Aggregate TB diagnosis and treatment data, Uganda 2017 and 2019

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.