Cough has been one of the most sensitive symptoms that is suggestive of tuberculosis in passive as well as ACF for TB. WHO’s screening guidelines reported a sensitivity of 42% for cough among HIV-negative individuals, and this finding corroborates with our survey (41.6%)3. However, this was lower than the prevalence survey from Kenya [52 (95% CI: 41–63)] which was done between 2005 and 0710. The specificity (72.8%) of cough in our survey was also lower than that of the Kenyan survey (89% (95% CI: 88–90)10. The Kenyan TB prevalence survey reported a sensitivity of 90% (95% CI:84–95) and a specificity of 32% (95% CI: 30–34) for the presence of any one symptom suggestive of TB. While we estimated a lower sensitivity (55.2%) than the Kenyan survey, our specificity was higher (50.9%) for any one symptom suggestive of TB. This is likely attributable to the fact that the survey conducted in Kenya included a substantial number of individuals who were HIV positive or had an unknown HIV status. Our sensitivity estimation for the presence of any one symptom is very similar to the survey conducted in Myanmar (59.8%), while the specificity (50.9%) is lower than that of the Myanmar survey (67.2%)8. A Cochrane review of 31 studies done among participants who were screened for tuberculosis estimated sensitivity of 70.6% (95% CI:61.7–78.2%) and specificity of 65.1% (95% CI:53.3–75.4%) for any tuberculosis symptoms. Symptom screening is the most simple screening tool that can be used even in limited resource settings. Though this tool is considered to have low accuracy, we found the specificity of symptom screening was higher than that of CXR11.
The sensitivity (87.25%) of CXR in our survey was almost similar to the Kenyan survey. However, the specificity (90.62%) was lower. CXR has been widely used in ACF and prevalence surveys and is known to be a screening tool with high sensitivity10. The aforesaid Cochrane review also included 19 studies and estimated sensitivity and specificity of 84.8% (95% CI:76.7–90.4) and 95.6% (95% CI: 92.6–97.4), respectively, for CXR abnormalities suggestive of TB11. Though CXR sensitivity in our survey was higher, the specificity was lower than in this review. When symptom screening was combined with CXR, it increased the sensitivity (98.04%) significantly at the cost of reduction in specificity. This indicates that CXR and symptom screening combined can significantly reduce false negatives in the screening population. Though CXR is a good screening test, it has a few limitations, such as the necessity for radiation-shielded vehicles, the higher cost associated with acquiring and upkeeping the vehicles and X-ray equipment, the availability of technicians; the crucial requirement for medical officers or radiologists to interpret CXR results; the inconsistencies in reporting between different observers and even within the same observer; and the exposure of apparently healthy individuals to radiation. However, these limitations and barriers may be surmounted by the recent advancements in portable X-ray machines and artificial intelligence to facilitate reporting. Given that our results unequivocally indicate that it is beneficial to allocate resources toward advanced CXR technology in both ACF and prevalence surveys.
We estimated a sensitivity of 71.88% (95% CI: 61.7–80.5) for Xpert MTB/RIF performed in the survey van. This is higher than the sensitivity of Xpert MTB/RIF in the recent prevalence surveys conducted in Kenya (69%), the Philippines (69%) and Vietnam (68%)7. However, our sensitivity estimate is lower than that of Bangladesh’s 84% (95% CI: 78–84) prevalence survey. The specificities of Xpert MTB/RIF from these surveys corroborate our findings. The sensitivity of the molecular test in the reference laboratory was significantly higher than the survey van [96.55% (95% CI: 88.0 −99.5)]. We could postulate several reasons for the decrease in sensitivity, such as temperature in the field setting, and environmental factors. The pooled sensitivity of Xpert Ultra (78%, 95% CI: 69–84%) was higher than Xpert MTB/RIF (73%, 95% CI: 62–82%) from the recent prevalence surveys (South Africa, Myanmar, Lesotho, and Zambia) conducted between 2017 and 20197. Future surveys should also consider WHO-recommended low complexity automated NAATs such as Truenat MTB Plus and Xpert Ultra to increase the yield in the survey as they have shown better performance in the healthcare setting and passive case finding. It is worthwhile to note that though smear microscopy had the lowest sensitivity (53.13% 95% CI: 42.6–63.9), it had the highest specificity (99.78% 95% CI: 99.6–99.8) among all the diagnostic tests. This implies that smear microscopy could still be used to confirm a diagnosis when molecular tests are unavailable in resource-limited settings.
When we combined our screening test and diagnostic tests, there was a significant increase in sensitivity with a reduction in specificity. However, our screening and diagnostic approach yielded a negative predictive value of 99.8%. Our participants underwent highly sensitive screening tests followed by a highly specific confirmatory test. CXR yielded a significant proportion of false positive results, which were later eliminated by the Xpert MTB/RIF. The possible reasons could be that the survey was done during and immediately after COVID-19, and the CXR was interpreted by both trained medical officers and specialists. The primary objective of the prevalence survey is to identify the true prevalence in the community, including cases that were not routinely detected by the passive case finding through healthcare system. This will enable us to estimate the prevalence notification gap, which is an initial step of the TB care cascade and an indicator of the efficiency of the TB management system in the state. Conducting an initial screening combining symptoms and chest X-rays may significantly reduce the likelihood of false negative results and enable participants to proceed with the specified diagnostic tests. We found that the True positive rate was significantly higher when all the tests were combined. We also noted similar estimates when the molecular test was replaced with smear microscopy.
