1 / 27

LECTURE#3 Data: Uses, Abuses & Analysis Marcia C. Castro mcastro@hsph.harvard

Collaborative Public Health Field Course January 2010. LECTURE#3 Data: Uses, Abuses & Analysis Marcia C. Castro mcastro@hsph.harvard.edu. Objectives. Describe types of data available for health-related research Introduce basic demographic methods Describe spatial analytical approaches

barto
Download Presentation

LECTURE#3 Data: Uses, Abuses & Analysis Marcia C. Castro mcastro@hsph.harvard

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Collaborative Public Health Field Course January 2010 • LECTURE#3 • Data: Uses, Abuses & Analysis • Marcia C. Castro • mcastro@hsph.harvard.edu

  2. Objectives • Describe types of data available for health-related research • Introduce basic demographic methods • Describe spatial analytical approaches • GIS, Remote Sensing, and Spatial Analysis

  3. Data Sources • Three types of data: • Events • Cases & deaths • Exposure (“denominators”) • Population • “Clues” on transmission mechanisms • Multidisciplinary: social, economic, demographic, behavior & individual perception/knowledge, ecological, political, biological

  4. Data Sources • What are we looking for? • Time • Space • Population groups you can’t always get what you want

  5. Data Sources: Events • Administrative data • Outpatient / Hospitalization • Reported cases • Active / Passive • Immunization • Deaths • Control activities • Survey-based data • Sample frame

  6. Data Sources: Denominators • Population counts • Census • Every 10 years • Civil registration system • Coverage • Household surveys • Sample • Periodicity & Coverage • Projections • Uncertainty

  7. Data Sources: Multidisciplinary • Census & household surveys • Agriculture • Environment • Land cover, land change, land use • Climate • Soil, hydrology • Infrastructure • Urban planning & expansion • Social programs (“cash transfers”)

  8. Demographic Methods • Population (P) numbers can only change by • Births (B) Fertility • Deaths (D) Mortality • Movements in (I) / out (E) Migration • Measuring each component • Direct methods • Indirect methods

  9. Demographic MethodsMortality • Key indicators: • Infant & Child mortality • Life expectancy at birth

  10. 160 1996 (TFR = 2.5) 2006 (TFR = 1.8) 128 96 Age-spefic fertility rates (1,000) 64 32 0 15 20 25 30 35 40 45 50 Age Demographic MethodsFertility • Key indicator: Total Fertility Rate PNDS 1996 and 2006

  11. Demographic MethodsMigration • Migratory flow • Direction • Intensity • Characteristics

  12. Spatial MethodsSpace is special • Visualization • Exploration • Modeling

  13. Framework for Spatial Data Analysis Attribute data Feature data Databases GIS & Remote Sensing Visualization Maps Describe significant patterns Exploration StatisticalSoftware Test hypotheses Modeling

  14. Visualization Source:Map 1. Published by C.F. Cheffins, Lith, Southhampton Buildings, London, England, 1854in Snow, John. On the Mode of Communication of Cholera, 2nd Ed, John Churchill, New Burlington Street, London, England, 1855.

  15. Visualization: Visceral Leishmaniasis, Pernambuco

  16. Visualization: incidence ratesDengue – Rio de Janeiro

  17. Visualization: prevalence rates Schistosomiasis Mansoni Bahia

  18. Visualization: % variation in prevalence rates Schistosomiasis Mansoni Bahia

  19. Virgin Mary in a pancake Mermaid in clouds Patterns?

  20. Exploration: clustering Machadinho Settlement Project, Rondônia State, Brazilian Amazon Significant clusters of low malaria rates No significant clusters Significant clusters of high malaria rates

  21. Modeling: predictive model

  22. Modeling: spatial diffusion

  23. Modeling: diffusion Schistosomiasis

  24. A final word of caution • Combining data … … from different sources … from different years … in different scales • Ecological X Individual analysis • Generalizability • Causality

  25. References • Anselin, L. 2006. How (Not) to Lie with Spatial Statistics. American Journal of Preventive Medicine 30(2): S3-S6. • Brownstein, J.S., C.A. Cassa, K.D. Mandl. 2006. No place to hide: Re-identifying patients from published maps. New England Journal of Medicine 355(16):1741-2. • Kenneth Hill, Alan D Lopez, Kenji Shibuya, Prabhat Jha, on behalf of the Monitoring of Vital Events (MoVE) writing group. 2007. Interim measures for meeting needs for health sector data: births, deaths, and causes of death. The Lancet 370(9600): 1726-1735. • Werneck, GL et al. 2002. The urban spread of visceral leishmaniasis: clues from spatial analysis. Epidemiology 13(3):364-367.

  26. Questions for Discussion:Data Sources • What are the main data limitations for health-related analysis? • What would be needed to fill in the gap(s)? • How could available data be used for evidence-based planning of public health interventions?

  27. Questions for Discussion:Data Analysis • How would a demographic analysis improve understanding of the 5 diseases focused in this course? • How do the use of spatial tools in public health improve the understanding of disease dynamics? • Is there a balance between local x global analysis? • How can each approach inform policy makers? • Which is most appropriate to evaluate the progress toward achieving the MDG?

More Related