Demographic and Health Survey 2015-2016
Malawi, 2015 - 2016
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MWI_2015_DHS_v01_M
Demographic and Health Survey 2015-2016
| Name | Country code |
|---|---|
| Malawi | MWI |
Demographic and Health Survey (Standard) - DHS VII
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 2015-16 MDHS is the fifth Demographic and Health Survey conducted in Malawi since 1992. This survey follows other surveys completed in 1992, 2000, 2004, and 2010. The survey provides reliable estimates of fertility levels, marriage, sexual activity, fertility preferences, family planning methods, breastfeeding practices, nutrition, childhood and maternal mortality, maternal and child health, HIV/AIDS and other sexually transmitted infections (STIs), women’s empowerment, and domestic violence that can be used by programme managers and policymakers to evaluate and improve existing programmes.
The 2016-16 Malawi Demographic and Health Survey (2015-16 MDHS) was conducted between October 2015 and February 2016 by the National Statistical Office (NSO) of Malawi in joint collaboration with the Ministry of Health (MoH) and the Community Health Services Unit (CHSU). Malawi conducted its first DHS in 1992 and again in 2000, 2004, and 2010. The 2015-16 MDHS is the fifth in the series. The survey is based on a nationally representative sample that provides estimates at the national and regional levels and for urban and rural areas with key indicator estimates at the district level. The survey included 26,361 households, 24,562 female respondents, and 7,478 male respondents.
The primary objective of the 2015-16 MDHS is to provide current estimates of basic demographic and health indicators. The MDHS provides a comprehensive overview of population, maternal, and child health issues in Malawi. More specifically, the 2015-16 MDHS:
- collected data that allow the calculation of key demographic indicators, particularly fertility and under 5 and adult mortality rates
- provided data to explore the direct and indirect factors that determine the levels and trends of fertility and child mortality
- measured the levels of contraceptive knowledge and practice
- obtained data on key aspects of family health, such as immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators that include antenatal visits and assistance at delivery
- obtained data on child feeding practices including breastfeeding
- collected anthropometric measures that assess nutritional status, and conducted anaemia testing for all eligible children under age 5 and women age 15-49
- collected data on knowledge and attitudes of women and men about sexually-transmitted diseases (STDs) and HIV/AIDS, potential exposure to the risk of HIV infection (risk behaviours and condom use) and coverage of HIV Testing and Counselling (HTC) and other key HIV programmes
- collected dried blood spot (DBS) specimens for HIV testing from women age 15-49 and men age 15-54 to provide information on the prevalence of HIV among the adult population in the prime reproductive ages.
The micronutrient component of the 2015-16 MDHS was designed to: (1) determine the prevalence of micronutrient deficiencies (vitamin A, B, iron, iodine, zinc) and anaemia among pre-school and school-age children, women, and men of child-bearing age; (2) estimate micronutrient supplementation and fortification coverage; and (3) assess the knowledge and practices in maternal and child nutrition.
The information collected in the 2015-16 MDHS will assist policy makers and programme managers in evaluating and designing programmes and strategies that can improve the health of the country’s population.
Sample survey data [ssd]
- Household
- Individual
- Children age 0-5
- Woman age 15-49
- Man age 15-54
Scope
The 2015-16 Malawi 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 bilogical parents, school attendance, highest educational attainment, domestic violence, and birth registration
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, type of fuel used for cooking, materials used for the floor, roof and walls of the house, and possessions of durable goods (including land) and mosquito nets.
INDIVIDUAL WOMAN
• Background characteristics: age, education, media exposure
• Reproduction: children ever born, birth history, current pregnancy
• Family planning: knowledge and use of contraception, sources of contraceptive methods, information on family planning
• Maternal and child health, breastfeeding, and nutrition
• Marriage and sexual activity: marital status, age at first marriage, number of unions, age at first sexual intercourse, recent sexual activity, number and type of sexual partners, use of condoms
• Fertility preferences: desire for more children, ideal number of children, gender preferences, intention to use family planning
• Husband’s background and woman’s work: husband’s age, level of education, and occupation, and woman’s occupation and sources of earnings
• STDs and HIV: knowledge of STDs and HIV, methods of transmission, sources of information, behaviours to avoid STDs and HIV, and stigma
• Knowledge, attitudes, and behaviours related to other health issues such as injections, smoking, fistula, tuberculosis
• Adult and maternal mortality
• Domestic violence
INDIVIDUAL MAN
• Respondent background
• Reproduction
• Contraception
• Marriage and sexual activity
• Fertility preferences
• Employment and gender roles
• HIV/AIDS
• Other health issues
BIOMARKER
• Weight, height, and hemoglobin measurement for children age 0-5
• Weight, height, hemoglobin measurements and HIV testing for women age 15-49
• HIV testing for men age 15-54
• Weight, height, hemoglobin measurements and HIV testing for men age 15-54
Coverage
National coverage
Northern Region: Chitipa, Karonga, Likoma, Mzimba, Nkhata Bay, and Rumphi
Central Region: Dedza, Dowa, Kasungu, Lilongwe, Mchinji, Nkhotakota, Ntcheu, Ntchisi, and Salima
Southern Region: Balaka, Blantyre, Chikhwawa, Chiradzulu, Machinga, Mangochi, Mulanje, Mwanza, Neno, Nsanje, Phalombe, Thyolo, and Zomba
The survey covered all de jure household members (usual residents), women age 15-49 years and men age 15-54 years resident in the household.
Producers and sponsors
| Name | Affiliation |
|---|---|
| National Statistical Office (NSO) | Government of Malawi |
| Name | Role |
|---|---|
| Ministry of Health | Collaborated |
| ICF International | Provided technical assistance through the DHS Program |
| Name | Role |
|---|---|
| Government of Malawi | Funded the study |
| United States Agency for International Development | Funded the study |
| National Aids Commission | Funded the study |
| United Nations Children’s Fund | Funded the study |
| United Nations Population Fund | Funded the study |
| World Bank | Funded the study |
| Irish Aid | Funded the study |
Sampling
The sampling frame used for the 2015-16 MDHS is the frame of the Malawi Population and Housing Census (MPHC), conducted in Malawi in 2008, and provided by the Malawi National Statistical Office (NSO). The census frame is a complete list of all census standard enumeration areas (SEAs) created for the 2008 MPHC. A SEA is a geographic area that covers an average of 235 households. The sampling frame contains information about the SEA location, type of residence (urban or rural), and the estimated number of residential households.
Administratively, Malawi is divided into 28 districts. The sample for the 2015-16 MDHS was designed to provide estimates of key indicators for the country as a whole, for urban and rural areas separately, and for each of the 28 districts.
The 2015-16 MDHS sample was stratified and selected in two stages. Each district was stratified into urban and rural areas; this yielded 56 sampling strata. Samples of SEAs were selected independently in each stratum in two stages. 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.
In the first stage, 850 SEAs, including 173 SEAs in urban areas and 677 in rural areas, were selected with probability proportional to the SEA size and with independent selection in each sampling stratum.
In the second stage of selection, a fixed number of 30 households per urban cluster and 33 per rural cluster were selected with an equal probability systematic selection from the newly created household listing.
For further details on sample selection, see Appendix B of the final report.
A total of 27,516 households were selected for the sample, of which 26,564 were occupied. Of the occupied households, 26,361 were successfully interviewed, for a response rate of 99%.
In the interviewed households, 25,146 eligible women were identified for individual interviews. Interviews were completed with 24,562 women, for a response rate of 98%. In the subsample of households selected for the male survey, 7,903 eligible men were identified and 7,478 were successfully interviewed, for a response rate of 95%.
A spreadsheet with all sampling parameters and selection probabilities was prepared to facilitate the calculation of the design weights. Design weights were adjusted for household nonresponse and individual nonresponse to obtain the sampling weights for households, women, and men, respectively. Nonresponse is adjusted at the sampling stratum level. For the household sampling weight, the household design weight is multiplied by the inverse of the household response rate, by stratum. For the women’s individual sampling weight, the household sampling weight is multiplied by the inverse of the women’s individual response rate, by stratum. For the men’s individual sampling weight, the household sampling weight for the male subsample is multiplied by the inverse of the men’s individual response rate, by stratum. After adjusting for nonresponse, the sampling weights are normalised to obtain the final standard weights that appear in the data files. The normalisation process obtains a total number of unweighted cases equal to the total number of weighted cases using normalised weights at the national level for the total number of households, women, and men. Normalisation is obtained by multiplying the sampling weight by the estimated total sampling fraction obtained from the survey for the household weight, and the individual women’s and men’s weights. The normalised weights are relative weights that are valid for estimating means, proportions, ratios, and rates, although they are not valid for estimating population totals or pooled data. The sampling weights for HIV testing are calculated in a similar way, although the normalisation of the HIV weights is different. The individual HIV testing weights are normalised at the national level for women and men together so that HIV prevalence estimates calculated for women and men together are valid.
For further details on sampling weights, see Appendix B.4 of the final report.
Survey instrument
Four questionnaires were used in the 2015-16 MDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to Malawi. Input was solicited from stakeholders who represented government ministries and agencies, nongovernmental organisations, and international donors. After the preparation of the definitive questionnaires in English, the questionnaires were then translated into Chichewa and Tumbuka languages. All four questionnaires were programmed into tablet computers to facilitate computer-assisted personal interviewing (CAPI) for data collection, and to offer the option to choose either English, Chichewa or Tumbuka for each questionnaire.
Data collection
| Start | End |
|---|---|
| 2015-10 | 2016-02 |
| Name | Affiliation |
|---|---|
| National Statistical Office | Government of Malawi |
Data collection was completed by 37 field teams, with each including one team leader, one field editor, three female interviewers, one male interviewer, two biomarker technicians, and one driver. Electronic data files were transferred to the NSO central office in Zomba every day via the secured IFSS. Senior staff from the NSO; University of Malawi-Chancellor College; the Ministry of Health; the Ministry of Finance, Economic Planning and Development; and a survey technical specialist from The DHS Program coordinated and supervised fieldwork activities. Data collection took place over a 4-month period, from 19 October 2015 through 17 February 2016.
Data processing
All electronic data collected in the 2015-16 MDHS were received via IFSS at the NSO central office in Zomba, where the data were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by four individuals who took part in the fieldwork training, and were supervised by two senior staff from NSO. Data editing was accomplished with CSPro software. Secondary editing and data processing were initiated in October 2015 and completed in March 2016.
Data appraisal
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 2015-16 Malawi Demographic and Health Survey (2015-16 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 year acronym 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 2015-16 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 by SAS programs developed by ICF International. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.
Note: A more detailed description of estimates of sampling errors are presented in APPENDIX C of the survey report.
Data Quality Tables
- Household age distribution
- Age distribution of eligible and interviewed women
- Age distribution of eligible and interviewed men
- Completeness of reporting
- Births by calendar years
- Reporting of age at death in days
- Reporting of age at death in months
- Nutritional status of children based on the NCHS/CDC/WHO International Reference Population
- Sibship size and sex ratio of siblings
- Pregnancy-related mortality
- Pregnancy-related mortality
Note: See details of the data quality tables in APPENDIX D of the report.
Access policy
The DHS Program
The DHS Program
http://dhsprogram.com/data/available-datasets.cfm
Cost: None
Data Access
| Name | URL | |
|---|---|---|
| The DHS Program | http://www.DHSprogram.com | archive@dhsprogram.com |
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.
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
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| Information about The DHS Program | The DHS Program | reports@DHSprogram.com | http://www.DHSprogram.com |
| General Inquiries | The DHS Program | info@dhsprogram.com | http://www.DHSprogram.com |
| Data and Data Related Resources | The DHS Program | archive@dhsprogram.com | http://www.DHSprogram.com |