Volume-Outcome Relationship: An Econometric Approach to CABG Surgery

Hsueh-Fen Chen (VCU)Gloria J. Bazzoli (VCU)

Askar Chukmaitov (FSU)

Funded by the Agency for Healthcare Research and Quality (HS 13094-03)

Rationale for the Study Clinicians and policymakers continue to debate

the basis for volume-quality relationships: Practice makes perfect Selective referral

Outcomes of CABG surgery are of great interest:

one of the most common surgeries in the US volume thresholds have been recommended by

Leapfrog Group regionalization vs non-regionalization

Research Question

Do volume-outcome relationships for CABG surgery in hospitals reflect selective referral, practice makes perfect, or both?

Findings from Prior Research Several studies have found high CABG

volume does not lead to better outcomes at the hospital level (Luft, 1980; Luft, et al., 1987; Shroyer, 1996)

At patient level, mixed results exist about CABG volume-outcome relationship (Hannan, et al., 1989; 1991; Shroyer, et al.,

1996; Sollano et al., 1999; Birkmeyer, et al., 2002; Wu, et al., 2004; Peterson et al., 2004).

Limitations of Prior Research: Contribution of Current Study

Is volume exogenous or endogenous?

Use of cross-sectional study design versus longitudinal study design

Generalizability of findings

Study Methods and Data Sources

Research Approach A longitudinal design: 1995 - 2000

Data Sources HCUP-SID (AZ, CA, CO, FL, IA, MD, MA, NJ, NY, WA,

WI) AHA ARF InterStudy

Sample 1,760 nonfederal, general short-term hospitals with

at least 6 CABG surgeries a year 1,200 of them had complete data

Analytical Approach The model for Practice Makes Perfect

Qualityit = β0+ β1 log( Volumeit )+ β2 Hospitalit + β3 Marketit + β4 IVQit+ β5 Statei + β6 Timeit + θi + εit

The model for Selective Referral log(Volume)it = γ0 + γ1Qualityit + γ2

Hospitalit + γ3 Marketit + γ4 IVVit + γ5 Statei + γ6 Timeit + Ψi + μit

Measures

Primary Variables of Interest: Quality: risk-adjusted in-hospital CABG mortality

rate; calculated with AHRQ IQI software Volume: log of the sum of discharges with the

procedure ICD-9-CM codes: 3610-3619

Control Variables Hospital Characteristics: ownership, teaching status, log

(total surgical operations), system/ network affiliation, case-mixed adjusted length of stay

Market factors: log (per capita income) and HMO penetration at the MSA level

State and time dummy variables

Results of Specification Tests Instruments are valid.

Instruments of volume (IVV): log (size), HHI, and tertiary services.

Instruments of quality (IVQ): Staffing: RN and LPN per 1,000 inpatient days. Severity of illness: patient acuity and case mix

index. Hospital-specific component of error

exists (i.e., θi ≠0 and Ψi ≠0 ). Fixed effects found to be preferred

estimation method to random effects

Results

Practice makes perfect (DV: mortality)

Selective Referral (DV: log (volume))

OLS OLS with IVs FE FE with IVs

Log (volume)

-.006(.00009)***

.0003(.0035)

-.0003(.0021)

-.0002(.0205)

OLS OLS with IVs FE FE with IVs

Mortality -3.75(.077)***

2.23(3.34)

-.709(.485)

-4.28(2.14)**

Study Limitations

Administrative data used for constructing risk adjusted mortality rates

Strictly examine in-hospital mortality not mortality that occurs after discharge

Lack of data on physician volume May be that practice makes perfect

hypothesis is more relevant for physicians than for hospitals

Study Implications Longitudinal study design with

instruments is recommended in future research on volume-quality relationships

From hospital perspective: Regionalization of care based on volume

thresholds may need to be reconsidered Competition based on quality may be

preferred.

Questions and Suggestions