Primary Healthcare Data Management Practice and Associated Factors: The Case of Health Extension Workers in Northwest Ethiopia
Segenet Yitayew1, Mulusew A. Asemahagn2, 3, *, Atinkut A. Zeleke2, 3
Identifiers and Pagination:Year: 2019
First Page: 2
Last Page: 7
Publisher Id: TOMINFOJ-13-2
Article History:Received Date: 15/01/2019
Revision Received Date: 06/02/2019
Acceptance Date: 05/04/2019
Electronic publication date: 24/07/2019
Collection year: 2019
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Collecting quality and timely healthcare data is crucial to improve health service performance.
This study aimed at assessing data management practice and associated factors among health extension workers in East Gojjam zone, Northwest Ethiopia.
Materials and Methods:
An institution based cross-sectional study was conducted in 2014 among 302 health extension workers. Data were collected using a self-administered questionnaire and analyzed using SPSS version 20. The study objectives were described by descriptive statistics, and factors in data management were identified by multivariable logistic regression analysis.
A total of 302 health extension workers participated in the study. About 47.4% and 53.3% of respondents had good data management knowledge and practice, respectively. Inaccessibility of transportation, communication services, reference materials, and data collection/reporting formats were the mentioned challenges. Workload, data management knowledge, supervision, urban residence, reference materials access and clarity of formats were positively associated with better data management practice (p <0.05).
Based on this study, the data management practice of health extension workers was low. Factors for low data management practice were organizational and technical related. Addressing knowledge gaps through professional development and improving supportive supervision are crucial to solve the problem.