1. Using Clinformatics Data Mart Data to Learn About Patients with Ocular Diseases Joshua D. Stein, MD, MS Associate Professor University of Michigan Dept. of Ophthalmology and Visual Sciences Edward T. and Ellen K. Dryer Career Development Professor Director, Center for Eye Policy and Innovation January 20, No Financial Disclosures

2. University of Michigan Center for Eye Policy and Innovation
Faculty:
Paul P. Lee, MD, JD
David C. Musch, PhD, MPH
Paula Anne Newman-Casey, MD, MS
Maria Woodward, MD, MS
Angela Elam, MD
Joshua Ehrlich, MD, MPH
Biostatisticians:
Chris Andrews, PhD
Nidhi Talwar, MA
Leslie Niziol, MS
Moshiur Rahman, PhD

3. What Information is Captured In The Clinformatics Data Mart?
Demographics
Age, sex, race/ethnicity
Socioeconomic Data
Income, education level
Patient Diagnoses (ICD-9-CM codes)
Procedures (CPT-4 codes)
Office visits, diagnostic procedures, therapeutic procedures
Outpatient Medication Prescriptions
Outpatient Laboratory Results
HbA1c, BUN/Cr

4. What’s in The CDM? Some additional variables Providers performing care
Hurra!
Es ist eine Goldgrube!
Some additional variables
Providers performing care
Ophthalmologist, optometrist, PCP
Health Plan Type
HMO, PPO, POS, Medicare Advantage
Charges
Copays
Deductibles

5. Clinformatics DataMart Database (2001–2014)
Unique individuals receiving eye care
18,498,694
Encounters (eye / non-eye)
2,183,833,742
Outpatient prescriptions
897,430,076
Outpatient lab test results
928,939,926
Patients with open-angle glaucoma
314,691

6. Epidemiology & Risk Factors for Ocular Diseases

12. Studying Utilization of Health Care Services

15. Assessing Disparities and Inequities in Eye Care

18. Does Race, Income, and Education Affect Rates of Visits to Ophthalmologists and Optometrists?
Studied persons with commercial health insurance
Assessed affect of race, income, and education on the odds of visits to ophthalmologists and optometrists

19. Visits to Ophthalmologists

20. Visits to Optometrists

21. Likelihood of Visit Based on Ocular Diagnosis
FIGURE (CRUDE):
For Ophthalmologists (top row) and Optometrists (bottom row). A similar row for ‘Any Eye Provider’ could be drawn.
Visit-probability vs BHH,BLL,WHH,WLL for each of eight conditions:
Overall, healthy (no eye condition), OAG, CAT, DR, AMD, at least 2, at least 3
Yearly estimates are computed from only those meeting criteria. Eleven values are averaged.
FIGURE (ADJUSTED):
These are from a model adjusted for: year, region, urban/rural, sex, age, ccindex, mood changes, diabetes, hypertension, and eye trauma.
LL = Low income / Low education, LH = Low income / High education, HL= High income / Low education, HH = High income / High education

22. Comparing Eye Care For Persons with Different Types of Health Insurance

23. Does Insurance Status Affect Monitoring of Patients with Glaucoma?
Medicaid
CDM
Eligible, n
2123
9444
Mean age at diagnosis, (sd)
56 (7)
63 (11)
40s, n (%)
464 (22)
1090 (12)
50s
967 (46)
2833 (30)
60s
653 (31)
2929 (31)
70+
39 (2)
2592 (27)
Female, n (%)
1268 (60)
5009 (53)
Race, n (%)
White
1034 (49)
7477 (79)
Black
846 (40)
1212 (13)
Latino
243 (11)
755 (8)
Relative to patients in CDM, Medicaid enrollees with newly diagnosed OAG are younger, more female, and more racially diverse.

24. Proportion of Patients Undergoing Diagnostic Testing for Glaucoma in 15 Months After Initial Diagnosis
For each individual test, Medicaid patients received significantly less testing across the board.
Rates of fundus photography were low in both populations but Medicaid patients still fared worse.
In Medicaid, almost 50% of patients diagnosed with OAG underwent no testing at all, compared to about 20% in Optum.
Probability and 95% confidence interval (CI)

25. Comparison of Odds of Each Glaucoma Test Among Those in Medicaid vs
Comparison of Odds of Each Glaucoma Test Among Those in Medicaid vs. Commercial Insurance
Tests less likely in Medicaid than in Commercial
Tests more likely in Medicaid than in Commercial
When look at odds ratios, with Optum patients being the reference at 1, again you can see that overall, Medicaid patients received significantly less testing, particularly for VF and OOI.
OR with 95% CI

26. Comparison of Odds of Each Glaucoma Test Among Those in Medicaid vs
Comparison of Odds of Each Glaucoma Test Among Those in Medicaid vs. Commercial Insurance
When adding race to the model, you can see that the disparity in testing is greatest in Blacks in Medicaid compared to Optum. For example, Black in Medicaid receive 75% less VF testing than Blacks in Optum. The findings for fundus photography were only statistically significant for Blacks.
OR with 95% CI

27. Comparison of Odds of No Glaucoma Testing Among Those in Medicaid vs
Comparison of Odds of No Glaucoma Testing Among Those in Medicaid vs. Commercial Insurance
Here we compared the odds of having no test. Overall, Medicaid patients had 244% increased odds of having no glaucoma testing despite being diagnosed with glaucoma. That number increased to 333% in Blacks, Latinos had 129% increased odds.
OR with 95% CI

28. Assessing Relationships Between Systemic Diseases or Medications and Ocular Diseases

32. Geographic Variation in Eye Care Utilization

35. Conclusion Array of Uses of Clinformatics Data Epidemiology
Risk Factors
Utilization of Health Care Services
Surgical Outcomes
Medication Adherence
Associations Between Systemic / Ocular Diseases
Geographic Variation in Care
Disparities in Care
Comparison to Persons with Other Types of Health Insurance

36. Questions?