DS_10.1177_1558944718787310 – Supplemental material for The Safety and Benefits of the Semisterile Technique for Closed Reduction and Percutaneous Pinning of Pediatric Upper Extremity Fractures

<p>Supplemental material, DS_10.1177_1558944718787310 for The Safety and Benefits of the Semisterile Technique for Closed Reduction and Percutaneous Pinning of Pediatric Upper Extremity Fractures by Karan Dua, Charles J. Blevins, Nathan N. O’Hara and Joshua M. Abzug in HAND</p

Relative importance of attributes.

<p>Rescaled to sum to 1 within each class.</p

Healthcare Worker Preferences for Active Tuberculosis Case Finding Programs in South Africa: A Best-Worst Scaling Choice Experiment

<div><p>Objective</p><p>Healthcare workers (HCWs) in South Africa are at a high risk of developing active tuberculosis (TB) due to their occupational exposures. This study aimed to systematically quantify and compare the preferred attributes of an active TB case finding program for HCWs in South Africa.</p><p>Methods</p><p>A Best–Worst Scaling choice experiment estimated HCW’s preferences using a random-effects conditional logit model. Latent class analysis (LCA) was used to explore heterogeneity in preferences.</p><p>Results</p><p>“No cost”, “the assurance of confidentiality”, “no wait” and testing at the occupational health unit at one’s hospital were the most preferred attributes. LCA identified a four class model with consistent differences in preference strength. Sex, occupation, and the time since a previous TB test were statistically significant predictors of class membership.</p><p>Conclusions</p><p>The findings support the strengthening of occupational health units in South Africa to offer free and confidential active TB case finding programs for HCWs with minimal wait times. There is considerable variation in active TB case finding preferences amongst HCWs of different gender, occupation, and testing history. Attention to heterogeneity in preferences should optimize screening utilization of target HCW populations.</p></div

Latent class analysis: Relative preferences for attribute levels in a four-class model.

<p>Latent class analysis: Relative preferences for attribute levels in a four-class model.</p

Characteristics of the respondents (n = 125).

<p>Characteristics of the respondents (n = 125).</p

Latent class analysis: Four-class model of variable coefficients and probability of class membership.

<p>^<b>1</b>Probability is interpreted as the probability that class membership is predicted by the given covariate. P-values represent the statistical significance of the covariate as a predictor of class membership.</p><p>Latent class analysis: Four-class model of variable coefficients and probability of class membership.</p