Defining a screening tool for post-traumatic stress disorder in East Africa: a penalized regression approach.
East Africa (Kenya)
low and middle income countries (LMIC)
posttraumatic stress disorder (PTSD)
primary care
screening tools
sub Saharan Africa
traumatic stress
Journal
Frontiers in public health
ISSN: 2296-2565
Titre abrégé: Front Public Health
Pays: Switzerland
ID NLM: 101616579
Informations de publication
Date de publication:
2024
2024
Historique:
received:
07
02
2024
accepted:
13
05
2024
medline:
1
7
2024
pubmed:
1
7
2024
entrez:
1
7
2024
Statut:
epublish
Résumé
Scalable PTSD screening strategies must be brief, accurate and capable of administration by a non-specialized workforce. 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. 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. In some East African settings, a 2-item PTSD screening tool may outperform longer screeners and is easily scaled by a non-specialist workforce.
Sections du résumé
Background
UNASSIGNED
Scalable PTSD screening strategies must be brief, accurate and capable of administration by a non-specialized workforce.
Methods
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
In some East African settings, a 2-item PTSD screening tool may outperform longer screeners and is easily scaled by a non-specialist workforce.
Identifiants
pubmed: 38947359
doi: 10.3389/fpubh.2024.1383171
pmc: PMC11211862
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1383171Informations de copyright
Copyright © 2024 Meffert, Mathai, Ongeri, Neylan, Mwai, Onyango, Akena, Rota, Otieno, Obura, Wangia, Opiyo, Muchembre, Oluoch, Wambura, Mbwayo, Kahn, Cohen, Bukusi, Aarons, Burger, Jin, McCulloch and Njuguna Kahonge.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.