Category Archives: Data

28 Feb

DHS Data in the News

Journalists worldwide use DHS, MIS, and SPA surveys as source data for essential stories – stories about domestic violence, HIV prevention, and child survival. Coverage of these topics brings awareness to these critical issues and often prompts policy change.

In any given month, DHS Program data are cited in hundreds of print, television, radio, and digital media across the world. While we can’t possibly review and share every example of accurate DHS data coverage in the news, we do highlight some of the best examples in The DHS Program’s News Room. The results from India’s 2015-16 National Family Health Survey have been featured in India’s biggest newspapers, and topics range from anemia prevalence to child marriage. A recent article from the Midrand Report in South Africa cites condom use data from the 2016 South Africa Demographic and Health Survey as an argument for voluntary male circumcision, and a Ghana News Agency article highlights adolescents’ needs for reproductive health services.

Using data from a reputable source like a DHS survey adds credibility and context to journalistic reporting. But covering topics such as mortality, fertility, and disease prevalence is not simple, and journalists often struggle to interpret DHS survey results and write about demographic and health data in language that is accessible for their audiences. Following a survey’s national release, The DHS Program’s dissemination team facilitates a workshop to educate journalists on reading and understanding DHS tables, accessing comparable data, and using data in reporting. Learn more about these media trainings in this reflections piece on a Journalist Workshop in Togo.

The DHS Program also has user-friendly tools, such as STATcompiler and the mobile app that allow journalists to verify the accuracy of DHS data used in their reporting. In addition to featuring news that accurately cites DHS data, we have a Journalists’ Guide to the Demographic and Health Surveys, available in both English and French. This guide provides tips on how journalists can properly use DHS data in their stories.

Connect with us on Facebook, Twitter, LinkedIn, or email press@dhsprogram.com to share your accurate news story with DHS data for a chance to be featured in The DHS Program’s News Room.

Photo credits: 1) Officials from the Ethiopia Ministry of Health and Central Statistics Agency answer questions at the 2016 Ethiopia DHS National Seminar press conference; 2) Dr. Thet Thet Mu of the Myanmar Ministry of Health and Sports responds to questions from the press at the launch of the 2015-16 Myanmar DHS. © 2017 ICF

22 Feb

A New Way to Interact with your Favorite Indicators

We are pleased to showcase a new mini-tool on our website that allows you to quickly interact with indicators for topics such as family planning, gender, malaria, and nutrition. We have preselected 10-15 key indicators per topic that you can view by country or globally.

Simply navigate to your favorite topic to see a trend visualization from the most recently released survey. Then, select either a country or indicator within the drop-down menus to instantly see results. To start over, click “Reset” to return to the featured trend graphic.

Indicators are pulled from The DHS Program Application Programming Interface (API). As you click on a country or indicator within the data table, hyperlinks direct you to STATcompiler. There, you can compare even more indicators over time and geographically.

With the 1,000s of demographic and health indicators available, grouping key indicators by topic allows you to quickly interact with DHS data. Visit The DHS Program Topics page for a list of the featured topic pages containing the mini-tool.

What other topics do you want to see? Let us know what you think in the comments section below! Don’t forget to subscribe to The DHS Program newsletter for more updates on our digital tools, surveys, and more.

Photo Credit: © 2001 Marcel Reyners, Courtesy of Photoshare

31 Jan

IPUMS-DHS Unlocks Research Possibilities with New Contextual Data

Have you ever wondered if high-levels of precipitation affect birthweights and infant and child survival? Is increased use of insecticide-treated bed nets associated with lower incidence of malaria? Do children in households near battle zones or other violent contexts have higher levels of child malnutrition? Do some staple crop regimes promote better health outcomes than others?

Now with IPUMS-DHS, you can easily study these questions and others on how environmental and social contexts affect human health and behavior.

Using GPS coordinates, we’ve linked contextual variables drawn from many data sources directly to individual DHS survey respondent records. All context variables describe the features of a small geographic area (5-10 kilometers) surrounding each DHS survey cluster location.

New variables include:

Environmental
Variables
Agricultural
Variables
Social
Variables
  • Soil type
  • Ecoregion
  • Level of vegetation
  • Precipitation
  • Proportion of land area used for agriculture or pastureland
  • Total harvested area and yield for 17 major crops
  • Dominant livelihood
  • Population density
  • Counts of violent episodes
  • Incidence of malaria

Keep checking back! Over the next year, IPUMS-DHS will still be adding more contextual variables, including summary statistics calculated from large census-based samples.

Plan a new research project linking individual characteristics and outcomes with the surrounding context, and let us know about it. We’re always eager to hear how people are using IPUMS-DHS!

________________________________

IPUMS-DHS is a system that makes it easy to find and review the thousands of DHS survey variables and to download a single fully-harmonized data file with precisely the variables and samples that interest you. The system currently includes variables from all DHS survey samples taken in India and 22 African countries; more samples are constantly being added.

For DHS survey samples with GPS cluster data that are not yet in IPUMS-DHS, the contextual variables are available in linkable CSV files.

To learn more about the IPUMS-DHS contextual variables, check out our Technical Note, Using IPUMS-DHS Contextual Variables, which provides much more detail.

24 Aug

How Things Have Changed! Looking Back at Data Distribution Practices from 20 Years Ago

A lot can change in 20 years. For The DHS Program, it’s the difference between over 250 datasets for 70 separate surveys to more than 10,000 datasets from over 300 surveys. The contents of the model survey questionnaires changed radically, as did the media used for data distribution. And two decades ago, the internet had only recently emerged as a potential means of communication around the world!

It might be hard to imagine life without internet access today – for us, we rely on the internet for many of our activities. In 1995, The DHS Program established a website which had the basics: an informational brochure, survey statuses, fact sheets, press releases, and newsletters.

Though the website has been updated several times since then, it still has these basic features. The crucial difference lies in how we only provided an archive of publications and data and information on how to place an order for them. Yes, users had to pay for the cost of media – which, at the time, included diskettes (AKA a floppy disk), Bernoulli cartridges, and CD-ROMS – and shipping. At one point, we were deciding on whether to charge for the data itself, to ensure the fullest use of the data.

That decision was part of a proposal from 20 years ago, which proposed the following data dissemination over the internet:

  1. DHS data
  2. India NFHS data
  3. Report text
  4. Online newsletter (tentatively named ‘DHS Discoveries’)
  5. User forum

These look familiar, don’t they? Today, both reports and datasets are free and available over the internet for download (though we still require users to apply for access to datasets), we email our newsletter to subscribers (which includes news, new publications and datasets, and articles that have cited DHS data), and the User Forum has been live since February 2013.

The DHS Program has utilized the internet beyond what was proposed 20 years ago; to name only a few ways, the creations of STATcompiler, development of eLearning courses for data visualization and social media for global health, and utilization of social media to engage with our users. And if you want to know what is coming next, be sure to Follow or Like us on social media, subscribe to our newsletter or even this very blog you are just a few clicks away!

This blog post is based on the rediscovery of the paper prepared for the Population Association of America (PAA) meeting back in 1996. Go back in time and read the original paper here!

11 Jul

World Population Day 2017

How well do you know your population pyramids? Celebrate World Population Day with The DHS Program’s Guess the Population Pyramid Quiz!

See how you stack up against others and share your results below in the comment section, on Facebook, or Twitter! We are also having a live version of Guess the #PopPyramid on Twitter July 11 at 10AM EST.

Take the full-screen version of the quiz here.

Good luck!

14 Jun

An Age of Change: A More Precise Way to Measure Children’s Age in DHS Surveys

DHS-7 surveys are using a more precise method to calculate children’s age. The change, though far-reaching, has very little impact on interpretation and use of DHS data for program managers and policymakers. It does, however, have major implications for researchers doing secondary analysis of DHS data. If you are working with DHS datasets, a full description of the changes to the age-related variables is documented on The DHS Program website, and a brief summary is presented below.

Background:

For most of DHS history, interviewers have collected age data by asking the month and year of birth of the respondent, her age in years, month and year of marriage or age at marriage, and month and year of birth of each of her children as well as the age of living children. For children under 5 who are weighed and measured to assess nutritional status, day of birth was collected in the household questionnaire but was not connected with the birth history. Beginning with the DHS-7 questionnaires (most surveys with fieldwork in 2015/2016 and beyond), we asked the day of birth for all children listed in the birth history.

Why was day of birth added for children in DHS-7?

Adding day of birth permits calculating the age of children more accurately. Calculating age in months using just month and year of birth and month and year of interview meant that age in months could be off by one month in approximately half of all cases. For example, a child born February 2017 was considered a 3-month-old in May 2017. However, if the birth took place on February 25, 2017, and the interview was May 3, 2017, then the child is actually only two completed months old. Thus, if the day of birth is greater than the day of interview (roughly half of all cases), then the age would be over-estimated by one month.

Why make the change now?

Historically DHS surveys have not collected the day of birth of all children as the quality of reporting of dates of births and ages was simply not reliable enough, especially for older children or those who have died. The quality of date and age reporting for children has improved over time and now appears to be sufficiently reliable for use throughout the survey data.

How is the age calculation different in DHS-7?

Previously, child’s age was calculated by subtracting the month and year of birth from the month and year of interview to give age in months. In DHS-7, we introduced the calculation of age taking into account the day of birth and the day of interview. To do this, we introduced a new concept – the century day code (CDC).  DHS datasets now contain several new variables related to the century day codes.

For more details on the definition of the CDC and a list of the new variables, a complete description of changes made to existing age variables (e.g. age of child in years, age of child in months, and birth intervals), and programming notes for STATA and SPSS users, visit The DHS Program website.

How do these changes affect analysis?

In surveys that introduced the day of birth of the child, changes have been made in the analysis of the data in two main ways:

  1. The restrictions on the denominator for tables now all use the age variables based on the calculation to the day, rather than to the month as was previously done.
  2. All background age group variables used in analysis are now based on the revised ages. Previously, on average, because the calculation method only considered month and year and not day of birth, the age group of 0 months would have roughly half the number of cases of age group 1 month or other older single month age groups. With the new method, age group 0 months will have a roughly similar number of cases as other single month age groups.

These changes affect virtually all tables related to children, particularly to children under 5.

It is important to note that fertility rate and childhood mortality rate tables are not impacted as these tables exclude the month of interview from calculations and effectively use complete months in the calculations.

More precise calculation results in a shift in age

The diagrams below show the age of the child calculated using the old and new methods, given a particular month of interview and month of birth, giving examples here for interviews in January to June 2017, and births in December 2015 to June 2017. For any birth taking place on a day in the month on or before the day of interview there is no change in the calculation, but for any birth taking place on a day in the month after the day of interview the age of the child is now calculated as 1 month less than previously. For example, a child born in late April 2017 and included in an interview in early June 2017 (equivalent to a point in the bottom right corner of box “2” in the first row below, marked with a red star) was calculated as 2 months using the old method, but looking at the equivalent position in the second example, this child is calculated as age 1 month in the new calculation method.

Old age calculation method example:

New age calculation method example:

This shift in age in months affects roughly half of all children, but only has an effect on age in years for roughly 1/24 of children – those previously classified as 12 months old, but now classified as 11 months old, and similarly around ages 24 months, 36 months, etc.

While these changes will unlikely have a major impact on the interpretation of trends, they do mark a significant shift towards a more precise, accurate measure of children’s age.  Dataset users striving to replicate DHS tabulations need to adjust their logic to match DHS results using some of the new or modified variables to capture the more accurate measure of child age.

Download the full PDF here.

Questions?  After reviewing the full guidance document, please visit the DHS User Forum and post additional questions there for discussion.

© 2012 Xinshu She/Boston Children’s Hospital Global Pediatrics Fellow, Courtesy of Photoshare

15 May

Everything You Need to Know about DHS Data and More

So, you’re new to DHS and you’ve registered as a DHS data user, downloaded the free available datasets, but now what? We have the perfect resources to get you started.

The following videos provide an overview of DHS data answering key questions such as, what is a data file or dataset? What is the difference between De Jure and De Facto? What types of data files are available for download?

Starting with the Introduction to DHS Datasets, this video provides a guide to units of analysis, basic terminology, and DHS data files.

As mentioned in the video above, separate data files are created for different units of analysis. DHS Dataset Types in 60 Seconds runs through the most common data files and what they contain.

De Jure and De Facto are terms that you will see often within DHS reports and datasets. The following video breaks down what the terms mean, and how they apply to analyzing DHS data.

And finally, where is the information about interviewed households and individuals located in different data files? The Introduction to DHS Data Structure examines DHS datasets in a hierarchical structure.

We will have more videos released this summer, but for those who are still eager to learn more about DHS data, check out DHS Dataset Names Explained below.

 

11 Apr

New Data Available from DHS-7 Questionnaire: Maternal and Pregnancy-Related Mortality

Baby Kabuche, 30 yrs old, 4 months pregnant, outside her house. Baby has 2 children: Eric, 12, living with granparents in Musoma and Judith, 6, living with her and her husband. She works in a factory manufacturing alluminium pots and iron rods. But as she becqme pregnant she took some unpaid leave as the factory uses acid and other toxic materials and she cares for the safety and health of her baby. Baby got malaria only once as she sleeps under mosquito net all the time. This new one makes her happy as it is treated with mosquito repellent and it is more effective.

© 2016 Riccardo Gangale/VectorWorks, Courtesy of Photoshare

In 2014, The DHS Program began the process of updating the standard DHS questionnaires. With input from stakeholders, feedback from in-country implementing agencies, and a host of lessons learned from the previous 5-year program, we added, modified, and, in some cases, deleted questions. For many indicators, the actual questionnaire did not require an adjustment, but the calculation of indicators or the tabulation of the data needed an update to reflect new international indicators and best practices.

While questionnaire revision started in 2014, it can take a long time to see this exercise bear fruit. The 2015-16 Malawi DHS, for example, went into the field with the DHS-7 updated questionnaires in October 2015. The final report and dataset for the 2015-16 Malawi DHS were released in March 2017, allowing us to explore the new data for the first time.

In this blog series, New Data Available from DHS-7 Questionnaire, we will be detailing, topic by topic, some of the key changes to the questionnaire, with a focus on why the changes were made, how the changes affect the tabulations, and some guidance on how the resulting data should be interpreted.

Part 1:  Maternal and Pregnancy-Related Mortality

DHS surveys now collect data to provide the maternal mortality ratio in line with the definition provided by WHO. For almost 30 years, The DHS Program has collected data on maternal mortality in a subset of countries. In previous DHS cycles, maternal mortality was defined as any death to a woman while pregnant, during childbirth, or within two months of delivery. The WHO definition of maternal mortality is more precise:  any death to a woman during pregnancy, childbirth, or within 42 days of delivery but not from accidental or incidental causes (see full WHO definition here). The new DHS-7 questionnaire allows us to calculate the maternal mortality ratio (MMR) in closer alignment with this more precise WHO definition.

As always, women interviewed in the DHS are asked to list their siblings. The interviewer then collects information about the siblings’ survival status. In the case of female siblings who have died at age 12 or older, the interviewer inquires whether or not the sister died during pregnancy, childbirth, or within the 2 months following delivery. If the sister died within 2 months after childbirth, the interviewer asks how many days after childbirth the sister died. This clarification on the number of days is a new addition to the DHS-7 questionnaire. The interviewer then asks additional questions to determine if the death was accidental or due to violence. In DHS-7 these deaths are excluded from the calculation of the MMR per the WHO definition.

Why?  These changes were made to improve the precision of the MMR, as well as to align the DHS estimation of the MMR with the standard definition provided by the WHO.

Implications:  While the newly added questions allow for a more precise and up-to-date measure of maternal mortality, the change does present challenges for interpretation. DHS has reported on maternal mortality for 30 years, but estimates obtained using the new definition of maternal mortality cannot be directly compared to the old definition of maternal mortality which included deaths up to 2 months after delivery and did not exclude deaths due to accidents and violence.

And yet, one of the main objectives for conducting DHS surveys is to provide trend data. Fortunately, the old definition of maternal mortality can still be applied to calculate the mortality ratio estimate comparable to estimates from previously collected mortality data. This less precise measure of mortality is referred to as the pregnancy-related mortality ratio (PRMR).

DHS reports that include the maternal mortality module will now contain both the maternal mortality ratio and the pregnancy-related mortality ratio. The maternal mortality ratio will be used as the primary point estimate, but the pregnancy-related mortality ratio will be shown in an additional table and in figures to illustrate the trend. Keep in mind that the new measure of maternal mortality, by definition, will result in a lower maternal mortality ratio than the old measure because the accidental and violence-related deaths to women during the maternal period and deaths occurring between 42 days and 2 months after childbirth are being excluded from maternal deaths while using the new definition but included while using the old definition.

Summary of Maternal Mortality and Pregnancy-related Mortality:

Maternal Mortality Ratio The number of maternal deaths to any woman during pregnancy, childbirth, or within 42 days of delivery excluding accidents and acts of violence per 100,000 live births More precise Not comparable to surveys before DHS-7
Pregnancy-related Mortality Ratio The number of pregnancy-related deaths (deaths to a woman during pregnancy or delivery or within 2 months of the termination of a pregnancy, from any cause, including accidents or violence per 100,000 live births Less precise Comparable to previous surveys; shown to allow for trend  interpretation

The DHS-7 questionnaire includes additional prompts to fully capture more siblings and siblings’ deaths. In previous DHS questionnaires, women were asked to list their siblings in order and then were asked follow-up questions about their survival status. In the DHS-7 adult mortality module, respondents are asked to list their siblings without worrying about their order but are then asked a list of probing questions to ensure that all siblings have actually been recorded. This change is likely to produce a more complete list of siblings for which information on adult and maternal mortality is collected. Once a complete list is produced they are then ordered and the questions on their survival status and age or age at death and years since death, as well as the maternal mortality related questions, are then asked as applicable. 

Why?  Several studies have suggested that respondents’ lists of siblings are not always complete. This often happens when the sibling is a half-brother or sister, when the sibling did not live with the respondent as a child, or when the sibling has died. A pre-test in Ghana indicated that the addition of these probing questions resulted in capturing additional siblings for about 10% of women.

Implications:  Omissions in the sibling history can affect the adult and maternal mortality ratios in different ways. The inclusion of more siblings tends to increase the adult mortality rate. This is because often the siblings who were previously omitted were not spontaneously mentioned because they have already died. However, studies suggest that these deaths are not disproportionately maternal deaths, so a more complete sibling listing might result in a lower maternal mortality ratio.

Key Take-Aways

The changes described above may sound confusing for non-demographers.  The major points to remember for DHS data users include:

  • The new Maternal Mortality Ratio is not comparable with previous measures of maternal mortality in DHS surveys
  • For trends, look at Pregnancy-related Mortality Ratio
  • Despite the different names, both measures include deaths during pregnancy. The MMR is a more precise measure as it excludes some of the deaths during pregnancy that were not related to pregnancy (i.e. accidents and acts of violence).
  • Maternal mortality is still a relatively rare event, and therefore both MMR and PRMR have wide confidence intervals. Both measures are always presented with their confidence interval so that the user can draw their own conclusions about the relative certainty of the point estimate.
21 Mar

7 Tips to Matching DHS Final Report Tables

Can't match DHS Final Report tables?
Feeling frustrated because you can’t match DHS Final Report tables in your statistical software?

 

Our new four-part video series shows the Top 7 Tips & Tricks for Matching DHS Final Report Tables.

In this four-part video series, we will be covering the top 7 tips and tricks to matching The DHS Program Final Reports using a statistical software program.

The videos will guide you through the following questions:

  1. Are you using the correct data file?
  2. Are you using the correct denominator of cases?
  3. Are you using the correct variable(s)?
  4. Are you properly recoding?
  5. Are you applying the correct weights?
  6. Are you selecting the correct software specific code?
  7. Are you properly coding the tabulation commands in your statistical program?

Watch the four videos in the series below on Matching DHS Final Report tables to get all the details on the top 7 tips and tricks.


Additional help can be found on our website and the User Forum.
10 Feb

Where Statistics are Beautiful

Hans Rosling created a world where “statistics are beautiful” and data are entertaining. The staff at The DHS Program have always believed these things to be true but found it difficult to convince the masses. And then came Gapminder and the juggernaut of Hans Rosling’s charismatic, informative, and perspective-changing data presentations.

The DHS Program was heartbroken to learn of Hans Rosling’s death earlier this week. DHS has enjoyed a long and enthusiastic relationship with Dr. Rosling. In 2009, The DHS Program and USAID had the honor of welcoming Dr. Rosling as our keynote speaker at the DHS 25th anniversary celebration in Washington, DC. What is particularly striking in watching the video again after 8 years, is the laughter. Before Hans Rosling, no one would have believed that a data presentation could be so engaging and witty while being so insightful.

In addition to being entertaining and informative, Dr. Rosling was exceptionally modest and gracious. He came to the DHS 25th anniversary event at his own cost, and credited USAID and DHS data with his own success. He thanked USAID and the US taxpayers saying, “Nothing in my career would have been possible without DHS data.”

But really we, at The DHS Program, owe Hans Rosling a tremendous debt of gratitude. Dr. Rosling was a great advocate not just for DHS data, but for all data. He understood, better than anyone else, that data are worthless unless they are used. And he succeeded in doing what many of us have attempted and failed:  he made data come alive.  He used the data to expose the many incorrect notions about development that even people working in the field have, and he did it with such unique charm and flair. His presentations inspired people to think in different ways and to take action.

To Hans Rosling’s family, we thank you for sharing Hans with the world, and for so willingly joining his mission to “edutain” us. All of us at The DHS Program mourn the loss of this warm, generous visionary. This week, more than ever, we commit to continue the work that Hans has started, and will be inspired by Hans Rosling’s leadership and ingenuity as we look for new ways to provide the world with actionable, understandable data.

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.

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