Spatiotemporal distribution of climate-sensitive disease incidences in ethiopia: a longitudinal retrospective analysis of Malaria, Meningitis, Cholera, Dysentery, Leishmaniasis and Dengue fever between 2010 and 2022/2023.

BACKGROUND: Understanding the temporal and geographic distribution of disease incidences is crucial for effective public health planning and intervention strategies. This study presents a comprehensive analysis of the spatiotemporal distribution of disease incidences in Ethiopia, focusing on six major diseases: Malaria, Meningitis, Cholera and Dysentery, over the period from 2010 to 2022, whereas Dengue Fever and Leishmaniasis from 2018 to 2023. METHODS: Using data from Ethiopian public health institute: public health emergency management (PHEM), and Ministry of Health, we examined the occurrence and spread of each disease across different regions of Ethiopia. Spatial mapping and time series analysis were employed to identify hotspots, trends, and seasonal variations in disease incidence. RESULTS: The findings reveal distinct patterns for each disease, with varying cases and temporal dynamics. Monthly wise, Malaria exhibits a cyclical pattern with a peak during the rainy and humid season, while Dysentery, Meningitis and Cholera displays intermittent incidences. Dysentery cases show a consistent presence throughout the years, while Meningitis remains relatively low in frequency but poses a potential threat due to its severity. Dengue fever predominantly occurs in the eastern parts of Ethiopia. A significant surge in reported incident cases occurred during the years 2010 to 2013, primarily concentrated in the Amhara, Sidama, Oromia, Dire Dawa, and Benishangul-Gumuz regions. CONCLUSIONS: This study helps to a better understanding of disease epidemiology in Ethiopia and can serve as a foundation for evidence-based decision-making in disease prevention and control. By recognizing the patterns and seasonal changes associated with each disease, health authorities can implement proactive measures to mitigate the impact of outbreaks and safeguard public health in the region.

Correction: Developing a patient flow visualization and prediction model using aggregated data for a healthcare network cluster in Southwest Ethiopia.

[This corrects the article DOI: 10.1371/journal.pdig.0000376.].

Developing a patient flow visualization and prediction model using aggregated data for a healthcare network cluster in Southwest Ethiopia.

A health information system has been created to gather, aggregate, analyze, interpret, and utilize data collected from diverse sources. In Ethiopia, the most popular digital tools are the Electronic Community Health Information System and the District Health Information System. However, these systems lack capabilities like real-time interactive visualization and a data-driven engine for evidence-based insights. As a result, it was challenging to observe and continuously monitor the flow of patients. To address the gap, this study used aggregated data to visualize and predict patient flow in a South Western Ethiopia healthcare network cluster. The South-Western Ethiopian healthcare network cluster was where the patient flow datasets were collected. The collected dataset encompasses a span of 41 months, from 2019 to 2022, and has been obtained from 21 hospitals and health centers. Python Sankey diagrams were used to develop and build patient flow visualizations. Then, using the random forest and K-Nearest Neighbors (KNN) algorithms, we achieved an accuracy of 0.85 and 0.83 for the outpatient flow modeling and prediction, respectively. The imbalance in the data was further addressed using the NearMiss Algorithm, Synthetic Minority Oversampling Technique (SMOTE), and SMOTE-Tomek methods. In conclusion, we developed a patient flow visualization and prediction model as a first step toward an end-to-end effective real-time patient flow data-driven and analytical dashboard in Ethiopia, as well as a plugin for the already-existing digital health information system. Moreover, the need for and amount of data created by these digital tools will grow along with their use, demanding effective data-driven visualization and prediction to support evidence-based decision-making.

Health workers' use of routine health information and related factors at public health institutions in Illubabor Zone, Western Ethiopia.

BACKGROUND: Proper utilization of health data has paramount importance for health service management. However, it is less practiced in developing countries, including Ethiopia. Therefore, this study aimed to assess routine health information utilization and identify factors associated with it among health workers in the Illubabor zone, Western Ethiopia. METHODS: A facility based cross-sectional study was conducted from March to June 2021 with a total of 423 randomly selected health workers. Data were collected using an interviewer-administered questionnaire that was developed based on the performance of routine information system management (PRISM) framework. We created composite variables for health workers' knowledge, attitude, abilities, and information utilization based on existing data. Multivariate logistic regression analysis was performed and the statistical association between the outcome and independent variables was declared using 95% CI and a P < 0.05. RESULTS: About two-thirds or 279 health workers (66.0%, 95% CI 61.3, 70.4) had good health information utilization. Two-thirds of health workers think organizational decision-making culture (67.1%, 95% CI 62.6, 71.5) and facility managers' or supervisors' promotion of information use (65.5%, 95% CI 60.9, 69.9) are positive. Over half of health workers (57.0%, 95% CI 52.2, 61.6) have a positive attitude toward data management, and the majority (85.8%, 95% CI 82.2, 88.9) believe they are competent of performing routine data analysis and interpretation activities. Only about two-thirds of health workers (65.5%, 95% CI 60.9, 69.9) were proficient in data analysis and interpretation. CONCLUSIONS: The use of routine health information was lower than the national target and data from other literatures. Unacceptably large number of health personnel did not use information. As a result, efforts should be made to increase health workers' data management knowledge and skills, as well as the organizational culture of data utilization.