Demographic and Health Survey 2016-2017
Maldives, 2016 - 2017
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Reference ID
MDV_2016_DHS_v01_M
Producer(s)
Ministry of Health (MOH)
Collection(s)
Metadata
Created on
Feb 27, 2019
Last modified
Feb 27, 2019
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Identification
Survey ID Number
MDV_2016_DHS_v01_M
Title
Demographic and Health Survey 2016-2017
Country/Economy
| Name | Country code |
|---|---|
| Maldives | MDV |
Study type
Demographic and Health Survey (Standard) - DHS VII
Series Information
Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition.
The 2016-17 Maldives Demographic and Health Survey (2016-17 MDHS) is the second DHS survey that was conducted in the Maldives, with the 2009 MDHS being the first. A nationally representative sample of about 6,700 households was selected. All Maldivian women and men age 15-49 who were usual residents of the selected households or who slept in the households the night before the survey were eligible for the survey.
The 2016-17 Maldives Demographic and Health Survey (2016-17 MDHS) is the second DHS survey that was conducted in the Maldives, with the 2009 MDHS being the first. A nationally representative sample of about 6,700 households was selected. All Maldivian women and men age 15-49 who were usual residents of the selected households or who slept in the households the night before the survey were eligible for the survey.
Abstract
The 2016-17 Maldives Demographic and Health Survey (MDHS) is the second Demographic and Health Survey conducted in the Maldives.
The primary objective of the 2016-17 MDHS is to provide up-to-date estimates of key demographic and health indicators. The MDHS provides a comprehensive overview of population, maternal, and child health issues in the Maldives. More specifically, the 2016-17 MDHS:
- Collected data at the national level that allowed calculation of key demographic indicators, particularly fertility and under-5 mortality rates
- Explored the direct and indirect factors that determine levels and patterns of fertility and child mortality
- Measured levels of contraceptive knowledge and practice
- Collected data on key aspects of family health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery
- Obtained data on child feeding practices, including breastfeeding
- Collected anthropometric measures to assess the nutritional status of children under age 5, women age 15-49, and men age 15-49
- Conducted haemoglobin testing on children age 6-59 months and women age 15-49 to provide information on the prevalence of anaemia in these groups
- Collected data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and assessed the coverage of past HIV testing
- Collected data on the prevalence of disabilities among all household members
- Collected data on early childhood education, support for children’s learning, and the level of inadequate care for young children
- Assessed the level of knowledge and self-reported prevalence of certain non-communicable diseases such as hypertension, diabetes, thalassemia, and tuberculosis
- Collected data on knowledge and prevalence of female circumcision among women age 15-49 and their daughters age 0-14
- Obtained data on women’s experience of emotional, physical, and sexual violence.
The primary objective of the 2016-17 MDHS is to provide up-to-date estimates of key demographic and health indicators. The MDHS provides a comprehensive overview of population, maternal, and child health issues in the Maldives. More specifically, the 2016-17 MDHS:
- Collected data at the national level that allowed calculation of key demographic indicators, particularly fertility and under-5 mortality rates
- Explored the direct and indirect factors that determine levels and patterns of fertility and child mortality
- Measured levels of contraceptive knowledge and practice
- Collected data on key aspects of family health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery
- Obtained data on child feeding practices, including breastfeeding
- Collected anthropometric measures to assess the nutritional status of children under age 5, women age 15-49, and men age 15-49
- Conducted haemoglobin testing on children age 6-59 months and women age 15-49 to provide information on the prevalence of anaemia in these groups
- Collected data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and assessed the coverage of past HIV testing
- Collected data on the prevalence of disabilities among all household members
- Collected data on early childhood education, support for children’s learning, and the level of inadequate care for young children
- Assessed the level of knowledge and self-reported prevalence of certain non-communicable diseases such as hypertension, diabetes, thalassemia, and tuberculosis
- Collected data on knowledge and prevalence of female circumcision among women age 15-49 and their daughters age 0-14
- Obtained data on women’s experience of emotional, physical, and sexual violence.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Household
- Individual
- Children age 0-5
- Woman age 15-49
- Man age 15-49
- Individual
- Children age 0-5
- Woman age 15-49
- Man age 15-49
Version
Version Date
2019-02
Version Notes
The data dictionary was generated from hierarchical data that was downloaded from the The DHS Program website (http://dhsprogram.com).
Scope
Notes
The 2016-17 Maldives Demographic and Health Survey covered the following topics:
HOUSEHOLD
• Identification
• 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 biological parents, school attendance, birth registration, and disability.
• Characteristics of the household's dwelling unit, such as main source of water, time taken to get water and come back, water treatment, type of toilet facility and location, type of fuel used for cooking, materials used for the floor, roof and walls of the house, and possessions of durable goods.
INDIVIDUAL WOMAN
• Identification
• Background characteristics (including age, marital status, education, and media exposure)
• Birth history and childhood mortality
• Family planning, including knowledge, use, and sources of contraceptive methods
• Fertility preferences (including desire for more children and ideal number of children)
• Antenatal, delivery, and postnatal care
• Breastfeeding and infant feeding practices
• Vaccinations and childhood illnesses
• Women’s work and husbands’ background characteristics
• Knowledge and awareness regarding HIV/AIDS and other sexually transmitted diseases (STDs)
• Self-reported prevalence of smoking and selected diseases (e.g., hypertension, diabetes)
• Female circumcision
• Early childhood development and support for learning
• Violence against women
INDIVIDUAL MAN
• Identification
• Respondent's background
• Reproduction
• Contraception
• Marriage and sexual activity
• Fertility preferences
• Employment and gender roles
• HIV/AIDS
• Other health issues
• Non communicable diseases
BIOMARKER
• Identification
• Weight, height and hemoglobin measurement for children age 0-5
• Weight, height and hemoglobin measurement for women age 15-49
• Weight and height measurement for men age 15-49
HOUSEHOLD
• Identification
• 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 biological parents, school attendance, birth registration, and disability.
• Characteristics of the household's dwelling unit, such as main source of water, time taken to get water and come back, water treatment, type of toilet facility and location, type of fuel used for cooking, materials used for the floor, roof and walls of the house, and possessions of durable goods.
INDIVIDUAL WOMAN
• Identification
• Background characteristics (including age, marital status, education, and media exposure)
• Birth history and childhood mortality
• Family planning, including knowledge, use, and sources of contraceptive methods
• Fertility preferences (including desire for more children and ideal number of children)
• Antenatal, delivery, and postnatal care
• Breastfeeding and infant feeding practices
• Vaccinations and childhood illnesses
• Women’s work and husbands’ background characteristics
• Knowledge and awareness regarding HIV/AIDS and other sexually transmitted diseases (STDs)
• Self-reported prevalence of smoking and selected diseases (e.g., hypertension, diabetes)
• Female circumcision
• Early childhood development and support for learning
• Violence against women
INDIVIDUAL MAN
• Identification
• Respondent's background
• Reproduction
• Contraception
• Marriage and sexual activity
• Fertility preferences
• Employment and gender roles
• HIV/AIDS
• Other health issues
• Non communicable diseases
BIOMARKER
• Identification
• Weight, height and hemoglobin measurement for children age 0-5
• Weight, height and hemoglobin measurement for women age 15-49
• Weight and height measurement for men age 15-49
Coverage
Geographic Coverage
National coverage
Universe
The survey covered all de jure household members (usual residents), children age 0-5 years, women age 15-49 years and men age 15-49 years resident in the household.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Ministry of Health (MOH) | Government of the Maldives |
Producers
| Name | Affiliation | Role |
|---|---|---|
| ICF | The DHS Program | Provided technical assistance through the DHS Program |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Government of the Maldives | GovMDV | Financial support |
| World Health Organization | WHO | Financial support |
| United Nations International Children’s Emergency Fund | UNICEF | Financial support |
| United Nations Population Fund | UNFPA | Financial support |
Sampling
Sampling Procedure
The sampling frame used for the 2016-17 MDHS is the 2014 Maldives Population and Housing Census, provided by the National Bureau of Statistics in Maldives. The census frame is a complete list of all 997 census blocks (CB) created for the 2014 census. A CB is a geographic area containing an average of 58 households. The sampling frame contains information about the CB location and estimated number of residential households. Each CB has accompanying cartographic materials. These materials delineate geographic locations, boundaries, main access, and landmarks in or outside the CB that help identify the CB.
The 2016-17 MDHS sample is designed to yield representative information for most indicators for the country as a whole, for residence, and for each of Maldives's six regions. Also, the MDHS sample is designed to yield representative information for some selected indicators for each of the atolls of the country.
The sample for the 2016-17 MDHS was a stratified sample selected in two stages from the sampling frame. Stratification was achieved by separating each region into atolls; in total, 21 sampling strata were created, within each of which samples were selected independently. In the first stage, 266 CBs were selected with probability proportional to size according to the sample allocated to each stratum. The CB size is the number of residential households residing in the CB based on the 2014 census. Because of the large variation in the size of atolls, a proportional allocation of the sample points to the atolls is not adequate since the small atolls will receive too few sample points. The allocation adopted is a somewhat adjusted equal size allocation at atoll level except Malé which consists of 38% of the total residential population of the Maldives. This allocation will guarantee a better precision at atoll level and comparability across atolls.
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.
After the selection of CBs and immediately before interviewing, a household listing operation was carried out. The household listing operation was implemented by the teams of fieldworkers who, upon entering a sampled CB, would disperse to record on their tablet computers all occupied Maldivian residential households found in the CB with the address and the name of the head of the household. The resulting list of households served as the sampling frame for the selection of households in the second stage.
In the second stage of selection, a fixed number of 25 households was selected in every CB (cluster) (except for Felidhu Atoll (V) where about 42 households on average were selected in all the six clusters of the atoll), by an equal probability systematic sampling based on the household listing. Selection of households was done on the supervisor's tablet in the field. A total of 6,750 households was sampled, 1,075 households in Malé region and 5,675 households in other areas. The survey interviewers were required to interview only the pre-selected households. No replacements and no changes of the preselected households were allowed in order to prevent bias.
For further details on sample design, see Appendix A of the final report.
The 2016-17 MDHS sample is designed to yield representative information for most indicators for the country as a whole, for residence, and for each of Maldives's six regions. Also, the MDHS sample is designed to yield representative information for some selected indicators for each of the atolls of the country.
The sample for the 2016-17 MDHS was a stratified sample selected in two stages from the sampling frame. Stratification was achieved by separating each region into atolls; in total, 21 sampling strata were created, within each of which samples were selected independently. In the first stage, 266 CBs were selected with probability proportional to size according to the sample allocated to each stratum. The CB size is the number of residential households residing in the CB based on the 2014 census. Because of the large variation in the size of atolls, a proportional allocation of the sample points to the atolls is not adequate since the small atolls will receive too few sample points. The allocation adopted is a somewhat adjusted equal size allocation at atoll level except Malé which consists of 38% of the total residential population of the Maldives. This allocation will guarantee a better precision at atoll level and comparability across atolls.
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.
After the selection of CBs and immediately before interviewing, a household listing operation was carried out. The household listing operation was implemented by the teams of fieldworkers who, upon entering a sampled CB, would disperse to record on their tablet computers all occupied Maldivian residential households found in the CB with the address and the name of the head of the household. The resulting list of households served as the sampling frame for the selection of households in the second stage.
In the second stage of selection, a fixed number of 25 households was selected in every CB (cluster) (except for Felidhu Atoll (V) where about 42 households on average were selected in all the six clusters of the atoll), by an equal probability systematic sampling based on the household listing. Selection of households was done on the supervisor's tablet in the field. A total of 6,750 households was sampled, 1,075 households in Malé region and 5,675 households in other areas. The survey interviewers were required to interview only the pre-selected households. No replacements and no changes of the preselected households were allowed in order to prevent bias.
For further details on sample design, see Appendix A of the final report.
Response Rate
A total of 6,697 households were selected for the sample, of which 6,608 were occupied. Of the occupied households, 6,050 were successfully interviewed, yielding a response rate of 92%. In the interviewed households, 9,170 women age 15-49 were identified for individual interviews; these interviews were completed with 7,699 women, yielding a response rate of 84%. In addition, 6,335 men age 15-49 were identified, of whom 4,342 were interviewed for a response rate of 69%.
All response rates are considerably lower in Malé region than in other atolls; for example, the response rate of women to individual interviews was only 68% in Malé, compared with 87% in other atolls. Overall, the response rate at the household level (92%) is slightly higher than it was for the 2009 MDHS (90%).
All response rates are considerably lower in Malé region than in other atolls; for example, the response rate of women to individual interviews was only 68% in Malé, compared with 87% in other atolls. Overall, the response rate at the household level (92%) is slightly higher than it was for the 2009 MDHS (90%).
Weighting
A spreadsheet containing all sampling parameters and selection probabilities was prepared to facilitate the calculation of the design weights. Design weights were adjusted for household non-response and as well as for individual non-response to calculate the following survey weights:
1. The household survey weight.
2. The individual survey weight for women 15-49.
3. The individual survey weight for men 15-49.
4. The survey weight for the domestic violence module.
The differences between the household survey weight and the individual survey weights are introduced by individual non-response. In the case of the household survey weight, the design weight was multiplied by the inverse of the strata-level household weighted response rates. In the case of the women’s individual survey weight, the household survey weight was multiplied by the inverse of the strata-level women’s individual weighted response rates. Similarly, in the case of the men’s individual survey weight, the household survey weight was multiplied by the inverse of the strata-level men’s individual weighted response rates.
In addition to the standard survey weights described above, a special weight was calculated for the domestic violence module, where one woman 15-49 was selected at random from each household to complete the domestic violence questionnaire. In the case of the domestic violence weight, for each household, the household survey weight was multiplied by the number of women 15-49 to account for the within-household selection probabilities; then the modified weights were adjusted for the nonresponse to the module similar to the nonresponse adjustment described earlier.
All the survey weights described earlier were then normalised in order to give a total number of weighted cases that equals the total number of unweighted cases at national level. Normalisation is done by multiplying the survey weight by the estimated total sampling fraction obtained from the survey for the household weight, the individual woman’s weight, the individual man’s weight, and the domestic violence weight. The normalised weights are relative weights which are valid for estimating means, proportions and ratios, but not valid for estimating population totals and for pooled data. The number of weighted cases using the normalised weight has no direct relation with the survey precision because it is relative; especially for oversampled areas, the number of weighted cases will be much smaller than the number of unweighted cases, which is directly related to survey precision.
For further details on sampling weights, see Appendix A.4 of the final report.
1. The household survey weight.
2. The individual survey weight for women 15-49.
3. The individual survey weight for men 15-49.
4. The survey weight for the domestic violence module.
The differences between the household survey weight and the individual survey weights are introduced by individual non-response. In the case of the household survey weight, the design weight was multiplied by the inverse of the strata-level household weighted response rates. In the case of the women’s individual survey weight, the household survey weight was multiplied by the inverse of the strata-level women’s individual weighted response rates. Similarly, in the case of the men’s individual survey weight, the household survey weight was multiplied by the inverse of the strata-level men’s individual weighted response rates.
In addition to the standard survey weights described above, a special weight was calculated for the domestic violence module, where one woman 15-49 was selected at random from each household to complete the domestic violence questionnaire. In the case of the domestic violence weight, for each household, the household survey weight was multiplied by the number of women 15-49 to account for the within-household selection probabilities; then the modified weights were adjusted for the nonresponse to the module similar to the nonresponse adjustment described earlier.
All the survey weights described earlier were then normalised in order to give a total number of weighted cases that equals the total number of unweighted cases at national level. Normalisation is done by multiplying the survey weight by the estimated total sampling fraction obtained from the survey for the household weight, the individual woman’s weight, the individual man’s weight, and the domestic violence weight. The normalised weights are relative weights which are valid for estimating means, proportions and ratios, but not valid for estimating population totals and for pooled data. The number of weighted cases using the normalised weight has no direct relation with the survey precision because it is relative; especially for oversampled areas, the number of weighted cases will be much smaller than the number of unweighted cases, which is directly related to survey precision.
For further details on sampling weights, see Appendix A.4 of the final report.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2016-03 | 2017-11 |
Data Collection Mode
Face-to-face [f2f]
Data Collection Notes
Data collection took place over a 20-month period, from 17 March 2016 to 27 November 2017. Fieldwork was carried out in two phases. The first phase was carried out from 17 March to 31 October 2016. During this phase, data collection was completed in the Malé region, Kaafu atoll (K), North Ari atoll (AA), and South Ari atoll (ADh). Initially, there were 6 field teams, each consisting of one team supervisor, one health worker, and either 6 or 8 interviewers (half female and half male) during the data collection in the Malé region. However, since a few team members were unable to join fieldwork in the atolls, the teams were regrouped to form five teams composing of one team supervisor and either 6 or 8 interviewers. Anaemia testing was carried out either by trained enumerators or with assistance from health facilities located on site.
The second phase of fieldwork took place from mid-April to 27 November 2017. Five teams, each composed of one team supervisor and 6-8 interviewers, were initially dispatched to complete data collection in the remaining atolls. Towards the end of the survey, teams were reduced to align with the few remaining atolls. Special attention was given to ensure that either an experienced nurse or community health worker was placed in each team to assist in anaemia testing.
Electronic data files were transferred to the MoH central office in Malé every few days via the secured IFSS. Staff from MoH coordinated and supervised fieldwork activities. Field check tables based on data from completed questionnaires were generated periodically by the central office and used to monitor progress and provide feedback to the field teams.
The second phase of fieldwork took place from mid-April to 27 November 2017. Five teams, each composed of one team supervisor and 6-8 interviewers, were initially dispatched to complete data collection in the remaining atolls. Towards the end of the survey, teams were reduced to align with the few remaining atolls. Special attention was given to ensure that either an experienced nurse or community health worker was placed in each team to assist in anaemia testing.
Electronic data files were transferred to the MoH central office in Malé every few days via the secured IFSS. Staff from MoH coordinated and supervised fieldwork activities. Field check tables based on data from completed questionnaires were generated periodically by the central office and used to monitor progress and provide feedback to the field teams.
Questionnaires
Questionnaires
Four questionnaires were used for the 2016-17 MDHS: the Household Questionnaire, Woman’s Questionnaire, Man’s Questionnaire, and Biomarker Questionnaire. All questionnaires were based on the DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires that were adapted to reflect the population and health issues relevant to the Maldives. Input was solicited from various stakeholders representing relevant department and divisions within MOH, other government agencies, universities, non-governmental organisations and international agencies. All questionnaires were translated from English to Dhivehi and back-translated into English.
Data Processing
Data Editing
All electronic data files for the 2016-17 MDHS were transferred via IFSS to the MoH central office in Malé, 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 openended questions. Data editing was accomplished using CSPro software. During the duration of fieldwork, 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 March 2016 and completed in April 2018.
Data Appraisal
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 2016-17 Maldives Demographic and Health Survey (MDHS) 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 2016-17 MDHS 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 2017-18 MDHS 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.
A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2016-17 MDHS 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 2017-18 MDHS 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.
A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final 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
- Height and weight data completeness and quality for children
See details of the data quality tables in Appendix C of the survey final 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
- Height and weight data completeness and quality for children
See details of the data quality tables in Appendix C of the survey final report.
Access policy
Contacts
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
Access authority
| Name | URL | |
|---|---|---|
| The DHS Program | archive@dhsprogram.com | Link |
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_MDV_2016_DHS_v01_M
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Economics Data Group | DECDG | The World Bank | Documentation of the DDI |
DDI Document version
Version 01 (February 2019). Metadata is excerpted from "Maldives Demographic and Health Survey 2016-17" Report.