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Community-based screening for tuberculosis (TB) could improve detection but is resource intensive. We set out to evaluate the accuracy of computer-aided TB screening using digital chest X-ray (CXR) to determine if this approach met target product profiles (TPP) for community-based screening. CXR images from participants in the 2016 Kenya National TB Prevalence Survey were evaluated using CAD4TBv6 (Delft Imaging), giving a probabilistic score for pulmonary TB ranging from 0 (low probability) to 99 (high probability). We constructed a Bayesian latent class model to estimate the accuracy of CAD4TBv6 screening compared to bacteriologically-confirmed TB across CAD4TBv6 threshold cut-offs, incorporating data on Clinical Officer CXR interpretation, participant demographics (age, sex, TB symptoms, previous TB history), and sputum results. We compared model-estimated sensitivity and specificity of CAD4TBv6 to optimum and minimum TPPs. Of 63,050 prevalence survey participants, 61,848 (98%) had analysable CXR images, and 8,966 (14.5%) underwent sputum bacteriological testing; 298 had bacteriologically-confirmed pulmonary TB. Median CAD4TBv6 scores for participants with bacteriologically-confirmed TB were significantly higher (72, IQR: 58–82.75) compared to participants with bacteriologically-negative sputum results (49, IQR: 44–57, p<0.0001). CAD4TBv6 met the optimum TPP; with the threshold set to achieve a mean sensitivity of 95% (optimum TPP), specificity was 83.3%, (95% credible interval [CrI]: 83.0%—83.7%, CAD4TBv6 threshold: 55). There was considerable variation in accuracy by participant characteristics, with older individuals and those with previous TB having lowest specificity. CAD4TBv6 met the optimal TPP for TB community screening. To optimise screening accuracy and efficiency of confirmatory sputum testing, we recommend that an adaptive approach to threshold setting is adopted based on participant characteristics.

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BackgroundTo facilitate priority-setting in health policymaking, we compiled the best available information to estimate the adult mortality (>30 years) burden attributable to 13 metabolic, lifestyle, infectious, and environmental risk factors in Taiwan.MethodsWe obtained data on risk factor exposure from nationally representative health surveys, cause-specific mortality from the National Death Registry, and relative risks from epidemiological studies and meta-analyses. We applied the comparative risk assessment framework to estimate mortality burden attributable to individual risk factors or risk factor clusters.ResultsIn 2009, high blood glucose accounted for 14,900 deaths (95% UI: 11,850–17,960), or 10.4% of all deaths in that year. It was followed by tobacco smoking (13,340 deaths, 95% UI: 10,330–16,450), high blood pressure (11,190 deaths, 95% UI: 8,190–14,190), ambient particulate matter pollution (8,600 deaths, 95% UI: 7,370–9,840), and dietary risks (high sodium intake and low intake of fruits and vegetables, 7,890 deaths, 95% UI: 5,970–9,810). Overweight-obesity and physical inactivity accounted for 7,620 deaths (95% UI: 6,040–9,190), and 7,400 deaths (95% UI: 6,670–8,130), respectively. The cardiometabolic risk factors of high blood pressure, high blood glucose, high cholesterol, and overweight-obesity jointly accounted for 12,120 deaths (95% UI: 11,220–13,020) from cardiovascular diseases. For domestic risk factors, infections from hepatitis B virus (HBV) and hepatitis C virus (HCV) were responsible for 6,300 deaths (95% UI: 5,610–6,980) and 3,170 deaths (95% UI: 1,860–4,490), respectively, and betel nut use was associated with 1,780 deaths from oral, laryngeal, and esophageal cancer (95% UI: 1,190–2,360). The leading risk factors for years of life lost were similar, but the impact of tobacco smoking and alcohol use became larger because the attributable deaths from these risk factors occurred among young adults aged less than 60 years.ConclusionsHigh blood glucose, tobacco smoking, and high blood pressure are the major risk factors for deaths from diseases and injuries among Taiwanese adults. A large number of years of life would be gained if the 13 modifiable risk factors could be removed or reduced to the optimal level.Electronic supplementary materialThe online version of this article (doi:10.1186/s12963-017-0134-4) contains supplementary material, which is available to authorized users.

BackgroundCommunity-based screening for tuberculosis (TB) could improve detection but is resource intensive. We set out to evaluate the accuracy of computer-aided TB screening using digital chest X-ray (CXR) to determine if this approach met target product profiles (TPP) for community-based screening.MethodsCXR images from participants in the 2016 Kenya National TB Prevalence Survey were evaluated using CAD4TBv6 (Delft Imaging), giving a probabilistic score for pulmonary TB ranging from 0 (low probability) to 99 (high probability). We constructed a Bayesian latent class model to estimate the accuracy of CAD4TBv6 screening compared to bacteriologically-confirmed TB across CAD4TBv6 threshold cut-offs, incorporating data on Clinical Officer CXR interpretation, participant demographics (age, sex, TB symptoms, previous TB history), and sputum results. We compared model-estimated sensitivity and specificity of CAD4TBv6 to optimum and minimum TPPs.ResultsOf 63,050 prevalence survey participants, 61,848 (98%) had analysable CXR images, and 8,966 (14.5%) underwent sputum bacteriological testing; 298 had bacteriologically-confirmed pulmonary TB. Median CAD4TBv6 scores for participants with bacteriologically-confirmed TB were significantly higher (72, IQR: 58-82.75) compared to participants with bacteriologically-negative sputum results (49, IQR: 44-57, p<0.0001). CAD4TBv6 met the optimum TPP; with the threshold set to achieve a mean sensitivity of 95% (optimum TPP), specificity was 83.3%, (95% credible interval [CrI]: 83.0%—83.7%, CAD4TBv6 threshold: 55). There was considerable variation in accuracy by participant characteristics, with older individuals and those with previous TB having lowest specificity.ConclusionsCAD4TBv6 met the optimal TPP for TB community screening. To optimise screening accuracy and efficiency of confirmatory sputum testing, we recommend that an adaptive approach to threshold setting is adopted based on participant characteristics.Take home messageCAD4TBv6 met the optimal WHO target product profile for a community TB screening tool. Specificity was lower in adults with previous TB and those aged 41 years or older; an adaptive approach to setting CAD thresholds will likely be required to optimize use.

Objectives. To describe and compare 3 garbage code (GC) redistribution models: naïve Bayes classifier (NB), coarsened exact matching (CEM), and multinomial logistic regression (MLR). Methods. We analyzed Taiwan Vital Registration data (2008–2016) using a 2-step approach. First, we used non-GC death records to evaluate 3 different prediction models (NB, CEM, and MLR), incorporating individual-level information on multiple causes of death (MCDs) and demographic characteristics. Second, we applied the best-performing model to GC death records to predict the underlying causes of death. We conducted additional simulation analyses for evaluating the predictive performance of models. Results. When we did not account for MCDs, all 3 models presented high average misclassification rates in GC assignment (NB, 81%; CEM, 86%; MLR, 81%). In the presence of MCD information, NB and MLR exhibited significant improvement in assignment accuracy (19% and 17% misclassification rate, respectively). Furthermore, CEM without a variable selection procedure resulted in a substantially higher misclassification rate (40%). Conclusions. Comparing potential GC redistribution approaches provides guidance for obtaining better estimates of cause-of-death distribution and highlights the significance of MCD information for vital registration system reform.

COVID-19 may impact on tuberculosis (TB) diagnosis and care. We analysed a city-wide electronic TB register in Blantyre, Malawi and interviewed TB officers. Malawi had no official 'lockdown' but closed schools and borders on 23-March 2020. In interrupted time series analysis, there was an immediate 35.9% reduction in TB notifications (95% CI 22.0 to 47.3%) in April, which recovered to near pre-pandemic numbers by December 2020, but with 333 (95% CI 291 to 375) fewer cumulative notifications than anticipated. Women and girls were impacted (30.7% fewer cases, 95% CI 28.4 to 33.0%) more than men and boys (20.9% fewer, 95% CI 18.5 to 23.3). Fear of COVID-19 infection, temporary facility closure, inadequate protective equipment and COVID-19 stigma with similar presenting symptoms to TB were mentioned. Public health measures could benefit both TB and COVID-19, but only if diagnostic services remain accessible and are considered safe to attend.