To estimate a meta-analytic regression coefficient related to the average annual change in prevalence, we used the PROC MIXED procedure in SAS to regress the logarithm of annual prevalence estimates on year with a class variable for survey incorporated as a random intercept term. Prevalence estimates were weighted by the inverse variance (square of the standard error). A random effect for survey was chosen over a fixed-effects approach because there was clear heterogeneity in the estimates for overall time trends. (For example, I2 is a parameter often used in meta-analyses to quantify the proportion of total variance that is due to differences in effect sizes as opposed to sampling error. I2 for both full-sample trend analyses of both outcomes was greater than 99%.) As with the trend analyses described previously, dummy variables were used to adjust for any effects that changes in item wording may have had on prevalence estimates while maintaining the assumption of linearity for year-to-year change in the logarithm of the prevalence. Sensitivity analyses were also conducted to gauge the relative influence of each survey on the
