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

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dc.contributor.authorWhite, Elizabeth B.
dc.contributor.authorHernandez-Ramırez, Raul U.
dc.contributor.authorKaos Majwala, Robert
dc.contributor.authorNalugwa, Talemwa
dc.contributor.authorReza, Tania
dc.contributor.authorCattamanchi, Adithya
dc.contributor.authorKatamba, Achilles
dc.contributor.authorDavis, J. Lucian
dc.date.accessioned2023-01-18T18:02:22Z
dc.date.available2023-01-18T18:02:22Z
dc.date.issued2021
dc.description.abstractTo 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 facilitylevel 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.en_US
dc.identifier.citationWhite EB, Herna´ndez-Ramı´rez RU, Majwala RK, Nalugwa T, Reza T, Cattamanchi A, et al. (2022) Is aggregated surveillance data a reliable method for constructing tuberculosis care cascades? A secondary data analysis from Uganda. PLOS Glob Public Health 2(11): e0000716. https://doi.org/10.1371/journal. pgph.0000716en_US
dc.identifier.urihttps://doi.org/10.1371/journal. pgph.0000716
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/7051
dc.language.isoenen_US
dc.publisherPLOS Glob Public Healthen_US
dc.subjectSurveillance dataen_US
dc.subjectReliable methoden_US
dc.subjectConstructing tuberculosis care cascadesen_US
dc.subjectSecondary data analysisen_US
dc.titleIs aggregated surveillance data a reliable method for constructing tuberculosis care cascades? A secondary data analysis from Ugandaen_US
dc.typeArticleen_US
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