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Enregistrement W4376603936 · doi:10.1101/2023.05.12.23288882

Barriers to engagement by people with active tuberculosis in the care cascade in India: a systematic review of two decades of quantitative research

2023· review· en· W4376603936 sur OpenAlexaffabout
Tulip A. Jhaveri, Disha Jhaveri, Amith Galivanche, Dominic Voehler, Maya Lubeck-Schricker, Mei Chung, Pruthu Thekkur, V. K. Chadha, Ruvandhi R. Nathavitharana, Ajay Kumar, Hemant Deepak Shewade, Katherine E. Powers, Kenneth H. Mayer, Jessica E. Haberer, Paul Bain, Madhukar Pai, Srinath Satyanarayana, Ramnath Subbaraman

Notice bibliographique

RevuemedRxiv · 2023
Typereview
Langueen
DomaineMedicine
ThématiqueTuberculosis Research and Epidemiology
Établissements canadiensMcGill University Health Centre
Organismes subventionnairesUnited States Agency for International Development
Mots-clésMedicineTuberculosisQuarter (Canadian coin)PovertyHealth careOddsPopulationFamily medicineEnvironmental healthLogistic regressionInternal medicineEconomic growth

Résumé

récupéré en direct d'OpenAlex

Abstract Background India has the highest burden of tuberculosis (TB), accounting for more than one-quarter of people with active TB and nearly one-third of TB deaths globally. Most people contracting active TB in India do not successfully navigate all stages of the care cascade to receive treatment and achieve TB recurrence-free survival. Understanding reasons for losses across the care cascade is critical to improve outcomes. In this paper, guided by a PECO (population/exposure/comparison/outcome) framework, we describe quantitative findings of a systematic review aimed at identifying factors contributing to unfavorable outcomes experienced by people with TB at each care cascade gap in India. Methods and findings We defined care cascade gaps as comprising people with confirmed or presumptive TB who did not: start the TB diagnostic workup (Gap 1), complete the diagnostic workup (Gap 2), start treatment (Gap 3), achieve treatment success (Gap 4), or achieve TB recurrence-free survival (Gap 5). Three systematic searches were conducted to identify 147 unique articles published from 2000 to 2021 that evaluated factors associated with unfavorable outcomes for each gap (reported as odds, relative risk, or hazard ratios) and, among people experiencing unfavorable outcomes, reasons reported for these outcomes (reported as proportions). Findings were organized into patient-, family-, society-, or health system-related factors, using a social-ecological framework. Some factors were common and associated with unfavorable outcomes across multiple care cascade stages. These included male sex, older age (variably defined across studies), a broad array of poverty-related factors, lower symptom severity or duration, undernutrition, alcohol use, smoking, and distrust of (or dissatisfaction with) local government health services. People who had been previously treated for TB were more likely to seek care and engage in the TB diagnostic workup (Gaps 1 and 2) but were also more likely to suffer pretreatment loss to follow-up (Gap 3) and unfavorable outcomes during TB treatment (Gap 4), especially those who had been lost to follow-up during their prior treatment episode. For individual care cascade gaps, multiple studies highlighted the importance of lack of TB knowledge and structural barriers to care (e.g., transport or financial challenges in reaching clinics) in contributing to lack of care-seeking for TB symptoms (Gap 1, 15 studies or analyses); lack of access to diagnostics (e.g., chest X-ray), non-identification of eligible patients for testing, and failure of providers to communicate concern for TB to patients in contributing to non-completion of the diagnostic workup (Gap 2, 20 studies or analyses); TB stigma, poor recording of patient contact information by providers, and early death due to diagnostic delays in contributing to pretreatment loss to follow-up (Gap 3, 25 studies); and medication adverse effects, TB stigma, and lack of TB knowledge in contributing to unfavorable treatment outcomes (Gap 4, 104 studies). Medication nonadherence contributed to unfavorable treatment outcomes (Gap 4) and post-treatment TB recurrence (Gap 5, 15 studies). Conclusions This extensive systematic review illuminates common patterns of risk that shape outcomes for people with TB in India, while also highlighting gaps in knowledge, particularly with regard to TB care for children or in the private sector, that can help to guide future research. These findings may help inform targeting of additional support services to people with TB who are at higher risk of poor outcomes and inform development of multi-component interventions to close gaps in India’s TB care cascade. Author Summary Why was this study done? India has the highest burden of tuberculosis (TB), accounting for more than one-quarter of people with active TB and nearly one-third of TB deaths globally. Of the nearly 3 million people contracting active TB every year in India, most do not successfully traverse all stages of the care needed to receive TB treatment and achieve an optimal long-term outcome, and serial losses of people with TB across these stages is often referred to as the “cascade of care.” Understanding risk factors among people with active or presumptive TB that contribute to losses from care, and why these losses occur, is crucial to inform the development of targeted interventions to prevent them from experiencing unfavorable outcomes. What did the researchers do and find? To understand reasons why people with TB are lost from care in India, we conducted three systematic searches of the medical literature to identify 147 unique and relevant articles published from 2000 to 2021. We extracted information from these studies on risk factors for unfavorable outcomes at each care cascade gap, as well as reasons reported by people with TB who experienced unfavorable outcomes and were surveyed by researchers. Some barriers to care or characteristics of people with TB contributed to losses at multiple stages of the care cascade, including male sex, older age, poverty-related factors, history of previous TB treatment, lower symptom severity or duration, undernutrition, alcohol use, smoking, and distrust of (or dissatisfaction with) local government health services. Other barriers contributed more substantially to individual care cascade gaps: lack of TB knowledge and structural barriers to care (e.g., transport barriers to clinics) contributed to lack of care-seeking for TB symptoms (Gap 1); poor accessibility of diagnostic testing, non-identification of eligible patients for testing, and failure of providers to communicate concern for TB to patients contributed to non-completion of the TB diagnostic workup (Gap 2); early death due to delays in diagnosis, TB stigma, and poor recording of patient contact information by healthcare providers contributed to losses of diagnosed patients before they started treatment (Gap 3); medication adverse effects, TB stigma, and lack of TB knowledge contributed to unfavorable TB treatment outcomes (Gap 4); and medication nonadherence contributed to unfavorable treatment outcomes and post-treatment TB recurrence (Gaps 4 and 5). What do these findings mean? The reasons for losses of people with TB across the care cascade are complex, vary by care cascade gap, and involve a mix of patient- and health system-related barriers. Given the complexity of the barriers contributing to unfavorable outcomes in India’s TB care cascade, future implementation interventions should consider involving multiple components that target different challenges faced by patients and the health system. In addition, India’s TB program and those in other high incidence settings should target additional services to people with TB who are at higher risk of experiencing poor outcomes.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,033
score de la tête « metaresearch » (Gemma)0,123
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,033
Score d'incertitude au seuil0,176

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0330,123
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0060,009
Bibliométrie0,0170,020
Études des sciences et des technologies0,0010,002
Communication savante0,0040,003
Science ouverte0,0020,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,125
Tête enseignante GPT0,483
Écart entre enseignants0,357 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission2
Résumé présentoui

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