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.
- Publisher:
- MDPI
- Publication Type:
- Journal Article
- Citation:
- Int J Environ Res Public Health, 2016, 13, (11), pp. E1156
- Issue Date:
- 2016-11-19
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Youl, PH | |
dc.contributor.author | Aitken, JF | |
dc.contributor.author | Turrell, G | |
dc.contributor.author | Chambers, SK | |
dc.contributor.author | Dunn, J | |
dc.contributor.author | Pyke, C | |
dc.contributor.author | Baade, PD | |
dc.date.accessioned | 2022-08-03T04:49:02Z | |
dc.date.available | 2016-11-15 | |
dc.date.available | 2022-08-03T04:49:02Z | |
dc.date.issued | 2016-11-19 | |
dc.identifier.citation | Int J Environ Res Public Health, 2016, 13, (11), pp. E1156 | |
dc.identifier.issn | 1661-7827 | |
dc.identifier.issn | 1660-4601 | |
dc.identifier.uri | http://hdl.handle.net/10453/159554 | |
dc.description.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. | |
dc.format | Electronic | |
dc.language | eng | |
dc.publisher | MDPI | |
dc.relation.ispartof | Int J Environ Res Public Health | |
dc.relation.isbasedon | 10.3390/ijerph13111156 | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject.classification | Toxicology | |
dc.subject.mesh | Adult | |
dc.subject.mesh | Aged | |
dc.subject.mesh | Breast Neoplasms | |
dc.subject.mesh | Cohort Studies | |
dc.subject.mesh | Early Detection of Cancer | |
dc.subject.mesh | Female | |
dc.subject.mesh | Healthcare Disparities | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Longitudinal Studies | |
dc.subject.mesh | Mammography | |
dc.subject.mesh | Middle Aged | |
dc.subject.mesh | Odds Ratio | |
dc.subject.mesh | Queensland | |
dc.subject.mesh | Rural Population | |
dc.subject.mesh | Socioeconomic Factors | |
dc.subject.mesh | Time Factors | |
dc.subject.mesh | Vulnerable Populations | |
dc.subject.mesh | Young Adult | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Breast Neoplasms | |
dc.subject.mesh | Mammography | |
dc.subject.mesh | Odds Ratio | |
dc.subject.mesh | Cohort Studies | |
dc.subject.mesh | Longitudinal Studies | |
dc.subject.mesh | Time Factors | |
dc.subject.mesh | Socioeconomic Factors | |
dc.subject.mesh | Adult | |
dc.subject.mesh | Aged | |
dc.subject.mesh | Middle Aged | |
dc.subject.mesh | Vulnerable Populations | |
dc.subject.mesh | Rural Population | |
dc.subject.mesh | Queensland | |
dc.subject.mesh | Female | |
dc.subject.mesh | Healthcare Disparities | |
dc.subject.mesh | Early Detection of Cancer | |
dc.subject.mesh | Young Adult | |
dc.title | 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. | |
dc.type | Journal Article | |
utslib.citation.volume | 13 | |
utslib.location.activity | Switzerland | |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Health | |
utslib.copyright.status | open_access | * |
dc.date.updated | 2022-08-03T04:49:00Z | |
pubs.issue | 11 | |
pubs.publication-status | Published online | |
pubs.volume | 13 | |
utslib.citation.issue | 11 |
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.
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