Improving measurement of tuberculosis care cascades to enhance people-centred care

Faust, Lena; Naidoo, Pren; Caceres-Cardenas, Guillermo; Ugarte-Gil, César; Muyoyeta, Monde; Kerkhoff, Andrew D; Nagarajan, Karikalan; Satyanarayana, Srinath; Rakotosamimanana, Niaina; Grandjean Lapierre, Simon; Adejumo, Olusola Adedeji; Kuye, Joseph; Oga-Omenka, Charity; Pai, Madhukar; Subbaraman, Ramnath

doi:10.1016/s1473-3099(23)00375-4

Cited by 4 publications

(4 citation statements)

References 54 publications

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“…1 Insufficient access to high-quality TB screening and diagnosis is recognized as one of the most important gaps in the cascade of care. 2 Here we describe a cough sound database including detailed demographic, clinical and microbiologic information for the development of AI-based sound classification TB triage models. As the WHO's End TB Strategy calls for intensified research and innovation including the discovery of new tools for community-based screening, digital cough monitoring and Acoustic Epidemiology could represent new tools that can help bend the TB pandemic curve and accelerate the achievement of global TB elimination goals.…”

Section: Background and Summarymentioning

confidence: 99%

“…The scoring mechanism can evaluate two different types of models:(1) those that use only cough sounds, or(2) those which also incorporate clinical metadata variables which have been provided in the training dataset (sex, age, height, weight, reported duration of cough, prior TB diagnosis and type, hemoptysis, heart rate, temperature, weight loss, smoking in the last week, fever and night sweats). Models are submitted to the scoring queues as Docker images.…”

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confidence: 99%

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Solicited Cough Sound Analysis for Tuberculosis Triage Testing: The CODA TB DREAM Challenge Dataset

Huddart,

Yadav,

Sieberts

et al. 2024

Preprint

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Cough is a common and commonly ignored symptom of lung disease. Cough is often perceived as difficult to quantify, frequently self-limiting, and non-specific. However, cough has a central role in the clinical detection of many lung diseases including tuberculosis (TB), which remains the leading infectious disease killer worldwide. TB screening currently relies on self-reported cough which fails to meet the World Health Organization (WHO) accuracy targets for a TB triage test. Artificial intelligence (AI) models based on cough sound have been developed for several respiratory conditions, with limited work being done in TB. To support the development of an accurate, point-of-care cough-based triage tool for TB, we have compiled a large multi-country database of cough sounds from individuals being evaluated for TB. The dataset includes more than 700,000 cough sounds from 2,143 individuals with detailed demographic, clinical and microbiologic diagnostic information. We aim to empower researchers in the development of cough sound analysis models to improve TB diagnosis, where innovative approaches are critically needed to end this long-standing pandemic.

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Section: Background and Summarymentioning

confidence: 99%

mentioning

confidence: 99%

Solicited Cough Sound Analysis for Tuberculosis Triage Testing: The CODA TB DREAM Challenge Dataset

Huddart,

Yadav,

Sieberts

et al. 2024

Preprint

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“…The inequity in gaps is more stark among individuals coinfected with HIV and especially among individuals with drug-resistant TB with an overall 88% loss for those with drug-resistant TB [ 23 ]. Increasing the proportion of individuals eligible to complete each step of the care cascade is essential to reduce the TB burden, as those individuals who go undiagnosed and untreated contribute to further TB transmission and many of those diagnosed failing to complete the cascade [ 1 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Systems analysis and improvement approach to optimize tuberculosis (SAIA-TB) screening, treatment, and prevention in South Africa: a stepped-wedge cluster randomized trial

van de Water,

Brooks,

Matji

et al. 2024

Implement Sci Commun

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Background The use of systems engineering tools, including the development and use of care cascades using routinely collected data, process mapping, and continuous quality improvement, is used for frontline healthcare workers to devise systems level change. South Africa experiences high rates of tuberculosis (TB) infection and disease as well as HIV co-infection. The Department of Health has made significant gains in HIV services over the last two decades, reaching their set “90–90-90” targets for HIV. However, TB services, although robust, have lagged in comparison for both disease and infection. The Systems Analysis and Improvement Approach (SAIA) is a five-step implementation science method, drawn from systems engineering, to identify, define, and implement workflow modifications using cascade analysis, process mapping, and repeated quality improvement cycles within healthcare facilities. Methods This stepped-wedge cluster randomized trial will evaluate the effectiveness of SAIA on TB (SAIA-TB) cascade optimization for patients with TB and high-risk contacts across 16 clinics in four local municipalities in the Sarah Baartman district, Eastern Cape, South Africa. We hypothesize that SAIA-TB implementation will lead to a 20% increase in each of: TB screening, TB preventive treatment initiation, and TB disease treatment initiation during the 18-month intervention period. Focus group discussions and key informant interviews with clinic staff will also be conducted to determine drivers of implementation variability across clinics. Discussion This study has the potential to improve TB screening, treatment initiation, and completion for both active disease and preventive measures among individuals with and without HIV in a high burden setting. SAIA-TB provides frontline health care workers with a systems-level view of their care delivery system with the aim of sustainable systems-level improvements. Trial registration Clinicaltrials.gov, NCT06314386. Registered 18 March 2024, https://clinicaltrials.gov/study/NCT06314386. NCT06314386.

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“…Losses of people with a disease across sequential care stages needed to achieve a favorable health outcome may be represented using care cascades (or continuums) [ 3 , 4 ]. TB care cascade analyses for India and other countries have provided insights into shortcomings in quality of care [ 5 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

Barriers to engagement in the care cascade for tuberculosis disease in India: A systematic review of quantitative studies

Jhaveri,

Galivanche

et al. 2024

PLoS Med

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Background India accounts for about one-quarter of people contracting tuberculosis (TB) disease annually and nearly one-third of TB deaths globally. Many Indians do not navigate all care cascade stages to receive TB treatment and achieve recurrence-free survival. Guided by a population/exposure/comparison/outcomes (PECO) framework, we report findings of a systematic review to identify factors contributing to unfavorable outcomes across each care cascade gap for TB disease 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 workup (Gap 2), start treatment (Gap 3), achieve treatment success (Gap 4), or achieve TB recurrence-free survival (Gap 5). Three systematic searches of PubMed, Embase, and Web of Science from January 1, 2000 to August 14, 2023 were conducted. We identified articles evaluating factors associated with unfavorable outcomes for each gap (reported as adjusted odds, relative risk, or hazard ratios) and, among people experiencing unfavorable outcomes, reasons for these outcomes (reported as proportions), with specific quality or risk of bias criteria for each gap. Findings were organized into person-, family-, and society-, or health system-related factors, using a social-ecological framework. Factors associated with unfavorable outcomes across multiple cascade stages included: male sex, older age, poverty-related factors, lower symptom severity or duration, undernutrition, alcohol use, smoking, and distrust of (or dissatisfaction with) health services. People previously treated for TB were more likely to seek care and engage in the diagnostic workup (Gaps 1 and 2) but more likely to suffer pretreatment loss to follow-up (Gap 3) and unfavorable treatment outcomes (Gap 4), especially those who were lost to follow-up during their prior treatment. For individual care cascade gaps, multiple studies highlighted lack of TB knowledge and structural barriers (e.g., transportation challenges) as contributing to lack of care-seeking for TB symptoms (Gap 1, 14 studies); lack of access to diagnostics (e.g., X-ray), non-identification of eligible people for testing, and failure of providers to communicate concern for TB as contributing to non-completion of the diagnostic workup (Gap 2, 17 studies); stigma, poor recording of patient contact information by providers, and early death from diagnostic delays as contributing to pretreatment loss to follow-up (Gap 3, 15 studies); and lack of TB knowledge, stigma, depression, and medication adverse effects as contributing to unfavorable treatment outcomes (Gap 4, 86 studies). Medication nonadherence contributed to unfavorable treatment outcomes (Gap 4) and TB recurrence (Gap 5, 14 studies). Limitations include lack of meta-analyses due to the heterogeneity of findings and limited generalizability to some Indian regions, given the country’s diverse population. Conclusions This systematic review illuminates common patterns of risk that shape outcomes for Indians with TB, while highlighting knowledge gaps—particularly regarding TB care for children or in the private sector—to guide future research. Findings may inform targeting of support services to people with TB who have higher risk of poor outcomes and inform multicomponent interventions to close gaps in the care cascade.

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Improving measurement of tuberculosis care cascades to enhance people-centred care

Cited by 4 publications

References 54 publications

Solicited Cough Sound Analysis for Tuberculosis Triage Testing: The CODA TB DREAM Challenge Dataset

Solicited Cough Sound Analysis for Tuberculosis Triage Testing: The CODA TB DREAM Challenge Dataset

Systems analysis and improvement approach to optimize tuberculosis (SAIA-TB) screening, treatment, and prevention in South Africa: a stepped-wedge cluster randomized trial

Barriers to engagement in the care cascade for tuberculosis disease in India: A systematic review of quantitative studies

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