Category Archives: Data

19 Aug 2019

DHS Data Users: Samuel Oppong, Ghana National Malaria Control Programme M&E Specialist

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. 


How are you involved in DHS Program surveys and analysis workshops?

My first time working on a DHS Program survey was for the 2016 Ghana Malaria Indicator Survey (GMIS). I helped with fieldwork monitoring and report writing. After the 2016 GMIS, I participated in the 2017 Regional DHS/MIS Malaria Analysis Workshop. At this workshop, I worked with my team members from the Ghana National Malaria Control Program (NMCP) to write an abstract, “Factors Influencing Malaria Prevalence in Children Under 5,” using the 2016 GMIS data.

Samuel Oppong (left) with participants from Ghana at
the 2017 DHS/MIS Malaria Analysis Workshop. © ICF

I then transitioned from being a workshop participant to a workshop co-facilitator, facilitating the 2017 Regional Malaria Indicator Trends Workshop in Uganda. This workshop brought together NMCP monitoring and evaluation (M&E) program managers from Liberia, Malawi, Nigeria, Sierra Leone, and Uganda to examine trends in malaria indicators.

More recently, I co-facilitated the 2018 Ghana Malaria Trends Workshop. This workshop brought together district malaria health officers to analyze trends in household survey indicators in Ghana. This was a great workshop because I was able to work with the data I am most familiar with! The output from this workshop is published on The DHS Program website.

Samuel Oppong (left) and Annē Linn co-facilitated the 2018
Ghana Malaria Indicator Trends Workshop. © ICF

How has NMCP used DHS data for programmatic decision making?

After the release of the 2016 GMIS, NMCP noticed a low uptake of artemisinin-based combination therapy (ACTs) in the Northern region, but the use of SP/Fansidar was high, which is not a recommended treatment for malaria in children. This triggered us to do additional research to figure out what was going on in this region and investigate which outlets were distributing SP. We realized that people were not receiving SP from public health facilities but from private clinical shops and other drug peddlers. The 2016 GMIS results provided a snapshot of the malaria case management situation in the Northern region and provided us justification to explore further. To solve this problem, NMCP implemented a sensitization activity to ensure people in the region know the recommended treatment and sources to get the correct treatment.

Another example of evidence-based decision making was the implementation of a malaria sensitization campaign using data the 2016 GMIS. Malaria prevalence by microscopy in the Eastern region increased between the 2014 GDHS and 2016 GMIS. This was a worrying trend because in Ghana we normally only see high malaria prevalence in the Northern and Upper West regions. NMCP looked more critically at the 2016 GMIS results and saw that while insecticide-treated net (ITN) ownership was high, the proportion of people who recognized the cause and symptoms of malaria was very low. As a result, NMCP implemented a community level sensitization activity in four districts of the Eastern region.

How do you use MIS survey data during your daily job?

I recently collaborated on a research paper using DHS data. The paper, published in The Malaria Journal, used survey data from the 2014 GDHS and the 2016 GMIS to examine ITN use behavior by exploring how several household and environmental variables related to use among Ghanaians with access to an ITN. This further analysis paper has been extremely helpful for programmatic decision making here at NMCP.

What data are you looking forward to in the upcoming 2019 GMIS?

I am interested in further examining the information about the type of nets in households. NMCP finished a mass long-lasting insecticidal net (LLIN) distribution campaign in 2018 and implemented a school-based piperonyl butoxide (PBO) net distribution campaign in 2019. The 2019 GMIS results will provide information on the reach and use of these nets across Ghana as well as where people obtained their nets.


Featured photo: Samuel Oppong (left) work on data analysis at the 2017 Regional DHS/MIS Malaria Analysis Workshop. © ICF


Written by: Samuel Oppong

Samuel Oppong is a Monitoring and Evaluation Specialist with the Ghana National Malaria Control Programme. He coordinators M&E activities in vector control interventions, routine data quality audits, and SMC. He is involved in capacity building of national, regional, district and health facility staff on capturing, reporting, and analyzing malaria-related data from routine health information systems as well as other malaria data sources. He also leads capacity building programs of national, regional, and district staff on conducting data quality audits as well as onsite training, supportive supervision (OTSS) on malaria data management.

10 Jul 2019

Luminare: Programming Code for DHS Indicators

DHS Staff Programming

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.

Questions? Email codeshare@DHSprogram.com 


Featured photo: Samuel Oppong (left) work on data analysis at the 2017 Regional DHS/MIS Malaria Analysis Workshop. © ICF

25 Jun 2019

DHS Data Users: More than 2,000 Users Accessing DHS Data through IPUMS-DHS

Students presenting posters using IPUMS-DHS data at the first-ever Student Poster Extravaganza

© 2019 Students presenting posters using IPUMS-DHS data at the first-ever Student Poster Extravaganza at the Institute for Social Research and Data Innovation.

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:

This year, IPUMS announced its first-ever IPUMS-DHS Award, an honor given to outstanding use of IPUMS-DHS data. The winning paper, Neonatal mortality in East Africa and West Africa: a geographic analysis of district-level demographic and health survey data (Grady et al., Geospatial Health 2017 volume 12:501) identifies high-risk districts and counties for neonatal mortality. This analysis aims to help prioritize intervention sites for countries as they strive to reach the Sustainable Development Goals.

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.

30 May 2019

Luminare: Customize Fertility and Childhood Mortality Rates with DHS.rates

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:

  • Using reference 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
  • Using different 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

This innovation was recently featured in PLOS One: Calculating fertility and childhood mortality rates from survey data using the DHS.rates R package

Download the DHS.rates package today!

Not an R user? Try the web-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.

21 Mar 2019

DHS Data Users: Insights on Health System Quality from the Service Provision Assessments

© 2017 Magali Rochat/VectorWorks, Courtesy of Photoshare

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) [4]. 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.
  • Across facilities with a similar level of readiness, provider adherence to clinical guidelines varied widely. Correlation between readiness and observed clinical quality was more consistent for observations of labor and delivery, though only two SPA surveys include these data [5].
  • 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 [6].
  • 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%) [7]. The median sick child consultation lasted only 8 minutes [8]. 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 [9].
  • 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 [10].

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.

References


Written by Dr. Hannah Leslie

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.

08 Mar 2019

International Women’s Day 2019

© 2016 Kato James, Courtesy of Photoshare

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:

  1. 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.

  2. 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.
  3. 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.
  4. 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.
  5. 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).
  6. 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.

28 Feb 2019

Strengthening Nutrition Data Quality at The DHS Program

A health technician tests a child for anemia during a survey training. © 2018 ICF/Sorrel Namaste

“Everything bad can go wrong at collecting the sample, and you can’t get any good results from a bad sample. ” – Informant from the Enhancing Nutrition Data Quality Report

Data for decision-making is vital as countries work to reduce the burden of malnutrition and to measure progress towards the Sustainable Development Goals and the Global Nutrition Targets 2025.

The DHS Program, a leading source of nutrition data globally, has invigorated its focus on the quality and depth of the types of nutrition data collected. To this end, a qualitative study was undertaken to identify how to enhance the quality of nutrition data. Interviews were conducted with 50 experts internal and external to The DHS Program, and DHS staff participated in focus group discussions. Informants highlighted critical challenges that exist in collecting anemia, anthropometry, and infant and young child feeding data in large surveys while also offering solutions to strengthen data quality.

The outcomes from the study are summarized in the report “Enhancing Nutrition Data Quality in The DHS Program” which calls for the implementation of 32 recommendations. The DHS Program is already addressing most of these recommendations (21 out of the 32) and plans to take up additional recommendations throughout DHS-8. These include revising hemoglobin cutoffs in STATcompiler, working with the WHO to develop a technical error of measurement value for passing an anthropometry standardization exercise, and testing new procedures and indicators for real-time monitoring of fieldwork. Future blog posts will explore the application of these recommendations across the stages of a DHS survey.

Recommendations to enhance nutrition data quality were identified across The DHS Program survey stages. © 2018 ICF

The DHS Program is committed to continuous quality improvement and is uniquely positioned to implement new data quality measures. Yet, the report is not only intended to inform operations at The DHS Program. The lessons learned are applicable to wider audiences involved in the collection and use of nutrition data throughout the world. Strengthening the quality of nutrition data will lead to improved data-driven nutrition actions.


Written by Sorrel Namaste and Rukundo K. Benedict

Dr. Sorrel Namaste is the Senior Nutrition Technical Advisor for The DHS Program. She is an epidemiologist with expertise in nutrition assessment and implementation research. 

Dr. Rukundo K. Benedict is the Nutrition Technical Specialist for The DHS Program. She is a public health nutrition practitioner with expertise in infant and young child feeding (IYCF), water-sanitation hygiene (WASH), community health systems, and the delivery of integrated interventions in low-resource settings. 

29 Jan 2019

Updated Recode Manual for DHS-VII

What is the DHS-VII Recode Manual?

The basic approach of The DHS Program is to collect data that are comparable across countries. This is achieved through the use of model questionnaires and the subsequent processing of the raw data into standardized data formats known as recode files. The DHS-VII Recode Manual is an introduction to the DHS standard recode files and serves as a reference document for those analyzing DHS data.

Who is the manual for?

Data users who are analyzing DHS datasets in statistical software receive the DHS recode data files for each survey along with the survey specific recode documentation. We strongly recommend that users download this documentation as well as the questionnaires used in the surveys they analyze. The questionnaire for a survey can be located in the appendix of the final report.

What is new in this version of the manual?

This updated manual describes the characteristics of the recode files defined for the seventh round of the DHS surveys (DHS-VII). The manual highlights the 234 new variables added during DHS-VII. In addition to an explanation of new variables, the manual now also contains:

  1. A description of the DHS Recode Data Files distributed and file naming convention used.
  2. An explanation of the Century Day Code (CDC). Beginning with the DHS-VII questionnaire (surveys with fieldwork in about 2015 and later), the woman’s questionnaire collects the day of birth for all children listed in the birth history in addition to their month and year of birth to calculate the age of children more accurately. The use of CDC affect virtually all tables related to children, particularly to children under the age of five.
  3. A list of the locations of DHS-VII core questionnaire variables in the DHS-VII standard recode variables.

Where can you find the guide?

Download the DHS-VII DHS Recode Manual Here

© ICF

03 Dec 2018

16 Days of Activism against Gender-based Violence

We are halfway through the 16 days of Activism against Gender-based Violence, but there is still time to get involved. The DHS Program has a variety of resources to help you learn about the prevalence of violence against women around the world.

Since 2000, The DHS Program has collected domestic violence data in more than 50 countries. Explore the domestic violence results in five recently released Demographic and Health Surveys from the Philippines, Senegal, Haiti, Timor-Leste, and Uganda in a new infographic developed for this year’s 16 Days of Activism against Gender-based Violence.

Share our infographic using the links below.  

Share the #16days infographic on Facebook

Tweet the #16days infographic

Additionally, try our easy-to-use mini tool to compare indicators of gender inequality, women’s empowerment, gender norms, and more. For even more domestic violence data, you can visualize these indicators by background characteristics, over time, and across countries using STATcompiler

Photo Credit: © 2004 Syed Ziaul Habib Roobon, Courtesy of Photoshare

 

25 Sep 2018

The New and Improved Guide to DHS Statistics

What is the Guide to DHS Statistics?

The purpose of the Guide to DHS Statistics is to provide transparent documentation to users to assist them in understanding DHS datasets and to enable them to reproduce the statistics in DHS reports. DHS surveys collect a wealth of information on a wide range of topics from a representative sample of the population in the countries that participate in The DHS Program. For each country, the information collected is processed, tabulated, and presented in a report that describes the living conditions and the demographic and health situation in the country.

Many of the procedures involved are straightforward and are familiar to demographic analysts. However, other procedures need special attention and have been developed based on experience accumulated over many years regarding the preferred way of calculating certain indicators, what to guard against, and what not to forget.

Who is the guide for?

The Guide to DHS Statistics is meant to be a tool for all data users: for those just starting out in data analysis and for those with advanced skills who need a tool for checking procedures. It is intended to serve as a reference document for those directly analyzing DHS data as well as for users who desire a deeper understanding of indicator definitions. The tool can help those who use DHS data to monitor and evaluate programs and assist in informed decision-making.

What’s new in this version of the guide?

The updated Guide to DHS Statistics serves as a replacement for the old tool, but also as an expansion. Though it provides the same basic indicator definitions and calculation information as the original tool for the indicators used in DHS-4, the new guide goes far beyond the original content by adding the many new indicators and topics that are now covered by the DHS-7 tabulation planNew features in the guide include variables, details of numerator and denominator calculations, discussions of changes over time, links to other relevant data use tools and resources, and links to API indicator data. Complex indicators include examples or figures to facilitate understanding. View an example of an indicator page here.

Where can you find the guide? What else can you expect?

The new guide was a team effort of many DHS Program staff members, and the result is a document that is available as a PDF and online as an interactive tool. In the near future, the guide will be expanded to include chapters on female genital cutting and fistula. The tool will be continuously updated as the DHS core questionnaires and tabulation plans change to ensure that data users always work with the most up-to-date reference guide to the universe of DHS data.

Online Guide to DHS Statistics

PDF Guide to DHS Statistics

The information provided on this Web site is not official U.S. Government information and does not represent the views or positions of the U.S. Agency for International Development or the U.S. Government.

The DHS Program, ICF
530 Gaither Road, Suite 500, Rockville, MD 20850
Tel: +1 (301) 407-6500 • Fax: +1 (301) 407-6501
dhsprogram.com