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

Alejandro Arrieta Slide 2/16 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?

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

Alejandro Arrieta Slide 4/16 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

Alejandro Arrieta Slide 5/16 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

Alejandro Arrieta Slide 6/16 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

Alejandro Arrieta Slide 7/16 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

Alejandro Arrieta Slide 8/16 Structural Misclassification Model However, with non-clinical factors Physician’s incentives: Physician chooses the inappropriate treatment when The probability of observing the treatment

Alejandro Arrieta Slide 9/16 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

Alejandro Arrieta Slide 10/16 Application: C-section in New Jersey C-sections in New Jersey grew from 22.5% to 27.5% between 1999 and 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)

Alejandro Arrieta Slide 11/16 Application: C-section in New Jersey 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)

Alejandro Arrieta Slide 12/16 C-section in New Jersey Results 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

Alejandro Arrieta Slide 13/16 C-section in New Jersey Results OBSERVED C-SECTIONS AND C-SECTIONS WITHOUT NON-CLINICAL INFLUENCE

Alejandro Arrieta Slide 14/16 C-section in New Jersey Results 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

Alejandro Arrieta Slide 15/16 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

Alejandro Arrieta Slide 16/16 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.

Alejandro Arrieta Thank you

Alejandro Arrieta Clinical Variables MARGINAL EFFECTS Structural Misclassification Model with dept errors

Alejandro Arrieta 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 % * Year % * Year % * Non-Clinical Variables MARGINAL EFFECTS Structural Misclassification Model with dept errors

Alejandro Arrieta Estimates RESULTS Structural Misclassification Model with dept errors