Health Management Information System Data Quality, Utilization and Associated Factors at Governmental Hospitals and Public Health Management System in Sidama Regional State

Background: A health information system is a tool used to gather, organize, transmit, analyze, store, and utilize health system data to help decision-makers and stakeholders allocate resources at every level of the healthcare system. Health information system activities are not well integrated at the district and facility levels, where the majority of the data is being generated. Objective: the aim of this study was to assess the utilization of the health information system data, its quality and influencing factors in the public health facilities residing in Sidama regional state. Methods An institution-based cross-sectional study using a mixed (quantitative and qualitative) approach was conducted. The sample size for quantitative data estimated was 422, and two months' documents were checked for accuracy and completeness for data quality. Twenty-five Key informants for qualitative data were chosen purposively. Quantitative data was analyzed using SPSS version 23.  The variables having a p-value of less than 0.25 were added to the multivariable analysis using binary logistic regression. Lastly, variables with p-value of less than 0.05 at the multivariable analysis were taken as significant. Manual transcription, coding, and thematic analysis of qualitative data were done. Result: out of 422 participants 407 healthcare professionals responded, 96.5% response rate. Majority 288 (69.5%) were younger age less than 30 years old, 173(42.5%) were male, 331 (81.3%) were degree holders, 219 (53.8%) married, 221(54.3%) were protestant. More than half (52.1%) had poor utilization of HMIS data for decision-making. participant’s knowledge on HMIS, the extent of participant confidence to complete HMIS tasks, and the level of management support for staff were found  to be significantly associated with utilization of HMIS data to the evidence based decision making for health care. Through qualitative study we identified five themes. These included Theme 1: awareness, knowledge and expertise gap on HMIS, Theme 2:  Perception on Significance of HMIS data, Theme 3: data quality and information use challenges, fourth theme: Facilitator characteristics and Theme five: perceptions on data quality and usage. Keywords: Health information system, Data quality, Health management information system DOI: 10.7176/IKM/13-1-01 Publication date: January 31st 202

Progress in health among regions of Ethiopia, 1990-2019: a subnational country analysis for the Global Burden of Disease Study 2019.

BACKGROUND: Previous Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) studies have reported national health estimates for Ethiopia. Substantial regional variations in socioeconomic status, population, demography, and access to health care within Ethiopia require comparable estimates at the subnational level. The GBD 2019 Ethiopia subnational analysis aimed to measure the progress and disparities in health across nine regions and two chartered cities. METHODS: We gathered 1057 distinct data sources for Ethiopia and all regions and cities that included census, demographic surveillance, household surveys, disease registry, health service use, disease notifications, and other data for this analysis. Using all available data sources, we estimated the Socio-demographic Index (SDI), total fertility rate (TFR), life expectancy, years of life lost, years lived with disability, disability-adjusted life-years, and risk-factor-attributable health loss with 95% uncertainty intervals (UIs) for Ethiopia's nine regions and two chartered cities from 1990 to 2019. Spatiotemporal Gaussian process regression, cause of death ensemble model, Bayesian meta-regression tool, DisMod-MR 2.1, and other models were used to generate fertility, mortality, cause of death, and disability rates. The risk factor attribution estimations followed the general framework established for comparative risk assessment. FINDINGS: The SDI steadily improved in all regions and cities from 1990 to 2019, yet the disparity between the highest and lowest SDI increased by 54% during that period. The TFR declined from 6·91 (95% UI 6·59-7·20) in 1990 to 4·43 (4·01-4·92) in 2019, but the magnitude of decline also varied substantially among regions and cities. In 2019, TFR ranged from 6·41 (5·96-6·86) in Somali to 1·50 (1·26-1·80) in Addis Ababa. Life expectancy improved in Ethiopia by 21·93 years (21·79-22·07), from 46·91 years (45·71-48·11) in 1990 to 68·84 years (67·51-70·18) in 2019. Addis Ababa had the highest life expectancy at 70·86 years (68·91-72·65) in 2019; Afar and Benishangul-Gumuz had the lowest at 63·74 years (61·53-66·01) for Afar and 64.28 (61.99-66.63) for Benishangul-Gumuz. The overall increases in life expectancy were driven by declines in under-5 mortality and mortality from common infectious diseases, nutritional deficiency, and war and conflict. In 2019, the age-standardised all-cause death rate was the highest in Afar at 1353·38 per 100 000 population (1195·69-1526·19). The leading causes of premature mortality for all sexes in Ethiopia in 2019 were neonatal disorders, diarrhoeal diseases, lower respiratory infections, tuberculosis, stroke, HIV/AIDS, ischaemic heart disease, cirrhosis, congenital defects, and diabetes. With high SDIs and life expectancy for all sexes, Addis Ababa, Dire Dawa, and Harari had low rates of premature mortality from the five leading causes, whereas regions with low SDIs and life expectancy for all sexes (Afar and Somali) had high rates of premature mortality from the leading causes. In 2019, child and maternal malnutrition; unsafe water, sanitation, and handwashing; air pollution; high systolic blood pressure; alcohol use; and high fasting plasma glucose were the leading risk factors for health loss across regions and cities. INTERPRETATION: There were substantial improvements in health over the past three decades across regions and chartered cities in Ethiopia. However, the progress, measured in SDI, life expectancy, TFR, premature mortality, disability, and risk factors, was not uniform. Federal and regional health policy makers should match strategies, resources, and interventions to disease burden and risk factors across regions and cities to achieve national and regional plans, Sustainable Development Goals, and universal health coverage targets. FUNDING: Bill & Melinda Gates Foundation