On the use of the outcome variable "small for gestational age" when gestational age is a potential mediator: a maternal asthma perspective

Preconception paternal/maternal BMI and risk of small/large for gestational age infant in over 4·7 million Chinese women aged 20-49 years: a population-based cohort study in China

Evidence of couples' BMI and its influence on birth weight is limited and contradictory. Therefore, this study aims to assess the association between couple's preconception BMI and the risk of small for gestational age (SGA)/large for gestational age (LGA) infant, among over 4·7 million couples in a retrospective cohort study based on the National Free Pre-pregnancy Checkups Project between 1 December 2013 and 30 November 2016 in China. Among the live births, 256 718 (5·44 %) SGA events and 506 495 (10·73 %) LGA events were documented, respectively. After adjusting for confounders, underweight men had significantly higher risk (OR 1·17 (95 % CI 1·15, 1·19)) of SGA infants compared with men with normal BMI, while a significant and increased risk of LGA infants was obtained for overweight and obese men (OR 1·08 (95 % CI 1·06, 1·09); OR 1·19 (95 % CI 1·17, 1·20)), respectively. The restricted cubic spline result revealed a non-linear decreasing dose-response relationship of paternal BMI (less than 22·64) with SGA. Meanwhile, a non-linear increasing dose-response relationship of paternal BMI (more than 22·92) with LGA infants was observed. Moreover, similar results about the association between maternal preconception BMI and SGA/LGA infants were obtained. Abnormal preconception BMI in either women or men were associated with increased risk of SGA/LGA infants, respectively. Overall, couple's abnormal weight before pregnancy may be an important preventable risk factor for SGA/LGA infants.

Global Exposure to Per- and Polyfluoroalkyl Substances and Associated Burden of Low Birthweight

Low birthweight (LBW) is a worldwide public health concern, while the global burden of LBW attributable to endocrine-disrupting chemicals, such as per- and polyfluoroalkyl substances (PFAS), has not yet been evaluated. Here, we established a large dataset for the biomonitoring of seven representative congeners of PFAS by examining data from 2325 publications. Global exposure to perfluorooctanesulfonic acid (PFOS) was the highest, followed by perfluorohexanesulfonic acid (PFHxS) and perfluorooctanoic acid (PFOA). Spatiotemporal exposure to PFAS varied considerably, with daily intake estimated in the range of 0.01-1.7 ng/kg/day. Moreover, decreasing trends in PFOS, PFHxS, and PFOA exposure were noted in most regions of the world over the past two decades, but such trends were not observed for other PFAS with long carbon chains, especially in East Asia. Furthermore, we estimated that human exposure to PFOA contributed to approximately 461,635 (95% confidence interval: 57,418 to 854,645) cases per year of LBW during the past two decades, predominantly from Asian regions. Although our estimation may be constrained by uncertainties from the dose-response curve and data availability, this study has unveiled that PFAS might be a contributor to global LBW prevalence during 2000-2019, supporting continuous actions to mitigate PFAS contamination.

Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation

PURPOSE

The application of simulated data in epidemiological studies enables the illustration and quantification of the magnitude of various types of bias commonly found in observational studies. This was a review of the application of simulation methods to the quantification of bias in reproductive and perinatal epidemiology and an assessment of value gained.

METHODS

A search of published studies available in English was conducted in August 2020 using PubMed, Medline, Embase, CINAHL, and Scopus. A gray literature search of Google and Google Scholar, and a hand search using the reference lists of included studies was undertaken.

RESULTS

Thirty-nine papers were included in this study, covering information (n = 14), selection (n = 14), confounding (n = 9), protection (n = 1), and attenuation bias (n = 1). The methods of simulating data and reporting of results varied, with more recent studies including causal diagrams. Few studies included code for replication.

CONCLUSIONS

Although there has been an increasing application of simulation in reproductive and perinatal epidemiology since 2015, overall this remains an underexplored area. Further efforts are required to increase knowledge of how the application of simulation can quantify the influence of bias, including improved design, analysis and reporting. This will improve causal interpretation in reproductive and perinatal studies.

A Bayesian finite mixture of bivariate regression model for causal mediation analyses

Building on the work of Schwartz et al, Joint Bayesian analysis of birthweight and censored gestational age using finite mixture models in Statistics in Medicine, we propose a Bayesian finite mixture of bivariate regression model for causal mediation analyses. Using an identifiability condition within each component of the mixture, we express the natural direct and indirect effects of the exposure on the outcome as functions of the component-specific regression coefficients. On the basis of simulated data, we examine the behavior of the model for estimating these effects in situations where the associations between exposure, mediator, and outcome are confounded or not. Additionally, we demonstrate that this mixture model can be used to account for heterogeneity arising through unmeasured binary or categorical mediator-outcome confounders. Considering gestational age as a potential mediator, we then illustrate our mediation mixture model to estimate the natural direct and indirect effects of exposure to inhaled corticosteroids during pregnancy on birthweight using a cohort of asthmatic women from the province of Quebec (Canada).

Correction to: On the use of the outcome variable "small for gestational age" when gestational age is a potential mediator: a maternal asthma perspective

CORRECTION

Following publication of the original article [1], the authors reported that the following four references in Table 2 are incorrect.