| dc.description.abstract | The quality of tuberculosis (TB) data is crucial for accurate diagnosis and effective public health
strategies, significantly influenced among others, human factors. While existing studies provide
insights into these human factors, they often fail to explore their interactions and collective
impact on data quality. This study investigates how sociodemographic characteristics, training,
data quality checking skills, and problem-solving abilities of healthcare workers affect TB data
quality in Kiambu County, Kenya. Utilizing a cross-sectional design and mixed-methods
approach, the research combined quantitative data from structured questionnaires with
qualitative insights from interviews, involving 110 health workers, predominantly mid-career
professionals aged 28-37—mainly nurses and clinicians—selected from 21 health facilities.
Pretesting of data collection tool for validity and reliability. Quantitative analysis included
descriptive statistics, regression analysis, and chi-square tests to assess the impact of various
factors on data integrity. Qualitative data were analyzed through thematic coding and
interpretation. Results indicated a significant relationship between age and training participation,
with younger workers more actively engaging in training and demonstrating better error
detection skills. Training emerged as a major determinant of data quality (P = 0.006), with 78.2%
of respondents having over a year of experience in TB clinics, enhancing productivity and
accountability. Moreover, strong correlations were found between data quality checking skills
and improved data integrity, with a chi-square value of 232.5 (9 degrees of freedom). Practical
applications, such as systematic verification processes, significantly enhanced data quality.
Problem-solving abilities also played a crucial role, evidenced by a chi-square value of 45.32 (5
degrees of freedom, P < 0.001), indicating that respondents effectively utilized these skills
through established guidelines. Qualitative findings revealed key informants emphasizing the
need for improved TB management training, highlighting standardized protocols, cultural
competency, ongoing education, and technology integration. These factors collectively
contributed to significant reductions in data entry errors, thereby enhancing data quality as
measured by accuracy, timeliness, completeness, reliability, and validity. In conclusion, the
study confirmed that sociodemographic characteristics, training, data quality checking skills, and
problem-solving abilities significantly influence TB data quality. Recommendations include
implementing age-specific training programs, continuous structured training, specialized
workshops, and supportive policies from health ministries to improve TB data quality and public
health outcomes. These measures are essential for fostering reliable TB data, ultimately
contributing to better health strategies and outcomes in the region. | en_US |
