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Background <p>Although 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.</p> Methods <p>We 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.</p> Results <p>Of 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).</p> Conclusions <p>Missed 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月17日

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  • Jonathan Izudi,
  • Adithya Cattamanchi,
  • Francis Bajunirwe

摘要

Background

Although 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.

Methods

We 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.

Results

Of 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).

Conclusions

Missed 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.