Results from a roving, active case finding initiative to improve tuberculosis detection among older people in rural cambodia using the Xpert MTB/RIF assay and chest X-ray

Evaluation of a population-wide, systematic screening initiative for tuberculosis on Daru island, Western Province, Papua New Guinea

BACKGROUND

A population-wide, systematic screening initiative for tuberculosis (TB) was implemented on Daru island in the Western Province of Papua New Guinea, where TB is known to be highly prevalent. The initiative used a mobile van equipped with a digital X-ray device, computer-aided detection (CAD) software to identify TB-related abnormalities on chest radiographs, and GeneXpert machines for follow-on diagnostic testing. We describe the results of the TB screening initiative, evaluate its population-level impact and examine risk factors associated with TB detection.

METHODS

Through a retrospective review of screening data, we assessed the effectiveness of the screening by examining the enrolment coverage and the proportion of people with TB among screened subjects. A cascade analysis was performed to illustrate the flow of participants in the screening algorithm. We conducted univariate and multivariate analyses to identify factors associated with TB. Furthermore, we estimated the number of additional cases detected by the project by examining the trend of routine TB case notifications during the intervention period, compared to the historical baseline cases and trend-adjusted expected cases.

RESULTS

Of the island's 18,854 residents, 8,085 (42.9%) were enrolled and 7,970 (98.6%) had chest X-ray interpreted by the CAD4TB software. A total of 1,116 (14.0%) participants were considered to have abnormal CXR. A total of 69 Xpert-positive cases were diagnosed, resulting in a detection rate of 853 per 100 000 population screened. 19.4% of people with TB had resistance to rifampicin. People who were in older age groups (aOR 6.6, 95%CI: 1.5-29.1 for the 45-59 age group), were severely underweight (aOR 2.5, 95%CI:1.0-6.1) or underweight (aOR 2.1, 95%CI: 1.1-3.8), lived in households < 5 people (aOR 3.4, 95%CI:1.8-6.6) and had a past history of TB (aOR 2.1, 95%CI: 1.2-3.6) were more likely to have TB. The number of bacteriologically confirmed TB notified during the intervention period was 79.3% and 90.8% higher than baseline notifications and forecasted notifications, respectively.

CONCLUSION

The screening project demonstrated its effectiveness with the high Xpert-positive TB prevalence among the participants and by successfully yielding additional cases of bacteriologically confirmed TB including rifampicin-resistant TB. The results and lessons learnt from the project should inform future TB screening initiatives in Papua New Guinea.

Applicability of artificial intelligence-based computer-aided detection (AI-CAD) for pulmonary tuberculosis to community-based active case finding

BACKGROUND

Artificial intelligence-based computer-aided detection (AI-CAD) for tuberculosis (TB) has become commercially available and several studies have been conducted to evaluate the performance of AI-CAD for pulmonary tuberculosis (TB) in clinical settings. However, little is known about its applicability to community-based active case-finding (ACF) for TB.

METHODS

We analysed an anonymized data set obtained from a community-based ACF in Cambodia, targeting persons aged 55 years or over, persons with any TB symptoms, such as chronic cough, and persons at risk of TB, including household contacts. All of the participants in the ACF were screened by chest radiography (CXR) by Cambodian doctors, followed by Xpert test when they were eligible for sputum examination. Interpretation by an experienced chest physician and abnormality scoring by a newly developed AI-CAD were retrospectively conducted for the CXR images. With a reference of Xpert-positive TB or human interpretations, receiver operating characteristic (ROC) curves were drawn to evaluate the AI-CAD performance by area under the ROC curve (AUROC). In addition, its applicability to community-based ACFs in Cambodia was examined.

RESULTS

TB scores of the AI-CAD were significantly associated with the CXR classifications as indicated by the severity of TB disease, and its AUROC as the bacteriological reference was 0.86 (95% confidence interval 0.83-0.89). Using a threshold for triage purposes, the human reading and bacteriological examination needed fell to 21% and 15%, respectively, detecting 95% of Xpert-positive TB in ACF. For screening purposes, we could detect 98% of Xpert-positive TB cases.

CONCLUSIONS

AI-CAD is applicable to community-based ACF in high TB burden settings, where experienced human readers for CXR images are scarce. The use of AI-CAD in developing countries has the potential to expand CXR screening in community-based ACFs, with a substantial decrease in the workload on human readers and laboratory labour. Further studies are needed to generalize the results to other countries by increasing the sample size and comparing the AI-CAD performance with that of more human readers.

Comparing tuberculosis symptom screening to chest X-ray with artificial intelligence in an active case finding campaign in Northeast Nigeria

BACKGROUND

Ultra-portable X-ray devices with artificial intelligence (AI) are increasingly used to screen for tuberculosis (TB). Few studies have documented their performance. We aimed to evaluate the performance of chest X-ray (CXR) and symptom screening for active case finding of TB among remote populations using ultra-portable X-ray and AI.

METHODS

We organized screening camps in rural northeast Nigeria, and all consenting individuals ≥ 15 years were screened for TB symptoms (cough, fever, night sweats, and weight loss) and received a CXR. We used a MinXray Impact system interpreted by AI (qXR V3), which is a wireless setup and can be run without electricity. We collected sputum samples from individuals with an qXR abnormality score of 0.30 or higher or if they reported any TB symptoms. Samples were tested with Xpert MTB/RIF. We documented the TB screening cascade and evaluated the performance of screening with different combinations of symptoms and CXR interpreted by AI.

RESULTS

We screened 5297 individuals during 66 camps: 2684 (51%) were females, and 2613 (49%) were males. Using ≥ 2 weeks of cough to define presumptive TB, 1056 people (20%) would be identified. If a cough of any duration was used, the number with presumptive TB increased to 1889 (36%) and to 3083 (58%) if any of the four symptoms were used. Overall, 769 (14.5%) had abnormality scores of 0.3 or higher, and 447 (8.4%) had a score of 0.5 or higher. We collected 1021 samples for Xpert testing and detected 85 (8%) individuals with TB. Screening for prolonged cough only identified 40% of people with TB. Any symptom detected 90.6% of people with TB, but specificity was 11.4%. Using an AI abnormality score of 0.50 identified 89.4% of people with TB with a specificity of 62.8%.

CONCLUSIONS

Ultra-portable CXR can be used to provide more efficient TB screening in hard-to-reach areas. Symptom screening missed large proportions of people with bacteriologically confirmed TB. Employing AI to read CXR can improve triaging when human readers are unavailable and can save expensive diagnostic testing costs.

Tuberculosis in older adults: challenges and best practices in the Western Pacific Region

The Western Pacific has one of the fastest-growing older adult populations globally, and tuberculosis (TB) remains one of the foremost infectious causes of disease and death in the region. Older adults are at higher risk of TB due to immunosenescence, comorbidities, and increased institutionalisation. Atypical symptoms and reduced access to health services may delay care-seeking and TB diagnosis, while co-morbidity and increased risk of adverse drug reactions complicate TB treatment. Post-TB sequelae and socioeconomic challenges may decrease the quality of life after TB treatment completion. Despite their high disease burden and special challenges, there is a lack of regionally coordinated policies and guidelines to manage TB among older adults. Routine TB screening at aged-care facilities, age-friendly infrastructure and services, awareness of atypical TB features, integration of TB and non-communicable diseases services, and person-centred approaches to treatment support could improve TB management among older adults. Addressing these challenges and adopting the best practices identified should inform policy formulation and implementation.

Funding

This project was funded by 1) the World Health Organization Regional Office for the Western Pacific, with financial contributions from the Government of the Republic of Korea through the Korean Disease Control and Prevention Agency and the Government of Japan through the Ministry of Health, Labour and Welfare, and 2) NUS Start-up Grant. The funders had no role in the paper design, collection, analysis, and interpretation of data and in writing of the paper.

Detection of Mycobacterium tuberculosis Complex Using the Xpert MTB/RIF Ultra Assay on the Stool of Pediatric Patients in Dushanbe, Tajikistan

We report the findings of a prospective laboratory diagnostic accuracy study to evaluate the sensitivity, specificity, and predictive values of the Xpert MTB/RIF Ultra assay for Mycobacterium tuberculosis detection in fresh stool specimens from children under 15 years of age with confirmed tuberculosis (TB) disease from Dushanbe, Tajikistan. Six hundred eighty-eight (688) participants were enrolled from April 2019 to October 2021. We identified 16 participants (2.3%) with confirmed TB disease, defined as ≥1 TB sign/symptom plus microbiologic confirmation. With the Xpert MTB/RIF Ultra assay for stool, we found a sensitivity of 68.8% (95% CI, 46.0 to 91.5) and a specificity of 98.7% (95% CI, 97.8 to 99.5) in confirmed TB disease. Our results are comparable to other published studies; however, our cohort was larger and our confirmed TB disease rate lower than most. We also demonstrated that this assay was feasible to implement in a centralized hospital laboratory in a low-middle-income Central Asian country. However, we encountered obstacles such as lack of staffing, material ruptures, outdated government protocols, and decreased case presentation due to COVID-19. We found eight patients whose only positive test was an Xpert Ultra stool assay. None needed treatment during the study; however, three were treated later, suggesting such cases require close observation. Our report is the first from Central Asia and one of a few from a low-middle-income country. We believe our study demonstrates the generalizability of the Xpert MTB/RIF Ultra assay on fresh stool specimens from children and provides further evidence supporting WHO's approval of this diagnostic strategy. IMPORTANCE The importance of this report is that it provides further support for WHO's recent recommendation that fresh stool is an acceptable sample for GeneXpert TB testing in children, especially small children who often cannot produce an adequate sputum sample. Diagnosing TB in this age group is difficult, and many cases are missed, leading to unacceptable rates of TB illness and death. In our large cohort of children from Dushanbe, Tajikistan, the GeneXpert stool test was positive in 69% of proven cases of TB, and there were very few false-positive tests. We also showed that this diagnostic strategy was feasible to implement in a low-middle-income country with an inefficient health care delivery system. We hope that many more programs will adopt this form of diagnosing TB in children.

Progress towards the 2020 milestones of the end TB strategy in Cambodia: estimates of age and sex specific TB incidence and mortality from the Global Burden of Disease Study 2019

BACKGROUND

Cambodia was recently removed from the World Health Organization's (WHO's) top 30 high tuberculosis (TB) burden countries. However, Cambodia's TB burden remains substantial, and the country is on the WHO's new global TB watchlist. We aimed to examine the levels and trends in the fatal and non-fatal TB burden in Cambodia from 1990 to 2019, assessing progress towards the WHO End TB interim milestones, which aim to reduce TB incidence rate by 20% and TB deaths by 35% from 2015 to 2020.

METHODS

We leveraged the Global Burden of Disease 2019 (GBD 2019) analytical framework to compute age- and sex-specific TB mortality and incidence by HIV status in Cambodia. We enumerated TB mortality utilizing a Bayesian hierarchical Cause of Death Ensemble modeling platform. We analyzed all available data sources, including prevalence surveys, population-based tuberculin surveys, and TB cause-specific mortality, to produce internally consistent estimates of incidence and mortality using a compartmental meta-regression tool (DisMod-MR 2.1). We further estimated the fraction of tuberculosis mortality among individuals without HIV coinfection attributable to the independent effects of alcohol use, smoking, and diabetes.

RESULTS

In 2019, there were 6500 (95% uncertainty interval 4830-8680) deaths due to all-form TB and 50.0 (43.8-57.8) thousand all-form TB incident cases in Cambodia. The corresponding age-standardized rates were 53.3 (39.9-69.4) per 100,000 population for mortality and 330.5 (289.0-378.6) per 100,000 population for incidence. From 2015 to 2019, the number of all-form TB deaths decreased by 11.8% (2.3-21.1), while the age-standardized all-form TB incidence rate decreased by 11.1% (6.3-15.6). Among individuals without HIV coinfection in 2019, alcohol use accounted for 28.1% (18.2-37.9) of TB deaths, smoking accounted for 27.0% (20.2-33.3), and diabetes accounted for 12.5% (7.1-19.0). Removing the combined effects of these risk factors would reduce all-form TB deaths by 54.2% (44.2-62.2).

DISCUSSION

Despite significant progress in reducing TB morbidity and mortality since 1990, Cambodia is not on track to achieve the 2020 WHO End TB interim milestones. Existing programs in Cambodia can benefit from liaising with risk factor control initiatives to accelerate progress toward eliminating TB in Cambodia.

A role for community-level socioeconomic indicators in targeting tuberculosis screening interventions

Tuberculosis screening programs commonly target areas with high case notification rates. However, this may exacerbate disparities by excluding areas that already face barriers to accessing diagnostic services. We compared historic case notification rates, demographic, and socioeconomic indicators as predictors of neighborhood-level tuberculosis screening yield during a mobile screening program in 74 neighborhoods in Lima, Peru. We used logistic regression and Classification and Regression Tree (CART) analysis to identify predictors of screening yield. During February 7, 2019-February 6, 2020, the program screened 29,619 people and diagnosed 147 tuberculosis cases. Historic case notification rate was not associated with screening yield in any analysis. In regression analysis, screening yield decreased as the percent of vehicle ownership increased (odds ratio [OR]: 0.76 per 10% increase in vehicle ownership; 95% confidence interval [CI]: 0.58-0.99). CART analysis identified the percent of blender ownership (≤ 83.1% vs > 83.1%; OR: 1.7; 95% CI: 1.2-2.6) and the percent of TB patients with a prior tuberculosis episode (> 10.6% vs ≤ 10.6%; OR: 3.6; 95% CI: 1.0-12.7) as optimal predictors of screening yield. Overall, socioeconomic indicators were better predictors of tuberculosis screening yield than historic case notification rates. Considering community-level socioeconomic characteristics could help identify high-yield locations for screening interventions.

Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis

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.

Determining the value of TB active case-finding: current evidence and methodological considerations

Active case-finding (ACF) is an important component of the End TB Strategy. However, ACF is resource-intensive, and the economics of ACF are not well-understood. Data on the costs of ACF are limited, with little consistency in the units and methods used to estimate and report costs. Mathematical models to forecast the long-term effects of ACF require empirical measurements of the yield, timing and costs of case detection. Pragmatic trials offer an opportunity to assess the cost-effectiveness of ACF interventions within a 'real-world´ context. However, such analyses generally require early introduction of economic evaluations to enable prospective data collection on resource requirements. Closing the global case-detection gap will require substantial additional resources, including continued investment in innovative technologies. Research is essential to the optimal implementation, cost-effectiveness, and affordability of ACF in high-burden settings. To assess the value of ACF, we must prioritize the collection of high-quality data regarding costs and effectiveness, and link those data to analytical models that are adapted to local settings.

Trade-offs between cost and accuracy in active case finding for tuberculosis: A dynamic modelling analysis

BACKGROUND

Active case finding (ACF) may be valuable in tuberculosis (TB) control, but questions remain about its optimum implementation in different settings. For example, smear microscopy misses up to half of TB cases, yet is cheap and detects the most infectious TB cases. What, then, is the incremental value of using more sensitive and specific, yet more costly, tests such as Xpert MTB/RIF in ACF in a high-burden setting?

METHODS AND FINDINGS

We constructed a dynamic transmission model of TB, calibrated to be consistent with an urban slum population in India. We applied this model to compare the potential cost and impact of 2 hypothetical approaches following initial symptom screening: (i) 'moderate accuracy' testing employing a microscopy-like test (i.e., lower cost but also lower accuracy) for bacteriological confirmation and (ii) 'high accuracy' testing employing an Xpert-like test (higher cost but also higher accuracy, while also detecting rifampicin resistance). Results suggest that ACF using a moderate-accuracy test could in fact cost more overall than using a high-accuracy test. Under an illustrative budget of US$20 million in a slum population of 2 million, high-accuracy testing would avert 1.14 (95% credible interval 0.75-1.99, with p = 0.28) cases relative to each case averted by moderate-accuracy testing. Test specificity is a key driver: High-accuracy testing would be significantly more impactful at the 5% significance level, as long as the high-accuracy test has specificity at least 3 percentage points greater than the moderate-accuracy test. Additional factors promoting the impact of high-accuracy testing are that (i) its ability to detect rifampicin resistance can lead to long-term cost savings in second-line treatment and (ii) its higher sensitivity contributes to the overall cases averted by ACF. Amongst the limitations of this study, our cost model has a narrow focus on the commodity costs of testing and treatment; our estimates should not be taken as indicative of the overall cost of ACF. There remains uncertainty about the true specificity of tests such as smear and Xpert-like tests in ACF, relating to the accuracy of the reference standard under such conditions.

CONCLUSIONS

Our results suggest that cheaper diagnostics do not necessarily translate to less costly ACF, as any savings from the test cost can be strongly outweighed by factors including false-positive TB treatment, reduced sensitivity, and foregone savings in second-line treatment. In resource-limited settings, it is therefore important to take all of these factors into account when designing cost-effective strategies for ACF.

The TB REACH Initiative: Supporting TB Elimination Efforts in the Asia-Pacific

After many years of TB 'control' and incremental progress, the TB community is talking about ending the disease, yet this will only be possible with a shift in the way we approach the TB response. While the Asia-Pacific region has the highest TB burden worldwide, it also has the opportunity to lead the quest to end TB by embracing the four areas laid out in this series: using data to target hotspots, initiating active case finding, provisioning preventive TB treatment, and employing a biosocial approach. The Stop TB Partnership's TB REACH initiative provides a platform to support partners in the development, evaluation and scale-up of new and innovative technologies and approaches to advance TB programs. We present several approaches TB REACH is taking to support its partners in the Asia-Pacific and globally to advance our collective response to end TB.

Islands of Tuberculosis Elimination: An Evaluation of Community-Based Active Case Finding in North Sumatra, Indonesia

Community-based active case finding (ACF) is needed to reach key/vulnerable populations with limited access to tuberculosis (TB) care. Published reports of ACF interventions in Indonesia are scarce. We conducted an evaluation of a multicomponent community-based ACF intervention as it scaled from one district to nine in Nias and mainland North Sumatra. Community and health system support measures including laboratory strengthening, political advocacy, sputum transport, and community awareness were instituted. ACF was conducted in three phases: pilot (18 months, 1 district), intervention (12 months, 4 districts) and scale-up (9 months, 9 districts). The pilot phase identified 215 individuals with bacteriologically positive (B+) TB, representing 42% of B+ TB notifications. The intervention phase yielded 509, representing 54% of B+ notifications and the scale-up phase identified 1345 individuals with B+ TB (56% of notifications). We observed large increases in B+ notifications on Nias, but no overall change on the mainland despite district variation. Overall, community health workers screened 377,304 individuals of whom 1547 tested positive, and 95% were initiated on treatment. Our evaluation shows that multicomponent community-based ACF can reduce the number of people missed by TB programs. Community-based organizations are best placed for accessing and engaging hard to reach populations and providing integrated support which can have a large positive effect on TB notifications.

Mobilising community networks for early identification of tuberculosis and treatment initiation in Cambodia: an evaluation of a seed-and-recruit model

Background and objectives

The effects of active case finding (ACF) models that mobilise community networks for early identification and treatment of tuberculosis (TB) remain unknown. We investigated and compared the effect of community-based ACF using a seed-and-recruit model with one-off roving ACF and passive case finding (PCF) on the time to treatment initiation and identification of bacteriologically confirmed TB.

Methods

In this retrospective cohort study conducted in 12 operational districts in Cambodia, we assessed relationships between ACF models and: 1) the time to treatment initiation using Cox proportional hazards regression; and 2) the identification of bacteriologically confirmed TB using modified Poisson regression with robust sandwich variance.

Results

We included 728 adults with TB, of whom 36% were identified via the community-based ACF using a seed-and-recruit model. We found community-based ACF using a seed-and-recruit model was associated with shorter delay to treatment initiation compared to one-off roving ACF (hazard ratio 0.81, 95% CI 0.68-0.96). Compared to one-off roving ACF and PCF, community-based ACF using a seed-and-recruit model was 45% (prevalence ratio (PR) 1.45, 95% CI 1.19-1.78) and 39% (PR 1.39, 95% CI 0.99-1.94) more likely to find and detect bacteriologically confirmed TB, respectively.

Conclusion

Mobilising community networks to find TB cases was associated with early initiation of TB treatment in Cambodia. This approach was more likely to find bacteriologically confirmed TB cases, contributing to the reduction of risk of transmission within the community.

Effect of community active case-finding strategies for detection of tuberculosis in Cambodia: study protocol for a pragmatic cluster randomized controlled trial

Background

Cambodia has made notable progress in the fight against tuberculosis (TB). However, these gains are impeded by a significant proportion of undiagnosed cases. To effectively reach people with TB, active case-finding (ACF) strategies have been adopted by countries affected by the epidemic, including Cambodia, alongside passive case finding (PCF). Despite increased efforts to improve case detection, approximately 40% of TB cases in Cambodia remained undiagnosed in 2018. In Cambodia, several community-based TB ACF modalities have been implemented, but their effectiveness has yet to be systematically assessed.

Methods

This pragmatic cluster randomized controlled trial will be conducted between December 2019 and June 2021. We will randomize eight operational districts (clusters) in seven provinces (Kampong Cham, Kampong Thom, Prey Veng, Thbong Khmum, Kampong Chhnang, Kandal, and Kampong Speu) to either the control group (PCF) or the intervention groups (ACF using a seed-and-recruit model, ACF targeting household and neighborhood contacts, and ACF targeting persons aged ≥ 55 years using mobile screening units). The primary endpoints will be TB case notification rates, additionality, and cumulative yield of TB cases. The secondary endpoints include treatment outcomes, the number needed to screen to find one TB case, and cost-effectiveness outcome measures. We will analyze the primary and secondary endpoints by intention to treat. We will compare cluster and individual-level characteristics using Student’s t test and hierarchical or mixed-effect models to estimate the ratio of these means. The incremental cost-effectiveness ratio per disability-adjusted life year averted will also be considered as a benchmark to determine whether the interventions are cost-effective.

Discussion

This study will build an evidence base to inform future scale-up, implementation, and sustainability of ACF strategies in Cambodia and other similar settings. Implementation of this study will also complement TB control strategies in Cambodia by conducting ACF in operational districts without active interventions to find TB cases currently. Those who are ill and might have TB will be promptly screened, diagnosed, and linked to care. Early diagnosis and treatment initiation will also benefit their community by interrupting transmission and prevent further infections. The experience gained from this project will inform future attempts in conducting pragmatic trials in low-resource settings.

Trial registration

ClinicalTrials.gov, NCT04094350. Registered on 18 September 2019.

Can the High Sensitivity of Xpert MTB/RIF Ultra Be Harnessed to Save Cartridge Costs? Results from a Pooled Sputum Evaluation in Cambodia

Despite the World Health Organization recommending the use of rapid molecular tests for diagnosing tuberculosis (TB), uptake has been limited, partially due to high cartridge costs. Other infectious disease programs pool specimens to save on diagnostic test costs. We tested a sputum pooling strategy as part of a TB case finding program using Xpert MTB/RIF Ultra (Ultra). All persons were tested with Ultra individually, and their remaining specimens were also grouped with 3–4 samples for testing in a pooled sample. Individual and pooled testing results were compared to see if people with TB would have been missed when using pooling. We assessed the potential cost and time savings which different pooling strategies could achieve. We tested 584 individual samples and also grouped them in 153 pools for testing separately. Individual testing identified 91 (15.6%) people with positive Ultra results. One hundred percent of individual positive results were also found to be positive by the pooling strategy. Pooling would have saved 27% of cartridge and processing time. Our results are the first to use Ultra in a pooled approach for TB, and demonstrate feasibility in field conditions. Pooling did not miss any TB cases and can save time and money. The impact of pooling is only realized when yield is low.

Performance of algorithms for tuberculosis active case finding in underserved high-prevalence settings in Cambodia: a cross-sectional study

Background: Most studies evaluate active case findings (ACF) for bacteriologically confirmed TB. Adapted diagnostic approaches are needed to identify cases with lower bacillary loads. Objectives: To assess the likelihood of diagnosing all forms of TB, including clinically diagnosed pulmonary and extra-pulmonary TB, using different ACF algorithms in Cambodia. Methods: Clients were stratified into 'high-risk' (presumptive TB plus TB contact, or history of TB, or presumptive HIV infection; n = 12,337) and 'moderate-risk' groups (presumptive TB; n = 28,804). Sputum samples were examined by sputum smear microscopy (SSM) or Xpert MTB/RIF (Xpert). Initially, chest X-ray using a mobile radiography unit was a follow-up test after a negative sputum examination [algorithms A (Xpert/X-ray) and B (SSM/X-ray)]. Subsequently, all clients received an X-ray [algorithms C (X-ray+Xpert) and D (Xray+SSM/Xpert)]. X-rays were interpreted on the spot. Results: Between 25 August 2014 and 31 March 2016, 2217 (5.4%) cases with all forms of TB cases were diagnosed among 41,141 adults. The majority of TB cases (1488; 67.1%) were diagnosed using X-ray. When X-rays were taken and interpreted the same day the sputum was collected, same-day diagnosis more than doubled. Overall, the number needed to test (NNT) to diagnose one case was 18.6 (95%CI:17.9-19.2). In the high-risk group the NNT was lower [algorithm D: NNT = 17.3(15.9-18.9)] compared with the 'moderate-risk group' [algorithm D: NNT = 20.8(19.6-22.2)]. In the high-risk group the NNT was lower when using Xpert as an initial test [algorithm A: NNT = 12.2(10.8-13.9) or algorithm C: NNT = 11.2(9.6-13.0)] compared with Xpert as a follow-up test [algorithm D: NNT = 17.3(15.9-18.9)]. Conclusion: To diagnose all TB forms, X-ray should be part of the diagnostic algorithm. The combination of X-ray and Xpert testing for high-risk clients was the most effective ACF approach in this setting.