Abstract

The Ethiopia Socioeconomic Panel Survey (ESPS) is a collaborative project between the Ethiopian Statistical Service (ESS) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology. ESPS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households on agriculture activities in the country. The ESPS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, and access to services and resources. The ability to follow the same households over time makes the ESPS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESPS is the first-panel survey to be carried out by the Ethiopian Statistical Service that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the second phase ESPS panel survey is based on the updated 2018 pre-census cartographic database of enumeration areas by the Ethiopian Statistical Service (ESS). The sample is a two-stage stratified probability sample. The ESPS EAs in rural areas are the subsample of the AgSS EA sample. That means the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e., the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematic PPS. This is designed to automatically result in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS, and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e., systematic random sampling. One important issue to note in ESPS sampling is that the total number of agriculture households per EA remains at 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA. For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA.

The ESPS-5 kept all the ESPS-4 samples except for those in the Tigray region and a few other places. A more detailed description of the sample design is provided in Section 3 of the Basic Information Document provided under the Related Materials tab.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The ESPS-5 survey consisted of four questionnaires (household, community, post-planting, and post-harvest questionnaires), similar to those used in previous waves but revised based on the results of those waves and on the need for new data they revealed. The following new topics are included in ESPS-5:

a. Dietary Quality: This module collected information on the household’s consumption of specified food items.

b. Food Insecurity Experience Scale (FIES): In this round the survey has implemented FIES. The scale is based on the eight food insecurity experience questions on the Food Insecurity Experience Scale | Voices of the Hungry | Food and Agriculture Organization of the United Nations (fao.org).

c. Basic Agriculture Information: This module is designed to collect minimal agriculture information from households. It is primarily for urban households. However, it was also used for a few rural households where it was not possible to implement the full agriculture module due to security reasons and administered for urban households. It asked whether they had undertaken any agricultural activity, such as crop farming and tending livestock) in the last 12 months. For crop farming, the questions were on land tenure, crop type, input use, and production. For livestock there were also questions on their size and type, livestock products, and income from sales of livestock or livestock products.

d. Climate Risk Perception: This module was intended to elicit both rural and urban households perceptions, beliefs, and attitudes about different climate-related risks. It also asked where and how households were obtaining information on climate and weather-related events.

e. Agriculture Mechanization and Video-Based Agricultural Extension: The rural area community questionnaire covered these areas rural areas. On mechanization the questions related to the penetration, availability and accessibility of agricultural machinery. Communities were also asked if they had received video-based extension services.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESPS-5 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). However, due to the security situation in northern Ethiopia and to a lesser extent in the western part of the country, only a total of 4999 households from 438 EAs were interviewed for both the agriculture and household modules. The security situation in northern parts of Ethiopia meant that, in Tigray, ESPS-5 did not cover any of the EAs and households previously sampled. In Afar, while 275 households in 44 EAs had been covered by both the ESPS-4 agriculture and household modules, in ESPS-5 only 252 households in 22 EAs were covered by both modules. During the fifth wave, security was also a problem in both the Amhara and Oromia regions, so there was a comparable reduction in the number of households and EAs covered there.

More detailed information is available in the BID.

Introduction: Reliable quality of life assessment is critical for identifying health issues, evaluating health interventions, and establishing the best health policies and care packages. The World Health Organization Quality of Life-Old Module is a tool for assessing the subjective quality of life in old age people. It's validated and available in more than 20 languages, except Amharic. Hence, this study was intended to translate it into Amharic language and validate it among old age people in Bahir Dar City, Ethiopia.
Methods: A cross-sectional study was conducted among 180 community-dwelling old age people in Bahir Dar City, Northwestern Ethiopia, from January 16 to March 13, 2021. Psychometric validation was achieved through Cronbach’s alpha of the internal consistency reliability test, and construct validity from confirmatory factor analysis.
Results: The study participants aged from 60 to 90 years old with a mean age of 69.44. Females made up 61.7% of the population and 40% of them could not read and write. The results showed a relatively low level of quality of life, with the total transformed score of 58.58 ± 23.15. The Amharic version of the World Health Organization Quality of Life-Old Module showed a Cronbach’s Alpha value of 0.96 and corrected item-total correlations of more than 0.74. Confirmatory factor analysis confirmed the six-factor model with a chi-square (X2) of 341.98 with a p-value less than 0.001. The comparative fit index (CFI) was 0.98, Tucker-Lewis’s index (TCL) was 0.97, and the root mean square error of approximation (RMSEA) was 0.046.
Conclusion: The Amharic version of the World Health Organization Quality of Life-Old Module indicated good internal consistency reliability and construct validity. The tool can be utilized to provide care to Ethiopian community-dwelling old age people.

Socio-demographic characteristics of radiology personnel in eastern Amhara, northeast Ethiopia, 2021.

Socio-demographic characteristics of health professionals working in all private hospitals Amhara region 2021 (N = 410).

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

Distribution of NoV genotypes across demographic characteristics and study settings of participants in Amhara National Regional State, May 2021 to November 2021.

Demographic characteristics and NoV positivity rates of diarrheic patients in Amhara National Regional State, May 2021 to November 2021.

Clinical characteristics of adult patients with epilepsy attending follow-up care at referral hospitals in Amhara region, Ethiopia, 2021 (n = 565).

The socio-demographic characteristics of a mother’s diagnosis with PROM at Amhara, a region-specialized hospital from July 08, 2019, to July 07, 2021, (n = 538).