Uganda - Demographic and Health Survey 2016

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The 2016 Uganda Demographic and Health Survey (2016 UDHS) was implemented by the Uganda Bureau of Statistics. The survey sample was designed to provide estimates of population and health indicators including fertility and child mortality rates for the country as a whole, for the urban and rural areas separately, and for each of the 15 regions in Uganda (South Central, North Central, Busoga, Kampala, Lango, Acholi, Tooro, Bunyoro, Bukedi, Bugisu, Karamoja, Teso, Kigezi, Ankole, and West Nile). The primary objective of the 2016 UDHS project is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2016 UDHS collected information on: • Key demographic indicators, particularly fertility and under-5, adult, and maternal mortality rates • Direct and indirect factors that determine levels of and trends in fertility and child mortality • Contraceptive knowledge and practice • Key aspects of maternal and child health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery • Child feeding practices, including breastfeeding, and anthropometric measures to assess the nutritional status of women, men, and children • Knowledge and attitudes of women and men about sexually transmitted infections (STIs) 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/AIDS programmes • Anaemia in women, men, and children • Malaria prevalence in children as a follow-up to the 2014-15 Uganda Malaria Indicator Survey • Vitamin A deficiency (VAD) in children • Key education indicators, including school attendance ratios, level of educational attainment, and literacy levels • The extent of disability • Early childhood development • The extent of gender-based violence The information collected through the 2016 UDHS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.

Acronym: 
DHS/ UDHS 2016
Type: 
Microdata
Topics: 
Topic not specified
Languages Supported: 
English
Geographical Coverage: 
Uganda
Reference ID: 
UGA_2016_DHS_v01_M
Release Date: 
March 19, 2018

Harvest Source

Harvest Source: 
Microdata

Harvest Source ID

Harvest Source ID: 
9723

Last Updated

Last Updated: 
March 19, 2018
Data Collector(s) Name: 
Bureau of Statistics
Study Type: 

Demographic and Health Survey [hh/dhs]

Data Collector(s) Name: 
Bureau of Statistics
Data Editing: 
All electronic data files for the 2016 UDHS were transferred via IFSS to the UBOS central office in Kampala, where they were stored on a password-protected computer. The data processing operation included registering and checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning included structure and consistency checks to ensure completeness of work in the field. The central office also conducted secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by four staff (two programmers and two data editors) who took part in the main fieldwork training. They were supervised by three senior staff from UBOS. Data editing was accomplished with CSPro software. Secondary editing and data processing were initiated in August 2016 and completed in January 2017.
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.
Estimates of Sampling Error: 
The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2016 Uganda Demographic and Health Survey (UDHS) 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 2016 UDHS 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 2016 UDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. 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.A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.
Funding Name, Abbreviation, Role: 
Government of Uganda; United States Agency for International Development; United Nations Children’s Fund; United Nations Population Fund
Primary Investigator Name, Affiliation: 
Bureau of Statistics (UBOS) - Government of Uganda
Response Rates: 
A total of 20,791 households were selected for the sample, of which 19,938 were occupied. Of the occupied households, 19,588 were successfully interviewed, which yielded a response rate of 98%.In the interviewed households, 19,088 eligible women were identified for individual interviews. Interviews were completed with 18,506 women, yielding a response rate of 97%. In the subsample of households selected for the male survey, 5,676 eligible men were identified and 5,336 were successfully interviewed, yielding a response rate of 94%. Response rates were higher in rural than in urban areas, with the ruralurban difference being more pronounced among men (95% and 90%, respectively) than among women (98% and 95%, respectively).
Sampling Procedure: 
The sampling frame used for the 2016 UDHS is the frame of the Uganda National Population and Housing Census (NPHC), conducted in 2014; the sampling frame was provided by the Uganda Bureau of Statistics. The census frame is a complete list of all census enumeration areas (EAs) created for the 2014 NPHC. In Uganda, an EA is a geographic area that covers an average of 130 households. The sampling frame contains information about EA location, type of residence (urban or rural), and the estimated number of residential households. The 2016 UDHS sample was stratified and selected in two stages. In the first stage, 697 EAs were selected from the 2014 Uganda NPHC: 162 EAs in urban areas and 535 in rural areas. One cluster from Acholi subregion was eliminated because of land disputes. Households constituted the second stage of sampling. For further details on sample design, see Appendix A of the final report.
Series Information: 
The 2016 Uganda Demographic and Health Survey (2016 UDHS) is the sixth in a series of Demographic and Health Surveys conducted in Uganda in 1988-89, 1995, 2000-01, 2006, and 2011. As with the prior surveys, the main objective of the 2016 UDHS is to provide up-to-date information on fertility and childhood mortality levels; fertility preferences; awareness, approval, and use of family planning methods; maternal and child health; domestic violence; knowledge and attitudes toward HIV/AIDS; and maternal mortality.
Unit of Analysis: 
- Household- Individual- Children age 0-5- Woman age 15-49- Man age 15-54
Weighting: 
A spreadsheet containing 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 and for 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. Similarly, domestic violence weights were calculated for women and men, where the design weights were adjusted for the within-household selection and the nonresponse for the domestic violence module. After adjusting for nonresponse, the sampling weights were normalized to get the final standard weights that appear in the data files. The normalization process is aimed at obtaining a total number of unweighted cases equal to the total number of weighted cases using normalized weights at the national level, for the total number of households, women, and men. Normalization is done by multiplying the sampling weight by the estimated total sampling fraction obtained from the survey for the household weight, the individual woman’s weight, and the individual man’s weight. The normalized weights are relative weights that are valid for estimating means, proportions, ratios, and rates, but they are not valid for estimating population totals or for pooled data.For further details on sampling weights, see Appendix A.4 of the final report.

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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 2016 Uganda Demographic and Health Survey (2016 UDHS) was implemented by the Uganda Bureau of Statistics. The survey sample was designed to provide estimates of population and health indicators including fertility and child mortality rates for the country as a whole, for the urban and rural areas separately, and for each of the 15 regions in Uganda (South Central, North Central, Busoga, Kampala, Lango, Acholi, Tooro, Bunyoro, Bukedi, Bugisu, Karamoja, Teso, Kigezi, Ankole, and West Nile). The primary objective of the 2016 UDHS project is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2016 UDHS collected information on: • Key demographic indicators, particularly fertility and under-5, adult, and maternal mortality rates • Direct and indirect factors that determine levels of and trends in fertility and child mortality • Contraceptive knowledge and practice • Key aspects of maternal and child health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery • Child feeding practices, including breastfeeding, and anthropometric measures to assess the nutritional status of women, men, and children • Knowledge and attitudes of women and men about sexually transmitted infections (STIs) 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/AIDS programmes • Anaemia in women, men, and children • Malaria prevalence in children as a follow-up to the 2014-15 Uganda Malaria Indicator Survey • Vitamin A deficiency (VAD) in children • Key education indicators, including school attendance ratios, level of educational attainment, and literacy levels • The extent of disability • Early childhood development • The extent of gender-based violence The information collected through the 2016 UDHS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.

FieldValue
Modified Date
2018-03-19
Release Date
Identifier
41593259-43b0-4a33-adea-03e053921d39
License
License Not Specified
Contact Email
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Reference ID: 
UGA_2016_DHS_v01_M
Acronym: 
DHS/ UDHS 2016
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 20,791 households were selected for the sample, of which 19,938 were occupied. Of the occupied households, 19,588 were successfully interviewed, which yielded a response rate of 98%.In the interviewed households, 19,088 eligible women were identified for individual interviews. Interviews were completed with 18,506 women, yielding a response rate of 97%. In the subsample of households selected for the male survey, 5,676 eligible men were identified and 5,336 were successfully interviewed, yielding a response rate of 94%. Response rates were higher in rural than in urban areas, with the ruralurban difference being more pronounced among men (95% and 90%, respectively) than among women (98% and 95%, respectively).
Weighting: 
A spreadsheet containing 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 and for 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. Similarly, domestic violence weights were calculated for women and men, where the design weights were adjusted for the within-household selection and the nonresponse for the domestic violence module. After adjusting for nonresponse, the sampling weights were normalized to get the final standard weights that appear in the data files. The normalization process is aimed at obtaining a total number of unweighted cases equal to the total number of weighted cases using normalized weights at the national level, for the total number of households, women, and men. Normalization is done by multiplying the sampling weight by the estimated total sampling fraction obtained from the survey for the household weight, the individual woman’s weight, and the individual man’s weight. The normalized weights are relative weights that are valid for estimating means, proportions, ratios, and rates, but they are not valid for estimating population totals or for pooled data.For further details on sampling weights, see Appendix A.4 of the final report.
Estimates of Sampling Error: 
The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2016 Uganda Demographic and Health Survey (UDHS) 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 2016 UDHS 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 2016 UDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. 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.A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.
Time Periods: 
March, 2018
Data Collector(s) Name: 
Bureau of Statistics
Primary Investigator Name, Affiliation: 
Bureau of Statistics (UBOS) - Government of Uganda
Unit of Analysis: 
- Household- Individual- Children age 0-5- Woman age 15-49- Man age 15-54
Geographical Coverage: 
Data Classification of a Dataset: 
Series Information: 
The 2016 Uganda Demographic and Health Survey (2016 UDHS) is the sixth in a series of Demographic and Health Surveys conducted in Uganda in 1988-89, 1995, 2000-01, 2006, and 2011. As with the prior surveys, the main objective of the 2016 UDHS is to provide up-to-date information on fertility and childhood mortality levels; fertility preferences; awareness, approval, and use of family planning methods; maternal and child health; domestic violence; knowledge and attitudes toward HIV/AIDS; and maternal mortality.
Sampling Procedure: 
The sampling frame used for the 2016 UDHS is the frame of the Uganda National Population and Housing Census (NPHC), conducted in 2014; the sampling frame was provided by the Uganda Bureau of Statistics. The census frame is a complete list of all census enumeration areas (EAs) created for the 2014 NPHC. In Uganda, an EA is a geographic area that covers an average of 130 households. The sampling frame contains information about EA location, type of residence (urban or rural), and the estimated number of residential households. The 2016 UDHS sample was stratified and selected in two stages. In the first stage, 697 EAs were selected from the 2014 Uganda NPHC: 162 EAs in urban areas and 535 in rural areas. One cluster from Acholi subregion was eliminated because of land disputes. Households constituted the second stage of sampling. For further details on sample design, see Appendix A of the final report.
Release Date: 
Monday, March 19, 2018
Last Updated Date: 
Monday, March 19, 2018
Data Editing: 
All electronic data files for the 2016 UDHS were transferred via IFSS to the UBOS central office in Kampala, where they were stored on a password-protected computer. The data processing operation included registering and checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning included structure and consistency checks to ensure completeness of work in the field. The central office also conducted secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by four staff (two programmers and two data editors) who took part in the main fieldwork training. They were supervised by three senior staff from UBOS. Data editing was accomplished with CSPro software. Secondary editing and data processing were initiated in August 2016 and completed in January 2017.
Harvest Source: 
Harvest Source ID: 
9723
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: 
17609
Study Type: 
Demographic and Health Survey [hh/dhs]
Primary Dataset: 
Yes

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