Demographic and Health Survey (Standard) - DHS VII
The 2018 Zambia Demographic and Health Survey (2018 ZDHS) is a nationwide survey with a nationally representative sample of approximately 13,625 selected households. All women age 15-49 and all men age 15-59 who are usual members of the selected households or who spent the night before the survey in the selected households were eligible for individual interviews. In all households, all women age 15-49 and all children under age 5 were eligible for height and weight measurements and anaemia testing. One woman age 15-49 was selected from each household to complete the domestic violence module. The survey was designed to produce reliable estimates for key indicators at the national level as well as for urban and rural areas and each of the 10 provinces: Central, Copperbelt, Eastern, Luapula, Lusaka, Muchinga, Northern,
North Western, Southern, and Western.
The 2018 ZDHS is the sixth in a series of Demographic and Health Surveys in Zambia. Previous surveys were conducted in 1992, 1996, 2001-02, 2007, and 2013-14.
The primary objective of the 2018 ZDHS was to provide up-to-date estimates of basic demographic and health indicators. Specifically, the ZDHS collected information on:
- Fertility levels and preferences; contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; maternal mortality; and gender, nutrition, and awareness regarding HIV/AIDS and other health issues relevant to the achievement of the Sustainable Development Goals (SDGs)
- Ownership and use of mosquito nets as part of the national malaria eradication programmes
- Health-related matters such as breastfeeding, maternal and childcare (antenatal, delivery, and postnatal), children’s immunisations, and childhood diseases
- Anaemia prevalence among women age 15-49 and children age 6-59 months
- Nutritional status of children under age 5 (via weight and height measurements)
- HIV prevalence among men age 15-59 and women age 15-49 and behavioural risk factors related to HIV
- Assessment of situation regarding violence against women
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Children age 0-5
- Woman age 15-49
- Man age 15-59
The data dictionary was generated from hierarchical data that was downloaded from the The DHS Program website (http://dhsprogram.com).
The 2018 Zambia Demographic and Health Survey covered the following topics:
• Usual members and visitors in the selected households
• Background information on each person listed, such as relationship to head of the household, age, sex, marital status, survivorship and residence of bilogical parents, highest educational attainment, and birth registration
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, type of fuel used for cooking, materials used for the floor, roof and walls of the house, and possessions of durable goods and mosquito nets
• Background characteristics (including age, education, and media exposure)
• Reproduction and child mortality
• Antenatal, delivery, and postnatal care
• Vaccinations and childhood illnesses
• Maternal and child health and nutrition
• Marriage and sexual activity
• Fertility preferences
• Women’s work and husbands’ background characteristics
• Knowledge, awareness, and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs)
• Knowledge, attitudes, and behaviour related to other health issues (e.g., injections, smoking, childhood illnesses, and pregnancy and childbirth)
• Adult and maternal mortality
• Domestic violence
• Women’s empowerment
• Background characteristics
• Marriage and sexual activity
• Fertility preferences
• Employment and gender roles
• Other health issues
• Weight, height and hemoglobin measurement for children age 0-5
• Hemoglobin measurement and HIV testing for women age 15-49
• HIV testing for men age 15-59
The survey covered all de jure household members (usual residents), all women age 15-49, all men age 15-59, and all children age 0-5 years who are usual members of the selected households or who spent the night before the survey in the selected households.
Producers and sponsors
Zambia Statistics Agency (ZamStats)
Government of Zambia
Ministry of Health
Government of Zambia
University Teaching Hospital Virology Laboratory
Department of Population Studies at the University of Zambia
Provided technical assistance through The DHS Program
Government of Zambia
United States Agency for International Development
The sampling frame used for the 2018 ZDHS is the Census of Population and Housing (CPH) of the Republic of Zambia, conducted in 2010 by ZamStats. Zambia is divided into 10 provinces. Each province is subdivided into districts, each district into constituencies, and each constituency into wards. In addition to these administrative units, during the 2010 CPH each ward was divided into convenient areas called census supervisory areas (CSAs), and in turn each CSA was divided into enumeration areas (EAs). An enumeration area is a geographical area assigned to an enumerator for the purpose of conducting a census count; according to the Zambian census frame, each EA consists of an average of 110 households.
The current version of the EA frame for the 2010 CPH was updated to accommodate some changes in districts and constituencies that occurred between 2010 and 2017. The list of EAs incorporates census information on households and population counts. Each EA has a cartographic map delineating its boundaries, with identification information and a measure of size, which is the number of residential households enumerated in the 2010 CPH. This list of EAs was used as the sampling frame for the 2018 ZDHS.
The 2018 ZDHS followed a stratified two-stage sample design. The first stage involved selecting sample points (clusters) consisting of EAs. EAs were selected with a probability proportional to their size within each sampling stratum. A total of 545 clusters were selected.
The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters. During the listing, an average of 133 households were found in each cluster, from which a fixed number of 25 households were selected through an equal probability systematic selection process, to obtain a total sample size of 13,625 households. Results from this sample are representative at the national, urban and rural, and provincial levels.
For further details on sample selection, see Appendix A of the final report.
Of the 13,595 households in the sample, 12,943 were occupied. Of these occupied households, 12,831 were successfully interviewed, yielding a response rate of 99%.
In the interviewed households, 14,189 women age 15-49 were identified as eligible for individual interviews; 13,683 women were interviewed, yielding a response rate of 96% (the same rate achieved in the 2013-14 survey). A total of 13,251 men were eligible for individual interviews; 12,132 of these men were interviewed, producing a response rate of 92% (a 1 percentage point increase from the previous survey).
Of the households successfully interviewed, 12,505 were interviewed in 2018 and 326 in 2019. As the large majority of households were interviewed in 2018 and the year for reference indicators is 2018.
The design weights were adjusted for household non-response and individual non-response to obtain the sampling weights for households and for women and men, respectively. Non-response is adjusted at the sampling stratum level. For the household sampling weight, the household design weight is multiplied by the inverse of the household response rate by stratum. For women’s individual sampling weight, the household sampling weight is multiplied by the inverse of women’s individual response rate by stratum. After adjusting for non-response, the sampling weights are normalised to obtain the final standard weights that appear in the data files. The normalisation process is done to obtain a total number of unweighted cases equal to the total number of weighted cases at the national level for the total number of households, women, and men. Normalisation is done by multiplying the sampling weight by the estimated sampling fraction obtained from the survey for the household weight and the individual women’s and men’s weights. The normalised weights are relative weights that are valid for estimating means, proportions, ratios, and rates but are not valid for estimating population totals or for pooled data. A special weight for domestic violence was calculated that accounts for the selection of one woman per household and for module nonresponse. HIV weights were produced that accounted for HIV testing nonresponse among women and men separately.
Dates of Data Collection
Data Collection Mode
Data Collection Notes
Data collection was carried out from 17 July 2018 to 24 January 2019 by 22 teams, with each team consisting of seven members typically featuring the following composition: one supervisor, three female interviewers, one male interviewer, and two biomarker technicians.
Fieldwork monitoring was an integral part of the ZDHS. Senior technical staff from ZamStats, the Department of Population Studies at the University of Zambia (UNZA), UTH-VL, and TDRC visited teams regularly to review their work and monitor data quality. ZamStats organised three groups of fieldwork monitors. The first group consisted of 10 provincial coordinators, each responsible for supervising the work of the teams in one province. They helped teams resolve any issues that arose in accessing clusters or while conducting their work, and they supported the technical work of the interviewers. The second group consisted of five biomarker monitors, each responsible for two provinces, who supervised the work of the biomarker technicians. The final supervisory group consisted of three information technology (IT) staff, who were deployed to teams on an as-needed basis to resolve CAPIrelated issues. Three staff members from The DHS Program each independently visited teams to monitor data collection and biomarker collection. These visits occurred 17-21 July, 17 September-1 October, and 2-5 October 2018.
During field visits, monitors provided the teams they visited with critical feedback to improve their performance. All monitors used the ZDHS field-check tables, based on data from the completed clusters, to illustrate problems specific to each team visited.
Zambia Statistics Agency
Four questionnaires were used in the 2018 ZDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s Model Questionnaires, were adapted to reflect the population and health issues relevant to Zambia. Input on questionnaire content was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international cooperating partners. After all questionnaires were finalised in English, they were translated into seven local languages: Bemba, Kaonde, Lozi, Lunda, Luvale, Nyanja, and Tonga. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.
All electronic data files were transferred via a secure internet file streaming system to the ZamStats central office in Lusaka, 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 two IT specialists and one secondary editor who took part in the main fieldwork training; they were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro 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 and data processing were initiated in July 2018 and completed in March 2019.
Estimates of Sampling Error
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 2018 Zambia Demographic and Health Survey (ZDHS) to minimise 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 2018 ZDHS 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 2018 ZDHS 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 linearisation 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 Quality Tables
- Household age distribution
- Age distribution of eligible and interviewed women
- Age distribution of eligible and interviewed men
- Completeness of reporting
- Births by calendar years
- Reporting of age at death in days
- Reporting of age at death in months
- Completeness of information on siblings
- Sibship size and sex ratio of siblings
- Height and weight data completeness and quality for children
- Number of enumeration areas completed by month, according to province, Zambia DHS 2018
Note: Data quality tables are presented in APPENDIX C of the report.
The DHS Program
Information about The DHS Program
The DHS Program
The DHS Program
Data and Data Related Resources
The DHS Program
Request Dataset Access
The following applies to DHS, MIS, AIS and SPA survey datasets (Surveys, GPS, and HIV).
To request dataset access, you must first be a registered user of the website. You must then create a new research project request. The request must include a project title and a description of the analysis you propose to perform with the data.
The requested data should only be used for the purpose of the research or study. To request the same or different data for another purpose, a new research project request should be submitted. The DHS Program will normally review all data requests within 24 hours (Monday - Friday) and provide notification if access has been granted or additional project information is needed before access can be granted.
DATASET ACCESS APPROVAL PROCESS
Access to DHS, MIS, AIS and SPA survey datasets (Surveys, HIV, and GPS) is requested and granted by country. This means that when approved, full access is granted to all unrestricted survey datasets for that country. Access to HIV and GIS datasets requires an online acknowledgment of the conditions of use.
A dataset request must include contact information, a research project title, and a description of the analysis you propose to perform with the data.
A few datasets are restricted and these are noted. Access to restricted datasets is requested online as with other datasets. An additional consent form is required for some datasets, and the form will be emailed to you upon authorization of your account. For other restricted surveys, permission must be granted by the appropriate implementing organizations, before The DHS Program can grant access. You will be emailed the information for contacting the implementing organizations. A few restricted surveys are authorized directly within The DHS Program, upon receipt of an email request.
When The DHS Program receives authorization from the appropriate organizations, the user will be contacted, and the datasets made available by secure FTP.
GPS/HIV Datasets/Other Biomarkers
Once downloaded, the datasets must not be passed on to other researchers without the written consent of The DHS Program. All reports and publications based on the requested data must be sent to The DHS Program Data Archive in a Portable Document Format (pdf) or a printed hard copy.
Datasets are made available for download by survey. You will be presented with a list of surveys for which you have been granted dataset access. After selecting a survey, a list of all available datasets for that survey will be displayed, including all survey, GPS, and HIV data files. However, only data types for which you have been granted access will be accessible. To download, simply click on the files that you wish to download and a "File Download" prompt will guide you through the remaining steps.
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
Archive where study is originally stored
The DHS Program
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.
DDI Document ID
Development Economics Data Group
The World Bank
Documentation of the DDI
Date of Metadata Production
DDI Document version
Version 01 (February 2020). Metadata is excerpted from "Zambia Demographic and Health Survey 2018" Report.