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.
Have you ever wondered how to write a Stata program for vaccination coverage or struggled to construct mortality rates using DHS data? Well, DHS Program staff are busy writing SPSS and Stata code for all indicators listed in the Guide to DHS Statistics, and you can use this code to jump-start your exploration of the data. And as they are completed, the code will be posted on GitHub for open access to the public.
The DHS Program GitHub site contains two repositories: DHS-Indicators-Stata and DHS-Indicators-SPSS. Users can download the code from these repositories or clone the repository to their own Github site. Users can also suggest changes to the code that will be reviewed by DHS Program staff before acceptance.
Don’t see what you need? The programming for all indicators listed in the Guide to DHS Statistics will be available by September 2020. The Guide corresponds to the topics/chapters that are typically found in a DHS survey final report in addition to the modules for malaria and HIV prevalence. As of July 2019, about half of the indicators have been coded and shared in Stata including indicators covering child health, family planning, and reproductive health. SPSS code will follow later in 2019 and 2020, along with the remainder of the indicators. Review the Readme text file for more details.
If you are interested in being featured in the ‘DHS Data Users’ blog series, let us know here by submitting your example of DHS Program data use.
Over the past four years, the IPUMS-DHS program has grown substantially, in both the magnitude of available data and in use. As of June 2019, more than 2,000 users have accessed the IPUMS-DHS database, and multiple papers have been published using DHS data through IPUMS-DHS.
One of the advantages of accessing DHS data through IPUMS-DHS is that variables are harmonized across surveys, facilitating comparative research. Recent research using IPUMS-DHS data highlight innovative methods and fascinating results:
Becker conducted a comparative study looking at control of female sexuality and male absenteeism in 34 countries and found that women in historically pastoralist societies face more restrictive norms.
Boyle and Svec recently published a paper on the international transmission of gender norms and female genital cutting (FGC) in six DHS countries. Results indicate that women’s decision making status is an important factor in FGC abandonment.
Di Brisco and Farina explored the methodological challenge of measuring gender disparities through individual perceptions and enlightening the pertinence of the poset methodology for the analysis of ordinal variables and response profiles. They used IPUMS-DHS data from 16 African countries.
IPUMS-DHS is also being used to train the next generation of analysts and data users. The Quantitative Global Health Analysis course taught at the University of Minnesota this spring relied on IPUMS-DHS as a primary data source for its students. Final products were research posters using the data. Research questions explored by students analyzing IPUMS-DHS data included:
How Violence against Women Affects Fertility and Family Planning in Uganda
Changes in and Predictors of Antenatal Care for Women in Mali
Effects of Family Size and Food Insecurity on Child Mortality in Ethiopia
Understanding Variation in Vaccination Status in Ethiopia
Vitamin A Vaccination and Deficiency in Uganda
Perceptions of HIV/AIDS in India in the Context of Education
IPUMS-DHS Data Update: As of June 2019, the IPUMS-DHS database includes 156 samples from 38 countries and nearly 15,000 consistently coded variables, including all standard DHS variables from DHS Phases 1 through 7 and many country-specific variables. Learn more on our website and read our previous blogs on the IPUMS-DHS collaboration here.
This blog post is part of Luminare, our new blog series exploring innovative solutions to data collection, quality assurance, biomarker measurement, data use, and further analysis.
Two needs are often expressed by both DHS host countries and donors: 1) for data to be made available more frequently, and 2) for the continued strengthening of implementing agencies’ capacity to conduct surveys. Among various innovations that The DHS Program has pursued to respond to these needs is the Continuous Survey (CS) model.
What is a Continuous Survey?
In a CS, data are collected and reported annually by a permanently maintained office and field staff. A smaller sample size is designed to provide estimates at the national level and for urban/rural residence every year. For regional-level estimates, data are pooled over multiple consecutive phases. Through both the smaller sample and continuously maintained staff, the model can lower costs and institutionalize the implementing agency’s ability to conduct a DHS survey. In 2004, Peru became the first country to conduct a CS, and the effort is still ongoing.
How did Senegal implement a Continuous Survey?
Inspired by the Peru experience, USAID and The DHS Program piloted the CS model in Africa. Senegal was chosen for its long survey history and the capacity of the local implementing agency, Agence Nationale de la Statistique et la Démographie (ANSD). The Senegal Continuous Survey (SCS) expanded on the original model to include an annual facility-based Continuous Service Provision Assessment (C-SPA), in addition to the household-based Continuous Demographic and Health Survey (C-DHS). The SCS was conducted in five phases, spanning the period from 2012 to 2018.
Covers from final reports from each of the five phases of the SCS.
What were the successes and challenges of the SCS?
ANSD partnered with Le Soleil newspaper to create an 8-page spread highlighting results from the 2016 SCS.
The SCS demonstrated many successes. Senegal is the only country in Africa to annually collect nationally representative demographic and health data, allowing Senegal to monitor progress towards the SDGs every year. This was also the first time a country releases both facility and household data at the same time. This model of releasing C-SPA data annually and in conjunction with the C-DHS resulted in flourishing data use for both surveys.
The SCS greatly strengthened capacity in Senegal. ANSD is now capable of conducting DHS and SPA surveys with only limited technical assistance. ANSD has the initiative to move beyond the pilot to implement the 2018 SCS with limited technical assistance and is already continuing the annual surveys.
Most surveys encounter challenges, and, in the Senegal experience, CS-specific design challenges emerged. Some stakeholders were concerned about the approach of pooling two consecutive years of CS data to generate a large enough sample size for regional-level estimates. Additionally, a census and an updated health facility master list in Senegal during the SCS pilot period resulted in new sampling frames for both the C-DHS and the C-SPA, and subsequent challenges in data interpretation. Finally, survey dissemination activities overlapped with the next phase’s design and implementation activities, increasing the burden on ANSD.
The CS model demands an overlap of activities. While one phase moves toward dissemination, planning is already occurring for the next phase of data collection, as evidenced in the SCS pilot experience.
Lessons learned from the SCS experience will inform The DHS Program’s continued efforts to innovate in the areas of data collection and use.
This blog post is part of Luminare, our new blog series exploring innovative solutions to data collection, quality assurance, biomarker measurement, data use, and further analysis. This is the second post in the series that focuses on innovations using DHS.rates data.
Ever struggled to calculate fertility or child mortality indicators from survey data? Want to customize the reference period? DHS.rates can do it for you!
What is DHS.rates?
The DHS.rates is a user-friendly R package to calculate fertility
and childhood mortality rates based on DHS datasets. First released in March
2018, the current version of DHS.rates calculates the Total Fertility Rate,
General Fertility Rate, Age-Specific Fertility Rates, Neonatal Mortality Rate,
Post-Neonatal Mortality Rate, Infant Mortality Rate, Child Mortality Rate, Under-5
Mortality Rate and mortality probabilities. For each indicator, the package
calculates standard error, design effect, relative standard error, and
confidence intervals. Data users can customize rates:
periods other than DHS standard reference periods
Based on calendar
years so the end of the reference period is not the date of the survey
sub-populations or domains other than those produced by The DHS Program
Based on other surveys
other than DHS if the required variables are available
Not an R user? Try
theweb-application, DHS.rates Shiny
This web-application provides all the DHS.rates functions without needing to download or use R. The DHS.rates Shiny web application includes two main tabs, fert and chmort. After uploading the relevant survey dataset, the application calculates fertility or childhood mortality rates according to the DHS methodology.
Just as with the R package, Shiny web application users can customize the reference period as well as the end date of the reference period. By adding a variable to “Class of the rate”, users can do the calculations for different subpopulations other than the ones produced by The DHS Program. Users also can change any of the fields on the screen allowing them to use the application with other surveys other than the DHS.
This blog post is part of Luminare, our new blog series exploring innovative solutions to data collection, quality assurance, biomarker measurement, data use, and further analysis. This is the first post in the series that focuses on innovations to improve the quality of anthropometry data.
Anthropometry, the measurement of the human body, gives a snapshot of the malnutrition situation in a country. Yet, the collection of accurate height and weight measurements, especially for young children, is difficult during data collection. To address this challenge, The DHS Program has tested new innovations to enhance the quality of the anthropometry data in the 2018 Nigeria Demographic and Health Survey (NDHS).
The 2018 NDHS is the sixth DHS survey conducted in Nigeria. The National Population Commission (NPC), in collaboration with the National Malaria Elimination Programme (NMEP) of the Federal Ministry of Health (FMOH), implemented the survey. Thirty-seven field teams closely monitored by coordinators and quality controllers collected data from August 14, 2018, to December 29, 2018. Each field team included a supervisor, field editor, two male interviewers, three female interviewers, and a biomarker team consisting of a lab scientist and nurse.
Introducing a Biomarker Checklist to strengthen supervision
The DHS Program has developed a Biomarker Checklist to assess the performance of and provide feedback to field staff. The checklist includes a core set of tasks required to collect biomarker data. Each task is a crucial action that, if missed, can result in poor quality data. The Biomarker Checklist is administered by supervisors and coordinators during collection of biomarkers in households.
The Biomarker Checklist was tested during the 2018 NDHS pre-test and biomarker main training using a mixed-method design which included administering anonymous questionnaires and conducting key informant interviews with the supervisory staff who used the checklist to assess its’ viability and usefulness. Feedback on the Biomarker Checklist was positive, so the Biomarker Checklist was used during the 2018 NDHS data collection. A Biomarker Checklist will be publicly available at a later date.
“it [Biomarker Checklist] has helped a lot because you are able to monitor what the biomarkers are doing so [you] can actually give corrective action”
Reducing errors while still in the field through re-measurement of children
The shift from paper questionnaires to a CAPI-based data collection approach provides an opportunity to easily identify children who may have been mismeasured and return to the household to measure these children again. To identify children with incorrect measurements requires performing a complicated calculation – a child’s body measurements are compared against a healthy population by transforming their measurements into anthropometry Z-scores. Extreme measurement results are then detected by calculating anthropometry Z-scores and flagging cases with higher or lower Z-scores than expected. The DHS Program has developed a program to automatically calculate anthropometry Z-scores and flag extreme cases in the CAPI system. A user-friendly interface on the tablet produces a report with the children who need to be re-measured.
The DHS Program relies on field check tables that are run periodically during data collection. While an important data quality tool, a major limitation of the field check tables is that enough data need to accumulate before problems can be identified. At that point, the information can only be used to improve collection of data moving forward; these corrections do not fix data previously collected. The real-time ability to re-measure children while still in the field is a major step forward and can easily be applied to other CAPI surveys.
Re-measuring anthropometry in a random subsample of children
A random re-measurement of height and weight in a subsample of children was also piloted in Nigeria. The DHS Program has developed a CAPI program that randomly selects one household in each cluster for the biomarker team to revisit. The program then compares differences between measurements and reports precision, or how close the first measurement is to the second measurement, as acceptable or unacceptable.
The aim is measurements will be of better quality as a result of instituting random re-measurement. The biomarker team may be more careful and not rush measurements if they know poor measurements will be exposed. The data produced on precision can be used as a motivational tool for biomarker teams and provide an opportunity to identify and re-train in cases where there is a high degree of discrepancy between measurements.Precision estimates will also help better assess data quality post-data collection.
A review of the anthropometry data from the 2018 NDHS indicates it meets data quality targets. Results from the 2018 NDHS Key Indicators Report (KIR) show that 37% of children under 5 are stunted. Stunting generally increases with age, peaking at 47% for children age 24-35 months. Overall, 7% of children under 5 are wasted, while 23% of children are underweight.
Lessons learned in the implementation of the quality assurance activities in Nigeria are being used to conduct similar activities in DHS surveys in other countries.
About the survey
The 2018 NDHS was implemented by the National Population Commission (NPC) in collaboration with the National Malaria Elimination Programme (NMEP) of the Federal Ministry of Health, Nigeria. Funding for the 2018 NDHS was provided by the United States Agency for International Development (USAID), Global Fund, Bill and Melinda Gates Foundation (BMGF), the United Nations Population Fund (UNFPA), and World Health Organization (WHO). ICF provided technical assistance through The DHS Program, a USAID-funded project that provides support and technical assistance in the implementation of population and health surveys in countries worldwide.
Additional information about the 2018 NDHS may be obtained from the headquarters of the National Population Commission (NPC), Plot 2031, Olusegun Obasanjo Way, Zone 7, Wuse, P.M.B. 0281, Abuja, Nigeria (telephone: 234-09-523-9173; fax: 243-09-523-1024; email: email@example.com; internet: www.population.gov.ng).
This blog post is part of Luminare: The DHS Program Blog Series on Innovation. You can find additional posts in the Luminare series here.
While The DHS Program is known for comparability and standard methods, it would not be relevant today without innovation. We’ve made big leaps – like moving from paper to tablet-based interviewing and from basic print publications to web and mobile data dissemination. We’ve integrated complex biomarker testing and developed weeks-long curricula in data analysis and use. We’ve also made hundreds of smaller, less flashy improvements, such as use of WhatsApp to communicate with field teams and the use of checklists to improve biomarker collection. Innovation – large and small – is part of life at The DHS Program, as we are constantly seeking new ways to solve problems, increase efficiency, and improve data quality while meeting the needs of an increasingly diverse audience.
Over the course of the next several years, we will be undertaking a systematic review of new ideas, from new biomarker assays to non-traditional partnerships. This new blog series is just one of the ways that we will be exploring and sharing innovations. We will also be holding topical consultations with experts, reviewing the academic literature, attending key conferences, and interviewing key informants such as external survey experts, staff, consultants, and implementing agency representatives.
In DHS-8, we have an even stronger mandate to innovate, and for the first time ever, we have a formal innovation strategy which is led by our new Senior Science Advisor, Joanna Lowell. Her job, in part, is to lead the “deliberate exploration and strategic incorporation of state-of-the-art advances in survey tools methodologies, and partnerships.”
We welcome your ideas. You can reach out to Joanna directly at Innovations@dhsprogram.com and look out for announcements regarding other ways to contribute.
Today marks the first day of the 2019 Population Association of America (PAA) Annual Meeting, and we’re excited to be back! The PAA Annual Meeting attracts demographers and other public health professionals from across the world to present their research, hear of others’ findings, and network with peers.
DHS Program staff will be available at booth #1 in the exhibition hall to answer your questions about DHS data, to provide tours of our web and mobile tools, and to distribute free DHS Program publications. So don’t be shy and stop by the exhibit hall to say hello!
Don’t read French? You can use the translate feature at the top of the page!
Nom : Didine K. Kaba
Pays d’origine : République Démocratique du Congo
Titre et organisation : Professeur (MD, PhD), Département d’Epidémiologie et Bio statistiques, Ecole de Santé Publique de l’Université de Kinshasa
Rôle dans l’EPSS RDC 2017-2018 : Co-Investigateur de l’enquête
Vous pouvez nous raconter un peu sur votre expérience avec l’EPSS RDC 2017-2018 ?
C’est une enquête intéressante qui nous a permis d’auto évaluer nos compétences dans la réalisation d’une enquête de grande envergure. Dans l’EPSS, il s’est agi d’un échantillon des formations sanitaires disséminées partout en RDC.
Le pays étant grand (26 provinces), les données ont été collectées par vague, qui a été décidée afin de diminuer les sites de formation, permettre une bonne supervision des formations, et assurer ainsi la qualité des données.
Une autre chose que nous avons expérimenté, c’est l’appropriation de l’enquête par le Ministère de la Santé Publique, présent de l’adaptation des questionnaires jusqu’à la rédaction du premier draft du rapport de l’enquête. Son implication à tous les niveaux a facilité la tâche à nos différentes équipes, plus particulièrement à celles chargées de la collecte des données.
L’EPSS RDC 2017-2018 est la première EPSS en RDC. Vous pouvez commenter sur quelques défis ou succès spécifiques à une telle enquête, surtout étant la première expérience ?
Un défi c’est l’immensité du pays, avec accès très difficile dans l’arrière-pays, sans compter le fait que des conflits armés étaient encours dans le pays pendant la collecte des données. Au sein d’une même province, le transport aérien était parfois nécessaire pour accéder à des formations sanitaires soit séparées des autres par des champs de guerre ou alors d’accès difficile par route. Nous disposions également dans l’échantillon des formations sanitaires qui ne pouvaient être atteintes que par pirogue ou hors-bord. Ainsi, nous devrions d’une part respecter l’échantillon des formations sanitaires pour assurer la représentativité et d’autre part veiller à la sécurité des agents de collecte des données. Ce défi a également été relevé. En effet, nous avons collecté les données dans 1380 formations sanitaires sur les 1412, seuls deux pourcents de formations sanitaires n’ont pas été enquêtées.
Comment espérez-vous que les données de l’EPSS RDC 2017-2018 soient utilisées ?
Ces données sont très importantes et très attendues dans le pays. Chaque programme s’intéressera aux données en lien avec son domaine d’intervention. Les données de cette évaluation serviront à l’identification des problèmes à résoudre en vue de l’amélioration de la qualité de l’offre de service des soins en RDC.
Quelles sont d’autres leçons apprises ou pensées que vous aimeriez partager ?
Une autre procédure de rédaction d’un rapport d’enquête : informations clés pour chaque chapitre, encadrés pour définir chaque indicateur et commentaires par caractéristique de base ;
Maitrise de la formation des adultes/Andragogie : utilisé dans la formation des agents de collecte des données ;
Analyse des éditions secondaires des données et des tableaux de qualité avec feedback vers les agents de terrain pour l’amélioration de la qualité des données collectées ;
La collaboration entre institutions et le fait d’avoir de la considération des uns envers les autres au sein de l’équipe de recherche ont été le gage de la réussite de l’EPSS RDC 2017-2018. Chaque membre de l’équipe de recherche (agent de collecte des données, facilitateurs/superviseurs, agent de saisie, équipe informatique, équipe de coordination, etc.) avait fait de cette enquête son affaire. Notre motivation était la satisfaction de voir l’enquête se dérouler avec succès. Oui, c’était ça la clé de notre réussite.
The 2017-18 Democratic Republic of the Congo SPA was released on March 22, 2019.
This new blog series, DHS Data Users, captures examples of how you, the data user, have incorporated data from DHS, MIS, and/or SPA surveys into your analyses, at your institution, or to influence policies or programs. If you are interested in being featured in the ‘DHS Data Users’ blog series, let us know here by submitting your example of DHS Program data use.
The year 2018 saw an upswell of interest in health system quality with the publication of three global reports highlighting critical deficits in quality in health systems in low- and middle-income countries [1,2,3]. Much of the empirical basis for these reports was drawn from the Service Provision Assessments (SPA), the lesser-known surveys conducted by The Demographic and Health Surveys (DHS) Program, which provide comprehensive assessments of health systems in low-resource settings from Haiti to Nepal.
These surveys include a detailed audit of facility resources, provider interviews, direct observations of primary care services, and exit interviews with patients or caretakers. Each assessment is a sample of the complete health system (public and private) or in some cases a complete census. The resulting wealth of data enables assessment of structural inputs to quality of care, the care process – both competent care and user experience – and some outcomes from care, primarily user confidence in the health system. A small but increasing number of researchers is delving into all the SPA data have to offer. Among the insights the SPA surveys have yielded just from my own research are:
Most health systems assessed are not fully prepared for basic health care.
A comparative study of 8,443 facilities in 9 countries based on SPA surveys between 2007 and 2015 found that hospitals averaged between 69% (Senegal 2012-2014) and 82% (Tanzania 2015, Namibia 2009) on the service readiness index defined by the World Health Organization for primary health facilities. Non-hospitals achieved at best 68% readiness (Namibia 2009) and at worst only 41% (Uganda 2007, Bangladesh 2014) . Within primary care services – antenatal care, family planning, and sick child care – service-specific service readiness is not highly predictive of competent care being delivered.
In Kenya, where the 2010 SPA did include direct observation of labor and delivery, both structural quality of maternity care and observed clinical quality was higher in facilities in wealthier areas than facilities in poorer areas, with women in the poorest areas receiving care that complied with only half of recommended clinical guidelines on average .
Across 8 countries, adherence to clinical guidelines was lower in sick child care, where providers completed only 38% of the standard Integrated Management of Childhood Illness (IMCI) items, than in family planning (46%) and antenatal care (57%) . The median sick child consultation lasted only 8 minutes . Focusing specifically on Malawi, where the survey team conducted a limited re-examination of sick children, providers diagnosed pneumonia in only 1 in 5 children who showed symptoms of pneumonia per the IMCI guidelines .
Analysis of the 2013-2014 Malawi SPA survey with a simultaneous household survey suggested that poor quality care may contribute to avertable neonatal mortality, with a predicted prevalence of neonatal mortality of 28.3 deaths per 1,000 in lower quality facilities and 5.2 deaths per 1,000 in higher quality facilities, among women who would choose higher quality if it were more accessible to them .
As attention shifts from describing health system quality to improving it at scale, robust and ongoing measurement will be an essential tool for governments and researchers alike, particularly the direct observation of care delivery and perspective from patients themselves that makes the SPA such a unique and valuable resource.
Dr. Hannah Leslie is a Research Associate at the Harvard Chan School of Public Health; she served as the Measurement Research Lead for the Lancet Global Health Commission on High-Quality Health Systems in the SDG Era. She received her MPH and Ph.D. in Epidemiology from the University of California, Berkeley. Her research has made extensive use of the Service Provision Assessment surveys to 1) develop metrics of structure and process quality in LMICs, 2) describe current quality of care, and 3) assess predictors and effects of poor quality. Her recent work focuses on effective coverage calculations, patient experience measurement, and quality of care as a driver of HIV testing and treatment retention.
The DHS Program is now in its 35th year with a long history of helping to collect, analyze, and disseminate data on women’s empowerment, gender equality, men’s engagement, and gender-based violence within the context of health and development. Historically, The DHS Program has integrated attention to gender in all its activities and aspects of its operations, from the types of data collected and disaggregated and analyses conducted, and the “how” and the “who” of data collection, capacity strengthening, dissemination, and use.
Over the coming five years, The DHS Program will continue its cross-cutting approach to gender integration into its work and surveys. In particular, The Program will endeavor to help achieve the agency-wide commitments mandated by USAID’s Gender Equality and Female Empowerment Policy. The DHS Program supports USAID’s objectives and has adopted an updated Gender Integration Strategy with the following priorities:
Continued collection of high-quality data for gender indicators and sex disaggregation: The project will continue to contribute to evidence-based, gender-integrated health programming by providing the data necessary for understanding gender disparities related to health, including disparities in wealth, access to resources, and decision making power. Similarly, it will continue to collect data on domestic violence; early marriage and skewed sex ratio; household headship; women’s relative earnings and control of their earnings; women’s ownership of a house, of land of a bank account, and of a mobile phone; as well as female genital cutting and fistula.
The DHS Program will monitor and respond to emerging needs for gender data important for women’s health and demographic behavior. The DHS Program is soliciting public feedback through March 15, 2019, on potential new areas/indicators/questions, including on the measurement of gender equality, male engagement, women’s empowerment, decision making, and domestic violence. This feedback will help identify some of the current gender-related data gaps.
Increased focus of dissemination efforts to highlight gender disparities in health and resource and opportunity access: Data collected on gender and women’s empowerment are widely disseminated using digital, print, and other means. Most indicators are readily available on the STATcompiler, The DHS Program’s Mobile App, and the DHS API. The DHS Program website also maintains a “Gender” topic page, which provides a one-stop shop for gender indicators from DHS surveys.
Enabling gender equality in access to opportunities, capabilities, learning, and resources: The DHS Program will continue its efforts to ensure that there is no discrimination by sex, pregnancy status, sexual orientation, or gender identity in access to opportunities for training, employment, and learning all along the survey continuum.
By maintaining confidentiality and gender-sensitive protections. The DHS Program has strict ethical guidelines to protect respondents and interviewers and ensure confidentiality of respondents, their families, and of the data. While these guidelines apply to all respondents, they also specifically recognize the need for special protections for women in certain circumstances.
By exploring technologies to ask highly sensitive questions: Several of the questions asked in DHS surveys are highly sensitive. While some of these sensitive questions are asked of both women and men, such as number of sexual partners, some others are mainly asked of women, including questions on experience of sexual violence. Improving the validity of responses to these questions remains a challenge for any survey program, and it is important to look for ways to both improve reporting and also provide respondents with a more secure platform to disclose sensitive information, such as audio computer assisted self-interviewing (ACASI).
By continuing to integrate gender into the research agenda: The DHS Program’s research agenda continues to include innovative studies that shed light on the linkages between gender and health. The DHS Program will undertake many new research projects that will contribute to a better understanding of the level and changes in women’s empowerment and the interface between gender and health outcomes as well as gender disparities in health, while also applying a gender lens to analyses that do not directly involve gender indicators. In the meantime, read the latest gender analytical publications.
For International Women’s Day 2019, The DHS Program invites you to explore the wealth of gender-related resources and publications available at dhsprogram.com. Learn more about Sustainable Development Goal #5, Gender Equality indicators available in DHS surveys in the infographic below.