The Influence of Behavioural Factors on HMIS Data Quality in Central Equatoria State, South Sudan
Matur T. Tieng Abele
*
School of Public Health, University of Juba, South Sudan and National Public Health Institute, South Sudan.
*Author to whom correspondence should be addressed.
Abstract
Background: The quality of HMIS data is essential for effective public health decision-making globally. However, in South Sudan, the fragility of the healthcare system, exacerbated by historical crises, has substantially affected data quality. A preliminary spot check by the National Ministry of Health revealed deficiencies in completeness, timeliness, consistency and accuracy across various datasets, prompting this study.
Aim: This study aimed to investigate factors contributing to poor HMIS data quality in Central Equatoria State. Although other factors, such as HIS infrastructure and the availability of human resources, were important in determining data quality, behavioural aspects, such as motivation, skills, and engagement, greatly influenced data quality outcomes.
Methods: A cross-sectional descriptive study was conducted in Central Equatoria State, targeting data clerks, monitoring and evaluation officers, and data managers working in health facilities. A total of 148 respondents were randomly selected to participate in this study. Stata software version 15 was used to analyse the data using descriptive statistics, binary logistic regression and multivariate logistic regression models. Odds ratios were interpreted to identify the significance level of the association between the factors influencing HMIS data quality.
Results: Of the 139 respondents who participated in the study, 65.5% were male and 34.5% were female. Regarding education level, 59% of respondents had higher education certificates, and 38.5% had secondary school certificates. Additionally, most respondents were young, with 77% between 26 and 41 years. Lack of financial payment was also identified as a major demotivating factor, as more than 60% of respondents confirmed this. Logistic regression analysis revealed that lack of motivation (OR = 0.407; CI, 0.212–0.779; p = 0.007) was a strong predictor of the timeliness data quality dimension and indicated a 59.3% likelihood that demotivated staff would not enter data into the system promptly. Having adequately skilled staff handling data would result in a 67.4% improvement across three dimensions of data quality, as skill (OR = 1.674; CI: 1.066–2.627; p = 0.025) exhibited high odds of data quality improvement.
Conclusion: The study concluded that behavioural factors, when considered together, have a significant effect on HMIS data quality at public health facilities in Central Equatoria State. It recommends establishing performance-based incentive payments, as most respondents reported that low or absent payment greatly demotivated staff responsible for handling data.
Keywords: HMIS data quality, behavioural factors, incentive payment, health information systems, data completeness, data timeliness, data consistency