Defining a screening tool for post-traumatic stress disorder in East Africa: a penalized regression approach

Abstract

Background: Scalable PTSD screening strategies must be brief, accurate and capable of administration by a non-specialized workforce.

Methods: We used PTSD as determined by the structured clinical interview as our gold standard and considered predictors sets of (a) Posttraumatic Stress Checklist-5 (PCL-5), (b) Primary Care PTSD Screen for the DSM-5 (PC-PTSD) and, (c) PCL-5 and PC-PTSD questions to identify the optimal items for PTSD screening for public sector settings in Kenya. A logistic regression model using LASSO was fit by minimizing the average squared error in the validation data. Area under the receiver operating characteristic curve (AUROC) measured discrimination performance.

Results: Penalized regression analysis suggested a screening tool that sums the Likert scale values of two PCL-5 questions-intrusive thoughts of the stressful experience (#1) and insomnia (#21). This had an AUROC of 0.85 (using hold-out test data) for predicting PTSD as evaluated by the MINI, which outperformed the PC-PTSD. The AUROC was similar in subgroups defined by age, sex, and number of categories of trauma experienced (all AUROCs>0.83) except those with no trauma history- AUROC was 0.78.

Conclusion: In some East African settings, a 2-item PTSD screening tool may outperform longer screeners and is easily scaled by a non-specialist workforce.

Keywords: East Africa (Kenya); low and middle income countries (LMIC); posttraumatic stress disorder (PTSD); primary care; screening tools; sub Saharan Africa; traumatic stress.