1. 1 USING ADMINISTRATIVE DATA TO MONITOR ACCESS, IDENTIFY DISPARITIES, AND ASSESS PERFORMANCE OF THE SAFETY NET John Billings September, 2003 NYU Center for Health and Public Service Research

2. 2 WHAT ARE “ADMINISTRATIVE DATA” Computerized records Gathered for some “administrative” purpose –Bill paying/reimbursement –Record keeping Typically containing information about individuals –Demographics –Utilization of services –Other events (birth, death, etc.)

3. 3 SOME EXAMPLES OF “ADMINISTRATIVE DATA” Birth/death records Hospital admission/discharge abstracts Emergency department billing records Medicare and Medicaid claims files

4. 4 ADVANTAGES OF “ADMINISTRATIVE DATA” They’re already there They’re electronic [computerized] They can be relatively inexpensive to analyze [sometimes] They can tell you a lot about what is going on [sometimes]

5. 5 DISADVANTAGES OF ADMINISTRATIVE DATA They can be “dirty” (caution is required) –Some data elements are a lot better than others –A good test is whether anyone will go to jail for bad data, or there is some other good reason to get it right They seldom tell the whole story (often raising more pesky questions) Not everyone is willing to share (which may be required) You’re probably going to be dealing with bureaucrats not particularly interested in being helpful

6. 6 USING BIRTH RECORDS TO MONITOR BIRTH “OUTCOMES” Late/no prenatal care Low birth weight (adjusted for gestational age) Preterm birth

7. 7 Percent Late/No Prenatal Care New York City 1997-8 Each “■ “ represents zip code area R 2 =.435 Source: NYU Center for Health and Public Service Research

8. 8 Staten Island Coney Island Source: NYU Center for Health and Public Service Research

9. 9 USING HOSPITAL DISCHARGE DATA Preventable/Avoidable Hospitalizations Ambulatory Care Sensitive (ACS) Conditions ACS Conditions - Where timely and effective ambulatory care help prevent the need for hospitalization Chronic conditions – Effective care can prevent flare-ups (asthma, diabetes, congestive heart disease, etc.) Acute conditions – Early intervention can prevent more serious progression (ENT infections, cellulitis, pneumonia, etc.) Preventable conditions – Immunization preventable illness

10. 10 ACS Admissions/1,000 By Zip Code Area Income Baltimore - Age 0-17 - 1999 Adms/1,000 R 2 =.595 LowInc/HiInc = 2.24 Mean Rate = 9.53 Each represents a zip code Percent of Households with Income <$15,000 Source: NYU Center for Health and Public Service Research

11. 11 ACS Admissions/1,000 By Zip Code Area Income Baltimore - Age 40-64 - 1999 Adms/1,000 R 2 =.893 LowInc/HiInc = 4.08 Mean Rate = 26.69 Each represents a zip code Percent of Households with Income <$15,000 Source: NYU Center for Health and Public Service Research

12. 12 Atlanta Metro Area ACS Admissions/1,000 Age 40-64 - 1999 Source: AHRQ/HCUP - NYU Center for Health and Public Service Research

13. 13 Atlanta Metro Area ACS Admissions/1,000 Age 40-64 - 1999 Source: AHRQ/HCUP - NYU Center for Health and Public Service Research

14. 14 ACS Admissions/1,000 By Zip Code Area Income New York City - Age 18-64 - 2000 Adms/1,000 R 2 =.613 LowInc/HiInc = 3.18 Mean Rate = 10.64 Each represents a zip code Percent of Households with Income <$15,000 Source: NYU Center for Health and Public Service Research Low income zip codes with large differences in ACS rates 10035 11239

15. 15 USING EMERGENCY DEPARTMENT DATA TO MONITOR THE SAFETY NET NYU ED CLASSIFICATION ALGORITHM Emergent Primary Care Treatable ED Care Needed Not preventable/avoidable Preventable/avoidable Non-Emergent Source: NYU Center for Health and Public Service Research

16. 16 Preventable/Avoidable ED Use/1,000 By Zip Code Area Income Baltimore - Age 18-64 - 2000 ED Visits/1,000 R 2 =.783 LowInc/HiInc = 3.77 Mean Rate = 80 Each represents a zip code Percent of Households with Income <$15,000 Source: AHRQ/HCUP - NYU Center for Health and Public Service Research

17. 17 Preventable/Avoidable ED Use/1,000 By Zip Code Area Income Austin Metro Area - Age 0-17 - 2000 Austin Metro Area Source: NYU Center for Health and Public Service Research

18. 18 SOME CAUTIONS FOR USING ADMINISTRATIVE DATA The data can be “dirty” (see above) If a number is way high or way low, it’s probably wrong –Unless it’s not –(Some disparities are huge) Don’t expect final answers Avoid the easy explanation – this stuff is complex