Syndromic Surveillance for Common Illnesses in the Lancaster Community: GIS Applied to Ambulatory Visit Data in a Large Health System

Transcription

1 Syndromic Surveillance for Common Illnesses in the Lancaster Community: GIS Applied to Ambulatory Visit Data in a Large Health System Mike Horst, PhD, MPHS, MS Director of Research Lancaster General Research Institute mahorst@lancastergeneral.org

2 IRB Approval: Lancaster General Hospital IRB Grant Funding: HG Barsumian Memorial Fund Acknowledgements Andrew S. Coco, MD, MPH Dwight O. Eichelberger, MD Angela S. Gambler David T. Mauger, PhD Software ArcView 9.3 Minitab 15 Stata 10 SaTScan GeoDA i5 MS Excel 2007

3 Syndromic Surveillance What is it? Post 9/11: Bioterrorism Electronic health records Lawson, A. & Kleinman, K. (eds.). Spatial and Syndromic Surveillance for Public Health. Wiley (2005) Syndromic Surveillance Common Illnesses

4 Research Questions Primary Research Questions: Determine if there is a spatial, temporal or spatial temporal pattern of common illnesses. Determine the feasibility of providing information to the community to prevent office visits. Discover how much we really don t know.

5 Common Illnesses: Respiratory & GI Definitions Definition #1 ESSENCE (Electronic Surveillance System for the Early Notification i of Community based Epidemics) DOD & CDC defined syndromes based upon ICD 9 codes urveillance/syndromedef/ Marsden Haug, N. et. al. Emerging Infectious Diseases vol. 13, No. 2 (2/2007) Definition #2 Review of ESSENCE Discussion with primary care providers Narrowed definitions to exclusively identify general colds, flu and GI illness & symptoms Definition #3 Flu only

6 Data Elements Collected Patient ID # (unique ID # or MRN) Encounter # Date of visit Practice site of visit DOB Gender Street address Insurance type Primary diagnosis (ICD9) 2 nd diagnosis (ICD9) (if applicable) 3 rd diagnosis (ICD9) (if applicable)

7 Inclusion Criteria Study Sample Allpatients visitingany of the Lancaster General primarycare physician offices or primary care oriented emergency room visits for any reason: 26 primary care sites, 1 ED site Visits occurred 9/1/2007 9/30/2008 Within dfi defined dstudy area Exclusion Criteria Geocoding of street address is less than a score of 72 (also excludes missing addresses, PO Boxes, etc.). Visits occurring in non complete weeks in the beginning of Sept and the end of Sept Initial Sample: 857,555visits Final Sample: 722,701 visits 90,668 ED visits 632,033 primarycare visits Geocoding rate of 87% with no correction

8 16.0% 14.0% 12.0% 10.0% Respiratory, GI and Flu Visits Respiratory GI Flu Flu Visits per 1, % 6.0% 4.0% 2.0% 0.0% Percent Vis its Respiratory and GI Week

9 Study Location

10 Study Location

11 Comparison of All and Study Area Census Tract Weekly Visits Slide omitted: data to be published

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14 Study Sites and Area

15 Spatial Results Management of areas with lower number of visits Evaluated unadjusted, weighted sampling, constant denominator and Bayesian smoothing Selected: Nearest Neighbor Bayesian Smoothing Continuous surface maps created with Kriging based upon Bayesian smoothed rates

16 Respiratory Legend 0% - 4% 4% - 5% 5% - 6% 6% - 7% 7% - 8% 8% - 9% 9% - 10% 10% - 11% 11% - 12% 12% - 13% 13% - 14% 14% - 100%

17 Legends for SaTScan Results

18 Respiratory SaTScan Outcome Slide omitted: data to be published

19 GI SaTScan Outcome Slide omitted: data to be published

20 Respiratory: Getis Ord Gi Getis Gi** SaTScan

21 GI: Getis Ord Gi Getis Gi** SaTScan

22 Population Density Quartiles US Census 2008 Estimates Population Density Quartiles (pop/sq mile) Q1: Q2: Q3: 892 3,267 Q4: 3, ,145

23 Boxplots of Weekly Visits & Population Density Quartiles

24 Spatial Analysis Conclusions Smoothing models provide one method to deal with variable denominators General increases and decreases of visit activity across entire study area Clustering does appear to occur GI appears to be more localized and higher in densely populated p areas Spatial scans pick up statistical clusters

25 Retrospective Limitations Proportion of clinics in geographic area used (marketshare) Represents only those seeking healthcare Codingvariability and code <> actual Does not account for mobility assumes billing/home address One site, one year Geocoding inaccuracies and unmatched

26 PREDICTOR NEWS 8 HEALTH TEAM Diarrhea Vomiting 2:00 PM Wed

27 Future Research Develop methods for defining study area Determine/validate optimal methods for spatially displaying visits iit Determine optimal SaTScan settings and/or determine best spatial scanning method(s) Correlation of visit rate with actual syndrome rate Study spatial differences in urban vs. rural Impacts on health system and community resources

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