Demographic and Health Survey 2013-2014
Zambia, 2013 - 2014
Central Statistical Office (CSO), Ministry of Health
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June 16, 2017
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Identification
Survey ID Number
ZMB_2013_DHS_v01_M
Title
Demographic and Health Survey 2013-2014
Country
| Name | Country code |
|---|---|
| Zambia | ZMB |
Study type
Demographic and Health Survey (Standard) - DHS VI
Series Information
The 2013-14 ZDHS is a follow-up to the 1992, 1996, 2001-02, and 2007 ZDHS surveys and provides updated estimates of basic demographic and health indicators covered in the earlier surveys. The 2013-14 survey is the third ZDHS to measure HIV prevalence in Zambia and the first to measure HIV incidence. It is also the third survey that includes information on violence against women.
Abstract
The Zambia Demographic and Health Survey (ZDHS) is a nationally representative sample survey of women and men of reproductive age. The main objective is to provide information on levels and trends in fertility, childhood mortality, use of family planning methods, maternal and child health indicators including HIV/AIDS. This information is necessary for programme managers, policymakers, and implementers to monitor and evaluate the impact of existing programmes and to design new initiatives for health policies in Zambia.
The primary objectives of the 2013-14 ZDHS are:
• To collect up-to-date information on fertility, infant and child mortality, and family planning.
• To collect information on health-related matters such as breastfeeding, antenatal care, children’s immunisations, and childhood diseases.
• To assess knowledge of contraceptive practices among women.
• To assess the nutritional status of mothers and children.
• To improve understanding of variations in HIV seroprevalence levels according to social and economic characteristics and behavioural risk factors.
• To estimate levels of HIV incidence in the general population of adults.
• To estimate unmet need for antiretroviral treatment.
In the case of HIV/AIDS, the testing component of the 2013-14 ZDHS was undertaken to provide information to address the monitoring and evaluation needs of government and nongovernmental programmes dealing with HIV/AIDS. It also provides programme managers and policymakers with the information they need to effectively plan and implement future interventions. The overall objective was to collect high-quality and representative data on knowledge, attitudes, and behaviours regarding HIV/AIDS and other STIs and on the prevalence and incidence of HIV among women and men.
The primary objectives of the 2013-14 ZDHS are:
• To collect up-to-date information on fertility, infant and child mortality, and family planning.
• To collect information on health-related matters such as breastfeeding, antenatal care, children’s immunisations, and childhood diseases.
• To assess knowledge of contraceptive practices among women.
• To assess the nutritional status of mothers and children.
• To improve understanding of variations in HIV seroprevalence levels according to social and economic characteristics and behavioural risk factors.
• To estimate levels of HIV incidence in the general population of adults.
• To estimate unmet need for antiretroviral treatment.
In the case of HIV/AIDS, the testing component of the 2013-14 ZDHS was undertaken to provide information to address the monitoring and evaluation needs of government and nongovernmental programmes dealing with HIV/AIDS. It also provides programme managers and policymakers with the information they need to effectively plan and implement future interventions. The overall objective was to collect high-quality and representative data on knowledge, attitudes, and behaviours regarding HIV/AIDS and other STIs and on the prevalence and incidence of HIV among women and men.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Household
- Individual
- Children age 0-5
- Woman age 15-49
- Man age 15-59
- Individual
- Children age 0-5
- Woman age 15-49
- Man age 15-59
Scope
Notes
The 2013 Zambia Demographic and Health Survey covered the following topics:
HOUSEHOLD
• Usual members and visitors in the selected households
• Background information on each person listed, such as relationship to head of the household, age, sex, and highest educational attainment
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor, roof and walls of the house, and ownership of various durable goods (these items are used as proxy indicators of the household's socioeconomic status)
• Weight and height measurement for children age 0-5
• Weight, height, and HIV testing for women age 15-49
• HIV testing for men age 15-59
INDIVIDUAL WOMAN
• Background characteristics (age, religion, education, literacy, media exposure, etc.)
• Reproductive history
• Knowledge, use, and source of family planning methods
• Fertility preferences
• Maternal health (antenatal, delivery, and postnatal care)
• Fistula prevalence
• Breastfeeding and infant feeding practices
• Child immunisation and childhood illnesses
• Treatment of malaria
• Child mortality
• Marriage and sexual activity
• Women’s work and husbands’ background characteristics
• Awareness of AIDS and other STIs
• Other health issues (e.g., tuberculosis, injection safety, and smoking)
• Maternal mortality
• Domestic violence
INDIVIDUAL MAN
• Background characteristics (age, education, religion, etc.)
• Reproduction
• Contraception
• Marriage and sexual activity
• Fertility preferences
• Employment and gender roles
• Knowledge, awareness, and behavior regarding AIDS and other sexually transmitted infections (STIs)
• Other health issues
• Maternal mortality
HOUSEHOLD
• Usual members and visitors in the selected households
• Background information on each person listed, such as relationship to head of the household, age, sex, and highest educational attainment
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor, roof and walls of the house, and ownership of various durable goods (these items are used as proxy indicators of the household's socioeconomic status)
• Weight and height measurement for children age 0-5
• Weight, height, and HIV testing for women age 15-49
• HIV testing for men age 15-59
INDIVIDUAL WOMAN
• Background characteristics (age, religion, education, literacy, media exposure, etc.)
• Reproductive history
• Knowledge, use, and source of family planning methods
• Fertility preferences
• Maternal health (antenatal, delivery, and postnatal care)
• Fistula prevalence
• Breastfeeding and infant feeding practices
• Child immunisation and childhood illnesses
• Treatment of malaria
• Child mortality
• Marriage and sexual activity
• Women’s work and husbands’ background characteristics
• Awareness of AIDS and other STIs
• Other health issues (e.g., tuberculosis, injection safety, and smoking)
• Maternal mortality
• Domestic violence
INDIVIDUAL MAN
• Background characteristics (age, education, religion, etc.)
• Reproduction
• Contraception
• Marriage and sexual activity
• Fertility preferences
• Employment and gender roles
• Knowledge, awareness, and behavior regarding AIDS and other sexually transmitted infections (STIs)
• Other health issues
• Maternal mortality
Coverage
Geographic Coverage
National coverage
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Central Statistical Office (CSO) | Government of Zambia |
| Ministry of Health | Government of Zambia |
Producers
| Name | Role |
|---|---|
| University Teaching Hospital - Virology Laboratory | Technical assistance |
| Tropical Diseases Research Centre | Technical assistance |
| Department of Population Studies at the University of Zambia | Technical assistance |
| ICF International | Technical assistance |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Government of Zambia | GovZMB | Provided funding through Ministry of Health and the Ministry of Finance |
| United States Agency for International Development | USAID | Funded the study |
| Centers for Disease Control and Prevention | CDC | Funded the study |
| United Nations Population Fund | UNFPA | Funded the study |
| United Nations Children’s Fund | UNICEF | Funded the study |
Sampling
Sampling Procedure
The sample for the 2013-14 ZDHS was designed to provide estimates at the national and provincial levels, as well as for rural and urban areas within the provinces. This is the first time the ZDHS has been designed to provide estimates at such disaggregated levels for many of the survey indicators. The updated list of enumeration areas (EAs) for the 2010 Population and Housing Census provided the sampling frame for the survey. The frame comprises 25,631 EAs and 2,815,897 households. An EA is a convenient geographical area with an average size of 130 households or 600 people. For each EA, information is available on its location, type of residence (rural or urban), number of households, and total population. Each EA has a cartographical map with delimited boundaries and main landmarks of the area. A 2013-14 ZDHS cluster is essentially representative of an EA.
A representative sample of 18,052 households was drawn for the 2013-14 ZDHS. The survey used a two-stage stratified cluster sample design, with EAs (or clusters) selected during the first stage and households selected during the second stage. In the first stage, 722 EAs (305 in urban areas and 417 in rural areas) were selected with probability proportional to size. Zambia is now administratively divided into 10 provinces (Central, Copperbelt, Eastern, Luapula, Lusaka, Muchinga,2 Northern, North Western, Southern, and Western). Stratification was achieved by separating each province into urban and rural areas. Therefore, the 10 provinces were stratified into 20 sampling strata. In the second stage, a complete list of households served as the sampling frame in the selection of households for enumeration. An average of 25 households was selected in each EA. It was during the second stage of selection that a representative sample of 18,052 households was selected.
For further details on sample selection, see Appendix A of the final report.
A representative sample of 18,052 households was drawn for the 2013-14 ZDHS. The survey used a two-stage stratified cluster sample design, with EAs (or clusters) selected during the first stage and households selected during the second stage. In the first stage, 722 EAs (305 in urban areas and 417 in rural areas) were selected with probability proportional to size. Zambia is now administratively divided into 10 provinces (Central, Copperbelt, Eastern, Luapula, Lusaka, Muchinga,2 Northern, North Western, Southern, and Western). Stratification was achieved by separating each province into urban and rural areas. Therefore, the 10 provinces were stratified into 20 sampling strata. In the second stage, a complete list of households served as the sampling frame in the selection of households for enumeration. An average of 25 households was selected in each EA. It was during the second stage of selection that a representative sample of 18,052 households was selected.
For further details on sample selection, see Appendix A of the final report.
Response Rate
A total of 18,052 households were selected from 722 clusters, of which 16,258 were occupied at the time of the fieldwork. Of the occupied households, 15,920 were successfully interviewed, yielding a household response rate of 98 percent.
In the interviewed households, a total of 17,064 women age 15-49 were identified as eligible for individual interviews, and 96 percent of these women were successfully interviewed. A total of 16,209 men age 15-59 were identified as eligible for interviews, and 91 percent were successfully interviewed. Individual response rates were slightly lower in urban areas than in rural areas.
In the interviewed households, a total of 17,064 women age 15-49 were identified as eligible for individual interviews, and 96 percent of these women were successfully interviewed. A total of 16,209 men age 15-59 were identified as eligible for interviews, and 91 percent were successfully interviewed. Individual response rates were slightly lower in urban areas than in rural areas.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2013-08 | 2014-04 |
Data Collection Mode
Face-to-face [f2f]
Data Collection Notes
Training of Field Staff
The CSO and MoH recruited and trained 306 participants. The MoH provided nurses, HIV counsellors, and laboratory technicians, while the CSO provided non-medical interviewers and data processing staff. Training on the survey methodology was conducted over a five-week period in May and June 2013 by resource personnel from the CDC, CSO, MoH, TDRC, UTH Virology, and UNZA Population Studies. Prior to the training of field staff, a two-week training workshop was conducted for resource personnel (training of trainers). Field staff were trained to serve as supervisors, field editors, and interviewers. The training course consisted of instruction on interviewing techniques and field procedures, a detailed review of questionnaire items, instruction and practice in weighing and measuring children, mock interviews between participants in the classroom, and practice interviews with real respondents in areas outside the 2013-14 ZDHS sample clusters. Field practice in rapid HIV testing, CD4 measurement, and DBS specimen preparation for HIV testing was also conducted. During this period, field editors and team supervisors were provided with additional training in methods of field editing, data quality control procedures, and fieldwork coordination. Twenty-four supervisors, 24 editors, 72 female interviewers, 48 HIV counsellors, 24 laboratory technicians, and 48 male interviewers made up the 24 data collection teams (each comprising 10 people) for the 2013-14 ZDHS.
Fieldwork
The survey was undertaken by 24 field teams. The 24 interviewing teams carrying out data collection each consisted of one supervisor (team leader), one field editor, three female interviewers, two male interviewers, two nurses/nurse counsellors, one laboratory technician, and one driver. Four senior staff members from the CSO, assisted by seven other staff members, coordinated supervision of fieldwork activities. Three staff members from UNZA assisted in field supervision and monitoring. In addition, two ICF International staff members conducted field supervision activities. To monitor implementation of the 2013-14 ZDHS biomarker components, laboratory staff from the TDRC and UTH Virology periodically supervised and monitored field laboratory technicians with respect to their compliance with survey biomarker procedures. Data collection took place over an eight-month period, from August 2013 to April 2014.
The CSO and MoH recruited and trained 306 participants. The MoH provided nurses, HIV counsellors, and laboratory technicians, while the CSO provided non-medical interviewers and data processing staff. Training on the survey methodology was conducted over a five-week period in May and June 2013 by resource personnel from the CDC, CSO, MoH, TDRC, UTH Virology, and UNZA Population Studies. Prior to the training of field staff, a two-week training workshop was conducted for resource personnel (training of trainers). Field staff were trained to serve as supervisors, field editors, and interviewers. The training course consisted of instruction on interviewing techniques and field procedures, a detailed review of questionnaire items, instruction and practice in weighing and measuring children, mock interviews between participants in the classroom, and practice interviews with real respondents in areas outside the 2013-14 ZDHS sample clusters. Field practice in rapid HIV testing, CD4 measurement, and DBS specimen preparation for HIV testing was also conducted. During this period, field editors and team supervisors were provided with additional training in methods of field editing, data quality control procedures, and fieldwork coordination. Twenty-four supervisors, 24 editors, 72 female interviewers, 48 HIV counsellors, 24 laboratory technicians, and 48 male interviewers made up the 24 data collection teams (each comprising 10 people) for the 2013-14 ZDHS.
Fieldwork
The survey was undertaken by 24 field teams. The 24 interviewing teams carrying out data collection each consisted of one supervisor (team leader), one field editor, three female interviewers, two male interviewers, two nurses/nurse counsellors, one laboratory technician, and one driver. Four senior staff members from the CSO, assisted by seven other staff members, coordinated supervision of fieldwork activities. Three staff members from UNZA assisted in field supervision and monitoring. In addition, two ICF International staff members conducted field supervision activities. To monitor implementation of the 2013-14 ZDHS biomarker components, laboratory staff from the TDRC and UTH Virology periodically supervised and monitored field laboratory technicians with respect to their compliance with survey biomarker procedures. Data collection took place over an eight-month period, from August 2013 to April 2014.
Questionnaires
Questionnaires
Three questionnaires were used in the 2013-14 ZDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. The three instruments were based on the questionnaires developed by the Demographic and Health Surveys Program and adapted to Zambia’s specific data needs. The questionnaires were translated into seven major languages: Bemba, Kaonde, Lozi, Lunda, Luvale, Nyanja, and Tonga. Questionnaires and field procedures were pretested prior to implementation of the main survey.
The Household Questionnaire was used to collect data such as:
• Age, sex, marital status, and education of all usual members and visitors
• Current school attendance and survivorship of parents among children under age 18
• Characteristics of the structural dwelling/housing unit
• Sanitation facilities and source of water
• Ownership of durable goods, land, and livestock
• Ownership and use of mosquito nets
The Household Questionnaire was also used to record biomarker data, including height and weight data for children and women and HIV and CD4 testing information for women and men. Data on age and sex of household members were used to identify the women and men eligible for individual interviews.
The Woman’s Questionnaire was used to collect information from all women age 15-49.
The Man’s Questionnaire was administered to all men age 15-59. It collected much of the same information as the Woman’s Questionnaire but it did not contain a detailed reproductive history or questions on maternal and child health or nutrition.
The Household Questionnaire was used to collect data such as:
• Age, sex, marital status, and education of all usual members and visitors
• Current school attendance and survivorship of parents among children under age 18
• Characteristics of the structural dwelling/housing unit
• Sanitation facilities and source of water
• Ownership of durable goods, land, and livestock
• Ownership and use of mosquito nets
The Household Questionnaire was also used to record biomarker data, including height and weight data for children and women and HIV and CD4 testing information for women and men. Data on age and sex of household members were used to identify the women and men eligible for individual interviews.
The Woman’s Questionnaire was used to collect information from all women age 15-49.
The Man’s Questionnaire was administered to all men age 15-59. It collected much of the same information as the Woman’s Questionnaire but it did not contain a detailed reproductive history or questions on maternal and child health or nutrition.
Data Processing
Data Editing
All questionnaires for the 2013-14 ZDHS were returned to the CSO headquarters in Lusaka for data processing, which consisted of office editing, coding of open-ended questions, data entry, and editing of computer-identified errors. Data processing staff included two data processing supervisors, 24 data entry clerks, five office editors, four secondary editors, one questionnaire administrator, and one biomarker administrator.
The processing of the data began in September 2013, one month after data collection commenced, and continued concurrently with the fieldwork. This offered an advantage because data were consistently checked and feedback was given to field teams, thereby improving data quality. Before being sent to the data processing centre in Lusaka, completed questionnaires were edited in the field by the field editors and checked by the supervisors. At the processing centre, data were edited and coded by office editors. Data were then entered using the CSPro computer package. All data were entered twice for 100 percent verification. This double entry of data enabled easy comparisons and identification of errors and inconsistencies. Inconsistencies were resolved by tallying the data with the paper questionnaire entries. Further inconsistencies that were identified were resolved through secondary editing of the data. The data files (excluding HIV testing data) were finalised in June 2014 after data cleaning.
The processing of the data began in September 2013, one month after data collection commenced, and continued concurrently with the fieldwork. This offered an advantage because data were consistently checked and feedback was given to field teams, thereby improving data quality. Before being sent to the data processing centre in Lusaka, completed questionnaires were edited in the field by the field editors and checked by the supervisors. At the processing centre, data were edited and coded by office editors. Data were then entered using the CSPro computer package. All data were entered twice for 100 percent verification. This double entry of data enabled easy comparisons and identification of errors and inconsistencies. Inconsistencies were resolved by tallying the data with the paper questionnaire entries. Further inconsistencies that were identified were resolved through secondary editing of the data. The data files (excluding HIV testing data) were finalised in June 2014 after data cleaning.
Data Appraisal
Estimates of Sampling Error
The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling 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 2014 Zambia DHS (ZDHS) to minimize this type of error, non-sampling 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 2014 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 between 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 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 2014 ZDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF International. These programs use the Taylor linearization method of variance estimation 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.
The Taylor linearization method treats any percentage or average as a ratio estimate, r = y x , where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.
Note: Detailed description of estimate of sampling error is presented in APPENDIX B of the survey report.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2014 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 between 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 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 2014 ZDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF International. These programs use the Taylor linearization method of variance estimation 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.
The Taylor linearization method treats any percentage or average as a ratio estimate, r = y x , where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.
Note: Detailed description of estimate of sampling error is presented in APPENDIX B of the survey report.
Data Appraisal
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
- Nutritional status of children based on the NCHS/CDC/WHO International Reference Population
- Completeness of information on siblings
- Sibship size and sex ratio of siblingss
Note: See detailed tables in APPENDIX C of the report.
- 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
- Nutritional status of children based on the NCHS/CDC/WHO International Reference Population
- Completeness of information on siblings
- Sibship size and sex ratio of siblingss
Note: See detailed tables in APPENDIX C of the report.
Access policy
Access authority
| Name | URL | |
|---|---|---|
| The DHS Program | archive@dhsprogram.com | http://www.DHSprogram.com |
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| Information about The DHS Program | The DHS Program | reports@DHSprogram.com | http://www.DHSprogram.com |
| General Inquiries | The DHS Program | info@dhsprogram.com | http://www.DHSprogram.com |
| Data and Data Related Resources | The DHS Program | archive@dhsprogram.com | http://www.DHSprogram.com |
Access conditions
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.
Required Information
A dataset request must include contact information, a research project title, and a description of the analysis you propose to perform with the data.
Restricted Datasets
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
Because of the sensitive nature of GPS, HIV and other biomarkers datasets, permission to access these datasets requires that you accept a Terms of Use Statement. After selecting GPS/HIV/Other Biomarkers datasets, the user is presented with a consent form which should be signed electronically by entering the password for the user's account.
Dataset Terms of Use
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.
Download Datasets
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.
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.
Required Information
A dataset request must include contact information, a research project title, and a description of the analysis you propose to perform with the data.
Restricted Datasets
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
Because of the sensitive nature of GPS, HIV and other biomarkers datasets, permission to access these datasets requires that you accept a Terms of Use Statement. After selecting GPS/HIV/Other Biomarkers datasets, the user is presented with a consent form which should be signed electronically by entering the password for the user's account.
Dataset Terms of Use
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.
Download Datasets
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.
Citation requirements
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
- 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
Location of Data Collection
The DHS Program
Archive where study is originally stored
The DHS Program
http://dhsprogram.com/data/available-datasets.cfm
Cost: None
http://dhsprogram.com/data/available-datasets.cfm
Cost: None
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.
Metadata production
DDI Document ID
DDI_ZMB_2013_DHS_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | The World Bank | Documentation of the DDI |
Date of Metadata Production
2015-04-17
DDI Document version
Version 01 (April 2015). Metadata is excerpted from "Zambia Demographic and Health Survey 2013-14" Report.