Sensitivity and specificity of tuberculosis signs and symptoms screening and adjunct role of social pathology characteristics in predicting bacteriologically confirmed tuberculosis in Myanmar

Predictors contributing to the estimation of pulmonary tuberculosis among adults in a resource-limited setting: A systematic review of diagnostic predictions

Background

Although tuberculosis is highly prevalent in low- and middle-income countries, millions of cases remain undetected using current diagnostic methods. To address this problem, researchers have proposed prediction rules.

Objective

We analyzed existing prediction rules for the diagnosis of pulmonary tuberculosis and identified factors with a moderate to high strength of association with the disease.

Methods

We conducted a comprehensive search of relevant databases (MEDLINE/PubMed, Cochrane Library, Science Direct, Global Health for Reports, and Google Scholar) up to 14 November 2022. Studies that developed diagnostic algorithms for pulmonary tuberculosis in adults from low and middle-income countries were included. Two reviewers performed study screening, data extraction, and quality assessment. The study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. We performed a narrative synthesis.

Results

Of the 26 articles selected, only half included human immune deficiency virus-positive patients. In symptomatic human immune deficiency virus patients, radiographic findings and body mass index were strong predictors of pulmonary tuberculosis, with an odds ratio of >4. However, in human immune deficiency virus-negative individuals, the biomarkers showed a moderate association with the disease. In symptomatic human immune deficiency virus patients, a C-reactive protein level ⩾10 mg/L had a sensitivity and specificity of 93% and 40%, respectively, whereas a trial of antibiotics had a specificity of 86% and a sensitivity of 43%. In smear-negative patients, anti-tuberculosis treatment showed a sensitivity of 52% and a specificity of 63%.

Conclusions

The performance of predictors and diagnostic algorithms differs among patient subgroups, such as in human immune deficiency virus-positive patients, radiographic findings, and body mass index were strong predictors of pulmonary tuberculosis. However, in human immune deficiency virus-negative individuals, the biomarkers showed a moderate association with the disease. A few models have reached the World Health Organization's recommendation. Therefore, more work should be done to strengthen the predictive models for tuberculosis screening in the future, and they should be developed rigorously, considering the heterogeneity of the population in clinical work.

Population-wide active case finding as a strategy to end TB

Tuberculosis (TB) is the leading infectious cause of morbidity and mortality globally. Despite available tools for preventing, finding, and treating TB, many people with TB remain undiagnosed. In high-incidence settings, TB transmission is ubiquitous within the community, affecting both high-risk groups and the general population. In fact, most people who develop TB come from the general population. To disrupt the chain of transmission that sustains the TB epidemic, we need to find and treat everyone with infectious TB as early as possible, including those with minimal symptoms or subclinical TB who are unlikely to present for care. Important elements of an effective active case-finding strategy include effective social mobilisation and community engagement, using sensitive screening tools that can be used at scale, and embracing population-wide screening in high-incidence ('hot spot') areas. We require a better description of feasible delivery models, 'real-life' impact and cost effectiveness to enable wider implementation.

Active Tuberculosis Screening via a Mobile Health App in Myanmar: Incremental Cost-Effectiveness Evaluation

BACKGROUND

A mobile app that calculates a tuberculosis (TB) risk score based on individual social and pathological characteristics has been shown to be a better predictor of the risk of contracting TB than conventionally used TB signs and symptoms (TBSS) in Myanmar, where the TB burden is high. Its cost-effectiveness, however, has not yet been assessed.

OBJECTIVE

This study aimed to determine the incremental costs of this mobile app and of chest x-rays (CXRs) in averting disability-adjusted life years (DALYs) among missed cases of active TB in the population being screened.

METHODS

Elements of incremental costs and effectiveness of 3 initial TB screening strategies were examined, including TBSS followed by CXR, the mobile app followed by CXR, and universal CXR. The incremental cost-effectiveness ratio (ICER; ie, the additional cost for each additional DALY averted) was compared to TBSS screening. Based on the latest 2020 gross domestic product (GDP) per capita of Myanmar (US $1477.50), the ICER was compared to willingness-to-pay (WTP) thresholds of 1, 2, and 3 times the GDP per capita. Probabilistic sensitivity analysis was conducted with a Monte Carlo simulation to compute the levels of probability that the ICER for each strategy was below each WTP threshold.

RESULTS

For each 100,000 population, the incremental cost compared to TBSS of active TB screening was US $345,942 for the mobile app and US $1,810,712 for universal CXR. The incremental effectiveness was 325 DALYs averted for the mobile app and 576 DALYs averted for universal CXR. For the mobile app, the estimated ICER was US $1064 (72% of GDP per capita) per 1 DALY averted. Furthermore, 100% of the simulated values were below an additional cost of 1 times the GDP per capita for 1 additional DALY averted. The universal CXR strategy has an estimated ICER of US $3143 (2.1 times the GDP per capita) per 1 DALY averted and an additional 77.2% DALYs averted compared to the app (ie, 576 - 325 / 325 DALYs); however, 0.5% of the simulated values were higher than an additional expenditure of 3 times the GDP per capita.

CONCLUSIONS

Based on the status of the economy in 2020, the mobile app strategy is affordable for Myanmar. The universal CXR strategy, although it could prevent an additional 77% of DALYs, is probably unaffordable. Compared to the TBSS strategy, the mobile app system based on social and pathological characteristics of TB has potential as a TB screening tool to identify missing TB cases and to reduce TB morbidity and mortality, thereby helping to achieve the global goal of "End TB" in resource-limited settings with a high TB burden.

Simple tuberculosis screening tool using signs, symptoms, and risk factors to reduce the missed opportunity in the older population

Background

There is a growing concern on how to increase tuberculosis (TB) case detection in resource-poor settings. The healthcare facilities routinely providing services to the elderly for chronic diseases often failed to detect TB cases, causing a missed opportunity. This study aimed to develop a simple and sensitive screening tool using signs, symptoms, and risk factors for TB case detection in the elderly.

Methods

This was a cross-sectional study conducted from August to December 2020. A random sample of 302 subjects was taken from the elderly aged ≥ 60 years attending the outpatient polyclinic at Mangunharjo hospital, Madiun, East Java, Indonesia, for a chronic disease problem. The test was developed using 16 TB signs, symptoms, and risk factors. Test performance was assessed by comparing it against the GeneXpert MTB/RIF.

Results

Marginal analysis resulted in the optimal cut-point of ≥ 7 for the test, which gave an area under the curve (AUC) of 0.62, with the maximum marginal AUC of 0.04 (p < 0.001). The sensitivity and specificity were 60.26% and 64.29%, meaning 60 TB cases for every 100 elderly with TB would be otherwise left undetected if this screening test did not take place.

Conclusion

A simple TB screening tool with moderate sensitivity and AUC has been developed using TB signs, symptoms, and risk factors. It can be used as an initial step of the systematic TB screening in the elderly visiting healthcare facilities for routine chronic disease examination, with the additional utility of reducing the missed opportunity.

Usability of a mobile health application in tuberculosis screening and compliance to perform chest x-ray examination among presumptive cases detected by the app in Myanmar (Preprint)

Background

In Myanmar, the use of a mobile app for tuberculosis (TB) screening and its operational effect on seeking TB health care have not been evaluated yet.

Objective

This study aims to report the usability of a simple mobile app to screen TB and comply with chest X-ray (CXR) examination of presumptive cases detected by the app.

Methods

A new “TB-screen” app was developed from a Google Sheet based on a previously published algorithm. The app calculates a TB risk propensity score from an individual’s sociodemographic characteristics and TB clinical history and suggests whether the individual should undergo a CXR. The screening program was launched in urban slum areas soon after the COVID-19 outbreak subsided. A standard questionnaire was used to assess the app’s usability rated by presumptive cases. Compliance to undergo CXR was confirmed by scanning the referral quick response (QR) code via the app.

Results

Raters were 453 presumptive cases detected by the app. The mean usability rating score was 4.1 out of 5. Compliance to undergo CXR examination was 71.1% (n=322). Active TB case detection among CXR compliances was 7.5% (n=24). One standard deviation (SD) increase in the app usability score was significantly associated with a 59% increase in the odds to comply with CXR (β=.464) after adjusting for other variables (P<.001).

Conclusions

This simple mobile app got a high usability score rated by 453 users. The mobile app usability score successfully predicted compliance to undergo CXR examination. Eventually, 24 (7.5%) of 322 users who were suspected of having TB by the mobile app were detected as active TB cases by CXR. The system should be upscaled for a large trial.