Paulo César Pereira Dos Santos1, Andrea da Silva Santos1, Roberto Dias de Oliveira2, Bruna Oliveira da Silva1, Thiego Ramon Soares1, Leonardo Martinez3, Renu Verma4, Jason R Andrews4, Julio Croda5. 1. Faculty of Health Sciences, Federal University of Grande Dourados, Dourados, Mato Grosso do Sul, Brazil. 2. School of Nursing, State University of Mato Grosso do Sul, Dourados, Mato Grosso do Sul, Brazil. 3. Department of Epidemiology, School of Public Health, Boston University, Boston, Massachusetts, USA. 4. Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA. 5. Oswaldo Cruz Foundation, Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.
Abstract
BACKGROUND: Although systematic tuberculosis screening in high-risk groups is recommended by the World Health Organization (WHO), implementation in prisons has been limited due to resource constraints. Whether Xpert Ultra sputum pooling could be a sensitive and efficient approach to mass screening in prisons is unknown. METHODS: In total, 1280 sputum samples were collected from incarcerated individuals in Brazil during mass screening and tested using Xpert G4. We selected samples for mixing in pools of 4, 8, 12, and 16, which were then tested using Ultra. In each pool, a single positive sample of differing Xpert mycobacterial loads was used. Additionally, 10 pools of 16 negative samples each were analyzed as controls. We then simulated tuberculosis screening at prevalences of 0.5-5% and calculated the cost per tuberculosis case detected at different sputum pooling sizes. RESULTS: The sensitivity and specificity of sputum pooling were high (sensitivity: 94%; 95% confidence interval [CI]: 88-98; specificity: 100%, 95% CI: 84-100). Sensitivity was greater in pools in which the positive sample had a high mycobacterial load compared to those that were very low (100% vs 88%). In settings with a higher tuberculosis prevalence, pools of 4 and 8 were more efficient than larger pool sizes. Larger pools decreased the costs by 87% at low prevalences, whereas smaller pools led to greater cost savings at higher prevalence at higher prevalences (57%). CONCLUSIONS: Sputum pooling using Ultra was a sensitive strategy for tuberculosis screening. This approach was more efficient than individual testing across a broad range of simulated tuberculosis prevalence settings and could enable active case finding to be scaled while containing costs.
BACKGROUND: Although systematic tuberculosis screening in high-risk groups is recommended by the World Health Organization (WHO), implementation in prisons has been limited due to resource constraints. Whether Xpert Ultra sputum pooling could be a sensitive and efficient approach to mass screening in prisons is unknown. METHODS: In total, 1280 sputum samples were collected from incarcerated individuals in Brazil during mass screening and tested using Xpert G4. We selected samples for mixing in pools of 4, 8, 12, and 16, which were then tested using Ultra. In each pool, a single positive sample of differing Xpert mycobacterial loads was used. Additionally, 10 pools of 16 negative samples each were analyzed as controls. We then simulated tuberculosis screening at prevalences of 0.5-5% and calculated the cost per tuberculosis case detected at different sputum pooling sizes. RESULTS: The sensitivity and specificity of sputum pooling were high (sensitivity: 94%; 95% confidence interval [CI]: 88-98; specificity: 100%, 95% CI: 84-100). Sensitivity was greater in pools in which the positive sample had a high mycobacterial load compared to those that were very low (100% vs 88%). In settings with a higher tuberculosis prevalence, pools of 4 and 8 were more efficient than larger pool sizes. Larger pools decreased the costs by 87% at low prevalences, whereas smaller pools led to greater cost savings at higher prevalence at higher prevalences (57%). CONCLUSIONS: Sputum pooling using Ultra was a sensitive strategy for tuberculosis screening. This approach was more efficient than individual testing across a broad range of simulated tuberculosis prevalence settings and could enable active case finding to be scaled while containing costs.
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