Statistics For Management 1 Pdf In Ethiopia
Angelique Syria
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Background: A well designed Health management information system is necessary for improving health service effectiveness and efficiency. It also helps to produce quality information and conduct evidence based monitoring, adjusting policy implementation and resource use. However, evidences show that data quality is poor and is not utilized for program decisions in Ethiopia especially at lower levels of the health care and it remains as a major challenge.
Method: Facility based cross sectional study design was employed. A total of 18 health centers and 302 health professionals were selected by simple random sampling using lottery method from each selected health center. Data was collected by health professionals who were experienced and had training on HMIS tasks after the tools were pretested. Data quality was assessed using accuracy, completeness and timeliness dimensions. Seven indicators from national priority area were selected to assess data accuracy and monthly reports were used to assess completeness and timeliness. Statistical software SPSS version 20 for descriptive statistics and binary logistic regression was used for quantitative data analysis to identify candidate variable.
Conclusion: This study found that the overall data quality was lower than the national target. Over reporting of all indicators were observed in all facilities. It needs major improvement on supervision quality, training status to increase confidence of individuals to do HMIS activities.
PMA uses innovative mobile technology to support low-cost, rapid-turnaround surveys monitoring key health and development indicators. Surveys are completed by resident enumerators, uploaded to a central server via a mobile data network, cleaned and analyzed. Results are disseminated shortly after.
Snapshot of Indicators (SOIs) are online tables that provide a summary of key family planning indicators and information on sample design, questionnaires, data processing, response rates and sample error estimates.
PMA has a variety of publications including briefs, reports and overview documents that may be used to inform health policy and programming decisions. Listed below are publications authored by PMA faculty, students, staff, and partners that draw upon PMA data.
The Department of Community Health was founded in 1964 under the medical faculty with the objective of training and equipping medical doctors with public health thinking and practice useful for a developing country setting. In these settings, the majority of the morbidities and mortalities are preventable and most people are living in rural areas with no or little access to health services. The Addis Ababa University School of Public Health was the first academic institution in the country to provide graduate training in public health -- offering an MPH degree program since 1984 and the doctoral program (PhD) since 2003/2004. The School of Public Health is organized into three departments: the Department of Epidemiology and Biostatistics; the Department of Reproductive, Family Health and Nutrition; and the Department of Health Management, Environmental and Behavioral Health Sciences.
Dr. Solomon Shiferaw is an Associate Professor at the Addis Ababa University in the School of Public Health, Department of Reproductive Health and Health Service Management. His research background includes adolescent reproductive health, use of mobile phone based applications to improve maternity service utilization, and inventory management of contraceptives at health facilities. His work on PMA builds upon a longstanding collaboration of Addis Ababa University with the Bill & Melinda Gates Institute for Population & Reproductive Health. Dr. Solomon holds an MD from the University of Gondar, an MPH from Addis Ababa University, and a PhD from Maastricht University, The Netherlands.
Mahari Yihdego is the Project Coordinator for PMA Ethiopia. Formerly he served as a lecturer at Mizan-Tepi University in the College of Health Sciences, Department of Public Health. His research background includes maternal and child health with a focus on neonatal health. Mahari has professional experience in coordinating research conducted by local and international nongovernmental organizations. He has worked as a regional and research coordinator in various quantitative and qualitative studies. Mahari holds a B.Sc. in Public Health from Mekelle University and an MPH in Reproductive and Family Health from Addis Ababa University.
Ayanaw Amogne is the Data Manager for PMA Ethiopia. He has also been working as a data manager at Mela Research, PLC in Addis Ababa, where he works on database design, Stata programming, and overseeing data processing. Ayanaw has extensive professional experience in data management and analysis. He worked as a statistician for the Central Statistical Agency of Ethiopia, where he contributed to data analysis, automation, and dissemination. He was also a data analyst at the Ethiopian Development Research Institute. Ayanaw holds an MA in International Development Studies from National Graduate Institute for Policy Studies (GRIPS) and a BSc major in Statistics and minor in Computer Science from Addis Ababa University.
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Health management information system (HMIS) is a system whereby health data are recorded, stored, retrieved and processed to improve decision-making. HMIS data quality should be monitored routinely as production of high quality statistics depends on assessment of data quality and actions taken to improve it. Thus, this study assessed accuracy of the routine HMIS data.
The Health Management Information System (HMIS) is a system whereby health data are recorded, stored, retrieved and processed to improve decision-making [1,2,3,4]. HMIS is one of the six core building blocks of the health system and provides data needed for other components (service delivery, health workforce, access to essential medicines, financing, and leadership) [3].
Data delivered through HMIS come from service delivery reports and administrative records kept as part of routine transactions at health facilities and management offices. Data must be collected, processed and transformed, communicated, and used to improve decisions toward improved health outcomes [3, 5].
High quality data are needed to enable safe and reliable healthcare delivery [6] and health facility data are critical inputs to monitor performances [7]. Though different organizations consider different dimensions of data quality, the World Health Organization (WHO) states that the dimensions of data quality are accuracy, validity, reliability, completeness, legibility, timeliness, accessibility, usefulness and confidentiality [5]. But in practice, no health data from any source can be considered perfect. All data are subject to a number of limitations related to data quality such as missing values, bias, measurement error, and human errors in data entry and computation [8] and factors associated with these errors are categorized in to technical, behavioral, and organizational factors [9].
Ethiopia has a three tier health system: primary, secondary and tertiary. Primary health care unit comprises health posts, health centers and primary hospitals. Health centers and health posts are networked by the linkage in which one health center is responsible for supporting approximately five health posts. Secondary level includes general hospitals while tertiary level includes teaching and referral (specialized) hospitals. Ethiopia has been implementing HMIS at all levels of the health system to ensure information use for evidence-based health planning and decision-making [10] with reforms focusing on rationalizing and standardizing the system and information use mechanisms [11].
All levels of health facilities use standard registers and individual cards to record and standard formats to report data. These registers and reporting formats are designed considering services provided at each levels of health facilities and are distributed by federal ministry of health. Except very few hospitals that use computerized data system, all service delivery points use printed materials for recording. Regarding reporting, health posts report to cluster supporting (supervising) health centers (or primary hospitals) which then report to district health office. General and teaching hospitals report to zones where they are located. Some health centers and all hospitals use computer for data entry and analysis. Facilities using computers enter data and submit softcopy while those facilities without computer submit hardcopy to district health office. HMIS reports submitted to district by hardcopy or softcopy are digitalized and shared by higher levels through web system. Except health posts where any of two health extension workers can compile reports, all organizations have person in charge of HMIS activities.
This study was stand-alone survey, was not linked to community (data verification was done only at facility level), and used both quantitative and qualitative methods. Public health facilities reporting data to government system through the routine HMIS for more than a year were included in the study.
Assuming p (proportion of health facilities reporting accurate data) to be 50% at 95% level of confidence and considering 20% relative error, design effect of 1.5 and finite population formula, final sample size for all facilities was 138. Distribution of sample size to facility type considered health center to hospital ratio and pairing health center (HC) with health post (HP). For every selected HC, one HP reporting to selected HC was selected.
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