Demographic and Health Survey 2022
Nepal, 2022
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Reference ID
NPL_2022_DHS_v01_M
Producer(s)
Ministry of Health and Population (MoHP)
Collection(s)
Metadata
Created on
Jul 05, 2023
Last modified
Jul 05, 2023
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Identification
Survey ID Number
NPL_2022_DHS_v01_M
Title
Demographic and Health Survey 2022
Country/Economy
| Name | Country code |
|---|---|
| Nepal | NPL |
Study type
Demographic and Health Survey [hh/dhs]
Series Information
The 2022 Nepal Demographic and Health Survey (2022 NDHS) is the sixth survey of its kind following the 1996 Nepal Family Health Survey and the 2001, 2006, 2011, and 2016 NDHS surveys. The 2022 NDHS incorporated a nationally representative sample of 14,280 households from 476 clusters. All women 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 2022 Nepal Demographic and Health Survey (NDHS) is the sixth survey of its kind implemented in the country as part of the worldwide Demographic and Health Surveys (DHS) Program. It was implemented by New ERA under the aegis of the Ministry of Health and Population (MoHP) of the Government of Nepal with the objective of providing reliable, accurate, and up-to-date data for the country.
The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2022 NDHS collected information on fertility, marriage, family planning, breastfeeding practices, nutrition, food insecurity, maternal and child health, childhood mortality, awareness and behavior regarding HIV/AIDS and other sexually transmitted infections (STIs), women’s empowerment, domestic violence, fistula, mental health, accident and injury, disability, and other healthrelated issues such as smoking, knowledge of tuberculosis, and prevalence of hypertension.
The information collected through the 2022 NDHS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of Nepal’s population. The survey also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nepal.
The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2022 NDHS collected information on fertility, marriage, family planning, breastfeeding practices, nutrition, food insecurity, maternal and child health, childhood mortality, awareness and behavior regarding HIV/AIDS and other sexually transmitted infections (STIs), women’s empowerment, domestic violence, fistula, mental health, accident and injury, disability, and other healthrelated issues such as smoking, knowledge of tuberculosis, and prevalence of hypertension.
The information collected through the 2022 NDHS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of Nepal’s population. The survey also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nepal.
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 Notes
The data dictionary was generated from hierarchical data that was downloaded from the The DHS Program website (http://dhsprogram.com).
- Contract Phase: DHS-VIII
- Recode Structure: DHS-VIII
- Contract Phase: DHS-VIII
- Recode Structure: DHS-VIII
Scope
Notes
The 2022 Nepal 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, educational attainment, and birth registration
• Characteristics of the household's dwelling unit, such as the source of water for drinking and other purposes such as cleaning and handwashing, water source location and how long it takes to get water, type of toilet facilities and where it is located, type of fuel used for cooking, main source of light for the home, type of fuel or energy used for heating the home, number of rooms, ownership of livestock, possessions of durable goods, and main material for the floor, roof and walls of the dwelling.
• Accidents and injuries
• Food insecurity
WOMAN
• Identification
• Background characteristics (including age, education, and media exposure)
• Pregnancy history and child mortality
• Knowledge, use, and source of family planning methods
• Fertility preferences (including desire for more children and ideal number of children)
• Antenatal, delivery, and postnatal care
• Vaccinations and childhood illnesses
• Breastfeeding and infant feeding practices
• Women’s work and husbands’ background characteristics
• Knowledge, awareness, and behavior regarding HIV and other sexually transmitted infections (STIs)
• Fistula
• Mental health
• Domestic violence
• Knowledge, attitudes, and behavior related to other health issues (for example, cervical and breast cancer, smoking, tuberculosis, and COVID-19)
MAN
• Identification
• Background characteristics
• Reproduction
• Contraception
• Marriage and sexual activity
• Fertility preferences
• Employment and gender roles
• HIV/AIDS
• Other health issues
• Mental health
BIOMARKER
• Identification
• Weight, height, and hemoglobin measurement for children age 0-4
• Weight, height, blood pressure, and hemoglobin measurement for women age 15 and above
• Weight, height, and blood pressure measurement for men age 15 and above
FIELDWORKER
• Background information on each fieldworkers
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, educational attainment, and birth registration
• Characteristics of the household's dwelling unit, such as the source of water for drinking and other purposes such as cleaning and handwashing, water source location and how long it takes to get water, type of toilet facilities and where it is located, type of fuel used for cooking, main source of light for the home, type of fuel or energy used for heating the home, number of rooms, ownership of livestock, possessions of durable goods, and main material for the floor, roof and walls of the dwelling.
• Accidents and injuries
• Food insecurity
WOMAN
• Identification
• Background characteristics (including age, education, and media exposure)
• Pregnancy history and child mortality
• Knowledge, use, and source of family planning methods
• Fertility preferences (including desire for more children and ideal number of children)
• Antenatal, delivery, and postnatal care
• Vaccinations and childhood illnesses
• Breastfeeding and infant feeding practices
• Women’s work and husbands’ background characteristics
• Knowledge, awareness, and behavior regarding HIV and other sexually transmitted infections (STIs)
• Fistula
• Mental health
• Domestic violence
• Knowledge, attitudes, and behavior related to other health issues (for example, cervical and breast cancer, smoking, tuberculosis, and COVID-19)
MAN
• Identification
• Background characteristics
• Reproduction
• Contraception
• Marriage and sexual activity
• Fertility preferences
• Employment and gender roles
• HIV/AIDS
• Other health issues
• Mental health
BIOMARKER
• Identification
• Weight, height, and hemoglobin measurement for children age 0-4
• Weight, height, blood pressure, and hemoglobin measurement for women age 15 and above
• Weight, height, and blood pressure measurement for men age 15 and above
FIELDWORKER
• Background information on each fieldworkers
Coverage
Geographic Coverage
National coverage
Universe
The survey covered all de jure household members (usual residents), all women aged 15-49, men ageed 15-49, and all children aged 0-4 resident in the household.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Ministry of Health and Population (MoHP) | Government of Nepal |
Producers
| Name | Affiliation | Role |
|---|---|---|
| ICF | The DHS Program | Provided technical assistance through The DHS Program |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Government of Nepal | Govt. NPL | Funding the study |
| United States Agency for International Development | USAID | Funding the study |
Sampling
Sampling Procedure
The sampling frame used for the 2022 NDHS is an updated version of the frame from the 2011 Nepal Population and Housing Census (NPHC) provided by the National Statistical Office. The 2022 NDHS considered wards from the 2011 census as sub-wards, the smallest administrative unit for the survey. The census frame includes a complete list of Nepal’s 36,020 sub-wards. Each sub-ward has a residence type (urban or rural), and the measure of size is the number of households.
In September 2015, Nepal’s Constituent Assembly declared changes in the administrative units and reclassified urban and rural areas in the country. Nepal is divided into seven provinces: Koshi Province, Madhesh Province, Bagmati Province, Gandaki Province, Lumbini Province, Karnali Province, and Sudurpashchim Province. Provinces are divided into districts, districts into municipalities, and municipalities into wards. Nepal has 77 districts comprising a total of 753 (local-level) municipalities. Of the municipalities, 293 are urban and 460 are rural.
Originally, the 2011 NPHC included 58 urban municipalities. This number increased to 217 as of 2015. On March 10, 2017, structural changes were made in the classification system for urban (Nagarpalika) and rural (Gaonpalika) locations. Nepal currently has 293 Nagarpalika, with 65% of the population living in these urban areas. The 2022 NDHS used this updated urban-rural classification system. The survey sample is a stratified sample selected in two stages. Stratification was achieved by dividing each of the seven provinces into urban and rural areas that together formed the sampling stratum for that province. A total of 14 sampling strata were created in this way. Implicit stratification with proportional allocation was achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units at the different levels, and by using a probability-proportional-to-size selection at the first stage of sampling. In the first stage of sampling, 476 primary sampling units (PSUs) were selected with probability proportional to PSU size and with independent selection in each sampling stratum within the sample allocation. Among the 476 PSUs, 248 were from urban areas and 228 from rural areas. A household listing operation was carried out in all of the selected PSUs before the main survey. The resulting list of households served as the sampling frame for the selection of sample households in the second stage. Thirty households were selected from each cluster, for a total sample size of 14,280 households. Of these households, 7,440 were in urban areas and 6,840 were in rural areas. Some of the selected sub-wards were found to be overly large during the household listing operation. Selected sub-wards with an estimated number of households greater than 300 were segmented. Only one segment was selected for the survey with probability proportional to segment size.
For further details on sample design, see APPENDIX A of the final report.
In September 2015, Nepal’s Constituent Assembly declared changes in the administrative units and reclassified urban and rural areas in the country. Nepal is divided into seven provinces: Koshi Province, Madhesh Province, Bagmati Province, Gandaki Province, Lumbini Province, Karnali Province, and Sudurpashchim Province. Provinces are divided into districts, districts into municipalities, and municipalities into wards. Nepal has 77 districts comprising a total of 753 (local-level) municipalities. Of the municipalities, 293 are urban and 460 are rural.
Originally, the 2011 NPHC included 58 urban municipalities. This number increased to 217 as of 2015. On March 10, 2017, structural changes were made in the classification system for urban (Nagarpalika) and rural (Gaonpalika) locations. Nepal currently has 293 Nagarpalika, with 65% of the population living in these urban areas. The 2022 NDHS used this updated urban-rural classification system. The survey sample is a stratified sample selected in two stages. Stratification was achieved by dividing each of the seven provinces into urban and rural areas that together formed the sampling stratum for that province. A total of 14 sampling strata were created in this way. Implicit stratification with proportional allocation was achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units at the different levels, and by using a probability-proportional-to-size selection at the first stage of sampling. In the first stage of sampling, 476 primary sampling units (PSUs) were selected with probability proportional to PSU size and with independent selection in each sampling stratum within the sample allocation. Among the 476 PSUs, 248 were from urban areas and 228 from rural areas. A household listing operation was carried out in all of the selected PSUs before the main survey. The resulting list of households served as the sampling frame for the selection of sample households in the second stage. Thirty households were selected from each cluster, for a total sample size of 14,280 households. Of these households, 7,440 were in urban areas and 6,840 were in rural areas. Some of the selected sub-wards were found to be overly large during the household listing operation. Selected sub-wards with an estimated number of households greater than 300 were segmented. Only one segment was selected for the survey with probability proportional to segment size.
For further details on sample design, see APPENDIX A of the final report.
Response Rate
A total of 14,243 households were selected for the sample, of which 13,833 were found to be occupied. Of the occupied households, 13,786 were successfully interviewed, yielding a response rate of more than 99%. In the interviewed households, 15,238 women age 15-49 were identified as eligible for individual interviews. Interviews were completed with 14,845 women, yielding a response rate of 97%. In the subsample of households selected for the men’s survey, 5,185 men age 15-49 were identified as eligible for individual interviews and 4,913 were successfully interviewed, yielding a response rate of 95%.
Weighting
A spreadsheet containing all sampling parameters and selection probabilities was prepared to facilitate the calculation of design weights. Design weights were adjusted for household nonresponse and for individual nonresponse to obtain the sampling weights for households and for women, respectively. Similar weights were calculated for the male survey. The differences between the household sampling weights and the individual sampling weights are introduced by individual nonresponse. The weight for domestic violence against women took the number of eligible women in the household into account. The final sampling weights were normalized so that the total number of unweighted cases was equal to the total number of weighted cases at the national level for both household weights and individual weights. Several sets of
weights were calculated:
• one set for all households selected for the survey
• one set for the women’s individual survey
• one set for all households selected for the men’s survey
• one set for the men’s individual survey
• one set for domestic violence against women
It is important to note that normalized weights are relative weights that are valid for estimating means, proportions, and ratios but not valid for estimating population totals or for pooled data. Also, the number of weighted cases using the normalized weight has no direct relation with survey precision because it is relative - specially for oversampled areas, where the number of weighted cases is much smaller than the number of unweighted cases and only the latter are directly related to survey precision.
For further details on sample weights, see APPENDIX A.4 of the final report.
weights were calculated:
• one set for all households selected for the survey
• one set for the women’s individual survey
• one set for all households selected for the men’s survey
• one set for the men’s individual survey
• one set for domestic violence against women
It is important to note that normalized weights are relative weights that are valid for estimating means, proportions, and ratios but not valid for estimating population totals or for pooled data. Also, the number of weighted cases using the normalized weight has no direct relation with survey precision because it is relative - specially for oversampled areas, where the number of weighted cases is much smaller than the number of unweighted cases and only the latter are directly related to survey precision.
For further details on sample weights, see APPENDIX A.4 of the final report.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2022-01-05 | 2022-06-22 |
Data Collection Mode
Computer Assisted Personal Interview [capi]
Data Collection Notes
Data collection for the 2022 NDHS was carried out by 19 teams. Each team consisted of a supervisor, one male interviewer, three female interviewers, and one biomarker specialist. The teams were first deployed in locations away from Kathmandu because at that time the capital city was a COVID-19 hotspot. The fieldwork began on January 5, 2022, in two central locations - Itahari and Chitwan under close supervision. On completion of the fieldwork in these first locations, a review session was held on January 9, and the teams departed to their respective assigned clusters on January 10 to continue with data collection for the survey. Caution was taken while mobilizing the teams throughout the data collection period to mitigate the risk of COVID-19. Except for a few mild cases, there were no major impacts of COVID-19 during data collection. The fieldwork was slightly disrupted when local elections took place. The field teams had to go home to cast their votes, and the local people were engaged in election activities. Data collection activities were completed on June 22, 2022.
Fieldwork monitoring was an integral part of the 2022 NDHS, and several rounds of monitoring were carried out by the New ERA core team and quality control teams. ICF provided technical assistance during the data collection period through weekly virtual meetings. The technical teams from the MoHP, NHRC, and USAID Nepal made several field visits to ensure that data collection was carried out according to the protocol. Regular feedback was provided to the teams by the New ERA core team.
Fieldwork monitoring was an integral part of the 2022 NDHS, and several rounds of monitoring were carried out by the New ERA core team and quality control teams. ICF provided technical assistance during the data collection period through weekly virtual meetings. The technical teams from the MoHP, NHRC, and USAID Nepal made several field visits to ensure that data collection was carried out according to the protocol. Regular feedback was provided to the teams by the New ERA core team.
Data Collectors
| Name |
|---|
| New ERA |
Questionnaires
Questionnaires
Four questionnaires were used in the 2022 NDHS: 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 Nepal. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.
Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors. After all questionnaires were finalized in English, they were translated into Nepali, Maithili, and Bhojpuri. The Household, Woman’s, and Man’s Questionnaires were programmed into tablet computers to facilitate computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the three languages for each questionnaire. The Biomarker Questionnaire was completed on paper during data collection and then entered in the CAPI system.
Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors. After all questionnaires were finalized in English, they were translated into Nepali, Maithili, and Bhojpuri. The Household, Woman’s, and Man’s Questionnaires were programmed into tablet computers to facilitate computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the three languages for each questionnaire. The Biomarker Questionnaire was completed on paper during data collection and then entered in the CAPI system.
Data Processing
Data Editing
Data capture for the 2022 NDHS was carried out with Microsoft Surface Go 2 tablets running Windows 10.1. Software was prepared for the survey using CSPro. The processing of the 2022 NDHS data began shortly after the fieldwork started. When data collection was completed in each cluster, the electronic data files were transferred via the Internet File Streaming System (IFSS) to the New ERA central office in Kathmandu. The data files were registered and checked for inconsistencies, incompleteness, and outliers. Errors and inconsistencies were immediately communicated to the field teams for review so that problems would be mitigated going forward. Secondary editing, carried out in the central office at New ERA, involved resolving inconsistencies and coding the open-ended questions. The New ERA senior data processor coordinated the exercise at the central office. The NDHS core team members assisted with the secondary editing. The paper Biomarker Questionnaires were compared with the electronic data file to check for any inconsistencies in data entry. The pictures of vaccination cards that were captured during data collection were verified with the data entered. Data processing and editing were carried out using the CSPro software package. The concurrent data collection and processing offered a distinct advantage because it maximized the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for effective monitoring. The secondary editing of the data was completed by July 2022, and the final cleaning of the data set was completed by the end of August.
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 result from mistakes made in implementing data collection and in data processing, such as failing to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and entering the data incorrectly. Although numerous efforts were made during the implementation of the 2022 Nepal Demographic and Health Survey (2022 NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, 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, and so on), 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 2022 NDHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed through the SAS program. This program uses the Taylor linearization method to estimate variances for estimated means, proportions, and 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 report.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, 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, and so on), 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 2022 NDHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed through the SAS program. This program uses the Taylor linearization method to estimate variances for estimated means, proportions, and 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 report.
Data Appraisal
Data Quality Tables
- Household age distribution
- Age distribution of eligible and interviewed women
- Age distribution of eligible and interviewed men
- Age displacement at age 14/15
- Age displacement at age 49/50
- Pregnancy outcomes by years preceding the survey
- Completeness of reporting
- Standardization exercise results from anthropometry training
- Height and weight data completeness and quality for children
- Height measurements from random subsample of measured children
- Interference in height and weight measurements of children
- Interference in height and weight measurements of women and men
- Heaping in anthropometric measurements for children (digit preference)
- Food insecurity data completeness, infit and outfit model statistics, and Rasch reliability
- Observation of handwashing facility
- School attendance by single year of age
- Vaccination cards photographed
See details of the data quality tables in Appendix C of the final report.
- Household age distribution
- Age distribution of eligible and interviewed women
- Age distribution of eligible and interviewed men
- Age displacement at age 14/15
- Age displacement at age 49/50
- Pregnancy outcomes by years preceding the survey
- Completeness of reporting
- Standardization exercise results from anthropometry training
- Height and weight data completeness and quality for children
- Height measurements from random subsample of measured children
- Interference in height and weight measurements of children
- Interference in height and weight measurements of women and men
- Heaping in anthropometric measurements for children (digit preference)
- Food insecurity data completeness, infit and outfit model statistics, and Rasch reliability
- Observation of handwashing facility
- School attendance by single year of age
- Vaccination cards photographed
See details of the data quality tables in Appendix C of the final report.
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| Information about The DHS Program | The DHS Program | reports@DHSprogram.com |
| General Inquiries | The DHS Program | info@dhsprogram.com |
| Data and Data Related Resources | The DHS Program | archive@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
Recommended citations are available at https://www.dhsprogram.com/publications/Recommended-Citations.cfm
Access authority
| Name | URL |
|---|---|
| The DHS Program | Link |
Metadata production
DDI Document ID
DDI_NPL_2022_DHS_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | The World Bank | Documentation of the DDI |
Date of Metadata Production
2023-07-05
DDI Document version
Version 01 (July 2023). Metadata is excerpted from "Nepal Demographic and Health Survey 2022" Report.