Abstract:
Tuberculosis (TB) epidemic is planned to be ended by the year 2035. This can be achieved
through employing an accurate means of monitoring progress of interventions. A recording
and reporting system, being of paramount importance has been in place since 2006. This
system is meant to provide assistance on assessment of quality of Tuberculosis services
offered, treatment outcome evaluation and TB notifications trends. Despite the presence of a
full functional health management Information system for monitoring TB services, the data
produced by this system is not of good quality as per 2018 DQA report by NTLP.
Objective: The aim of this study was to assess the factors affecting quality of data of a
tuberculosis surveillance system for case-based reporting in Lindi Municipality.
Methodology: This study was a cross sectional study assessing TB data quality by using
three dimensions namely data availability, Data completeness and data accuracy. Data were
collected from all 11 facilities in Lindi Municipality offering Direct Observed Therapy
whereby the focus was on all cases recorded in TB 03 facility registers a year before date of
data collection, also one DOT nurse or clinician at the DOT center was provided with a
validated RDQA tool for TB monitoring and reviewing available collected data, Adopted and
modified questionnaires from MEASURE evaluation RHMIS Performance diagnostic tool
was administered. Data from the questionnaires was analyzed quantitatively using Statistical
Package for Social Scientists (SPSS) version 23, whereby frequency distribution tables were
used to present data. Results: This study found out that majority (54.5%) of DOT providers
from all facilities involved were female,30 years or older, 63.6% were educated to attain a
certificate level. On level of data quality assessed data availability was 81.8%, completeness
was 93% and on accuracy an underreporting of 5% was observed. Factors that influence data
quality were found to be supportive supervision, more than five years of working experience,
being trained on basic computer and TB HMIS. However, having budget for HMIS and being
queried for delay of report was found to have no effect on Tb data quality.
Recommendations: The RCHMT and implementing partner(s) should further invest in
conducting supportive supervisions and arrange trainings on data management as they have
found to have an influence in production of data of good quality.
Conclusion: The overall quality of data is good, and most of the factors that have been
revealed to have effect on Tb data quality are modifiable. If they are addressed the quality of
TB data will improve more.
