Maldives - Demographic and Health Survey 2016-2017

Primary tabs

The 2016-17 Maldives Demographic and Health Survey (MDHS) is the second Demographic and Health Survey conducted in the Maldives. The primary objective of the 2016-17 MDHS is to provide up-to-date estimates of key demographic and health indicators. The MDHS provides a comprehensive overview of population, maternal, and child health issues in the Maldives. More specifically, the 2016-17 MDHS: - Collected data at the national level that allowed calculation of key demographic indicators, particularly fertility and under-5 mortality rates - Explored the direct and indirect factors that determine levels and patterns of fertility and child mortality - Measured levels of contraceptive knowledge and practice - Collected data on key aspects of family 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 - Obtained data on child feeding practices, including breastfeeding - Collected anthropometric measures to assess the nutritional status of children under age 5, women age 15-49, and men age 15-49 - Conducted haemoglobin testing on children age 6-59 months and women age 15-49 to provide information on the prevalence of anaemia in these groups - Collected data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and assessed the coverage of past HIV testing - Collected data on the prevalence of disabilities among all household members - Collected data on early childhood education, support for children’s learning, and the level of inadequate care for young children - Assessed the level of knowledge and self-reported prevalence of certain non-communicable diseases such as hypertension, diabetes, thalassemia, and tuberculosis - Collected data on knowledge and prevalence of female circumcision among women age 15-49 and their daughters age 0-14 - Obtained data on women’s experience of emotional, physical, and sexual violence.

Acronym: 
DHS / MDHS 2016-17
Type: 
Microdata
Topics: 
Topic not specified
Economy Coverage: 
Economy Coverage not specified
Languages Supported: 
English
Geographical Coverage: 
Maldives
Reference ID: 
MDV_2016_DHS_v01_M
Version Production Date: 
February 1, 2019
Release Date: 
February 27, 2019

Harvest Source

Harvest Source: 
Microdata

Harvest Source ID

Harvest Source ID: 
10315

Last Updated

Last Updated: 
February 27, 2019
Study Type: 

Demographic and Health Survey [hh/dhs]

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 2016-17 Maldives Demographic and Health Survey (MDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically. Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2016-17 MDHS 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 2017-18 MDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed 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 the Maldives; World Health Organization; United Nations International Children’s Emergency Fund; United Nations Population Fund
Primary Investigator Name, Affiliation: 
Ministry of Health (MOH) - Government of the Maldives
Questionnaires: 
Four questionnaires were used for the 2016-17 MDHS: the Household Questionnaire, Woman’s Questionnaire, Man’s Questionnaire, and Biomarker Questionnaire. All questionnaires were based on the DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires that were adapted to reflect the population and health issues relevant to the Maldives. Input was solicited from various stakeholders representing relevant department and divisions within MOH, other government agencies, universities, non-governmental organisations and international agencies. All questionnaires were translated from English to Dhivehi and back-translated into English.
Response Rates: 
A total of 6,697 households were selected for the sample, of which 6,608 were occupied. Of the occupied households, 6,050 were successfully interviewed, yielding a response rate of 92%. In the interviewed households, 9,170 women age 15-49 were identified for individual interviews; these interviews were completed with 7,699 women, yielding a response rate of 84%. In addition, 6,335 men age 15-49 were identified, of whom 4,342 were interviewed for a response rate of 69%. All response rates are considerably lower in Malé region than in other atolls; for example, the response rate of women to individual interviews was only 68% in Malé, compared with 87% in other atolls. Overall, the response rate at the household level (92%) is slightly higher than it was for the 2009 MDHS (90%).
Sampling Procedure: 
The sampling frame used for the 2016-17 MDHS is the 2014 Maldives Population and Housing Census, provided by the National Bureau of Statistics in Maldives. The census frame is a complete list of all 997 census blocks (CB) created for the 2014 census. A CB is a geographic area containing an average of 58 households. The sampling frame contains information about the CB location and estimated number of residential households. Each CB has accompanying cartographic materials. These materials delineate geographic locations, boundaries, main access, and landmarks in or outside the CB that help identify the CB. The 2016-17 MDHS sample is designed to yield representative information for most indicators for the country as a whole, for residence, and for each of Maldives's six regions. Also, the MDHS sample is designed to yield representative information for some selected indicators for each of the atolls of the country. The sample for the 2016-17 MDHS was a stratified sample selected in two stages from the sampling frame. Stratification was achieved by separating each region into atolls; in total, 21 sampling strata were created, within each of which samples were selected independently. In the first stage, 266 CBs were selected with probability proportional to size according to the sample allocated to each stratum. The CB size is the number of residential households residing in the CB based on the 2014 census. Because of the large variation in the size of atolls, a proportional allocation of the sample points to the atolls is not adequate since the small atolls will receive too few sample points. The allocation adopted is a somewhat adjusted equal size allocation at atoll level except Malé which consists of 38% of the total residential population of the Maldives. This allocation will guarantee a better precision at atoll level and comparability across atolls. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units in different levels, and by using a probability proportional to size selection at the first stage of sampling. After the selection of CBs and immediately before interviewing, a household listing operation was carried out. The household listing operation was implemented by the teams of fieldworkers who, upon entering a sampled CB, would disperse to record on their tablet computers all occupied Maldivian residential households found in the CB with the address and the name of the head of the household. The resulting list of households served as the sampling frame for the selection of households in the second stage. In the second stage of selection, a fixed number of 25 households was selected in every CB (cluster) (except for Felidhu Atoll (V) where about 42 households on average were selected in all the six clusters of the atoll), by an equal probability systematic sampling based on the household listing. Selection of households was done on the supervisor's tablet in the field. A total of 6,750 households was sampled, 1,075 households in Malé region and 5,675 households in other areas. The survey interviewers were required to interview only the pre-selected households. No replacements and no changes of the preselected households were allowed in order to prevent bias. For further details on sample design, see Appendix A of the final report.
Series Information: 
Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The 2016-17 Maldives Demographic and Health Survey (2016-17 MDHS) is the second DHS survey that was conducted in the Maldives, with the 2009 MDHS being the first. A nationally representative sample of about 6,700 households was selected. All Maldivian women and men age 15-49 who were usual residents of the selected households or who slept in the households the night before the survey were eligible for the survey.
Unit of Analysis: 
- Household - Individual - Children age 0-5 - Woman age 15-49 - Man age 15-49
Universe: 
The survey covered all de jure household members (usual residents), children age 0-5 years, women age 15-49 years and men age 15-49 years resident in the household.
Version Notes: 
The data dictionary was generated from hierarchical data that was downloaded from the The DHS Program website (http://dhsprogram.com).
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 non-response and as well as for individual non-response to calculate the following survey weights: 1. The household survey weight. 2. The individual survey weight for women 15-49. 3. The individual survey weight for men 15-49. 4. The survey weight for the domestic violence module. The differences between the household survey weight and the individual survey weights are introduced by individual non-response. In the case of the household survey weight, the design weight was multiplied by the inverse of the strata-level household weighted response rates. In the case of the women’s individual survey weight, the household survey weight was multiplied by the inverse of the strata-level women’s individual weighted response rates. Similarly, in the case of the men’s individual survey weight, the household survey weight was multiplied by the inverse of the strata-level men’s individual weighted response rates. In addition to the standard survey weights described above, a special weight was calculated for the domestic violence module, where one woman 15-49 was selected at random from each household to complete the domestic violence questionnaire. In the case of the domestic violence weight, for each household, the household survey weight was multiplied by the number of women 15-49 to account for the within-household selection probabilities; then the modified weights were adjusted for the nonresponse to the module similar to the nonresponse adjustment described earlier. All the survey weights described earlier were then normalised in order to give a total number of weighted cases that equals the total number of unweighted cases at national level. Normalisation is done by multiplying the survey weight by the estimated total sampling fraction obtained from the survey for the household weight, the individual woman’s weight, the individual man’s weight, and the domestic violence weight. The normalised weights are relative weights which are valid for estimating means, proportions and ratios, but not valid for estimating population totals and for pooled data. The number of weighted cases using the normalised weight has no direct relation with 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 is directly related to survey precision. For further details on sampling weights, see Appendix A.4 of the final report.

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 2016-17 Maldives Demographic and Health Survey (MDHS) is the second Demographic and Health Survey conducted in the Maldives. The primary objective of the 2016-17 MDHS is to provide up-to-date estimates of key demographic and health indicators. The MDHS provides a comprehensive overview of population, maternal, and child health issues in the Maldives. More specifically, the 2016-17 MDHS: - Collected data at the national level that allowed calculation of key demographic indicators, particularly fertility and under-5 mortality rates - Explored the direct and indirect factors that determine levels and patterns of fertility and child mortality - Measured levels of contraceptive knowledge and practice - Collected data on key aspects of family 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 - Obtained data on child feeding practices, including breastfeeding - Collected anthropometric measures to assess the nutritional status of children under age 5, women age 15-49, and men age 15-49 - Conducted haemoglobin testing on children age 6-59 months and women age 15-49 to provide information on the prevalence of anaemia in these groups - Collected data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and assessed the coverage of past HIV testing - Collected data on the prevalence of disabilities among all household members - Collected data on early childhood education, support for children’s learning, and the level of inadequate care for young children - Assessed the level of knowledge and self-reported prevalence of certain non-communicable diseases such as hypertension, diabetes, thalassemia, and tuberculosis - Collected data on knowledge and prevalence of female circumcision among women age 15-49 and their daughters age 0-14 - Obtained data on women’s experience of emotional, physical, and sexual violence.

FieldValue
Modified Date
2019-03-08
Release Date
Identifier
8c26f05d-15e8-4976-8480-d160ba2c7f47
License
License Not Specified
Contact Email
Public Access Level
Public
Rating: 
0
No votes yet
Reference ID: 
MDV_2016_DHS_v01_M
Acronym: 
DHS / MDHS 2016-17
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 6,697 households were selected for the sample, of which 6,608 were occupied. Of the occupied households, 6,050 were successfully interviewed, yielding a response rate of 92%. In the interviewed households, 9,170 women age 15-49 were identified for individual interviews; these interviews were completed with 7,699 women, yielding a response rate of 84%. In addition, 6,335 men age 15-49 were identified, of whom 4,342 were interviewed for a response rate of 69%. All response rates are considerably lower in Malé region than in other atolls; for example, the response rate of women to individual interviews was only 68% in Malé, compared with 87% in other atolls. Overall, the response rate at the household level (92%) is slightly higher than it was for the 2009 MDHS (90%).
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 non-response and as well as for individual non-response to calculate the following survey weights: 1. The household survey weight. 2. The individual survey weight for women 15-49. 3. The individual survey weight for men 15-49. 4. The survey weight for the domestic violence module. The differences between the household survey weight and the individual survey weights are introduced by individual non-response. In the case of the household survey weight, the design weight was multiplied by the inverse of the strata-level household weighted response rates. In the case of the women’s individual survey weight, the household survey weight was multiplied by the inverse of the strata-level women’s individual weighted response rates. Similarly, in the case of the men’s individual survey weight, the household survey weight was multiplied by the inverse of the strata-level men’s individual weighted response rates. In addition to the standard survey weights described above, a special weight was calculated for the domestic violence module, where one woman 15-49 was selected at random from each household to complete the domestic violence questionnaire. In the case of the domestic violence weight, for each household, the household survey weight was multiplied by the number of women 15-49 to account for the within-household selection probabilities; then the modified weights were adjusted for the nonresponse to the module similar to the nonresponse adjustment described earlier. All the survey weights described earlier were then normalised in order to give a total number of weighted cases that equals the total number of unweighted cases at national level. Normalisation is done by multiplying the survey weight by the estimated total sampling fraction obtained from the survey for the household weight, the individual woman’s weight, the individual man’s weight, and the domestic violence weight. The normalised weights are relative weights which are valid for estimating means, proportions and ratios, but not valid for estimating population totals and for pooled data. The number of weighted cases using the normalised weight has no direct relation with 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 is directly related to survey precision. 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 2016-17 Maldives Demographic and Health Survey (MDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically. Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2016-17 MDHS 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 2017-18 MDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed 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.
Primary Investigator Name, Affiliation: 
Ministry of Health (MOH) - Government of the Maldives
Unit of Analysis: 
- Household - Individual - Children age 0-5 - Woman age 15-49 - Man age 15-49
Universe: 
The survey covered all de jure household members (usual residents), children age 0-5 years, women age 15-49 years and men age 15-49 years resident in the household.
Geographical Coverage: 
Data Classification of a Dataset: 
Version Production Date: 
Friday, February 1, 2019
Series Information: 
Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The 2016-17 Maldives Demographic and Health Survey (2016-17 MDHS) is the second DHS survey that was conducted in the Maldives, with the 2009 MDHS being the first. A nationally representative sample of about 6,700 households was selected. All Maldivian women and men age 15-49 who were usual residents of the selected households or who slept in the households the night before the survey were eligible for the survey.
Sampling Procedure: 
The sampling frame used for the 2016-17 MDHS is the 2014 Maldives Population and Housing Census, provided by the National Bureau of Statistics in Maldives. The census frame is a complete list of all 997 census blocks (CB) created for the 2014 census. A CB is a geographic area containing an average of 58 households. The sampling frame contains information about the CB location and estimated number of residential households. Each CB has accompanying cartographic materials. These materials delineate geographic locations, boundaries, main access, and landmarks in or outside the CB that help identify the CB. The 2016-17 MDHS sample is designed to yield representative information for most indicators for the country as a whole, for residence, and for each of Maldives's six regions. Also, the MDHS sample is designed to yield representative information for some selected indicators for each of the atolls of the country. The sample for the 2016-17 MDHS was a stratified sample selected in two stages from the sampling frame. Stratification was achieved by separating each region into atolls; in total, 21 sampling strata were created, within each of which samples were selected independently. In the first stage, 266 CBs were selected with probability proportional to size according to the sample allocated to each stratum. The CB size is the number of residential households residing in the CB based on the 2014 census. Because of the large variation in the size of atolls, a proportional allocation of the sample points to the atolls is not adequate since the small atolls will receive too few sample points. The allocation adopted is a somewhat adjusted equal size allocation at atoll level except Malé which consists of 38% of the total residential population of the Maldives. This allocation will guarantee a better precision at atoll level and comparability across atolls. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units in different levels, and by using a probability proportional to size selection at the first stage of sampling. After the selection of CBs and immediately before interviewing, a household listing operation was carried out. The household listing operation was implemented by the teams of fieldworkers who, upon entering a sampled CB, would disperse to record on their tablet computers all occupied Maldivian residential households found in the CB with the address and the name of the head of the household. The resulting list of households served as the sampling frame for the selection of households in the second stage. In the second stage of selection, a fixed number of 25 households was selected in every CB (cluster) (except for Felidhu Atoll (V) where about 42 households on average were selected in all the six clusters of the atoll), by an equal probability systematic sampling based on the household listing. Selection of households was done on the supervisor's tablet in the field. A total of 6,750 households was sampled, 1,075 households in Malé region and 5,675 households in other areas. The survey interviewers were required to interview only the pre-selected households. No replacements and no changes of the preselected households were allowed in order to prevent bias. For further details on sample design, see Appendix A of the final report.
Release Date: 
Wednesday, February 27, 2019
Last Updated Date: 
Wednesday, February 27, 2019
Questionnaires: 
Four questionnaires were used for the 2016-17 MDHS: the Household Questionnaire, Woman’s Questionnaire, Man’s Questionnaire, and Biomarker Questionnaire. All questionnaires were based on the DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires that were adapted to reflect the population and health issues relevant to the Maldives. Input was solicited from various stakeholders representing relevant department and divisions within MOH, other government agencies, universities, non-governmental organisations and international agencies. All questionnaires were translated from English to Dhivehi and back-translated into English.
Harvest Source: 
Harvest Source ID: 
10315
Version Notes: 
The data dictionary was generated from hierarchical data that was downloaded from the The DHS Program 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: 
17954
Study Type: 
Demographic and Health Survey [hh/dhs]
Primary Dataset: 
Yes

Data Access and Licensing

This dataset is classified as Public under the Access to Information Classification Policy. Users inside and outside the Bank can access this dataset.

This dataset is available from an external third-party website. Visit the website to obtain license information. More information

Share Metadata

The information on this page (the dataset metadata) is also available in these formats.

PRINT EMAIL JSON RDF