Epidemiology最新文献

Background: Hypospadias is a male genital tract defect for which an increase in prevalence has been documented over the last few decades. A role for environmental risk factors is suspected, including prenatal exposure to pesticides.

Objectives: To study the risk of hypospadias in association with multiple pesticide measurements in meconium samples.

Methods: The Brittany Registry of Congenital Anomalies (France) conducted a case-control study between 2012 and 2018. Cases were hypospadias, ascertained by a pediatrician and a pediatric surgeon, excluding genetic conditions, following European Surveillance of Congenital Anomalies guidelines (N = 69). Controls (N = 135) were two male infants without congenital anomaly born after each case in the same maternity unit. Mothers in the maternity units completed a self-administered questionnaire, we collected medical data from hospital records, and medical staff collected meconium samples. We performed chemical analysis of 38 pesticides (parent compound and/or metabolite) by UHPLC/MS/MS following strict quality assurance/quality control criteria and blind to case-control status. We carried out logistic regression accounting for frequency-matching variables and major risk factors.

Results: Among the 38 pesticides measured, 16 (42%) were never detected in the meconium samples, 18 (47%) were in <5% of samples, and 4 (11%) in ≥5% of the samples. We observed an association between the detection of fenitrothion in meconium and the risk of hypospadias (OR = 2.6 [1.0-6.3] with n cases = 13, n controls = 21), but not the other pesticides.

Conclusions: Our small study provides a robust assessment of fetal exposure. Fenitrothion's established antiandrogenic activities provide biologic plausibility for our observations. Further studies are needed to confirm this hypothesis.

Background: Instrumental variable (IV) analysis provides an alternative set of identification assumptions in the presence of uncontrolled confounding when attempting to estimate causal effects. Our objective was to evaluate the suitability of measures of prescriber preference and calendar time as potential IVs to evaluate the comparative effectiveness of buprenorphine/naloxone versus methadone for treatment of opioid use disorder (OUD).

Methods: Using linked population-level health administrative data, we constructed five IVs: prescribing preference at the individual, facility, and region levels (continuous and categorical variables), calendar time, and a binary prescriber's preference IV in analyzing the treatment assignment-treatment discontinuation association using both incident-user and prevalent-new-user designs. Using published guidelines, we assessed and compared each IV according to the four assumptions for IVs, employing both empirical assessment and content expertise. We evaluated the robustness of results using sensitivity analyses.

Results: The study sample included 35,904 incident users (43.3% on buprenorphine/naloxone) initiated on opioid agonist treatment by 1585 prescribers during the study period. While all candidate IVs were strong (A1) according to conventional criteria, by expert opinion, we found no evidence against assumptions of exclusion (A2), independence (A3), monotonicity (A4a), and homogeneity (A4b) for prescribing preference-based IV. Some criteria were violated for the calendar time-based IV. We determined that preference in provider-level prescribing, measured on a continuous scale, was the most suitable IV for comparative effectiveness of buprenorphine/naloxone and methadone for the treatment of OUD.

Conclusions: Our results suggest that prescriber's preference measures are suitable IVs in comparative effectiveness studies of treatment for OUD.

Background: There is debate as to whether a coronavirus infection (SARS-CoV-2) affects older adults' physical activity, sleeping problems, weight, feelings of social isolation, and quality of life (QoL). We investigated differences in these outcomes between older adults with and without coronavirus infection over 180 days following infection.

Methods: We included 6789 older adults (65+) from the Lifelines COVID-19 cohort study who provided data between April 2020 and June 2021. Older adults (65+) with and without coronavirus infection were matched on sex, age, education, living situation, body mass index, smoking status, vulnerable health, time of infection, and precoronavirus health outcome. Weighted linear mixed models, adjusted for strictness of governmental policy measures, were used to compare health outcomes after infection between groups.

Results: In total, 309 participants were tested positive for coronavirus. Eight days after infection, older adults with a coronavirus infection engaged in less physical activity, had more sleeping problems, weighed less, felt more socially isolated, and had a lower QoL than those without an infection. Differences in weight, feelings of social isolation, and QoL were absent after 90 days. However, differences in physical activity were still present at 90 days following infection and sleeping problems were present at 180 days.

Conclusion: Our findings found negative associations of coronavirus infection with all the examined outcomes, which for physical activity persisted for 90 days and sleeping problems for 180 days. Magnitudes of estimated effects on physical activity and sleeping problems remain uncertain.

Differential participation in observational cohorts may lead to biased or even reversed estimates. In this article, we describe the potential for differential participation in cohorts studying the etiologic effects of long-term environmental exposures. Such cohorts are prone to differential participation because only those who survived until the start of follow-up and were healthy enough before enrollment will participate, and many environmental exposures are prevalent in the target population and connected to participation via factors such as geography or frailty. The relatively modest effect sizes of most environmental exposures also make any bias induced by differential participation particularly important to understand and account for. We discuss key points to consider for evaluating differential participation and use causal graphs to describe two example mechanisms through which differential participation can occur in health studies of long-term environmental exposures. We use a real-life example, the Canadian Community Health Survey cohort, to illustrate the non-negligible bias due to differential participation. We also demonstrate that implementing a simple washout period may reduce the bias and recover more valid results if the effect of interest is constant over time. Furthermore, we implement simulation scenarios to confirm the plausibility of the two mechanisms causing bias and the utility of the washout method. Since the existence of differential participation can be difficult to diagnose with traditional analytical approaches that calculate a summary effect estimate, we encourage researchers to systematically investigate the presence of time-varying effect estimates and potential spurious patterns (especially in initial periods in the setting of differential participation).

Background: In the presence of effect measure modification, estimates of treatment effects from randomized controlled trials may not be valid in clinical practice settings. The development and application of quantitative approaches for extending treatment effects from trials to clinical practice settings is an active area of research.

Methods: In this article, we provide researchers with a practical roadmap and four visualizations to assist in variable selection for models to extend treatment effects observed in trials to clinical practice settings and to assess model specification and performance. We apply this roadmap and visualizations to an example extending the effects of adjuvant chemotherapy (5-fluorouracil vs. plus oxaliplatin) for colon cancer from a trial population to a population of individuals treated in community oncology practices in the United States.

Results: The first visualization screens for potential effect measure modifiers to include in models extending trial treatment effects to clinical practice populations. The second visualization displays a measure of covariate overlap between the clinical practice populations and the trial population. The third and fourth visualizations highlight considerations for model specification and influential observations. The conceptual roadmap describes how the output from the visualizations helps interrogate the assumptions required to extend treatment effects from trials to target populations.

Conclusions: The roadmap and visualizations can inform practical decisions required for quantitatively extending treatment effects from trials to clinical practice settings.

Background: When a randomized controlled trial fails to demonstrate statistically significant efficacy against the primary endpoint, a potentially costly new trial would need to be conducted to receive licensure. Incorporating data from previous trials might allow for more efficient follow-up trials to demonstrate efficacy, speeding the availability of effective vaccines.

Methods: Based on the outcomes from a failed trial of a maternal vaccine against respiratory syncytial virus (RSV), we simulated data for a new Bayesian group-sequential trial. We analyzed the data either ignoring data from the previous trial (i.e., weakly informative prior distributions) or using prior distributions incorporating the historical data into the analysis. We evaluated scenarios where efficacy in the new trial was the same, greater than, or less than that in the original trial. For each scenario, we evaluated the statistical power and type I error rate for estimating the vaccine effect following interim analyses.

Results: When we used a stringent threshold to control the type I error rate, analyses incorporating historical data had a small advantage over trials that did not. If control of type I error is less important (e.g., in a postlicensure evaluation), the incorporation of historical data can provide a substantial boost in efficiency.

Conclusions: Due to the need to control the type I error rate in trials used to license a vaccine, incorporating historical data provides little additional benefit in terms of stopping the trial early. However, these statistical approaches could be promising in evaluations that use real-world evidence following licensure.