Factors of loss to follow-up during tuberculosis treatment in a low-incidence region

Tétart, Macha; Meybeck, Agnès; Assaf, A.; Valette, M.; Choisy, P.; Blondiaux, Nicolas; Senneville, Éric

doi:10.1016/j.medmal.2019.02.007

Cited by 6 publications

(10 citation statements)

References 22 publications

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“…A non-significant decrease in the pooled risk of loss from treatment estimated was 0.82 times lower in HIV-infected patients than in those HIV-uninfected. In agreement to this finding, study reports indicated 11.8% versus 26.2% (P<0.001) and 14.2% versus 35.2% rates of loss to follow-up among the HIV-infected versus HIV-uninfected patients; and the loss to follow-up was 7.67 (95% CI: 1.00-59.0) times more likely in the HIV-uninfected patients than in those HIV-infected [80][81][82]. Also, reminding information via cell phone as a part of the HIV/TB integrated program enabled tracing lost patients and resulted in twice more likely returnee for traced patients than untracked ones [83].…”

Section: Discussionsupporting

confidence: 70%

“…Alternatively, a study report indicated 1.6 times higher odds of treatment failure in HIV-uninfected patients than in those HIV-infected [79]. Again, a previous study explained more frequent support for compliance in HIV-infected patients (23%) than in those HIV-uninfected (7%) [81]. Accordingly, good compliance with treatment is a key component of strategies that can reduce the rates of treatment failure [88][89][90][91][92][93][94].…”

Section: Discussionmentioning

confidence: 99%

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Unfavorable outcomes to second-line tuberculosis therapy among HIV-infected versus HIV-uninfected patients in sub-Saharan Africa: A systematic review and meta-analysis

2020

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Background Drug resistance is a key obstacle to the global target set to end tuberculosis by 2030. Clinical complexities in drug-resistant tuberculosis and HIV-infection co-management could worsen outcomes of second-line anti-tuberculosis drugs. A comprehensive estimate for risks of unsuccessful outcomes to second-line tuberculosis therapy in HIV-infected versus HIV-uninfected patients is mandatory to address such aspects in segments of the target set. Therefore, this meta-analysis was aimed to estimate the pooled risk ratios of unfavorable outcomes to second-line tuberculosis therapy between HIV-infected and HIV-uninfected patients in sub-Saharan Africa. Methods We conducted a literature search from PubMed/MEDLINE, EMBASE, SCOPUS and Google Scholar. We screened the retrieved records by titles and abstracts. Finally, we assessed eligibility and quality of full-text articles for the records retained by employing appraisal checklist of the Joanna Briggs Institute. We analyzed the data extracted from the included studies by using Review Manager Software, version 5.3 and presented our findings in forest and funnel plots. Protocol for this study was registered on PROSPERO (ID: CRD42020160473). Results A total of 19 studies with 1,766 from 4,481 HIV-infected and 1,164 from 3,820 HIV-uninfected patients had unfavorable outcomes. The risk ratios we estimated between HIV-infected and HIV-uninfected drug-resistant tuberculosis patients were 1.18 (95% CI: 1.07–1.30; I 2 = 48%; P = 0.01) for the overall unfavorable outcome; 1.50 (95% CI: 1.30–1.74) for death; 0.66 (95% CI: 0.38–1.13) for treatment failure; and 0.82 (95% CI: 0.74–0.92) for loss from treatment. Variable increased risks of unfavorable outcomes estimated for subgroups with significance in mixed-age patients (RR: 1.22; 95% CI: 1.10–1.36) and eastern region of sub-Saharan Africa (RR: 1.47; 95% CI: 1.23–1.75). Conclusions We found a higher risk of unfavorable treatment outcome in drug-resistant tuberculosis patients with death highly worsening in HIV-infected than in those HIV-uninfected patients. The risks for the unfavorable outcomes were significantly higher in mixed-age patients and in the eastern region of sub-Saharan Africa. Therefore, special strategies that reduce the risks of death should be discovered and implemented for HIV and drug-resistant tuberculosis co-infected patients on second-line tuberculosis therapy with optimal integration of the two programs in the eastern region of sub-Saharan Africa.

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Section: Discussionsupporting

confidence: 70%

Section: Discussionmentioning

confidence: 99%

Unfavorable outcomes to second-line tuberculosis therapy among HIV-infected versus HIV-uninfected patients in sub-Saharan Africa: A systematic review and meta-analysis

2020

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“…Included studies were published 1986-2019, from 10 different countries, including the UK and Ireland (n=7) [37][38][39][40][41][42][43] , USA (n=6) 36,[44][45][46][47][48] , and Spain (n=5) [49][50][51][52][53] . The most common study design was retrospective cohort (n=12) 37,40,46,48,49,[53][54][55][56][57][58][59] . Sample sizes ranged from 62 to 73,591 (median= 1009; interquartile range (IQR)= 184-2576).…”

Section: Description Of Included Studiesmentioning

confidence: 99%

“…Most studies (n=20) included all patients starting treatment in a given setting [36][37][38][39][40][41][42][43][44][45]47,48,[50][51][52][54][55][56][57][58] . Three studies sampled specific high-risk groups, of people experiencing homelessness or unstable living arrangements 60 , individuals with multidrug-resistant TB (MDR-TB) 59 , or HIV/TB co-infection 53 .…”

Section: Description Of Included Studiesmentioning

confidence: 99%

“…Supplementary Material 2 demonstrates the considerable variability in definitions of adherence. Most study outcomes (n=15) related to treatment discontinuation (stopping treatment early) 38,40,[44][45][46][47]49,50,[53][54][55][56][58][59][60] . Fewer study outcomes (n=7) appeared to record suboptimal implementation (missed doses during treatment) 36,37,39,41,43,48,57 .…”

Section: Non-adherence: Definitions and Assessmentmentioning

confidence: 99%

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Determinants of non-adherence to anti-TB treatment in high income, low TB incidence settings: a scoping review

Jones

Bidad

Horne

et al. 2021

int j tuberc lung dis

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BACKGROUND: Improving adherence to anti-TB treatment is a public health priority in high-income, low incidence (HILI) regions. We conducted a scoping review to identify reported determinants of non-adherence in HILI settings.METHODS: Key terms related to TB, treatment and adherence were used to search MEDLINE, EMBASE, Web of Science, PsycINFO and CINAHL in June 2019. Quantitative studies examining determinants (demographic, clinical, health systems or psychosocial) of non-adherence to anti-TB treatment in HILI settings were included.RESULTS: From 10,801 results, we identified 24 relevant studies from 10 countries. Definitions and methods of assessing adherence were highly variable, as were documented levels of non-adherence (0.9–89%). Demographic factors were assessed in all studies and clinical factors were frequently assessed (23/24). Determinants commonly associated with non-adherence were homelessness, incarceration, and alcohol or drug misuse. Health system (8/24) and psychosocial factors (6/24) were less commonly evaluated.CONCLUSION: Our review identified some key factors associated with non-adherence to anti-TB treatment in HILI settings. Modifiable determinants such as psychosocial factors are under-evidenced and should be further explored, as these may be better targeted by adherence support. There is an urgent need to standardise definitions and measurement of adherence to more accurately identify the strongest determinants.

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Predictive machine learning models for anticipating loss to follow-up in tuberculosis patients throughout anti-TB treatment journey

Chen,

Jiang,

et al. 2024

Sci Rep

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Loss to follow-up (LTFU) in tuberculosis (TB) management increases morbidity and mortality, challenging effective control strategies. This study aims to develop and evaluate machine learning models to predict loss to follow-up in TB patients, improving treatment adherence and outcomes. Retrospective data encompassing tuberculosis patients who underwent treatment or registration at the National Center for Clinical Medical Research on Infectious Diseases from January 2017 to December 2021 were compiled. Employing machine learning techniques, namely SVM, RF, XGBoost, and logistic regression, the study aimed to prognosticate LTFU. A comprehensive cohort of 24,265 tuberculosis patients underwent scrutiny, revealing a LTFU prevalence of 12.51% (n = 3036). Education level, history of hospitalization, alcohol consumption, outpatient admission, and prior tuberculosis history emerged as precursors for pre-treatment LTFU. Employment status, outpatient admission, presence of chronic hepatitis/cirrhosis, drug adverse reactions, alternative contact availability, and health insurance coverage exerted substantial influence on treatment-phase LTFU. XGBoost consistently surpassed alternative models, boasting superior discriminative ability with an average AUC of 0.921 for pre-treatment LTFU and 0.825 for in-treatment LTFU. Our study demonstrates that the XGBoost model provides superior predictive performance in identifying LTFU risk among tuberculosis patients. The identification of key risk factors highlights the importance of targeted interventions, which could lead to significant improvements in treatment adherence and patient outcomes.

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Factors of loss to follow-up during tuberculosis treatment in a low-incidence region

Cited by 6 publications

References 22 publications

Unfavorable outcomes to second-line tuberculosis therapy among HIV-infected versus HIV-uninfected patients in sub-Saharan Africa: A systematic review and meta-analysis

Unfavorable outcomes to second-line tuberculosis therapy among HIV-infected versus HIV-uninfected patients in sub-Saharan Africa: A systematic review and meta-analysis

Determinants of non-adherence to anti-TB treatment in high income, low TB incidence settings: a scoping review

Predictive machine learning models for anticipating loss to follow-up in tuberculosis patients throughout anti-TB treatment journey

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