Maternal Mortality Survey 2019
Pakistan, 2019
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Reference ID
PAK_2019_DHS-MMS_v01_M
Producer(s)
National Institute of Population Studies (NIPS)
Collection(s)
Metadata
Created on
Dec 23, 2020
Last modified
Dec 23, 2020
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Identification
Survey ID Number
PAK_2019_DHS-MMS_v01_M
Title
Maternal Mortality Survey 2019
Country/Economy
| Name | Country code |
|---|---|
| Pakistan | PAK |
Study type
Demographic and Health Survey, Special [hh/dhs-sp]
Abstract
The 2019 Pakistan Maternal Mortality Survey (2019 PMMS) was the first stand-alone maternal mortality survey conducted in Pakistan. A nationally representative sample of 1,396 primary sampling units were randomly selected. The survey was expected to result in about 14,000 interviews with ever-married women age 15-49.
The primary objective of the 2019 PMMS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the survey was designed and carried out with the purpose of assessing where Pakistan stands on maternal health indicators and how well the country is moving toward these targets. Overall aims of the 2019 PMMS were as follows:
- To estimate national and regional levels of maternal mortality for the 3 years preceding the survey and determine whether the MMR has declined substantially since 2006-07
- To identify medical causes of maternal deaths and the biological and sociodemographic risk factors associated with maternal mortality
- To assess the impact of maternal and newborn health services, including antenatal and postnatal care and skilled birth attendance, on prevention of maternal mortality and morbidity
- To estimate the prevalence and determinants of common obstetric complications and morbidities among women of reproductive age during the 3 years preceding the survey
The primary objective of the 2019 PMMS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the survey was designed and carried out with the purpose of assessing where Pakistan stands on maternal health indicators and how well the country is moving toward these targets. Overall aims of the 2019 PMMS were as follows:
- To estimate national and regional levels of maternal mortality for the 3 years preceding the survey and determine whether the MMR has declined substantially since 2006-07
- To identify medical causes of maternal deaths and the biological and sociodemographic risk factors associated with maternal mortality
- To assess the impact of maternal and newborn health services, including antenatal and postnatal care and skilled birth attendance, on prevention of maternal mortality and morbidity
- To estimate the prevalence and determinants of common obstetric complications and morbidities among women of reproductive age during the 3 years preceding the survey
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Household
- Individual
- Woman age 15-49
- Community
- Individual
- Woman age 15-49
- Community
Version
Version Notes
The data dictionary was generated from hierarchical data that was downloaded from the The DHS Program website (http://dhsprogram.com).
Scope
Notes
The 2019 Pakistan Malaria Indicator Survey covered the following topics:
HOUSEHOLD
• Identification
• Usual members and visitors in the selected households
• Background information on each person listed, such as relationship to head of the household, age, and sex
• Information about births and deaths in the household in the previous 3 years
• Household characteristics
EVER- WOMAN
• Identification
• Respondent's background
• Reproduction
• Contraception
• Pregnancy and postnatal care
• Maternal morbidity
• Health services utilization
DECEASED WOMAN
• Identification
• Information about respondents
• Deceased woman's background
• Birth and pregnancy information
• Verbatim description of illness and death
• Symptoms identification
• Deceased illness history
• Antenatal care and characteristics of last pregnancy
• For deaths during labour, delivery, or within 40 days after delivery
• Deaths due to injury / accident / violence
• Care-seeking behavior
• Household characteristics
• Additional household characteristics
COMMUNITY
• Identification
• Household characteristics
• Availability of facilities and services
• Availability of health facilities
FIELDWORKER
• Background information on each fieldworker
HOUSEHOLD
• Identification
• Usual members and visitors in the selected households
• Background information on each person listed, such as relationship to head of the household, age, and sex
• Information about births and deaths in the household in the previous 3 years
• Household characteristics
EVER- WOMAN
• Identification
• Respondent's background
• Reproduction
• Contraception
• Pregnancy and postnatal care
• Maternal morbidity
• Health services utilization
DECEASED WOMAN
• Identification
• Information about respondents
• Deceased woman's background
• Birth and pregnancy information
• Verbatim description of illness and death
• Symptoms identification
• Deceased illness history
• Antenatal care and characteristics of last pregnancy
• For deaths during labour, delivery, or within 40 days after delivery
• Deaths due to injury / accident / violence
• Care-seeking behavior
• Household characteristics
• Additional household characteristics
COMMUNITY
• Identification
• Household characteristics
• Availability of facilities and services
• Availability of health facilities
FIELDWORKER
• Background information on each fieldworker
Coverage
Geographic Coverage
National coverage
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| National Institute of Population Studies (NIPS) | Government of Pakistan |
Producers
| Name | Affiliation | Role |
|---|---|---|
| ICF | The DHS Program | Provided technical assistance through The DHS Program |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Government of Pakistan | GoP | Financial support |
| United States Agency for International Development | USAID | Financial support |
| Department for International Development | DFID | Financial support |
| United Nations Population Fund | UNFPA | Financial support |
| Bill and Melinda Gates Foundation | BMGF | Financial support |
Sampling
Sampling Procedure
The 2019 PMMS used a multistage and multiphase cluster sampling methodology based on updated sampling frames derived from the 6th Population and Housing Census, which was conducted in 2017 by the Pakistan Bureau of Statistics (PBS). The sampling universe consisted of urban and rural areas of the four provinces of Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa, and Balochistan), Azad Jammu and Kashmir (AJK), Gilgit Baltistan (GB), Federally Administered Tribal Areas (FATA), and the Islamabad Capital Territory (ICT). A total of 153,560 households (81,400 rural and 72,160 urban) were selected using a two-stage and two-phase stratified systematic sampling approach. The survey was designed to provide representative results for most of the survey indicators in 11 domains: four provinces (by urban and rural areas with Islamabad combined with Punjab and FATA combined with Khyber Pakhtunkhwa), Azad Jammu and Kashmir (urban and rural), and Gilgit Baltistan. Restricted military and protected areas were excluded from the sample.
The sampled households were randomly selected from 1,396 primary sampling units (PSUs) (740 rural and 656 urban) after a complete household listing. In each PSU, 110 randomly selected households were administered the various questionnaires included in the survey. All 110 households in each PSU were asked about births and deaths during the previous 3 years, including deaths among women of reproductive age (15-49 years). Households that reported at least one death of a woman of reproductive age were then visited, and detailed verbal autopsies were conducted to determine the causes and circumstances of these deaths to help identify maternal deaths. In the second phase, 10 households in each PSU were randomly selected from the 110 households selected in the first phase to gather detailed information on women of reproductive age. All eligible ever-married women age 15-49 residing in these 10 households were interviewed to gather detailed information, including a complete pregnancy history.
Note: A detailed description of the sample design is provided in Appendix A of the final report.
The sampled households were randomly selected from 1,396 primary sampling units (PSUs) (740 rural and 656 urban) after a complete household listing. In each PSU, 110 randomly selected households were administered the various questionnaires included in the survey. All 110 households in each PSU were asked about births and deaths during the previous 3 years, including deaths among women of reproductive age (15-49 years). Households that reported at least one death of a woman of reproductive age were then visited, and detailed verbal autopsies were conducted to determine the causes and circumstances of these deaths to help identify maternal deaths. In the second phase, 10 households in each PSU were randomly selected from the 110 households selected in the first phase to gather detailed information on women of reproductive age. All eligible ever-married women age 15-49 residing in these 10 households were interviewed to gather detailed information, including a complete pregnancy history.
Note: A detailed description of the sample design is provided in Appendix A of the final report.
Response Rate
In the four provinces, the sample contained a total of 116,169 households. All households were visited by the field teams, and 110,483 households were found to be occupied. Of these households, 108,766 were successfully interviewed, yielding a household response rate of 98%. The subsample selected for the Long Household Questionnaire comprised 11,080 households, and interviews were carried out in 10,479 of these households. A total of 12,217 ever-married women age 15-49 were eligible to be interviewed based on the Long Household Questionnaire, and 11,859 of these women were successfully interviewed (a response rate of 97%).
In Azad Jammu and Kashmir, 16,755 households were occupied, and interviews were successfully carried out in 16,588 of these households (99%). A total of 1,707 ever-married women were eligible for individual interviews, of whom 1,666 were successfully interviewed (98%). In Gilgit Baltistan, 11,005 households were occupied, and interviews were conducted in 10,872 households (99%). A total of 1,219 ever-married women were eligible for interviews, of whom 1,178 were successfully interviewed (97%).
A total of 944 verbal autopsy interviews were conducted in Pakistan overall, 150 in Azad Jammu and Kashmir, and 88 in Gilgit Baltistan. The Verbal Autopsy Questionnaire was used in almost all of the interviews, and response rates were nearly 100%.
In Azad Jammu and Kashmir, 16,755 households were occupied, and interviews were successfully carried out in 16,588 of these households (99%). A total of 1,707 ever-married women were eligible for individual interviews, of whom 1,666 were successfully interviewed (98%). In Gilgit Baltistan, 11,005 households were occupied, and interviews were conducted in 10,872 households (99%). A total of 1,219 ever-married women were eligible for interviews, of whom 1,178 were successfully interviewed (97%).
A total of 944 verbal autopsy interviews were conducted in Pakistan overall, 150 in Azad Jammu and Kashmir, and 88 in Gilgit Baltistan. The Verbal Autopsy Questionnaire was used in almost all of the interviews, and response rates were nearly 100%.
Weighting
A spreadsheet containing all sampling parameters and selection probabilities was developed to facilitate the calculation of the design weights. Design weights were adjusted for cluster-level non-response, household-level non-response, and individual non-response to obtain sampling weights for households and individual surveys. The differences in the household sampling weights and the individual sampling weights were introduced by individual non-response. The final sampling weights were normalised to obtain 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. Three sets of weights were calculated:
- one set for all households selected for the phase one survey
- one set for households selected for the phase two women’s survey
- one set for women’s individual survey
It is important to note that the normalised weights are relative weights that are valid for estimating means, proportions, and ratios but are not valid for estimating population totals or pooled data. Also, the number of weighted cases obtained using the normalised weight has no direct relation to survey precision because it is relative, especially for oversampled areas. The number of weighted cases is much smaller than the number of unweighted cases; the latter is directly related to survey precision.
- one set for all households selected for the phase one survey
- one set for households selected for the phase two women’s survey
- one set for women’s individual survey
It is important to note that the normalised weights are relative weights that are valid for estimating means, proportions, and ratios but are not valid for estimating population totals or pooled data. Also, the number of weighted cases obtained using the normalised weight has no direct relation to survey precision because it is relative, especially for oversampled areas. The number of weighted cases is much smaller than the number of unweighted cases; the latter is directly related to survey precision.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2019-01 | 2019-05 |
Data Collection Mode
Face-to-face [f2f]
Data Collection Notes
A comprehensive household listing of all sampled PSUs was conducted in December 2018 to January 2019 to identify sampled households to be visited. Data collection took place from 20 January to 30 September 2019 in all provinces and regions other than Balochistan and Gilgit Baltistan, where fieldwork was completed in October 2019. Forty-one teams consisting of a supervisor, a field editor, and four interviewers were deployed for data collection. All data entry was conducted by the field editors at the end of each day’s fieldwork.
Fieldwork monitoring was an integral part of the 2019 PMMS, and several rounds of monitoring were carried out by the core team members and the provincial coordinators. The monitors were provided with guidelines for overseeing the fieldwork. Quality Control Teams and Field Editors focused on various quality of data matters in all regions. The quality and progress of data collection were also monitored through weekly field check tables that were generated from completed interviews received at the NIPS central office, and regular feedback was sent out to the teams and monitors.
Fieldwork monitoring was an integral part of the 2019 PMMS, and several rounds of monitoring were carried out by the core team members and the provincial coordinators. The monitors were provided with guidelines for overseeing the fieldwork. Quality Control Teams and Field Editors focused on various quality of data matters in all regions. The quality and progress of data collection were also monitored through weekly field check tables that were generated from completed interviews received at the NIPS central office, and regular feedback was sent out to the teams and monitors.
Questionnaires
Questionnaires
Six questionnaires were used in the 2019 PMMS: the Short Household Questionnaire, the Long Household Questionnaire, the Woman’s Questionnaire, the Verbal Autopsy Questionnaire, the Community Questionnaire, and the Fieldworker Questionnaire. A Technical Advisory Committee was established to solicit comments on the questionnaires from various stakeholders, including representatives of government ministries and agencies, nongovernmental organisations, and international donors. The survey protocol was reviewed and approved by the National Bioethics Committee, the Pakistan Health Research Council, and the ICF Institutional Review Board. After being finalised in English, the questionnaires were translated into Urdu and Sindhi. The 2019 PMMS used paper-based questionnaires for data collection, while computer-assisted field editing (CAFE) was used to edit questionnaires in the field.
Data Processing
Data Editing
The processing of the 2019 PMMS data began simultaneously with the fieldwork. As soon as data collection was completed in each cluster, all electronic data files were transferred via the Internet File Streaming System (IFSS) to the NIPS central office in Islamabad. These data files were registered and checked for inconsistencies, incompleteness, and outliers. A double entry procedure was adopted by NIPS to ensure data accuracy. The field teams were alerted about any inconsistencies and errors. Secondary editing of completed questionnaires, which involved resolving inconsistencies and coding open-ended questions, was carried out in the central office. The survey core team members assisted with secondary editing, and the NIPS data processing manager coordinated the work at the central office. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate.
Data Appraisal
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 2019 Pakistan Maternal Mortality Survey (2019 PMMS) 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 2019 PMMS is only one of many samples that could have been selected from the same population, using the same design and sample 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 by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2019 PMMS sample was the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed using SAS programmes developed by ICF. These programmes use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios and use the Jackknife repeated replication method 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 report.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2019 PMMS is only one of many samples that could have been selected from the same population, using the same design and sample 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 by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2019 PMMS sample was the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed using SAS programmes developed by ICF. These programmes use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios and use the Jackknife repeated replication method 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 report.
Data Appraisal
Data Quality Tables
- Household age distribution
- Household age distribution: Short Household Questionnaire
- Household age distribution: Long Household Questionnaire
- Household age distribution: Azad Jammu and Kashmir
- Household age distribution (short questionnaire): Azad Jammu and Kashmir
- Household age distribution (long questionnaire): Azad Jammu and Kashmir
- Household age distribution: Gilgit Baltistan
- Household age distribution (short questionnaire): Gilgit Baltistan
- Household age distribution (long questionnaire): Gilgit Baltistan
- Age distribution of eligible and interviewed women
- Age distribution of eligible and interviewed women: Azad Jammu and Kashmir
- Age distribution of eligible and interviewed women: Gilgit Baltistan
- Completeness of reporting
- Completeness of reporting: Azad Jammu and Kashmir
- Completeness of reporting: Gilgit Baltistan
- Births by calendar years
- Births by calendar years: Azad Jammu and Kashmir
- Births by calendar years: Gilgit Baltistan
- Reporting of age at death in days
- Reporting of age at death in days: Azad Jammu and Kashmir
- Reporting of age at death in days: Gilgit Baltistan
- Reporting of age at death in months
- Reporting of age at death in months: Azad Jammu and Kashmir
- Reporting of age at death in months: Gilgit Baltistan
See details of the data quality tables in Appendix C of the final report.
- Household age distribution
- Household age distribution: Short Household Questionnaire
- Household age distribution: Long Household Questionnaire
- Household age distribution: Azad Jammu and Kashmir
- Household age distribution (short questionnaire): Azad Jammu and Kashmir
- Household age distribution (long questionnaire): Azad Jammu and Kashmir
- Household age distribution: Gilgit Baltistan
- Household age distribution (short questionnaire): Gilgit Baltistan
- Household age distribution (long questionnaire): Gilgit Baltistan
- Age distribution of eligible and interviewed women
- Age distribution of eligible and interviewed women: Azad Jammu and Kashmir
- Age distribution of eligible and interviewed women: Gilgit Baltistan
- Completeness of reporting
- Completeness of reporting: Azad Jammu and Kashmir
- Completeness of reporting: Gilgit Baltistan
- Births by calendar years
- Births by calendar years: Azad Jammu and Kashmir
- Births by calendar years: Gilgit Baltistan
- Reporting of age at death in days
- Reporting of age at death in days: Azad Jammu and Kashmir
- Reporting of age at death in days: Gilgit Baltistan
- Reporting of age at death in months
- Reporting of age at death in months: Azad Jammu and Kashmir
- Reporting of age at death in months: Gilgit Baltistan
See details of the data quality tables in Appendix C of the final report.
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| Information about The DHS Program | The DHS Program | reports@DHSprogram.com |
| General Inquiries | The DHS Program | info@dhsprogram.com |
| Data and Data Related Resources | The DHS Program | archive@dhsprogram.com |
Access conditions
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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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When The DHS Program receives authorization from the appropriate organizations, the user will be contacted, and the datasets made available by secure FTP.
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Once downloaded, the datasets must not be passed on to other researchers without the written consent of The DHS Program. All reports and publications based on the requested data must be sent to The DHS Program Data Archive in a Portable Document Format (pdf) or a printed hard copy.
Download Datasets
Datasets are made available for download by survey. You will be presented with a list of surveys for which you have been granted dataset access. After selecting a survey, a list of all available datasets for that survey will be displayed, including all survey, GPS, and HIV data files. However, only data types for which you have been granted access will be accessible. To download, simply click on the files that you wish to download and a "File Download" prompt will guide you through the remaining steps.
The following applies to DHS, MIS, AIS and SPA survey datasets (Surveys, GPS, and HIV).
To request dataset access, you must first be a registered user of the website. You must then create a new research project request. The request must include a project title and a description of the analysis you propose to perform with the data.
The requested data should only be used for the purpose of the research or study. To request the same or different data for another purpose, a new research project request should be submitted. The DHS Program will normally review all data requests within 24 hours (Monday - Friday) and provide notification if access has been granted or additional project information is needed before access can be granted.
DATASET ACCESS APPROVAL PROCESS
Access to DHS, MIS, AIS and SPA survey datasets (Surveys, HIV, and GPS) is requested and granted by country. This means that when approved, full access is granted to all unrestricted survey datasets for that country. Access to HIV and GIS datasets requires an online acknowledgment of the conditions of use.
Required Information
A dataset request must include contact information, a research project title, and a description of the analysis you propose to perform with the data.
Restricted Datasets
A few datasets are restricted and these are noted. Access to restricted datasets is requested online as with other datasets. An additional consent form is required for some datasets, and the form will be emailed to you upon authorization of your account. For other restricted surveys, permission must be granted by the appropriate implementing organizations, before The DHS Program can grant access. You will be emailed the information for contacting the implementing organizations. A few restricted surveys are authorized directly within The DHS Program, upon receipt of an email request.
When The DHS Program receives authorization from the appropriate organizations, the user will be contacted, and the datasets made available by secure FTP.
GPS/HIV Datasets/Other Biomarkers
Because of the sensitive nature of GPS, HIV and other biomarkers datasets, permission to access these datasets requires that you accept a Terms of Use Statement. After selecting GPS/HIV/Other Biomarkers datasets, the user is presented with a consent form which should be signed electronically by entering the password for the user's account.
Dataset Terms of Use
Once downloaded, the datasets must not be passed on to other researchers without the written consent of The DHS Program. All reports and publications based on the requested data must be sent to The DHS Program Data Archive in a Portable Document Format (pdf) or a printed hard copy.
Download Datasets
Datasets are made available for download by survey. You will be presented with a list of surveys for which you have been granted dataset access. After selecting a survey, a list of all available datasets for that survey will be displayed, including all survey, GPS, and HIV data files. However, only data types for which you have been granted access will be accessible. To download, simply click on the files that you wish to download and a "File Download" prompt will guide you through the remaining steps.
Citation requirements
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
Access authority
| Name | URL | |
|---|---|---|
| The DHS Program | archive@dhsprogram.com | Link |
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Metadata production
DDI Document ID
DDI_PAK_2019_DHS-MMS_v01_M
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Economics Data Group | DECDG | The World Bank | Documentation of the DDI |
Date of Metadata Production
2020-12-14
DDI Document version
Version 01 (December 2020). Metadata is excerpted from "Pakistan Maternal Mortality Survey 2019" Report.