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Chances of late surgery in relation to length of wait lists Sobolev, Boris G; Levy, Adrian R; Kuramoto, Lisa; Hayden, Robert Sep 26, 2005

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ralssBioMed CentBMC Health Services ResearchOpen AcceResearch articleChances of late surgery in relation to length of wait listsBoris G Sobolev*1,2, Adrian R Levy1,3, Lisa Kuramoto2 and Robert Hayden4Address: 1Department of Health Care and Epidemiology, University of British Columbia, Vancouver, Canada, 2Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, Vancouver, Canada, 3Centre for Health Evaluation and Outcome Sciences, St. Paul's Hospital, Vancouver, Canada and 4Department of Surgery, Royal Columbian Hospital, New Westminster, CanadaEmail: Boris G Sobolev* - bsobolev@shaw.ca; Adrian R Levy - alevy@cheos.ubc.ca; Lisa Kuramoto - Lisa.Kuramoto@vch.ca; Robert Hayden - erh@telus.net* Corresponding author    AbstractBackground: The proportion of patients who undergo surgery within a clinically safe time is an importantperformance indicator in health systems that use wait lists to manage access to care. However, little is knownabout chances of on-time surgery according to variations in existing demand. We sought to determine whatproportion of patients have had late coronary bypass surgery after registration on wait lists of different size in anetwork of hospitals with uniform standards for timing of surgery.Methods: Using records from a population-based registry, we studied wait-list times prospectively collected ina cohort of patients registered on wait lists for coronary artery bypass grafting procedures. We compared thenumber of weeks from registration to surgery against target access times established for three urgency groups.The chances of undergoing surgery within target time have been evaluated in relation to wait-list size atregistration and the number of surgeries performed without registration on a wait list.Results: In 1991–2001, two in three patients were at risk of late surgery when registered on wait lists for isolatedcoronary bypass procedures in British Columbia, Canada. Although urgent patients had never seen a wait list withclearance time exceeding one week, the odds of on-time surgery were reduced by 25%, odds ratio [OR] = 0.75(95% confidence interval [CI] 0.65–0.87) for every additional operation performed without registration on a list.When the wait list at registration required a clearance time of over one month, semi-urgent patients had 51%lower odds of on-time surgery as compared to lists with clearance time less than one week, OR = 0.49 (95%CI0.41–0.60), after adjustment for age, sex, comorbidity, calendar period, hospital and week on the list. In the non-urgent group, the odds were 69% lower, OR = 0.31 (95%CI 0.20–0.47). Every time an operation in the samehospital was performed without registration on a wait list, the odds of on-time surgery for listed patients werereduced by 7%, OR = 0.93 (95%CI 0.91–0.95) in the semi-urgent group, and by 10%, OR = 0.90 (95%CI 0.87–0.94), in the non-urgent group.Conclusion: Chances of late surgery increase with the wait-list size for semi-urgent and non-urgent patientsneeding coronary bypass surgery. The weekly number of patients who move immediately from angiography tothe operation without registration on a wait list reduced chances of surgery within target time in all urgencygroups of listed patients. When advising patients who will be placed on the wait list about the expected time totreatment, hospital managers should take into account the current list size as well as the weekly number ofpatients who require CABG immediately after undergoing coronary angiography.Published: 26 September 2005BMC Health Services Research 2005, 5:63 doi:10.1186/1472-6963-5-63Received: 01 June 2005Accepted: 26 September 2005This article is available from: http://www.biomedcentral.com/1472-6963/5/63© 2005 Sobolev et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 7(page number not for citation purposes)BMC Health Services Research 2005, 5:63 http://www.biomedcentral.com/1472-6963/5/63BackgroundIn health systems that provide universal access to care,efforts to contain costs for stand-by hospital capacity usu-ally result in waiting lists for surgical procedures [1]. Fromclinical perspective, however, delay in necessary treatmentdue to surgical wait lists is a major concern [2,3]. Estab-lishing a clinically appropriate time that patients cansafely wait for the operation is generally perceived as amethod to prevent adverse outcomes of delay [4]. Forexample, priority wait lists [5] are commonly used forqueuing patients with coronary artery disease requiringbypass surgery based on the severity of condition [6,7].The proportion of patients who undergo surgery withinclinically acceptable time is an important performanceindicator in health systems that use wait lists to manageaccess to care [8,9]. Describing variations in waiting timesfor coronary artery bypass surgery (CABG), Katz et al sug-gested that the wait-list size may be an important factor indelaying surgery [10]. Indeed, if there are patients on thelist, then for a patient who just arrived to be admittedwithin a certain time all patients ahead must have beenserved. Sobolev et al performed an empirical analysis of apopulation-based registry and found that the length ofqueue at registration affected the time to elective surgery[11]. Surprisingly, little is known about how the list sizeat registration affects the chances of undergoing electivesurgical procedures within acceptable time. The commonconcern for evaluation purposes is, therefore, whether onecan accurately estimate the proportion of late surgerieswithout considering the length of a wait list.In theory, queuing procedures should ensure access tocare according to urgency of treatment if implementeduniformly across a health system [8]. However, thechances of admission for elective surgery within targettime can be easily altered if surgical services experience anuneven influx of more urgent case [12]. In the Canadianprovince of British Columbia, there are two pathways tosurgical revascularization: registration on a wait list, ordirect admission after coronary angiography, as describedin [11]. Patients presenting with symptoms of coronaryartery disease are referred to cardiologist to assess the needfor coronary revascularization. The cardiologist evaluatesthe coronary angiogram and decides on treatment. If cor-onary angioplasty is not indicated, then a cardiac surgeonis consulted to assess the patients' suitability for coronarybypass surgery. Following the consultation in which sur-gery is indicated, surgeons register on their wait listspatients who require and decide to undergo the opera-tion. Alternatively, patients may be admitted to a hospitalcardiac ward directly from the catheterization laboratorywhen urgent assessment is deemed necessary. If suitableThe objective of this study was to determine the propor-tion of patients that have had late surgery after registrationon surgical wait list of different sizes. To examine the asso-ciation between the length of wait lists and timely accessto elective CABG surgery, we used data on registrationsand waiting times for elective coronary bypass surgery col-lected at a provincial cardiac surgery registry in BC. Wealso examined the relation between the number of surger-ies performed without wait-list registration and thechances of surgery within recommended time amongwait-listed patients.MethodsData sourcesData were taken from a population-based registry set upto capture the time of registration for surgery, the time ofsurgery or removal from wait lists without surgery, for allpatients accepted for coronary bypass surgery in the fourtertiary hospitals delivering adult cardiac care to residentsof BC [15]. Offices of all cardiac surgeons weekly provideinformation to the registry on registrations for surgery,operations performed, waitlist reconciliation (removals),and discharge summaries. Coexisting medical conditionswere identified in the BC Linked Health Database Hospi-tal Separations File via a deterministic link with the regis-try records.PatientsThere were 9,366 records of registration for isolated CABGadded to the registry between January 1991 and Decem-ber 2000. We excluded 147 records of patients who wereemergency cases (44), were removed on the registrationdate (99), or who had missing operating room reports (4).The remaining 9,219 records had either the surgery dateor the date and reason of removal from the list withoutsurgery.As patients who moved from angiography to surgery onan expedited basis were not added to the wait lists, theywere not included in the analysis of wait-list times. Thesepatients contributed to demand figures only.Urgency groupsAll cardiac surgeons in BC use a common guideline forprioritizing patients and assigning a target time for surgerybased on angina symptoms, affected coronary anatomy,and left ventricular function impairment as describedelsewhere [15]. Each patient was classified as urgent if thesuggested time to surgery was three days, semi-urgent ifthe time was six weeks, and non-urgent if the time was 12weeks.Demand for surgeryPage 2 of 7(page number not for citation purposes)for surgery, such patients remain in hospital until theoperation.For each calendar week during the study period, thedemand for surgery was characterized by the existing listBMC Health Services Research 2005, 5:63 http://www.biomedcentral.com/1472-6963/5/63size and the number of direct admissions immediatelyafter angiography. For each patient, the list size was a cen-sus of patients with higher or equal priority present at reg-istration on the list in a hospital. Patients contributed onecount to the list size for each week they remained on thelist, except for the week of arrival. As operations are sched-uled a week in advance, patients who underwent surgeryare considered removed from the wait list in the week pre-vious to their admission dates. The number of directadmissions was a weekly count of surgeries performedwithout wait-list registration.ComorbidityWe used diagnoses reported in discharge abstracts withinone year prior to registration for coronary bypass surgery.Each patient was classified as 1) presenting with no co-existing conditions, 2) presenting with congestive heartfailure, diabetes, chronic obstructive pulmonary disease,cancer or rheumatoid arthritis, or 3) presenting with otherco-existing chronic conditions as defined in [16].Statistical analysisWaiting timeEach patient had a waiting time computed as the numberof calendar weeks between registration and surgery orremoval for other reasons. The date at which a surgeon'soffice submits the operating room booking request forsurgery served as the date of registration on the list. Forprocedures delayed beyond target access time, we studiedthe number of weeks to target time.Study variablesThe list size was categorized in relation to clearance time,that is, a hypothetical time within which the list will becleared at a maximum weekly service capacity if there areno new arrivals. We divided the list size in four categories:1) lists requiring less than a week of clearance time, 2) halfa month, 3) a month, and 4) over one month. In threehospitals with the service capacity of 15 operations aweek, the following numbers of patients on the list – 0 to14; 15 to 29; 30 to 59; and over 60 – correspond to clear-ance time of a week, half a month, a month, over onemonth. In a hospital with the service capacity of 25 oper-ations weeks, the same clearance times correspond to 0 to24, 25 to 49, 50 to 99 and over 100 patients on the list.The weekly number of direct admissions was treated as acontinuous variable.Regression modelsPrimary outcome was admission to surgery within targetaccess time. Primary comparisons were between wait-listsize categories. To evaluate the effect of the list size, weestimated the odds ratios associated with list-size catego-patients for surgery has provided a weekly opportunity foradmission to occur. Measured as the number of servicescheduling cycles, waiting time is inherently discrete andis best measured as the number of new scheduling cyclesfrom registration to admission or removal for other rea-sons. For this analysis, we performed a pooled analysis ofbinary regression models developed for each week on thelist, treating weeks as ordered strata [13]. All casesremoved from waiting lists without surgery or thatexceeded target access time were treated as censored obser-vations at one week after the target time. In multivariateanalysis we adjusted for age, sex, comorbidity, period, thenumber of direct admissions and hospital.We entered an indicator variable for each hospital in themodels in order to obtain regression estimates for thestudy variables adjusted for possible variations in accessmanagement. Hospital 1 was coded as referent.For the direct admissions, we interpret the odds ratioderived from the model as a change in the weekly odds ofon-time surgery associated with one additional surgeryperformed immediately after angiography.ResultsWaiting outcomesThe baseline characteristics of registered patients areshown in Table 1. The most prevalent groups at registra-tion were patients aged 60–69 (38%) and 70–79 (30%)years, men (82%), those registered in semi-urgent group(71%), those without major comorbidities (52%), andthose registered in 1995–1996 (22%). Among the fourhospitals, the majority of patients were registered at hos-pital 2 (34%). At registration most patients (43%) saw alist-size requiring one month of clearance time, whereasthe minority (14%) required half a month.Among those who was removed before or on a targetaccess time (TAT), 2959 (93.6%) received surgery, 37(1.2%) died, 64 (2.0%) continued with medical treat-ment, 31 (1.0%) declined surgery, 22 (0.7%) were trans-ferred to another surgeon or hospital, and 47 (1.5%) wereremoved for other reasons (data not shown).Among those who was removed after TAT, 5018 (82.2%)eventually underwent surgery, 55 (0.9%) died, 112(1.9%) continued with medical treatment, 157 (2.6%)declined surgery, 77 (1.3%) were transferred to anothersurgeon or hospital, 165 (2.7%) were removed for otherreasons, and 475 (7.8%) were still on the wait list at 52weeks (data not shown).Of the 652 urgent patients, 22 (3.4%) were removed fromPage 3 of 7(page number not for citation purposes)ries using discrete-time survival regression models foreach urgency group [14]. In this service, schedulingthe wait list without surgery, and in 106 (16.3%) patientsBMC Health Services Research 2005, 5:63 http://www.biomedcentral.com/1472-6963/5/63the urgency was downgraded to semi-urgent or non-urgent.Access to surgery in urgency groupsOverall, the proportion of patients who underwent sur-gery within the target access time was 32% (95% confi-dence interval [CI] 31–33%). The proportion variedsignificantly across urgency groups. Table 2 shows thatamong urgent, semi-urgent, and non-urgent patients,of CABG performed within target time were similar inthree hospitals ranging between 34 and 38%, with only19% in hospital 3, Table 2.Access to surgery by list sizeThe percentage of patients receiving on-time surgerydecreases with the list size from 40% (37–42%) in list-sizecategory 1 (clearance time less than one week) to 30%(28–32%) in list-size category 4 (clearance time over oneTable 1: Characteristics of 9,219 subjects registered for isolated coronary artery bypass surgery in British Columbia 1991–2000Characteristic N (%)Age group (yr)<50 731 (7.9)50–59 2006 (21.8)60–69 3526 (38.2)70–79 2764 (30.0)≥80 192 (2.1)SexWomen 1630 (17.7)Men 7589 (82.3)Urgency at registrationUrgent 652 (7.1)Semi-urgent 6553 (71.1)Non-urgent 2014 (21.8)Major comorbidity at registrationNone 4775 (51.8)CHF, diabetes, COPD, rheumatoid arthritis, cancer 2435 (26.4)Other conditions 2009 (21.8)Calendar period1991–1992 1725 (18.7)1993–1994 1889 (20.5)1995–1996 1997 (21.7)1997–1998 1888 (20.5)1999–2000 1720 (18.7)Hospital ID at booking1 1903 (20.6)2 3137 (34.0)3 2124 (23.0)4 2055 (22.3)Wait-list size category1 – <1 week 1502 (16.3)2 – half month 1276 (13.8)3 – 1 month 3954 (42.9)4 – >1 month 2487 (27.0)CHF – congestive heart failure, COPD – chronic obstructive pulmonary diseasePage 4 of 7(page number not for citation purposes)21% (18–24%), 34% (33–35%), and 29% (27–31%)underwent surgery on time, respectively. The proportionsmonth), Table 2. Among all patients, the crude odds ofon-time surgery was 58% lower in list-size category 2, ORBMC Health Services Research 2005, 5:63 http://www.biomedcentral.com/1472-6963/5/63= 0.42 (0.37–0.48), 66% lower in category 3, OR = 0.34(0.31–0.38), and 71% lower in category 4, OR = 0.29(0.25–0.32), compared to list-size category 1 (data notshown).For semi-urgent and non-urgent patients, Table 3 showsthe association between list size and the probability of on-time surgery as measured by unadjusted odds ratios. Inthe semi-urgent group, the crude odds of on-time surgerywere 51% lower in list-size category 2, OR = 0.49 (0.42–0.57), 56% lower in category 3, OR = 0.44 (0.39–0.50),and 43% lower in category 4, OR = 0.57 (0.49–0.65).In the non-urgent group, the crude odds of on-time sur-gery were 12% lower in list-size category 1, OR = 0.88(0.62–1.25), 61% lower in category 3, OR = 0.39 (0.28–0.55), and 70% lower in category 4, OR = 0.30 (0.21–0.41).All urgent patients had a list-size category 1. Therefore theeffect of the list size was examined using regressionanalysis.Regression analysisIn urgent patients wait-list size was studied as continuousvariable ranging between 0 (28.1%) and 10 or more(1.7%). The effect of additional patient on the wait lists atregistration was not significant, OR = 0.97 (0.86–1.10),after adjustment for age, sex, comorbidity, calendarperiod, hospital, and week on the list.time surgery as measured by the adjusted odds ratios. Inthe semi-urgent group, the odds of on-time surgery were36% lower in list-size category 2, OR = 0.64 (0.54–0.75),47% lower in category 3, OR = 0.53 (0.45–0.62), and 51%lower in category 4, OR = 0.49 (0.41–0.60), compared tolist-size category 1 (clearance time less than one week).In the non-urgent group, the odds of on-time surgery were25% lower in list-size category 2, OR = 0.75 (0.51–1.09),62% lower in category 3, OR = 0.38 (0.26–0.56), and 69%lower in category 4, OR = 0.31 (0.20–0.47), compared tolist-size category 1.Every time an additional patient was operated withoutbeing registered on wait lists, for non-urgent patients reg-istered in that week the odds of on-time surgery werereduced by 10%, OR = 0.90 (0.87–0.94). Similarly, forsemi-urgent patients the odds of on-time surgery werereduced by 7%, OR = 0.93 (0.91–0.95), and for urgentpatients were reduced by 25%, OR = 0.75 (0.65–0.87).DiscussionWhether waiting times vary due to chance alone afteradjustment for clinical factors and variations in demandremains an important question in health services researchon access to care. However, chances of late surgery havenot been previously described according to the length ofwait list at registration in a multiple-list setting. This paperexamines the relationship between the proportion ofpatients undergoing surgery within accepted standardsand the length of the wait lists at registration for CABGTable 2: Probability of undergoing surgery within target time in relation to urgency, hospital, and wait-list sizeCharacteristic N % (95% CI)Urgency at registrationUrgent 137 21.0 (17.9, 24.1)Semi-urgent 2235 34.1 (33.0, 35.3)Non-urgent 587 29.1 (27.2, 31.1)Hospital ID at booking1 695 36.5 (34.4, 38.7)2 1079 34.4 (32.7, 36.1)3 405 19.1 (17.4, 20.7)4 780 38.0 (35.9, 40.1)Wait-list size category1 – <1 week 596 39.7 (37.2, 42.2)2 – half month 454 35.6 (33.0, 38.2)3 – 1 month 1163 29.4 (28.0, 30.8)4 – >1 month 746 30.0 (28.2, 31.8)Page 5 of 7(page number not for citation purposes)For semi-urgent and non-urgent patients, Table 3 showsthe association between list size and the probability of on-surgery on multiple wait lists in a health system where allmedically necessary services are publicly funded.BMC Health Services Research 2005, 5:63 http://www.biomedcentral.com/1472-6963/5/63Using records from the provincial population-based regis-try of patients identified as needing isolated CABG, wedetermined the proportion of listed patients undergoingthe operation within target access times across the list sizecategories. The list size was a simple count of patients withhigher or equal priority present on the list at registrationof a new patient on a given list.We found that two in three patients were at risk of late sur-gery if registered on long wait lists. Our results provideevidence that list size had an effect on chances of under-going elective surgery on time, with lower chances forlonger lists in semi-urgent and non-urgent patients. Whenclearance time exceeded one week, half a month, or onemonth, the odds of on-time surgery were, respectively,36%, 47%, and 51% lower in the semi-urgent group, and25%, 62%, and 69% lower in the non-urgent group, com-pared to a shorter list, after adjustment for potential con-founders. We also found an independent effect of thenumber of patients who were operated without being reg-istered on wait lists.Individual waiting times were analyzed as prospectiveobservations beginning at the time of registration. We rep-resented the wait for each patient by a sequence of binaryindicators that indicate if the patient left the list at a cer-tain wait-list week [13]. The likelihood function of suchindicators can be factored into contributions that involvethe conditional probabilities of surgery in a certain weekamong those remaining on the list. This observation justi-fies the practice of treating the binary indicators from onepatient as independent Bernoulli trials [14]. To fit a poolof binary regression models developed for each patient byusing the maximum likelihood method, it was assumedthat binary indicators were independent across patients.urgency category is a composite variable based on a vari-ety of clinical factors. No audit has been performed toevaluate the quality of these records. The observation thathigher priority patients were more likely to undergoCABG through the direct admission indicates that thedegree of misclassification of priority was likely small.Another concern is that urgency of some patients were re-classified at the time of surgery. However, the timing ofchanges in urgency is not recorded.The contribution of this paper to the research on access tocare is three-fold. It provides evidence that for evaluationpurposes one can not accurately estimate the proportionof late surgeries without considering the length of a waitlist. It quantifies the effect of the queue length on the pro-portion of patients having late coronary bypass surgery ina network of hospitals with uniform standards for timingof surgery. It quantifies the effect of the operation per-formed without registration on a wait list, on the odds ofon-time surgery in the patients registered on the list in thatweek.ConclusionChances of late surgery increase with the wait-list size forsemi-urgent and non-urgent patients needing coronarybypass surgery. The weekly number of patients who moveimmediately from angiography to the operation withoutregistration on a wait list reduced chances of surgerywithin target time in all urgency groups of listed patients.Our findings have implications for policies on access toelective cardiac surgery in a network of hospitals. If queuelength varies substantially from hospital to hospital,policy makers may consider re-distribution of cases acrosshospitals with the aim of reducing the proportion of latesurgeries. Our results also suggest that an informed deci-sion on choosing a surgeon requires cardiologists andTable 3: Association between wait-list size and chances of on-time surgery as measured by odds ratios derived from discrete-time survival regression modelsSemi-urgent Non-urgentCrude Adjusted* Crude Adjusted*Surgery demand OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)Wait-list size category1 – <1 week list-size 1.00 1.00 1.00 1.002 – half month list-size 0.49 (0.42, 0.57) 0.64 (0.54, 0.75) 0.88 (0.62, 1.25) 0.75 (0.51, 1.09)3 – 1 month list-size 0.44 (0.39, 0.50) 0.53 (0.45, 0.62) 0.39 (0.28, 0.55) 0.38 (0.26, 0.56)4 – >1 month list-size 0.57 (0.49, 0.65) 0.49 (0.41, 0.60) 0.30 (0.21, 0.41) 0.31 (0.20, 0.47)Direct admission† - 0.93 (0.91, 0.95) - 0.90 (0.87, 0.94)* Adjusted for age, sex, comorbidity, calendar period, hospital, and week on the list.† Associated with one additional surgery performed without wait-list registrationPage 6 of 7(page number not for citation purposes)Misclassification of the recorded urgency of treatment is aconcern in this analysis. Retrieved from the registry, thepatients to consider information about the chance ofundergoing surgery beyond a target time and associatedPublish with BioMed Central   and  every scientist can read your work free of charge"BioMed Central will be the most significant development for disseminating the results of biomedical research in our lifetime."Sir Paul Nurse, Cancer Research UKYour research papers will be:available free of charge to the entire biomedical communitypeer reviewed and published immediately upon acceptancecited in PubMed and archived on PubMed Central BMC Health Services Research 2005, 5:63 http://www.biomedcentral.com/1472-6963/5/63risks. When projecting the expected time to treatment forpatients who will be placed on the wait list, hospital man-agers should take into account the current list size as wellas the weekly number of patients who require CABGimmediately after undergoing coronary angiography.More research is needed to evaluate whether referral pat-terns across hospitals depend on wait-list size.Competing interestsThe author(s) declare that they have no competinginterests.Authors' contributionsStudy concept and design: Sobolev. Data acquisition: Levy,Hayden. Analysis and interpretation: Sobolev, Kuramoto,Levy, Hayden. Drafting of the manuscript: Sobolev,Kuramoto.AcknowledgementsThis study received financial support from Canada Research Chairs Pro-gram (BS), Canada Foundation for Innovation (BS, AL), Michael Smith Foun-dation for Health Research (AL), Vancouver Coastal Health Research Institute (BS, LK), and St Paul's Hospital Foundation (AL). None of the sponsors had any role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or the decision to submit the paper for publication.The following cardiac surgeons are members of the Surgical Research Committee: James Abel, Richard Brownlee, Larry Burr, Anson Cheung, James Dutton, Guy Fradet, Virginia Gudas, Robert Hayden, Eric Jamieson, Michael Janusz, Shahzad Karim, Tim Latham, Jacques LeBlanc, Sam Lichten-stein, Hilton Ling, John Ofiesh, Michael Perchin-sky, Peter Skarsgard and Frank Tyers.The authors gratefully acknowledge the contributions of Mark FitzGerald, Martin Schechter, and Rita Sobolyeva. We are grateful to the external reviewers for thoughtful and useful suggestions.References1. Pierskalla WP, Brailer DJ: Applications of operations research inhealth care delivery.  1994:469-508.2. Noseworthy TW, McGurran JJ, Hadorn DC: Waiting for sched-uled services in Canada: development of priority-settingscoring systems.  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Levy A, Sobolev B, Hayden R, Kiely M, FitzGerald M, Schechter M:Time on wait lists for coronary bypass surgery in BritishColumbia, Canada, 1991 – 2000.  BMC Health Services Research2005, 5:22-32.16. Romano PS, Roos LL, Jollis JG: Adapting a clinical comorbidityindex for use with ICD-9-CM administrative data: differingperspectives.  Journal of Clinical Epidemiology 1993, 46:1075-1079.Pre-publication historyThe pre-publication history for this paper can be accessedhere:http://www.biomedcentral.com/1472-6963/5/63/prepubyours — you keep the copyrightSubmit your manuscript here:http://www.biomedcentral.com/info/publishing_adv.aspBioMedcentralPage 7 of 7(page number not for citation purposes)


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