Demographic and Health Survey and Malaria Indicator Survey 2015-2016
Demographic and Health Survey (Standard) - DHS VII
The 2015-16 Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS) is the ninth in a series of national sample surveys conducted in Tanzania to measure levels, patterns, and trends in demographic and health indicators. The first TDHS, conducted in 1991-92, was followed by the 1994 Tanzania Knowledge, Attitudes, and Practices Survey (TKAPS), the 1996 TDHS, the 1999 Tanzania Reproductive and Child Health Survey (TRCHS), the 2003-04 Tanzania HIV/AIDS Indicator Survey (THIS), the 2004-05 TDHS, the 2007-08 Tanzania HIV/AIDS and Malaria Indicator Survey (THMIS), and the 2010 Tanzania Demographic and Health Survey (TDHS 2010).
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.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Children age 0-5
- Women age 15-49
- Men age 15-59
Recode Data Alert (Version 7B)
Corrections have been made to the Recode Data files. The changes implemented in the new version of the data are as follows:
HV035: Selection for height and weight now based on Century Day Code instead of Century Month code.
HV107: Highest year of education corrected
HV108: Education in single years corrected
HV109: Educational attainment corrected
HV115: Current marital status for married and living together changed from 6 to 1
HV123: Grade of education during current school year corrected
HV124: Education in single years for current school year corrected
HV213: Value set added for main floor material
HV207A: Wealth index for urban/rural corrected
SHDIST: District number taken out
SH124KU: Now combined into one variable SH124K
SH124KN: Now combined in one variable SH124K
SHIOD: Decimal point taken out of salt iodine level (now one implied decimal level)
SH03: Relationship to household head taken out, since this is available from HV101
SH17: Ever attended school added
SH19: Attended school anytime during 2015 school year added
SH20A: Level of school attended during 2015 school year added
SH20B: Grade attended during 2015 school year added
HC15: Not measured for height lying or standing now changed from not applicable to 0
HML19: Person slept under an ever-treated net adjusted
SB105: Weight in kilograms taken out, since this available from HC2
SB106: Height in centimeters taken out, since this available from HC3
SB112FW: Fieldworker number for anemia test added
SB112BFW: Fieldworker number for malaria test added
SB116: Hemoglobin result taken out since it is a filter
SB119: Illness symptoms present taken out since it is a filter
SB120: Hemoglobin result taken out since it is a filter
SB126: Read consent for malaria information and treatment taken out since it is a filter
SB205: Weight in kilograms taken out since it is available in HA2
SB206: Height in centimeters taken out since it is available in HA3
SB209: Marital status taken out since it is a filter
SB209A: Relationship to household head taken out since it is a filter
SB211FW: Fieldworker number adult respondent consent for anemia now assigned correctly
SB218FW: Fieldworker number parental/responsible adult consent for anemia added
SB221: Eligibility for male interview and salt and iodine corrected
SB222: Adult age taken out since it is a filter
SB223: Marital status taken out since it is filter
SB223A: Relationship to head of household taken out since it is a filter
SB230: Hemoglobin level taken out since it is available in HA53
SHA52AI: Result of iodine measurement added
HMLA1: Person who slept under net 1 taken out since it is available in HMLA
HMLA2: Person who slept under net 2 taken out since it is available in HMLB
HMLA3: Person who slept under net 3 taken out since it is available in HMLC
HMLA4: Person who slept under net 4 taken out since it is available in HMLD
HV801: Time interview started changed to standard 24 hour clock
HV802: Time interview ended changed to standard 24 hour clock
HV803: Length of interview corrected and based on standard 24 hour clock
V019A: Number of calendar columns changed from 1 to 2
V133: Education in single years adjusted
V190A: Wealth index for rural/urban corrected
V191A: Wealth index scores for rural/urban corrected
V222: Period for Last birth to interview not based on Century Day Code instead of Century Month Code
V304: Correction to knowledge any method for occurrences 10 and 17 (other traditional methods and other modern methods reversed)
V307: Correction to method currently used for occurrences 10 and 17 (other traditional methods and other modern methods reversed)
V327: Last source for users by type corrected
V359: Last method discontinued in last 5 years corrected
V380: Source knows for any method corrected
M67: Period between discharge/home delivery and health check for respondent corrected (now includes TBA premises)
M68: Person who checked respondent health after discharge/delivery corrected (now includes TBA premises)
M69: Place where respondent check after discharge/home delivery took place corrected (now includes TBA premises)
M71: Time after home delivery and postnatal check corrected (now includes TBA premises)
M72: Person who performed postnatal check corrected (now includes TBA premises)
M73: Place where baby was checked for first time corrected (now includes TBA premises)
M77: Child put on mother?s chest and bear skin corrected
V463AB: Frequency smokes or uses other types of tobacco corrected
V465: Number of cases for disposal of youngest child?s stools when not using toilet now based on Century Day Code instead of Century Month Code
H32A-H32: Denominator for place where medical treatment or advice was sought for last episode of fever and/or cough now restricted to H31B instead of H31 as in the standard recode
V702: Husband/partner?s highest year of education corrected
V715: Husband/partner?s total number of years of education corrected
V729: Husband/partner?s educational attainment corrected
V801: Time interview started changed to standard 24 hour clock
V802: Time interview ended changed to standard 24 hour clock
V803: Length of interview corrected and based on standard 24 hour clock
ML13A-13Z: Number of cases now includes children with difficulty breathing (H32B = 1)
D115J: Physical abuse by former partner added
SHHV191: Wealth index factor score set to not applicable since this is available in V191
S108A: Highest educational level taken out since this is available in V149
S120: Used the internet in last 12 months taken out since this is available in V171A
SV201-212: Taken out since these variables are available in the household file
S646A: Denominator no longer restricted to children born in the last 5 years to match questionnaire
S729: Brand name for condom used during last sex removed since this is available in V762A
S410I, J, K, N: Prenatal care given by religious voluntary hospital, health center, dispensary and clinic added because this is combined in standard variable M57T
S612I to N: Seek advice or treatment for diarrhea by several religious/voluntary facilities added, because they are combined in H12U
S625I to N: Seek advice or treatment for fever by several religious/voluntary facilities added, because they are combined in H32U
S630AA-AX: The number of cases for place received drugs for fever and cough expanded to include cough (H31B = 1).
MV133: Education in single years adjusted
MV190A: Wealth index for rural/urban corrected
MV191A: Wealth index scores for rural/urban corrected
MV304: Correction to knowledge any method for occurrences 10 and 17 (other traditional methods and other modern methods reversed)
MV307: Correction to method currently used for occurrences 10 and 17 (other traditional methods and other modern methods reversed)
MV3A00Y: Knows no source of family planning for non-users added
MV3A00Z: Knows any source of family planning for non-users added
MV801: Time interview started changed to standard 24 hour clock
MV802: Time interview ended changed to standard 24 hour clock
MV803: Length of interview corrected and based on standard 24 hour clock
SM108A: Highest educational level taken out since this is available in MV149
SM302A: Value set added to seen any information about family planning on poster
The 2015-16 Tanzania Demographic and Health and Malaria Indicator Survey covered the following topics:
• Background information on each person listed, such as relationship to head of the household, age, sex, highest educational attainment, and health insurance coverage
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, main source of energy for lighting, main source of fuel for cooking, materials used for the floor, roof and walls of the dwelling, ownership of land and livestock, and ownership of various durable goods (these items are used as proxy indicators of the household's socioeconomic status)
• Mosquito nets
• Inpatient health expenditures
• Outpatient health expenditures
• Respondent's background (age, education, etc.)
• Pregnancy and postnatal care
• Child immunization
• Child health and nutrition
• Marriage and sexual activity
• Fertility preferences
• Husband's background and woman's work
• Other health issues
• Female genital cutting/mutilation
• Maternal mortality
• Domestic violence
• Respondent's background (age, education etc.)
• Marriage and sexual activity
• Fertility preferences
• Employment and gender roles
• Other health issues
• Weight, height, hemoglobin measurement and malaria testing for children age 0-5 years
• Weight and height measurement, hemoglobin and urine (for iodine) test for women age 15-49
Western Zone Northern Zone Central Zone Southern Highlands Zone Southern Zone South West Highlands Zone Lake Zone Eastern Zone Zanzibar
Producers and sponsors
National Bureau of Statistics (NBS)
Government of Tanzania
Office of the Chief Government Statistician (OCGS)
Demographic and Health Surveys (DHS) Program
Ministry of Health
Community Development, Gender, Elderly and Children
Ministry of Health
Government of Tanzania
Funded the study
United States Agency for International Development
Funded the study
Global Affairs Canada
Funded the study
Funded the study
United Nations Children’s Fund
Funded the study
United Nations Population Fund
Funded the study
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.
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.
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.
Dates of Data Collection
Data Collection Mode
Data Collection Notes
Training of Field Staff
The main training of the 2015-16 TDHS-MIS enumerators, supervisors, and editors took place in Kilimanjaro region from July 20, 2015, to August 21, 2015. A total of 74 female nurses, 20 male nurses, 20 supervisors, and 20 editors from all over the country were invited to participate in the training. The training sessions were conducted by NBS, Office of the Chief Government Statistician (OCGS), and trainers from ministries responsible for health on both Tanzania Mainland and Zanzibar with support from ICF International. Training on biomarkers was provided by trainers from Ifakara Health Institute (IHI) and Tanzania Food and Nutrition Centre (TFNC), with support from ICF International.
Participants were evaluated through in-class exercises, quizzes, and observations made during field practice. By the end of the main training, 16 teams were formed, consisting of 16 individuals to serve as team leaders, 16 to serve as field editors, 16 as male interviewers, and 64 as female interviewers. All interviewers were nurses. The team leaders received additional training on how to identify the selected households, different subsamples, data quality control procedures, and fieldwork coordination. The field editors received additional training on how to edit the questionnaires, data quality control procedures, and how to enter data in tablets.
Data collection was carried out by 16 field teams: three teams in Zanzibar and 13 teams on Tanzania Mainland. Each team was provided with a four-wheel drive vehicle with a driver. The teams consisted of a team supervisor, four female interviewers, one male interviewer, and one field editor, who also entered data into a tablet. The field editor and supervisor were responsible for reviewing all questionnaires for completeness, quality, and consistency before entering data into the tablet. All questionnaires, dried blood smears, table salt, and urine specimens were transferred to the NBS head office almost every 2 weeks by a quality control team from NBS, OCGS, TFNC, and ministries responsible for health for both Tanzania Mainland and Zanzibar. The dried blood smears, table salt and urine specimens were sent later to IHI and TFNC laboratories for testing. The NBS also coordinated and supervised all fieldwork activities. ICF International provided technical assistance during the entire 5-month data collection period, from August 22, 2015, through February 14, 2016.
National Bureau of Statistics
Government of Tanzania
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.
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.
Estimates of Sampling Error
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.
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.
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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.
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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.
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GPS/HIV Datasets/Other Biomarkers
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- the Identification of the Primary Investigator
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- the survey reference number
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DDI Document ID
Development Data Group
The World Bank
Documentation of the DDI
DDI Document version
Version 01 (December 2016). Metadata is excerpted from "Tanzania Demographic and Health Survey and Malaria Indicator Survey 2015-2016" Report.