Alejandro Arrieta Department of Economics Rutgers University

1. A Structural Misclassification Model to Estimate the Impact of Non-Clinical Factors on Healthcare Utilization Alejandro Arrieta Department of Economics Rutgers University June 7th, 2008

2. Health Care Utilization • Over-utilization: Back surgery, heartburn surgery, cesarean section • Under-utilization: Cardiovascular surgery for minorities Research Questions • What is appropriate level of treatment? • How health outcomes are affected by non-clinical factors? • What is the degree of over/under treatment? • What drives over/under treatment?

3. Health Care Utilization: Application OVERTREATMENT? • C-sections in New Jersey grew from 22.5% to 27.5% between 1999 and 2002. • WHO and Healthy People recommend a rate of 15%. Slide 3/16

4. Physician Agency • Physician is the agent with informational advantage • Monetary or non-monetary incentives to deviate from appropriate treatment • Health outcomes • Clinical factors • Non-clinical factors

5. Physician Agency • Physician observes health status h: healthy (h<0) or sickly (h≥0) • A is the appropriate treatment for sickly patient • B is the appropriate treatment for healthy patient • Physician chooses a treatment conditional on patient health status

6. Physician Agency • Physician incentives (i) depend on perceived cost-benefits for each treatment • Inappropriate treatment arises when physician incentives are big (i≥0) • Physician chooses the treatment associated to the highest utility (U) • Patient observed medical information

7. Structural Misclassification Model • Health status: • Patient requires treatment A if h≥0 • Econometrician cannot observe the appropriate treatment . She only observes the physician treatment choice y. • Without non-clinical factors , and binary models (probit/logit) will return efficient estimators

8. Structural Misclassification Model • However, with non-clinical factors • Physician’s incentives: • Physician chooses the inappropriate treatment when • The probability of observing the treatment

9. Structural Misclassification Model Cesarean section deliveries • For the c-section case: • Estimation using Maximum Likelihood • Bivariate probit (Amemiya, 1985) with Partial observability (Poirier, 1980) • Conventional approach: • Monte Carlo study: Conventional approach reports inconsistent estimates

10. Application:C-section in New Jersey 1999-2002 • C-sections in New Jersey grew from 22.5% to 27.5% between 1999 and 2002. • WHO and Healthy People recommend a c-section rate of 15%. • What drives the rapid growth in c-section rates? DATA • Dependent variable: Mode of Delivery c-section (y=1) or vaginal delivery (y=0) • Patient discharge hospital data (NJ Dept of Health) • US Census (zip code matching)

11. Application:C-section in New Jersey 99-02 • Clinical variables: Most relevant according to medical literature (14 variables, ICD codes). • Non-clinical variables: • Direct physician incentives drivers (insurance condition, hospital size, physician specialty) • Signaling of patient-obtained medical information and preferences (ethnicity/race, zip code income, social support, full employed woman)

12. C-section in New Jersey 99-02Results DEGREE OF OVER-TREATMENT • 3.2% of non at-risk women had a c-section due to non-clinical Each year, around 2,500 women have c-sections for non-medical reasons Each year, $17.5 million paid in excess BUT THIS PERCENTAGE IS GROWING

13. C-section in New Jersey 99-02Results OBSERVED C-SECTIONS AND C-SECTIONS WITHOUT NON-CLINICAL INFLUENCE

14. C-section in New Jersey 99-02Results WHAT DRIVES PHYSICIAN INCENTIVES? • Direct Physician Incentives drivers • Insurance matters: women without insurance less likely to have a c-section followed by Medicaid (prospective payment) and HMO (capitated fees). • Hospital size matters: probability of c-section is higher if delivery is in a big hospital. • Specialization: more specialized doctors (Ob/Gyn) more likely to do a c-section. • Signaling of patient’s information and preferences • Physician’s perception of informed patients • Income: Higher income implies a lower probability of c-section. • Ethnicity: Latin and Black women have higher probability of c-sections, and white women lower probability. • Social support: Married women or with partners have a lower probability of c-sections. • Full-time employed women have a higher probability of c-section

15. Conclusions Contribution: • A new methodology to efficiently measure over- or/and under- healthcare utilization • Methodology allows us to neatly separate out the impact of non-clinical factors on risk-adjusted utilization rates • Methodology allows us to estimate the degree of over-treatment or under-treatment

16. Extensions • Is racial bias in cardiovascular surgery originated by under-use for African Americans or over-use for White patients? • Deeper analysis of physician incentives in c-section rates. Do unnecessary c-sections increase newborn mortality and length of stay? Comparing risk-adjusted c-section rates.

17. Thank you

19. Woman is married * -2.20% Zip code mean household income (thousands) * -0.10% Yearly average of births in Hospital (thousands) * 0.50% Obs&Gyn Physician * 3.30% Woman is full time employed * 8.60% Patient payment (uninsured) * -8.50% Medicaid payment * -3.50% HMO payment * -1.40% White (non-Hispanic) * -2.40% Black (non-Hispanic) * 2.70% Hispanic * 2.70% Year 2000 * 3.00% Year 2001 * 4.70% Year 2002 * 8.30%