Is aggregated surveillance data a reliable method for constructing tuberculosis care cascades? A secondary data analysis from Uganda
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
PLOS Glob Public Health
Abstract
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 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.
Description
Keywords
Surveillance data, Reliable method, Constructing tuberculosis care cascades, Secondary data analysis
Citation
White 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.0000716