Improvements in Ohio’s Vital Statistics residence data with geo-coding software

1. Improvements in Ohio’s Vital Statistics residence data with geo-coding software Presented at NAPHSIS/VSCP meeting Cincinnati, Ohio June 5, 2005 John O. Paulson, MS Ohio Department of Health jpaulson@odh.ohio.gov

2. Place: geographic location of events and residence Perhaps the most important data element available in VS birth file • Indicators needed at local level • Birth numbers used in school enrollment projections, population estimations

3. In 1909 these places available for study in Ohio: • State • Counties (88) • Registration districts (1,150) cities, villages, townships

4. Mid 1900’s: Consolidation of Registration Districts leading to fewer places to study • Concerted effort to consolidate districts • Shifted from place of occurrence analysis to place of residence analysis (1945) • Statistical Analysis available for: • Counties (88) • Cities of 5,000 population or more 110 in 1939 260 in 2000 --Added Census Tracts for some cities during the 1960s

5. Cities added/removed as statistical places in the VS information

6. Recent developments • Need for more granular view of residence for births and deaths • Wish to improve the accuracy of residential assignment • Potential for reporting FIPS type codes to NCHS instead of traditional geo-codes

7. Geographic Information System Street address/zip code leads to: X-Y latitude longitude coordinate Census block Census block group Census tract Places (cities used in VS) Metropolitan area Urbanized areas (19 in Ohio, densely settled with 50,000 + people) Urban clusters (2,500 to 49,999 people)

8. Geographic Information System Street address/zip code leads to: Townships School districts House/Senate legislative districts Congressional districts

9. Geo-coding Process • Key the street address and zip • Submit to centralized geo-coding service for state government • Batch system primarily, but we are working on incorporating real time geo-coding into our data entry application • File returned from geo-coding service

10. Geo-coding Process (cont.) • Evaluate quality score for the address and for the location • Set a threshold for quality score • Acceptance rates for geo-coded data highest in the cities • Fairly high even for out of state addresses • Develop logic for using the returned data values • e.g. we get FIPS place codes in three different variables • For cases not meeting the quality criteria, use text city name and city limit field to ascertain FIPS place code

11. Changes to NCHS submission process • Change in data submission layout • Separate FIPS error report (.pdf format)

12. Geo-coding influence on place-level health statistics • Traditional method uses self-reported residence • Geo-coding method uses street address, should be more accurate • When we re-assign residence based on new method, does that cause health indicators to change?

13. Place of residence agrees

14. Place of residence disagrees

15. Residence agreement status, Franklin County, 2004

16. LBW rates for Franklin County places, by whether residence assignments agree, 2004

17. LBW rates for Franklin County places, by whether residence assignments agree, 2004

18. LBW rates for Franklin County using traditional vs. GIS-enhanced methods of residence, 2004

19. Geography and VS: the future • Local registrar feedback to improve rate of geo-coding success • Spatial analysis: correlating geo-coded VS data with other spatial variables • Online mapping for users