai et al (Am J Epidemiol. 2024;193(11):1600-1608), which uses simulation methods to quantify the impact of an unmeasured confounder when it is completely missing or when a proxy of the confounder is measured. We discuss how we can use simulations of sources of bias to ensure that we generate better and more valid study estimates, and we discuss the importance of simulating realistic datasets with plausible bias structures to guide data collection. This article is part of a Special Collection on Pharmacoepidemiology., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Inconsistent reporting of patient characteristics in clinical research hampers reproducibility and limits analysis opportunities. This paper proposes condition-specific 'Core Descriptor Sets' comprising key factors like demographics, disease severity, comorbidities, and prognosis to standardize Table 1 reporting., Methods: Development entails stakeholder involvement, systematic identification of descriptors, value rating, and consensus-building using multiple Delphi rounds. Final agreement comes at an expert meeting., Conclusion: Benefits include easier cross-study comparison, for example, through individual patient meta-analysis, facilitated by comparison of consistently reported individual data rather than group-level analysis. This may also support routine data analyses, subgroup and risk identification, and reduced research waste. Core Descriptor Sets describe cohorts thoroughly while minimizing research burden. They are intended to enable improved clinical characterization, personalization, reproducibility, data sharing, and knowledge building., Competing Interests: Declaration of competing interest None to declare., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Real-world evidence (RWE) can complement and fill knowledge gaps from randomized controlled trials to assist in health-technology assessment (HTA) for regulatory decision-making. However, the generation of RWE is an intricate process with many sequential decision points, and different methods and approaches may impact the quality and reliability of evidence. Standardization and transparency in reporting these decisions is imperative to appraise RWE and incorporate it into HTA decision-making. A partnership between Canadian health system stakeholders, namely Health Canada and Canada's Drug Agency (formerly the Canadian Agency for Drugs and Technologies in Health (CADTH)), was established to develop a guidance for standardization of reporting of RWE for regulatory and HTA decision-making in Canada. In this article, we describe the methods to develop the Guidance for Reporting Real-World Evidence document and checklist for reporting RWE for regulatory and HTA decision-making in Canada. This guidance can be adapted for other jurisdictions and will have future extensions to incorporate emerging issues with RWE and HTA decision-making., (Copyright © 2024. Published by Elsevier Inc.)

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• Exposure to medium low and low levels of positive leadership behaviours was associated with an increased risk of onset of treatment for depression or anxiety, when compared to exposure to high levels. • The association between the different levels of leadership behaviours and treatment for depression or anxiety showed a dose-response relationship. • The association between low levels of positive leadership behaviours and treatment for depression or anxiety was moderated by age; the association was stronger in younger than in older participants. Positive leadership behaviours at work are associated with worker well-being and performance. However there is less knowledge about whether exposure to low levels of positive leadership behaviours increase workers' risk of clinical mental disorders. We investigated whether low levels of positive leadership behaviours are prospectively associated with risk of treatment for depressive and anxiety disorders. In a cohort study, we linked survey data from 59,743 respondents from the Work Environment and Health in Denmark survey with national health register data. Leadership behaviours were measured with an eight-item scale. Treatment was defined as redeemed prescription for antidepressants or anxiolytics or hospital treatment for depression or anxiety. Using Cox proportional hazard regression, adjusting for demographic variables, job type and sector, adverse life events and childhood adversities, we estimated the association between leadership behaviours at baseline and risk of treatment during follow-up. We identified 999 cases of depression and anxiety treatment during follow-up. Compared to high levels of leadership behaviours, exposure to medium low and low levels were associated with an increased risk of treatment after adjustment for covariates. The results suggest that low levels of positive leadership behaviours are associated with an increased risk of treatment for depressive or anxiety disorders. [ABSTRACT FROM AUTHOR]

The recombinant herpes zoster vaccine (RZV), approved as a 2-dose series in the United States in October 2017, has proven highly effective and generally safe. However, a small risk of Guillain-Barré syndrome after vaccination was identified after approval, and questions remain about other possible adverse events. This data-mining study assessed RZV safety in the United States using the self-controlled tree-temporal scan statistic, scanning data on thousands of diagnoses recorded during follow-up to detect any statistically unusual temporal clustering of cases within a large hierarchy of diagnoses. IBM MarketScan data on commercially insured persons at least 50 years of age receiving RZV between January 1, 2018, and May 5, 2020, were used, including 56 days of follow-up; 1,014,329 doses were included. Statistically significant clustering was found within a few days of vaccination for unspecified adverse effects, complications, or reactions to immunization or other medical substances/care; fever; unspecified allergy; syncope/collapse; cellulitis; myalgia; and dizziness/giddiness. These findings are consistent with the known safety profile of this and other injected vaccines. No cluster of Guillain-Barré syndrome was detected, possibly due to insufficient sample size. This signal-detection method has now been applied to 5 vaccines, with consistently plausible results, and seems a promising addition to vaccine-safety evaluation methods. [ABSTRACT FROM AUTHOR]

PurposeWe examined symptom variability in men and women with urological chronic pelvic pain syndrome. We describe symptom fluctuations as related to early symptom regression and its effect on estimated 1-year symptom change. We also describe a method to quantify patient specific symptom variability.Materials and methodsSymptoms were assessed biweekly in 424 subjects with urological chronic pelvic pain syndrome during 1 year. To evaluate the impact of early symptom regression subjects were classified as improved, no change or worse according to the rate of change using 1) all data, 2) excluding week 0 and 3) excluding weeks 0 and 2. Patient specific, time varying variability was calculated at each interval using a sliding window approach. Patients were classified as high, medium or low variability at each time and ultimately as high or low variability overall based on the variability for the majority of contacts.ResultsPrior to excluding early weeks to adjust for early symptom regression 25% to 38% and 5% to 6% of patients were classified as improved and worse, respectively. After adjustment the percent of patients who were improved or worse ranged from 15% to 25% and 6% to 9%, respectively. High and low variability phenotypes were each identified in 25% to 30% of participants.ConclusionsPatients with urological chronic pelvic pain syndrome show symptom variability. At study enrollment patients had worse symptoms on average, resulting in a regression effect that influenced the estimated proportion of those who were improved or worse. Prospective studies should include a run-in period to account for regression to the mean and other causes of early symptom regression. Further, symptom variability may be quantified and used to characterize longitudinal symptom profiles of urological chronic pelvic pain syndrome.

There has been an increased interest in identifying gene-environment interaction (G × E) in the context of multiple environmental exposures. Most G × E studies analyze one exposure at a time, but we are exposed to multiple exposures in reality. Efficient analysis strategies for complex G × E with multiple environmental factors in a single model are still lacking. Using the data from the Multiethnic Study of Atherosclerosis, we illustrate a two-step approach for modeling G × E with multiple environmental factors. First, we utilize common clustering and classification strategies (e.g., k-means, latent class analysis, classification and regression trees, Bayesian clustering using Dirichlet Process) to define subgroups corresponding to distinct environmental exposure profiles. Second, we illustrate the use of an additive main effects and multiplicative interaction model, instead of the conventional saturated interaction model using product terms of factors, to study G × E with the data-driven exposure subgroups defined in the first step. We demonstrate useful analytical approaches to translate multiple environmental exposures into one summary class. These tools not only allow researchers to consider several environmental exposures in G × E analysis but also provide some insight into how genes modify the effect of a comprehensive exposure profile instead of examining effect modification for each exposure in isolation.

Revision anterior cruciate ligament (ACL) reconstruction can present a series of unique challenges for the both the patient and the treating surgeon. The Multicenter ACL Revision Study (MARS) was established to identify the factors contributing to ACL graft failure and report on the outcomes following revision reconstruction. In this article we review the development and early to midterm findings of the MARS group.

Influenza viruses undergo frequent antigenic changes. As a result, the viruses circulating change within and between seasons, and the composition of the influenza vaccine is updated annually. Thus, estimation of the vaccines effectiveness is not constant across seasons. In order to provide annual estimates of the influenza vaccines effectiveness, health departments have increasingly adopted the test-negative design, using enhanced data from routine surveillance systems. In this design, patients presenting to participating general practitioners with influenza-like illness are swabbed for laboratory testing; those testing positive for influenza virus are defined as cases, and those testing negative form the comparison group. Data on patients vaccination histories and confounder profiles are also collected. Vaccine effectiveness is estimated from the odds ratio comparing the odds of testing positive for influenza among vaccinated patients and unvaccinated patients, adjusting for confounders. The test-negative design is purported to reduce bias associated with confounding by health-care-seeking behavior and misclassification of cases. In this paper, we use directed acyclic graphs to characterize potential biases in studies of influenza vaccine effectiveness using the test-negative design. We show how studies using this design can avoid or minimize bias and where bias may be introduced with particular study design variations.

There is high demand in environmental health for adoption of a structured process that evaluates and integrates evidence while making decisions and recommendations transparent. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework holds promise to address this demand. For over a decade, GRADE has been applied successfully to areas of clinical medicine, public health, and health policy, but experience with GRADE in environmental and occupational health is just beginning. Environmental and occupational health questions focus on understanding whether an exposure is a potential health hazard or risk, assessing the exposure to understand the extent and magnitude of risk, and exploring interventions to mitigate exposure or risk. Although GRADE offers many advantages, including its flexibility and methodological rigor, there are features of the different sources of evidence used in environmental and occupational health that will require further consideration to assess the need for method refinement. An issue that requires particular attention is the evaluation and integration of evidence from human, animal, in vitro, and in silico (computer modeling) studies when determining whether an environmental factor represents a potential health hazard or risk. Assessment of the hazard of exposures can produce analyses for use in the GRADE evidence-to-decision (EtD) framework to inform risk-management decisions about removing harmful exposures or mitigating risks. The EtD framework allows for grading the strength of the recommendations based on judgments of the certainty in the evidence (also known as quality of the evidence), as well as other factors that inform recommendations such as social values and preferences, resource implications, and benefits. GRADE represents an untapped opportunity for environmental and occupational health to make evidence-based recommendations in a systematic and transparent manner. The objectives of this article are to provide an overview of GRADE, discuss GRADE's applicability to environmental health, and identify priority areas for method assessment and development.

The use and functionality of electronic health records (EHRs) have increased rapidly in the past decade. Although the primary purpose of EHRs is clinical, researchers have used them to conduct epidemiologic investigations, ranging from cross-sectional studies within a given hospital to longitudinal studies on geographically distributed patients. Herein, we describe EHRs, examine their use in population health research, and compare them with traditional epidemiologic methods. We describe diverse research applications that benefit from the large sample sizes and generalizable patient populations afforded by EHRs. These have included reevaluation of prior findings, a range of diseases and subgroups, environmental and social epidemiology, stigmatized conditions, predictive modeling, and evaluation of natural experiments. Although studies using primary data collection methods may have more reliable data and better population retention, EHR-based studies are less expensive and require less time to complete. Future EHR epidemiology with enhanced collection of social/behavior measures, linkage with vital records, and integration of emerging technologies such as personal sensing could improve clinical care and population health.

The Sichuan Mental Health Survey (SMHS) is a provincially representative survey with a coherent methodology to obtain the prevalence of multiple mental disorders and data of services used and to analyze the psychological and social risk factors or correlates in Sichuan, China. Mental disorders include anxiety disorders, mood disorders, schizophrenia, and other psychotic disorders, drug use and alcohol use disorders, impulse control disorder, and eating disorders. A cross-sectional design is employed to sample adults from 200 communities/villages in all 21 prefectural-level municipalities of Sichuan Province in a five-stage provincially representative disproportionate stratified sampling design. The participants need to be interviewed face to face by trained interviewers from local primary healthcare institutions and by psychiatrists. The quality control staff implement data quality control by checking records and statistics in the interview system, and then re-interviewing checks are done by the psychiatrists. Data is weighted to adjust the sample distribution to match the whole population. The outcomes of the SMHS would not only demonstrate the serious challenges posed by the high burdens of mental disorders but also offer baseline data for policymakers and healthcare professionals to study and resolve the factors that influence mental health in Sichuan, China. [ABSTRACT FROM AUTHOR]

This paper proposes a new estimation procedure for the survival time distribution with left-truncated and right-censored data, where the distribution of the truncation time is known up to a finite-dimensional parameter vector. The paper expands on the Vardis multiplicative censoring model (Vardi, 1989. Multiplicative censoring, renewal processes, deconvolution and decreasing density: non-parametric estimation. Biometrika 76: , 751-761), establishes the connection between the likelihood under a generalized multiplicative censoring model and that for left-truncated and right-censored survival time data, and derives an Expectation-Maximization algorithm for model estimation. A formal test for checking the truncation time distribution is constructed based on the semiparametric likelihood ratio test statistic. In particular, testing the stationarity assumption that the underlying truncation time is uniformly distributed is performed by embedding the null uniform truncation time distribution in a smooth alternative (Neyman, 1937. Smooth test for goodness of fit. Skandinavisk Aktuarietidskrift 20: , 150-199). Asymptotic properties of the proposed estimator are established. Simulations are performed to evaluate the finite-sample performance of the proposed methods. The methods and theories are illustrated by analyzing the Canadian Study of Health and Aging and the Channing House data, where the stationarity assumption with respect to disease incidence holds for the former but not the latter.

The impaired ability to make correct antisaccades (i.e., antisaccade performance) is well documented among schizophrenia subjects, and researchers have successfully demonstrated that antisaccade performance is a valid schizophrenia endophenotype that is useful for genetic studies. However, it is unclear how the ascertainment biases that unavoidably result from recruitment differences in schizophrenia subjects identified in family versus case-control studies may influence patient-control differences in antisaccade performance. To assess the impact of ascertainment bias, researchers from the Consortium on the Genetics of Schizophrenia (COGS) compared antisaccade performance and antisaccade metrics (latency and gain) in schizophrenia and control subjects from COGS-1, a family-based schizophrenia study, to schizophrenia and control subjects from COGS-2, a corresponding case-control study. COGS-2 schizophrenia subjects were substantially older; had lower education status, worse psychosocial function, and more severe symptoms; and were three times more likely to be a member of a multiplex family than COGS-1 schizophrenia subjects. Despite these variations, which were likely the result of ascertainment differences (as described in the introduction to this special issue), the effect sizes of the control-schizophrenia differences in antisaccade performance were similar in both studies (Cohen's d effect size of 1.06 and 1.01 in COGS-1 and COGS-2, respectively). This suggests that, in addition to the robust, state-independent schizophrenia-related deficits described in endophenotype studies, group differences in antisaccade performance do not vary based on subject ascertainment and recruitment factors.

BackgroundNeurocognitive deficits in schizophrenia (SZ) are established and the Consortium on the Genetics of Schizophrenia (COGS) investigated such measures as endophenotypes in family-based (COGS-1) and case-control (COGS-2) studies. By requiring family participation, family-based sampling may result in samples that vary demographically and perform better on neurocognitive measures.MethodsThe Penn computerized neurocognitive battery (CNB) evaluates accuracy and speed of performance for several domains and was administered across sites in COGS-1 and COGS-2. Most tests were included in both studies. COGS-1 included 328 patients with SZ and 497 healthy comparison subjects (HCS) and COGS-2 included 1195 patients and 1009 HCS.ResultsDemographically, COGS-1 participants were younger, more educated, with more educated parents and higher estimated IQ compared to COGS-2 participants. After controlling for demographics, the two samples produced very similar performance profiles compared to their respective controls. As expected, performance was better and with smaller effect sizes compared to controls in COGS-1 relative to COGS-2. Better performance was most pronounced for spatial processing while emotion identification had large effect sizes for both accuracy and speed in both samples. Performance was positively correlated with functioning and negatively with negative and positive symptoms in both samples, but correlations were attenuated in COGS-2, especially with positive symptoms.ConclusionsPatients ascertained through family-based design have more favorable demographics and better performance on some neurocognitive domains. Thus, studies that use case-control ascertainment may tap into populations with more severe forms of illness that are exposed to less favorable factors compared to those ascertained with family-based designs.

The Sichuan Mental Health Survey (SMHS) is a provincially representative survey with a coherent methodology to obtain the prevalence of multiple mental disorders and data of services used and to analyze the psychological and social risk factors or correlates in Sichuan, China. Mental disorders include anxiety disorders, mood disorders, schizophrenia, and other psychotic disorders, drug use and alcohol use disorders, impulse control disorder, and eating disorders. A cross-sectional design is employed to sample adults from 200 communities/villages in all 21 prefectural-level municipalities of Sichuan Province in a five-stage provincially representative disproportionate stratified sampling design. The participants need to be interviewed face to face by trained interviewers from local primary healthcare institutions and by psychiatrists. The quality control staff implement data quality control by checking records and statistics in the interview system, and then re-interviewing checks are done by the psychiatrists. Data is weighted to adjust the sample distribution to match the whole population. The outcomes of the SMHS would not only demonstrate the serious challenges posed by the high burdens of mental disorders but also offer baseline data for policymakers and healthcare professionals to study and resolve the factors that influence mental health in Sichuan, China.

PurposeSelection bias is a form of systematic error that can be severe in compromised study designs such as case-control studies with inappropriate selection mechanisms or follow-up studies that suffer from extensive attrition. External adjustment for selection bias is commonly undertaken when such bias is suspected, but the methods used can be overly simplistic, if not unrealistic, and fail to allow for simultaneous adjustment of associations of the exposure and covariates with the outcome, when of interest. Internal adjustment for selection bias via inverse probability weighting allows bias parameters to vary with the levels of covariates but has only been formalized for longitudinal studies with covariate data on patients up until loss to follow-up.MethodsWe demonstrate the use of inverse probability weighting and externally obtained bias parameters to perform internal adjustment of selection bias in studies lacking covariate data on unobserved participants.ResultsThe "true" or selection-adjusted odds ratio for the association between exposure and outcome was successfully obtained by analyzing only data on those in the selected stratum (i.e., responders) weighted by the inverse probability of their being selected as function of their observed covariate data.ConclusionsThis internal adjustment technique using user-supplied bias parameters and inverse probability weighting for selection bias can be applied to any type of observational study.

General Internal Medicine research evolves in response to the needs of the patients to whom we provide care. Currently, many studies exclude older adults who deeply affect the clinical care of this population. With the number of older adults increasing, creating research protocols that include older adults with multiple chronic comorbidities is imperative. Through funding from the Association of Specialty Physicians, a working group of aging-responsive researchers from the Society of General Internal Medicine was convened to tackle this issue. The goal of this article is threefold: 1) to shed light on the current exclusion of older adults in research; 2) to identify and propose research protocol solutions for overcoming barriers to including older adults in research; and 3) to provide suggestions for research funding. The extent to which these recommendations can create change depends greatly on our researcher colleagues. By embracing these challenges, we hope that the care provided to older adults with multiple chronic conditions will no longer be extrapolated, but become evidence-based.

In environmental epidemiology, measurements of exposure biomarkers often fall below the assay's limit of detection. Existing methods for handling this problem, including deletion, substitution, parametric regression, and multiple imputation, can perform poorly if the proportion of "nondetects" is high or parametric models are mis-specified. We propose an approach that treats the measured analyte as the modeled outcome, implying a role reversal when the analyte is a putative cause of a health outcome. Following a scale reversal as well, our approach uses Cox regression to model the analyte, with confounder adjustment. The method makes full use of quantifiable analyte measures, while appropriately treating nondetects as censored. Under the proportional hazards assumption, the hazard ratio for a binary health outcome is interpretable as an adjusted odds ratio: the odds for the outcome at any particular analyte concentration divided by the odds given a lower concentration. Our approach is broadly applicable to cohort studies, case-control studies (frequency matched or not), and cross-sectional studies conducted to identify determinants of exposure. We illustrate the method with cross-sectional survey data to assess sex as a determinant of 2,3,7,8-tetrachlorodibenzo-p-dioxin concentration and with prospective cohort data to assess the association between 2,4,4'-trichlorobiphenyl exposure and psychomotor development.

We developed, implemented, and evaluated a myocardial infarction (MI) adjudication protocol for cohort research of human immunodeficiency virus. Potential events were identified through the centralized Centers for AIDS Research Network of Integrated Clinical Systems data repository using MI diagnoses and/or cardiac enzyme laboratory results (1995-2012). Sites assembled de-identified packets, including physician notes and results from electrocardiograms, procedures, and laboratory tests. Information pertaining to the specific antiretroviral medications used was redacted for blinded review. Two experts reviewed each packet, and a third review was conducted if discrepancies occurred. Reviewers categorized probable/definite MIs as primary or secondary and identified secondary causes of MIs. The positive predictive value and sensitivity for each identification/ascertainment method were calculated. Of the 1,119 potential events that were adjudicated, 294 (26%) were definite/probable MIs. Almost as many secondary (48%) as primary (52%) MIs occurred, often as the result of sepsis or cocaine use. Of the patients with adjudicated definite/probable MIs, 78% had elevated troponin concentrations (positive predictive value = 57%, 95% confidence interval: 52, 62); however, only 44% had clinical diagnoses of MI (positive predictive value = 45%, 95% confidence interval: 39, 51). We found that central adjudication is crucial and that clinical diagnoses alone are insufficient for ascertainment of MI. Over half of the events ultimately determined to be MIs were not identified by clinical diagnoses. Adjudication protocols used in traditional cardiovascular disease cohorts facilitate cross-cohort comparisons but do not address issues such as identifying secondary MIs that may be common in persons with human immunodeficiency virus.

The purpose of this study was to provide data regarding the prevalence and trends of adolescents' healthy lifestyles from 32 countries between 2006 and 2014 by sex and age interval. The data used in the present study were derived from the Health Behavior in School-aged Children (HBSC) 2006, 2010, and 2014 international database. Healthy lifestyle was assessed using the combination of daily physical activity, daily fruit and vegetable consumption,

The use of administrative health datasets is increasingly important for research on disease trends and outcome. The Western Australian (WA) Rheumatic Disease Epidemiological Registry contains longitudinal health data for over 10,000 patients with rheumatoid arthritis (RA). Accurate coding for RA is essential to the validity of this dataset. Investigate the diagnostic accuracy of International Classification of Diseases (ICD)-based discharge codes for RA at WA's largest tertiary hospital. Medical records for a sample of randomly selected patients with ICD-10 codes (M05.00–M06.99) in the hospital discharge database between 2008 and 2020 were retrospectively reviewed. Rheumatologist‐reported diagnoses and ACR/EULAR classification criteria were used as reference standards to determine accuracy measures. Medical chart review was completed for 87 patients (mean (± SD) age 64.7 ± 17.2 years), 67.8% female). A total of 80 (91.9%) patients had specialist confirmed RA diagnosis, while seven patients (8%) had alternate clinical diagnoses. Among 87 patients, 69 patients (79.3%) were fulfilled ACR/EULAR classification criteria. The agreement between the reference standards was moderate (Kappa 0.41). Based on rheumatologist‐reported diagnoses and ACR/EULAR classification criteria, primary diagnostic codes for RA alone had a sensitivity of (90% vs 89.8%), and PPV (90.9% vs 63.6%), respectively. A combination of a diagnostic RA code with biologic infusion codes in two or more codes increased the PPV to 97.9%. Hospital discharge diagnostic codes in WA identify RA patients with a high degree of accuracy. Combining a primary diagnostic code for RA with biological infusion codes can further increase the PPV. [ABSTRACT FROM AUTHOR]

We introduce a new and time-efficient memory-encoding paradigm for functional magnetic resonance imaging (fMRI). This paradigm is optimized for mapping multiple contrasts using a mixed design, using auditory (environmental/vocal) and visual (scene/face) stimuli. We demonstrate that the paradigm evokes robust neuronal activity in typical sensory and memory networks. We were able to detect auditory and visual sensory-specific encoding activities in auditory and visual cortices. Also, we detected stimulus-selective activation in environmental-, voice-, scene-, and face-selective brain regions (parahippocampal place and fusiform face area). A subsequent recognition task allowed the detection of sensory-specific encoding success activity (ESA) in both auditory and visual cortices, as well as sensory-unspecific positive ESA in the hippocampus. Further, sensory-unspecific negative ESA was observed in the precuneus. Among others, the parallel mixed design enabled sustained and transient activity comparison in contrast to rest blocks. Sustained and transient activations showed great overlap in most sensory brain regions, whereas several regions, typically associated with the default-mode network, showed transient rather than sustained deactivation. We also show that the use of a parallel mixed model had relatively little influence on positive or negative ESA. Together, these results demonstrate a feasible, versatile, and brief memory-encoding task, which includes multiple sensory stimuli to guarantee a comprehensive measurement. This task is especially suitable for large-scale clinical or population studies, which aim to test task-evoked sensory-specific and sensory-unspecific memory-encoding performance as well as broad sensory activity across the life span within a very limited time frame.

Pragmatic clinical trials are commonly used in patient-centered outcomes research to assess heterogeneity of treatment effects. Patient-Centered Outcomes Research Institute (PCORI) methodology standards for assessing heterogeneity of treatment effects are extremely rigorous, but their implementation in real-world settings can be difficult. Predicting recruitment effectiveness and subgroup characteristics is often challenging and may require mid-stream revision of projected group and subgroup sizes. Yet, little real-world data are available to demonstrate methodologically valid approaches to address situations where such revisions are necessary. These data were used for mid-stream revision of group and subgroup sizes in the Management of Diabetes in Everyday Life (MODEL) clinical trial. The planned number of randomized participants retained over the one-year study period was reduced from 800 to 581 due to recruitment difficulties among potential participants residing in rural areas. Prospective power analyses are based on the revised target of 581 participants retained and the proportions of 167 participants with various key baseline characteristics, who had been randomized in MODEL by January 2018, as reported to the Patient Center Outcomes Research Institute (PCORI) and the MODEL Data Safety and Monitoring Committee. Power calculations are based on two-sided t-tests with type-I error rates of 0.05 and the assumption that effect sizes will range from small (standardized difference = 0.36) to medium (= 0.50). The primary outcome variables are how many days in the previous week participants 1) ate healthy meals, 2) participated in at least 30 minutes of physical activity, and 3) took medications as prescribed. The POWER procedure of SAS 9.4 was used for all analyses. These data, along with the approach, can assist statisticians as they plan future pragmatic clinical trials evaluating heterogeneity of treatment effects. These data can help inform investigators, conducting patient-centered outcomes research, as they define subgroups for either confirmatory analyses for testing heterogeneity of treatment effects or for exploratory analyses where estimation of confidence bounds may be useful for generating future hypotheses. (This work was supported through a Patient-Centered Outcomes Research Institute (PCORI) Project Program Award (SC15-1503-28336), www.ClinicalTrials.gov and Identifier: NCT02957513 [1].)

The development of nutrition and health guidelines and policies requires reliable scientific information. Unfortunately, theoretical considerations and empirical evidence indicate that a large percentage of science-based claims rely on studies that fail to replicate. The session "Strategies to Optimize the Impact of Nutrition Surveys and Epidemiological Studies" focused on the elements of design, interpretation, and communication of nutritional surveys and epidemiological studies to enhance and encourage the production of reliable, objective evidence for use in developing dietary guidance for the public. The speakers called for more transparency of research, raw data, consistent data-staging techniques, and improved data analysis. New approaches to collecting data are urgently needed to increase the credibility and utility of findings from nutrition epidemiological studies. Such studies are critical for furthering our knowledge and understanding of the effects of diet on health.

Purpose of reviewResearch directed toward an HIV cure presents ethical as well as scientific challenges.Recent findingsInternational guidelines, regulations, and the medical literature provide helpful guidance on protecting research participants.SummaryThis review presents points to consider for researchers, sponsors, oversight committees, community advisory boards, and HIV advocates to help resolve ethical challenges that are particularly complex or difficult or that are not adequately addressed by current ethical guidelines. The points to consider are: collaborative partnership among international scientists from both the private and public sectors, as well as engagement of HIV-affected communities, social value, scientific validity, fair selection of participants and study sites, a favorable and acceptable risk-benefit balance, independent scientific and ethical review, informed and voluntary consent, and respect for enrolled patients and communities. To prevent therapeutic misconception, participants' comprehension of key features of the study may need to be assessed. Participants who suffer study-related adverse events should receive appropriate medical care and compensation. If interventions are shown capable of curing HIV, sponsors and international funding agencies should plan how to make them available and affordable in resource-poor countries.

We introduce a new and time-efficient memory-encoding paradigm for functional magnetic resonance imaging (fMRI). This paradigm is optimized for mapping multiple contrasts using a mixed design, using auditory (environmental/vocal) and visual (scene/face) stimuli. We demonstrate that the paradigm evokes robust neuronal activity in typical sensory and memory networks. We were able to detect auditory and visual sensory-specific encoding activities in auditory and visual cortices. Also, we detected stimulus-selective activation in environmental-, voice-, scene-, and face-selective brain regions (parahippocampal place and fusiform face area). A subsequent recognition task allowed the detection of sensory-specific encoding success activity (ESA) in both auditory and visual cortices, as well as sensory-unspecific positive ESA in the hippocampus. Further, sensory-unspecific negative ESA was observed in the precuneus. Among others, the parallel mixed design enabled sustained and transient activity comparison in contrast to rest blocks. Sustained and transient activations showed great overlap in most sensory brain regions, whereas several regions, typically associated with the default-mode network, showed transient rather than sustained deactivation. We also show that the use of a parallel mixed model had relatively little influence on positive or negative ESA. Together, these results demonstrate a feasible, versatile, and brief memory-encoding task, which includes multiple sensory stimuli to guarantee a comprehensive measurement. This task is especially suitable for large-scale clinical or population studies, which aim to test task-evoked sensory-specific and sensory-unspecific memory-encoding performance as well as broad sensory activity across the life span within a very limited time frame. [ABSTRACT FROM AUTHOR]

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 Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)