“Before Xpert I only had my expertise”: A qualitative study on the utilization and effects of Xpert technology among pediatricians in 4 Indian cities
Notice bibliographique
Résumé
BACKGROUND: Diagnosing tuberculosis (TB) in children presents considerable challenges. Upfront testing on Xpert® MTB/RIF ('Xpert')-a rapid molecular assay with high sensitivity and specificity-for pediatric presumptive TB patients, as recommended by India's Revised National Tuberculosis Control Program (RNTCP), can pave the way for early TB diagnosis. As part of an ongoing project implemented by Foundation for Innovative New Diagnostics (FIND) dedicated to providing upfront free-of-cost (FOC) Xpert testing to children seeking care in the public and private sectors, a qualitative assessment was designed to understand how national guidelines on TB diagnosis and Xpert technology have been integrated into the pediatric TB care practices of different health providers. METHODS: We conducted semi-structured interviews with a sample of health providers from public and private sectors engaged in the ongoing pediatric project in 4 major cities of India. Providers were sampled from intervention data based on sector of practice, number of Xpert referrals, and TB detection rates amongst referrals. A total of 55 providers were interviewed with different levels of FOC Xpert testing uptake. Data were transcribed and analyzed inductively by a medical anthropologist using thematic content analysis and narrative analysis. RESULTS: It was observed that despite guidance from RNTCP on the use of Xpert and significant efforts by FIND and state authorities to disseminate these guidelines, there was notable diversity in their implementation by different health care providers. Xpert, apart from being utilized as intended, i.e. as a first diagnostic test for children, was utilized variably-as an initial screening test (to rule out TB), confirmatory test (once TB diagnosis is established based on antibiotic trial or clinically) and/or only for drug susceptibility testing after TB diagnosis was confirmed. Most providers who used Xpert frequently reported that Xpert was an important tool for managing pediatric TB cases, by reducing the proportion of cases diagnosed only on clinical suspicion and by providing upfront information on drug resistance, which is seldom suspected in children. Despite non-standard use, these results showed that Xpert access helped raise awareness, aided in antibiotic stewardship, and reduced dependence on clinical diagnosis among those who diagnose and treat TB in children. CONCLUSION: Access to free and rapid Xpert testing for all presumptive pediatric TB patients has had multiple positive effects on pediatricians' diagnosis and treatment of TB. It has important effects on speed of diagnosis, empirical treatment, and awareness of drug resistance among TB treatment naive children. In addition, our study shows that access to public sector Xpert machines may be an important way to encourage Public-Private integration and facilitate the movement of patients from the private to public sector for anti-TB treatment. Despite availability of rapid and free Xpert testing, our study showed an alarming diversity of Xpert utilization strategies across different providers who may be moving toward suggested practice over time. The degree of diversity in TB diagnostic approaches in children reported here highlights the urgent need for concerted efforts to place Xpert early in diagnostic algorithms to positively impact the pediatric TB care pathway. A positive change in diagnostic algorithms may be possible with continued advocacy, time, and increased access.
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 | 0,014 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,015 | 0,015 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».