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Acknowledgements

Acknowledgements. Epidemiologic Query & Mapping System. Sherrilynn Fuller Principal Investigator. Public Health System Linkages – Bench to Bedside and Beyond. Richard Hoskins Cathy O’Connor. Clark Johnson. Patrick O’Carroll. EpiQMS URL. http://198.187.0.45/EpiQMS/.

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Acknowledgements

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  1. Acknowledgements Epidemiologic Query & Mapping System Sherrilynn FullerPrincipal Investigator Public Health System Linkages – Bench to Bedside and Beyond Richard Hoskins Cathy O’Connor Clark Johnson Patrick O’Carroll

  2. EpiQMS URL http://198.187.0.45/EpiQMS/ Also EPIQMS in www.google.com

  3. Components Components • WWW based - high speed access by local health • Three user levels (public, practitioner, need-to-know) • Rates with statistical measures • Charts & graphs, time trends • Deals with small numbers (empirical Bayes spatial modeling) • Static, dynamic and full GIS mapping • Multiple geographies • Queries a dynamic database (no or little on-line calculation) • Allows central Q&A (software & data & statistical measures) • Comprehensive security model • Individual id protection (available dimensions) • Only aggregated data • Tutorials

  4. ABORTIONS 1991-1999BIRTHS CERTIFICATE 1980-1999CANCER Registry CENSUS DATA  1980 1990 – 1998 2000 (state, county, census tract, legislative district, school district, Zip Code, SES cluster zones, climate zones, rural areas)  COMMUNICABLE DISEASE 1980-1997DEATHS 1980-2000 ICD9 – ICD10 HOSPITALIZATIONS 1990 -1999HOSPITALIZATIONS (EPI FILE) 1990-1999 INFANT DEATHS 1981-1999STD 1993-1999 TUBERCULOSIS 1992-1999 HIV 1992 – 1999 Health of Washington State Youth Violence Crime , housing Datasets for EpiQMS Available now In preparation Requested

  5. Why do this? • Original and still primary objective: Communicable disease tracking • Geographically oriented (maps) • Small numbers • Ease of use and access • Low cost for users

  6. Objectives • Ease of access to public health data by all citizens while paying strict attention to individual privacy. • Allow medical practitioners routine access to support assessment and surveillance in local health departments, communities, WA DOH, and public health research. • Get people who use public health data to think geographically. Many geographies, some non-standard. • Uniformity of epidemiologic measures. • Offer on-line instruction in how to use and intrepret public health data. • Software burden is on DOH not users. • Allow down loading ofinformation– tables, charts, maps.

  7. Diagram How it works … DOH databases Population data Web server Geocoded data Dynamic mappingengine SASpreprosessing Data formating PreProcessing SAS Full GIS DOH:Secure Data Server No identifiers ! Static Maps EpiQMS Internet Engine EpiQMS database Aggregated data SQL server EpiQMS: Data Server WWW users need-to-know citizen users practitioners

  8. Indexing - primary key Aggregating events by: Disease Geography Year Age Yes Race Yes No Sex Yes No No Generates Index: 15 X O X O O 15XOXOO Breast cancer

  9. Cancer Registry security model level I

  10. Concurrent dimensions security model level II

  11. Who decides which users get various levels of access? • Data “owners” • Not EpiQMS team • Tools to help data owners decide: • Probability studies • Count suppression

  12. Map Beginningto think geographically ... Thematic Maps Trivial pursuit word: choropleth

  13. Map Issues in thematic mapping Natural break Equal ranges Equal counts Different conclusionscan be drawn from maps of thesame data.

  14. MAUP The Modifiable ArealUnit Problem (MAUP) A form of ecological fallacy associated with the aggregation of data into areal units for geographical analysis. There are two effects: Scale effect: The larger the unit of aggregation, the larger, on average, is the correlation between two variables. Aggregation effect: By aggregating data into different blocks, you can get different correlations. 1960 election: +0.44 correlation between rural non-farm voting for Nixon in using Census nine-region division -0. 22 correlation using the Census four-region division.

  15. Empirical Bayes estimation How to estimate disease rates in “small” areas? • previous data • intuition • good guess (or even a bad one) • the data itself - Empirical Bayes • Smoothing to reflect confidence of local estimation of risk • Prior knowledge of about rates and the observed data are used to develop a priordistribution posterior likelihood prior distribution of datadistribution Mean (smoothed rates) std error (Bayesian confidence intervals)

  16. Map Breast Cancer Bayesian Rate Ratio 0.00 to 0.75 0.75 to 1.25 1.25 to 2.25 2.25 to 12.00 Other 0 30 60 90 Miles Deaths from breast cancer in age 35-44 women No Bayes Blank areas indicate no deaths Bayes Zipcodes

  17. What does it take to run EpiQMS? Fast! User • Internet Explorer • Internet connection 56k or > • Two plug-ins which are easy to deal with. (SVG for maps, ChartFX for charts) DOH • SQL server • ChartFX – charting software • SAS for the prep of data • Visual Interdev – standard Internet site development tool. • RoboHelp – help system development package http://198.187.0.45/EpiQMS/

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