Molecular Detection of Airborne Mycobacterium tuberculosis in South African High Schools.

Rationale: South African adolescents carry a high tuberculosis disease burden. It is not known if schools are high-risk settings for Mycobacterium tuberculosis (MTB) transmission. Objectives: To detect airborne MTB genomic DNA in classrooms. Methods: We studied 72 classrooms occupied by 2,262 students in two South African schools. High-volume air filtration was performed for median 40 (interquartile range [IQR], 35-54) minutes and assayed by droplet digital PCR (ddPCR)-targeting MTB region of difference 9 (RD9), with concurrent CO2 concentration measurement. Classroom data were benchmarked against public health clinics. Students who consented to individual tuberculosis screening completed a questionnaire and sputum collection (Xpert MTB/RIF Ultra) if symptom positive. Poisson statistics were used for MTB RD9 copy quantification. Measurements and Main Results: ddPCR assays were positive in 13/72 (18.1%) classrooms and 4/39 (10.3%) clinic measurements (P = 0.276). Median ambient CO2 concentration was 886 (IQR, 747-1223) ppm in classrooms versus 490 (IQR, 405-587) ppm in clinics (P < 0.001). Average airborne concentration of MTB RD9 was 3.61 copies per 180,000 liters in classrooms versus 1.74 copies per 180,000 liters in clinics (P = 0.280). Across all classrooms, the average risk of an occupant inhaling one MTB RD9 copy was estimated as 0.71% during one standard lesson of 35 minutes. Among 1,836/2,262 (81.2%) students who consented to screening, 21/90 (23.3%) symptomatic students produced a sputum sample, of which one was Xpert MTB/RIF Ultra positive. Conclusions: Airborne MTB genomic DNA was detected frequently in high school classrooms. Instantaneous risk of classroom exposure was similar to the risk in public health clinics.

Performance of diagnostic and predictive host blood transcriptomic signatures for Tuberculosis disease: A systematic review and meta-analysis.

INTRODUCTION: Host blood transcriptomic biomarkers have potential as rapid point-of-care triage, diagnostic, and predictive tests for Tuberculosis disease. We aimed to summarise the performance of host blood transcriptomic signatures for diagnosis of and prediction of progression to Tuberculosis disease; and compare their performance to the recommended World Health Organisation target product profile. METHODS: A systematic review and meta-analysis of the performance of host blood mRNA signatures for diagnosing and predicting progression to Tuberculosis disease in HIV-negative adults and adolescents, in studies with an independent validation cohort. Medline, Scopus, Web of Science, and EBSCO libraries were searched for articles published between January 2005 and May 2019, complemented by a search of bibliographies. Study selection, data extraction and quality assessment were done independently by two reviewers. Meta-analysis was performed for signatures that were validated in ≥3 comparable cohorts, using a bivariate random effects model. RESULTS: Twenty studies evaluating 25 signatures for diagnosis of or prediction of progression to TB disease in a total of 68 cohorts were included. Eighteen studies evaluated 24 signatures for TB diagnosis and 17 signatures met at least one TPP minimum performance criterion. Three diagnostic signatures were validated in clinically relevant cohorts to differentiate TB from other diseases, with pooled sensitivity 84%, 87% and 90% and pooled specificity 79%, 88% and 74%, respectively. Four studies evaluated signatures for progression to TB disease and performance of one signature, assessed within six months of TB diagnosis, met the minimal TPP for a predictive test for progression to TB disease. CONCLUSION: Host blood mRNA signatures hold promise as triage tests for TB. Further optimisation is needed if mRNA signatures are to be used as standalone diagnostic or predictive tests for therapeutic decision-making.

Burden of disease in adults admitted to hospital in a rural region of coastal Kenya: an analysis of data from linked clinical and demographic surveillance systems.

BACKGROUND: Estimates of the burden of disease in adults in sub-Saharan Africa largely rely on models of sparse data. We aimed to measure the burden of disease in adults living in a rural area of coastal Kenya with use of linked clinical and demographic surveillance data. METHODS: We used data from 18,712 adults admitted to Kilifi District Hospital (Kilifi, Kenya) between Jan 1, 2007, and Dec 31, 2012, linked to 790,635 person-years of observation within the Kilifi Health and Demographic Surveillance System, to establish the rates and major causes of admission to hospital. These data were also used to model disease-specific disability-adjusted life-years lost in the population. We used geographical mapping software to calculate admission rates stratified by distance from the hospital. FINDINGS: The main causes of admission to hospital in women living within 5 km of the hospital were infectious and parasitic diseases (303 per 100,000 person-years of observation), pregnancy-related disorders (239 per 100,000 person-years of observation), and circulatory illnesses (105 per 100,000 person-years of observation). Leading causes of hospital admission in men living within 5 km of the hospital were infectious and parasitic diseases (169 per 100,000 person-years of observation), injuries (135 per 100,000 person-years of observation), and digestive system disorders (112 per 100,000 person-years of observation). HIV-related diseases were the leading cause of disability-adjusted life-years lost (2050 per 100,000 person-years of observation), followed by non-communicable diseases (741 per 100,000 person-years of observation). For every 5 km increase in distance from the hospital, all-cause admission rates decreased by 11% (95% CI 7­14) in men and 20% (17­23) in women. The magnitude of this decline was highest for endocrine disorders in women (35%; 95% CI 22­46) and neoplasms in men (30%; 9­45). INTERPRETATION: Adults in rural Kenya face a combined burden of infectious diseases, pregnancy-related disorders, cardiovascular illnesses, and injuries. Disease burden estimates based on hospital data are affected by distance from the hospital, and the amount of underestimation of disease burden differs by both disease and sex. FUNDING: The Wellcome Trust, GAVI Alliance.