Author
Listed:
- Philippa H. Youl
(Cancer Council Queensland, P.O. Box 201, Spring Hill, QLD 4004, Australia
Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia
School of Public Health and Social Work, Queensland University of Technology, Herston Road, Kelvin Grove, QLD 4059, Australia)
- Joanne F. Aitken
(Cancer Council Queensland, P.O. Box 201, Spring Hill, QLD 4004, Australia
School of Public Health and Social Work, Queensland University of Technology, Herston Road, Kelvin Grove, QLD 4059, Australia
School of Population Health, University of Queensland, Brisbane, QLD 4072, Australia
Institute for Resilient Regions, University of Southern Queensland, Toowoomba, QLD 4350, Australia)
- Gavin Turrell
(Institute of Health and Ageing, Australian Catholic University, Fitzroy, VIC 3115, Australia)
- Suzanne K. Chambers
(Cancer Council Queensland, P.O. Box 201, Spring Hill, QLD 4004, Australia
Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia
Institute for Resilient Regions, University of Southern Queensland, Toowoomba, QLD 4350, Australia)
- Jeffrey Dunn
(Cancer Council Queensland, P.O. Box 201, Spring Hill, QLD 4004, Australia
Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia
Institute for Resilient Regions, University of Southern Queensland, Toowoomba, QLD 4350, Australia
School of Social Science, University of Queensland, Brisbane, QLD4072, Australia)
- Christopher Pyke
(Mater Medical Centre, 293 Vulture Street, South Brisbane, QLD 4101, Australia)
- Peter D. Baade
(Cancer Council Queensland, P.O. Box 201, Spring Hill, QLD 4004, Australia
Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia
School of Mathematical Sciences, Queensland University of Technology, Gardens Point, Brisbane, QLD 4000, Australia)
Abstract
Delays in diagnosing breast cancer (BC) can lead to poorer outcomes. We investigated factors related to the diagnostic interval in a population-based cohort of 3202 women diagnosed with BC in Queensland, Australia. Interviews ascertained method of detection and dates of medical/procedural appointments, and clinical information was obtained from medical records. Time intervals were calculated from self-recognition of symptoms (symptom-detected) or mammogram (screen-detected) to diagnosis (diagnostic interval (DI)). The cohort included 1560 women with symptom-detected and 1642 with screen-detected BC. Symptom-detected women had higher odds of DI of >60 days if they were Indigenous (OR = 3.12, 95% CI = 1.40, 6.98); lived in outer regional (OR = 1.50, 95% CI = 1.09, 2.06) or remote locations (OR = 2.46, 95% CI = 1.39, 4.38); or presented with a “non-lump” symptom (OR = 1.84, 95% CI = 1.43, 2.36). For screen-detected BC, women who were Indigenous (OR = 2.36, 95% CI = 1.03, 5.80); lived in remote locations (OR = 2.35, 95% CI = 1.24, 4.44); or disadvantaged areas (OR = 1.69, 95% CI = 1.17, 2.43) and attended a public screening facility (OR = 2.10, 95% CI = 1.40, 3.17) had higher odds of DI > 30 days. Our study indicates a disadvantage in terms of DI for rural, disadvantaged and Indigenous women. Difficulties in accessing primary care and diagnostic services are evident. There is a need to identify and implement an efficient and effective model of care to minimize avoidable longer diagnostic intervals.
Suggested Citation
Philippa H. Youl & Joanne F. Aitken & Gavin Turrell & Suzanne K. Chambers & Jeffrey Dunn & Christopher Pyke & Peter D. Baade, 2016.
"The Impact of Rurality and Disadvantage on the Diagnostic Interval for Breast Cancer in a Large Population-Based Study of 3202 Women in Queensland, Australia,"
IJERPH, MDPI, vol. 13(11), pages 1-20, November.
Handle:
RePEc:gam:jijerp:v:13:y:2016:i:11:p:1156-:d:83259
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