#WSD2015 Data Visualization Challenge

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1 #WSD2015 Data Visualization Challenge 1. A web link (url) to a working (live) demo Data visualization on health inequity among urban residents in 60 countries focused on key indicators from MDGs 1, 4, 5, 6 and 7: 2. Original code, data files and other electronic files hosted in a public repository Original files are accessible from the Dropbox link provided A PDF document describing your project, which should include: Abstract: Provide a brief summary of your data visualization project, in 120 words or less Given our focus on urban health, the project intended to elicit the magnitude of health inequities in urban areas using MDG indicators. We want to highlight that sections of the urban population are disadvantaged in accessing services and achieving healthy outcomes. The project, therefore, focused on providing a quick and clear understanding of the current situation as well as trends, while providing benchmarks for each country to compare their performance with others. We used a pooled dataset to obtain MDG indicators and used data visualization tools to construct country profiles. The results of this project have been acknowledged by the WHO Global Health Observatory in 2015 and have contributed to raising awareness on urban health inequity. Problem and Motivation: Explain the development policy question you have addressed, highlighting why it is relevant and identifying the main variables involved. In the joint WHO-UNHABITAT report in 2010, Hidden Cities, a strong argument was made on reducing health inequities in urban areas by acting on a variety of urban determinants of health. This could only be done through identifying the worse-off populations and the key issues across which inequities exist. In this project we have identified the urban poor (poorest 20%) as a particularly vulnerable population in addition to those who live in slums. Our objective was then to reveal the magnitude of disadvantage that these populations

2 face in living health lives. Therefore, indicators were categorized as health outcomes, health systems coverage, health risk factors, and health determinants. This is extremely relevant to MDGs as it highlights the need to act urgently to improve lives of large sections of disadvantaged populations in urban areas, who are not always at the forefront of attention in international development discussions. Improvements in their lives will contribute much to national achievement of global goals. The main variables identified in this project are mapped to respective MD Gs in Table 1. Table 1 Mapping MDGs to included indicators in country profiles MDG Included Indicators #1 Eradicate extreme poverty and hunger Chronic malnutrition among under-5 years (stunting) #4 Reduce child mortality Under-five mortality rate, coverage of measles immunization #5 Improve maternal health Antenatal care coverage #6 Combat HIV/AIDS, malaria and other diseases Correct knowledge of HIV/AIDS transmission #7 Ensure environmental sustainability Slum population, access to improved water sources In addition, two key risk factors for noncommunicable diseases, prevalence of smoking and obesity, were included in the profiles. An indicator of the growth of urban population was also considered (using UNDESA projections). Approaches: Describe the approaches you used to examine and analyze the data. What were the major steps involved in your work (from beginning to end)? What kind of statistical methods or data transformations did you apply (if any), and why? W hat difficulties did you find during the process, and how did you overcome them? While the MDGs have focused on national achievement, it has been equally important to understand who the beneficiaries of progress have been. Much attention has been given to urban-rural differentials and differences across socioeconomic status by international agencies such as WHO, UNICEF, UNDP, and the World Bank among others. However, in this analysis, a new rapidly increasing population group the urban poor has been particularly considered. We recognize that major differences exist between other

3 socioeconomic or demographic groups in urban areas but for clarity we focused our project on the urban poor. The most reliable datasets for comparing results across countries fo r a number of MDGs are the Demographic and Health Surveys (DHS, USAID) and the Multiple Indicator Cluster Surveys (MICS, UNICEF). We re-analysed all datasets from the two sources with sufficient urban sample sizes and focused our attention on inequities ac ross wealth and education groups in 21 indicators. Details of the analysis are provided in the document titled Methodological Notes in the Dropbox folder. In addition, as the data is published on the Global Health Observatory, metadata on indicators can be accessed from the website: The main goal of this project was to develop the most effective way of visualizing our analysed data. Therefore, we needed to test different visualization samples and examine its messaging power and readability without second guesses or misunderstandings. Once the sample indicator s visualization method was considered satisfactory, the selection of indicators was conducted. The criteria of the selection of indicators were: 1. The indicator should be an MDG or linked to MDGs; 2. Data should be available across a large number of countries as well as UN regions; 3. Data should be complete and reliable. If MICS indicators were also included their comparability with DHS was tested. We found gaps in terms of regional representativeness and completeness of the data. The MICS and DHS datasets were merged to develop the completed dataset. However, like the other big data projects, definitions and dimensions of each measure were not completely aligned among the two major sources of data. This was one of our key difficulties which resulted in only 60 complete country profiles. Indicators were combined only when the definition and dimension were interpretable and comparable. Sometimes, indicators were dropped when the sample size for measuring urban inequities were considered too low. Tools Utilized: Describe the main features of the technologies you leveraged to develop your project, and why did you choose them There were three main phases of the project. Data analysed by the WHO Kobe Centre on urban health equity for nearly 80 countries was used. First, the datasets were reformatted and collated to align the result with target MDG indicators. STATA and MS-Excel were the main tools/software used at this stage. STATA was used at the initial stage to merge data, conduct data refactoring, as well as a basic descriptive statistics analysis. As a backup to the STATA refactoring, MS- Excel was used for verification and inspection of reorganized databases.

4 In the second phase we tested different designs of visualization to maximize readability and effective communication without misinterpretation. Tableau Desktop was the main software package utilized in this phase. Tableau is currently recognized as the most adequate software package for the purpose of trying different visualization methods due to its various basic visualization modules with intuitive interface. Nearly all the graphs for each country were constructed in Tableau. One infographic per count ry on slum populations was created using a combination of MS-Excel and MS-PowerPoint. Finally, MS-PowerPoint was used to combine entire sets of Tableau graphs for each country into an urban health profile. Data was double-checked for each country to minimize errors. Results: Describe how the results of your work may contribute to improve the use of data for development. Our country profiles are particularly useful for decision-makers as well as development professionals in the following ways: 1. They focus on an often ignored population group critical for achieving development objectives: the urban poor; 2. Presentation of data on 10 indicators linked to various MDGs is succinct and clear; 3. The profiles provide an indication of urban inequalities at one po int in time, as well as across different time periods; 4. Inequalities are presented for a variety of issues including health outcomes, health systems coverage, risk factors, and determinants of health; 5. Within the profiles, visuals are embedded to reveal the country s achievements in comparison with global achievements over the different time periods. With the wealth of information on MDGs presented in a short space, decision -makers can not only get a snapshot of their achievements or gaps, but the profiles can also encourage more targeted action or improvements in areas where the country may be performing below the global median achievement. Short bio: Please introduce yourself in 1 or 2 sentences This project was undertaken by two WHO Kobe Centre [WKC] members: Mr. Amit Prasad and Dr Doohee You, with the support and feedback of other staff of WHO. Mr Prasad is a Technical Officer who provided oversight, led the design of the profiles, and validated data quality. Dr You, consultant, collated the MDG indicators dataset, developed the graphics, and implemented data visualization for the country profiles. Mr Prasad has a Master in Public Administration in International Development from Harvard University and has

5 actively led various global health projects at WHO. Dr You is an epidemiologist trained at doctoral program in environmental health science in the University of California, Berkeley, with experience in big data projects and development of effective data visualization.

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