OBJECTIVE: To examine the effect of hospital volume on in-hospital surgical outcomes for knee replacement using six years of Medicare claims data. DATA SOURCES/STUDY SETTING: The data include inpatient claims for a 100 percent sample of Medicare patients who underwent primary knee replacement during 1985-1990. We supplemented these data with information from HCFA's denominator files, the Area Resource File, and the American Hospital Association survey files. STUDY DESIGN: We estimated the probability that a patient has an in-hospital complication in the initial hospitalization for the first primary knee replacement, using a Logit model, for three definitions of complication. The models controlled for hospital volume, other hospital characteristics, patient demographics, and patient health status. We tested for the endogeneity of hospital volume. DATA COLLECTION/EXTRACTION METHODS: A panel of two orthopaedic surgeons and two internists reviewed diagnosis codes to determine whether a complication was likely, possible, or due to anemia. After removing the few observations with bad or missing data, the final population has 295,473 observations. PRINCIPAL FINDINGS: The probability of a likely in-hospital complication declines rapidly from 53 through 107 operations per year, then levels off. Statistical tests imply that hospital volume is exogenous in this patient-level data. Complication rates increased steadily through the study period. Although obesity appeared to lower the probability of a complication, a counterintuitive result, further investigation revealed this to be an artifact of the claims data limit of listing no more than five diagnoses. Controlling for this restriction reversed the effect of obesity. CONCLUSIONS: Rather than uncontrolled expansion of knee surgery to small hospitals, decentralization to regional centers where at least about 50, and preferably about 100, operations per year are assured appears to be the optimal policy to reduce in-hospital complications.
OBJECTIVE: To examine the effect of hospital volume on in-hospital surgical outcomes for knee replacement using six years of Medicare claims data. DATA SOURCES/STUDY SETTING: The data include inpatient claims for a 100 percent sample of Medicare patients who underwent primary knee replacement during 1985-1990. We supplemented these data with information from HCFA's denominator files, the Area Resource File, and the American Hospital Association survey files. STUDY DESIGN: We estimated the probability that a patient has an in-hospital complication in the initial hospitalization for the first primary knee replacement, using a Logit model, for three definitions of complication. The models controlled for hospital volume, other hospital characteristics, patient demographics, and patient health status. We tested for the endogeneity of hospital volume. DATA COLLECTION/EXTRACTION METHODS: A panel of two orthopaedic surgeons and two internists reviewed diagnosis codes to determine whether a complication was likely, possible, or due to anemia. After removing the few observations with bad or missing data, the final population has 295,473 observations. PRINCIPAL FINDINGS: The probability of a likely in-hospital complication declines rapidly from 53 through 107 operations per year, then levels off. Statistical tests imply that hospital volume is exogenous in this patient-level data. Complication rates increased steadily through the study period. Although obesity appeared to lower the probability of a complication, a counterintuitive result, further investigation revealed this to be an artifact of the claims data limit of listing no more than five diagnoses. Controlling for this restriction reversed the effect of obesity. CONCLUSIONS: Rather than uncontrolled expansion of knee surgery to small hospitals, decentralization to regional centers where at least about 50, and preferably about 100, operations per year are assured appears to be the optimal policy to reduce in-hospital complications.
Authors: B P Katz; D A Freund; D A Heck; R S Dittus; J E Paul; J Wright; P Coyte; E Holleman; G Hawker Journal: Health Serv Res Date: 1996-06 Impact factor: 3.402
Authors: J Michael Paterson; J Ivan Williams; Hans J Kreder; Nizar N Mahomed; Nadia Gunraj; Xuesong Wang; Andreas Laupacis Journal: Can J Surg Date: 2010-06 Impact factor: 2.089