Evidence map›Paper›PMID 40247175›Full record

ArticleBMC medical research methodology2025

Causal inference methodologies to assess the effect of missed clinic visits on treatment success rate among people with tuberculosis in rural Uganda.

Jonathan Izudi, Adithya Cattamanchi, Francis Bajunirwe

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Article in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Jonathan IzudiDepartment of Community Health, Faculty of Medicine, Mbarara University of Science and Technology, Mbarara, Uganda. jonahzd@gmail.com.
Adithya CattamanchiCenter for Tuberculosis, San Francisco General Hospital, University of California San Francisco, San Francisco, CA, USA.
Francis BajunirweDepartment of Community Health, Faculty of Medicine, Mbarara University of Science and Technology, Mbarara, Uganda.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough randomized controlled trials are the gold standard design for cause-effect analysis, high costs and challenges around practicability, feasibility, and ethics may limit their use. In such situations, causal inference methods can improve the rigor of cause-effect analysis using observational data but such methods have infrequently been applied in tuberculosis (TB) research. We conducted a parallel comparison across three causal inference methods in order to assess the causal association between missed clinic visit/s and treatment success among people with drug-susceptible bacteriologically confirmed pulmonary TB.

methodsWe used causal inference methods to analyze cross-sectional data of adults with drug-susceptible bacteriologically confirmed pulmonary TB at clinics in rural eastern Uganda. We compared effect estimates from three causal inference methods, namely instrumental variable analysis, propensity-score analysis (adjustment, matching, weighting, and stratification), and double-robust estimation for cause-effect analysis. The exposure was missing a TB clinic visit/s and the outcome was treatment success defined as cure or treatment completion, both measured on a binary scale. Covariates were selected based on the literature, and their social and biological relevance to the outcome. We report the odds ratio and 95% confidence interval from each causal analysis.

resultsOf 762 participants (mean age of 39.3 ± 15.8 years) included, 186 (24.4%) had missed a clinic visit/s while 687 (90.2%) were successfully treated for TB. Missed clinic visit/s lowered treatment success across all analyses with instrumental variable analysis (OR 0.41, 95% CI 0.20-0.82), propensity-score analysis (adjustment [OR 0.49, 95% CI 0.30-0.82], matching [OR 0.43, 95% CI 0.21-0.91)], weighting [OR 0.52, 95% CI 0.30-0.91], and stratification [OR 0.34, 95% CI 0.19-0.62]), and double-robust estimation (OR 0.49, 95% CI 0.28-0.85).

conclusionsMissed clinic visit/s reduced the likelihood of TB treatment success rate across all causal inference methods, supporting a causal relationship. Studies are needed to examine interventions that enhance retention in TB treatment.

Indexed as

Antitubercular AgentsTuberculosisTuberculosis, PulmonaryAdultCross-Sectional StudiesFemaleHumansMaleMiddle AgedPropensity ScoreRural PopulationTreatment OutcomeUgandaYoung AdultAntitubercular AgentsCausal inferenceDouble-robust EstimationPropensity score analysisTreatment successTuberculosis

Identifiers

PMID40247175
PMCPMC12004605

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.