Please use this identifier to cite or link to this item: http://hdl.handle.net/11434/2042
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dc.contributor.authorPrince, Miles-
dc.contributor.otherDoo, Nicole-
dc.contributor.otherWhite, Victoria-
dc.contributor.otherMartin, Kara-
dc.contributor.otherBassett, Julie-
dc.contributor.otherHarrison, Simon-
dc.contributor.otherJefford, Michael-
dc.contributor.otherWinship, Ingrid-
dc.contributor.otherMillar, Jeremy-
dc.contributor.otherMilne, Roger-
dc.contributor.otherSeymour, John-
dc.contributor.otherGiles, Graham-
dc.date2019-07-
dc.date.accessioned2021-11-22T03:06:20Z-
dc.date.available2021-11-22T03:06:20Z-
dc.date.issued2019-07-
dc.identifier.citationCancers, 11(7), 928.en_US
dc.identifier.issn2072-6694en_US
dc.identifier.urihttp://hdl.handle.net/11434/2042-
dc.description.abstractIntroduction: Diffuse large B cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma for which a cure is usually the therapeutic goal of optimal treatment. Using a large population-based cohort we sought to examine the factors associated with optimal DLBCL treatment and survival. Methods: DLBCL cases were identified through the population-based Victorian Cancer Registry, capturing new diagnoses for two time periods: 2008-2009 and 2012-2013. Treatment was pre-emptively classified as 'optimal' or 'suboptimal', according to compliance with current treatment guidelines. Univariable and multivariable logistic regression models were fitted to determine factors associated with treatment and survival. Results: Altogether, 1442 DLBCL cases were included. Based on multivariable analysis, delivery of optimal treatment was less likely for those aged ≥80 years (p < 0.001), women (p = 0.012), those with medical comorbidity (p < 0.001), those treated in a non-metropolitan hospital (p = 0.02) and those who were ex-smokers (p = 0.02). Delivery of optimal treatment increased between 2008-2009 and the 2012-2013 (from 60% to 79%, p < 0.001). Delivery of optimal treatment was independently associated with a lower risk of death (hazard ratio (HR) = 0.60 (95% confidence interval (CI) 0.45-0.81), p = 0.001). Conclusion: Delivery of optimal treatment for DLBCL is associated with hospital location and category, highlighting possible demographic variation in treatment patterns. Together with an increase in the proportion of patients receiving optimal treatment in the more recent time period, this suggests that treatment decisions in DLBCL may be subject to non-clinical influences, which may have implications when evaluating equity of treatment access. The positive association with survival emphasizes the importance of delivering optimal treatment in DLBCL.en_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.subjectCancer Survivalen_US
dc.subjectChemotherapyen_US
dc.subjectDiffuse Large B Cell Lymphoma (DLBCL)en_US
dc.subjectEpidemiologic Studiesen_US
dc.subjectPatterns of Careen_US
dc.subjectNon-Hodgkin Lymphomaen_US
dc.subjectVictorian Cancer Registryen_US
dc.subjectSuboptimal Treatmenten_US
dc.subjectOptimal Treatmenten_US
dc.subjectMultivariable Analysisen_US
dc.subjectDemographic Variationen_US
dc.subjectUnivariable/multivariable Logistic Regression modelsen_US
dc.subjectTherapeutic Modalitiesen_US
dc.subjectR-CHOPen_US
dc.subjectNon-Clinical Factorsen_US
dc.subjectSocioeconomic Status (SES)en_US
dc.subjectArea of Remoteness Index of Australiaen_US
dc.subjectEpworth Centre for Immunotherapies and Snowdome Laboratoriesen_US
dc.subjectMolecular Oncology and Cancer Immunologyen_US
dc.subjectCancer Services Clinical Institute, Epworth HealthCare, Victoria, Australiaen_US
dc.titleThe use of optimal treatment for DLBCL is improving in all age groups and is a key factor in overall survival, but non-clinical factors influence treatment.en_US
dc.typeJournal Articleen_US
dc.identifier.doi10.3390/cancers11070928en_US
dc.identifier.journaltitleCancers (Basel)en_US
dc.description.pubmedurihttps://pubmed.ncbi.nlm.nih.gov/31269764en_US
dc.description.affiliatesCancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australiaen_US
dc.description.affiliatesConcord Repatriation General Hospital, Sydney Medical School, University of Sydney, Sydney, NSW 2139, Australiaen_US
dc.description.affiliatesConcord Clinical School, University of Sydney, Concord, NSW 2139, Australiaen_US
dc.description.affiliatesSchool of Psychology, Faculty of Health, Deakin University, Geelong, VIC 3220, Australiaen_US
dc.description.affiliatesCentre for Behavioural Research in Cancer, Cancer Council Victoria, Melbourne, VIC 3004, Australiaen_US
dc.description.affiliatesDepartment of Haematology, Peter MacCallum Cancer Centre & Royal Melbourne Hospital, Melbourne, VIC 3000, Australiaen_US
dc.description.affiliatesSir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC 3010, Australiaen_US
dc.description.affiliatesDepartment of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australiaen_US
dc.description.affiliatesGenomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, VIC 3050, Australiaen_US
dc.description.affiliatesDepartment of Medicine, The University of Melbourne, Parkville, VIC 3010, Australiaen_US
dc.description.affiliatesAlfred Health Radiation Oncology, Alfred and LaTrobe Regional Hospital, Melbourne, VIC 3004, Australiaen_US
dc.description.affiliatesSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australiaen_US
dc.description.affiliatesCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australiaen_US
dc.description.affiliatesPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3800, Australiaen_US
dc.type.studyortrialCohort Studyen_US
dc.type.contenttypeTexten_US
Appears in Collections:Cancer Services
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