Utilization of Health Information for Control of Communicable Diseases in Habaswein Sub County Public Health Facilities, Wajir County

Abstract

Utilizing health information for controlling communicable diseases is a critical aspect of effective public health management. Despite the existence of a health management information system (HMIS), Wajir County continues to experience recurrent outbreaks of infectious diseases, raising concerns about the effective use of health data for disease control. This study therefore assessed the effect of the use of health management information systems (HMIS) on the control of communicable diseases in public health facilities in Habaswein Sub-County, Wajir County. The study adopted a descriptive cross-sectional research design and was guided by the Evidence-Based Health Information Systems Theory, the Health Metrics Network (HMN) Framework, and the Performance of Routine Information System Management (PRISM) Framework. The dependent variable was utilization of health management information systems, while the independent variables were effectiveness of use, data metrics, technology, and government policy. Primary data were collected using structured questionnaires from a census of 74 health service providers working in public health facilities within the sub-county. Quantitative data was analyzed using the Statistical Package for Social Sciences (SPSS) Version 25, employing both descriptive and inferential statistics, including multiple regression analysis to determine the relationships between variables. The regression model yielded R = 0.733 and R² = 0.537, indicating that 53.7% of the variance in utilization of HMIS for controlling communicable diseases was explained by the four independent variables. The Adjusted R² = 0.513, suggesting a moderate explanatory power of the model. The ANOVA test results (F = 267.639, p < 0.001) confirmed that the model was statistically significant, implying that the independent variables collectively have a significant influence on HMIS utilization. Further regression analysis revealed that effectiveness of use (β = 0.220, p = 0.035), data metrics (β = 0.608, p < 0.001), and technology (β = 0.551, p < 0.001) significantly influenced HMIS utilization, while government policy (β = 0.008, p = 0.931) had no statistically significant effect. These results indicate that enhanced use of data metrics, improved technological capacity, and effective utilization practices significantly contribute to better use of health information for communicable disease control. The study concludes that optimal use of HMIS in controlling communicable diseases in Habaswein Sub-County depends largely on the accuracy of data metrics, the adoption of modern technology, and the effective use of health data by healthcare providers. It recommends the implementation of comprehensive data quality audits, capacity-building programs on data utilization, and increased technological integration to enhance timely disease detection, reporting, and intervention across all public health facilities.