1. 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)

2. 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

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

4. 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).

5. 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

6. 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

7. Analytical Approach The model for Practice Makes Perfect Quality it = β 0 + β 1 log( Volume it )+ β 2 Hospital it + β 3 Market it + β 4 IVQ it + β 5 State i + β 6 Time it + θ i + ε it The model for Selective Referral log(Volume) it = γ 0 + γ 1 Quality it + γ 2 Hospital it + γ 3 Market it + γ 4 IVV it + γ 5 State i + γ 6 Time it + Ψ i + μ it

8. 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

9. 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., θ i ≠0 and Ψ i ≠0 ). Fixed effects found to be preferred estimation method to random effects

10. 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)**

11. 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

12. 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.

13. Questions and Suggestions