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.
November 18, 2020, is GIS Day, a day to celebrate Geographic Information Systems (GIS) technology and their application to DHS data. This GIS Day, we highlight recent work from The DHS Program Geospatial team.
Highlighting Subnational Inequalities That May Hinder COVID-19 Mitigation
The Geospatial team produced a story map using DHS spatial data on handwashing and the average number of people per sleeping room in some countries in Africa and Asia to illuminate subnational variations and inform COVID-19 mitigation strategies.
Health Data Mapping Workshop Facilitated Virtually
In February 2020, The DHS Program released a call for applications for a Health Data Mapping regional workshop, to be held in Hyderabad, Telangana, India in May. When this workshop was canceled due to the COVID-19 pandemic, the Geospatial team quickly pivoted and developed an online course with the same basic objectives. A new call for applications was launched targeting countries in East Africa. Of 175 applicants, 25 were selected according to established criteria, including 15 men and 10 women.
The redesigned course, a semi-synchronous workshop, was conducted from July to September and included self-paced eLearning and instructor-led virtual sessions. Each week, participants completed readings, videos, and assignments. Participants acquired knowledge on data cleaning, converting GPS data in Excel, and joining data in QGIS, an open-source GIS software package. For their capstone assignment, participants created and presented maps using data from DHS surveys.
The DHS Program periodically hosts virtual instructor-led courses like Health Data Mapping on The DHS Program Learning Hub. For future offerings of the Health Data Mapping and other courses, check The DHS Program website for calls for applications.
Two Spatial Analysis Reports Published
Predicting HIV/AIDS at Subnational Levels using DHS Covariates related to HIV uses GPS data collected in DHS surveys to produce spatially interpolated maps to predict indicator values at non-surveyed locations. This report produces fine spatial and lower level estimates of HIV prevalence for seven Global Fund-supported countries in sub-Saharan Africa. A multi-task Gaussian process is used to leverage HIV prevalence data collected in DHS surveys along with HIV-related indicators such as sexually transmitted infection (STI), condom use, and sex partners. The model produces estimates of HIV prevalence along with uncertainty at the second subnational administrative level (ADMIN 2) where health programs are designed and implemented. This geospatial modeling approach was first described in Interpolation of DHS Survey Data at Subnational Administrative Level 2, and summarized in a previous blog post.
Geospatial Covariates: Proxies for Mapping Urban-Related Indicators uses DHS data from three East African countries to explore what makes an enumeration area rural or urban. The DHS Program does not define urbanicity; countries determine official urban and rural classifications themselves. This can impair comparative analyses of the relationship between urbanicity and health outcomes. In this Spatial Analysis Report, researchers sought to determine if geospatial covariates of urbanicity, including intensity of nightlights and travel times to hospitals, can predict urban-correlated health and demographic indicators from DHS surveys.
Spatial Anonymization in Public-Use Household Survey Datasets
In March 2020, The DHS Program released a call for applications for the 2020 DHS Data Processing Procedures – Data Tabulation and Data Finalization (DPPII) workshop, to be held in Accra, Ghana in June. The DPPII workshop includes online pre-work and face-to-face instruction. DHS Program Data Processing staff members assist participants through one-on-one coaching, and participants gain proficiency through hands-on practice. Due to the COVID-19 pandemic, this in-person workshop was canceled.
The DHS Program’s Data Processing team worked with the Capacity Strengthening team to adapt the DPPII workshop to an online course focused on data tabulation. The course was delivered on The DHS Program Learning Hub and included self-paced modules with readings, videos, and activities, as well as updated CSPro manuals. These up-to-date materials will be used in future data processing courses and workshops, plus trainings for new Data Processing staff at The DHS Program.
The restructured DPPII course is semi-synchronous, including eLearning modules and assignments that participants work through independently. The course also includes four virtual instructor-led sessions, in which participants and DHS Program facilitators login to the same virtual learning space to learn new content, watch presentations, ask and address questions, and receive feedback on assignments in real-time. For their capstone assignment, participants recreate a standard DHS table using CSPro by defining their own variables and data.
Staff from implementing agencies in countries with ongoing DHS surveys are targeted for participation in the DPPII workshop, as participants build competencies required to process DHS data and produce country-specific tables found in DHS final reports. For this first-ever virtual DPPII course, participants included five women and fourteen men from eight Anglophone countries which recently implemented a DHS survey: the Gambia, Ghana, Liberia, Nigeria, Pakistan, Rwanda, Uganda, and Zambia.
What Participants Say
“I’m glad to have been part of this training. [It gave me a] better understanding of the use of DHS data, generation of DHS recode and tables. I hope to practice my new skills with the country-specific tables.”
“Attending training and combining with other duties from work was not helpful but I will take time and continue reading and finish all as they are clear and useful.”
Converting face-to-face workshops to virtual learning sessions comes with challenges. It can be difficult for participants to balance coursework with work and other responsibilities, which is not an issue with in-person residential workshops. Throughout the virtual DPPII training, it became clear that more one-on-one instruction time was needed. To address this, facilitators began holding optional office hours. These and other lessons learned about virtual facilitation will be applied to future online courses, remote technical assistance, and webinars.
Interested in learning more about capacity strengthening opportunities at The DHS Program? The DHS Program periodically makes Workshop and Training Announcements for upcoming training opportunities.
This blog post is part of Luminare, our blog series exploring innovative solutions to data collection, quality assurance, biomarker measurement, data use, and further analysis.
The DHS Program recently published a Methodological Report providing a framework for estimating “level-weights” in DHS surveys – weights that correspond to each stage of sampling. These weights are required for multilevel modeling. While the audience for the framework itself is academic researchers, the challenge of protecting respondent confidentiality while supporting data analysis is of general interest.
We sat down with two of the authors, Mahmoud Elkasabi, Senior Sampling Statistician, and Tom Pullum, Senior Advisor for Research and Analysis, to learn more about this innovative strategy.
How did the idea for this activity come about?
Post from Data User:
I have been reading the posts on the forum regarding the use of weights with multilevel analyses and wanted to check to see if there were any updates on recommendations on how to go about this. . . Since we cannot separate out the household weights from the cluster weights to incorporate them in the statistical coding, does the DHS have any recommendations on how to go about running multilevel models with DHS data? . . . I would like to run multilevel models looking at childhood vaccinations and want to make sure I am going about it in the most proper way. Any help or guidance on this from those at DHS or out in the forum would be greatly appreciated!
Mahmoud: There has been huge user demand for DHS survey level-weights. We have seen many posts on The DHS Program User Forum over the years, where analysts are trying to apply weights in multilevel analysis. It is a common type of research question, to use multilevel modeling to understand the effects of cluster-level characteristics such as region on individual-level outcomes, such as contraceptive use or children’s nutritional status.
For those of us who aren’t statistically inclined, why do researchers need to include sampling weights in their analysis?
Mahmoud: Sampling weights compensate for different probabilities of selection within the samples, and for different levels of non-response. Providing weights at multiple levels allows for the best level of representativeness for that unit. That is, the data from each interviewed woman becomes as representative as possible of similar women in the population. That is ultimately the goal of a survey: to obtain data that are nationally and subnationally representative without interviewing the entire population.
Why aren’t level-weights standardly provided with DHS datasets?
Mahmoud: After a survey is completed, The DHS Program destroys the information required for exact calculation of the cluster weights. Providing the true cluster-level weight for each cluster would pose a risk to respondent confidentiality—anyone with access to the sampling frame could use the cluster-level weights to identify the specific clusters that were drawn in the sample—and then, potentially, identify households or individuals. For that reason, The DHS Program only releases the final survey weights in the datasets.
How does the level-weights framework respond to the challenge of protecting confidentiality?
Tom: We propose a framework that uses publicly available data from DHS datasets and Final Reports, along with a process to estimate other inputs. The framework starts with the household final weight from the household recode file or the woman final weight from the woman recode file. Most of the numbers required to separate the final weight into a cluster-level weight and a household-level (or woman-level) weight are included in the data files or in Appendix A – Sample Design of DHS Final Reports. Some of the required information is not available there (see Table 1), but we provide guidance on how to estimate these inputs with other publicly available data. In this way, we can estimate or approximate the level-weights for the clusters and households (or women).
Have these level-weights been used in any DHS analysis?
Tom: This report shows how to use data from the 2015 Zimbabwe DHS to estimate level-weights and then include them in a multilevel regression model. We fitted several regression models with data for married women in 400 clusters to examine modern contraceptive use with age, education, residence, and number of children as covariates. We provide the STATA code for this example.
The recently released Analytical Study Contraceptive Use, Method Mix, and Method Availabilityis the first DHS research to use the proposed methodology. This analysis used the method described here to estimate cluster-level and woman-level weights and then to assess the effect of cluster-level and woman-level factors on contraceptive use in Haiti and Malawi.
Dr. Mahmoud Elkasabi is a Sampling Statistician at The DHS Program. He joined The DHS Program in 2013 after earning his Ph.D in Survey Methodology from the University of Michigan at Ann Arbor, with a specialty in Survey Statistics and Sampling. Dr. Elkasabi is responsible for the sampling design for the DHS surveys as well as building sampling capacity in many countries, such as Ghana, Egypt, Nigeria, India, Malawi, Zambia, Bangladesh, and Afghanistan. Dr. Elkasabi likes to work closely with the sampling statisticians in different countries. In these win-win relationships, he shares his knowledge in sampling and gains new knowledge & experiences.
Dr. Tom Pullum directs the research program, including the analysis of DHS data beyond the country reports, such as the analytical studies, comparative reports, further analysis studies, and methodological reports. He also has overall responsibility for The DHS Fellows Program and workshops. Current interests include maternal mortality and the measurement of child vulnerability. A continuing effort is the adaptation of demographic methods to statistical frameworks and software. His work with DHS has included methodological reports on data quality. He joined the DHS staff in 2011, following a lengthy career in academia, primarily at the University of Texas at Austin. Dr. Pullum has a Ph.D. in sociology from the University of Chicago.
Languages spoken: French, Spanish, a little Swahili, a word or two in Arabic, a touch of Portuguese
When did you start at The DHS Program? I was hired in 1999 as the first GIS Analyst after completing my master’s degree in Population Geography. The DHS Program at that time had just started collecting GPS data. I established the first protocol and manual for the collection of GPS data which remains part of the core survey methodology today.
I grew in that role until 2006. I wanted to learn more and be able to do more, so I went back to pursue my Doctorate in Public Health at Harvard School of Public Health. Following that, I became the Director of Research for the Center for Population and Development Studies at Harvard for five years before returning to The DHS Program in May 2019.
What is your role at The DHS Program? My role as Deputy Director is to translate all of our day to day survey work into the big picture in The DHS Program. Specifically, the country work falls under me, so I try to keep up-to-date on what’s happening in all the surveys all the time.
I also bring an outside perspective to the program. Having been away from The DHS Program for a number of years, I feel like I have a better perspective of what the community of DHS data users cares about and how people value and use the data, while at the same time understanding how the extraordinary work is achieved with all of our partners.
COVID-19 has affected all ongoing DHS surveys in one way or another. We have postponed a number of surveys that were supposed to take place this year, and we have shifted to virtual technical assistance where we can. The country demand for DHS surveys has not decreased, so I am closely monitoring when we will be able to resume field activities. For the latest updates on COVID-19 from The DHS Program, visit the new COVID-19 feature page.
What’s your favorite trip to date? One trip I enjoyed was to Cambodia in 2005. We had completed the 2005 Cambodia DHS and were in the planning stages for the next DHS survey. I traveled with Bernard Barrère, the previous Deputy Director. He was working on the new survey design, and I was teaching a workshop to staff from the Ministry of Health and Ministry of Planning on using GIS to combine their health information system data with DHS to explore some of the findings in the DHS survey. Bernard and I took little motorcycles to dinner at a very nice French restaurant together. That’s a nice memory I have of the previous Deputy Director.
What work are you most proud of? Working with country counterparts to develop and carry out the surveys. I loved teaching workshops, seeing when participants get what you are trying to teach them. Capacity strengthening was always part of The DHS Program, but it has grown tremendously and is now a formalized effort through The DHS Program Learning Hub for instance.
What developments in data collection or global health, in general, are you excited about right now? Biomarkers. DHS surveys always collect data about people’s demographic and health histories, but there are many health conditions and risk factors that we can measure directly, such as testing for malaria, hemoglobin levels, micronutrients, and things we have yet to consider that can provide additional information to help us understand health and population change.
Even though COVID-19 has paused survey fieldwork and kept DHS Program staff from traveling, DHS data continue to inform the COVID-19 conversation around the world. The just-launched COVID-19 feature page on The DHS Program website provides a hub for all of this essential information.
This new page features the tools released this year by The DHS Program to support the use of DHS COVID-19-related data:
Our story map on availability of handwashing facilities and sleeping space allows users to explore national and subnational variation in these infection-prevention measures.
SPA data on health facility readiness to manage infection control, diagnose respiratory infections, and provide treatment were compiled in a new publication.
The global community relies on DHS data in their work to understand COVID-19, and we are eager to share those resources. The new web hub provides frequently updated news and journal articles that feature DHS data. These articles highlight the broad impact of COVID-19. The effects of lockdowns, social distancing, and isolation are far-reaching and have caused great concern in the global health community. DHS data help to contextualize these other effects of the pandemic: food insecurity in Nepal, concerns about the impact of COVID-19 on family planning use in Nigeria, and an increase in domestic violence in the Philippines are just a few examples. The journal articles feed lists COVID-19-related peer-reviewed journal articles that use DHS data. Recently posted articles cover the public health consequences of COVID-19 on malaria in Africa (Nature Medicine), preventing COVID-19 in Indian slums (World Medical and Health Policy), and a vulnerability index for COVID-19 in India (Lancet Global Health).
Finally, the COVID-19 feature page will be home to any COVID-19-related press releases issued by The DHS Program. This is where you can find more detailed information about the status of survey operations, including plans for returning to the field and adjusted survey timelines.
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.