Variations in the quality of tuberculosis care in urban India: A cross-sectional, standardized patient study in two cities
Notice bibliographique
Résumé
BACKGROUND: India has the highest burden of tuberculosis (TB). Although most patients with TB in India seek care from the private sector, there is limited evidence on quality of TB care or its correlates. Following our validation study on the standardized patient (SP) method for TB, we utilized SPs to examine quality of adult TB care among health providers with different qualifications in 2 Indian cities. METHODS AND FINDINGS: During 2014-2017, pilot programs engaged the private health sector to improve TB management in Mumbai and Patna. Nested within these projects, to obtain representative, baseline measures of quality of TB care at the city level, we recruited 24 adults to be SPs. They were trained to portray 4 TB "case scenarios" representing various stages of disease and diagnostic progression. Between November 2014 and August 2015, the SPs visited representatively sampled private providers stratified by qualification: (1) allopathic providers with Bachelor of Medicine, Bachelor of Surgery (MBBS) degrees or higher and (2) non-MBBS providers with alternative medicine, minimal, or no qualifications. Our main outcome was case-specific correct management benchmarked against the Standards for TB Care in India (STCI). Using ANOVA, we assessed variation in correct management and quality outcomes across (a) cities, (b) qualifications, and (c) case scenarios. Additionally, 2 micro-experiments identified sources of variation: first, quality in the presence of diagnostic test results certainty and second, provider consistency for different patients presenting the same case. A total of 2,652 SP-provider interactions across 1,203 health facilities were analyzed. Based on our sampling strategy and after removing 50 micro-experiment interactions, 2,602 interactions were weighted for city-representative interpretation. After weighting, the 473 Patna providers receiving SPs represent 3,179 eligible providers in Patna; in Mumbai, the 730 providers represent 7,115 eligible providers. Correct management was observed in 959 out of 2,602 interactions (37%; 35% weighted; 95% CI 32%-37%), primarily from referrals and ordering chest X-rays (CXRs). Unnecessary medicines were given to nearly all SPs, and antibiotic use was common. Anti-TB drugs were prescribed in 118 interactions (4.5%; 5% weighted), of which 45 were given in the case in which such treatment is considered correct management. MBBS and more qualified providers had higher odds of correctly managing cases than non-MBBS providers (odds ratio [OR] 2.80; 95% CI 2.05-3.82; p < 0.0001). Mumbai non-MBBS providers had higher odds of correct management than non-MBBS in Patna (OR 1.79; 95% CI 1.06-3.03), and MBBS providers' quality of care did not vary between cities (OR 1.15; 95% CI 0.79-1.68; p = 0.4642). In the micro-experiments, improving diagnostic certainty had a positive effect on correct management but not across all quality dimensions. Also, providers delivered idiosyncratically consistent care, repeating all observed actions, including mistakes, approximately 75% of the time. The SP method has limitations: it cannot account for patient mix or care-management practices reflecting more than one patient-provider interaction. CONCLUSIONS: Quality of TB care is suboptimal and variable in urban India's private health sector. Addressing this is critical for India's plans to end TB by 2025. For the first time, we have rich measures on representative levels of care quality from 2 cities, which can inform private-sector TB interventions and quality-improvement efforts.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».