Every year, hundreds of papers are published using DHS Program data. Datasets for individual DHS surveys are available for download from The DHS Program website by completing a simple registration form. Researchers and students can also access the IPUMS DHS data collection to facilitate comparative analysis of DHS Program data. Harmonized across time and space, IPUMS DHS features consistently named and coded variables for 32 African countries and 9 Asian countries, with more datasets constantly being added.
The IPUMS DHS team recently announced the winners of this year’s IPUMS Global Health paper awards, which highlight some of the most exciting research to take advantage of the IPUMS DHS integrated datasets. These papers were selected from a highly competitive field that included nearly 50 studies released in 2020.
Fan and Loria resolve a puzzle in prior research on intimate partner violence (IPV): Why is the relationship between IPV and contraceptive use negative in some countries and positive in others? Using 30 IPUMS DHS samples from 17 countries, the authors demonstrate that the relationship between IPV and family planning is modified by macro contextual factors, including legal prohibitions and national levels of female empowerment. This study stands out not just for answering an important social science question but also in its creative use of the broad range of information collected in DHS Program surveys, including variables on contraceptive use and type, family size preferences, husband-wife disagreement on fertility goals, various indicators of women’s status (e.g., education, employment, decision-making), and domestic violence. In addition, the authors draw on IPUMS DHS variables to determine the direction of causality: from the experience of IPV to increased contraceptive use, rather than from contraceptive use to increased incidence of IPV.
This study leverages geographic heterogeneity to determine the effect of reduced malaria burden on low-birth-weight rates across communities in 19 sub-Saharan African countries. Low birth weight is a serious health risk associated with cognitive and physical difficulties among children. This careful and cleverly designed study analyzes IPUMS DHS data from countries with at least two surveys and GPS data on survey cluster locations. After using optimal matching to pair DHS Program survey clusters separated in time, the authors use a difference-in-difference approach to compare the incidence of low birth weight in areas that did and did not experience malaria decline. Results reveal a substantial decline in low birth weight resulting from declines in malaria prevalence, especially for first-born children.
Congratulations to these scholars on this impressive accomplishment!
IPUMS DHS is a system that makes it easy to find and review thousands of DHS survey variables and to download a single fully harmonized data file with only the variables and samples that interest you. IPUMS DHS currently includes variables from DHS survey samples from 32 African and 9 Asian countries; more samples are constantly being added.
Special thanks to our guest blog contributors, Elizabeth Boyle and Miriam King!
Elizabeth Heger Boyle is Professor of Sociology & Law at the University of Minnesota. She studies the role of international laws and policies on women and children’s health around the world. She has written extensively on the impetus for and impact of laws related to female genital cutting, including the book Female Genital Cutting: Cultural Conflict in the Global Community. Her current research focuses on abortion policies globally and their effects; this includes a 2015 article in the American Journal of Sociology. Professor Boyle is currently co-Principal Investigator (with Dr. Miriam King) on IPUMS DHS, a National Institute of Child Health and Human Development grant that integrates Demographic and Health Surveys over time and across countries to make them more user-friendly for researchers. Professor Boyle has a Ph.D. in Sociology from Stanford University and a J.D. from the University of Iowa.
Miriam L. King is a Senior Research Scientist at the Institute for Social Research and Data Innovation at the University of Minnesota. She has managed data integration projects on the U.S. Current Population Survey, the U.S. National Health Interview Survey, and, most recently, the Demographic and Health Surveys. Her research has focused on the history of the U.S. census, data integration methods, U.S. historical fertility differences, living arrangements, and disparities in access to insurance for same-sex couples. Dr. King has a Ph.D. jointly in Demography and History from the University of Pennsylvania.
Over the next four days, demographers and public health professionals will present research, view and comment on posters, and network with peers. For those attending the PAA Annual Meeting, DHS Program staff will be presenting their findings using DHS data. Find the schedule below:
The DHS Program recently released three YouTube tutorial videos to help DHS data users access The DHS Program’s Code Share Library on GitHub. The Code Share Library was started in 2018 to provide code for producing DHS indicators listed in the Guide to DHS Statistics using statistical software like Stata or SPSS. This year, The DHS Program has begun preparing R code as well. You do not need to create a GitHub account to copy or download any code to produce DHS indicators; it is publicly available for use.
The first video, Overview of The DHS Program’s Code Share Library on GitHub, explains the main components of the Code Share Library, including Stata and SPSS repositories, the indicator list, ReadMe file, and chapter folder contents. In each repository on GitHub, there is an important ReadMe file with instructions for users to read before using the code provided.
One way to start using the Code Share Library is to download the entire repository on your computer. If any update is made to the code in the future, you will need to download the updated code from the Code Share Library. Another way to run the code is to copy and paste the code for your indicator of interest from GitHub to your own personal do file, without having to download the entire repository.
The third video, Running The DHS Program’s Shared Code on Stata, demonstrates how to run the code in Stata to construct indicator variables and produce tables for the indicators. The tables provide a simple tabulation that follows the standard DHS tabulation plan used for survey final reports.
The DHS Fellows Program builds the long-term institutional capacity of universities in DHS countries to train students and faculty to analyze DHS data. Since 2011, the DHS Fellows Program has trained more than 150 researchers from over 40 universities in 25 countries in Africa, Asia, and the Middle East. Typically, Fellows attend two separate in-person workshops, prepare publication-quality research papers in teams using DHS datasets, and implement capacity strengthening activities at their home universities.
For the 2020 DHS Fellows Program, a cohort of university faculty from Bangladesh, Burkina Faso, Cameroon, Guinea, Jordan, and Pakistan convened for the first workshop in Nairobi, Kenya, in February 2020. The second workshop, scheduled to begin in April 2020, was canceled due to the COVID-19 pandemic. The DHS Program worked quickly to convert the second workshop into online activities. A remote teaching space was created on The DHS Program Learning Hub with presentations and assignments for the Fellows to complete. Virtual meetings were held with each Fellows team to discuss drafts of their working papers.
The 2020 DHS Fellows produced working papers that addressed a variety of research topics including:
As of this blog’s publication, the teams from Jordan and Cameroon have published their working papers in peer-reviewed journals. Visit The DHS Program Fellows page to see all DHS Fellows’ working papers and publications in peer-reviewed journals.
We interviewed teams of 2020 Fellows from the Asian University for Women in Bangladesh and the Gamal Abdel Nasser University of Conakry in Guinea about the virtual DHS capacity strengthening activities conducted for faculty and students.
For their working paper, Nazmul Alam, Mohammad Manir Hossain Mollah, and Sharin Shajahan Naomi wrote about the prevalence and determinants of adolescent fertility. They conducted two virtual capacity strengthening sessions via Zoom, one for 21 faculty members, researchers, and development practitioners, and another session for 25 students. In the sessions, the Fellows introduced participants to The DHS Program, reviewed basic characteristics of DHS data, and highlighted how one can effectively generate new ideas from available DHS data without needing to conduct field research, which has become difficult during the COVID-19 pandemic. “Although we were a bit hesitant about the outcome of online sessions, they appeared to be beneficial…faculty members from social sciences, public health, and natural sciences joined…after the workshop, we got very positive feedback.”
Bienvenu Salim Camara, Sidikiba Sidibé, and Nafissatou Dioubate wrote about non-use of contraceptives among married women. Days before their planned capacity strengthening presentations, Guinea declared a health emergency due to COVID-19. Universities were closed and gatherings of more than 20 people were prohibited, so the Fellows recorded video presentations introducing The DHS Program survey questionnaires and datasets and uploaded them to Google drive. Students watched the videos at their own pace and emailed the Fellows with questions. Now some students are using DHS data in their research. “One of my students is currently working on his Master’s thesis in maternal health using DHS data, and I am supporting him in the data analysis,” explains Camara. Dioubate notes, “I am proud that I was able to pass on the knowledge gained from the DHS Fellows Program to others and show the opportunities that DHS data can offer.”
Explore indicators related to women’s empowerment, reproductive health, and more using STATcompiler. Download Stata and SPSS code for these and other topics from The DHS Program’s code library on GitHub, and check out the wealth of gender-related resources and publications available at dhsprogram.com.
Learn more about Sustainable Development Goal #5, gender equality indicators from recent DHS surveys in the infographic below.
Location is an important factor in population and health outcomes. Knowing the geospatial location of household survey clusters allows researchers to analyze the impact of location on peoples’ health, nutrition, and access to health care services. Geospatial data provide a clearer picture of where progress towards the Sustainable Development Goals is and is not being made at subnational levels.
The DHS Program has collected GPS coordinates for household survey clusters since 1996. To ensure respondent confidentiality and prevent positive identification (disclosure) of respondent locations, the GPS position of each urban cluster is displaced by up to two kilometers and up to 5-10 kilometers for rural clusters. This method of geomasking coordinates developed by The DHS Program is straightforward and has been widely accepted by analysts using DHS geospatial data. Nonetheless, there are legitimate concerns that urban points may be overly displaced, reducing the analytical usefulness of the geospatial data, and that some rural points may not be adequately displaced to ensure respondent confidentiality. In response, the spatial anonymization task force convened to explore more sophisticated methods of anonymizing geospatial data.
The task force developed and tested new population-based displacement tools on multiple DHS survey datasets. These tools use an area’s population to determine the minimum distance a cluster’s GPS position must be displaced. These new methods show promise over current spatial anonymization methods to better protect survey respondents while minimizing any adverse impact on analysis and continue to be explored using DHS datasets.
The task force also outlines immediate steps that can be taken to protect respondents. “Even without switching to a new population-based approach [to anonymize geospatial data], we should take steps to verify that we are within an acceptable level of disclosure risk and that our current anonymization objectives are being achieved,” explains Trinadh Dontamsetti, Lead, Geospatial Research. Standards in data protection and security have evolved—the European Union General Data Protection Regulation requires that personal data, including location data, be safeguarded. The task force recommends assessing the risk of disclosure. By quantifying and measuring spatial disclosure risk, the risk can be managed.
The DHS Program is hosting another Health Data Mapping online course on The DHS Program Learning Hub. The 12-week course focuses on the application of geographic information systems (GIS) in public health, specifically using maps for better program and policy decision making. Participants will be introduced to GIS concepts, manage and clean data in Microsoft Excel, and get a hands-on introduction to QGIS, an open-source GIS software package.
This course is for people who:
Have little to no GIS experience, but have an interest in learning QGIS and strong data skills.
Have at least an undergraduate degree in public health, demography, statistics, monitoring & evaluation, or a related subject, and basic training in statistics.
Currently work for government ministries, development partners, NGOs, or universities in the field of public health.
Can understand and communicate in English—the course will be conducted in English and participants will be expected to give presentations in English.
Have experience using Excel and have a computer that can run the latest stable release of QGIS.
The Health Data Mapping online course begins April 12 and ends July 3, 2021. Participants can expect to spend two to four hours a week working independently on self-paced lessons and completing assignments. Course facilitators will give feedback on assignments and answer questions on the course discussion forum and during periodic instructor-led virtual sessions.
What are the 3 objectives of The Demographic and Health Surveys (DHS) Program?
How many months does a standard DHS survey take, from design to data dissemination?
How many questionnaires are used in a standard DHS survey?
Have you ever wondered about the questions above? There is always something new to learn about Demographic and Health Surveys! Even the most experienced survey implementers and researchers will discover something they did not know in our new 30-minute introductory course. The newest addition to The DHS Program Learning Hub is a short and engaging orientation of The DHS Program. The course covers the main objectives of the survey, key terms, survey types and topics, and the survey process.
This introductory course is available for free to anyone. To access the course, you must complete a short registration form. The course can be taken independently and will also be a pre-requisite for other courses offered on The DHS Program Learning Hub.
An animated video from the course showcases the DHS survey process and is also available on our YouTube channel.
The DHS Program Research and Analysis team has recently published several studies that analyze new DHS data or employ novel approaches to analyze existing DHS data.
Analysis of New Sickle Cell Data
The 2018 Nigeria DHS includes sickle cell genotyping of a subsample of 11,186 children age 6-59 months, the first population-based household survey to do so at a national level. A new Working Paper, Analysis of Sickle Cell Genotypes of Young Children in Nigeria Using the 2018 DHS Survey, finds that the siblings of genotyped children with sickle cell disease are about 2.5 times as likely to have died as the siblings of other genotyped children. The main value of the data is the description of the spatial distribution of the genotypes within Nigeria. The S and C alleles, which result in sickle cell disease, sickle cell trait, or Hemoglobin C trait, are primarily concentrated in states in the South West Zone, including Lagos, and secondarily in the North Central Zone. This information is helpful for estimating the burden of risk and for prioritizing interventions in different areas of Nigeria.
New Insights Into Wealth Inequality Using DHS Wealth Index Data
In 9 of 10 countries, households that are poor relative to their communities were more likely to use at least one maternal health care (antenatal care and facility delivery) or vaccination service, suggesting that a household that is poor relative to the community is potentially better able to access the services of a relatively wealthy community. Read the analysis brief for this Analytical Study, a user-friendly summary of the methods, key findings, and relevant action steps. Analysis briefs are available for many recent analytical reports from The DHS Program.
New Analysis of DHS Contraceptive Calendar Data
A new web feature highlights a series of publications that put to new use retrospective, longitudinal data from DHS contraceptive calendars. Three working papers were recently published. In Fertility and Family Planning Characteristics of Contraceptive Clusters in Burundi researchers apply sequence and cluster analysis to identify six discrete clusters that characterize women’s dynamic contraceptive and pregnancy behaviors over the previous five years. Factors most consistently associated with cluster membership are the need for family planning, lifetime experience of contraceptive use, marital status, pregnancy experience, and age.
Anthropometry measurement (height and weight) is a core component of DHS surveys that is used to generate indicators on nutritional status. The Biomarker Questionnaire now includes questions on clothing and hairstyle interference on measurements for both women and children for improved interpretation.