Demographic and Health Survey 2019
Sierra Leone, 2019
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Reference ID
SLE_2019_DHS_v01_M
Producer(s)
Statistics Sierra Leone
Collection(s)
Metadata
Created on
Dec 23, 2020
Last modified
Jan 20, 2021
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Identification
Survey ID Number
SLE_2019_DHS_v01_M
Title
Demographic and Health Survey 2019
Country/Economy
| Name | Country code |
|---|---|
| Sierra Leone | SLE |
Study type
Demographic and Health Survey [hh/dhs]
Abstract
The 2019 Sierra Leone Demographic and Health Survey (2019 SLDHS) is a nationwide survey with a nationally representative sample of approximately 13,872 selected households. All women age 15-49 who are usual household members or who spent the night before the survey in the selected households were eligible for individual interviews.
The primary objective of the 2019 SLDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the survey collected information on fertility, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and children, maternal and child health, adult and childhood mortality, women’s empowerment, domestic violence, female genital cutting, prevalence and awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), and other health-related issues such as smoking.
The information collected through the 2019 SLDHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.
The primary objective of the 2019 SLDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the survey collected information on fertility, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and children, maternal and child health, adult and childhood mortality, women’s empowerment, domestic violence, female genital cutting, prevalence and awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), and other health-related issues such as smoking.
The information collected through the 2019 SLDHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.
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
Version
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 2019 Sierra Leone 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, and sex
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, type of fuel used for cooking, number of rooms, ownsership of livestock, possessions of durable goods, mosquito nets, and main material for the floor, roof and walls of the dwelling.
INDIVIDUAL WOMAN
• Identification
• Background characteristics (including age, education, and media exposure)
• Birth history and child mortality
• Knowledge, use, and source of family planning methods
• Antenatal, delivery, and postnatal care
• Vaccinations and childhood illnesses
• Breastfeeding and infant feeding practices
• Minimum dietary diversity
• Marriage and sexual activity
• Fertility preferences (including desire for more children and ideal number of children)
• Women’s work and husbands’ background characteristics
• Knowledge, awareness, and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs)
• Knowledge, attitudes, and behaviour related to other health issues (e.g., smoking)
• Female genital cutting
• Adult and maternal mortality
• Domestic violence
INDIVIDUAL MAN
• Identification
• Respondent's background
• Reproduction
• Contraception
• Marriage and sexual activity
• Fertility preferences
• Employment and gender roles
• HIV/AIDS
• Other health issues
BIOMARKER
• Identification
• Weight, height, and hemoglobin measurement for children age 0-5
• Weight, height, hemoglobin measurement and HIV testing for women age 15-49
• Weight, height, hemoglobin measurement and HIV testing for men age 15-59
FIELDWORKER
• Background information on each fieldworke
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, and sex
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, type of fuel used for cooking, number of rooms, ownsership of livestock, possessions of durable goods, mosquito nets, and main material for the floor, roof and walls of the dwelling.
INDIVIDUAL WOMAN
• Identification
• Background characteristics (including age, education, and media exposure)
• Birth history and child mortality
• Knowledge, use, and source of family planning methods
• Antenatal, delivery, and postnatal care
• Vaccinations and childhood illnesses
• Breastfeeding and infant feeding practices
• Minimum dietary diversity
• Marriage and sexual activity
• Fertility preferences (including desire for more children and ideal number of children)
• Women’s work and husbands’ background characteristics
• Knowledge, awareness, and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs)
• Knowledge, attitudes, and behaviour related to other health issues (e.g., smoking)
• Female genital cutting
• Adult and maternal mortality
• Domestic violence
INDIVIDUAL MAN
• Identification
• Respondent's background
• Reproduction
• Contraception
• Marriage and sexual activity
• Fertility preferences
• Employment and gender roles
• HIV/AIDS
• Other health issues
BIOMARKER
• Identification
• Weight, height, and hemoglobin measurement for children age 0-5
• Weight, height, hemoglobin measurement and HIV testing for women age 15-49
• Weight, height, hemoglobin measurement and HIV testing for men age 15-59
FIELDWORKER
• Background information on each fieldworke
Coverage
Geographic Coverage
National coverage
Universe
The survey covered all de jure household members (usual residents), all women aged 15-49, all men age 15-59, and all children aged 0-5 resident in the household.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Statistics Sierra Leone | Government of Sierra Leone |
Producers
| Name | Affiliation | Role |
|---|---|---|
| ICF | The DHS Program | Provided technical assistance |
| Ministry of Health and Sanitation | Government of Sierra Leone | Technical support |
| National AIDS Secretariat | Government of Sierra Leone | Technical support |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Government of Sierra Leone | GoSL | Financial support |
| United States Agency for International Development | USAID | Financial support |
| Global Fund | GF | Financial support |
| World Bank | WB | Financial support |
| Department of International Development | DFID | Financial support |
| World Health Organization | WHO | Financial support |
| United Nations Population Fund | UNFPA | Financial support |
Sampling
Sampling Procedure
The sampling frame used for the 2019 SLDHS is the Population and Housing Census of the Republic of Sierra Leone, which was conducted in 2015 by Statistics Sierra Leone. Administratively, Sierra Leone is divided into provinces. Each province is subdivided into districts, each district is further divided into chiefdoms/census wards, and each chiefdom/census ward is divided into sections. During the 2015 Population and Housing Census, each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2019 SLDHS, is defined based on EAs from the 2015 EA census frame. The 2015 Population and Housing Census provided the list of EAs that served as a foundation to estimate the number of households and distinguish EAs as urban or rural for the survey sample frame.
The sample for the 2019 SLDHS was a stratified sample selected in two stages. Stratification was achieved by separating each district into urban and rural areas. In total, 31 sampling strata were created. Samples were selected independently in every stratum via a two-stage selection process. Implicit stratifications were achieved at each of the lower administrative levels by sorting the sampling frame before sample selection according to administrative order and by using probability-proportional-to-size selection during the first sampling stage.
In the first stage, 578 EAs were selected with probability proportional to EA size. EA size was the number of households residing in the EA. A household listing operation was carried out in all selected EAs, and the resulting lists of households served as a sampling frame for the selection of households in the second stage. In the second stage’s selection, a fixed number of 24 households were selected in every cluster through equal probability systematic sampling, resulting in a total sample size of approximately 13,872 selected households. The household listing was carried out using tablets, and random selection of households was carried out through computer programming. The survey interviewers interviewed only the pre-selected households. To prevent bias, no replacements and no changes of the pre-selected households were allowed in the implementing stages.
For further details on sample selection, see Appendix A of the final report.
The sample for the 2019 SLDHS was a stratified sample selected in two stages. Stratification was achieved by separating each district into urban and rural areas. In total, 31 sampling strata were created. Samples were selected independently in every stratum via a two-stage selection process. Implicit stratifications were achieved at each of the lower administrative levels by sorting the sampling frame before sample selection according to administrative order and by using probability-proportional-to-size selection during the first sampling stage.
In the first stage, 578 EAs were selected with probability proportional to EA size. EA size was the number of households residing in the EA. A household listing operation was carried out in all selected EAs, and the resulting lists of households served as a sampling frame for the selection of households in the second stage. In the second stage’s selection, a fixed number of 24 households were selected in every cluster through equal probability systematic sampling, resulting in a total sample size of approximately 13,872 selected households. The household listing was carried out using tablets, and random selection of households was carried out through computer programming. The survey interviewers interviewed only the pre-selected households. To prevent bias, no replacements and no changes of the pre-selected households were allowed in the implementing stages.
For further details on sample selection, see Appendix A of the final report.
Response Rate
A total of 13,793 households were selected for the sample, of which 13,602 were occupied. Of the occupied households, 13,399 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 16,099 women age 15-49 were identified for individual interviews; interviews were completed with 15,574 women, yielding a response rate of 97%. In the subsample of households selected for the male survey, 7,429 men age 15-59 were identified, and 7,197 were successfully interviewed, yielding a response rate of 97%.
Weighting
Due to the non-proportional allocation of samples to different provinces and to their urban and rural areas and the possible differences in response rates, sampling weights will be required for any analysis using the 2019 SLDHS data to ensure the actual representativeness of the survey results at the national level as well as at the domain level. Since the 2019 SLDHS sample is a two-stage stratified cluster sample, sampling weights were calculated based on sampling probabilities separately for each sampling stage and for each cluster.
For further details on sampling weights, see Appendix A.4 of the final report.
For further details on sampling weights, see Appendix A.4 of the final report.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2019-05-15 | 2019-08-08 |
Data Collection Mode
Computer Assisted Personal Interview [capi]
Data Collection Notes
The fieldwork for the 2019 SLDHS was launched under close supervision on 15 May 2019 in clusters in Freetown. Twenty-four teams, each consisting of one supervisor, one field editor, one male interviewer, two female interviewers, one HIV counsellor, and one nurse, were assigned across the different clusters in Freetown. The teams were closely monitored by the coordinators and the biomarker monitors. After completion of the fieldwork in Freetown in the first week, teams were brought back to the Stats SL office in Freetown for a review session in which the teams had an opportunity to clarify issues and ask questions. The teams were then dispatched to their assigned provinces. Data collection lasted until 31 August 2019. The fieldwork in some provinces took longer than expected due to the various perceptions of the Ebola outbreak and its relationship to SLDHS data collection.
Fieldwork monitoring was an integral part of the 2019 SLDHS, and several rounds of monitoring were carried out by the Stats SL and MOHS core team, the coordinators from Stats SL, and ICF staff. The monitors were provided with guidelines for overseeing the fieldwork. Weekly field check tables were generated from the completed interviews sent to the central office to monitor fieldwork progress, and regular feedback was sent to the teams.
Fieldwork monitoring was an integral part of the 2019 SLDHS, and several rounds of monitoring were carried out by the Stats SL and MOHS core team, the coordinators from Stats SL, and ICF staff. The monitors were provided with guidelines for overseeing the fieldwork. Weekly field check tables were generated from the completed interviews sent to the central office to monitor fieldwork progress, and regular feedback was sent to the teams.
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Statistics Sierra Leone | Stats SL | Government of Sierra Leone |
Questionnaires
Questionnaires
Five questionnaires were used for the 2019 SLDHS: The Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, and the Fieldworker Questionnaire. The questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Sierra Leone. Comments were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. The survey protocol was reviewed and approved by the Sierra Leone Ethics and Scientific Review Committee and the ICF Institutional Review Board. All questionnaires were finalised in English, and the 2019 SLDHS used computer-assisted personal interviewing (CAPI) for data collection.
Data Processing
Data Editing
The processing of the 2019 SLDHS data began almost as soon as the fieldwork started. As data collection was completed in each cluster, all electronic data files were transferred via the IFSS to the Stats SL central office in Freetown. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams received alerts on any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding open-ended questions. The Stats SL data processor coordinated the exercise at the central office. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro Systems software package. Concurrent processing of the data offered a distinct advantage because it maximised 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 in mid-October 2019.
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 2019 Sierra Leone Demographic and Health Survey (SLDHS) 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 2019 SLDHS 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 errors are 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 2019 SLDHS 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 programmes developed by ICF. These programmes use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.
Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2019 SLDHS 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 errors are 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 2019 SLDHS 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 programmes developed by ICF. These programmes use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.
Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.
Data 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
- Standardisation exercise results from anthropometry training
- Height measurements from random subsample of measured children
- Sibship size and sex ratio of siblings
- Pregnancy-related mortality trends
- Completeness of information on siblings
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
- Completeness of reporting
- Births by calendar years
- Reporting of age at death in days
- Reporting of age at death in months
- Standardisation exercise results from anthropometry training
- Height measurements from random subsample of measured children
- Sibship size and sex ratio of siblings
- Pregnancy-related mortality trends
- Completeness of information on siblings
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
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_SLE_2019_DHS_v01_M
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Economics Data Group | DECDG | The World Bank | Documentation of the DDI |
Date of Metadata Production
2020-12-17
DDI Document version
Version 01 (December 2020). Metadata is excerpted from "Sierra Leone Demographic and Health Survey 2019" Report.