07 Sep

Providing Geospatial Covariate Data for Use with DHS Datasets

GIS participants at the 2017 Regional Health Data Mapping Workshop in Cambodia.

When users of The DHS Program’s survey data request access to our geospatial data, they usually do so with the intention of linking survey cluster location data to outside datasets – such as rainfall measurements, population density, and distance to road networks. These additional data, when coupled with geographic location, are known as spatial covariates and may shed light on the impact of location on health outcomes. However, linking these covariates to geographic data can often be a challenge as multiple sources of these covariate data exist, often with varying quality. It can be difficult for researchers to know which data source will provide the covariate data that will best complement the GPS cluster data they acquire from The DHS Program.

Having recognized both the demand for DHS geospatial data and the subsequent challenge in linking them to spatial covariates, the DHS Geospatial team endeavored to prepare and make a freely available set of standardized geospatial covariate datasets which do away with the need for linking to clusters’ GPS location data. This allows individuals with little to no Geographic Information Systems (GIS) experience to conduct geospatial statistical analysis in software such as STATA, SAS, or SPSS. Even experienced GIS analysts may benefit from these datasets as they no longer have to take the time to source the proper covariate data and link them to cluster GPS data themselves.

After gathering data from users and experts, we identified the covariates that are most commonly used in published literature in conjunction with The DHS Program’s survey data, that included key topic areas. Further, we reached out to users to get a sense of how they would potentially utilize and benefit from a set of spatial covariates prepared in-house. As a result of these two activities, we identified dozens of potential covariates that are used or that users would like to use in conjunction with our geospatial data.

Working closely with our partners at Blue Raster, we then extracted, at each displaced DHS survey cluster, measurements of selected geospatial covariates. These covariates were selected if they: a) had global or regional extent, b) were publicly available, c) had well-documented acquisition or creation processes with detailed metadata, and d) were available for relevant time frames.

We strove to include those covariates that would be in high demand by our users, including rainfall, ITN net coverage, cases of malaria, travel times to nearest cities, urbanization, and more. A detailed methodology used to extract them can be found on the Spatial Data Repository website.

We hope the spatial covariate datasets will prove to be valuable for a wide range of DHS data users. We are continuing to look into ways to further improve the datasets, including the extraction process used to create these files and release similar extracts for other covariates that weren’t addressed in the first round of this activity. User feedback will be critical in helping us understand what is truly desired out of these datasets, so we strongly encourage those who download and use these files to email us with their thoughts, advice, and requests for future covariates.

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!

09 Aug

The First-ever DHS in Myanmar: The Value of a Nationally Representative Survey

Representatives of the Myanmar Ministry of Health and Sports, USAID, the 3MDG Fund, and other key stakeholders share the results of the 2015-16 Myanmar DHS on March 23, 2017, in Nay Pyi Taw.

2015-16 Myanmar DHS Final Report

Many DHS countries have completed 3, 4, or 5 surveys, and look forward to their next DHS to examine trends and assess progress. But the 2015-16 Myanmar Demographic and Health Survey (MDHS) was the first DHS conducted, providing, for the first time ever, internationally comparable and nationally representative DHS data. For Myanmar, this is an especially meaningful achievement, as some areas of Myanmar have previously been too insecure for inclusion in national surveys.

The Myanmar DHS team, including the Ministry of Health and Sports, USAID/Burma, the 3MDG Fund, and ICF staff decided at the beginning of the survey process to prioritize inclusion of all people in Myanmar. This meant that many extra efforts were taken to collect data in even the hardest-to-reach areas, including clusters that had previously been unreachable by survey programs due to insecurity and violence. Deliberate efforts were made to hire interviewers from all regions and states and to ensure that interviewers could speak minority languages. In one case, data collection teams traveled to a selected cluster in ambulances to ensure fieldworker safety. Extensive advocacy efforts took place before the survey teams arrived at sensitive locations to make certain that communities were informed about the survey and felt comfortable participating. Ultimately, 98% of selected households participated in the MDHS. You can read more about sampling here.

With the 2015-16 MDHS, Myanmar joins the DHS club with nationally representative, transparent, and freely available data for decision makers in Myanmar and worldwide. During the national seminar releasing the MDHS data, the Minister of Health urged 150 eager audience members,

“I do not want this survey to be on a shelf… it must be on the desk of program managers and state and regional health directors”.

The Ministry of Health and Sports has been working towards this goal, holding dissemination workshops in all 15 states and regions in May.

As someone who has been with The DHS Program for 13 years and helped to support dozens of surveys, the release of a new survey final report never gets old. But in Myanmar, the survey signifies more than new data. It represents a new era in Myanmar where information is shared, all people are included, and representative data are used to inform decision making.

All of us at The DHS Program offer our congratulations to the Myanmar Ministry of Health and Sports. Your hard work and dedication over the last two years have paid off. We look forward to working with you again. And next time we can talk about trends.

26 Jul

Five Ways IPUMS-DHS Can Simplify Your Life

Have you ever formatted what you thought were your final models only to discover that:

  • The survey question you used for your dependent variable had five rather than four variations across surveys?
  • There are two other samples (not in your analysis) in which respondents were asked precisely the question that interests you?
  • There is a better question on women’s employment than the one you’re currently using?
  • A key question was asked about all daughters under 14 in one country but all daughters under 19 in another?
  • The survey skip patterns differ significantly across surveys?

These are among the DHS equivalents of missing the nail and hammering your thumb. Ouch!

Fortunately, with IPUMS-DHS, you can put the metaphorical Band-Aids away. IPUMS-DHS, constructed at the Minnesota Population Center, is a web-based tool for accessing DHS data. It makes error-free comparative analysis (across time or countries) easy. IPUMS-DHS currently covers Africa and Asia and includes 23 countries, 101 samples, and 5000 variables. Why not give it a try?

1) See at a glance which surveys asked certain questions, how, and of whom.

Choose a topic from the drop-down list to see which samples include the groups of questions you want. Click on a variable name to see a comparison across countries. The tabs will guide you to codes and a description (which is especially great for constructed variables, like “Unmet Need”) and a discussion of comparability issues.

2) Compare the frequency of responses to questions and more without downloading a data file.

Clicking on the variable name will also bring up, for every sample, frequencies of responses, an explanation of who was asked the question (called the “Universe”) and an English-language version of the question text.

3) Trust that the same variable name and codes have same substantive content.

While the DHS standard variables simplify researchers’ work, even standard variables (such as V130, RELIGION) may have different responses or varying amounts of detail across samples. Non-standard variables’ names differ widely across DHS samples. IPUMS-DHS gives variables with the same substantive meaning consistent names and codes. This “integration” of the DHS data lets you analyze the data immediately, without investigating and resolving differences across samples.

 

4) Create a customized data file with multiple samples in minutes, and change it just as quickly.

With IPUMS-DHS, you can create a dataset tailored to your specific needs in a snap. Just log in using your existing DHS Program user ID and password, browse variables and samples, and add the ones you want to your “data cart.” (Despite the analogy, the data are completely free.) Indicate your preferred file format and, a minute or two later, your data will be ready to download, unzip, and analyze.

Did you forget a control variable? Want to add information from an additional sample? No problem. Just return to your data cart, click “Revise” and then “Change,” and you can instantly add or subtract variables and samples, and download the new, revised data file.

We encourage you to check out IPUMS-DHS. It could change your life (or at least your research).

Special thanks to our guest blog contributors, Elizabeth Boyle and Miriam King!

Elizabeth Heger Boyle, is Professor of Sociology & Law at the University of Minnesota. She studies the role of international laws and policies on women and children’s health around the world. She has written extensively on the impetus for and impact of laws related to female genital cutting, including the book Female Genital Cutting: Cultural Conflict in the Global Community. Her current research focuses on abortion policies globally and their effects; this includes a 2015 article in the American Journal of Sociology. Professor Boyle is currently co-Principal Investigator (with Dr. Miriam King) on IPUMS-DHS, a National Institute for Child Health and Development grant that integrates Demographic and Health Surveys over time and across countries to make them more user-friendly for researchers. Professor Boyle has a Ph.D. in Sociology from Stanford University and a J.D. from the University of Iowa.

Miriam L. King is a Senior Research Scientist at the Minnesota Population Center at the University of Minnesota.  She has managed data integration projects on the U.S. Current Population Survey, the U.S. National Health Interview Survey, and, most recently, the Demographic and Health Surveys.  Her research has focused on the history of the U.S. census, data integration methods, U.S. historical fertility differences, living arrangements, and disparities in access to insurance for same-sex couples.  Dr. King has a Ph.D. jointly in Demography and History from the University of Pennsylvania.

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

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

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.

01 Jun

New Data Available from DHS-7 Questionnaire: Literacy, Ownership of Goods, Internet Use, Finances, and Tobacco Use

This is Part 3 in the DHS-7 questionnaire blog series that explores the new data that are available in DHS reports resulting from changes made to the DHS questionnaires in 2014. This week’s post focuses on changes made to gather additional information about DHS respondents.

Part 3:  Respondents’ Characteristics

Understanding DHS survey respondents is critical to interpreting DHS data. In addition to fertility and health data, the DHS captures information on education and literacy; exposure to mass media; ownership of goods, homes, and land; employment; and use of tobacco. Some of these topics are tabulated in Chapter 3 on Respondent Characteristics, while others are discussed in the chapter on Women’s Empowerment. Changes to these topics are outlined below.

More precise collection of literacy data. In previous DHS surveys, women and men who had attended at least some secondary education were assumed to be literate and only those with primary education and below were asked to read a card in their local language to test for literacy. In DHS-7, only those who have gone to “higher than secondary school” are assumed to be literate; all others, including those who have attended or completed secondary school, are asked to read the literacy card (pictured right).

Why?  This change was made to improve the precision of literacy measures. Not all people who have attended some secondary school are literate. In some cases, this confirmation of literacy may also point to a misclassification of educational levels of respondents.

Implications: In some countries, this change may affect the interpretation of trends, as a more inclusive group of respondents is actually being tested for literacy in DHS-7 surveys. Recently released surveys do not suggest a major impact, however. In the 2015-16 Malawi DHS, for example, 72% of women were found to be literate (when women with primary, secondary, or secondary completed were asked to read the card). This includes about 40 female respondents (out of over 24,000) with some secondary education who previously would have been assumed to be literate but were identified in the 2015-16 survey as illiterate because they could not read the card. This more precise measure adjusts the national literacy rate in Malawi by only 0.15%; both methodologies result in a 72% literacy rate at the national level.

Additional questions on mobile phone ownership. Previous DHS surveys collected data on mobile phone ownership at the household level. In DHS-7, women and men are asked about mobile phone ownership individually. These data are presented in the Women’s Empowerment chapter.

Why? Having one mobile phone per household is not very informative when programs are designing mobile interventions to reach pregnant women or facilitate receiving HIV results.

New finance-related questions. In DHS-7, women and men are now asked whether or not they have used their mobile phone for financial transactions, and whether or not they have an account in a bank or other financial institute. These data are tabulated in the Women’s Empowerment chapter.

New question on internet usage. Respondents to woman’s and man’s questionnaires are now asked if they have ever used the internet. Those who answer yes are asked if they’ve used the internet in the past 12 months. For those who have used the internet in the year before the survey, they are also asked, “during the last month, how often did you use the internet?”New question on ownership of title or deed for house or land. Previously, women and men were asked if they owned a house or land alone or jointly. Now they are asked two follow-up questions if they say yes to the ownership questions:  whether or not they have a title deed, and whether or not their name is on the title deed. These data are tabulated in the Women’s Empowerment chapter. Because these questions may be considered sensitive not all countries will elect to include them in their surveys.

New and more detailed questions on tobacco use. In DHS-7, women and men are asked more detailed questions about tobacco use to capture how often the respondent smokes or uses other tobacco products. Men are also asked whether they have previously been a daily smoker, how many of different types of tobacco products are used per day and per week, and whether or not the man uses smokeless tobacco.

To learn more, read the full blog series or download the DHS-7 model questionnaire.

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.

 

05 May

New data available from DHS-7 Questionnaire: WASH Indicators

DHS Survey manager Joanna Lowell washes her hands in Zimbabwe during fieldwork in 2010.

This is Part 2 in the New Data Available from DHS-7 Questionnaire blog series that explores the new data that are available in DHS reports resulting from changes made to the DHS-7 questionnaire in 2014. This post focuses on changes made to improve the quality and quantity of data collected about water and sanitation.

Part 2:  Water and Sanitation

There has been increasing demand from the water, sanitation, and hygiene (WASH) community to gather more detailed information to measure the Sustainable Development Goal of access to water and sanitation for all. The major DHS-7 questionnaire enhancements in this area are outlined below.

Bottled water is now defined as an improved or unimproved source of drinking water depending on the source of water for cooking and handwashing. In the previous DHS-6 questionnaire, if a household indicated that the main source of drinking water for household members was bottled water, this was categorized as an improved source of drinking water. In the DHS-7 questionnaire (as in DHS-5), a household that uses bottled water for drinking is asked a follow-up question about the source of water used for cooking and handwashing (see questionnaire). For example, a household that uses bottled water for drinking but surface water (an unimproved source) for cooking and handwashing is considered to have an unimproved source. A household that uses bottled water for drinking and piped water (an improved source) for cooking and handwashing is considered to have an improved source. Both categories are listed in Table 2.1 (see figure).

Why? This change was made to align DHS data with recommendations from the WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation (JMP) which monitors progress towards Sustainable Development Goal 6: Ensure access to water and sanitation for all. It is important to note that while surveys like the DHS can assess the main source of household water, the source of water is only a proxy measure for quality. Sometimes water from an improved source is not safe to drink.

Implications: For most countries, this change will simply add insight into how households use water sources for different purposes. In countries where there is heavy reliance on bottled drinking water, reclassification of some of the bottled water users as having an unimproved water source may affect interpretation of trends in the larger “improved source” and “unimproved source” categories between surveys that include information about the source of water for cooking and handwashing and those that do not.

New category of improved source of drinking water added. Respondents to the household questionnaire can now indicate that their drinking water source is water piped to a neighbor.

Why? This response category was added because it is a common source of drinking water in some countries.

New question and table on availability of water. For households using piped water or water from a borehole or tubewell, a new question has been added asking if water was available without an interruption of at least one day in the past 2 weeks (see Table 2. 2).

Why? Scheduled or unscheduled interruptions in the water supply may force households to use unimproved sources.  All persons should have sustainable access to adequate quantities of affordable and safe water. The new question helps determine whether or not households have a sustainable supply of water.

Implications: Water availability from some improved sources, such as piped water or tubewells, is not always consistent. Intermittent and unreliable water services result in inconvenience to water users and increased risk of compromised water safety.

Sanitation and toilet facilities language clarified. The collection of data about toilet facilities has changed only marginally, however the language used to describe the different types of unimproved sanitation has been clarified. In DHS-7 reports, sanitation is divided into the categories seen in Table 2.3 from the 2015-16 Malawi DHS. Improved sanitation includes flush/pour systems, VIP latrines, and composting toilets, among others. Unimproved sanitation now includes three subcategories: a shared facility (this may still be a flush system, but by definition a shared facility is not improved); an unimproved facility, such as a pit latrine without a slab, an open pit latrine or a bucket; and open defecation, that is, the household has no facility and uses the field or bush.

Why? Improved sanitation facilities are meant to separate human excreta from human contact. If an otherwise improved sanitation facility is shared with other households, the likelihood of exposure to fecal materials is increased.

Implications: In this case, the labeling of these categories is all that has changed. The DHS STATcompiler has been updated with new labels to reflect these categories. Interpretation of data for trend analysis is not affected.

New question added on location of toilet facilities. The DHS-7 questionnaire now also asks where the toilet facility is located. Table 2.3 categorizes these locations as “in own dwelling,” “in own yard/plot,” and “elsewhere.”

Why? If the sanitation facility used by the household is not in the dwelling or yard/plot, it is more difficult to access when needed, and it may pose a safety issue, especially for women and children.

Implications of this addition are not yet known; analysis of future survey data may provide insight.

DHS Survey manager Joanna Lowell washes her hands in Zimbabwe during fieldwork in 2010.

DHS Survey manager Joanna Lowell washes her hands in Zimbabwe during fieldwork in 2010.

Mobile sites for handwashing now captured. In previous surveys, interviewers asked household respondents to show them where members of the household usually wash their hands. The DHS-7 questionnaire allows for interviewers to indicate whether this handwashing site was fixed (such as a sink) or mobile (such as a pitcher or basin) (see Table 2.7 from the 2015-16 Malawi DHS).

Why? Many households without piped water do not have a fixed place for handwashing. In some countries (particularly in Africa), many households rely on mobile items for handwashing. When hands need to be washed, the individual may move a jug, basin, and soap from inside the home to the outdoor courtyard in order to wash hands. The ability to determine whether handwashing relies on a fixed or mobile place helps to interpret the handwashing data and to understand the physical and social norm-related barriers to handwashing with soap.

Implications: Early review of data from DHS-7 countries suggest that adding the mobile site for handwashing increases the percentage of households that will report that they have a handwashing site. Trends in this area should be interpreted with caution, as an increase in reported handwashing sites may be a function of the questionnaire change rather than a true change in handwashing practices.

24 Apr

The DHS Program at the 2017 PAA Annual Meeting

The DHS Program research team at the 2016 PAA Annual Meeting

We are pleased to announce that The DHS Program and staff will be attending this year’s Population Association of America (PAA) Annual Meeting in Chicago from April 27-29.

PAA is a nonprofit, scientific, professional organization established to promote the improvement, advancement, and progress of the human race through research of problems related to human population.

The DHS Program has been participating in the PAA Annual Meeting over the last few years and we are excited to share our recent surveys and other publications.

If you plan to attend PAA, visit booth #200 for your copy of free survey publications and tours of our new web and mobile tools. Several DHS staff will also be presenting posters, sessions, and will be available to answer any questions you may have about DHS data and results.

View the full DHS staff participation schedule here.

We are looking forward to seeing you there!

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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