European Journal of Epidemiology最新文献

In causal inference, parametric models are usually employed to address causal questions estimating the effect of interest. However, parametric models rely on the correct model specification assumption that, if not met, leads to biased effect estimates. Correct model specification is challenging, especially in high-dimensional settings. Incorporating Machine Learning (ML) into causal analyses may reduce the bias arising from model misspecification, since ML methods do not require the specification of a functional form of the relationship between variables. However, when ML predictions are directly plugged in a predefined formula of the effect of interest, there is the risk of introducing a “plug-in bias” in the effect measure. To overcome this problem and to achieve useful asymptotic properties, new estimators that combine the predictive potential of ML and the ability of traditional statistical methods to make inference about population parameters have been proposed. For epidemiologists interested in taking advantage of ML for causal inference investigations, we provide an overview of three estimators that represent the current state-of-art, namely Targeted Maximum Likelihood Estimation (TMLE), Augmented Inverse Probability Weighting (AIPW) and Double/Debiased Machine Learning (DML).

We aimed to determine the association of family history of dementia with structural brain measures and cognitive performance in childhood and mid-life adulthood. We studied 1,259 parents (mean age: 47.3 years, range 31.9–67.4) and 866 of their children (mean age [range] at brain MRI: 9.9 years [8.8–11.9], and for cognition: 13.5 years [12.6–15.8]) of the population-based Generation R Study. Parents filled in a questionnaire on family history, and both parents and children underwent cognitive assessment and neuroimaging. Of all participants, 109 parents (8.6%) reported a parental family history of dementia and 73 children (8.4%) had a grandparental history of dementia with mean age of dementia diagnosis in those affected 75 years (± 7.3). We observed no associations of dementia family history with cognitive ability in either parents or their children, except for worse Purdue pegboard in parents with a parental history of dementia, compared to those without (mean difference [95%CI]: -1.23 [-2.15; -0.31], test range: 21–52). In parents and children, neuroimaging measures did not differ significantly by family history. Results did not depend on age, sex, and APOE genotype. Family history of dementia was associated with worse manual dexterity in mid-life adulthood, but not with any other measures of cognitive ability or subclinical brain health in childhood and mid-life. These findings suggest that the association of family history with dementia risk is due chiefly to neurodegenerative rather than neurodevelopmental processes, and might first present with reduced motor skills.

Background: Dietary strategies for type 2 diabetes (T2DM) prevention have mainly focused on solid foods and nutrients. Emanating evidence suggests that beverage consumption in adulthood may also influence T2DM development, whereas the role of beverages during adolescence remains unknow.

Objective: To examine adolescent beverages consumption, and their changes from adolescence to adulthood in relation to T2DM risk in adulthood.

Methods: This prospective cohort study, conducted within the Nurses' Health Study II (NHS II), enrolled 41,317 women who completed a food-frequency questionnaire (FFQ) regarding their diet in high school and had no diabetes, cardiovascular disease, or cancer at baseline (1997). Beverage consumption including coffee, tea, regular or diet soda, fruit juice or milk, was assessed using the FFQ. Cox proportional hazards models were utilized to estimate hazard ratios (HRs) for the association between beverage consumption in adolescence and risk of incident type 2 diabetes (T2DM) in adulthood, adjusting for potential confounders.

Results: During 725,650 person-years of follow-up, 2,844 participants developed T2DM. After adjustment for demographic, lifestyle and dietary risk factors, comparing ≥ 1 serving/day with non-consumers, adolescent coffee [HR, 0.86 (95% confidence interval: 0.75 to 0.98); P-trend = 0.02)] and orange juice [HR, 0.83 (0.71 to 0.96); P-trend = 0.0008)] consumption was associated with lower T2DM risk, whereas, regular soda [HR, 1.37 (1.20 to 1.57); P-trend < 0.0001)] and iced tea [HR, 1.41 (1.21 to 1.65); P-trend < 0.0001)] intake was associated with higher T2DM risk. Increased coffee intake from adolescence to adulthood in 1991 was associated with a lower T2DM risk [HR, 0.70 (0.61 to 0.80); P-trend < 0.0001), comparing ≥ + 3 servings/day with no change], whereas the opposite was observed for increased regular soda [HR, 1.20 (1.06 to 1.35); P-trend = 0.004), comparing ≥ + 1 or more servings/week with no change)] and diet soda consumption [HR, 1.59 (1.41 to 1.80); P-trend = 0.0002), comparing ≥ + 2 servings/day with no change].

Conclusion: Adolescent consumption of coffee or orange juice intake was associated with a lower risk of T2DM, whereas the opposite was observed for intake of regular soda or iced tea. In addition, increased coffee intake was associated with a lower diabetes risk, whereas the opposite was observed for regular or diet soda intake. These data highlight a potentially important role of beverage intake at early life in the etiology of diabetes during adulthood.

The mechanism underlying the co-occurrence of major depressive disorder (MDD) and gynecological diseases remains unclear. This study aimed to investigate the familial co-aggregation and shared genetic loading between MDD and gynecological diseases, namely dysmenorrhea, endometriosis, uterine leiomyomas (UL), and polycystic ovary syndrome (PCOS). Overall, 2,121,632 females born 1970-1999 with parental information were enrolled from the Taiwan National Health Insurance Research Database (NHIRD); 25,142 same-sex twins and 951,779 persons with full-sibling(s) were selected. Genome-wide genotyping data were available for 67,882 unrelated female participants from the Taiwan Biobank linked to the NHIRD. A generalized linear model with a logistic link function was used to examine the associations of individual history, family history in parents/full-siblings/same-sex twins, and polygenic risk scores (PRS) for MDD with the risk of gynecological diseases; generalized estimating equations were used to consider the non-independence of data. Both parents affected with MDD was associated with four gynecological diseases, and its magnitude of association was higher than either affected parent; maternal MDD showed a higher magnitude of association than paternal MDD. Full-siblings of patients with MDD had a higher risk of four gynecological diseases; same-sex twins of patients with MDD had a greater association with dysmenorrhea and PCOS. PRS for MDD was associated with dysmenorrhea and endometriosis. Familial co-aggregation was observed in the co-occurrence of MDD and four gynecological diseases. There exists a shared polygenic liability between MDD and dysmenorrhea and endometriosis. Individuals with MDD-affected relatives or a higher PRS for MDD should be monitored for gynecological diseases.

This study evaluated the relationship between Covid-19 vaccination and menstrual bleeding disturbances using a large national registry linkage including 666,467 women between 20 and 40 years of age residing in Norway on January 1st, 2019. Information on vaccination-BNT162b2 and mRNA-1273 - was obtained from the Norwegian vaccination registry. Diagnoses of menstrual disturbances (absent/scanty, excessive, irregular/frequent menstruation, and intermenstrual bleeding) was obtained from the general practitioner database. We examined new-onset menstrual bleeding disturbances using a Cox regression comparing vaccinated to unvaccinated women, where women contributed follow-up time as unvaccinated until the day of vaccination. In addition, we conducted a self-controlled case-series analysis, and a sensitivity analysis excluding all those who remained unvaccinated throughout the pandemic, to evaluate the role of unmeasured confounding. We observed an increased risk of several menstrual bleeding disturbances after vaccination against Covid-19, ranging from an adjusted HR (aHR) of 1.18 (95% CI: 1.04, 1.33) for intermenstrual bleeding to 1.29 (95% CI: 1.23, 1.36) for irregular/frequent menstrual periods. However, estimates were fully attenuated when excluding women who remained unvaccinated at the end of follow-up (aHRs between 0.97 and 1.08). No differences were identified according to vaccine dose or type. Our self-controlled case series analysis confirmed no increased risk after a first dose of vaccination, though there was a slightly increased risk of menstrual bleeding disturbances from 61 days after vaccination with dose 2. In conclusion, the modestly increased risk of menstrual bleeding disturbances after Covid-19 vaccination appeared to reflect a role of unmeasured confounding by women who never received Covid-19 vaccinations, as associations did not remain when risk after vaccination were compared to risk before vaccination among ever vaccinated women.

Mortality statistics are critical to determine the burden of disease. Certain causes of death are prone to being misclassified on cause of death certificates. This poses a serious risk for public health and safety, as accurate death certificates form the basis for mortality statistics, which in turn are crucial for research, funding allocation and health interventions. This study uses generalised estimating equations and regression modelling to investigate for which cause of death categories suicide and accident deaths are misclassified as. National mortality statistics and autopsy rates from North America and Europe covering the past forty years were analysed to determine the associations between the different causes of death in cross-sectional and longitudinal models. We find that suicides and deaths by accidents are frequently mutually misclassified. We also find that suicides are frequently misclassified as drug use disorder deaths, in contrast to accident deaths, which are not misclassified as drug use disorder deaths. Furthermore, suicides do not seem to be misclassified as undetermined deaths or ill-defined deaths. The frequency of misclassification shows that the quality of death certificates should be improved, and autopsies may be used systematically to control the quality of death certificates.

Adverse neonatal outcomes following in utero antipsychotic exposure remain unclear. This systematic review and meta-analysis aimed to investigate associations between in utero first- and second-generation antipsychotic exposure and various neonatal outcomes. The primary outcome was small for gestational age. Secondary outcomes included other birth weight-related measures, prematurity and neonatal outcomes. MEDLINE, EMBASE, CENTRAL, ICTRP, and ClinicalTrials.gov were searched for on 8th July 2023. Two reviewers independently selected studies reporting associations between exposure and neonatal outcomes (all designs were eligible, no language or time restriction) and extracted data. ROBINS-I was used for risk of bias assessment. Meta-analyses were performed. Measures of association were odds ratios and mean differences. Thirty-one observational studies were included. Regarding small for gestational age < 10th percentile, meta-analysis was only performed for second-generation antipsychotics and showed no evidence for an association (OR 1.31 [95%CI 0.83; 2.07]; I²=46%; p_het=0.13, n = 4 studies). First-generation antipsychotics were associated with an increased risk of small for gestational age < 3rd percentile (OR 1.37 [95%CI 1.02; 1.83]; I²=60%; p_het=0.04, n = 5) and a lower mean birthweight (MD -135 g [95%CI -203; -66]; I²=53%; p_het=0.07, n = 5). Second-generation antipsychotics were associated with large for gestational age > 97th percentile (OR 1.56 [95%CI 1.31; 1.87]; I²=4%; p_het=0.37, n = 4) and Apgar score < 7 (OR 1.64 [95%CI 1.09; 2.47]; I²=47%; p_het=0.13, n = 4). Both types of antipsychotics were associated with increased risks of preterm birth and neonatal hospitalization. Despite potential confounding in the studies, this systematic review and meta-analysis showed that newborns of mothers using antipsychotics during pregnancy are potentially at risk of adverse neonatal outcomes. Data sources: MEDLINE, EMBASE, CENTRAL, ICTRP, ClinicalTrials.gov. Prospero Registration Number CRD42023401805.