Contains data from World Health Organization's data portal covering various indicators (one per resource).

This dataset contains many indicators in health such as Infant mortality rate, Proportion of population with advanced HIV infection with access to antiretroviral drugs, Death rate associated with malaria per 100,000 population, Tuberculosis prevalence rate per 100,000 population, etc. The whole list and their description can be find in this link https://bit.ly/2NZBRH3

The 2016 Ethiopia Demographic and Health Survey (EDHS) is the fourth Demographic and Health Survey conducted in Ethiopia. It was implemented by the Central Statistical Agency (CSA) at the request of the Federal Ministry of Health (FMoH). The primary objective of the 2016 EDHS is to provide up-to-date estimates of key demographic and health indicators. The EDHS provides a comprehensive overview of population, maternal, and child health issues in Ethiopia. More specifically, the 2016 EDHS: Collected data at the national level that allowed calculation of key demographic indicators, particularly fertility and under-5 and adult mortality rates Explored the direct and indirect factors that determine levels and trends of fertility and child mortality ? Measured levels of contraceptive knowledge and practice Collected data on key aspects of family health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery Obtained data on child feeding practices, including breastfeeding Collected anthropometric measures to assess the nutritional status of children under age 5, women age 15-49, and men age 15-59 Conducted haemoglobin testing on eligible children age 6-59 months, women age 15-49, and men age 15-59 to provide information on the prevalence of anaemia in these groups Collected data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and evaluated potential exposure to the risk of HIV infection by exploring high-risk behaviours and condom use Conducted HIV testing of dried blood spot (DBS) samples collected from women age 15-49 and men age 15-59 to provide information on the prevalence of HIV among adults of reproductive age Collected data on the prevalence of injuries and accidents among all household members Collected data on knowledge and prevalence of fistula and female genital mutilation or cutting (FGM/C) among women age 15-49 and their daughters age 0-14 Obtained data on women’s experience of emotional, physical, and sexual violence.

Ethiopia ET: External Resources for Health: % of Total Expenditure on Health data was reported at 41.695 % in 2014. This records an increase from the previous number of 30.603 % for 2013. Ethiopia ET: External Resources for Health: % of Total Expenditure on Health data is updated yearly, averaging 31.669 % from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 50.149 % in 2010 and a record low of 10.339 % in 2002. Ethiopia ET: External Resources for Health: % of Total Expenditure on Health data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank: Health Statistics. External resources for health are funds or services in kind that are provided by entities not part of the country in question. The resources may come from international organizations, other countries through bilateral arrangements, or foreign nongovernmental organizations. These resources are part of total health expenditure.; ; World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates).; Weighted average;

Abstract

The 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) is a nationwide survey with a nationally representative sample of 9,150 selected households. All women age 15-49 who were usual members of the selected households and those who spent the night before the survey in the selected households were eligible to be interviewed in the survey. In the selected households, all children under age 5 were eligible for height and weight measurements. The survey was designed to produce reliable estimates of key indicators at the national level as well as for urban and rural areas and each of the 11 regions in Ethiopia.

The primary objective of the 2019 EMDHS is to provide up-to-date estimates of key demographic and health indicators. Specifically, the main objectives of the survey are: ▪ To collect high-quality data on contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; child nutrition; and other health issues relevant to achievement of the Sustainable Development Goals (SDGs) ▪ To collect information on health-related matters such as breastfeeding, maternal and child care (antenatal, delivery, and postnatal), children’s immunizations, and childhood diseases ▪ To assess the nutritional status of children under age 5 by measuring weight and height

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Health facility

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 and all children aged 0-5 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2019 EMDHS is a frame of all census enumeration areas (EAs) created for the 2019 Ethiopia Population and Housing Census (EPHC) and conducted by the Central Statistical Agency (CSA). The census frame is a complete list of the 149,093 EAs created for the 2019 EPHC. An EA is a geographic area covering an average of 131 households. The sampling frame contains information about EA location, type of residence (urban or rural), and estimated number of residential households.

Administratively, Ethiopia is divided into nine geographical regions and two administrative cities. The sample for the 2019 EMDHS was designed to provide estimates of key indicators for the country as a whole, for urban and rural areas separately, and for each of the nine regions and the two administrative cities.

The 2019 EMDHS sample was stratified and selected in two stages. Each region was stratified into urban and rural areas, yielding 21 sampling strata. Samples of EAs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units in different levels, and by using a probability proportional to size selection at the first stage of sampling.

To ensure that survey precision was comparable across regions, sample allocation was done through an equal allocation wherein 25 EAs were selected from eight regions. However, 35 EAs were selected from each of the three larger regions: Amhara, Oromia, and the Southern Nations, Nationalities, and Peoples’ Region (SNNPR).

In the first stage, a total of 305 EAs (93 in urban areas and 212 in rural areas) were selected with probability proportional to EA size (based on the 2019 EPHC frame) and with independent selection in each sampling stratum. A household listing operation was carried out in all selected EAs from January through April 2019. The resulting lists of households served as a sampling frame for the selection of households in the second stage. Some of the selected EAs for the 2019 EMDHS were large, with more than 300 households. To minimise the task of household listing, each large EA selected for the 2019 EMDHS was segmented. Only one segment was selected for the survey, with probability proportional to segment size. Household listing was conducted only in the selected segment; that is, a 2019 EMDHS cluster is either an EA or a segment of an EA.

In the second stage of selection, a fixed number of 30 households per cluster were selected with an equal probability systematic selection from the newly created household listing. All women age 15-49 who were either permanent residents of the selected households or visitors who slept in the household the night before the survey were eligible to be interviewed. In all selected households, height and weight measurements were collected from children age 0-59 months, and women age 15-49 were interviewed using the Woman’s Questionnaire.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Five questionnaires were used for the 2019 EMDHS: (1) the Household Questionnaire, (2) the Woman’s Questionnaire, (3) the Anthropometry Questionnaire, (4) the Health Facility Questionnaire, and (5) the Fieldworker’s Questionnaire. These questionnaires, based on The DHS Program’s standard questionnaires, were adapted to reflect the population and health issues relevant to Ethiopia. They were shortened substantially to collect data on indicators of particular relevance to Ethiopia and donors to child health programmes.

Cleaning operations

All electronic data files were transferred via the secure internet file streaming system (IFSS) to the EPHI central office in Addis Ababa, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by EPHI staff members and an ICF consultant who took part in the main fieldwork training. They were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro System software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing, double data entry from both the anthropometry and health facility questionnaires, and data processing were initiated in April 2019 and completed in July 2019.

Response rate

A total of 9,150 households were selected for the sample, of which 8,794 were occupied. Of the occupied households, 8,663 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 9,012 eligible women were identified for individual interviews; interviews were completed with 8,885 women, yielding a response rate of 99%. Overall, there was little variation in response rates according to residence; however, rates were slightly higher in rural than in urban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2019 EMDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2019 EMDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Household age distribution

- Age distribution of eligible and interviewed women

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Millenium development goals, Climate Change, External Debt, Trade.

In 2015, the Ethiopian Federal Ministry of Health (FMOH) developed the Saving Lives through Safe Surgery (SaLTS) initiative to improve national surgical care. Previous work led to development and implementation of 15 surgical key performance indicators (KPIs) to standardize surgical data practices. The objective of this project is to investigate current practices of KPI data collection and assess quality to improve data management and strengthen surgical systems. The first portion of the study documented the surgical data collection process including methods, instruments, and effectiveness at 10 hospitals across 2 regions in Ethiopia. Secondly, data for KPIs of focus [1. Surgical Volume, 2. Perioperative Mortality Rate (POMR), 3. Adverse Anesthetic Outcome (AAO), 4. Surgical Site Infection (SSI), and 5. Safe Surgery Checklist (SSC) Utilization] were compared between registries, KPI reporting forms, and the DHIS2 (district health information system) electronic database for a 6-month period (January—June 2022). Quality was assessed based on data completeness and consistency. The data collection process involved hospital staff recording data elements in registries, quality officers calculating KPIs, completing monthly KPI reporting forms, and submitting data into DHIS2 for the national and regional health bureaus. Data quality verifications revealed discrepancies in consistency at all hospitals, ranging from 1–3 indicators. For all hospitals, average monthly surgical volume was 57 cases, POMR was 0.38% (13/3399), inpatient SSI rate was 0.79% (27/3399), AAO rate was 0.15% (5/3399), and mean SSC utilization monthly was 93% (100% median). Half of the hospitals had incomplete data within the registries, ranging from 2–5 indicators. AAO, SSC, and SSI were commonly missing data in registries. Non-standardized KPI reporting forms contributed significantly to the findings. Facilitators to quality data collection included continued use of registries from previous interventions and use of a separate logbook to document specific KPIs. Delayed rollout of these indicators in each region contributed to issues in data quality. Barriers involved variable indicator recording from different personnel, data collection tools that generate false positives (i.e. completeness of SSC defined as paper form filled out prior to patient discharge) or missing data because of reporting time period (i.e. monthly SSI may miss infections outside of one month), inadequate data elements in registries, and lack of standardized monthly KPI reporting forms. As the FMOH introduces new indicators and changes, we recommend continuous and consistent quality checks and data capacity building, including the use of routinely generated health information for quality improvement projects at the department level.

Ethiopia ET: Health Expenditure: Public: % of Government Expenditure data was reported at 15.750 % in 2014. This records a decrease from the previous number of 15.942 % for 2013. Ethiopia ET: Health Expenditure: Public: % of Government Expenditure data is updated yearly, averaging 10.637 % from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 19.837 % in 2010 and a record low of 6.921 % in 1999. Ethiopia ET: Health Expenditure: Public: % of Government Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank: Health Statistics. Public health expenditure consists of recurrent and capital spending from government (central and local) budgets, external borrowings and grants (including donations from international agencies and nongovernmental organizations), and social (or compulsory) health insurance funds.; ; World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates).; Weighted average;

Ethiopia ET: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 18.000 NA in 2016. This records a decrease from the previous number of 18.100 NA for 2015. Ethiopia ET: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 18.600 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 21.800 NA in 2000 and a record low of 18.000 NA in 2016. Ethiopia ET: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

The urban indicators data available here are analyzed, compiled and published by UN-Habitat’s Global Urban Observatory which supports governments, local authorities and civil society organizations to develop urban indicators, data and statistics. Urban statistics are collected through household surveys and censuses conducted by national statistics authorities. Global Urban Observatory team analyses and compiles urban indicators statistics from surveys and censuses. Additionally, Local urban observatories collect, compile and analyze urban data for national policy development. Population statistics are produced by the United Nations Department of Economic and Social Affairs, World Urbanization Prospects.

Bland-Altman summary statistics for the agreement analysis between HMIS and survey data from three districts in Jimma Zone, Ethiopia.

The 2011 Ethiopia Demographic and Health Survey (EDHS) was conducted by the Central Statistical Agency (CSA) under the auspices of the Ministry of Health. The principal objective of the 2011 Ethiopia Demographic and Health Survey (EDHS) is to provide current and reliable data on fertility and family planning behaviour, child mortality, adult and maternal mortality, children’s nutritional status, use of maternal and child health services, knowledge of HIV/AIDS, and prevalence of HIV/AIDS and anaemia. The specific objectives are these: Collect data at the national level that will allow the calculation of key demographic rates; Analyse the direct and indirect factors that determine fertility levels and trends; Measure the levels of contraceptive knowledge and practice of women and men by family planning method, urban-rural residence, and region of the country; Collect high-quality data on family health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under ge five, and maternity care indicators, including antenatal visits and assistance at delivery; Collect data on infant and child mortality and maternal mortality; Obtain data on child feeding practices, including breastfeeding, and collect anthropometric measures to assess the nutritional status of women and children; Collect data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and evaluate patterns of recent behaviour regarding condom use; Conduct haemoglobin testing on women age 15-49 and children 6-59 months to provide information on the prevalence of anaemia among these groups; Carry out anonymous HIV testing on women and men of reproductive age to provide information on the prevalence of HIV. This information is essential for informed policy decisions, planning, monitoring, and evaluation of programmes on health in general and reproductive health in particular at both the national and regional levels. A long-term objective of the survey is to strengthen the technical capacity of the Central Statistical Agency to plan, conduct, process, and analyse data from complex national population and health surveys. Moreover, the 2011 EDHS provides national and regional estimates on population and health that are comparable to data collected in similar surveys in other developing countries and to Ethiopia’s two previous DHS surveys, conducted in 2000 and 2005. Data collected in the 2011 EDHS add to the large and growing international database of demographic and health indicators. The survey was intentionally planned to be fielded at the beginning of the last term of the MDG reporting period to provide data for the assessment of the Millennium Development Goals (MDGs). The survey interviewed a nationally representative population in about 18,500 households, and all women age 15-49 and all men age 15-59 in these households. In this report key indicators relating to family planning, fertility levels and determinants, fertility preferences, infant, child, adult and maternal mortality, maternal and child health, nutrition, women’s empowerment, and knowledge of HIV/AIDS are provided for the nine regional states and two city administrations. In addition, this report also provides data by urban and rural residence at the country level. Major stakeholders from various government, non-government, and UN organizations have been involved and have contributed in the technical, managerial, and operational aspects of the survey.

Ethiopia ET: Mortality Rate: Under-5: Female: per 1000 Live Births data was reported at 52.100 Ratio in 2017. This records a decrease from the previous number of 57.400 Ratio for 2015. Ethiopia ET: Mortality Rate: Under-5: Female: per 1000 Live Births data is updated yearly, averaging 75.700 Ratio from Dec 1990 (Median) to 2017, with 5 observations. The data reached an all-time high of 188.500 Ratio in 1990 and a record low of 52.100 Ratio in 2017. Ethiopia ET: Mortality Rate: Under-5: Female: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank.WDI: Health Statistics. Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year.; ; Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

The Land Degradation Surveillance Framework (LDSF) (http://landscapeportal.org/blog/2015/03/25/the-land-degradation-surveillance-framework-ldsf/) was developed by the World Agroforestry (ICRAF) in response to the need for consistent field methods and indicator frameworks to assess land and soil health across landscapes, including quantifying SOC and understanding land degradation dynamics and other factors that drive changes in SOC.

Ethiopia: Healthcare price index, world average = 100: The latest value from 2021 is 23.6 index points, a decline from 29.84 index points in 2017. In comparison, the world average is 67.78 index points, based on data from 165 countries. Historically, the average for Ethiopia from 2017 to 2021 is 26.72 index points. The minimum value, 23.6 index points, was reached in 2021 while the maximum of 29.84 index points was recorded in 2017.

Births attended by skilled health staff (% of total) in Ethiopia was reported at 49.8 % in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. Ethiopia - Births attended by skilled health staff (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Prescribing care indicators data. (SAV 10 kb)

Background: For decades, health targeted aid in the form of development assistance for health has been an important source of financing health sectors in developing countries. Health sectors in Sub Saharan countries in general and Ethiopia in particular, are even more heavily reliant upon donors. Consequently, a more audible donors support to health sectors was seen during the last four decades, consistent with the donor's response to the global goal of Alma-Ata declaration of "health for all by the year 2000" through primary health care in 1978. Ever since, a massive surge of development assistance for health has followed the out gone of the 2015 United Nations Millennium Declaration Goals in which three out of the eight goals were directly related to health. In spite of the long history of health targeted aid, with an ever increasing volumes, there is an increasing controversy on the extent to which health targeted aid is producing the intended health outcomes in the recipient countries. Despite the vast empirical literatures considering the effect of foreign development aid on economic growth of the recipient countries, systematic evidence that health sector targeted aid improves health outcomes is relatively scarce. The main contribution of this study is, therefore, to present a comprehensive country level, and cross-country evidences on the effect of development assistance for health on health outcomes. Objectives: The overall objective of this study was to analyze the effect of development assistance for health on health outcomes in Ethiopia, and in Sub Saharan Africa. Methods: For the Ethiopian (country level) study, a dynamic time series data analytic approach was employed. A retrospective sample of 36-year observations from 1978 to 2013 was analyzed using an econometric technique - vector error correction model. Beside including time dependency between the variables of interest and allowing for stochastic trends, the model provides valuable information on the existence of long-run and short-run relationships among the variables under study. Furthermore, to estimate the co-integrating relations and the other parameters in the model, the standard procedure of Johansen's approach was used. While development assistance for health expenditure was used as an explanatory variable of interest, life expectancy at birth was used as a dependent variable for the fact that it has long been used with or without mortality measures as health status indicators in the literatures.In the Sub Saharan Africa (cross-country level) study, a dynamic panel data analytic approach was employed using fixed effect, random effect, and the first difference-generalized method of moments estimators in the period confined to the year 1995-2013 over the cross section of 43 SSA countries. While development assistance for health expenditure was used as an explanatory variable of interest here again, infant mortality rate was used for health status measure done for its advantage over other mortality measures in cross-country studies. Results: In Ethiopia, the immediate one and two prior year of development assistance for health was shown to have a significant positive effect on life expectancy at birth. Other things being equal, an increase of development assistance for health expenditure per capita by 1% leads to an improvement in life expectancy at birth by about 0.026 years (P=0.000) in the immediate year following the period, and 0.008 years following the immediate prior two years period (P= 0.025). Similarly, in Sub-Saharan Africa, development assistance for health was found to have a strong negative effect on the reduction of infant mortality rate. The estimates of the study result indicated that during the covered period of study, in the region, a 1% increase in development assistance for health expenditure, which is far less than 10 cents per capita at the mean level, saves the life of two infants per 1000 live births (P=0.000). Conclusion: Contrary to the views of health aid skeptics, this study indicates strong favorable effect of development assistance for health sector in improving health status of people in Sub Saharan Africa in general and the Ethiopia in particular. Recommendations: The policy implication of the current findings is that development assistance for health sector should continue as an interim necessity means. However, domestic health financing system should also be sought, as the targeted countries cannot rely upon external resources continuously for improving the health status of the population. At the same time, the current development assistance stakeholders assumption of targeting facility based primary health care provision should be augmented by a more strong parallel strategy of improving socioeconomic status of the population that promotes sustainable improvement of health status in the targeted countries.

Selected health, development and poverty indicators of ethiopia[7, 10,11].

Health facility indicators data. (SAV 2 kb)