Data analysis
REGRESSION, REGRESSION
Results obtained at the CDT were immediately entered in a digital case report form [Regression analysis and evaluation of the diagnostic performance were based on the participants HAT status (Fig. Regression analysis using Stata Statistical Software (Release 14, College Station, TX: StataCorp LP) was performed to assess for associations with the HAT status and thus identify which demographic features and clinical presentations could be used as criteria to target future testing for HAT. Continuous variables were assessed for normal distribution, and the correlation between the thirteen clinical signs and symptoms was determined. Unconditional associations between HAT status and the explanatory variables (gender, age, and clinical signs and symptoms) were investigated. Subsequently, mixed logistic regression models were developed for the HAT status, with prefecture included as a random effect to account for spatial clustering within each prefecture. Backward elimination was then used to screen variables, and only statistically significant variables (The diagnostic performance of the clinical presentation (only those that were retained in mixed logistic regression), the three RDTs (individually, in parallel, and in series), and of the four reference laboratory tests was determined. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy for diagnosis of HAT were calculated with 95% Clopper Pearson confidence intervals (GraphPad Prism 9). The Kappa agreement for combinations of RDTs and reference laboratory tests was also determined, and interpreted as poor (< 0.00), slight (0.00–0.20), fair (0.21–0.40), moderate (0.41–0.60), substantial (0.61–0.80), or almost perfect (0.81–1.00) [