Virginia Sharp Center for Children with Special Needs

1. Methods for Evaluating Within-State Variations Using the National Survey of Children with Special Health Care Needs Virginia Sharp Center for Children with Special Needs

2. Overview • Why bother? • What sub-state geographic identifiers are available in national surveys? • What alternatives are available? • Accessing the data • Example of Rural-Urban differences in access to care in Washington state

3. Why bother? • Statewide summary measures do not accurately reflect conditions in any particular segment of the state and may mask significant local issues • Health services are delivered locally • Limited health service dollars require targeting programs to meet specific needs • Local health jurisdictions need to be able to “see themselves” in the data.

4. Geographic Identifiers in SLAITS • State • MSA status • MSAs define metropolitan areas as a core area with 50,000 or more inhabitants, such as a central city, along with the counties economically and socially connected to it • MSAs are not static; both their definition and application change over time

5. Why not just use MSA Status? Why not just use MSA Status? • Not available for all states • Required edits to protect confidentiality result in MSA status being suppressed whenever the total Census 2000 population of either the combined MSA counties or the combined non-MSA counties is less than 500,000 persons. • 16 states affected in NS-CSHCN

6. Suppressed MSA status in NS-CSHCN Alaska Hawaii Non-MSA population too small MSA population too small BOTH non-MSA & MSA populations too small

7. Why not just use MSA Status? Why not just use MSA Status? • Not available for all states • Too little variation within many states • In Washington state, 85% of Census 2000 population in MSAs (12 counties) • 2003 MSA definition adds 6 more counties, increasing to 89% of WA population in MSAs

8. MSAs in Washington State, 1999

9. Why not just use MSA Status? Why not just use MSA Status? • Not available for all states • Too little variation within many states • Differences in county size obscure differences across states • Yakima county, an MSA, is 4296 square miles • State of Delaware is 2489 square miles • Differences in county size obscure differences within states • Average population density in Yakima county was 51.8 persons per square mile

10. Rural Urban Commuting Area Codes • Census-tract based classification scheme • Uses standard Bureau of the Census urban area and place definitions in combination with commuting information • Characterizes each census tract based on population density, urbanization & daily commuting • Identifies urban core areas and adjacent territory that is economically integrated with those cores

11. Rural Urban Commuting Area Codes • First developed from 1990 census data • Released for both census tracts & zip code delivery areas • Revised for 2000 decennial census • Census tract versions released March ‘05 • Zip code versions coming soon • Developed by UW Rural Health Research Center with funding from US Dept. of Agriculture, Economic Research Service

12. Why RUCAs? • Flexibility • 10 primary codes • 30 secondary codes • Allows for selective combinationof codes to meet varying definitional needs • Based on smaller geographic areas • Census tracts/zip areas vs. counties • Consistent with OMB concepts of metropolitan and micropolitan areas

13. Zip Code RUCAs, Washington 2000 Urban Core Suburban Large Town Small Town/Rural

14. Accessing RUCAs for NS-CSHCN • Obtain zip code RUCAs for area of interest • http://www.ers.usda.gov/Data/RuralUrbanCommutingAreaCodes/ • Submit proposal to NCHS Research Data Center • http://www.cdc.gov/nchs/r&d/rdc.htm • Jump through the RDC’s hoops

15. RDC Decisions • What specific survey files do you want to merge RUCA codes into? • $500 charge per file prepared by RDC • On-site or Remote Access? • On-site: • SAS, SUDAAN, STATA, Fortran • $200/day • Normal business hours • Output reviewed for disclosure issues

16. RDC Decisions • Remote Access: • SAS programs only • (certain procedures/functions not allowed) • Submit programs via e-mail; results returned next day • $500/month for any one data set

17. RDC Issues • RDC is not “customer friendly” • Processes not always clear • If on-site, must allow sufficient time for RDC staff to review all output at end of day • RDC staff do not provide consultation on surveys themselves or statistical analysis • Must advocate for needs

18. Examples from Washington State 514 112 73 47 Sample Size

19. Needed Routine Preventive Care Statewide mean = 74.6%

20. Needed Specialized Therapies Statewide mean = 26.5%

21. Received All Needed Dental Care Statewide mean = 89.5%

22. Received All Needed Mental Health Care Statewide mean = 79.9%

23. Child has 2+ Unmet Health Needs Statewide mean = 5.8%

24. Family has Unmet Service Needs Statewide mean = 8.4%

25. Topics for Further Study • Is statistical significance at the 95% confidence level necessary for within-state disparities to be important to public health agencies? • How can these findings be used by MCH agencies to reduce geographic disparities? • To what extent are within-state disparities in access to care for CSHCN a function of agency structure? Is there a “most efficient” structure for equitable access to CSHCN-related services? • Would alternate rural-urban definitions yield the same or similar results?

26. Conclusions • Within-state variation in access to care based on rural-urban setting for CSHCN can be significant • Working through the Research Data Center at NCHS, analyses of within-state variation can be conducted on the NS-CSHCN & NS-CH • These data cannot be used to identify issues in specific places within a state • States interested in improving their ability to understand within-state patterns should invest in additional sample size in future surveys

27. Contact Information:Ginny.Sharp@seattlechildrens.org(206) 987-5311 Funding from the Washington State Department of Health, CSHCN Program and an MCHB CSHCN Financing Grant supported this research.