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, 2017 No Financial Disclosures

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

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

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

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

Epidemiology & Risk Factors for Ocular Diseases

Studying Utilization of Health Care Services

Assessing Disparities and Inequities in Eye Care

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

Visits to Ophthalmologists

Visits to Optometrists

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

Comparing Eye Care For Persons with Different Types of Health Insurance

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.

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)

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

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

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

Assessing Relationships Between Systemic Diseases or Medications and Ocular Diseases

Geographic Variation in Eye Care Utilization

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

Questions?