Modeling prognostic heterogeneity in multinational tuberculosis outcomes using quantile regression on multimodal...

Area of Science:

• Global Health
• Epidemiology
• Biostatistics

Background:

• Tuberculosis (TB) remains a significant global health challenge.
• Understanding factors influencing TB treatment outcomes is crucial for effective control strategies.
• Existing research often focuses on average outcomes, potentially missing critical insights into outcome heterogeneity.

Purpose of the Study:

• To investigate the heterogeneity of TB treatment outcomes across 75 countries.
• To identify determinants of TB incidence and treatment success at different risk levels (low to high extremes).
• To move beyond average-centric analyses and characterize the full conditional distribution of TB outcomes.

Main Methods:

• Applied quantile regression modeling to a multimodal dataset.
• Integrated socioeconomic, demographic, and comorbidity factors (e.g., HIV prevalence, GDP, healthcare expenditure).
• Evaluated model performance using mean absolute error, R-squared, and quantile loss.

Main Results:

• HIV prevalence and healthcare spending strongly influence unfavorable TB treatment quantiles.
• Higher Gross Domestic Product (GDP) and robust health infrastructure correlate with favorable TB outcomes.
• Quantile regression revealed complex interactions between predictors and outcome variability.

Conclusions:

• A distribution-aware approach provides nuanced insights into global TB disparities.
• Findings offer actionable guidance for resource allocation and targeted TB interventions.
• Highlights the need to consider diverse epidemiological profiles and risk factors in TB control.