In a mathematical model of 33 neighborhoods in Blantyre, Malawi, targeting active TB case finding to areas with higher estimated annual risk of TB infection (ARTI) was more efficient than screening neighborhoods without that targeting. The model estimated that screening 48% of the study population could identify 80% of people with TB disease, at a cost of US$1,100 per disability-adjusted life year (DALY) averted, versus US$1,600 for untargeted screening. These are modeled estimates—not results observed in a completed public-health program—and the authors say lower-cost ways to collect the survey data are needed before wider adoption.
What the study asked
Kim and colleagues examined whether neighborhood-level ARTI estimates could help public-health programs decide where to conduct active case finding (ACF) for tuberculosis. ARTI is an estimate of the rate at which people acquire TB infection over a year. The study used results from an immunoreactivity survey in children younger than five as a proxy for differences in underlying TB burden between neighborhoods.
The authors compared three modeled strategies: passive case finding (PCF) alone, PCF plus ACF conducted without neighborhood targeting, and PCF plus ACF focused on neighborhoods with higher estimated ARTI. PCF refers to diagnosis after people seek care; ACF involves actively screening people in the community.
Where and how the model was built
The analysis covered 33 urban neighborhoods with a combined estimated population of 266,710. Neighborhood boundaries followed community health worker catchment areas. The areas were selected based on population density and proximity to three study clinics, so the results describe this study area rather than all of Blantyre or Malawi. The reported neighborhood ARTI estimates ranged from 0% to 9%; these are estimates of infection risk, not measurements of TB disease prevalence.
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The researchers used a Markov microsimulation parameterized with local data. It modeled a one-time, community-wide ACF intervention and compared outcomes under different assumptions about how well ARTI predicts neighborhood TB prevalence. Outcomes included life expectancy, TB-attributable mortality, DALYs and costs, assessed from both health-system and societal perspectives.
Modeled screening pathway
- People reporting any cough were sent for Xpert testing.
- People without cough received a chest X-ray.
- People with any chest X-ray abnormality were sent for Xpert testing.
- An Xpert-positive result was treated as a TB diagnosis. The model assumed that 5% of people diagnosed with TB were lost to follow-up before starting treatment.
Xpert, chest X-ray and immunoreactivity testing are components of the modeled public-health strategy, not consumer products evaluated for purchase.
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What the three strategies produced in the model
| Strategy | Modeled coverage and case finding | Cost-effectiveness |
|---|---|---|
| PCF alone | ACF not included; specific coverage and case counts are not stated in Kim and colleagues’ 2025 medRxiv preprint. | Not stated in the preprint. |
| PCF plus untargeted ACF | Specific coverage and case counts are not stated in the preprint. | US$1,600 per DALY averted, as modeled in the preprint. |
| PCF plus ARTI-guided targeted ACF | Screening 48% of the modeled study population was estimated to identify 80% of people with TB disease. | US$1,100 per DALY averted, as modeled in the preprint. |
The authors reported that both ACF strategies’ cost-per-DALY ratios exceeded the cost-effectiveness thresholds available for Malawi. The reported ratios exclude the cost of collecting the immunoreactivity survey data: those data came from a separate research study embedded in primary-care visits. They therefore do not represent the full cost of routinely collecting surveillance data and then running targeted screening.
Estimated health benefit and the role of ARTI
Compared with PCF alone, untargeted ACF was estimated to improve life expectancy among people with TB disease by 3.7 years (95% credible interval, 1.9–5.9). This is a model output, not an observed treatment effect from a prospective program.
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Targeting’s apparent efficiency rests on a key assumption: that higher ARTI estimates reliably identify neighborhoods with higher underlying TB prevalence. The authors varied the assumed predictive power of ARTI, making that relationship an important source of uncertainty in the results. The 48%-to-80% finding should therefore be understood as a modeled result under the study’s assumptions, not a guaranteed yield for an operational program.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the findings mean for public-health programs
The model suggests a potential advantage to using infection-survey results to focus ACF: a smaller share of the population could be screened while reaching a larger share of people estimated to have TB disease than with untargeted screening. But putting that approach into practice requires reliable neighborhood-level estimates, a screening and diagnostic pathway, and resources to collect the underlying survey data. Because survey collection costs were excluded, the reported cost-effectiveness advantage may not carry over unchanged to a routine surveillance-and-screening program.
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The study is an October 2025 medRxiv preprint by Kim and colleagues. Its findings are useful as evidence about a modeled strategy in the selected Blantyre neighborhoods; they do not establish that targeted ACF has already achieved these health or economic outcomes in Malawi.
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