Objectives: To evaluate the cost-effectiveness of testing and treatment for Mycobacterium tuberculosis (Mtb) infection among Asian and Hispanic persons with diagnosed diabetes in the United States.
Methods: We estimated population size and Mtb infection prevalence for Asian and Hispanic persons aged ≥15 years with diagnosed, nongestational diabetes, by age and US-born status. We assumed a 1-time test for Mtb infection intervention, with positive-testing persons offered treatment. Using a deterministic, transmission-dynamic model of incident tuberculosis (TB) in the United States, we estimated costs, TB cases and deaths averted, and quality-adjusted life years gained under the intervention compared with no intervention. We estimated incremental cost-effectiveness ratios (ICERs), calculated as costs per quality-adjusted life years gained, from a TB health services perspective, including diagnosis and treatment for TB infection and disease. We also assessed health services and societal perspectives. We estimated 95% uncertainty intervals via probabilistic sensitivity analysis.
Results: TB cases averted per 100 000 persons tested ranged from 7.5 (95% uncertainty interval: 6.9-8.1) among US-born Hispanic persons to 238.9 (225.2-254.3) among non-US-born Asian persons. TB deaths averted per 100 000 persons tested ranged from 1.3 (1.2-1.4) among US-born Hispanic persons to 53.7 (51.4-56.1) among non-US-born Asian persons. ICERs for US-born Asian and Hispanic populations were $856 671 ($533 506-$1 234 032) and $1 081 646 ($673 142-$1 551 264), respectively. ICERs for non-US-born Asian and Hispanic populations were lower: $66 664 ($41 456-$93 625) and $68 749 ($43 136-$97 044), respectively. ICERs were 2% to 19% higher under a societal perspective.
Conclusions: Although the intervention produced health benefits for all populations assessed, health benefits were greater-and ICERs more favorable-for non-US-born Asian and Hispanic populations with diagnosed diabetes.
Keywords: Mycobacterium tuberculosis infection; cost-effectiveness analysis; mathematical modeling; persons with diabetes.
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