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Is aggregated surveillance data a reliable method for constructing tuberculosis care cascades? A secondary data analysis from Uganda

To accelerate tuberculosis (TB) control and elimination, reliable data is needed to improve the quality of TB care. We assessed agreement between a surveillance dataset routinely collected for Uganda’s national TB program and a high-fidelity dataset collected from the same source documents for a res...

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Autores principales: White, Elizabeth B., Hernández-Ramírez, Raúl U., Majwala, Robert Kaos, Nalugwa, Talemwa, Reza, Tania, Cattamanchi, Adithya, Katamba, Achilles, Davis, J. Lucian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045605/
https://www.ncbi.nlm.nih.gov/pubmed/36962541
http://dx.doi.org/10.1371/journal.pgph.0000716
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author White, Elizabeth B.
Hernández-Ramírez, Raúl U.
Majwala, Robert Kaos
Nalugwa, Talemwa
Reza, Tania
Cattamanchi, Adithya
Katamba, Achilles
Davis, J. Lucian
author_facet White, Elizabeth B.
Hernández-Ramírez, Raúl U.
Majwala, Robert Kaos
Nalugwa, Talemwa
Reza, Tania
Cattamanchi, Adithya
Katamba, Achilles
Davis, J. Lucian
author_sort White, Elizabeth B.
collection PubMed
description To accelerate tuberculosis (TB) control and elimination, reliable data is needed to improve the quality of TB care. We assessed agreement between a surveillance dataset routinely collected for Uganda’s national TB program and a high-fidelity dataset collected from the same source documents for a research study from 32 health facilities in 2017 and 2019 for six measurements: 1) Smear-positive and 2) GeneXpert-positive diagnoses, 3) bacteriologically confirmed and 4) clinically diagnosed treatment initiations, and the number of people initiating TB treatment who were also 5) living with HIV or 6) taking antiretroviral therapy. We measured agreement as the average difference between the two methods, expressed as the average ratio of the surveillance counts to the research data counts, its 95% limits of agreement (LOA), and the concordance correlation coefficient. We used linear mixed models to investigate whether agreement changed over time or was associated with facility characteristics. We found good overall agreement with some variation in the expected facility-level agreement for the number of smear positive diagnoses (average ratio [95% LOA]: 1.04 [0.38–2.82]; CCC: 0.78), bacteriologically confirmed treatment initiations (1.07 [0.67–1.70]; 0.82), and people living with HIV (1.11 [0.51–2.41]; 0.82). Agreement was poor for Xpert positives, with surveillance data undercounting relative to research data (0.45 [0.099–2.07]; 0.36). Although surveillance data overcounted relative to research data for clinically diagnosed treatment initiations (1.52 [0.71–3.26]) and number of people taking antiretroviral therapy (1.71 [0.71–4.12]), their agreement as assessed by CCC was not poor (0.82 and 0.62, respectively). Average agreement was similar across study years for all six measurements, but facility-level agreement varied from year to year and was not explained by facility characteristics. In conclusion, the agreement of TB surveillance data with high-fidelity research data was highly variable across measurements and facilities. To advance the use of routine TB data as a quality improvement tool, future research should elucidate and address reasons for variability in its quality.
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spelling pubmed-100456052023-03-29 Is aggregated surveillance data a reliable method for constructing tuberculosis care cascades? A secondary data analysis from Uganda White, Elizabeth B. Hernández-Ramírez, Raúl U. Majwala, Robert Kaos Nalugwa, Talemwa Reza, Tania Cattamanchi, Adithya Katamba, Achilles Davis, J. Lucian PLOS Glob Public Health Research Article To accelerate tuberculosis (TB) control and elimination, reliable data is needed to improve the quality of TB care. We assessed agreement between a surveillance dataset routinely collected for Uganda’s national TB program and a high-fidelity dataset collected from the same source documents for a research study from 32 health facilities in 2017 and 2019 for six measurements: 1) Smear-positive and 2) GeneXpert-positive diagnoses, 3) bacteriologically confirmed and 4) clinically diagnosed treatment initiations, and the number of people initiating TB treatment who were also 5) living with HIV or 6) taking antiretroviral therapy. We measured agreement as the average difference between the two methods, expressed as the average ratio of the surveillance counts to the research data counts, its 95% limits of agreement (LOA), and the concordance correlation coefficient. We used linear mixed models to investigate whether agreement changed over time or was associated with facility characteristics. We found good overall agreement with some variation in the expected facility-level agreement for the number of smear positive diagnoses (average ratio [95% LOA]: 1.04 [0.38–2.82]; CCC: 0.78), bacteriologically confirmed treatment initiations (1.07 [0.67–1.70]; 0.82), and people living with HIV (1.11 [0.51–2.41]; 0.82). Agreement was poor for Xpert positives, with surveillance data undercounting relative to research data (0.45 [0.099–2.07]; 0.36). Although surveillance data overcounted relative to research data for clinically diagnosed treatment initiations (1.52 [0.71–3.26]) and number of people taking antiretroviral therapy (1.71 [0.71–4.12]), their agreement as assessed by CCC was not poor (0.82 and 0.62, respectively). Average agreement was similar across study years for all six measurements, but facility-level agreement varied from year to year and was not explained by facility characteristics. In conclusion, the agreement of TB surveillance data with high-fidelity research data was highly variable across measurements and facilities. To advance the use of routine TB data as a quality improvement tool, future research should elucidate and address reasons for variability in its quality. Public Library of Science 2022-11-23 /pmc/articles/PMC10045605/ /pubmed/36962541 http://dx.doi.org/10.1371/journal.pgph.0000716 Text en © 2022 White et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
White, Elizabeth B.
Hernández-Ramírez, Raúl U.
Majwala, Robert Kaos
Nalugwa, Talemwa
Reza, Tania
Cattamanchi, Adithya
Katamba, Achilles
Davis, J. Lucian
Is aggregated surveillance data a reliable method for constructing tuberculosis care cascades? A secondary data analysis from Uganda
title Is aggregated surveillance data a reliable method for constructing tuberculosis care cascades? A secondary data analysis from Uganda
title_full Is aggregated surveillance data a reliable method for constructing tuberculosis care cascades? A secondary data analysis from Uganda
title_fullStr Is aggregated surveillance data a reliable method for constructing tuberculosis care cascades? A secondary data analysis from Uganda
title_full_unstemmed Is aggregated surveillance data a reliable method for constructing tuberculosis care cascades? A secondary data analysis from Uganda
title_short Is aggregated surveillance data a reliable method for constructing tuberculosis care cascades? A secondary data analysis from Uganda
title_sort is aggregated surveillance data a reliable method for constructing tuberculosis care cascades? a secondary data analysis from uganda
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045605/
https://www.ncbi.nlm.nih.gov/pubmed/36962541
http://dx.doi.org/10.1371/journal.pgph.0000716
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