Demographic and Health Survey 1988-1989
Uganda, 1988 - 1989
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UGA_1988_DHS_v01_M
Demographic and Health Survey 1988-1989
| Name | Country code |
|---|---|
| Uganda | UGA |
Demographic and Health Survey (standard) - DHS I
The Uganda Demographic and Health Survey 1988-89 is the first survey of its kind to be conducted in Uganda.
The primary objective of the UDHS was to provide data on fertility, family planning, childhood mortality and basic indicators of maternal and child health. Additional information was collected on educational level, literacy, sources of household water and housing conditions. The available demographic data were incomplete and hardly any recent information concerning family planning or other health and social indicators existed at the national level.
A more specific objective was to provide baseline data for the South West region and the area in Central region known as the Luwero Triangle, where the Uganda government and UNICEF are currently supporting a primary health care project. In order to effectively plan strategies and to evaluate progress in meeting the project goals and objectives, there was a need to collect data on the health of the target population.
Another important goal of UDHS was to enhance the skills of those participating in the project so that they could conduct high-quality surveys in the future. Finally, the contribution of Ugandan data to an expanding international data set was an objective of the UDHS.
Sample survey data
- Household
- Women age 15-49
Scope
The Uganda Demographic and Health Survey 1998 covers the following topics:
- Anthropometry
- Familiy planning
- Fertility
- Maternal and child health
- Micronutrients
- Service Availability
Coverage
The Uganda Demographic and Health Survey (UDHS) was conductedin 24 districts. Nine northern districts were not surveyed due to security reasons.
The population covered by the 1988 UDHS is defined as the universe of all women age 15-49 in Uganda and all men age 15-54 living in the household. But due to security problems at the time of sample selection, 9 districts, containing an estimated 20 percent of the country's population, were excluded from the sample frame
Producers and sponsors
| Name |
|---|
| Ministry of Health |
| Name | Role |
|---|---|
| Ministry of Planning and Economic Development | Collaboration |
| Department of Geography, Makerere University | Collaboration |
| Institute of Statistics and Applied Economics | Collaboration |
| Institute for Resource Development/Macro Systems | Technical assistance |
| Name | Role |
|---|---|
| U.S. Agency for International Development | Funding |
Sampling
The UDHS used a stratified, weighted probability sample of women aged 15-49 selected from 206 clusters. Due to security problems at the time of sample selection, 9 districts, containing an estimated 20 percent of the country's population, were excluded from the sample frame. Primary sampling units in rural areas were sub-parishes, which, in the absence of a more reliable sampling frame, were selected with a probability proportional to the number of registered taxpayers in the sub-parish. Teams visited each selected sub-parish and listed all the households by name of the household head. Individual households were then selected for interview from this list.
Because Ugandans often pay taxes in rural areas or in their place of work instead of their place of residence, it was not possible to use taxpayer rolls as a sampling frame in urban areas. Consequently, a complete list of all administrative urban areas known as Resistance Council Ones (RCls) was compiled, and a sampling frame was created by systematically selecting 200 of these units with equal probability. The households in these RCls were listed, and 50 RCls were selected with probability proportional to size. Finally, 20 households were then systematically selected in each of the 50 RCls for a total of 1,000 urban households.
SAMPLE DESIGN
The sample used for the Uganda Demographic and Health Survey was a stratified, weighted probability sample of women aged 15-49 selected from 206 clusters. Due to security problems at the time of sample selection, 9 of the country's 34 districts, containing an estimated 20 percent of the population, were excluded from the sample frame. Primary sampling units in rural areas were sub-parishes, which, in the absence of a more reliable sampling frame, were selected with a probability proportional to the number of registered taxpayers in the sup-parish.
The South West region and the area in Central region known as Luwero Triangle were each over-sampled to provide a sample with sufficient size to produce independent estimates of certain variables for these two areas.
The urban sector was over-sampled by a factor of three compared with a proportionate urban/rural sample. Since it was not possible to use an appropriate sampling frame in the urban area, it was necessary to look for an altemative procedure. A convenient solution avoiding excessive cost was to use a two-phase sampling:
-
1st Phase: A complete list of all administrative urban areas known as Resistance Council Ones (RCls) was compiled and a sampling frame was created by systematically selecting 200 of these units with equal probability for a complete household updating.
-
2nd Phase: After the first phase selection and updating was completed, a sub-sample of 50 RCls were selected with probability proportional to size (size as reported in the housing listing). At the subsequent stage, 20 households were then systematically selected in each of the 50 RCls for a total of 1,000 urban households.
Contact was not made with 127 eligible women, either because the respondent was not at home during any of the visits by the interviewer, or because the respondent refused to be interviewed, or because of other reasons. In any case, the overall level of nonresponse is very low.
Households and eligible women: Out of 5,587 addresses visited, 5,123 households were located. The remaining addresses (8.3 percent) were not valid households, either because the dwelling had been vacated or destroyed, or the household could not be located or did not exist. Of the located households, 5101 were successfully interviewed, producing a household response rate of 99.6 percent.
The household questionnaires identified 4,857 women eligible for the individual interview (that is, they were aged 15-49 and had spent the night before the interview in the selected household). This represents an average of slightly under one eligible women per household. Questionnaires were completed for 4,730 women, indicating an individual response rate of 98.4 percent. The overall response rate, that is, the product of response rates at the household and individual levels was 98.0 percent
The response rates for the urban-rural areas, and regions were similar. In the urban areas, the overall individual response rate was 96.0 percent, compared with 97.7 percent for the rural areas. These lower rates of response in the urban areas are influenced by the low rates of response observed for Kampala.
Survey instrument
Three questionnaires were used for the UDHS: the household questionnaire, the individual woman's questionnaire, and the service availability questionnaire.
a) The household questionnaire listed all usual members of the household and their visitors, together with information on their age and sex and information on the fostering of children under 15. It was used to identify women who were eligible for the individual interview, namely, those aged 15-49 who slept in the household the night before the household interview, whether they normally lived there or were visiting.
b) For those women who were either absent or could not be interviewed during the first visit, a minimum of three revisits were made before recording nonr esponse. Women were interviewed with the individual questionnaire, which contained questions on fertility, family planning and maternal and child health.
c) The service availability (SA) questionnaire collected information on family planning and health services and other socioeconomic characteristics of the selected areas and was completed for each rural cluster and for each urban area. The SA questionnaire was administered by a different team of interviewers from the one carrying out the individual women's interview. The same clusters chosen for the individual interviews were visited by the SA interviewer who was instructed to assemble 3 or 4 "knowledgeable" residents. These people were asked about the services available in the community and the distances to them. Based on this information, interviewers visited the facilities close to the cluster and collected information about equipment, staffing, services available, and general infrastructure. Results on service availability are not included in this report.
The household and the individual questionnaires were translated into four languages: Luganda, Lugbara, Runyankole-Rukiga and Runyoro-Rutom. Luganda questionnaires were used in the East region, where there are a number of languages, but most people speak Luganda. A pretest of the translated questionnaires was conducted in October 1987 by interviewers who completed a three-week training course.
Data collection
| Start | End |
|---|---|
| 1988-09 | 1989-02 |
| Name |
|---|
| Ministry of Health |
A three-week training course for the main survey was held in September 1988. Fifty-six interviewers, six field editors and six supervisors took part in the survey. All interviewers were women, although some of the supervisors and field editors were men. Field staff were recruited from the Ministries of Health and Planning and from among people who answered advertisements in the national press and passed selection interviews. A major qualification of the interviewers was educational achievement and a good command of at least one of the local languages covered by the four translations. All field staff had at least Senior Four secondary school education and several were university graduates. Senior survey staff came from the Ministries of Health and Planning, as well as Makerere University. The National Director of the UDHS was the Assistant Director of Medical Services in charge of Maternal and Child Health. IRD provided technical collaboration through periodic staff visits regarding sample selection, questionnaire design, anthropemetric measurement, training of interviewers, and data processing and analysis.
Data processing
Completed questionnaires were sent to the data processing room at Makerere University where data entry and machine editing proceeded concurrently with fieldwork. Four desktop computers and ISSA, the Integrated System for Survey Analysis, were used to process the UDHS data. Of the households sampled, 5,101 were successfully interviewed, a completion rate of 91.3 percent. A total of 4,857 eligible women were identified in these households, of which 4,730 were interviewed, a completion rate of 97.4 percent. Data entry and editing were completed a few days after fieldwork ended.
Data appraisal
The sample of women selected in the UDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each one would have yielded results that differed somewhat from the actual sample selected. The sampling error is a measure of the variability between all possible samples; although it is not known exactly, it can be estimated from the survey results. Sampling error is usually measured in terms of the "standard error" of 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 one can be reasonably assured that, apart from non-sampling errors, the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples with the same design (and expected size) will fall within a range of plus or minus two times the standard error of that statistic.
If the sample of women had been selected as a simple random sample, it would have been possible to use strightforward formulas for calculating sampling errors. However, the UDHS sample design depended on stratification, stages, and clusters; consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS was used to assist in computing the sampling errors with the proper statistical methodology.
In addition to the standard errors, CLUSTERS computes the design effect (DEFT) for each estimate, which is defined as the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used. A DEFT value of 1.0 indicates that the sample design is as efficient as a simple random sample; a value greater than 1.0 indicates the increase in the sampling error due to the use of a more complex and less statistically efficient design.
Sampling errors are presented in Tables in appendice of the Final Report for 35 variables considered to be of major interest. Results are presented for the whole country, for urban and rural areas, for women in three broad age groups, and for the six regions. For each variable, the type of statistic (mean, proportion) and the base population are given in Table B.1 of the Final Report. For each variable, Table presents the value of the statistic, its standard error, the number of unweighted and weighted cases, the design effect, the relative standard error, and the 95 percent confidence limits. The confidence interval has the following interpretation. For the mean number of children ever born (CEB), the overall average from the sample is 3.493 and its standard error is 0.049. Therefore, to obtain the 95 percent confidence limits, one adds and subtracts twice the standard error to the sample estimate, i.e., 3.493 + or - (2 x 0.049), which means that there is a high probability (95 percen0 that the true average number of children ever born falls within the interval of 3.395 to 3.592.
The relative standard error for most estimates for the country as a whole is small, except for estimates of very small proportions. The magnitude of the error increases as estimates for subpopulations such as particular age groups, and especially geographical areas, are considered. For the variable CEB, for example, the relative standard error (as a percentage of the estimated mean) for the whole country, rural areas, and Kampala is, respectively, 1.4 percent, 1.4 percent, and 7.1 percent. This means that the survey can provide estimates of CEB only with a margin of uncertainty (at the 95 percent confidence level) of +/- 2.8 percent, 2.8 percent, and 14.2 percent respectively for these three domains.
Nonsampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way questions are asked, misunderstanding of the questions on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the UDHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate analytically.
Data Access
| Name | Affiliation | URL | |
|---|---|---|---|
| MEASURE DHS | ICF International | www.measuredhs.com | archive@measuredhs.com |
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
Disclaimer and copyrights
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.
Contacts
| Name | URL | |
|---|---|---|
| General Inquiries | info@measuredhs.com | www.measuredhs.com |
| Data and Data Related Resources | archive@measuredhs.com | www.measuredhs.com |
Metadata production
DDI_UGA_1988_DHS_v01_M
| Name | Role |
|---|---|
| World Bank, Development Economics Data Group | Generation of DDI documentation |
2012-04-05