Background: To achieve the WHO End TB Strategy targets, it is necessary to detect and treat more people with active TB early. Scale-up of active case finding (ACF) may be one strategy to achieve that goal. Given human resource constraints in the health systems of most high TB burden countries, volunteer community health workers (CHW) have been widely used to economically scale up TB ACF. However, more evidence is needed on the most cost-effective compensation models for these CHWs and their potential impact on case finding to inform optimal scale-up policies. Methods: We conducted a two-year, controlled intervention study in 12 districts of Ho Chi Minh City, Viet Nam. We engaged CHWs as salaried employees (3 districts) or incentivized volunteers (3 districts) to conduct ACF among contacts of people with TB and urban priority groups. Eligible persons were asked to attend health services for radiographic screening and rapid molecular diagnosis or smear microscopy. Individuals diagnosed with TB were linked to appropriate care. Six districts providing routine NTP care served as control area. We evaluated additional cases notified and conducted comparative interrupted time series (ITS) analyses to assess the impact of ACF by human resource model on TB case notifications. Results: We verbally screened 321,020 persons in the community, of whom 70,439 were eligible for testing and 1138 of them started TB treatment. ACF activities resulted in a + 15.9% [95% CI: + 15.0%, + 16.7%] rise in All Forms TB notifications in the intervention areas compared to control areas. The ITS analyses detected significant positive post-intervention trend differences in All Forms TB notification rates between the intervention and control areas (p = 0.001), as well as between the employee and volunteer human resource models (p = 0.021).

acknowledgements All authors are members of the Health and Social Protection Action Research & Knowledge Sharing Network (SPARKS), an international interdisciplinary research network. SPARKS' multisectoral team characterises and evaluates the direct and indirect effects of social protection strategies on health, economic and wider outcomes. Website: https:// sparksnetwork. ki. se.

There have been few independent evaluations of computer-aided detection (CAD) software for tuberculosis (TB) screening, despite the rapidly expanding array of available CAD solutions. We developed a test library of chest X-ray (CXR) images which was blindly re-read by two TB clinicians with different levels of experience and then processed by 12 CAD software solutions. Using Xpert MTB/RIF results as the reference standard, we compared the performance characteristics of each CAD software against both an Expert and Intermediate Reader, using cut-off thresholds which were selected to match the sensitivity of each human reader. Six CAD systems performed on par with the Expert Reader (Qure.ai, DeepTek, Delft Imaging, JF Healthcare, OXIPIT, and Lunit) and one additional software (Infervision) performed on par with the Intermediate Reader only. Qure.ai, Delft Imaging and Lunit were the only software to perform significantly better than the Intermediate Reader. The majority of these CAD software showed significantly lower performance among participants with a past history of TB. The radiography equipment used to capture the CXR image was also shown to affect performance for some CAD software. TB program implementers now have a wide selection of quality CAD software solutions to utilize in their CXR screening initiatives.

Across Asia, a large proportion of people with tuberculosis (TB) do not report symptoms, have mild symptoms or only experience symptoms for a short duration. These individuals may not seek care at health facilities or may be missed by symptom screening, resulting in sustained TB transmission in the community. We evaluated the yields of TB from 114 days of community-based, mobile chest X-ray (CXR) screening. The yields at each step of the TB screening cascade were tabulated and we compared cohorts of participants who reported having a prolonged cough and those reporting no cough or one of short duration. We estimated the marginal yields of TB using different diagnostic algorithms and calculated the relative diagnostic costs and cost per case for each algorithm. A total of 34,529 participants were screened by CXR, detecting 256 people with Xpert-positive TB. Only 50% of those diagnosed with TB were detected among participants reporting a prolonged cough. The study’s screening algorithm detected almost 4 times as much TB as the National TB Program’s standard diagnostic algorithm. Community-based, mobile chest X-ray screening can be a high yielding strategy which is able to identify people with TB who would likely otherwise have been missed by existing health services.

Background Many tuberculosis (TB) patients incur catastrophic costs. Active case finding (ACF) may have socio-protective properties that could contribute to the WHO End TB Strategy target of zero TB-affected families suffering catastrophic costs, but available evidence remains limited. This study measured catastrophic cost incurrence and socioeconomic impact of an episode of TB and compared those socioeconomic burdens in patients detected by ACF versus passive case finding (PCF). Methods This cross-sectional study fielded a longitudinal adaptation of the WHO TB patient cost survey alongside an ACF intervention from March 2018 to March 2019. The study was conducted in six intervention (ACF) districts and six comparison (PCF) districts of Ho Chi Minh City, Viet Nam. Fifty-two TB patients detected through ACF and 46 TB patients in the PCF cohort were surveyed within two weeks of treatment initiation, at the end of the intensive phase of treatment, and after treatment concluded. The survey measured income, direct and indirect costs, and socioeconomic impact based on which we calculated catastrophic cost as the primary outcome. Local currency was converted into US$ using the average exchange rates reported by OANDA for the study period (VNĐ1 = US$0.0000436, 2018–2019). We fitted logistic regressions for comparisons between the ACF and PCF cohorts as the primary exposures and used generalized estimating equations to adjust for autocorrelation. Results ACF patients were poorer than PCF patients (multidimensional poverty ratio: 16 % vs. 7 %; p = 0.033), but incurred lower median pre-treatment costs (US$18 vs. US$80; p < 0.001) and lower median total costs (US$279 vs. US$894; p < 0.001). Fewer ACF patients incurred catastrophic costs (15 % vs. 30 %) and had lower odds of catastrophic cost (aOR = 0.17; 95 % CI: [0.05, 0.67]; p = 0.011), especially during the intensive phase (OR = 0.32; 95 % CI: [0.12, 0.90]; p = 0.030). ACF patient experienced less social exclusion (OR = 0.41; 95 % CI: [0.18, 0.91]; p = 0.030), but more often resorted to financial coping mechanisms (OR = 5.12; 95 % CI: [1.73, 15.14]; p = 0.003). Conclusions ACF can be effective in reaching vulnerable populations and mitigating the socioeconomic burden of TB, and can contribute to achieving the WHO End TB Strategy goals. Nevertheless, as TB remains a catastrophic life event, social protection efforts must extend beyond ACF.