Maternal body mass index in early pregnancy and severe asphyxia-related complications in preterm infants

Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions

BACKGROUND

Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions ("missing completely at random", "missing at random" [MAR], "missing not at random") are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation.

METHODS

We searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically.

RESULTS

Of the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis.

CONCLUSION

Effort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.

Obesity in Pregnancy as a Risk Factor in Maternal and Child Health-A Retrospective Cohort Study

The prevalence of overweight and obesity has risen dramatically in the last few years. This has led to an increase in both conditions in pregnant women. Obesity and overweight are associated with complications for both the mother and the newborn. The aim of this study is to determine the prevalence of obesity and its association with the risk of complications during pregnancy. Materials and Methods: We conducted a retrospective cohort study of pregnant women who delivered from 1 January 2012 to 31 December 2018. Results: A higher prevalence of obesity is observed in the group of women aged 35 or older. Women with a BMI > 25 present a higher risk of cesarean section (aOR 1.49, 95% CI: 1.37-1.61), preeclampsia (aOR 1.64, 95% CI: 1.19-2.26), high-risk pregnancy (aOR 2.34, 95% CI: 1.68-2.6), Apgar < 7 at one minute (aOR 1.53, 95% CI: 1.25-1.89) and macrosomia (aOR 2.08, 95% CI: 1.83-2.37). Maternal overweight and obesity are important determinants of the risk of complications for both the mother and the newborn.

The Association of Prepregnancy Body Mass Index with Pregnancy Outcomes in Chinese Women

Our study was to evaluate the association between prepregnancy body mass index (BMI) and pregnancy outcomes. A total of 1546 women who attended prenatal care clinics and delivered at the Peking University International Hospital, Beijing, China, from October 2018 to April 2020 was included. This research explored gestational, perinatal, and postpartum outcomes, including gestational diabetes, anemia, preeclampsia, preterm premature rupture of membranes (PPROM), and postpartum hemorrhage. Participants were divided into underweight (BMI < 18.5 kg/m²), normal weight (18.5 kg/m² ≤ BMI ≤ 23.9 kg/m²), overweight (24 kg/m² ≤ BMI ≤ 27.9 kg/m²), and obese (BMI ≥ 28 kg/m²) groups. Logistic regression analysis was used to analyze the association between prepregnancy BMI and pregnancy outcomes, and odds ratio (OR) with 95% confidence interval (95% CI) was calculated. After adjusting potential confounders, the risk of PPROM was higher in the underweight group than the normal weight group (OR = 1.864, 95% CI: 1.269-2.737, P < 0.01). Prepregnancy obesity was associated with higher odds of gestational diabetes (OR = 2.649, 95% CI: 1.701-4.126, P < 0.001) and preeclampsia (OR = 3.654, 95% CI: 1.420-9.404, P < 0.01) than the normal weight group, whereas it correlated with the lower risk of anemia (OR = 0.300, 95% CI: 0.128-0.704, P < 0.01). Our findings may provide evidence for the importance of keeping normal weight for Chinese women when preparing for pregnancy.