Breast cancer surgery volume-cost associations: hierarchical linear regression and propensity score matching analysis in a nationwide Taiwan population.

BACKGROUND No outcome studies have longitudinally and systematically compared the effects of hospital and surgeon volume on breast cancer surgery costs in an Asian population. This study purposed to evaluate the use of hospital and surgeon volume for predicting breast cancer surgery costs. METHODS This cohort study retrospectively analyzed 97,215 breast cancer surgeries performed from 1996 to 2010. Relationships between volumes and costs were analyzed by propensity score matching and by hierarchical linear regression. RESULTS The mean breast cancer surgery costs for all breast cancer surgeries performed during the study period was $1485.3 dollars. The average breast cancer surgery costs for high-volume hospitals and surgeons were 12% and 26% lower, respectively, than those for low-volume hospitals and surgeons. Propensity score matching analysis showed that the average breast cancer surgery costs for breast cancer surgery procedures performed by high-volume hospitals ($1428.6 dollars) significantly differed from the average breast cancer surgery costs of those performed by low-/medium-volume hospitals ($1514.0 dollars) and that the average breast cancer surgery costs of procedures performed by high-volume surgeons ($1359.0 dollars) significantly differed from the average breast cancer surgery costs of those performed by low-/medium-volume surgeons ($1550.3 dollars) (P < 0.001). CONCLUSIONS The factors significantly associated with hospital resource utilization for this procedure included age, surgical type, Charlson co-morbidity index score, hospital type, hospital volume, and surgeon volume. The data indicate that analyzing and emulating the treatment strategies used by high-volume hospitals and by high-volume surgeons may reduce overall breast cancer surgery costs.

[1]  B. Given,et al.  Observation Interval for Evaluating the Costs of Surgical Interventions for Older Women With a New Diagnosis of Breast Cancer , 2001, Medical care.

[2]  P. Rosenbaum,et al.  Invited commentary: propensity scores. , 1999, American journal of epidemiology.

[3]  E. Lamont,et al.  Evaluation of trends in the cost of initial cancer treatment. , 2008, Journal of the National Cancer Institute.

[4]  A. Montero,et al.  The economic burden of metastatic breast cancer: a U.S. managed care perspective , 2012, Breast Cancer Research and Treatment.

[5]  C. Begg,et al.  The Effect of Clustering of Outcomes on the Association of Procedure Volume and Surgical Outcomes , 2003, Annals of Internal Medicine.

[6]  Jinn-Tsong Tsai,et al.  Predicting two-year quality of life after breast cancer surgery using artificial neural network and linear regression models , 2012, Breast Cancer Research and Treatment.

[7]  P. Tekkis,et al.  The national bowel cancer audit project: the impact of organisational structure on outcome in operative bowel cancer within the United Kingdom. , 2011, Surgical oncology.

[8]  Shu-Ping Lin,et al.  Study on doctor shopping behavior: insight from patients with upper respiratory tract infection in Taiwan. , 2010, Health policy.

[9]  A. J. Smith,et al.  A structured strategy to combine education for advanced MIS training in surgical oncology training programs. , 2011, Surgical oncology.

[10]  E. Livingston,et al.  Procedure volume as a predictor of surgical outcomes. , 2010, JAMA.

[11]  S. Clare,et al.  The Effect of Dedicated Breast Surgeons on the Short-Term Outcomes in Breast Cancer , 2008, Annals of surgery.

[12]  T. Chua,et al.  Exploring the role of resection of extrahepatic metastases from hepatocellular carcinoma. , 2012, Surgical oncology.

[13]  Donald Rubin,et al.  Estimating Causal Effects from Large Data Sets Using Propensity Scores , 1997, Annals of Internal Medicine.

[14]  F. Brunicardi,et al.  Cost analysis of breast conservation surgery compared with modified radical mastectomy with and without reconstruction. , 2000, American journal of surgery.

[15]  R. Deyo,et al.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. , 1992, Journal of clinical epidemiology.

[16]  Bradley P Carlin,et al.  Hierarchical Commensurate and Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials , 2011, Biometrics.

[17]  T. Lash,et al.  Breast cancer treatment of older women in integrated health care settings. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[18]  A. Enthoven,et al.  Should operations be regionalized? The empirical relation between surgical volume and mortality. , 1980, The New England journal of medicine.

[19]  Ó. Zurriaga,et al.  Bayesian Factor Analysis to Calculate a Deprivation Index and Its Uncertainty , 2011, Epidemiology.

[20]  Shou-Hsia Cheng,et al.  Physician performance information and consumer choice: a survey of subjects with the freedom to choose between doctors , 2004, Quality and Safety in Health Care.

[21]  R. Brook,et al.  Disparities in the utilization of high-volume hospitals for complex surgery. , 2006, JAMA.

[22]  M. Lai,et al.  Application of propensity score model to examine the prognostic significance of lymph node number as a care quality indicator. , 2012, Surgical oncology.

[23]  S. Varadarajulu,et al.  Endoscopic stenting versus surgical colostomy for the management of malignant colonic obstruction: comparison of hospital costs and clinical outcomes , 2011, Surgical Endoscopy.

[24]  H. Luft,et al.  The volume-outcome relationship: practice-makes-perfect or selective-referral patterns? , 1987, Health services research.