A Systematic Review of Obesity Disparities Research.

CONTEXT: A review of interventions addressing obesity disparities could reveal gaps in the literature and provide guidance on future research, particularly for populations with a high prevalence of obesity and obesity-related cardiometabolic risk. EVIDENCE ACQUISITION: A systematic review of clinical trials in obesity disparities research that were published in 2011-2016 in PubMed/MEDLINE resulted in 328 peer-reviewed articles. Articles were excluded if they had no BMI, weight, or body composition measure as primary outcome or were foreign (n=201); were epidemiologic or secondary data analyses of clinical trials (n=12); design or protocol papers (n=54); systematic reviews (n=3); or retracted or duplicates (n=9). Forty-nine published trials were summarized and supplemented with a review of ongoing obesity disparities grants being funded by the National, Heart, Lung and Blood Institute. EVIDENCE SYNTHESIS: Of the 49 peer-reviewed trials, 27 targeted adults and 22 children only or parent-child dyads (5 of 22). Interventions were individually focused; mostly in single settings (e.g., school or community); of short duration (mostly ≤12 months); and primarily used behavioral modification (e.g., self-monitoring) strategies. Many of the trials had small sample sizes and moderate to high attrition rates. A meta-analysis of 13 adult trials obtained a pooled intervention effect of BMI -1.31 (95% CI=-2.11, -0.52, p=0.0012). Institutional review identified 140 ongoing obesity-related health disparities grants, but only 19% (n=27) were clinical trials. CONCLUSIONS: The reviews call for cardiovascular-related obesity disparities research that is long term and includes population research, and multilevel, policy, and environmental, or "whole of community," interventions.

Estimation of a Matrix of Heterogeneity Parameters in Multivariate Meta-Analysis of Random-Effects Models.

Multivariate meta-analysis has potential over its univariate counterpart. The most common challenge in univariate or multivariate meta-analysis is estimating heterogeneity parameters in non-negative domains under the random-effects model assumption. In this context, two new multivariate estimation methods are demonstrated; first, by extending the Sidik and Jonkman (2005) univariate estimates to a multivariate setting, and second, by considering an iterative version of the Sidik and Jonkman method, namely, a Hybrid method developed in Wouhib (2013). These two methods are compared with extended DerSimonian and Laird methods (Jackson et al. 2009; Chen et al. 2012) by using an example and simulation in random-effects multivariate meta-analysis. Finally, the benefits of the proposed estimates are evaluated in terms of precision in estimating vectors of effect sizes and associated covariance matrices via simulation. Also, some limitations and remedies resulting from negative definite matrix in estimating heterogeneity parameters will be discussed.