Spatial Data for Health: What’s Changed in Terms of Availability and Quality?


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Presented by Nate Heard, HIU/Dept of State, at the March 2014 MEASURE GIS Working Group meeting.

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Spatial Data for Health: What’s Changed in Terms of Availability and Quality?

  1. 1. Spatial data for health: what’s changed in terms of availability and quality? MEASURE GIS Working Group March 4, 2014 Nate Heard 1 UNCLASSIFIED
  2. 2. Overview • Spatial data for analysis in health • Tracks of spatial data development – Authority – The crowd – The academy • Open data • What’s next? 2 UNCLASSIFIED
  3. 3. Attribute data Feature data Databases Describe patterns Analyze patterns Explain or predict patterns Visualization Exploration Modelling GIS DBMS Statistical analysis Pfeiffer et al. 2008. Spatial Analysis in Epidemiology. Oxford University Press Conceptual Framework for Spatial Epidemiological Data Analysis UNCLASSIFIED
  4. 4. Sub-national HIV Prevalence – 2001-2007 Percent of women and men age 15-49 who are HIV-positive Kimberly Forkner - Macro International UNCLASSIFIED
  5. 5. Clara Burgert, ICF Macro Sub-national HIV Prevalence – 2001-2013 Percent of women and men age 15-49 who are HIV-positive UNCLASSIFIED 5
  6. 6. Spatial Data Repository Before… After! UNCLASSIFIED
  7. 7. # CVL (mean) 0-20 21-100 101-500 500+ Brazil: Community Viral Load, 2012 # CVL (quartile) 0 – 449,948 449,949 – 1,570,941 1570942 - 5433788 5433788+ UNCLASSIFIED 7
  8. 8. DHIS2 • Used in 46 countries, 25,000 users monthly • National standard for HMIS in 11 countries • In Uganda, DHIS2 is the facility registry 8 UNCLASSIFIED
  9. 9. Global AIDS Response Progress Reporting 2014 9 “To facilitate data integration and analysis, geographic markers for data should be maintained with indicators at the appropriate level of precision and using standardized geographic references and naming conventions... Attaching geographic information to the more granular data that compose aggregate indicators can enable a wide array of analysis, such as geographic coverage of services, spatial distribution of human resource and expenditures, and the estimation of change over time for small areas.” UNCLASSIFIED
  10. 10. Geographic Location as Required Data in Master Lists of Health Facilities, 2012 10 UNCLASSIFIED
  11. 11. Open Street Map (OSM): Johannesburg, 2013 11 UNCLASSIFIED
  12. 12. OSM: Rio de Janeiro, 2013 12 UNCLASSIFIED
  13. 13. OSM: Mathare, Nairobi, Kenya 13 UNCLASSIFIED
  14. 14. OSM: Bangui, Central Africa Republic 14 UNCLASSIFIED
  15. 15. 15 OSM: Bangui, Central Africa Republic UNCLASSIFIED
  16. 16. OSM: Lubumbashi, DRC 16 UNCLASSIFIED
  17. 17. OSM: Ta‘izz, Yemen 17 UNCLASSIFIED
  18. 18. OSM: Nalayh, Mongolia 18 UNCLASSIFIED
  19. 19. Haiti MSPP v. OSM 1 19 UNCLASSIFIED
  20. 20. Haiti MSPP v. OSM 2 20 UNCLASSIFIED
  21. 21. Haiti MSPP v. OSM 3 21 UNCLASSIFIED
  22. 22. Haiti MSPP v. OSM 4 22 UNCLASSIFIED
  23. 23. This Wormy World 23 Brooker et al. 2009. An updated atlas of human helminth infections: the example of East Africa. International Journal of Health Geographics 2009, 8:42 Atlas of Helminth Infections UNCLASSIFIED
  24. 24. 24 Patil, A.P., Gething, P.W., Piel, F.B. and Hay, S.I. (2011). Bayesian geostatistics in health cartography: the perspective of malaria. Trends in Parasitology 27(6): 246-253 The clinical burden of Plasmodium falciparum map in 2007 in Papua New Guinea The spatial limits of Plasmodium falciparum malaria transmission map in 2010 in Dominican Republic Malaria Atlas Project UNCLASSIFIED
  25. 25. Messina et al. 2010. "Spatial and socio-behavioral patterns of HIV prevalence in the Democratic Republic of Congo" Social Science & Medicine 71 (2010) 1428e1435. Montana, L. 2007. Spatial Modeling of HIV Prevalence in Kenya. DHS Working Papers. MEASURE DHS, Macro International Inc., Calverton, MD Larmarange, J. 2011. Methods for mapping regional trends of HIV prevalence from Demographic and Health Surveys (DHS) Cybergeo: European Journal of Geography. HIV Interpolation Using DHS UNCLASSIFIED 25
  26. 26. Gridded Population 26U.S. Census: Demobase Landscan WorldPop UNCLASSIFIED
  27. 27. Watch This Space 27 Map generated by more than 250 million public tweets with high-resolution location information, March 2011 and January 2012. Salathé M, Bengtsson L, Bodnar TJ, Brewer DD, et al. (2012) Digital Epidemiology. PLoS Comput Biol 8(7): e1002616. doi:10.1371/journal.pcbi.1002616 al.pcbi.1002616 UNCLASSIFIED
  28. 28. “The three principles of transparency, participation, and collaboration form the cornerstone of an open government. Transparency promotes accountability by providing the public with information about what the Government is doing. Participation allows members of the public to contribute ideas and expertise so that their government can make policies with the benefit of information that is widely dispersed in society. Collaboration improves the effectiveness of Government by encouraging partnerships and cooperation within the Federal Government, across levels of government, and between the Government and private institutions.” 1. Publish Government Information Online 2. Improve the quality of USG information 3. Create and Institutionalize a Culture of Open Government 4. Create an Enabling Policy Framework for Open Government Open Government Directive UNCLASSIFIED
  29. 29. Federal agencies with more than $100M in R&D expenditures to develop plans to make the published results of federally funded research freely available to the public within one year of publication and requiring researchers to better account for and manage the digital data resulting from federally funded scientific research. Expanding Public Access to the Results of Federally Funded Research UNCLASSIFIED
  30. 30. PLOS’ New Data Policy: Public Access to Data 30 … We are now revising our data- sharing policy for all PLOS journals: authors must make all data publicly available, without restriction, immediately upon publication of the article. Beginning March 3rd, 2014, all authors who submit to a PLOS journal will be asked to provide a Data Availability Statement, describing where and how others can access each dataset that underlies the findings. This Data Availability Statement will be published on the first page of each article. UNCLASSIFIED
  31. 31. Conclusion • Beyond visualization • New tools for new data • Getting closer to the E 31 UNCLASSIFIED
  32. 32. Boundary representation is not necessarily authoritative. The views and conclusions contained in this presentation are those of the author and do not necessarily reflect the policies of the United States Government. Any use of trade, product, or firm names in this presentation is for descriptive purposes only and does not imply endorsement by the U.S. Government. UNCLASSIFIED Nathan Heard, DSc Public Health Analyst Humanitarian Information Unit U.S. Department of State 32