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Investigating the impact of TB case-detection strategies and the consequences of false positive diagnosis through mathematical modelling
BACKGROUND: Increasing case notifications is one of the top programmatic priorities of National TB Control Programmes (NTPs). To find more cases, NTPs often need to consider expanding TB case-detection activities to populations with increasingly low prevalence of disease. Together with low-specifici...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6054844/ https://www.ncbi.nlm.nih.gov/pubmed/30031378 http://dx.doi.org/10.1186/s12879-018-3239-x |
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author | Lalli, Marek Hamilton, Matthew Pretorius, Carel Pedrazzoli, Debora White, Richard G. Houben, Rein M. G. J. |
author_facet | Lalli, Marek Hamilton, Matthew Pretorius, Carel Pedrazzoli, Debora White, Richard G. Houben, Rein M. G. J. |
author_sort | Lalli, Marek |
collection | PubMed |
description | BACKGROUND: Increasing case notifications is one of the top programmatic priorities of National TB Control Programmes (NTPs). To find more cases, NTPs often need to consider expanding TB case-detection activities to populations with increasingly low prevalence of disease. Together with low-specificity diagnostic algorithms, these strategies can lead to an increasingly high number of false positive diagnoses, which has important adverse consequences. METHODS: We apply TIME, a widely-used country-level model, to quantify the expected impact of different case-finding strategies under two scenarios. In the first scenario, we compare the impact of implementing two different diagnostic algorithms (higher sensitivity only versus higher sensitivity and specificity) to reach programmatic screening targets. In the second scenario, we examine the impact of expanding coverage to a population with a lower prevalence of disease. Finally, we explore the implications of modelling without taking into consideration the screening of healthy individuals. Outcomes considered were changes in notifications, the ratio of additional false positive to true positive diagnoses, the positive predictive value (PPV), and incidence. RESULTS: In scenario 1, algorithm A of prolonged cough and GeneXpert yielded fewer additional notifications compared to algorithm B of any symptom and smear microscopy (n = 4.0 K vs 13.8 K), relative to baseline between 2017 and 2025. However, algorithm A resulted in an increase in PPV, averting 2.4 K false positive notifications thus resulting in a more efficient impact on incidence. Scenario 2 demonstrated an absolute decrease of 11% in the PPV as intensified case finding activities expanded into low-prevalence populations without improving diagnostic accuracy, yielding an additional 23 K false positive diagnoses for an additional 1.3 K true positive diagnoses between 2017 and 2025. Modelling the second scenario without taking into account screening amongst healthy individuals overestimated the impact on cases averted by a factor of 6. CONCLUSION: Our findings show that total notifications can be a misleading indicator for TB programme performance, and should be interpreted carefully. When evaluating potential case-finding strategies, NTPs should consider the specificity of diagnostic algorithms and the risk of increasing false-positive diagnoses. Similarly, modelling the impact of case-finding strategies without taking into account potential adverse consequences can overestimate impact and lead to poor strategic decision-making. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12879-018-3239-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6054844 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-60548442018-07-23 Investigating the impact of TB case-detection strategies and the consequences of false positive diagnosis through mathematical modelling Lalli, Marek Hamilton, Matthew Pretorius, Carel Pedrazzoli, Debora White, Richard G. Houben, Rein M. G. J. BMC Infect Dis Research Article BACKGROUND: Increasing case notifications is one of the top programmatic priorities of National TB Control Programmes (NTPs). To find more cases, NTPs often need to consider expanding TB case-detection activities to populations with increasingly low prevalence of disease. Together with low-specificity diagnostic algorithms, these strategies can lead to an increasingly high number of false positive diagnoses, which has important adverse consequences. METHODS: We apply TIME, a widely-used country-level model, to quantify the expected impact of different case-finding strategies under two scenarios. In the first scenario, we compare the impact of implementing two different diagnostic algorithms (higher sensitivity only versus higher sensitivity and specificity) to reach programmatic screening targets. In the second scenario, we examine the impact of expanding coverage to a population with a lower prevalence of disease. Finally, we explore the implications of modelling without taking into consideration the screening of healthy individuals. Outcomes considered were changes in notifications, the ratio of additional false positive to true positive diagnoses, the positive predictive value (PPV), and incidence. RESULTS: In scenario 1, algorithm A of prolonged cough and GeneXpert yielded fewer additional notifications compared to algorithm B of any symptom and smear microscopy (n = 4.0 K vs 13.8 K), relative to baseline between 2017 and 2025. However, algorithm A resulted in an increase in PPV, averting 2.4 K false positive notifications thus resulting in a more efficient impact on incidence. Scenario 2 demonstrated an absolute decrease of 11% in the PPV as intensified case finding activities expanded into low-prevalence populations without improving diagnostic accuracy, yielding an additional 23 K false positive diagnoses for an additional 1.3 K true positive diagnoses between 2017 and 2025. Modelling the second scenario without taking into account screening amongst healthy individuals overestimated the impact on cases averted by a factor of 6. CONCLUSION: Our findings show that total notifications can be a misleading indicator for TB programme performance, and should be interpreted carefully. When evaluating potential case-finding strategies, NTPs should consider the specificity of diagnostic algorithms and the risk of increasing false-positive diagnoses. Similarly, modelling the impact of case-finding strategies without taking into account potential adverse consequences can overestimate impact and lead to poor strategic decision-making. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12879-018-3239-x) contains supplementary material, which is available to authorized users. BioMed Central 2018-07-21 /pmc/articles/PMC6054844/ /pubmed/30031378 http://dx.doi.org/10.1186/s12879-018-3239-x Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Lalli, Marek Hamilton, Matthew Pretorius, Carel Pedrazzoli, Debora White, Richard G. Houben, Rein M. G. J. Investigating the impact of TB case-detection strategies and the consequences of false positive diagnosis through mathematical modelling |
title | Investigating the impact of TB case-detection strategies and the consequences of false positive diagnosis through mathematical modelling |
title_full | Investigating the impact of TB case-detection strategies and the consequences of false positive diagnosis through mathematical modelling |
title_fullStr | Investigating the impact of TB case-detection strategies and the consequences of false positive diagnosis through mathematical modelling |
title_full_unstemmed | Investigating the impact of TB case-detection strategies and the consequences of false positive diagnosis through mathematical modelling |
title_short | Investigating the impact of TB case-detection strategies and the consequences of false positive diagnosis through mathematical modelling |
title_sort | investigating the impact of tb case-detection strategies and the consequences of false positive diagnosis through mathematical modelling |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6054844/ https://www.ncbi.nlm.nih.gov/pubmed/30031378 http://dx.doi.org/10.1186/s12879-018-3239-x |
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