Survival after breast cancer treatment: the impact of provider volume
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1 Blackwell Publishing LtdOxford, UKJEPJournal of Evaluation in Clinical Practice The Authors; Journal compilation 2006 Blackwell Publishing Ltd Original Article Provider volume and breast cancer sur vivalk. Bailie et al. Journal of Evaluation in Clinical Practice ISSN Survival after breast cancer treatment: the impact of provider volume Karen Bailie MD PhD MSc FRCP FRCPath, 1 Iain Dobie MD FRCSI FRCS, 2 Stephen Kirk MB BCh FRCS 3 and Michael Donnelly PhD 4 1 Director, Clinical Research Support Centre, Education & Research Centre, Royal Hospitals, Belfast, Northern Ireland, UK 2 SpR Surgery, Ulster Hospital, Dundonald, Belfast, Northern Ireland, UK 3 Consultant Surgeon, Chair of the N Ireland Breast Cancer Group, Ulster Hospital, Dundonald, Belfast, Northern Ireland, UK 4 Reader in Health Services Research, Epidemiology and Health Services Research Group, Centre for Clinical and Population Sciences, Queen s University Belfast, Belfast, Northern Ireland, UK Key words breast cancer, case mix, surgeon workload, survival Correspondence Dr Karen Bailie Clinical Research Support Centre Education & Research Centre Royal Hospitals Grosvenor Road Belfast Northern Ireland BT12 6BA UK karen.bailie@crsc.n-i.nhs.uk Accepted for publication: 19 April 2006 doi: /j x Abstract Background Research has not paid sufficient attention to the need for adequate case-mix adjustment in studies of the relationship between provider volume and performance. This study attempted to address this limitation by capturing and including 5-year survival outcomes and a wide range of case-mix variables in multivariate analyses of the volume outcome relationship relating to breast cancer treatments. Methods All patients diagnosed with invasive primary breast cancer during 1996 (n = 809) were included. Patient, disease and treatment data were extracted from medical records; survival data were corroborated using official death registrations. A Cox proportional hazards approach was used to model relationships between patient, disease and service variables and risk of death. Results There were 262 deaths among 807 patients followed up; overall 5-year survival was 70%. Advancing age, higher levels of co-morbidity, late-stage disease, more positive nodes, and high-grade tumour were independently associated with lower survival (P < 0.05). Patients who received hormonal therapy (HR 0.50, 95% CI ) and radiotherapy (HR 0.73, 95% CI ) had a survival advantage. Using a cut-off point of 30 cases per annum, survival was lower for patients treated in low volume settings (HR 1.47, 95% CI ) after adjustment for case mix. Conclusions There was some evidence to support treatment in high volume settings although patient and disease variables were the major determinants of survival for patients with breast cancer. Introduction The drive towards the reorganization of cancer services in the UK, including Northern Ireland [1,2], was influenced mainly by reports of poor survival in the UK compared with European countries [3], compounded by evidence of significant variations in the use of all available cancer treatments [4 6]. A central tenet of the reconfiguration of services was that high volume treatment settings compared with low volume settings would lead to better care and improved cancer outcomes. However, the relationship between volume and outcome is complex; and the extent to which factors such as confounding by case mix, the influence of screening services and the specialisation of the work of clinicians, hospitals and services impact on this relationship remains unclear. Research purporting to provide support for the centralisation recommendation has been criticised on methodological grounds. For example, some studies lack, or incorrectly assign, key data variables on case mix and treatments received [7]. Furthermore, the definition of volume (hospital or clinician) varies across studies. A systematic review [8] did not find strong evidence for a high volume better outcome relationship in a variety of disease settings, including breast cancer, and concluded that the size of the relationship was overestimated because confounding due to case mix was not given sufficient attention in studies. The review noted the need for further research that took account of case-mix variables and examined longer-term survival. Thus, this study was designed to investigate the extent to which survival of patients with breast cancer at 5 years from diagnosis is associated with volume of clinical practice while adjusting for case mix. Methods The study comprised all patients diagnosed with invasive primary breast cancer in Northern Ireland during 1996 (n = 809). Salient 2007 The Authors. Journal compilation 2007 Blackwell Publishing Ltd, Journal of Evaluation in Clinical Practice 13 (2007)
2 Provider volume and breast cancer survival K. Bailie et al. patient, disease and service variables and data about treatment decisions were extracted in 1997 from the hospital record of each patient (Table 1). Records were re-examined during 2002 to ascertain patient status and to establish the date and cause of any death that had occurred on or before 31 December This information was corroborated by examination of the General Registrar s Office mortality files from January 1996 to December The survival period for each patient was calculated from the date of diagnosis to date of death or censoring. Postcode at diagnosis was used to assign a Townsend score [9] calculated from the corresponding census-derived small area statistics. Co-morbidity was estimated by a count of other conditions at the time of diagnosis [10]. Stage was based on surgical and pathological findings where possible; in those patients in whom pathology details were missing, stage was assigned based on clinical findings. Volume assignment was categorized according to the number of patients with breast cancer seen during the study period at the level of each surgeon, oncologist and hospital [11,12]. The relationship between patient, disease and service variables and the risk of death was examined first in univariate Kaplan Meier survival analyses. Subsequently, a multivariate model was constructed using a Cox proportional hazards approach to examine Table 1 Characteristics of patients included in study Variable Category Number Percentage Patient characteristics Age at presentation <50 years years years >80 years Socio-economic status (derived from postcode) Quintiles over the range of to 8.9 in Townsend deprivation score Menopausal status Pre-menopausal Post-menopausal Co-morbidity at presentation None conditions or more Disease characteristics Stage at presentation* Tumour size <2 cm cm >4 cm unknown Tumour grade [11] unknown Nodal involvement (axilla) None > unknown Nottingham Prognostic Index (NPI) [12] Good (NPI < 3.4) Moderate (NPI ) Poor (NPI > 5.4) Unknown Treatments received Breast surgery None Conservative procedure Total mastectomy Radiotherapy No Yes Hormonal therapy No Yes Chemotherapy No Yes The Authors. Journal compilation 2007 Blackwell Publishing Ltd
3 K. Bailie et al. Provider volume and breast cancer survival Table 1 Continued Variable Category Number Percentage Service characteristics Health Board of residence East North South West Distance to radiotherapy center (single centre in Northern Ireland) Quintiles over the range of miles Surgeon caseload Low (<30 cases pa) High ( 30 cases pa) Surgeon caseload 100 cases pa cases pa cases pa <10 cases pa *Classification of stage: Stage 1: Micro-invasive disease, tumour size <2 cm, and no nodes palpable or involved pathologically. Stage 2: Tumour size 2 5 cm, or nodes involved clinically, not fixed, or any pathological involvement. Stage 3: Tumour size >5 cm, or clinically fixed nodes, or skin involvement. Stage 4: Metastatic disease. pa, per annum. this relationship and to determine independent effects. Inclusion of variables in the model was guided by clinical and biological plausibility and the effect size observed in univariate analyses. A best fit model was created by entering clinical (patient and disease) variables and then examining the addition of service variables. All analyses were conducted using the statistical software STATA version 8.0 (Stata Corporation, College Station, TX, USA). Results Of the 809 patients diagnosed with breast cancer in 1996, 807 (99%) were traced for follow-up in Patients were treated in 17 different hospitals by 35 individual consultant surgeons and nine individual consultant oncologists. The quality of data available from hospital records was good; missing data on key variables were infrequent, ranging from 0% to 7% (Table 1). Volume of surgical practice was associated with several key case-mix factors patients treated in higher volume settings tended to be younger (P = 0.004), have smaller tumours (P = 0.02), earlier-stage disease (P < 0.001) and a more favourable prognostic index (P = 0.005). These characteristics reflect to some extent larger centres role as preferred referral centres for screendetected cancers. These centres also tended to use more conservative surgery (P < 0.001) and radiotherapy (P = 0.001). The use of hormonal therapy (predominantly tamoxifen) was widespread (95%), but was highest (98%) in middle volume settings. High volume centres were also more likely to have breast surgeons regarded as specialists (P < 0.001). There was no statistically significant difference across settings in the distribution of deprivation index, tumour grade, number of positive nodes, or the use of chemotherapy. There was a tendency for lower volume settings to treat a population with higher levels of co-morbidity (P = 0.07). Median follow-up time was 5.3 years. In 3585 person-years of follow-up, there were 262 deaths, of which 174 were attributable to breast cancer. A single case was diagnosed at post-mortem and was thus excluded from the analysis. The Kaplan Meier survival function (95% CI) for 5-year survival was 70% (66 73%), and 79% (76 81%) when non-breast cancer deaths were censored. Univariate analyses When examined in isolation, all variables had a statistically significant relationship (P < 0.05) with the risk of dying, with the exception of geographical factors (Health Board and distance of residence from the radiotherapy centre) and the use of chemotherapy. The unadjusted hazard ratios for each variable are shown in Table 2. The crude Kaplan Meier survival plots by selected variables are shown in Fig. 1. The greatest influence on the risk of death was stage at presentation, with those with metastatic disease having a 25-times increase compared with those presenting at a very early stage. Other clinical variables older age; higher deprivation levels; greater co-morbidity; advanced stage disease; larger tumour size; increasing numbers of involved lymph nodes at surgical staging; higher grade of tumour; and a poor Nottingham Prognostic Index were all associated with an adverse 5-year survival probability. A survival advantage was found for patients who received radiotherapy or hormonal therapy. No such advantage was evident for patients who received chemotherapy; their survival was poorer compared with the group in receipt of chemotherapy, although the difference did not reach statistical significance. Survival following conservative surgery and total mastectomy was similar, but patients who did not have surgical treatment fared worse The Authors. Journal compilation 2007 Blackwell Publishing Ltd 751
4 Provider volume and breast cancer survival K. Bailie et al. Table 2 Univariate analysis unadjusted hazard ratios (HRs) for survival by patient, disease and service variables Variable Category Crude HR 95% CI Patient characteristics Age at presentation <50 years years years >80 years Socio-economic status 1 (highest) (lowest) Menopausal status Pre-menopausal 1 Post-menopausal Co-morbidity at presentation None conditions or more Disease characteristics Stage at presentation Stage 1 1 Stage Stage Stage Tumour size <2 cm cm >4 cm Tumour grade Grade 1 1 Grade Grade Nodal involvement None nodes >4 nodes Nottingham Prognostic Index Good 1 Moderate Poor Treatments received Surgery Conservative 1 Total mastectomy None Radiotherapy No 1 Yes Hormonal therapy No Chemotherapy Yes No Yes Service characteristics Health Board of residence East 1 North South West Distance to radiotherapy centre 1 (nearest) (farthest) Surgeon caseload 30 cases pa 1 <30 cases pa Surgeon caseload >100 cases pa cases pa cases pa <10 cases pa Hospital caseload >100 cases pa cases pa cases pa <10 cases pa Treatment by a specialist breast surgeon Yes 1 No CI, confidence interval; HR, hazard ratio, pa, per annum The Authors. Journal compilation 2007 Blackwell Publishing Ltd
5 K. Bailie et al. Provider volume and breast cancer survival (a) (b) Stage at diagnosis stage 1 stage 2 stage 3 stage 4 Survival time (years from diagnosis) Age category <50 years years years >80 years (c) (d) Number of comorbid conditions at diagnosis none or more (e) Grade of tumour low intermediate high (f) Number of involved axillary nodes none 1 4 >4 Surgical procedure conservative total mastectomy none Figure 1 Unadjusted Kaplan Meier survival function by selected patient, disease and service variables. (a) by stage at diagnosis; (b) by age at diagnosis; (c) by level of co-morbidity at diagnosis; (d) by tumour grade; (e) by degree of nodal involvement; (f) by type of breast surgery performed; (g) by use of breast radiotherapy; (h) by surgical caseload (1); and (i) by surgical caseload (2). A total of 607 patients (75%) received treatment from a specialist breast surgeon. Treatment in a lower volume setting, defined in terms of hospital or surgeon, as less than 30 cases per annum, was associated with reduced survival compared with higher volume settings. This was apparent using 30 cases per annum as a cut point, and was accentuated for very low volume settings defined as less than 10 cases per annum The Authors. Journal compilation 2007 Blackwell Publishing Ltd 753
6 Provider volume and breast cancer survival K. Bailie et al. (g) (h) Treatment by radiotherapy no yes Surgeon caseload per annum > <10 (i) Surgeon caseload per annum 30 or more <30 Figure 1 Continued Multivariate analyses The clinical factors of age, co-morbidity, stage of disease, number of positive nodes, and tumour grade were independently associated with survival in the multivariate model with the direction of effect in keeping with expectation based on their known prognostic significance (Table 3). The survival advantage found for patients treated with hormonal therapy was retained; the effect of radiotherapy was tempered by adjustment for other factors, but there was moderate evidence for an independent effect (P = 0.07), justifying its retention in the model. The analysis found a surgical workload effect even after adjustment was made for clinical and treatment variables. This was apparent using a cut-off point of 30 cases per annum to distinguish high and low volume categories and using four categories (see Fig. 2). The lowest volume category, defined as less than 10 cases per annum, showed the highest relative risk compared with the highest volume level (>100 cases per annum) (Table 3). There was a trend towards impaired survival with increasing levels of deprivation, but this did not reach statistical significance. Discussion The aim of this study was to investigate the relationship between provider volume and survival outcome for patients with breast cancer and, in particular, to adjust adequately for case mix, which has been postulated to account for much of the purported association between volume and outcome. Previous studies have found survival advantages for higher volume hospitals [4] and surgeons [5,13]. A survival advantage has also been suggested for patients treated by specialist surgeons [14 16]. Other investigators have concluded that volume effects are explained largely by case-mix factors or chance [17,18]. A pervading criticism of many studies is that they have limited ability to adjust for the effects of important confounding factors, such as patient and disease characteristics, and the impact of treatment received (e.g. [7]). The study reported here attempted to address the issue of confounding by collecting a comprehensive data set, including patient, disease and service variables, that have been shown or postulated to impact on survival. A further criticism of the research in this area is that many existing studies are based on the experience of patients treated The Authors. Journal compilation 2007 Blackwell Publishing Ltd
7 K. Bailie et al. Provider volume and breast cancer survival Table 3 Multivariate analysis adjusted hazard ratios (HRs) for survival by patient, disease and service variables with independent effects Variable Category HR adjusted for all other variables 95% CI P-value Patient characteristics Age at presentation <50 years years years >80 years Co-morbidity at presentation None conditions or more Disease characteristics Stage at presentation Stage 1 1 Stage Stage < Stage Tumour grade Grade 1 1 Grade Grade Nodal involvement None nodes < >4 nodes Treatments received Surgery Conservative 1 Total mastectomy < None Radiotherapy No 1 Yes Hormonal therapy No 1 Yes Service characteristics Surgeon caseload >100 cases pa cases pa cases pa <10 cases pa Each hazard ratio is adjusted only for the effects of all other variables included in the model. pa, per annum. some time ago, typically the 1980s. The role and impact of treatment advances, diffusion of treatment guidelines and the volume outcome debate itself have not been given sufficient consideration. Our study reports on a cohort of patients treated in 1996, a period covered previously by only one study from the USA [15] and one from the UK [13]. Bias in existing published studies as a result of excluding older patients or patients with advanced-stage disease at presentation add further problems to the interpretation of results emanating from these studies. In contrast, the study reported here was population-based, including patients of all ages and all stages of presentation. The expected pattern of mortality among elderly people, and individuals with advanced disease at presentation, was noted. In addition, there was an independent survival advantage for patients who received adjuvant therapy in the form of radiotherapy and hormonal therapy, but not for patients who received chemotherapy. This pattern of results is in keeping with randomized controlled trial (RCT)-based support for the benefit of tamoxifen [19], the most widely used form of hormonal therapy in this context. The benefit of radiotherapy following partial mastectomy has been demonstrated, and this is now standard practice for virtually every invasive tumour [20]. The findings presented here suggest that radiotherapy has a beneficial effect that is independent of surgical treatment, a finding in keeping with a recent systematic review of the effects of radiotherapy in breast cancer [20]. The failure to demonstrate a beneficial effect for systemic chemotherapy is at odds with previous studies. Under use of chemotherapy in the study population is a potential explanation. At the time of this study, chemotherapy was not used widely (25% of cases). In another study of compliance with recommended treatment guidelines in the same population, chemotherapy would have been indicated in an additional 14% of cases (unpublished data). In keeping with previous studies, case-mix adjustment reduced the hazard ratio in favour of clinicians who treated more than 30 patients per year, and poorer survival was accentuated further among clinicians with less than 10 cases per year. This was independent of treatment effects, and being treated by a specialist breast surgeon did not appear to influence survival. The attribute of care that is associated with higher caseload and accounts for the residual survival advantage is unclear. A multidisciplinary team approach to care delivery and the availability of more comprehensive infrastructure such as diagnostic facilities and supportive care 2007 The Authors. Journal compilation 2007 Blackwell Publishing Ltd 755
8 Provider volume and breast cancer survival K. Bailie et al. (a) (b) Surgeon caseload per annum 30 or more <30 Surgeon caseload per annum > <10 Figure 2 Kaplan Meier survival function by surgeon caseload adjusted to baseline level of stage (stage 1) at diagnosis. (a) by surgeon caseload in two categories; and (b) by surgeon caseload in four categories. in larger hospitals has been offered as an explanation. Residual confounding as an explanation would require that one or more hitherto unknown factors are operating and exerting a relatively large impact. Overall, this study adds to the body of evidence in support of higher volume breast cancer services. However, given the size of the relevant hazard ratios, the impact at a population level of investing in high volume services is unlikely to be as great as measures to encourage presentation for treatment of breast cancer at an early stage in the disease process. Valid assessment and interpretation of service quality measures in terms of structures, processes and outcomes are important issues in improving and maintaining health services. Clinical decision making is complex, driven by patient, disease and service-related factors, each of which may influence the ultimate outcome experienced by patients, necessitating inclusion in a valid assessment of service quality. The survival outcome for patients following a diagnosis of breast cancer in Northern Ireland is influenced primarily by clinical factors such as stage of disease at presentation, age and levels of co-morbidity. These factors tend largely to be outside the control of health services and the treatment modalities employed, which clearly are service responsibilities. The relative impact of higher caseload, as measured at the surgeon level, appears to have a more modest independent effect on survival than other factors. Acknowledgements The Faculty of Medicine Research Ethics Committee, Queen s University Belfast granted ethical approval for the study (Reference number 279/96). A Medical Research Council (MRC) Special Training Fellowship in Health Services Research held by KB funded this research. References 1. Department of Health (1995) A policy framework for commissioning cancer services. A report by the Expert Advisory Group on Cancer to the Chief Medical Officers of England and Wales (The Calman-Hine Report). London: HMSO. 2. Department of Health Social Services (1996) Cancer services: Investing for the future. Report of the N. Ireland Cancer Working Group (The Campbell Report). Belfast: DHSS. 3. Berrino, F., Sant, M., Verdecchia, A., Capocaccia, R., Hakulinen, T. & Esteve, J. (eds) (1995) Survival of cancer patients in Europe: The Eurocare study. IARC Scientific Publications No Lyon: IARC. 4. Lee-Feldstein, A., Anton-Culver, H. & Feldstein, J. (1994) Treatment differences and other prognostic factors related to breast cancer survival. Delivery systems and medical outcomes. Journal of the American Medical Association, 271, Sainsbury, R., Haward, B., Rider, L., Johnston, C. & Round, C. (1995) Influence of clinician workload and patterns of treatment on survival from breast cancer. Lancet, 345, Farrow, D. C., Samet, J. M. & Hunt, W. C. (1996) Regional variation in survival following the diagnosis of breast cancer. Journal of Clinical Epidemiology, 49, Malin, J. L., Kahn, K. L., Adams, J., Kwan, L., Laouri, M. & Ganz, P. A. (2002) Validity of cancer registry data fore measuring the quality of breast cancer care. Journal of the National Cancer Institute, 94, Sowden, A. J., Grilli, R. & Rice, N. (1997) Concentration and choice in the provision of hospital services: The relationship between hospital volume and quality of health outcomes. York: NHS Centre for Reviews and Dissemination. 9. Townsend, P., Philimore, P. & Beattie, A. (1988) Health and deprivation: Inequalities and the North. London: Croom-Helm. 10. Satariano, W. A. & Ragland, D. R. (1994) The effect of co-morbidity on 3-year survival of women with primary breast cancer. Annals of Internal Medicine, 120, Bloom, H. J. G. & Richardson, W. W. (1957) Histological grading and prognosis in breast cancer. British Journal of Cancer, 11, Elston, C. W. & Ellis, I. O. (1991) Pathological prognostic factors in breast cancer. 1. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology, 19, Mikelijevic, J. S., Haward, R. A., Jihnston, C., Sainsbury, R. & Forman, D. (2003) Surgeon workload and survival from breast cancer. British Journal of Cancer, 89, Gillis, C. R. & Hole, D. J. (1996) Survival outcome of care by specialist surgeons in breast cancer: a study of 3786 patients in the west of Scotland. British Medical Journal, 312, Skinner, K. A., Helsper, J. T., Deapen, D., Ye, W. & Sposto, R. (2003) Breast cancer: do specialists make a difference? Annals of Surgical Oncology, 10, The Authors. Journal compilation 2007 Blackwell Publishing Ltd
9 K. Bailie et al. Provider volume and breast cancer survival 16. Kingsmore, D., Ssmwogerere, A., Hole, D. & Gillis, C. (2003) Specialisation and breast cancer survival in the screening era. British Journal of Cancer, 88, Karjalainen, S. (1990) Geographical variation in cancer patient survival in Finland: chance, confounding, or effect of treatment? Journal of Epidemiology and Community Health, 44, Twelves, C. J., Thomson, C. S., Dewar, J. A., Brewster, D. H. on behalf of the Scottish Cancer Therapy Network (2001) Variation in survival of women with breast cancer: health board remains a factor at 10 years. British Journal of Cancer, 85, EBCTCG Early Breast Cancer Trialists Collaborative Group (1992) Systemic treatment of early breast cancer by hormonal, cytotoxic or immune therapy. Lancet, 339, 1 15, Rutqvist, L. E., Rose, C. & Cavallin-Stahl, E. (2003) A systematic review of radiation therapy effects in breast cancer. Acta Oncologica, 42, The Authors. Journal compilation 2007 Blackwell Publishing Ltd 757
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