Considering pregnancies as repeated versus independent events: An empirical comparison of common approaches across selected perinatal outcomes.

BACKGROUND: In population-based research, pregnancy may be a repeated event. Despite published guidance on how to address repeated pregnancies to the same individual, a variety of approaches are observed in perinatal epidemiological studies. While some of these approaches are supported by the chosen research question, others are consequences of constraints inherent to a given dataset (e.g., missing parity information). These decisions determine how appropriately a given research question can be answered and overall generalizability. OBJECTIVE: To compare common cohort selection and analytic approaches used for perinatal epidemiological research by assessing the prevalence of two perinatal outcomes and their association with a clinical and a social independent variable STUDY DESIGN: Using vital records linked to maternal hospital discharge records for singleton births, we created four cohorts: (1) all-births (2) randomly selected one birth per individual (3) first observed birth per individual (4) primiparous-births (parity 1). Sampling of births was not conditional on cluster (i.e., we did not sample all births by a given mother, but rather sampled individual births). Study outcomes were severe maternal morbidity and preeclampsia/eclampsia, and the independent variables were self-reported race/ethnicity (as a social factor) and systemic lupus erythematosus. Comparing the four cohorts, we assessed the distribution of maternal characteristics, the prevalence of outcomes, overall and stratified by parity, and risk ratios for the associations of outcomes with independent variables. Among all-births, we then compared risk ratios from three analytic strategies: with standard inference that assumes independently sampled births to the same mother in the model, with cluster-robust inference, and adjusting for parity. RESULTS: We observed minor differences in the population characteristics between the all-birth (N=2,736,693), random-selection, and first-observed birth cohorts (both N=2,284,660), with more substantial differences between these cohorts and the primiparous-births cohort (N=1,054,684). Outcome prevalence was consistently lowest among all-births and highest among primiparous-births (e.g., severe maternal morbidity 18.9 per 1,000 births among primiparous-births vs. 16.6 per 1,000 births among all-births). When stratified by parity, outcome prevalence was always the lowest in births of parity 2 and highest among births of parity 1 for both outcomes. Risk ratios differed for study outcomes across all four cohorts, with the most pronounced differences between the primiparous-birth cohort and other cohorts. Among all-births, robust inference minimally impacted the confidence bounds of estimates, compared to the standard inference, i.e., crude estimates (e.g., lupus-severe maternal morbidity association: 4.01, 95% CI 3.54-4.55 vs. 4.01, 95% CI 3.53-4.56 for crude estimate), while adjusting for parity slightly shifted estimates, towards the null for severe maternal morbidity and away from the null for preeclampsia/eclampsia. CONCLUSION: Researchers should consider the alignment between the methods they use, their sampling strategy, and their research question. This could include refining the research question to better match inference possible for available data, considering alternative data sources, and appropriately noting data limitations and resulting bias, as well as the generalizability of findings. If parity is an established effect modifier, stratified results should be presented.

The Risk of Aircraft-Acquired SARS-CoV-2 Transmission during Commercial Flights: A Systematic Review.

The aircraft-acquired transmission of SARS-CoV-2 poses a public health risk. Following PRISMA guidelines, we conducted a systematic review and analysis of articles, published prior to vaccines being available, from 24 January 2020 to 20 April 2021 to identify factors important for transmission. Articles were included if they mentioned index cases and identifiable flight duration, and excluded if they discussed non-commercial aircraft, airflow or transmission models, cases without flight data, or that were unable to determine in-flight transmission. From the 15 articles selected for in-depth review, 50 total flights were analyzed by flight duration both as a categorical variable-short (<3 h), medium (3-6 h), or long flights (>6 h)-and as a continuous variable with case counts modeled by negative binomial regression. Compared to short flights without masking, medium and long flights without masking were associated with 4.66-fold increase (95% CI: [1.01, 21.52]; p < 0.0001) and 25.93-fold increase in incidence rates (95% CI: [4.1, 164]; p < 0.0001), respectively; long flights with enforced masking had no transmission reported. A 1 h increase in flight duration was associated with 1.53-fold (95% CI: [1.19, 1.66]; p < 0.001) increase in the incidence rate ratio (IRR) of cases. Masking should be considered for long flights.

Simple graphical rules for assessing selection bias in general-population and selected-sample treatment effects.

When analyzing a selected sample from a general population, selection bias can arise relative to the causal average treatment effect (ATE) for the general population, and also relative to the ATE for the selected sample itself. We provide simple graphical rules that indicate: (1) if a selected-sample analysis will be unbiased for each ATE; (2) whether adjusting for certain covariates could eliminate selection bias. The rules can easily be checked in a standard single-world intervention graph. When the treatment could affect selection, a third estimand of potential scientific interest is the "net treatment difference", namely the net change in outcomes that would occur for the selected sample if all members of the general population were treated versus not treated, including any effects of the treatment on which individuals are in the selected sample . We provide graphical rules for this estimand as well. We decompose bias in a selected-sample analysis relative to the general-population ATE into: (1) "internal bias" relative to the net treatment difference; (2) "net-external bias", a discrepancy between the net treatment difference and the general-population ATE. Each bias can be assessed unambiguously via a distinct graphical rule, providing new conceptual insight into the mechanisms by which certain causal structures produce selection bias.

Toward evidence-based communication on overweight body mass index and mortality.

BACKGROUND: Reducing overweight and obesity has been a longstanding focus of public health messaging and physician-patient interactions. Clinical guidelines by major public health organizations describe both overweight and obesity as risk factors for mortality and other health conditions. Accordingly, a majority of primary care physicians believe that overweight BMI (even without obesity) strongly increases mortality risk. MAIN POINTS: The current evidence base suggests that although both obese BMI and underweight BMI are consistently associated with increased all-cause mortality, overweight BMI (without obesity) is not meaningfully associated with increased mortality. In fact, a number of studies suggest modest protective, rather than detrimental, associations of overweight BMI with all-cause mortality. Given this current evidence base, clinical guidelines and physician perceptions substantially overstate all-cause mortality risks associated with the range of BMIs classified as "overweight" but not "obese." Discrepancies between evidence and communication regarding mortality raise the question of whether similar discrepancies exist for other health outcomes. CONCLUSIONS: Health communication that inaccurately conveys current evidence may do more harm than good; this applies to communication from health authorities to health practitioners as well as to communication from health practitioners to individual patients. We give three recommendations to better align health communication with the current evidence. First, recommendations to the public and health practitioners should distinguish overweight from obese BMI and at this time should not describe overweight BMI as a risk factor for all-cause mortality. Second, primary care physicians' widespread misconceptions about overweight BMI should be rectified. Third, the evidence basis for other potential risks or benefits of overweight BMI should be rigorously examined and incorporated appropriately into health communication.

Evidence for Infant-directed Speech Preference Is Consistent Across Large-scale, Multi-site Replication and Meta-analysis.

There is substantial evidence that infants prefer infant-directed speech (IDS) to adult-directed speech (ADS). The strongest evidence for this claim has come from two large-scale investigations: i) a community-augmented meta-analysis of published behavioral studies and ii) a large-scale multi-lab replication study. In this paper, we aim to improve our understanding of the IDS preference and its boundary conditions by combining and comparing these two data sources across key population and design characteristics of the underlying studies. Our analyses reveal that both the meta-analysis and multi-lab replication show moderate effect sizes (d ≈ 0.35 for each estimate) and that both of these effects persist when relevant study-level moderators are added to the models (i.e., experimental methods, infant ages, and native languages). However, while the overall effect size estimates were similar, the two sources diverged in the effects of key moderators: both infant age and experimental method predicted IDS preference in the multi-lab replication study, but showed no effect in the meta-analysis. These results demonstrate that the IDS preference generalizes across a variety of experimental conditions and sampling characteristics, while simultaneously identifying key differences in the empirical picture offered by each source individually and pinpointing areas where substantial uncertainty remains about the influence of theoretically central moderators on IDS preference. Overall, our results show how meta-analyses and multi-lab replications can be used in tandem to understand the robustness and generalizability of developmental phenomena.

Do Forgiveness Campaign Activities Improve Forgiveness, Mental Health, and Flourishing?

Objectives: To evaluate the effectiveness of a forgiveness public health intervention at promoting forgiveness, mental health, and flourishing. Methods: Colombian students (N = 2,878) at a private, nonreligious university were exposed to a 4-week forgiveness community campaign and were assessed pre- and post-campaign. Results: Forgiveness, mental health, and flourishing outcomes showed improvements after the campaign. On average, participants reported engaging in 7.18 (SD = 3.99) of the 16 types of campaign activities. The number of types of campaign activities that participants engaged in evidenced a positive linear association with forgiveness, although some activities were more popular than others and some activities were more strongly associated with increased forgiveness. For depression, anxiety, and flourishing, engaging in more activities was generally associated with greater improvements, but the patterns were less consistent relative to forgiveness. Conclusion: This forgiveness public health intervention effectively promoted forgiveness, mental health, and flourishing. Effective campaigns in diverse communities involve promoting mental and physical health through forgiveness. However, recent conflict may hinder acceptance, necessitating political capital for leadership advocating forgiveness initiatives.

P-hacking in meta-analyses: A formalization and new meta-analytic methods.

As traditionally conceived, publication bias arises from selection operating on a collection of individually unbiased estimates. A canonical form of such selection across studies (SAS) is the preferential publication of affirmative studies (i.e., those with significant, positive estimates) versus nonaffirmative studies (i.e., those with nonsignificant or negative estimates). However, meta-analyses can also be compromised by selection within studies (SWS), in which investigators "p-hack" results within their study to obtain an affirmative estimate. Published estimates can then be biased even conditional on affirmative status, which comprises the performance of existing methods that only consider SAS. We propose two new analysis methods that accommodate joint SAS and SWS; both analyze only the published nonaffirmative estimates. First, we propose estimating the underlying meta-analytic mean by fitting "right-truncated meta-analysis" (RTMA) to the published nonaffirmative estimates. This method essentially imputes the entire underlying distribution of population effects. Second, we propose conducting a standard meta-analysis of only the nonaffirmative studies (MAN); this estimate is conservative (negatively biased) under weakened assumptions. We provide an R package (phacking) and website (metabias.io). Our proposed methods supplement existing methods by assessing the robustness of meta-analyses to joint SAS and SWS.

Sensitivity analysis for the interactive effects of internal bias and publication bias in meta-analyses.

Meta-analyses can be compromised by studies' internal biases (e.g., confounding in nonrandomized studies) as well as publication bias. These biases often operate nonadditively: publication bias that favors significant, positive results selects indirectly for studies with more internal bias. We propose sensitivity analyses that address two questions: (1) "For a given severity of internal bias across studies and of publication bias, how much could the results change?"; and (2) "For a given severity of publication bias, how severe would internal bias have to be, hypothetically, to attenuate the results to the null or by a given amount?" These methods consider the average internal bias across studies, obviating specifying the bias in each study individually. The analyst can assume that internal bias affects all studies, or alternatively that it only affects a known subset (e.g., nonrandomized studies). The internal bias can be of unknown origin or, for certain types of bias in causal estimates, can be bounded analytically. The analyst can specify the severity of publication bias or, alternatively, consider a "worst-case" form of publication bias. Robust estimation methods accommodate non-normal effects, small meta-analyses, and clustered estimates. As we illustrate by re-analyzing published meta-analyses, the methods can provide insights that are not captured by simply considering each bias in turn. An R package implementing the methods is available (multibiasmeta).

Exogenous female sex steroid hormones and new-onset asthma in women: a matched case-control study.

BACKGROUND: Evidence on the role of exogenous female sex steroid hormones in asthma development in women remains conflicting. We sought to quantify the potential causal role of hormonal contraceptives and menopausal hormone therapy (MHT) in the development of asthma in women. METHODS: We conducted a matched case-control study based on the West Sweden Asthma Study, nested in a representative cohort of 15,003 women aged 16-75 years, with 8-year follow-up (2008-2016). Data were analyzed using Frequentist and Bayesian conditional logistic regression models. RESULTS: We included 114 cases and 717 controls. In Frequentist analysis, the odds ratio (OR) for new-onset asthma with ever use of hormonal contraceptives was 2.13 (95% confidence interval [CI] 1.03-4.38). Subgroup analyses showed that the OR increased consistently with older baseline age. The OR for new-onset asthma with ever MHT use among menopausal women was 1.17 (95% CI 0.49-2.82). In Bayesian analysis, the ORs for ever use of hormonal contraceptives and MHT were, respectively, 1.11 (95% posterior interval [PI] 0.79-1.55) and 1.18 (95% PI 0.92-1.52). The respective probability of each OR being larger than 1 was 72.3% and 90.6%. CONCLUSIONS: Although use of hormonal contraceptives was associated with an increased risk of asthma, this may be explained by selection of women by baseline asthma status, given the upward trend in the effect estimate with older age. This indicates that use of hormonal contraceptives may in fact decrease asthma risk in women. Use of MHT may increase asthma risk in menopausal women.

Effects of an educational planetary plate graphic on meat consumption in a Stanford University dining hall: a randomized controlled trial.

BACKGROUND: Assess the impact of an educational Planetary Health Plate (PHP) graphic on meat-related dietary choices of Stanford University dining hall patrons using a randomized controlled trial crossover design. All patrons entering the dining hall during study periods were enrolled as participants. Control, n = 631; PHP, n = 547. METHODS: Compare dietary behavior without signage to behavior while exposed to PHP during four equivalent dinner meals. The primary outcome was total meat-dish weight adjusted for the number of people entering the dining hall. Secondary outcomes included the number of meat-dish servings and average meat-dish serving weight. Analysis using T-tests, Poisson generalized linear model. RESULTS: Differences in total meat-dish weight, (1.54 kg; 95% Confidence Interval [CI] = -4.41,1.33; P = .19) and average meat-dish serving weight (0.03 kg; 95% CI = 0.00, 0.06; P = .07) between PHP and control patrons did not reach significance. The rate at which PHP patrons took meat was significantly lower (Incidence Rate Ratio 0.80; 95% CI = 0.71, 0.91; P < .001). CONCLUSION: Exposure to an educational plate graphic decreased the proportion of patrons taking meat but had no impact on total meat consumption or meat-dish serving weight. Statistical methods used in this study may inform future investigations on dietary change in the dining hall setting. Further research on the role of educational signage in influencing dietary behavior is warranted, with an aim to improve human health and environmental sustainability. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05565859, registered 4 October 2022.

A Social Media-Based Public Health Campaign to Reduce Indoor Tanning in High-Risk Populations.

Introduction: Indoor tanning beds cause more than 450,000 new skin cancers each year, yet their use remains common, with a global indoor tanning prevalence of 10.4%. Social media provides an opportunity for cost-effective, targeted public health messaging. We sought to direct Instagram users at high risk of indoor tanning to accurate health information about the risks of indoor tanning and to reduce indoor tanning bed use. Methods: We disseminated a public health campaign on Instagram on April 6-27, 2022 with 34 video and still-image advertisements. We had 2 target audiences at high risk of indoor tanning: women aged 18-30 years in Kentucky, Nebraska, Ohio, or Tennessee interested in indoor tanning and men aged 18-45 years in California interested in indoor tanning. To evaluate the impact of the campaign, we tracked online metrics, including website visits, and conducted an interrupted time-series analysis of foot traffic data in our target states for all tanning salons documented on SafeGraph from January 1, 2018 to 3 months after the campaign. Results: Our indoor tanning health information advertisements appeared on Instagram feeds 9.1 million times, reaching 1.06 million individuals. We received 7,004 views of our indoor tanning health information landing page (Average Time on Page of 56 seconds). We did not identify a significant impact on foot traffic data on tanning salons. Conclusions: We show the successful use of social media advertising to direct high-risk groups to online health information about indoor tanning. Future research quantifying tanning visits before and after indoor tanning interventions is needed to guide future public health efforts.

A Specific Carbohydrate Diet Virtual Teaching Kitchen Curriculum Promotes Knowledge and Confidence in Caregivers of Pediatric Patients with Inflammatory Bowel Disease.

Diet-based approaches such as the Specific Carbohydrate Diet (SCD) have proposed health benefits for patients with Inflammatory Bowel Disease (IBD). Despite its potential effectiveness, patients and caregivers identified barriers towards implementing the SCD, and a majority expressed interest in formal education surrounding the SCD. This study aimed to determine the impact of a virtual teaching kitchen curriculum on caregivers' knowledge and perspectives on implementing the SCD. Inclusion criteria included pediatric patients with IBD aged 3-21 years and their caregivers. Participants should have fewer than 12 months of experience with the SCD or have no experience with the SCD but with an interest in learning it. Twenty-three caregivers took part in a 90-min virtual teaching kitchen curriculum and completed pre- and post-session surveys. Caregivers had statistically significant increases in total curriculum scores (p < 0.0001) as well as increases in all curricular elements post-curriculum teaching. Caregivers indicated that they plan to apply the newly acquired recipes and cooking concepts and appreciated the encouragement and support they received during the course. Curricular strengths identified included the innovative multimodal curriculum structure and professional and community support. IBD centers can use this pilot study to create or expand SCD and other nutritional curricula for the IBD community.

Marital transitions during earlier adulthood and subsequent health and well-being in mid- to late-life among female nurses: An outcome-wide analysis.

Comparing outcomes for individuals remaining married to those for single or divorced individuals might overstate the positive effects of the decision to marry, since marriage carries an inherent risk of divorce and its associated negative outcomes. While a growing literature has examined marital transitions, confounding by past marital history remains a concern and only a limited set of outcomes have been examined. To address these issues, this study examined incident first-time marriage and incident divorce/separation in relation to multiple subsequent physical health, health behavior, psychological distress, and psychosocial well-being outcomes in a large sample of female nurses in the U.S.. Data from the Nurses' Health Study II were studied (1993 to 2015/2017 questionnaire wave, Nmarriage analyses = 11,830, Ndivorce/separation analyses = 73,018, interquartile range of baseline age = 35 to 42 years). A set of regression models were used to regress each outcome on marital transition status, adjusting for a wide range of initial health and wellbeing status in addition to other covariates. Bonferroni correction was performed to account for multiple testing. Among the initially never married, those who became married had lower mortality (RR = 0.65, 95%CI = 0.50, 0.84), lower risks of cardiovascular diseases (e.g., RRstroke = 0.64, 95%CI = 0.50, 0.82), greater psychological wellbeing and less psychological distress (e.g., ßdepressive symptoms = -0.10, 95%CI = -0.15, -0.06). Among the initially married, those who became divorced/separated had lower social integration (ß = -0.15, 95%CI = -0.19, -0.11), greater psychosocial distress (e.g., RRdepression = 1.23, 95%CI = 1.10, 1.37), and possibly greater risks of mortality, cardiovascular diseases, and smoking. Future research could study similar questions using data from more recent cohorts, examine potential mechanisms and heterogeneity, and also examine alternative social relationship types.

Effect Measure Modification by Covariates in Mediation: Extending Regression-based Causal Mediation Analysis.

Existing methods for regression-based mediation analysis assume that the exposure-mediator effect, exposure-outcome effect, and mediator-outcome effect are constant across levels of the baseline characteristics of patients. However, investigators often have insight into how these underlying effects may be modified by baseline characteristics and are interested in how the resulting mediation effects, such as the natural direct effect (NDE), the natural indirect effect. (NIE), and the proportion mediated, are modified by these baseline characteristics. Motivated by an empirical example of anti-interleukin-1 therapy's benefit on incident anemia reduction and its mediation by an early change in an inflammatory biomarker, we extended the closed-form regression-based causal mediation analysis with effect measure modification (EMM). Using a simulated numerical example, we demonstrated that naive analysis without considering EMM can give biased estimates of NDE and NIE and visually illustrated how baseline characteristics affect the presence and magnitude of EMM of NDE and NIE. We then applied the extended method to the empirical example informed by pathophysiologic insights into potential EMM by age, diabetes, and baseline inflammation. We found that the proportion modified through the early post-treatment inflammatory biomarker was greater for younger, nondiabetic patients with lower baseline level of inflammation, suggesting differential usefulness of the early post-treatment inflammatory biomarker in monitoring patients depending on baseline characteristics. To facilitate the adoption of EMM considerations in causal mediation analysis by the wider clinical and epidemiologic research communities, we developed a free- and open-source R package, regmedint.

A brief primer on conducting regression-based causal mediation analysis.

OBJECTIVE: We provide an overview of regression-based causal mediation analysis in the field of traumatic stress and guidance on how to conduct mediation analysis using our R package regmedint. METHOD: We discuss the causal interpretations of the quantities that causal mediation analysis estimates, including total, direct, and indirect effects, especially when the interaction between exposure and mediator is permitted. We discuss the assumptions that must be fulfilled for mediation analyses to validly estimate these causal quantities, discuss suitable study designs for assessing mediation, and describe how causal mediation analysis differs from traditional methods of mediation. To illustrate how to conduct and interpret mediation analysis using our R package regmedint, we use data from a published longitudinal study to assess the extent to which children's externalizing behavior mediates changes in parental negative feelings during the COVID-19 lockdown. We compare the results to those obtained using traditional methods, thus illustrating the importance of accounting for exposure-mediator interaction when an interaction may be present. RESULTS: When the exposure and the mediator interact, traditional methods can provide estimates of direct and indirect effects that differ from those provided by more flexible causal mediation methods. When the exposure and the mediator do not interact, traditional methods and causal mediation method may estimate similar direct and indirect effects depending on the model specification. CONCLUSIONS: In contrast to traditional methods of mediation analysis, regression-based causal mediation methods seek to estimate specific interventional quantities, not mere associations, and the causal methods explicitly allow for exposure-mediator interactions. We recommend using these methods by default rather than using more restrictive traditional methods. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Toward Open and Reproducible Epidemiology.

Starting in the 2010s, researchers in the experimental social sciences rapidly began to adopt increasingly open and reproducible scientific practices. These practices include publicly sharing deidentified data when possible, sharing analytical code, and preregistering study protocols. Empirical evidence from the social sciences suggests such practices are feasible, can improve analytical reproducibility, and can reduce selective reporting. In academic epidemiology, adoption of open-science practices has been slower than in the social sciences (with some notable exceptions, such as registering clinical trials). Epidemiologic studies are often large, complex, conceived after data have already been collected, and difficult to replicate directly by collecting new data. These characteristics make it especially important to ensure their integrity and analytical reproducibility. Open-science practices can also pay immediate dividends to researchers' own work by clarifying scientific reasoning and encouraging well-documented, organized workflows. We consider how established epidemiologists and early-career researchers alike can help midwife a culture of open science in epidemiology through their research practices, mentorship, and editorial activities.

Is There Evidence of P-Hacking in Imaging Research?

BACKGROUND: P-hacking, the tendency to run selective analyses until they become significant, is prevalent in many scientific disciplines. PURPOSE: This study aims to assess if p-hacking exists in imaging research. METHODS: Protocol, data, and code available here https://osf.io/xz9ku/?view_only=a9f7c2d841684cb7a3616f567db273fa. We searched imaging journals Ovid MEDLINE from 1972 to 2021. Text mining using Python script was used to collect metadata: journal, publication year, title, abstract, and P-values from abstracts. One P-value was randomly sampled per abstract. We assessed for evidence of p-hacking using a p-curve, by evaluating for a concentration of P-values just below .05. We conducted a one-tailed binomial test (α = .05 level of significance) to assess whether there were more P-values falling in the upper range (e.g., .045 < P < .05) than in the lower range (e.g., .04 < P < .045). To assess variation in results introduced by our random sampling of a single P-value per abstract, we repeated the random sampling process 1000 times and pooled results across the samples. Analysis was done (divided into 10-year periods) to determine if p-hacking practices evolved over time. RESULTS: Our search of 136 journals identified 967,981 abstracts. Text mining identified 293,687 P-values, and a total of 4105 randomly sampled P-values were included in the p-hacking analysis. The number of journals and abstracts that were included in the analysis as a fraction and percentage of the total number was, respectively, 108/136 (80%) and 4105/967,981 (.4%). P-values did not concentrate just under .05; in fact, there were more P-values falling in the lower range (e.g., .04 < P < .045) than falling just below .05 (e.g., .045 < P < .05), indicating lack of evidence for p-hacking. Time trend analysis did not identify p-hacking in any of the five 10-year periods. CONCLUSION: We did not identify evidence of p-hacking in abstracts published in over 100 imaging journals since 1972. These analyses cannot detect all forms of p-hacking, and other forms of bias may exist in imaging research such as publication bias and selective outcome reporting.