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

ObjectiveThe prevalence of modern contraceptive use in Ethiopia has increased in the past two decades. Despite these efforts, unmet needs for modern contraception persist, with limited knowledge on regional variations and determining factors.MethodWe analyzed data from the Ethiopian Demographic and Health Surveys (EDHS) from 2000, 2005, 2011, 2016, and 2019. Descriptive statistics examined regional variations and trends in modern contraceptive use among married, non-pregnant women. Multilevel analysis identified individual and community-level factors influencing modern contraceptive use.ResultNationally, modern contraceptive prevalence (mCP) increased, but regional disparities widened, notably between Addis Ababa and Somalia, from 34.8 to 51.8 percentage points. Factors such as community wealth, residence, age, education, and number of children significantly influenced contraceptive use. The greatest increase in mCP was among women aged 15–24, with a rise of 49.4 percentage points from 2000 to 2019.ConclusionTailored reproductive health services at both individual and community levels are essential to address the growing regional disparities in modern contraceptive use among married women in Ethiopia.

Frequency of individual-level and community-level factors in EMDHS (2019), Ethiopia (n = 4306).

Detail decomposition of stunting by place of residence among Ethiopian under five children using 2019 Mini-Ethiopian Demographic and Health Survey, 2022.

Socio-demographic and economic, maternal and childhood characteristics by consumption of foods rich in vitamin A in Ethiopia; mini EDHS 2019 [N = 1497].

Socio-demographic characteristics of study participants based on place of residence using 2019 Mini-Ethiopian Demographic and Health Survey, 2022.

Individual and community level characteristics of women who had live births in the last five years preceding (EDHS) 2019, Ethiopia.

Multilevel analysis for individual-level factors and community-level factors of the short birth interval using EMDHS 2019, Ethiopia (n = 4306).

Results from random intercept model (a measure of variation) for the short birth interval at cluster level using multilevel logistic regression analysis using EMDHS 2019, Ethiopia, 2023(n = 4306).

Show that the most likely clusters from a purely spatial scan statistic (Bernoulli model) of high rate of home delivery among 15–49 age women who had live births in the last five years preceding EDHS 2019 survey.

Background Antenatal care (ANC) services are essential to reduce maternal and newborn morbidity and mortality rates. However, the trends and socioeconomic inequality of utilizing recommended ANC services has not been well studied in Ethiopia. Therefore, this study aims to investigate the trends and socioeconomic disparities in receiving recommended ANC services among Ethiopian women. Methods This study used recent Ethiopian Demographic Health Surveys (EDHS) conducted in 2011, 2016, and 2019. Binary logistic regression model was employed to assess the association between receiving the recommended ANC services and explanatory variables and socioeconomic disparities were estimated through concentration index (CIX) analysis. Moreover, Wagstaff approach was used to decompose the relative CIX to the contribution of explanatory variables for the observed disparities. Results This study found that 37.37% (95%CI: 36.46–38.28%) of mothers utilized the recommended ANC services in Ethiopia. The trend in the coverage of recommended ANC services increased from ~ 30% in 2011 to 44.70% in 2019. Mother’s age and education, household wealth status, distance of the nearest health facility, and experiencing domestic abuse (i.e., wife beating) were significantly associated with utilization of recommended ANC services. The relative estimated CIX for wealth index, mothers education, Ethiopian administrative regions, and residence were 0.15 (P 

Socio-demographic characteristics and improved shared toilet usage in Ethiopia, mini 2019 EDHS.

Significant spatial clusters of improved shared toilet facilities among households in Ethiopia using 2019 mini-EDHS.

Multivariable multilevel logistic regression analysis of individual-level and community level factors associated measles vaccination dropout rates among under-five children in Ethiopia, Mini EDHS 2019.

Characteristics of women who gave birth within the last five years preceding EDHS 2005 (n = 11,163) and 2019 (n = 5,527), Ethiopia.

IntroductionAccording to the 2019 Ethiopian Demographic and Health Survey (EDHS) only 11% of married reproductive-age women in Ethiopia use long-acting reversible contraceptives (LARCs). This study aimed to identify individual characteristics associated with LARC uptake compared to short-acting contraceptives, traditional and barrier methods, and non-contraceptive use.MethodsData from the 2019 Performance Monitoring for Action (PMA) Ethiopia survey (n = 8,182) were used to run multilevel logistic regression models. The sample includes sexually active reproductive-age women (15–49 years). Independent variables were grouped into predisposing and enabling factors guided by the Andersen Behavioral Model of Health Services.ResultsLARC uptake in this study was 9.7%. Older, single, nulliparous, and Muslim women had lower LARC use than non-contraceptive and traditional/barrier method use. When compared to short-acting method use, low LARC use was associated with smaller household size and no exposure to family planning information. When compared to all other groups, contraceptive autonomy was associated with higher LARC uptake. Younger women and women living in rural areas were less likely to use IUDs than implants.DiscussionPolicymakers could use these findings to tailor interventions to specific populations with low LARC uptake. Training providers on counseling and LARC eligibility could help improve LARC uptake among populations, including less-empowered women. Involving religious leaders in contraceptive health education has the potential to increase LARC use.

Multilevel analysis of factors associated with improved shared toilet facilities usage among households in Ethiopia.

A list of the study’s variables along with an explanation of each measurement.

Factors contributing to HRFB inequality among women who had birth within the five years preceding EDHS 2005 (n = 11,163) and 2019 (n = 5,527), Ethiopia.

Change in PNC uptake over time among women who gave birth two years preceding the survey based on EDHS 2016 (n = 4,308) and 2019 (n = 2,105), Ethiopia.