Understanding the gaps in DR-TB care cascade in Nigeria: A sequential mixed-method study
Notice bibliographique
Résumé
BACKGROUND: Despite the availability of free drug-resistant tuberculosis (DR-TB) care in Nigeria since 2011, the country continues to tackle low case notification and treatment rates. In 2018, 11% of an estimated 21,000 cases were diagnosed and 9% placed on treatment. These low rates are nevertheless a marked improvement from 2015 when only 3.4% were diagnosed and 2.3% placed on treatment of an estimated 29,000 cases. This study describes the Nigerian DR-TB care cascade from 2013 to 2017 and considers factors influencing gaps in care. METHODS: Our study utilized a mixed-method design. For the quantitative component, we utilized the national diagnosis and treatment databases, as well as the World Health Organization's estimates for prevalence to construct a 5-year care cascade: numbers of patients at each level of DR-TB care, including incident cases, individuals who accessed testing, were diagnosed, initiated treated and completed treatment in Nigeria between 2013 and 2017. Using retrospective data for patients diagnosed in 2015, we performed the Fisher's exact test to determine the association between patient (age and gender) and provider/patient (region- north or south) variables, permitting a closer look at the gaps in care revealed across the 5 years. Barriers to care were explored using framework thematic analysis of 57 qualitative interviews and focus group discussions with patients, including 5 cases not initiated on treatment from the 2015 cohort, treatment supporters, community members, healthcare workers and program managers in 2017. RESULTS: A 5-year analysis of cascade of care data shows significant, but inadequate, increases in overall numbers of cases accessing care. On average, between 2013 and 2017, 80% of estimated cases did not access testing; 75% of those who tested were not diagnosed; 36% of those diagnosed were not initiated on treatment and 23% of these did not finish treatment. In 2015, children and patients in Northern Nigeria had odds of 0.3 [95% CI 0.1-0.7] and 0.4 [0.3-0.5] of completing treatment once diagnosed; while males were shown to have a 1.34 [95% CI 1.0-1.7] times greater chance of completing treatment after diagnosis. The main themes from qualitative data identified barriers to care along the care cascade at individual, family and community, as well as health systems levels. At the individual level, a lack of awareness of the true cause of disease and the availability of 'free' care was a recurring theme. Family interference was found to be a particular challenge for children and women. At the health system level, low index of suspicion, lack of rapid diagnostic tools and human resource shortages appeared to limit patients' access. CONCLUSIONS: Any gains in diagnostic technology and shorter regimens are lost with inadequate access to DR-TB services. The biggest losses in the Nigerian cascade happen before treatment initiation. There is a need for urgent action on identified gaps in the DR-TB cascade in order to improve care continuity at multiple stages, improve health service delivery and facilitate TB control in Nigeria.
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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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».