DOI: 10.5061/dryad.1g1jwsv0w
title: Aggregate TB diagnosis and treatment data, Uganda 2017 and 2019

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). The 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 the 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). The average agreement was similar across study years
 for all six measurements, but facility-level agreement varied yearly 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: This dataset contains counts of TB diagnoses and treatment
 initiations, as well as HIV diagnosis and treatment counts among TB
 patients, at the health facility level from two data sources: surveillance
 data and research data. Surveillance data: In Uganda,
 all hospitals and health centers that treat TB patients report data on a
 quarterly basis. A trained staff member at each facility reviews
 handwritten TB laboratory and treatment registers quarterly and records
 aggregated counts of diagnosis and treatment data on a standardized paper
 reporting form within pre-specified strata. Aggregate data are entered
 into a DHIS2 database at the Ministry of Health. This dataset includes
 annual, facility-level data on diagnoses and treatment initiations from
 the quarterly reports in DHIS2. Research data: In the
 XPEL-TB study, trained facility staff photographed handwritten TB
 laboratory and treatment registers, uploaded them to a secure server,
 entered the data into a patient-level database, and conducted quality
 assurance to ensure accuracy, including resolving missing data and other
 discrepancies with health facility staff. To make direct comparisons to
 the surveillance data, this dataset includes aggregated patient data by
 year and health center using the same strata reported in the DHIS2
 system.
description: The data are contained in a .csv file that can be opened using
 most statistical softwares. Our analysis was conducted in R (version
 4.1.2), but other programs may be used.