Ethiopia demographic and health survey 2005 pdf

The areas selected for the pretest were urban Addis Ababa and both urban and rural parts of Mekele, Ambo and Debre Birhan areas. Accommodation was arranged for the trainees as well as the trainers at a training site in Addis Ababa. The training of interviewers, editors and supervisors was conducted from March 14 to April 20, The Amharic questionnaires were used during the training, while the Tigrigna and Oromiffa versions were simultaneously checked against the Amharic questionnaires to ensure accurate translation.

In addition to classroom training, trainees did several days of field practice to gain more experience on interviewing in the three local languages and fieldwork logistics.

A total of trainees were trained in five classrooms. On the basis of the scores on the exam and overall performances in the classroom, trainees were selected to participate in the main fieldwork. From the group 30 of the best male trainees were selected as supervisors and 30 of the best female interviewers were identified as field editors.

The remaining trainees were selected to be interviewers. The trainees not selected to participate in the fieldwork were kept as reserve. Thirty male interviewers and 30 female interviewers were selected to attend the biomarker training.

In addition, the 30 field editors also attended the training, as a backup to the biomarker interviewers. Thirteen regional laboratory technicians who were recruited from Private Laboratory Consortium Unit PLCU to serve as regional coordinators for the HIV testing were also trained, of whom 11 were eventually selected to supervise the blood collection.

During the one-week biomarker training, six experienced experts from ORC Macro and EHNRI provided theoretical training followed by practical classroom demonstrations of the techniques for testing of haemoglobin and collection of dried blood spots from a finger prick for HIV testing.

In addition to the classroom training, trainees did several days of field practice to gain more experience on blood collection. A total of 30 data collection teams, each composed of four female interviewers, two male interviewers, one female editor, and a male team supervisor, were organized for the main fiedwork. Furthermore, the 30 field teams were organized into 11 regional groups, each headed by an experienced senior staff of PHCCO and accompanied by a regional coordinator from PLCU.

The survey was fielded from April 27 to August 30, Data quality was also monitored through field check tables generated from completed clusters simultaneously data entered and produced during the fieldwork. Five senior experts from PHCCO were permanently assigned to monitor the fieldwork throughout the survey period by moving from one region to another.

Continuous communication was maintained between the field staff and the headquarters through cell phones. Fieldwork was successfully completed in of the clusters, with the 5 clusters not covered primarily due to reasons of inaccessibility. In one cluster in the Gambela Region, households refused to be finger-pricked for cultural and traditional reasons.

Questionnaires Questionnaires. In order to adapt the standard DHS core questionnaires to the specific socio-cultural settings and needs in Ethiopia, its contents were revised through a technical committee composed of senior and experienced demographers of PHCCO. A one-day workshop was organized on November 22, at the Ghion Hotel in Addis Ababa to discuss the contents of the questionnaire. Over 50 participants attended the national workshop and their comments and suggestions collected.

Based on these comments, further revisions were made on the contents of the questionnaires. The questionnaires were finalized in English and translated into the three main local languages: Amharic, Oromiffa and Tigrigna. The Household Questionnaire was used to list all the usual members and visitors in the selected households.

Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household.

The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. Data Processing Data Editing.

The processing of the EDHS results began soon after the start of fieldwork. Completed questionnaires were returned periodically from the field to the data processing department at the PHCCO headquarters. After the actual entry of the data began, additional data entry operators were recruited and entry was performed in two shifts. A total of 22 data entry operators and 4 office editors carried out data entry and primary office editing activities.

Each of the questionnaires was keyed twice by two separate entry clerks. Consistency checks were made and entry errors were manually checked by going back to the questionnaires.

A secondary editing program was then run on the data to indicate questions that showed inconsistency and these were also corrected by secondary editors. The data entry for the clusters that started on 9 May was completed on 24 September Data Appraisal Estimates of Sampling Error. The estimates from a sample survey are affected by two types of errors: 1 nonsampling errors, and 2 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 Ethiopia Demographic and Health Survey EDHS 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 EDHS 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 between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results. A sampling error is usually measured in terms of the standard error for a particular statistic mean, percentage, etc. 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 percent 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 EDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. This module used the Taylor linearization method of variance estimation for survey estimates that are means or proportions.

The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates. Data Appraisal. Access policy Access authority. Citation requirements. Disclaimer and copyrights Disclaimer. The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.

This is inconsistent Ethiopia, it remains important to remove the existing with a previous study of sub-Saharan African countries, disparities around the other dimensions of inequality. The WHO states that disparities from wider and sometimes conflicting perspec- correct care includes properly diagnosing and treating tives; a single summary measure of inequality is not pneumonia [27].

The simple summary measures i. The overall results of our work demonstrate exist- the sub-groups in the middle. This could lead to a biased ence of inequalities in health seeking behavior favoring conclusion, particularly when there is a population shift in households with higher wealth and more education liv- a subpopulation of interest over time [24]. Socioeconomic and residence inequalities in Difference, Ratio, SII and RII were significantly higher health seeking behavior in childhood pneumonia per- in , and only continued to with complex mea- sisted over the 11 years.

The health seeking behaviors. We suggest focusing on pro- study also revealed existence of education-based dispar- motion strategies, with a holistic approach, to address ity in under-five children with pneumonia symptoms health seeking behavior in childhood pneumonia in- who were taken to a health facility - more educated sub- equality.

By reducing inequities, increasing promotion groups had higher access to treatment in Available and strengthening prevention, the country can work to- evidence supports the finding that higher maternal edu- wards the SDGs. In , inequality ter diagnostics to detect pneumonia [8]. The report was evident since twice as many urban residents sought highlighted the need to train health care professionals in health treatment for pneumonia than rural residents.

To Integrated Management of Childhood Illness IMCI and date, there have been mixed findings regarding rural- integrated Community based Case Management iCCM urban residence; some studies indicate they are more to better diagnose and treat multiple conditions of pneu- disadvantaged because of poverty and little access to monia [8].

While local governments work towards de- health services [27] while others have not shown a sig- tecting all cases of pneumonia, special attention must be nificant association between rural-urban residence and directed towards the most affected subpopulations. As dis- vantaged children are reached.

We recommend spatial analysis of the ment in small scale divisions i. We Ethiopia is far from the goal. The WHO equity poorest and less educated. To reach the global targets monitor database does not disaggregate age for pneumo- for child health, and ultimately meet the universal health nia help seeking inequality; age should have been used coverage goal for child health service including inter- as an equity stratifier to know the specific age bracket ventions for pneumonia , nations should adopt the re- treatment for pneumonia.

Finally, the study did not de- search and intervention protocols of the WHO to compose the observed inequality in under-five children identify and treat pneumonia cases in a timely fashion. With increased efforts to introduce The study showed both socioeconomic and area-based the interventions into communities, there will be a ten- inequalities in health seeking behavior of childhood dency to exacerbate the existing inequity in access, with pneumonia disfavoring children in the lower socioeco- the already disadvantaged continuing to receive little of nomic status and households residing in rural areas in the services.

Therefore, work is required to increase the Ethiopia. Inequality in under-five children with pneumo- coverage of the interventions among the disadvantaged nia symptoms taken to health facility was observed in groups while also maintaining coverage for the advan- three of the survey years and across economic status, taged groups [30].

Interestingly, we did not ob- requires government level policy that addresses issues of serve sex related inequality in all the survey periods. In Ethiopia, Government and key stakeholders i. First, the inequality ing behaviour. We used the update of terminants related to the observed inequality in health the database, so it captured the current status of under- seeking behavior of childhood pneumonia. It used nationally represen- Acknowledgments tative EDHS data which could not be generalized to We acknowledge the WHO for making the software available to the public areas below the sub-national regions.

Countries around domain for free. Health Sector All authors made significant contributions to the study. Addis Ababa: conceptualized this study. GS and BZ collected and analyzed the data. SY Ministry of Health; SY had final responsibility to submit.

All How do health authors have read and approved the final version of the manuscript. Hum Resour Health. Funding Division World population prospects , online edition.

World Bank. International Monetary Fund. Accessed 18 Aug Software for exploring and comparing health inequalities in countries.

Built-in database edition. Version 3. Geneva: World Health Organization; Consent for publication Health equity assessment toolkit HEAT : software for exploring and individual person. Competing interests All other authors declare that they have no competing interests. Int J Epidemiol. Author details 1 Ethiopia Shewarobit, Ethiopia. Studies, University of Ottawa, Ottawa, Canada. Rutstein SO, Johnson K. The DHS wealth index. Accessed 17 Aug World Health Organization.

Handbook on health inequality monitoring with Received: 12 March Accepted: 13 January a special focus on low- and middle-income countries. Accessed 14 Aug References Updated on August The strengthening the reporting of observational studies in who. Int J Surg. Accessed Care seeking 18 Aug Marangu DJ, Zar H. Childhood pneumonia in low-and-middle-income Saharan Africa with high pneumonia mortality.

PLoS One. Paediatr Respir Rev. Prevalence of Care seeking for pneumonia. BMC Pediatr. Accessed 19 Aug Care seeking for childhood Jan 16]. Our world in data. J Family Med Prim Care. Ethiopia demographic BMJ Glob Health. Estimates of the ;3:e Equity and child- respiratory infections in countries, — a systematic analysis for survival strategies. Bull World Health Organ. National [cited Jan 16]. One is too many. Every death counts. End preventable deaths: global action plan for Accessed 10 Dec Geneva: WHO; Watkins K, Sridhar D.

Pneumonia: a global cause without champions. Alliance for Health Policy and Systems Research. Case study from Ethiopia. Related Papers Prevalence of and socioeconomic gradient in low birth weight in Ethiopia: further analysis of the demographic and health survey data By Mulugeta Tamire.