Demographic and Health Survey and Malaria Indicator Survey 2015-2016
Tanzania, 2015 - 2016
National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS)
Last modified
September 17, 2018
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Metadata
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Identification
IDNO
TZA_2015_DHS-MIS_v01_M
Title
Demographic and Health Survey and Malaria Indicator Survey 2015-2016
Countries
| Name | Code |
|---|---|
| Tanzania | TZA |
Study notes
The primary objective of the 2015-16 Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS) is to provide up-to-date estimates of basic demographic and health indicators. This survey collected information on fertility levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutrition, childhood and maternal mortality, maternal and child health, malaria, and other health-related issues. In addition, the 2015-16 TDHS-MIS provided estimates of anaemia prevalence among children age 6-59 months and women age 15-49 years, estimates of malaria prevalence among children age 6-59 months, and estimates of iodine concentration in household salt and women’s urine.
The information collected through the 2015-16 TDHS-MIS is intended to assist policy makers and programme managers in evaluating and designing programmes and strategies to improve the health of the country’s population.
The information collected through the 2015-16 TDHS-MIS is intended to assist policy makers and programme managers in evaluating and designing programmes and strategies to improve the health of the country’s population.
Kind of data
Sample survey data [ssd]
Version
Version notes
The data dictionary was generated from CSPro hierarchical data that was downloaded from the DHS website(http://dhsprogram.com).
Coverage
Geographic coverage
National
Geographic unit
Western Zone Northern Zone Central Zone Southern Highlands Zone Southern Zone South West Highlands Zone Lake Zone Eastern Zone Zanzibar
Unit of analysis
- Household
- Children age 0-5
- Women age 15-49
- Men age 15-59
- Children age 0-5
- Women age 15-49
- Men age 15-59
Producers and sponsors
Authoring entity
| Name | Affiliation |
|---|---|
| National Bureau of Statistics (NBS) | Government of Tanzania |
| Office of the Chief Government Statistician (OCGS) | Zanzibar |
Producer(s)
| Name | Affiliation | Role |
|---|---|---|
| ICF International | Demographic and Health Surveys (DHS) Program | Technical assistance |
| Ministry of Health | Mainland | Collaboration |
| Community Development, Gender, Elderly and Children | Mainland | Collaboration |
| Ministry of Health | Zanzibar | Collaboration |
Funding agencies
| Name | Abbreviation | Role |
|---|---|---|
| Government of Tanzania | GovTZA | Funded the study |
| United States Agency for International Development | USAID | Funded the study |
| Global Affairs Canada | DFATD | Funded the study |
| Irish Aid | Irish Aid | Funded the study |
| United Nations Children’s Fund | UNICEF | Funded the study |
| United Nations Population Fund | UNFPA | Funded the study |
Sampling
Sampling procedure
Sample Design
The sample design for the 2015-16 TDHS-MIS was done in two stages and was intended to provide estimates for the entire country, for urban and rural areas in Tanzania Mainland, and for Zanzibar. For specific indicators such as contraceptive use, the sample design allowed the estimation of indicators for each of the 30 regions (25 regions from Tanzania Mainland and 5 regions from Zanzibar). The first stage involved selecting sample points (clusters), consisting of enumeration areas (EAs) delineated for the 2012 Tanzania Population and Housing Census. A total of 608 clusters were selected.
In the second stage, a systematic selection of households was involved. A complete households listing was carried out for all 608 selected clusters prior to the fieldwork. From the list, 22 households were then systematically selected from each cluster, yielding a representative probability sample of 13,376 households for the 2015-16 TDHS-MIS. To estimate geographic differentials for certain demographic indicators, Tanzania was divided into nine geographic zones. Although these zones are not official administrative areas, this classification system is also used by the Reproductive and Child Health Section of the MoHCDGEC. Grouping the regions into zones allowed a relatively large number of people in the denominator and a reduced sampling error. Note that the zones, defined below, differ slightly from the zones used in previous DHS surveys. Therefore, comparisons across the zones and from survey to survey should be made with caution. The zones are as follows:
Western Zone: Tabora, Kigoma
Northern Zone: Kilimanjaro, Tanga, Arusha
Central Zone: Dodoma, Singida, Manyara
Southern Highlands Zone: Iringa, Njombe, Ruvuma
Southern Zone: Lindi, Mtwara
South West Highlands Zone: Mbeya, Rukwa, Katavi
Lake Zone: Kagera, Mwanza, Geita, Mara, Simiyu, Shinyanga
Eastern Zone: Dar es Salaam, Pwani, Morogoro
Zanzibar: Kaskazini Unguja, Kusini Unguja, Mjini Magharibi, Kaskazini Pemba, Kusini Pemba
All women age 15-49 who were either usual residents or visitors in the household on the night before the survey were included in the 2015-16 TDHS-MIS and were eligible to be interviewed. In a subsample of one-third of all the households selected for the survey, all men age 15-49 were eligible to be interviewed if they were either usual residents or visitors in the household on the night before the survey. In all households, with the parent's or guardian's consent, children age 6-59 months were tested for anaemia and malaria. All interviewed women were tested for anaemia. In the households selected for interviews with men, interviewed women were asked to provide a urine sample and a sample of household salt for laboratory testing to detect the presence of iodine.
For further details of sample design and implementation, see Appendix A of the final report.
The sample design for the 2015-16 TDHS-MIS was done in two stages and was intended to provide estimates for the entire country, for urban and rural areas in Tanzania Mainland, and for Zanzibar. For specific indicators such as contraceptive use, the sample design allowed the estimation of indicators for each of the 30 regions (25 regions from Tanzania Mainland and 5 regions from Zanzibar). The first stage involved selecting sample points (clusters), consisting of enumeration areas (EAs) delineated for the 2012 Tanzania Population and Housing Census. A total of 608 clusters were selected.
In the second stage, a systematic selection of households was involved. A complete households listing was carried out for all 608 selected clusters prior to the fieldwork. From the list, 22 households were then systematically selected from each cluster, yielding a representative probability sample of 13,376 households for the 2015-16 TDHS-MIS. To estimate geographic differentials for certain demographic indicators, Tanzania was divided into nine geographic zones. Although these zones are not official administrative areas, this classification system is also used by the Reproductive and Child Health Section of the MoHCDGEC. Grouping the regions into zones allowed a relatively large number of people in the denominator and a reduced sampling error. Note that the zones, defined below, differ slightly from the zones used in previous DHS surveys. Therefore, comparisons across the zones and from survey to survey should be made with caution. The zones are as follows:
Western Zone: Tabora, Kigoma
Northern Zone: Kilimanjaro, Tanga, Arusha
Central Zone: Dodoma, Singida, Manyara
Southern Highlands Zone: Iringa, Njombe, Ruvuma
Southern Zone: Lindi, Mtwara
South West Highlands Zone: Mbeya, Rukwa, Katavi
Lake Zone: Kagera, Mwanza, Geita, Mara, Simiyu, Shinyanga
Eastern Zone: Dar es Salaam, Pwani, Morogoro
Zanzibar: Kaskazini Unguja, Kusini Unguja, Mjini Magharibi, Kaskazini Pemba, Kusini Pemba
All women age 15-49 who were either usual residents or visitors in the household on the night before the survey were included in the 2015-16 TDHS-MIS and were eligible to be interviewed. In a subsample of one-third of all the households selected for the survey, all men age 15-49 were eligible to be interviewed if they were either usual residents or visitors in the household on the night before the survey. In all households, with the parent's or guardian's consent, children age 6-59 months were tested for anaemia and malaria. All interviewed women were tested for anaemia. In the households selected for interviews with men, interviewed women were asked to provide a urine sample and a sample of household salt for laboratory testing to detect the presence of iodine.
For further details of sample design and implementation, see Appendix A of the final report.
Response rate
A total of 13,360 households were selected for the survey, of which 12,767 were occupied. Of the occupied households, 12,563 were successfully interviewed, yielding a response rate of 98%.
In the interviewed households, 13,634 eligible women were identified for individual interviews; interviews were completed with 13,266 women, yielding a response rate of 97%. In the subsample of households selected for the male survey, 3,822 eligible men were identified and 3,514 were successfully interviewed, yielding a response rate of 92%. There is little variation in household response rates between rural and urban residences.
In the interviewed households, 13,634 eligible women were identified for individual interviews; interviews were completed with 13,266 women, yielding a response rate of 97%. In the subsample of households selected for the male survey, 3,822 eligible men were identified and 3,514 were successfully interviewed, yielding a response rate of 92%. There is little variation in household response rates between rural and urban residences.
Weighting
The final sampling weights are normalized in order to give the total number of unweighted cases equal to the total number of weighted cases at the national level, for both household weights and individual weights, respectively. The normalized weights are relative weights, which are valid for estimating means, proportions, and ratios, but are not valid for estimating population totals and for pooled data.
Sampling weights for the domestic violence surveys are calculated based on the number of eligible respondents in the households selected for the domestic violence module, for male and female surveys, respectively. A large number of sets of weights are calculated:
- One set for all households selected for the survey
- One set for women selected for the individual survey
- One set for households selected for the male survey
- One set for the male individual survey
It is important to note that the normalized weights are relative weights, which are valid for estimating means, proportions, and ratios, but not for estimating population totals and for pooled data. Also the number of weighted cases resulting from using the normalized weight has no direct relation to 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 are directly related to survey precision.
Sampling weights for the domestic violence surveys are calculated based on the number of eligible respondents in the households selected for the domestic violence module, for male and female surveys, respectively. A large number of sets of weights are calculated:
- One set for all households selected for the survey
- One set for women selected for the individual survey
- One set for households selected for the male survey
- One set for the male individual survey
It is important to note that the normalized weights are relative weights, which are valid for estimating means, proportions, and ratios, but not for estimating population totals and for pooled data. Also the number of weighted cases resulting from using the normalized weight has no direct relation to 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 are directly related to survey precision.
Data Collection
Dates of collection
| Start | End |
|---|---|
| 2015-08 | 2016-02 |
Mode of data collection
Face-to-face [f2f]
Questionnaires
Four questionnaires were used for the 2015-16 TDHS-MIS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. These questionnaires were based on the DHS Program’s standard Demographic and Health Survey (DHS) questionnaires. They were adapted to reflect the population and health issues relevant to Tanzania. Inputs were solicited from various stakeholders representing government ministries, departments, and agencies; non-governmental organizations; and development partners. After the preparation of the definitive questionnaires in English, the questionnaires were translated into Kiswahili.
Data collector(s)
| Name | Abbreviation | Affiliation |
|---|---|---|
| National Bureau of Statistics | NBS | Government of Tanzania |
Data Processing
Data editing
In the 2015-16 TDHS-MIS the first data entry was done concurrently with data collection in the field. After the paper questionnaires were completed, edited, and checked by both the field editor and the supervisor, the data was entered into a tablet equipped with a data entry programme. This was done by the editor. Completed questionnaires were then sent to NBS headquarters, where they were entered for the second time and edited by data processing personnel who were given special training for this task. ICF International provided technical assistance during the entire data processing period.
Processing the data concurrently with data collection allowed for regular monitoring of team performance and data quality. Field check tables were generated regularly during data processing to check various data quality parameters. As a result, feedback was given on a regular basis, encouraging teams to continue in areas of good performance and to correct areas in need of improvement. Feedback was individually tailored to each team. Data entry, which included 100% double entry to minimise keying errors, and data editing, were completed on March 21, 2016. Data cleaning and finalization were completed on April 22, 2016.
Processing the data concurrently with data collection allowed for regular monitoring of team performance and data quality. Field check tables were generated regularly during data processing to check various data quality parameters. As a result, feedback was given on a regular basis, encouraging teams to continue in areas of good performance and to correct areas in need of improvement. Feedback was individually tailored to each team. Data entry, which included 100% double entry to minimise keying errors, and data editing, were completed on March 21, 2016. Data cleaning and finalization were completed on April 22, 2016.
Data Appraisal
metadata.study_desc.method.analysis_info.sampling_error_estimates
The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) 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 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 Tanzania Demographic and Health Survey (TDHS) 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 2015 TDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.
A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.
If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2015 TDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2015 TDHS is a SAS program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method was used for variance estimation of more complex statistics such as fertility and mortality rates.
The Taylor linearization method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.
For further details on sampling error calculations see Appendix B of the final report.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2015 TDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.
A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.
If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2015 TDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2015 TDHS is a SAS program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method was used for variance estimation of more complex statistics such as fertility and mortality rates.
The Taylor linearization method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.
For further details on sampling error calculations see Appendix B of the final report.
Other forms of data appraisal
Data quality tables were produced to review the quality of the data:
- 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
Note: The tables are presented 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
Note: The tables are presented in Appendix C of the final report.
Data access
Access authorities
| Name | URI | |
|---|---|---|
| The DHS Program | archive@dhsprogram.com | http://www.DHSprogram.com |
Access conditions
Request Dataset Access
The following applies to DHS, MIS, AIS and SPA survey datasets (Surveys, GPS, and HIV).
To request dataset access, you must first be a registered user of the website. You must then create a new research project request. The request must include a project title and a description of the analysis you propose to perform with the data.
The requested data should only be used for the purpose of the research or study. To request the same or different data for another purpose, a new research project request should be submitted. The DHS Program will normally review all data requests within 24 hours (Monday - Friday) and provide notification if access has been granted or additional project information is needed before access can be granted.
DATASET ACCESS APPROVAL PROCESS
Access to DHS, MIS, AIS and SPA survey datasets (Surveys, HIV, and GPS) is requested and granted by country. This means that when approved, full access is granted to all unrestricted survey datasets for that country. Access to HIV and GIS datasets requires an online acknowledgment of the conditions of use.
Required Information
A dataset request must include contact information, a research project title, and a description of the analysis you propose to perform with the data.
Restricted Datasets
A few datasets are restricted and these are noted. Access to restricted datasets is requested online as with other datasets. An additional consent form is required for some datasets, and the form will be emailed to you upon authorization of your account. For other restricted surveys, permission must be granted by the appropriate implementing organizations, before The DHS Program can grant access. You will be emailed the information for contacting the implementing organizations. A few restricted surveys are authorized directly within The DHS Program, upon receipt of an email request.
When The DHS Program receives authorization from the appropriate organizations, the user will be contacted, and the datasets made available by secure FTP.
GPS/HIV Datasets/Other Biomarkers
Because of the sensitive nature of GPS, HIV and other biomarkers datasets, permission to access these datasets requires that you accept a Terms of Use Statement. After selecting GPS/HIV/Other Biomarkers datasets, the user is presented with a consent form which should be signed electronically by entering the password for the user's account.
Dataset Terms of Use
Once downloaded, the datasets must not be passed on to other researchers without the written consent of The DHS Program. All reports and publications based on the requested data must be sent to The DHS Program Data Archive in a Portable Document Format (pdf) or a printed hard copy.
Download Datasets
Datasets are made available for download by survey. You will be presented with a list of surveys for which you have been granted dataset access. After selecting a survey, a list of all available datasets for that survey will be displayed, including all survey, GPS, and HIV data files. However, only data types for which you have been granted access will be accessible. To download, simply click on the files that you wish to download and a "File Download" prompt will guide you through the remaining steps.
The following applies to DHS, MIS, AIS and SPA survey datasets (Surveys, GPS, and HIV).
To request dataset access, you must first be a registered user of the website. You must then create a new research project request. The request must include a project title and a description of the analysis you propose to perform with the data.
The requested data should only be used for the purpose of the research or study. To request the same or different data for another purpose, a new research project request should be submitted. The DHS Program will normally review all data requests within 24 hours (Monday - Friday) and provide notification if access has been granted or additional project information is needed before access can be granted.
DATASET ACCESS APPROVAL PROCESS
Access to DHS, MIS, AIS and SPA survey datasets (Surveys, HIV, and GPS) is requested and granted by country. This means that when approved, full access is granted to all unrestricted survey datasets for that country. Access to HIV and GIS datasets requires an online acknowledgment of the conditions of use.
Required Information
A dataset request must include contact information, a research project title, and a description of the analysis you propose to perform with the data.
Restricted Datasets
A few datasets are restricted and these are noted. Access to restricted datasets is requested online as with other datasets. An additional consent form is required for some datasets, and the form will be emailed to you upon authorization of your account. For other restricted surveys, permission must be granted by the appropriate implementing organizations, before The DHS Program can grant access. You will be emailed the information for contacting the implementing organizations. A few restricted surveys are authorized directly within The DHS Program, upon receipt of an email request.
When The DHS Program receives authorization from the appropriate organizations, the user will be contacted, and the datasets made available by secure FTP.
GPS/HIV Datasets/Other Biomarkers
Because of the sensitive nature of GPS, HIV and other biomarkers datasets, permission to access these datasets requires that you accept a Terms of Use Statement. After selecting GPS/HIV/Other Biomarkers datasets, the user is presented with a consent form which should be signed electronically by entering the password for the user's account.
Dataset Terms of Use
Once downloaded, the datasets must not be passed on to other researchers without the written consent of The DHS Program. All reports and publications based on the requested data must be sent to The DHS Program Data Archive in a Portable Document Format (pdf) or a printed hard copy.
Download Datasets
Datasets are made available for download by survey. You will be presented with a list of surveys for which you have been granted dataset access. After selecting a survey, a list of all available datasets for that survey will be displayed, including all survey, GPS, and HIV data files. However, only data types for which you have been granted access will be accessible. To download, simply click on the files that you wish to download and a "File Download" prompt will guide you through the remaining steps.
Citation requirements
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
Data collection locations
The DHS Program
Original archive where collection stored
The DHS Program
http://dhsprogram.com/data/available-datasets.cfm
Cost: None
http://dhsprogram.com/data/available-datasets.cfm
Cost: None
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Contacts
Contact(s)
| Name | Affiliation | URI | |
|---|---|---|---|
| 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 |
Metadata production
IDNo
DDI_TZA_2015_DHS-MIS_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | The World Bank | Documentation of the DDI |
Version
Version 01 (December 2016). Metadata is excerpted from "Tanzania Demographic and Health Survey and Malaria Indicator Survey 2015-2016" Report.