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LECTURE 3 Data Sources Data Analysis Marcia C. Castro mcastrohsph.harvard

Objectives. Describe the main data sources available for health-related research in BrazilIntroduce basic demographic methodsDescribe spatial analytical approachesGIS, Remote Sensing, and Spatial Analysis. Questions for Discussion: Data Sources. What are the main data limitations for health-rel

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LECTURE 3 Data Sources Data Analysis Marcia C. Castro mcastrohsph.harvard

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    2. Objectives Describe the main data sources available for health-related research in Brazil Introduce basic demographic methods Describe spatial analytical approaches GIS, Remote Sensing, and Spatial Analysis

    3. 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?

    4. 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 increase knowledge 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?

    5. 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

    6. Data Sources What are we looking for? Time Space Population groups List of important sources available on the course website

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

    8. Data Sources: Denominators Population counts Census Every 10 years Civil register system Under enumeration Household surveys Sample Periodicity & Coverage Projections Uncertainty

    9. 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”)

    10. RIPSA - http://www.ripsa.org.br RIPSA – Inter-agency Network of Information for Health (Rede Interagencial de Informaçőes para a Saúde) Joint initiative of the Ministry of Health and the Pan-American Health Organization (PAHO) Annual publication of summary indicators (available on-line)

    11. RIPSA On-line database: http://www.datasus.gov.br/idb

    12. Demographic Methods Population (P) numbers can only change by Births (B) Fertility Deaths (D) Mortality Movements in (I) / out (E) Migration Balancing equation Pt+1 = Pt + Bt – Dt +It – Et

    13. Demographic Methods Mortality Key indicators: Infant mortality Child mortality Life expectancy at birth

    14. Demographic Methods Mortality Key tools: Life tables Total deaths By cause of death Simulated scenarios of cause elimination Decompose differentials Inequalities Modeled mortality schedules Standards – in the absence of good data Indirect methods of estimation

    15. Demographic Methods Fertility Key indicator: Total Fertility Rate

    16. Demographic Methods Fertility Key indicator: Net Reproduction Rate Key tools: Modeled fertility schedules Standards – in the absence of good data Indirect methods of estimation

    17. Demographic Methods Migration Migratory flow Direction Intensity Characteristics Interactive flow mapping

    18. Demographic Methods Age and sex structure

    19. Demographic Methods Projections Projecting “denominators” Use 3 population components Assumptions about future patterns Uncertainty

    20. Spatial Tools Visualization Exploration Modeling

    21. Framework for Spatial Data Analysis spatial analysis essential component of epidemiological analysis: visualization -> extremely effective for analysis and presentation exploration -> cluster detection methods (beware of type I error) modelling -> Bayesian modelling and decision analysis techniquesspatial analysis essential component of epidemiological analysis: visualization -> extremely effective for analysis and presentation exploration -> cluster detection methods (beware of type I error) modelling -> Bayesian modelling and decision analysis techniques

    25. Visualization: incidence rates Dengue – Rio de Janeiro

    26. Visualization: prevalence rates

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

    31. Modeling: predictive model

    32. Modeling: spatial diffusion

    35. 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.

    36. 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?

    37. 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?

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