The impact of modifiable social determinants on tuberculosis incidence: insights from a Bayesian spatiotemporal and counterfactual analysis

Background: Addressing social determinants is critical to achieving the End TB Strategy targets. However, the impact of tuberculosis (TB) programmatic indicators amenable to short-term interventions on TB incidence remains underexplored. We aimed to identify modifiable social determinants and evaluate their impact on TB incidence in different risk-specific areas. Methods: We compiled individual-level surveillance data on reported pulmonary TB (PTB) cases and county-level data on 12 social determinants between 2011 and 2018 across 108 counties in Shaanxi Province, China. A Bayesian spatiotemporal model quantified associations between PTB incidence rate and social determinants. Model-estimated relative risks (RRs; x-axis) and their annual percentage change (APC; y-axis) were integrated to construct an RR–APC plane diagram for classifying different risk-specific counties. Counterfactual analysis estimated potential reduction in PTB incidence rate if the referral and tracing percentage for PTB patients and suspected cases or the percentage of PTB cases confirmed bacteriologically in these counties were increased to 2018 provincial benchmarks. Results: Five social determinants were significantly associated with PTB incidence rate. A 1% increase in the referral and tracing percentage for PTB patients and suspected cases, per capita housing area, and the percentage of PTB cases confirmed bacteriologically corresponded to reductions in PTB incidence rate of 0.39%, 0.39%, and 0.04%, respectively. In contrast, a 1% increase in the percentage of population aged ≥ 50 years and in the percentage of illiterate population was associated with increases of 0.35% and 0.07%, respectively. The RR–APC plane diagram identified three types of risk areas: four counties in Ankang Municipality with rapidly escalating risks, three counties in Yulin Municipalities with persistently high risks, and three newly emerging high-risk counties in Ankang and Shangluo Municipalities. Counterfactual analysis suggested that increasing the referral and tracing percentage to 94% could reduce PTB incidence rate by 9.24/100,000 to 38.54/100,000 (relative reductions of 8.49% to 28.06%) across different risk-specific counties, and simultaneous improvement in both indicators could achieve reductions of 10.48/100,000 to 38.99/100,000 (9.62% to 28.38%). Conclusion: The referral and tracing percentage for PTB patients and suspected cases and the percentage of PTB cases confirmed bacteriologically are modifiable social determinants on TB incidence rate. Strengthening the effectiveness of referral and tracing mechanisms and expanding bacteriological diagnostic capacity across risk areas may substantially reduce TB burden and accelerate progress towards End TB Strategy targets.