Tanzania - Malaria Indicator Survey 2017

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The 2017 Tanzania Malaria Indicator Survey (2017 TMIS) was the second stand-alone malaria indicator survey conducted in the country, following the one implemented in 2011-2012 (2011-12 THMIS). The survey involved a nationally representative sample of 9,724 households from 442 sample clusters. The primary objective of the 2017 TMIS is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the survey collected information on vector control interventions such as mosquito nets, intermittent preventive treatment of malaria in pregnant women, and care seeking and treatment of fever in children. Young children were also tested for anaemia and for malaria infection. Overall, the key aims of the 2017 TMIS are to: • Measure the level of ownership and use of mosquito nets • Assess coverage of intermittent preventive treatment for pregnant women • Identify health care seeking behaviours and treatment practices, including the use of specific antimalarial medications to treat malaria among children under age 5 • Identify diagnostic trends prior to administration of antimalarial medications for treatment of fever and other malaria-like symptoms • Measure the prevalence of malaria and anaemia among children age 6-59 months • Assess malaria knowledge, attitudes, and practices among women age 15-49 • Assess housing conditions • Assess the cost of malaria-related services The information collected through the 2017 TMIS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.

Type: 
Microdata
Acronym: 
MIS / TMIS 2017
Languages Supported: 
English
Topics: 
Topic not specified
Geographical Coverage: 
Tanzania
Release Date: 
October 26, 2018

Last Updated

Last Updated: 
October 26, 2018

Harvest System ID

Harvest System ID: 
Microdata

Harvest Source ID

Harvest Source ID: 
10252
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.
Version Notes: 
The data dictionary was generated from hierarchical data that was downloaded from the DHS website (http://dhsprogram.com).
Funding Name, Abbreviation, Role: 
Government of Tanzania; United States Agency for International Development; Global Fund; United States President’s Malaria Initiative
Study Type: 
Demographic and Health Survey [hh/dhs]
Series Information: 
The 2017 Tanzania Malaria Indicator Survey (2017 TMIS) was the second stand-alone malaria indicator survey conducted in the country, following the one implemented in 2011-2012 (2011-12 THMIS). The survey involved a nationally representative sample of 9,724 households from 442 sample clusters. The survey was expected to interview 9,287 women age 15-49 and cover about 7,842 children under age 5.
Unit of Analysis: 
- Household- Woman age 15 to 49- Child age 0 to 5
Primary Investigator Name, Affiliation: 
National Bureau of Statistics (NBS) - Government of the United Republic of Tanzania; Office of the Chief Government Statistician (OCGS) - Zanzibar
Sampling Procedure: 
The sampling frame used for the 2017 TMIS was the 2012 Tanzania Population and Housing Census (PHC). The sampling frame was a complete list of enumeration areas (EAs) covering the whole country provided by the National Bureau of Statistics (NBS) of Tanzania, the implementing agency for the 2017 TMIS. This frame was created for the 2012 PHC, and the EAs served as counting units for the census. In rural areas, an EA is a natural village, a segment of a large village, or a group of small villages; in urban areas, an EA is a street or a city block. Each EA includes identification information, administrative information, and, as a measure of size, the number of residential households residing in the EA. Each EA is also classified into one of two types of residence, urban or rural. For each EA, there are cartographical materials that delineate its geographical locations, boundaries, main access, and landmarks inside or outside the EA, helping to identify the different areas. Note: See Appendix A of the final report for additional details on the sampling procedure.
Response Rates: 
A total of 9,724 households selected for the sample, 9,390 were occupied at the time of fieldwork. Among the occupied households, 9,330 were successfully interviewed, yielding a total household response rate of 99%. In the interviewed households, 10,136 eligible women were identified for individual interviews and 10,018 were successfully interviewed, yielding a response rate of 99%.
Weighting: 
A spreadsheet containing all sampling parameters and selection probabilities was prepared to facilitate the calculation of design weights. Design weights were adjusted for household non-response and individual non-response to obtain sampling weights for households and women, respectively. Differences between household sampling weights and individual sampling weights were a result of non-response among women. The final sampling weights were normalised to produce unweighted cases equal to weighted cases at the national level for both household weights and individual weights.It is important to note that normalised weights are relative weights that are valid for estimating means, proportions, and ratios but are not valid for estimating population totals or for pooled data. Also, the number of weighted cases obtained using normalised weights has no direct relation with 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. It is the number of unweighted cases that is directly related to survey precision.Details of sampling weight calculation is available in Appendix A.4 of the final report.
Questionnaires: 
Three questionnaires—the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire—were used for the 2017 TMIS. Core questionnaires available from the Roll Back Malaria Monitoring & Evaluation Reference Group (RBM-MERG) were adapted to reflect the population and health issues relevant to Tanzania. The questionnaires were initially prepared in English, later translated to Kiswahili, and then programmed onto tablet computers, enabling use of computer-assisted personal interviewing (CAPI) for the survey.
Data Collector(s) Name: 
National Bureau of Statistics
Data Editing: 
Data for the 2017 TMIS were collected through questionnaires programmed onto the CAPI application. The CAPI application was programmed by ICF in collaboration with NBS and OCGS and loaded with the Household and Woman’s Questionnaires. The Biomarker Questionnaire measurements were entered on a hard copy and later transferred to the CAPI application. Using a secure internet file streaming system (IFSS), the field supervisors transferred data to a server located at NBS headquarters in Dar es Salaam on a daily basis. To facilitate communication and monitoring, each field worker was assigned a unique identification number. At NBS headquarters, data received from the field teams’ CAPI applications were registered and checked for inconsistencies and outliers. Data editing and cleaning included an extensive range of structural and internal consistency checks. Any anomalies were communicated to the teams so that, together with the data processing teams, they could resolve data discrepancies. The corrected results were maintained in master Census and Survey Processing System (CSPro) data files at NBS and were used in producing tables for analysis and report writing. ICF provided technical assistance in processing the data using CSPro for data editing, cleaning, weighting, and tabulation.
Estimates of Sampling Error: 
The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling 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 2017 Tanzania Malaria Indicator Survey (2017 TMIS) to minimise this type of error, non-sampling 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 2017 TMIS 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 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% 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 2017 TMIS 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 2017 TMIS is an SAS program. This program uses the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios.Note: Detailed description of sampling error estimates is presented in APPENDIX B of the final report.
Access Authority Name, Affiliation, Email: 
Time Periods: 
November, 2018

No Visualizations Available.

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 2017 Tanzania Malaria Indicator Survey (2017 TMIS) was the second stand-alone malaria indicator survey conducted in the country, following the one implemented in 2011-2012 (2011-12 THMIS). The survey involved a nationally representative sample of 9,724 households from 442 sample clusters. The primary objective of the 2017 TMIS is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the survey collected information on vector control interventions such as mosquito nets, intermittent preventive treatment of malaria in pregnant women, and care seeking and treatment of fever in children. Young children were also tested for anaemia and for malaria infection. Overall, the key aims of the 2017 TMIS are to: • Measure the level of ownership and use of mosquito nets • Assess coverage of intermittent preventive treatment for pregnant women • Identify health care seeking behaviours and treatment practices, including the use of specific antimalarial medications to treat malaria among children under age 5 • Identify diagnostic trends prior to administration of antimalarial medications for treatment of fever and other malaria-like symptoms • Measure the prevalence of malaria and anaemia among children age 6-59 months • Assess malaria knowledge, attitudes, and practices among women age 15-49 • Assess housing conditions • Assess the cost of malaria-related services The information collected through the 2017 TMIS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.

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Modified Date
2018-11-01
Release Date
Identifier
290fcc1d-de3a-4af9-894f-6ff0d45352de
License
License Not Specified
Contact Email
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Acronym: 
MIS / TMIS 2017
Type: 
Languages Supported: 
Access Authority Name, Affiliation, Email: 
The DHS Program, [email protected], http://www.DHSprogram.com
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.
Response Rates: 
A total of 9,724 households selected for the sample, 9,390 were occupied at the time of fieldwork. Among the occupied households, 9,330 were successfully interviewed, yielding a total household response rate of 99%. In the interviewed households, 10,136 eligible women were identified for individual interviews and 10,018 were successfully interviewed, yielding a response rate of 99%.
Weighting: 
A spreadsheet containing all sampling parameters and selection probabilities was prepared to facilitate the calculation of design weights. Design weights were adjusted for household non-response and individual non-response to obtain sampling weights for households and women, respectively. Differences between household sampling weights and individual sampling weights were a result of non-response among women. The final sampling weights were normalised to produce unweighted cases equal to weighted cases at the national level for both household weights and individual weights.It is important to note that normalised weights are relative weights that are valid for estimating means, proportions, and ratios but are not valid for estimating population totals or for pooled data. Also, the number of weighted cases obtained using normalised weights has no direct relation with 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. It is the number of unweighted cases that is directly related to survey precision.Details of sampling weight calculation is available in Appendix A.4 of the final report.
Estimates of Sampling Error: 
The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling 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 2017 Tanzania Malaria Indicator Survey (2017 TMIS) to minimise this type of error, non-sampling 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 2017 TMIS 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 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% 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 2017 TMIS 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 2017 TMIS is an SAS program. This program uses the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios.Note: Detailed description of sampling error estimates is presented in APPENDIX B of the final report.
Time Periods: 
November, 2018
Data Collector(s) Name: 
National Bureau of Statistics
Primary Investigator Name, Affiliation: 
National Bureau of Statistics (NBS) - Government of the United Republic of Tanzania; Office of the Chief Government Statistician (OCGS) - Zanzibar
Funding Name, Abbreviation, Role: 
Government of Tanzania; United States Agency for International Development; Global Fund; United States President’s Malaria Initiative
Unit of Analysis: 
- Household- Woman age 15 to 49- Child age 0 to 5
Geographical Coverage: 
Data Classification of a Dataset: 
Series Information: 
The 2017 Tanzania Malaria Indicator Survey (2017 TMIS) was the second stand-alone malaria indicator survey conducted in the country, following the one implemented in 2011-2012 (2011-12 THMIS). The survey involved a nationally representative sample of 9,724 households from 442 sample clusters. The survey was expected to interview 9,287 women age 15-49 and cover about 7,842 children under age 5.
Sampling Procedure: 
The sampling frame used for the 2017 TMIS was the 2012 Tanzania Population and Housing Census (PHC). The sampling frame was a complete list of enumeration areas (EAs) covering the whole country provided by the National Bureau of Statistics (NBS) of Tanzania, the implementing agency for the 2017 TMIS. This frame was created for the 2012 PHC, and the EAs served as counting units for the census. In rural areas, an EA is a natural village, a segment of a large village, or a group of small villages; in urban areas, an EA is a street or a city block. Each EA includes identification information, administrative information, and, as a measure of size, the number of residential households residing in the EA. Each EA is also classified into one of two types of residence, urban or rural. For each EA, there are cartographical materials that delineate its geographical locations, boundaries, main access, and landmarks inside or outside the EA, helping to identify the different areas. Note: See Appendix A of the final report for additional details on the sampling procedure.
Release Date: 
Friday, October 26, 2018
Last Updated Date: 
Friday, October 26, 2018
Questionnaires: 
Three questionnaires—the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire—were used for the 2017 TMIS. Core questionnaires available from the Roll Back Malaria Monitoring & Evaluation Reference Group (RBM-MERG) were adapted to reflect the population and health issues relevant to Tanzania. The questionnaires were initially prepared in English, later translated to Kiswahili, and then programmed onto tablet computers, enabling use of computer-assisted personal interviewing (CAPI) for the survey.
Data Editing: 
Data for the 2017 TMIS were collected through questionnaires programmed onto the CAPI application. The CAPI application was programmed by ICF in collaboration with NBS and OCGS and loaded with the Household and Woman’s Questionnaires. The Biomarker Questionnaire measurements were entered on a hard copy and later transferred to the CAPI application. Using a secure internet file streaming system (IFSS), the field supervisors transferred data to a server located at NBS headquarters in Dar es Salaam on a daily basis. To facilitate communication and monitoring, each field worker was assigned a unique identification number. At NBS headquarters, data received from the field teams’ CAPI applications were registered and checked for inconsistencies and outliers. Data editing and cleaning included an extensive range of structural and internal consistency checks. Any anomalies were communicated to the teams so that, together with the data processing teams, they could resolve data discrepancies. The corrected results were maintained in master Census and Survey Processing System (CSPro) data files at NBS and were used in producing tables for analysis and report writing. ICF provided technical assistance in processing the data using CSPro for data editing, cleaning, weighting, and tabulation.
Harvest Source: 
Harvest System ID: 
10252
Version Notes: 
The data dictionary was generated from hierarchical data that was downloaded from the DHS website (http://dhsprogram.com).
Citation Text: 
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
Modified date: 
17830
Study Type: 
Demographic and Health Survey [hh/dhs]
Primary Dataset: 
Yes

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