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1 UC Berkeley UC Berkeley Previously Publishe Works Title Variability in Costs Associate with Total Hip an Knee Replacement Implants Permalink Journal The Journal of Bone an Joint urgery (American), 94(18) IN Authors Robinson, James C. Pozen, Alexis Tseng, amuel et al. Publication Date DOI /JBJ.K Peer reviewe echolarship.org Powere by the California Digital Library University of California

2 1693 COPYRIGHT Ó 2012 BY THE JOURNAL OF BONE AND JOINT URGERY, INCORPORATED Variability in Costs Associate with Total Hip an Knee Replacement Implants James C. Robinson, PhD, MPH, Alexis Pozen, BA, amuel Tseng, PhD, an Kevin J. Bozic, MD, MBA Investigation performe at the University of California, Berkeley, Berkeley, an the University of California, an Francisco, an Francisco, California Backgroun: Implant costs associate with total hip replacement an total knee replacement proceures account for a large share of total costs an reimbursements to hospitals. Feeral policymakers are promoting episoe-of-care payment an other value-base elivery an payment reforms in part to encourage physicians an hospitals to cooperate in managing costs for these an other proceures. The present stuy quantifies the patient, hospital, an market characteristics associate with variation in implant an total proceure costs for hip an knee arthroplasty. Methos: Clinical, emographic, an economic ata were collecte on 10,155 unilateral primary total knee replacement proceures an 5013 unilateral primary total hip replacement proceures from sixty-one hospitals in Variation in implant costs per proceure was measure within an across hospitals. Multivariate statistical analyses were use to measure the association between patient an hospital characteristics an implant costs an total proceure costs. Results: The average implant cost per case range from $1797 to $12,093 for total knee replacement proceures an from $2392 to $12,651 for total hip replacement proceures. For total knee replacement, 2.5% of total variation in evice costs was attributable to patient characteristics an 61.0% was attributable to hospital characteristics; the remaining 36.5% of variance was attributable to within-hospital variation not ue to patient or hospital characteristics. For total hip replacement, 4.4% of variance was attribute to patient characteristics, 36.1% was attribute to hospital characteristics, an 59.5% was attribute to within-hospital variation not ue to patient or hospital characteristics. Conclusions: There are substantial variations in total hip replacement an total knee replacement implant costs within an across hospitals after controlling for patient iagnoses an comorbiities. This variation is responsible for the majority of variation in the overall cost of total hip an knee replacement surgery. Level of Evience: Economic an ecision analysis, Level III. ee Instructions for Authors for a complete escription of levels of evience. The Unite tates health-care system is struggling to moerate growth in spening while maintaining qualityimproving innovation in iagnostic an therapeutic interventions. Attention is being focuse on proceures that are experiencing rapi growth in volume an cost per case, incluing total joint replacement of the hip an knee, which alreay constitute the largest hospital expeniture category for Meicare. Proceure volumes continue to grow as the population ages an the inications are expane to inclue younger, more active patients 1. Implant costs make up the largest Disclosure: One or more of the authors receive payments or services, either irectly or inirectly (i.e., via his or her institution), from a thir party in support of an aspect of this work. In aition, one or more of the authors, or his or her institution, has ha a financial relationship, in the thirty-six months prior to submission of this work, with an entity in the biomeical arena that coul be perceive to influence or have the potential to influence what is written in this work. No author has ha any other relationships, or has engage in any other activities, that coul be perceive to influence or have the potential to influence what is written in this work. The complete Disclosures of Potential Conflicts of Interest submitte by authors are always provie with the online version of the article. This article was chosen to appear electronically on August 8, 2012, in avance of publication in a regularly scheule issue. AcommentarybyNitinB.Jain,MD, MPH, is linke to the online version of this article at jbjs.org. J Bone Joint urg Am. 2012;94:

3 1694 T HE J OURNAL OF B ONE &JOINT URGERY JBJ. ORG VARIABILITY IN C OT A OCIATED WITH TOTAL H IP AND K NEE R EPLACEMENT I MPLANT expense associate with hip an knee replacement proceures, an the average selling prices of hip an knee implants have increase >100% over the past ecae 2. In response to these trens, both public policymakers an private purchasers are proposing or experimenting with evice registries 3, physicianhospital gainsharing 4, bunle episoe-of-care payment methos 5, physician-patient share-ecision-making programs 6, an other initiatives to improve quality an to reuce costs associate with total joint replacement proceures. While many surgical proceures have migrate to the outpatient setting, total joint replacement proceures continue to be performe largely on an inpatient basis an constitute a very important component of the economic viability of many hospitals. The financial attractiveness of these proceures to hospitals epens in large part on the type an cost of the total joint replacement implants use. Devices are selecte by the attening surgeon but are pai for by the hospital, which has limite ability to influence the choice of evice or the rates at which it is reimburse by Meicare an private insurers for evices. The purpose of the present stuy was to measure the variance in total hip an knee replacement implant costs, both within an across hospitals, an to quantify the association between implant costs an hospital characteristics after ajusting for patient iagnoses, comorbiities, an hospital characteristics. Materials an Methos Data on Patients, Devices, Hospitals, an Hospital Markets We obtaine ata on patients who ha been amitte to sixty-one hospitals in 2008 for total knee replacement or total hip replacement. These sixty-one hospitals either participate in the value-base purchasing initiative of the Integrate Healthcare Association, a coalition of large hospitals, meical groups, an health plans in California, or worke on value purchasing with Aspen Healthcare Metrics, a hospital consulting firm. We limite the analysis to patients who unerwent unilateral primary total hip or knee replacement surgery. We inclue patients who were covere by Meicare or commercial insurance but exclue the small number of patients who ha no insurance or who were covere by Meicai. Aspen Healthcare Metrics provie an analytic file with etaile e-ientifie information on each patient an the hospital in which each proceure was performe. Device costs were measure in terms of the aggregate price pai by the hospital for the entire joint implant construct. For knee replacement, the construct typically inclue a femoral component, a tibial component, a tibial insert, an, in some cases, a polyethylene patellar button. For hip replacement, the construct typically inclue a femoral stem, a femoral hea, an acetabular component (incluing screws an hole covers, when use), an, in some cases, an acetabular liner. The variance in evice costs across patients reflecte both the prices charge by each venor to each hospital for each component an type of evice an the surgeon s choice of evice. These cost ata were erive from the hospitals financial accounting systems, operating room logs, an patient recors an inicate the amount actually pai by the hospital to the evice manufacturer or istributor. We exclue patients whose recore evice costs were below the 1st percentile or above the 99th percentile in the istribution of evice costs across the entire stuy population in orer to reuce the sensitivity of the analysis to ata-entry errors or outlier cases. As a check on this ata trimming, we also conucte all analyses on the full patient population. The ata file inclue information on patient age, principal iagnoses, number of comorbiities, ischarge estination, an number of in-hospital complications. Coe iagnoses inclue fracture, osteoarthritis, rheumatoi arthritis, an osteonecrosis. We obtaine information on the number of comorbiities, which were efine by Aspen Healthcare Metrics as preexisting conitions that, because of their presence in a patient unergoing the target surgical proceure, resulte in an increase in the length of stay by at least one ay. urgical complications were efine as incluing in-hospital events serious enough to result in at least one extra ay of hospital stay. We also collecte ata on the characteristics of the hospital where the proceure was performe, incluing the number of total joint replacement proceures performe uring the year an the percentage of cases attributable to each evice manufacturer within each of the sixty-one hospitals. To measure the extent to which the hospital consoliate its evice purchases from a small number of venors, we calculate the percentage of hip an knee replacement evices purchase from the two venors with the largest market shares in each hospital. As a comparison, we also analyze the percentage of evices purchase by each hospital from its single largest venor an from its four largest venors. Aitional ata on the hospitals where the proceures were performe were obtaine from the American Hospital Association s 2008 Annual urvey of Hospitals 7, incluing number of staffe bes an the teaching status of the institution. The Herfinahl-Hirschman Inex 8 was use as a measure of market competition, with low values (minimum, 0) inicating a competitive market an high values (maximum, 100) inicating a monopoly. The sixty-one hospitals in the present stuy were istribute across eight states. To control for the effect of market size, we also measure the population of the metropolitan regions serve by each hospital. tatistical Methos We calculate the istribution of implant costs across patients, incluing the minimum an maximum costs as well as the 1st, 25th, 50th (meian), 75th, an 99th percentiles. We also calculate the istribution of total proceure costs an the percentage of total proceure costs accounte for by the cost of the implante evice. We calculate the average evice cost across the sixty-one hospitals in our sample, plus the percentage of each hospital s evice purchases uring the year that came from the two evice venors from which the hospital purchase the largest share of its evices. Device costs vary as a result of patient-specific an non-patient-specific factors that iffer within an across hospitals. Patient-specific factors inclue ifferences in iagnosis an severity of illness. Across-hospital factors inclue the hospital s evice-purchasing strategy an other hospital characteristics, such as number of bes, annual proceure volume, an teaching status. Within-hospital, non-patient-specific factors inclue physician-specific preferences that influence evice choice as well as ifferences in the prices charge by ifferent manufacturers to the same hospital. One purpose of the present stuy was to allocate the total measure variance in evice costs across these three sets of factors. It shoul be emphasize that our measure of cost is the amount actually pai by the hospital to the evice manufacturer or istributor. It is not the list price (the hospital typically negotiates an actual price lower than the list price) or the manufacturer s cost (e.g., the resources expene to evelop, manufacture, an istribute the evice). We isaggregate the total variation in evice costs across patients into three components. The first component was the variation associate with patient characteristics such as age, iagnoses, an comorbiities. The secon component was the variation associate with the hospital at which the patient was manage ue to the ability of some hospitals to negotiate lower prices with evice manufacturers. Both of these components of variation were calculate irectly with use of multivariate regression techniques. The thir component was the variation associate with ifferent choices of evice by the surgeons working in the same hospital, base on their clinical preferences. This thir component of total variation was estimate by subtracting the first two components (those associate with patient characteristics an hospital characteristics) from total variation. We also conucte multivariate statistical analyses of the factors associate with evice costs for iniviual patients. Device costs for each patient were regresse on a set of hospital characteristics, incluing meical evice venor concentration, annual total knee replacement an total hip replacement proceure volume, staffe bes, an teaching status, plus patient characteristics (age, iagnoses, comorbiities, complications, ischarge estination, Meicare versus commercial insurance coverage), hospital market structure, an population size in the local market 9.

4 1695 T HE J OURNAL OF B ONE &JOINT URGERY JBJ. ORG VARIABILITY IN C OT A OCIATED WITH TOTAL H IP AND K NEE R EPLACEMENT I MPLANT TABLE I Costs Across Patients Unergoing Knee an Hip Replacement urgery Knee Replacement Hip Replacement Device Cost (U.. ollars) Total urgical Cost (U.. ollars) Ratio of Device Cost: urgical Cost Device Cost (U.. ollars) Total urgical Cost (U.. ollars) Ratio of Device Cost: urgical Cost Minimum % % 1st percentile % % 25th percentile % , % Meian , % , % 75th percentile , % , % 99th percentile 11,143 21, % 11,643 21, % Maximum 12,093 23, % 12,651 23, % ource of Funing The present stuy was fune by the California HealthCare Founation an Blue hiel of California Founation. The funs were use for salaries an supplies. The California HealthCare Founation an Blue hiel of California Founation ha no role in the esign an conuct of the stuy; in the collection, management, analysis, an interpretation of the ata; or in the preparation, review, or approval of the manuscript. Results The hospitals participating in the present stuy were major proviers of hip an knee replacement proceures in 2008, performing a total of 10,155 total knee replacements an 5013 total hip replacements. Meicare covere 67% of the patients unergoing total knee replacement an 60% of the patients unergoing total hip replacement, with commercial insurance covering the remainer. Table I presents the istribution of implant costs, total proceure costs, an implant costs as a percentage of total proceure costs per case for total knee an hip replacement. Implant costs varie by a factor of almost seven, from $1797 to $12,093 (in 2008 U.. ollars), for total knee replacement an by a factor of more than five, from $2392 to $12,651, for total hip replacement. Total proceure costs varie by a factor of Fig. 1 Histogram showing the percentage of implants purchase from the largest an secon-largest evice venors across sixty-one hospitals in 2008.

5 1696 T HE J OURNAL OF B ONE &JOINT URGERY JBJ. ORG VARIABILITY IN C OT A OCIATED WITH TOTAL H IP AND K NEE R EPLACEMENT I MPLANT TABLE II Percentage of Meical Device Costs Attributable to Hospital Characteristics, Patient Conition, everity, an Physician Preferences Knee Replacement (N = 10,155) Hip Replacement (N = 5013) Patient characteristics 2.5% 4.4% Hospital characteristics 61.0% 36.1% Resiual factors, incluing physician preferences 36.5% 59.5% more than three for total knee replacement an for total hip replacement. The cost of the evice represente a large share of the total cost of each proceure, ranging from 13% to 87% for total knee replacement an from 15% to 87% for total hip replacement. Table II presents the allocation of total variance in evice costs across the three sets of factors. Patient characteristics accounte for only a very small percentage of total variance: 2.5% for knee replacement an 4.4% for hip replacement. After ajusting for patient characteristics, 61.0% of the variance for total knee replacement implants an 36.1% of the variance for total hip replacement implants was associate with hospital characteristics. The resiual variance not accounte for by patient an hospital characteristics accounte for 36.5% of total variance for total knee replacement implants an 59.5% of total variance for total hip replacement implants. As shown in Figure 1, the percentage of total knee replacement an total hip replacement implants obtaine from the two venors with the highest market share within a particular hospital range across hospitals from a low of 52% to a high of 100%, with a mean of 90%. By way of comparison, national sales for knee an hip replacement implants are istribute among five major venors, with the top three accounting for 70% of the market 10. Table III presents multivariate regression results for the correlates of implant costs across the two stuy proceures. Age, payer (Meicare), complications, ischarge isposition (ischarge to acute or post-acute care facility), an a iagnosis of fracture (for total knee replacement only) were all associate with higher evice costs after controlling for all other patient, hospital, an market factors. The concentration of evice costs in the two venors with the highest market shares was positively associate with evice costs for both proceures, but the association was moest in scale an was not significant. Ten aitional points in the percentage of evices purchase from the two largest venors were associate with evice costs that were higher by $205 (p = 0.09). Teaching hospitals ha lower ajuste costs for both total hip replacement (2$876) an total knee replacement (2$119), but these ifferences were not significant. After ajusting for patient an hospital characteristics, proceure volume was not associate with evice costs for total knee replacement or total hip replacement. Our statistical results were not sensitive to the assumptions mae concerning the measurement of evice costs an proceures. imilar results were obtaine when evice costs were TABLE III Multivariate Regression Coefficients for Covariates of Cost of Implantable Device per Patient (U.. Dollars)* Knee Replacement (N = 10,532) Hip Replacement (N = 5214) Annual number of proceures (21.52 to 1.19) 0.54 (20.39 to 1.46) Percent of evices from two largest venors ( to 50.69) (23.63 to 45.66) Teaching hospital ( to ) ( to ) Number of staffe bes 0.21 (21.55 to 1.98) 0.07 (21.35 to 1.50) Market concentration (28.05 to 31.58) 7.63 ( to 35.05) Market population (1000s) (20.12 to 0.04) (20.11 to 0.03) Age ( to 21.88) ( to ) Meicare patient (3.09 to ) (64.80 to ) Comorbiities ( to ) ( to 90.53) Complications (24.50 to ) ( to ) Discharge to home ( to ) ( to ) Osteoarthritis ( to ) ( to ) Rheumatoi arthritis ( to ) ( to ) Osteonecrosis ( to ) ( to ) Fracture ( to ) 5.04 ( to ) R *The 95% confience intervals are given in parentheses, where appropriate. P < P < 0.01.

6 1697 T HE J OURNAL OF B ONE &JOINT URGERY JBJ. ORG VARIABILITY IN C OT A OCIATED WITH TOTAL H IP AND K NEE R EPLACEMENT I MPLANT measure in logarithmic rather than natural (ollar) units, when the parameters were estimate with use of the general linearize moel with a gamma istribution an log link rather than orinary least squares, when evice cost ata were not trimme at the 1st an 99th percentiles, or when the concentration of purchases was measure in terms of the percentage of purchases from the venor with the largest total volume or the four venors with the largest volumes (as a test of the robustness of measuring the concentration of purchases in terms of the percentage obtaine from the two venors with the highest volumes). Discussion The costs of joint implant constructs use for total knee replacement or total hip replacement vary wiely an are major contributors to the variation in the total cost of care for patients unergoing total joint replacement. Policymakers an health services researchers have pointe out the geographic variations in the rate at which proceures are performe 11,12. The results of the present stuy suggest that commensurate attention shoul be evote to variation in the costs of the proceures themselves an of their principal components, incluing implant costs. Only 3% to 4% of the variation in evice costs for these proceures was relate to patient age, iagnosis, an comorbiities. Another 36% to 61% was associate with the hospital in which the proceure was performe. However, the hospital characteristics that we were able to measure an that are frequently referre to in iscussions of implant pricing o not explain much of this hospital-associate variation. In particular, the annual volume of knee an hip replacements performe in the hospital, the concentration of implant purchases among a small number of venors, an hospital be size were only weakly associate with implant costs. Moreover, we foun that much of the variance in evice costs was relate to variance within, rather than across, hospitals. Even after ajusting for patient characteristics an taking full account of acrosshospital variance in costs, 37% to 60% of total variance in implant costs remaine unexplaine. This resiual variance (the fraction not explaine by patient or hospital characteristics) was ue to within-hospital factors that we were unable to measure. As the choice of total joint replacement implant is mae by the surgeon, we believe that a major eterminant of this resiual variance in implant costs is within-hospital ifferences in physician preferences for ifferent implants an alignment between the physicians an the implant manufacturer or the hospital. However, we have no irect information on the training or implant preferences of the iniviual surgeons practicing in the hospitals involve in the present stuy. Furthermore, we have no irect information on the economic association of the physicians to the implant manufacturers or the hospitals where they practice. Our results nee to be interprete within the limitations of the stuy. The ata were erive from hospital cost accounting systems an patient recors, which are imperfectly stanarize across facilities. Differences across hospitals in accounting methos, however, cannot explain the within-hospital variations in evice costs. Furthermore, there is no reason to believe that accounting methos are correlate systematically with the number of knee an hip replacement proceures performe per year or with the percentage of evices purchase from the largest two venors. Therefore, variation in cost accounting methos woul not be expecte to bias our statistical analyses. We were unable to evaluate the clinical outcomes of the hip an knee replacement proceures stuie. While our ata i capture in-hospital complications, they were not structure to measure either post-ischarge or long-term outcomes such as enhance patient functional ability, reuce pain, reamission, or evice failure. Furthermore, our ata only measure the number, not the type, of comorbiities an complications severe enough to cause a one-ay extension in length of stay. An American Joint Replacement Registry is currently being evelope to track the outcomes of hip an knee replacement proceures in the Unite tates 4,13. When combine with cost ata of the sort presente here, utilization an performance ata from joint replacement registries will be able to istinguish whether patients who receive higher-cost evices have commensurately better outcomes than patients who receive lower-cost evices. The principal conclusion that we erive from our analysis was that there is wie variance in evice costs that cannot be explaine by patient characteristics. Approximately half of this unexplaine variance is ue to variance across hospitals an approximately half is ue to variance within hospitals, e.g., across the ifferent surgeons working in the same facility. The unexplaine variance may be ue to factors such as iiosyncrasies in physician choice of implant or alignment between the physician an the hospital (e.g., gainsharing) or the physician an the evice manufacturer (e.g., consulting arrangements). The large unexplaine variance suggests that efforts can be mae to reuce implant costs if better ata on the comparative effectiveness of implants are evelope an better alignment is achieve between physicians an hospitals. n James C. Robinson, PhD, MPH Alexis Pozen, BA amuel Tseng, PhD University of California, Berkeley, Berkeley Center for Health Technology, 300 Lakesie Drive, uite 1980, Oaklan, CA Kevin J. Bozic, MD, MBA UCF Department of Orthopaeic urgery an Philip R. Lee Institute for Health Policy tuies, 500 Parnassus, MU 320W, an Francisco, CA aress: kevin.bozic@ucsf.eu

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