Pre-pregnancy Maternal Weight and Gestational Weight Gain Increase the Risk for Childhood Asthma and Wheeze: An Updated Meta-Analysis

Abstract Publication bias is a type of systematic error when synthesizing evidence that cannot represent the underlying truth. Clinical studies with favorable results are more likely published and thus exaggerate the synthesized evidence in meta-analyses. The trim-and-fill method is a popular tool to detect and adjust for publication bias. Simulation studies have been performed to assess this method, but they may not fully represent realistic settings about publication bias. Based on real-world meta-analyses, this article provides practical guidelines and recommendations for using the trim-and-fill method. We used a worked illustrative example to demonstrate the idea of the trim-and-fill method, and we reviewed three estimators (R 0, L 0, and Q 0) for imputing missing studies. A resampling method was proposed to calculate P values for all 3 estimators. We also summarized available meta-analysis software programs for implementing the trim-and-fill method. Moreover, we applied the method to 29,932 meta-analyses from the Cochrane Database of Systematic Reviews, and empirically evaluated its overall performance. We carefully explored potential issues occurred in our analysis. The estimators L 0 and Q 0 detected at least one missing study in more meta-analyses than R 0, while Q 0 often imputed more missing studies than L 0. After adding imputed missing studies, the significance of heterogeneity and overall effect sizes changed in many meta-analyses. All estimators generally converged fast. However, L 0 and Q 0 failed to converge in a few meta-analyses that contained studies with identical effect sizes. Also, P values produced by different estimators could yield different conclusions of publication bias significance. Outliers and the pre-specified direction of missing studies could have influential impact on the trim-and-fill results. Meta-analysts are recommended to perform the trim-and-fill method with great caution when using meta-analysis software programs. Some default settings (e.g., the choice of estimators and the direction of missing studies) in the programs may not be optimal for a certain meta-analysis; they should be determined on a case-by-case basis. Sensitivity analyses are encouraged to examine effects of different estimators and outlying studies. Also, the trim-and-fill estimator should be routinely reported in meta-analyses, because the results depend highly on it.

Meta-analysis, a statistical tool for combining results across studies, is becoming popular as a method for resolving discrepancies in genetic association studies. Persistent difficulties in obtaining robust, replicable results in genetic association studies are almost certainly because genetic effects are small, requiring studies with many thousands of subjects to be detected. In this article, we describe how meta-analysis works and consider whether it will solve the problem of underpowered studies or whether it is another affliction visited by statisticians on geneticists. We show that meta-analysis has been successful in revealing unexpected sources of heterogeneity, such as publication bias. If heterogeneity is adequately recognized and taken into account, meta-analysis can confirm the involvement of a genetic variant, but it is not a substitute for an adequately powered primary study.

Pre-pregnancy obesity poses risks to both pregnant women and their infants. This study used a large population-based data source to examine trends, from 1993 through 2003, in the prevalence of pre-pregnancy obesity among women who delivered live infants. Data from the Pregnancy Risk Assessment Monitoring System in nine states were analyzed for trends in pre-pregnancy obesity (BMI>29.0 kg/m2) overall and by maternal demographic and behavioral characteristics. Pre-pregnancy BMI was calculated from self-reported weight and height on questionnaires administered after delivery, and demographic characteristics were taken from linked birth certificates. The sample of 66,221 births was weighted to adjust for survey design, non-coverage, and non-response, and it is representative of all women delivering a live birth in each particular state. The sampled births represented 18.5% of all births in the United States. Pre-pregnancy obesity increased 69.3% during the study period, from 13.0% in 1993 to 1994 to 22.0% in 2002 to 2003. The percentage increase ranged from 45% to 105% for individual states. Subgroups of women with the highest prevalence of obesity in 2002 to 2003 were those who were 20 to 29 years of age, black, had three or more children, had a high school education, enrolled in Women, Infants, and Children, or were non-smokers. However, all subgroups of women examined experienced at least a 43% increase in pre-pregnancy obesity over this time period. The prevalence of pre-pregnancy obesity is increasing among women in these nine states, and this trend has important implications for all stages of reproductive health care.