Study setting and design
The study was conducted in Health Facilities in Hadiya Zone, which is found in the Southern Nations and Nationalities Region of Ethiopia from March 15–30/2108. Its capital town is Hosanna Town, located at 194Km south of Hawassa, the capital of southern regional state; and 230 km southwest of Addis Ababa the capital city of Ethiopia. According to the data obtained from the zonal health department, 2017/2018 projected population of the zone was around 1,611,756.There are 1 general hospital, 2 primary hospital, 61 health centers, and 305 health posts. It has two-town administration and currently there are 10 woredas covers an estimated area of 3542.66sq km.Facility based cross-sectional study design was employed.
Population: The source population was all health workers involved in HMIS activities at public health facilities of Hadiya zone while the study population was randomly selected health workers involved in HMIS activities in selected health Facilities of Hadiya zone. Those health workers who had less than six months experience were excluded from the study.
Sample size determination and sampling technique
The sample size was calculated by using single population proportion formulae using the following assumptions. P=0.7(the level of utilization ofhealth management information system in Hadiya zone health facilities at 2014) (12), marginal of error (d) of 5%, confidence interval of 95% and Zα/2 is the value of the standard normal distribution corresponding to a significant level of alpha (α) of 0.05, which is 1.96.This yields a sample size of 323. Since the study employed multistage sampling technique, it was necessary to apply design effect of 2. This yields a sample size of 646 (13). Because the total populations who were involved in HMIS data compilation and reports was less than 10,000 which are 1,753 (14), population correction formula used was used and the sample size was 472. By considering 5%non response rate the final sample size was 496 and distributed proportionally to each health facilities to obtain representative sample. Finally, the sample were selected be simple random sampling technique from each facility.
The dependent variable was health management information system use. The independent variables were; socio-demographic factors: age, sex, education status, types of institution, position in case team technical factor: perceived complexity of formats. Behavioral factors: knowledge on HMIS, data quality checking skill, confidence level of HMIS tasks, level of motivation. Organizational factors: training, supervision, regular feedback, management support. HMIS Processes: aggregation of data, collecting data on daily basis
Data collection tools
Self-questionnaires were adopted from World Health Organization(WHO) measure evaluation PRISM frame work and HMIS users guideline (15,16). The questionnaire containing background information of the respondents, knowledge on HMIS, self-efficacy test to know confidence level of staffs in HMIS tasks, motivation of staffs to collect data and utilization of HMIS data for decision-making were prepared. WHO measure evaluation tested the reliability and validity of these tools in African countries and the Cronbach's alpha was greater than 0.7 (17).
Health management information Use: Respondents were categorized as good health information users and poor information users based on number of utilization questions they practiced. Those health professionals who used HMIS data for four or more purpose from eight utilization questions were said to be good health information users and those who used blow four said to be poor health information users.
Complexity of formats: Inability of formats to be user friendly/understandable. Four questions were asked to assess complexity of HMIS formats. Those who answered "Yes "for more than two questions were categorized as health professionals who perceived formats were no complex.
Level of Knowledge: A health professional said to have good knowledge if he/she responds knowledge questions above mean score.
Data quality checking Skill: A health worker said to have data quality checking skill if he/she was able to calculate data accuracy.
Method of data analysis
After checking completeness, the data were coded and entered to Epi data version 3.1 then export to SPSS version 23 for analysis. Recoding, categorizing and computing of variables were made. In order to determine the association between dependent and independent variable binary logistic regression was used. Candidate variables were selected at p-value of less than 0.25, and then entered in to multi-variable logistic regression model. P-value of less than 0.05 at multi-variable logistic regression was independent predictors of routine health information utilization. Odds Ratio with 95% confidence intervals was computed to show the strengths of associations.
The ethical approval and letter of support was obtained from Jimma University, institute of health, institutional review board. An official permission was sought from Hadiya zone health department; Woreda Health Office and each facility participate in the study. Data collection for the evaluation was done with all consideration of the norm and values of the study participants. Moreover, oral consent was obtained from participants during data collection time. Confidentiality was assured for the information provided using anonymous code.