A Structural Misclassification Model to Estimate the Impact of Non-Clinical Factors on Healthcare

Utilization

Alejandro ArrietaDepartment of EconomicsRutgers University

June 7th, 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?

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Health Care Utilization: Application

Cesarean Section Rates: New Jersey

18%

20%22%

24%

26%28%

30%

1994 1995 1996 1997 1998 1999 2000 2001 2002

Observed c-section rates: 1994-2002

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%.

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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

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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

Nature Physician

h≥0

h<0

U(i<0|h≥0 )

U(i≥0|h≥0 )

U(i<0|h<0 )

U(i≥0|h<0 )

appropriate

inappropriate

appropriate

inappropriate

AB

BA

Nature Physician

h≥0

h<0

U(i<0|h≥0 )

U(i≥0|h≥0 )

U(i<0|h<0 )

U(i≥0|h<0 )

appropriate

inappropriate

appropriate

inappropriate

AB

BA

Alejandro Arrieta Slide 6/16

Nature Physician

h≥0

h<0

U(i<0|h≥0 )

U(i≥0|h≥0 )

U(i<0|h<0 )

U(i≥0|h<0 )

appropriate

inappropriate

appropriate

inappropriate

AB

BA

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

Nature Physician

h≥0

h<0

U(i<0|h≥0 )

U(i≥0|h≥0 )

U(i<0|h<0 )

U(i≥0|h<0 )

appropriate

inappropriate

appropriate

inappropriate

AB

BA

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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

hxh

0 if 1~ hxhy

)0Pr()1~Pr()1Pr( hyy)0Pr()0~Pr()0Pr( hyy

y~

yy ~

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Structural Misclassification Model

However, with non-clinical factors Physician’s incentives: Physician chooses the inappropriate treatment when

The probability of observing the treatment

izi

)0|0Pr()1~|0Pr()0|0Pr()0~|1Pr(

1

0

hiyyhiyy

)0Pr()1( )0Pr()0|0Pr()0Pr()0|0Pr()1Pr(

100

hhhihhiy

yy ~

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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

)0Pr()0|0Pr()0Pr()1Pr( hhihy

)0Pr()1Pr( ihy

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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)

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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)

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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 excessBUT THIS PERCENTAGE IS GROWING

dhhidhhii )0,0Pr()0,0Pr()0Pr(

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C-section in New Jersey 99-02Results

OBSERVED C-SECTIONS AND C-SECTIONS WITHOUT NON-CLINICAL INFLUENCE

Figure 2: Cesarean Section Rates: New J ersey

18%

20%22%

24%26%

28%30%

1994 1995 1996 1997 1998 1999 2000 2001 2002Observed c-section rates: 1994-2002Estimated c-section rates (health related only): 1999-2002

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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

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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

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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 EFFECTSStructural Misclassification

Model with dept errors

Age (years) 0.00% *Breech or transverse lie presentation 0.40% *Diabetes 0.20% *Hypertension 0.10% *Pre-eclampsia 0.00% *Oligohydramnios 0.00%Polyhydramnios 0.40% *Multiple gestation 0.30% *Previous cesarean delivery 0.50% *Abruptio placenta 0.10% **Full or partial placenta previa 0.50% *Elderly primigravida >=35 y.o. 0.40% *Long labor 0.20% *Admission by emergency -0.30% *

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 2000 3.00% *Year 2001 4.70% *Year 2002 8.30% *

Non-Clinical Variables

MARGINAL EFFECTSStructural Misclassification

Model with dept errors

Alejandro Arrieta

EstimatesRESULTS

Structural Misclassification Model with dept errors

Correlation -0.422 *(0.018)

Inducement (Marginal probability) 0.032(0.006)

Log-Likelihood function -160307.28Pseudo-R2 0.522Number of Observations 403660

Estimation was done in GAUSS 5.0. Program code is available under request.Standard errors in parenthesis. * Significant at 1%. ** Significant at 5%. *** Significant at 10%

Dependent variable is mode of delivery. 1 if it was a cesarea section, 0 if it was a vaginal delivery.