Person:

Blood, Emily Alice

Linear mixed models (LMMs) are frequently used to analyze longitudinal data. Although these models can be used to evaluate mediation, they do not directly model causal pathways. Structural equation models (SEMs) are an alternative technique that allows explicit modeling of mediation. The goal of this paper is to evaluate the performance of LMMs relative to SEMs in the analysis of mediated longitudinal data with time-dependent predictors and mediators. We simulated mediated longitudinal data from an SEM and specified delayed effects of the predictor. A variety of model specifications were assessed, and the LMMs and SEMs were evaluated with respect to bias, coverage probability, power, and Type I error. Models evaluated in the simulation were also applied to data from an observational cohort of HIV-infected individuals. We found that when carefully constructed, the LMM adequately models mediated exposure effects that change over time in the presence of mediation, even when the data arise from an SEM.

Background: Structural equation models (SEMs) provide a general framework for analyzing mediated longitudinal data. However when interest is in the total effect (i.e. direct plus indirect) of a predictor on the binary outcome, alternative statistical techniques such as non-linear mixed models (NLMM) may be preferable, particularly if specific causal pathways are not hypothesized or specialized SEM software is not readily available. The purpose of this paper is to evaluate the performance of the NLMM in a setting where the SEM is presumed optimal. Methods: We performed a simulation study to assess the performance of NLMMs relative to SEMs with respect to bias, coverage probability, and power in the analysis of mediated binary longitudinal outcomes. Both logistic and probit models were evaluated. Models were also applied to data from a longitudinal study assessing the impact of alcohol consumption on HIV disease progression. Results: For the logistic model, the NLMM adequately estimated the total effect of a repeated predictor on the repeated binary outcome and were similar to the SEM across a variety of scenarios evaluating sample size, effect size, and distributions of direct vs. indirect effects. For the probit model, the NLMM adequately estimated the total effect of the repeated predictor, however, the probit SEM overestimated effects. Conclusions: Both logistic and probit NLMMs performed well relative to corresponding SEMs with respect to bias, coverage probability and power. In addition, in the probit setting, the NLMM may produce better estimates of the total effect than the probit SEM, which appeared to overestimate effects.

Introduction: The Planet Health obesity prevention curriculum has prevented purging and abuse of diet pills (disordered weight control behavior [DWCB]) in middle-school girls in randomized trials, but the effects of Planet Health on DWCB when implemented by schools under dissemination conditions are not known. Methods: Massachusetts Department of Public Health and Blue Cross Blue Shield of Massachusetts disseminated Planet Health as part of the 3-year, Healthy Choices obesity prevention program in middle schools. We conducted an evaluation in 45 schools from fall 2005 to spring 2008. We gathered data from school staff to quantify intervention activities, and we gathered anonymous cross-sectional survey data from students on DWCB at baseline and Year 3 follow-up (n = 16,369). Multivariate logistic analyses with generalized estimating equations examined the effect of intervention activities on odds of students reporting DWCB at follow-up. Results: Students in schools reaching a high number of youth with Planet Health lessons on reducing television viewing had lower odds of DWCB at follow-up (odds ratio [OR], 0.80 per 100 lesson-exposures; 95% confidence interval [CI], 0.74–0.85). In addition, reduced odds of DWCB at follow-up were found in schools with active staff teamwork (OR, 0.76; 95% CI, 0.66–0.86) and the presence of programs addressing television viewing goals with staff (OR, 0.38; 95% CI, 0.28–0.53). Conclusion: Combined evidence from efficacy and effectiveness trials and now from dissemination research indicates that appropriately designed obesity prevention programs can achieve DWCB prevention on a large scale.

Cosmetic procedures have proliferated rapidly over the past few decades, with over $11 billion spent on cosmetic surgeries and other minimally invasive procedures and another $2.9 billion spent on U.V. indoor tanning in 2012 in the United States alone. While research interest is increasing in tandem with the growth of the industry, methods have yet to be developed to identify and geographically locate the myriad types of businesses purveying cosmetic procedures. Geographic location of cosmetic-procedure businesses is a critical element in understanding the public health impact of this industry; however no studies we are aware of have developed valid and feasible methods for spatial analyses of these types of businesses. The aim of this pilot validation study was to establish the feasibility of identifying businesses offering surgical and minimally invasive cosmetic procedures and to characterize the spatial distribution of these businesses. We developed and tested three methods for creating a geocoded list of cosmetic-procedure businesses in Boston (MA) and Seattle (WA), USA, comparing each method on sensitivity and staff time required per confirmed cosmetic-procedure business. Methods varied substantially. Our findings represent an important step toward enabling rigorous health-linked spatial analyses of the health implications of this little-understood industry.

Backgroun: Linear mixed effects models (LMMs) are a common approach for analyzing longitudinal data in a variety of settings. Although LMMs may be applied to complex data structures, such as settings where mediators are present, it is unclear whether they perform well relative to methods for mediational analyses such as structural equation models (SEMs), which have obvious appeal in such settings. For some researchers, SEMs may be more difficult than LMMs to implement, e.g. due to lack of training in the methodology or the need for specialized SEM software. It therefore is of interest to evaluate whether the LMM performs sufficiently in a scenario particularly suitable for SEMs. We focus on evaluation of the total effect (i.e. direct and indirect) of an exposure on an outcome of interest when a mediating factor is present. Our aim is to explore whether the LMM performs as well as the SEM in a setting that is conducive to using the SEM.Methods We simulated mediated longitudinal data from an SEM where a binary, main independent variable has both direct and indirect effects on a continuous outcome. We conducted analyses with both the LMM and SEM to evaluate the performance of the LMM in a setting where the SEM is expected to be preferable. Models were evaluated with respect to bias, coverage probability and power. Sample size, effect size and error distribution of the simulated data were varied. Results: Both models performed well in a range of settings. Marginal increases in power estimates were observed for the SEM, although generally there were no major differences in performance. Power for both models was good with a sample of size of 250 and a small to medium effect size. Bias did not substantially increase for either model when data were generated from distributions that were both skewed and kurtotic. Conclusions: In settings where the goal is to evaluate the overall effects, the LMM excluding mediating variables appears to have good performance with respect to power, bias and coverage probability relative to the SEM. The major benefit of SEMs is that it simultaneously and efficiently models both the direct and indirect effects of the mediation process.

Introduction: Few dissemination evaluations exist to document the effectiveness of evidence-based childhood obesity interventions outside the research setting. Objective: Evaluate Healthy Choices (HC), a multi-component obesity prevention program, by examining school-level changes in weight-related behaviors and weight status and the association of implementation components with odds of overweight/obesity. Methods: We compared baseline and Year 3 school-level behavioral and weight status outcomes with paired t-tests adjusted for schools’ socio-demographic characteristics. We used generalized estimating equations to examine the odds of overweight/obesity associated with program components. Setting/Participants Consecutive sample of 45 of 51 middle schools participating in the HC program with complete baseline and follow-up survey data including a subsample of 35 schools with measured anthropomentry for 5,665 7th grade students. Intervention Schools developed a multi-disciplinary team and implemented an obesity prevention curriculum, before and after school activities, environmental and policy changes and health promotions targeting a 5-2-1 theme: eat ≥ 5 servings/day of fruits and vegetables (FV), watch ≤ 2 hours of television (TV) and participate in ≥ 1 hours/day of physical activity (PA) on most days Main Outcome Measures: 1) School-level percent of students achieving targeted behaviors and percent overweight/obese; and 2) individual odds of overweight/obesity. Results: The percent achieving behavioral goals over three years increased significantly for FV: 16.4 to 19.4 (p = 0.001), TV: 53.4 to 58.2 (p = 0.003) and PA: 37.1 to 39.9 (p = 0.02), adjusting for school size, baseline mean age and percent female, non-Hispanic White, and eligible for free and reduced price lunch. In 35 schools with anthropometry, the percent of overweight/obese 7th grade students decreased from 42.1 to 38.4 (p = 0.016). Having a team that met the HC definition was associated with lower odds of overweight/obesity (OR = 0.83, CI: 0.71–0.98). Conclusions and Relevance The HC multi-component intervention demonstrated three-year improvements in weight-related behaviors and weight status across diverse middle schools. Team building appears important to the program’s effectiveness.