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\textbf{Screening and data extraction}  We used Covidence (covidence.org) to initially screen articles based on title and abstract. To qualify as a systematic review, studies had to summarize evidence across multiple studies and provide information on the search strategy, such as search terms, databases, or inclusion/exclusion criteria. Meta-analyses were classified as quantitative syntheses of results across multiple studies (Onishi 2014). Two screeners independently reviewed the titles and abstracts of each citation and made a decision regarding its suitability for inclusion based on the definitions previously described. Next, the screeners held a meeting to revisit the citations in conflict and arrive at a final consensus. Following the screening process, full-text versions of included articles were obtained via EndNote.   To standardize the coding process, an abstraction manual was developed and pilot tested. After completing this process, a training session was conducted to familiarize coders with abstracting the data elements. A subset of studies were jointly coded as a group. After the training exercise, each coder was provided with 3 new articles to code independently. Inter-rater agreement of this data was calculated using Cohen's kappa. Since inter-rater agreement was high (k=0.86; agreement=91 percent), each coder was assigned an equal subset of articles for data abstraction. We coded the following elements: a) statistical test used to evaluate heterogeneity; b) a priori threshold for statistical significance; c) type of model (random, fixed, mixed, or both); d) whether authors selected a random effects model based on significance of the heterogeneity test; e) whether authors used a random effects model without explanation; f) what type of plot was used to evaluate heterogeneity, if any; g) whether the plot was published as a figure in the manuscript; h) whether follow-up analysis was conducted, and if so, the type of analysis (subgroup, meta-regression, and/or sensitivity analysis); i) whether heterogeneity was mentioned in writing only; and j) whether authors concluded there was too much heterogeneity to perform a meta-analysis. After the initial coding process, validation checks were conducted such that each coded element was verified by the other coder. After these checks were performed, coders met to discuss disagreements and settle them by consensus. Analysis of the final data was conducted using STATA 13.1. Data from this study are publically available on Figshare (enter doi number here).  \textbf{Evidence-Based Mapping}  We modified an approach by Althuis, Weed, and Frankenfield to perform the evidence mapping process \cite{Althuis_2014}. This approach focused on observational studies and included a step to evaluate covariates adjusted for across the primary studies. In this study, we included an evaluation of particular risk of bias components pertinent to the selected articles. We performed the following steps during the evidence mapping exercise: 
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