Design and methods of the Longitudinal Eating Disorders Assessment Project research consortium for veterans

Abstract

Introduction: Military service members must maintain a certain body mass index and body fat percentage. Due to weight-loss pressures, some service members may resort to unhealthy behaviors that place them at risk for the development of an eating disorder (ED).

Objectives: To understand the scope and impact of EDs in military service members and veterans, we formed the Longitudinal Eating Disorders Assessment Project (LEAP) Consortium. LEAP aims to develop novel screening, assessment, classification, and treatment tools for veterans and military members with a focus on EDs and internalizing psychopathology.

Methods: We recruited two independent nationally representative samples of post-9/11 veterans who were separated from service within the past year. Study 1 was a four-wave longitudinal survey and Study 2 was a mixed-methods study that included surveys, structured-clinical interviews, and qualitative interviews.

Results: Recruitment samples were representative of the full population of recently separated veterans. Sample weights were created to adjust for sources of non-response bias to the baseline survey. Attrition was low relative to past studies of this population, with only (younger) age predicting attrition at 1-week follow-up.

Conclusions: We expect that the LEAP Consortium data will contribute to improved information about EDs in veterans, a serious and understudied problem.

Keywords: VADIR; assessment; department of defense; disordered eating; eating disorders; longitudinal; mental health; nationally representative; psychiatric disorders; veterans.

FIGURE 1

Response bias analysis. Plot of odds ratios with 95% confidence intervals for logistic regression model predicting participation in the baseline survey from the group of potential recruits. For each predictor, the associated reference group is listed in parentheses. For instance, we see that the third coefficient from the bottom reflects the odds ratio for males (vs. females), and indicates that males were significantly less likely to participate in the study than females. One coefficient, for warrant officers, was omitted from the plot due to an extremely large confidence interval (resulting from low representation for that group; N = 12). For warrant officers (in comparison with those who were enlisted), the model returned: OR = 4.97, 95% CI = [1.32, 18.80]. The total sample size for the baseline survey was N = 816

FIGURE 2

One‐week follow‐up attrition analysis. Plot of odds ratios with 95% confidence intervals for baseline demographic characteristics predicting participation in the 1‐week follow‐up survey. For each predictor, the associated reference group is listed in parentheses. For instance, the second coefficient from the bottom reflects the odds ratio for Black or African American (vs. White) individuals, and indicates that Black or African American participants were significantly less likely to participate in the 1‐week follow‐up than White participants. One case was removed from the analysis, as there was only one individual who identified their race as Native Hawaiian/Pacific Islander in the sample that completed the baseline survey. The total sample size at the 1‐week follow‐up was N = 560

FIGURE 3

Response Bias Analysis due to Invalid Addresses. This figure provides a plot of odds ratios with 95% confidence intervals for VADIR sample characteristics predicting invalid address. For each predictor, the associated reference group is listed in parentheses like Figures 1 and 2