Data-driven supervision to optimize the effectiveness of proactive case detection for mental health care among children: a proof-of-concept study
This proof-of-concept study evaluated an optimization strategy for the Community Case Detection Tool (CCDT) aimed at improving community-level mental health detection and help-seeking among children aged 6–18 years. The optimization strategy, CCDT+, combined data-driven supervision with motivational...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2025-01-01
|
| Series: | Cambridge Prisms: Global Mental Health |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S205442512400150X/type/journal_article |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841526413265469440 |
|---|---|
| author | Myrthe van den Broek M. Claire Greene Anthony F. Guevara Sandra Agondeze Erimiah Kyanjo Olivier Irakoze Rosco Kasujja Brandon A. Kohrt Mark J. D. Jordans |
| author_facet | Myrthe van den Broek M. Claire Greene Anthony F. Guevara Sandra Agondeze Erimiah Kyanjo Olivier Irakoze Rosco Kasujja Brandon A. Kohrt Mark J. D. Jordans |
| author_sort | Myrthe van den Broek |
| collection | DOAJ |
| description | This proof-of-concept study evaluated an optimization strategy for the Community Case Detection Tool (CCDT) aimed at improving community-level mental health detection and help-seeking among children aged 6–18 years. The optimization strategy, CCDT+, combined data-driven supervision with motivational interviewing techniques and behavioural nudges for community gatekeepers using the CCDT. This mixed-methods study was conducted from January to May 2023 in Palorinya refugee settlement in Uganda. We evaluated (1) the added value of the CCDT+ in improving the accuracy of detection and mental health service utilization compared to standard CCDT, and (2) implementation outcomes of the CCDT+. Of the 1026 children detected, 801 (78%) sought help, with 656 needing mental health care (PPV = 0.82; 95% CI: 0.79, 0.84). The CCDT+ significantly increased detection accuracy, with 2.34 times higher odds compared to standard CCDT (95% CI: 1.41, 3.83). Additionally, areas using the CCDT+ had a 2.05-fold increase in mental health service utilization (95% CI: 1.09, 3.83). The CCDT+ shows promise as an embedded quality-optimization process for the detection of mental health problems among children and enhance help-seeking, potentially leading to more efficient use of mental health care resources. |
| format | Article |
| id | doaj-art-402f9255b61a47508c7694610935bb15 |
| institution | Kabale University |
| issn | 2054-4251 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Cambridge Prisms: Global Mental Health |
| spelling | doaj-art-402f9255b61a47508c7694610935bb152025-01-16T21:53:10ZengCambridge University PressCambridge Prisms: Global Mental Health2054-42512025-01-011210.1017/gmh.2024.150Data-driven supervision to optimize the effectiveness of proactive case detection for mental health care among children: a proof-of-concept studyMyrthe van den Broek0https://orcid.org/0000-0001-9317-3258M. Claire Greene1https://orcid.org/0000-0002-4631-4764Anthony F. Guevara2Sandra Agondeze3Erimiah Kyanjo4Olivier Irakoze5Rosco Kasujja6Brandon A. Kohrt7https://orcid.org/0000-0002-3829-4820Mark J. D. Jordans8https://orcid.org/0000-0001-5925-8039Research and Development, War Child Alliance, Amsterdam, The Netherlands Amsterdam Institute for Social Science Research, University of Amsterdam, Amsterdam, The NetherlandsProgram on Forced Migration and Health, Columbia University Mailman School of Public Health, NY, USAResearch and Development, War Child Alliance, Amsterdam, The NetherlandsResearch and Development, War Child Alliance, Kampala, UgandaTranscultural Psychosocial Organization Uganda, Kampala, UgandaTranscultural Psychosocial Organization Uganda, Kampala, UgandaDepartment of Mental Health, School of Psychology, College of Humanities and Social Sciences, Makerere University, Kampala, UgandaCenter for Global Mental Health Equity, Department of Psychiatry and Behavioral Health, George Washington University, DC, USAResearch and Development, War Child Alliance, Amsterdam, The Netherlands Amsterdam Institute for Social Science Research, University of Amsterdam, Amsterdam, The NetherlandsThis proof-of-concept study evaluated an optimization strategy for the Community Case Detection Tool (CCDT) aimed at improving community-level mental health detection and help-seeking among children aged 6–18 years. The optimization strategy, CCDT+, combined data-driven supervision with motivational interviewing techniques and behavioural nudges for community gatekeepers using the CCDT. This mixed-methods study was conducted from January to May 2023 in Palorinya refugee settlement in Uganda. We evaluated (1) the added value of the CCDT+ in improving the accuracy of detection and mental health service utilization compared to standard CCDT, and (2) implementation outcomes of the CCDT+. Of the 1026 children detected, 801 (78%) sought help, with 656 needing mental health care (PPV = 0.82; 95% CI: 0.79, 0.84). The CCDT+ significantly increased detection accuracy, with 2.34 times higher odds compared to standard CCDT (95% CI: 1.41, 3.83). Additionally, areas using the CCDT+ had a 2.05-fold increase in mental health service utilization (95% CI: 1.09, 3.83). The CCDT+ shows promise as an embedded quality-optimization process for the detection of mental health problems among children and enhance help-seeking, potentially leading to more efficient use of mental health care resources.https://www.cambridge.org/core/product/identifier/S205442512400150X/type/journal_articleproof-of-conceptproactive case detectionoptimization strategydashboardsub-Saharan Africachild and adolescent mental health |
| spellingShingle | Myrthe van den Broek M. Claire Greene Anthony F. Guevara Sandra Agondeze Erimiah Kyanjo Olivier Irakoze Rosco Kasujja Brandon A. Kohrt Mark J. D. Jordans Data-driven supervision to optimize the effectiveness of proactive case detection for mental health care among children: a proof-of-concept study Cambridge Prisms: Global Mental Health proof-of-concept proactive case detection optimization strategy dashboard sub-Saharan Africa child and adolescent mental health |
| title | Data-driven supervision to optimize the effectiveness of proactive case detection for mental health care among children: a proof-of-concept study |
| title_full | Data-driven supervision to optimize the effectiveness of proactive case detection for mental health care among children: a proof-of-concept study |
| title_fullStr | Data-driven supervision to optimize the effectiveness of proactive case detection for mental health care among children: a proof-of-concept study |
| title_full_unstemmed | Data-driven supervision to optimize the effectiveness of proactive case detection for mental health care among children: a proof-of-concept study |
| title_short | Data-driven supervision to optimize the effectiveness of proactive case detection for mental health care among children: a proof-of-concept study |
| title_sort | data driven supervision to optimize the effectiveness of proactive case detection for mental health care among children a proof of concept study |
| topic | proof-of-concept proactive case detection optimization strategy dashboard sub-Saharan Africa child and adolescent mental health |
| url | https://www.cambridge.org/core/product/identifier/S205442512400150X/type/journal_article |
| work_keys_str_mv | AT myrthevandenbroek datadrivensupervisiontooptimizetheeffectivenessofproactivecasedetectionformentalhealthcareamongchildrenaproofofconceptstudy AT mclairegreene datadrivensupervisiontooptimizetheeffectivenessofproactivecasedetectionformentalhealthcareamongchildrenaproofofconceptstudy AT anthonyfguevara datadrivensupervisiontooptimizetheeffectivenessofproactivecasedetectionformentalhealthcareamongchildrenaproofofconceptstudy AT sandraagondeze datadrivensupervisiontooptimizetheeffectivenessofproactivecasedetectionformentalhealthcareamongchildrenaproofofconceptstudy AT erimiahkyanjo datadrivensupervisiontooptimizetheeffectivenessofproactivecasedetectionformentalhealthcareamongchildrenaproofofconceptstudy AT olivierirakoze datadrivensupervisiontooptimizetheeffectivenessofproactivecasedetectionformentalhealthcareamongchildrenaproofofconceptstudy AT roscokasujja datadrivensupervisiontooptimizetheeffectivenessofproactivecasedetectionformentalhealthcareamongchildrenaproofofconceptstudy AT brandonakohrt datadrivensupervisiontooptimizetheeffectivenessofproactivecasedetectionformentalhealthcareamongchildrenaproofofconceptstudy AT markjdjordans datadrivensupervisiontooptimizetheeffectivenessofproactivecasedetectionformentalhealthcareamongchildrenaproofofconceptstudy |