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Record 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 on 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

Bibliographic record

VenuemedRxiv · 2023
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University Health Centre
FundersUnited States Agency for International Development
KeywordsMedicineTuberculosisQuarter (Canadian coin)PovertyHealth careOddsPopulationFamily medicineEnvironmental healthLogistic regressionInternal medicineEconomic growth

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.123
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.033
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.123
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.009
Bibliometrics0.0170.020
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.125
GPT teacher head0.483
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes2
Has abstractyes

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