Creation of a tool for the Identification of Neurodevelopmental Disabilities to Improve Global Outcomes (INDIGO) in children aged 0-3 years in Malawi, Pakistan and Uganda – feasibility of implementation and diagnostic accuracy
Notice bibliographique
Résumé
Abstract Background Over 50 million children under five have disabilities, the majority residing in resource-limited settings. Many children with neurodevelopmental disabilities (NDDs) remain undetected due to lack of culturally appropriate tools for identification. This study aimed to develop and evaluate the feasibility of the Identification of Neurodevelopmental Disabilities to Improve Global Outcomes (INDIGO) tool to identify NDDs in children aged 0 −3 years. Methods A systematic approach was used to construct the INDIGO tool. First, 114 items of the Malawi Developmental Assessment Tool (MDAT) were mapped onto the International Classification of Functioning, Disability and Health for Children and Youth (ICF-CY). Gaps were identified leading to review of 2,158 items from existing tools. After consensus, 59 additional items (28 existing, 31 newly created) were incorporated, ensuring comprehensive coverage of ICF-CY domains. The preliminary INDIGO tool (203 items) was pilot tested in Uganda, Malawi and Pakistan with a gold standard assessment protocol developed to validate diagnostic accuracy. Feasibility outcomes including recruitment, diagnostic accuracy, cultural applicability and practical implementation were assessed. Items with over 80% diagnostic accuracy were retained for the final INDIGO prototype. Results A total of 425 children (151 Malawi, 145 Pakistan, 129 Uganda) were assessed using both INDIGO and gold standard evaluation. The feasibility study highlighted challenges in recruiting younger children with behavioural or socioemotional conditions and difficulties assessing sensory impairment due to resource constraints. Despite challenges, 99 high performing items were selected for the final INDIGO prototype emphasizing diagnostic accuracy, feasibility and cultural neutrality. Conclusions The INDIGO tool is a novel, rigorously developed instrument designed for early detection of NDDs in children 0-3 years in LMICs. By integrating a broad range of developmental domains, INDIGO addressed key limitations of existing tools. Future large-scale validation and implementation studies are needed to assess its effectiveness in routine child health surveillance programmes. Key Messages What is already known on this topic Most existing developmental screening tools used globally are not culturally adapted for LMICs, require specialized training, and fail to comprehensively assess structural, functional, and environmental factors important for detecting NDDs in early childhood. What this study adds This study presents the development and feasibility testing of the INDIGO tool, a novel screening instrument for identifying moderate to severe NDDs in children under 3 years in resource-limited settings, incorporating items to assess structural anomalies, sensory impairments, and participation, offering a more holistic and contextually relevant approach to NDD detection. The study provides strong initial evidence of diagnostic accuracy across multiple LMIC settings, with high feasibility and acceptability among caregivers and health workers. How this study might affect research, practice or policy Future studies could enable scaled up implementation of tools such as INDIGO integrated into national child health systems for early NDD detection, enhancing referral and intervention in LMICs.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,024 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».