American journal of epidemiology最新文献

Correlates of diagnosed and probable polycystic ovary syndrome (PCOS) among parous women were assessed in this study. A total of 557 women were recruited from multi-specialty clinics in eastern Massachusetts. The women were categorized as being diagnosed with PCOS based on medical records and self-reported clinician-diagnoses. A category of "probable PCOS" was created for women without a diagnosis but with ≥ 2 of the following: ovulatory dysfunction (cycle length < 21 or ≥ 35 days), hyperandrogenism (free testosterone concentration > 75th percentile), or elevated anti-Müllerian hormone (AMH) concentration (> 75th percentile). The remaining participants were placed in the "no PCOS" category, and characteristics were compared across groups. Of the total cohort, 9.7% had diagnosed and 9.2% had probable PCOS. The frequency of irregular cycles was similar for diagnosed and probable PCOS. Free testosterone and AMH levels were higher in women with probable than with diagnosed PCOS. Frequency of irregular cycles and both hormones were higher for the 2 PCOS groups vs the no PCOS group. Obesity prevalence for diagnosed PCOS was twice that of probable PCOS (43.9% vs 19.6%), yet the 2 groups had similar HbA1c and adiponectin values. Women with probable PCOS are leaner but have comparable glycemic traits to those with a formal diagnosis, highlighting the importance of assessing biochemical profiles among women with irregular cycles, even in the absence of overweight/obesity.

It is unclear how the risk of post-COVID symptoms evolved during the pandemic, especially before the spread of Severe Acute Respiratory Syndrome Coronavirus 2 variants and the availability of vaccines. We used modified Poisson regressions to compare the risk of six-month post-COVID symptoms and their associated risk factors according to the period of first acute COVID: during the French first (March-May 2020) or second (September-November 2020) wave. Nonresponse weights and multiple imputation were used to handle missing data. Among participants aged 15 years or older in a national population-based cohort, the risk of post-COVID symptoms was 14.6% (95% confidence interval [CI], 13.9%-15.3%) in March-May 2020, vs 7.0% (95% CI, 6.3%-7.7%) in September-November 2020 (adjusted relative risk [RR], 1.36; 95% CI, 1.20-1.55). For both periods, the risk was higher in the presence of baseline physical condition(s), and it increased with the number of acute symptoms. During the first wave, the risk was also higher for women, in the presence of baseline mental condition(s), and it varied with educational level. In France in 2020, the risk of six-month post-COVID symptoms was higher during the first than the second wave. This difference was observed before the spread of variants and the availability of vaccines.

Understanding health risks from methylmercury (MeHg) exposure is complicated by its link to fish consumption, which may confound or modify toxicities. One solution is to include fish intake and a biomarker of MeHg exposure in the same analytical model, but resulting estimates do not reflect the independent impact of accumulated MeHg or fish exposure. In fish-eating populations, this can be addressed by separating MeHg exposure into fish intake and average mercury content of the consumed fish. We assessed the joint association of prenatal MeHg exposure (maternal hair mercury level) and fish intake (among fish-eating mothers) with neurodevelopment in 361 children aged 8 years from the New Bedford Cohort (New Bedford, Massachusetts; born in 1993-1998). Neurodevelopmental assessments used standardized tests of IQ, language, memory, and attention. Covariate-adjusted regression assessed the association of maternal fish consumption, stratified by tertile of estimated average fish mercury level, with neurodevelopment. Associations between maternal fish intake and child outcomes were generally beneficial for those in the lowest average fish mercury tertile but detrimental in the highest average fish mercury tertile, where, for example, each serving of fish was associated with 1.3 fewer correct responses (95% CI, -2.2 to -0.4) on the Boston Naming Test. Standard analyses showed no outcome associations with hair mercury level or fish intake. This article is part of a Special Collection on Environmental Epidemiology.

When studying the impact of policy interventions or natural experiments on air pollution, such as new environmental policies or the opening or closing of an industrial facility, careful statistical analysis is needed to separate causal changes from other confounding factors. Using COVID-19 lockdowns as a case study, we present a comprehensive framework for estimating and validating causal changes from such perturbations. We propose using flexible machine learning-based comparative interrupted time series (CITS) models for estimating such a causal effect. We outline the assumptions required to identify causal effects, showing that many common methods rely on strong assumptions that are relaxed by machine learning models. For empirical validation, we also propose a simple diagnostic criterion, guarding against false effects in baseline years when there was no intervention. The framework is applied to study the impact of COVID-19 lockdowns on atmospheric nitrogen dioxide (NO2) levels in the eastern United States. The machine learning approaches guard against false effects better than common methods and suggest decreases in NO2 levels in 4 US cities (Boston, Massachusetts; New York, New York; Baltimore, Maryland; and Washington, DC) during the pandemic lockdowns. The study showcases the importance of our validation framework in selecting a suitable method and the utility of a machine learning-based CITS model for studying causal changes in air pollution time series. This article is part of a Special Collection on Environmental Epidemiology.

We test the robustness of the self-controlled risk interval (SCRI) design in a setting where time between doses may introduce time-varying confounding, using both negative control outcomes (NCOs) and quantitative bias analysis (QBA). All vaccinated cases identified from 5 European databases between September 1, 2020, and end of data availability were included. Exposures were doses 1-3 of the Pfizer, Moderna, AstraZeneca, and Janssen COVID-19 vaccines; outcomes were myocarditis and, as the NCO, otitis externa. The SCRI used a 60-day control window and dose-specific 28-day risk windows, stratified by vaccine brand and adjusted for calendar time. The QBA included two scenarios: (1) baseline probability of the confounder was higher in the control window and (2) vice versa. The NCO was not associated with any of the COVID-19 vaccine types or doses except Moderna dose 1 (IRR = 1.09; 95% CI 1.01-1.09). The QBA suggested that even the strongest literature-reported confounder (COVID-19; RR for myocarditis = 18.3) could only explain away part of the observed effect, from IRR = 3 to IRR = 1.40. The SCRI seems robust to unmeasured confounding in the COVID-19 setting, although a strong unmeasured confounder could bias the observed effect upward. Replication of our findings for other safety signals would strengthen this conclusion. This article is part of a Special Collection on Pharmacoepidemiology.

Evidence is limited regarding the effect of prenatal benzodiazepine and z-hypnotic exposure and long-term neurodevelopment in childhood. The objective of this study was to investigate the effects of initiating benzodiazepine or z-hypnotic treatment in early, mid, and late pregnancy on fifth-grade numeracy and literacy scholastic skills in children by emulating 3 target trials. The trials are identical except for the timing of enrollment and the number of eligible individuals. Eligibility to the trials required a history of anxiety and/or depression prior to pregnancy. We used data from the Norwegian Mother, Father and Child Cohort Study, linked to the Medical Birth Registry of Norway, to emulate the trials. We adjusted for baseline covariates that were available at time 0 for each trial by inverse probability of treatment weighting using the propensity score. The findings of this study did not show any effect of mothers' initiation of treatment with benzodiazepines or z-hypnotics in early, mid, or late pregnancy on the children's fifth-grade test scores in numeracy and literacy. The study results provide reassurance for patients in need of benzodiazepines and z-hypnotics during pregnancy; however, these findings need to be interpreted with caution due to low study power in some of the analyses. This article is part of a Special Collection on Pharmacoepidemiology.

Associations of pesticide exposures during preconception with stillbirth have not been well explored. We linked Arizona pesticide use records with birth certificates from 2006 to 2020 and estimated associations of living within 500 m of any pyrethroid, organophosphate (OP), or carbamate pesticide applications during a 90-day preconception window or the first trimester, with stillbirth. We considered a binary measure of exposure (any exposure), as well as log-pounds and log-acres applied within 500 m, in a negative control exposure framework with log-binomial regression. We included 1 237 750 births, 2290 stillbirths, and 27 pesticides. During preconception, any exposure to pesticides was associated with stillbirth, including cyfluthrin (risk ratio [RR] = 1.97; 95% CI, 1.17-3.32); zeta-cypermethrin (RR = 1.81; 95% CI, 1.20-2.74); OPs as a class (RR = 1.60; 95% CI, 1.16-2.19); malathion (RR = 2.02; 95% CI, 1.26-3.24); carbaryl (RR = 6.39; 95% CI, 2.07-19.74); and propamocarb hydrochloride (RR = 7.72; 95% CI, 1.10-54.20). During the first trimester, fenpropathrin (RR = 4.36; 95% CI, 1.09-17.50); permethrin (RR = 1.57; 95% CI, 1.02-2.42); OPs as a class (RR = 1.50; 95% CI, 1.11-2.01); acephate (RR = 2.31; 95% CI, 1.22-4.40); and formetanate hydrochloride (RR = 7.22; 95% CI, 1.03-50.58) were associated with stillbirth. Interpretations were consistent when using continuous measures of pounds or acres of exposure. Pesticide exposures during preconception and first trimester may be associated with stillbirth. This article is part of a Special Collection on Environmental Epidemiology.

We recently questioned the utility of testing for proportional hazards in survival analysis. Here we expand on why the proportional hazards assumption is both implausible and unnecessary in most medical studies, particularly in randomized trials. We conclude that using survival analysis methods that do not rely on proportional hazards is typically the preferred course of action.

Exposure to the United States criminal legal system - whether through contact with law enforcement, incarceration in a jail or prison, or community supervision - is associated with a range of adverse health outcomes. There is mounting evidence that mass incarceration drives health inequities, particularly for Black, Indigenous, and People of Color. However, relative to its outsized impacts on health and health inequities, the criminal legal system has received limited attention in epidemiology. In this commentary, we use a public health prevention framework to highlight opportunities for epidemiological research aiming to: 1) reduce the number of people entering the criminal legal system (primary prevention), 2) improve conditions of confinement (secondary), and 3) reduce recidivism and re-involvement in the system (tertiary). We describe common biases (confounding, selection, measurement, and missingness) encountered in research at each prevention level and identify ways in which epidemiologists can help to address these challenges. Our goal is to emphasize the unique strengths that epidemiologists can bring to investigating and intervening on the wide-ranging health consequences of a societal system that disproportionately impacts its most marginalized members.