A multi-level spatio-temporal analysis on prostate cancer outcomes.


Journal

Cancer epidemiology
ISSN: 1877-783X
Titre abrégé: Cancer Epidemiol
Pays: Netherlands
ID NLM: 101508793

Informations de publication

Date de publication:
06 2021
Historique:
received: 11 02 2021
revised: 01 04 2021
accepted: 05 04 2021
pubmed: 17 4 2021
medline: 8 7 2021
entrez: 16 4 2021
Statut: ppublish

Résumé

Geographic and temporal variation in positive surgical margins (PSM) for prostate cancer after radical prostatectomy (RP) has been observed. However, it is unclear how much of this variation could be attributed to patient, surgeon, institution, or socioeconomic-related factors and the impact of PSM on death among localized prostate cancer patients. This study aimed to assess the independent and relative contribution of the patient, surgeon, institution and area-level risk factors on geographic and temporal variation of PSM and evaluate the impact of PSM on five-year all-cause and prostate cancer-specific mortality among localized prostate cancer patients. Within the hierarchical-related regression approach, we utilised Bayesian spatial-temporal multi-level models to study individual and area-level predictors with the outcomes, while accounting for geographically structured and unstructured correlation and non-linear trends. Individual-level data included 10,075 localized prostate cancer cases with RP reported to the Prostate Cancer Outcomes Registry Victoria between 2009 and 2018. Area-level data comprised socio-economic disadvantage and remoteness data at the local government area level in Victoria, Australia. 26 % of patients had PSM, and the rates varied across areas by years. This variation was mainly associated with NCCN risk, followed by RP techniques, surgical institution type, surgeon volume and socio-economic disadvantage. Intermediate (Odds ratio/OR = 1.21,95 % credible interval/Crl = 1.05-1.41), high/very-high risk groups (OR = 2.24,95 % Crl = 1.91-2.64) and public surgical institution (OR = 1.64, 95 % Crl = 1.46-1.84) were independently associated with a higher likelihood of PSM. Robot-assisted (OR = 0.61, 95 % Crl = 0.55-0.68), laparoscopic RP (OR = 0.76, 95 % Crl = 0.62-0.93), high-volume surgeon (OR = 0.84, 95 % Crl = 0.76-0.93) and socio-economically least disadvantaged status (OR = 0.78, 95 % Crl = 0.64-0.94) showed a lower likelihood of PSM. PSM was also independently associated with a higher five-year all-cause and prostate cancer-specific mortality. Aggressive tumour characteristics and RP techniques were the main contributors to the likelihood of PSM following RP. Reducing the prevalence of PSM will generally improve prostate cancer-specific and all-cause mortality.

Sections du résumé

BACKGROUND
Geographic and temporal variation in positive surgical margins (PSM) for prostate cancer after radical prostatectomy (RP) has been observed. However, it is unclear how much of this variation could be attributed to patient, surgeon, institution, or socioeconomic-related factors and the impact of PSM on death among localized prostate cancer patients.
METHODS
This study aimed to assess the independent and relative contribution of the patient, surgeon, institution and area-level risk factors on geographic and temporal variation of PSM and evaluate the impact of PSM on five-year all-cause and prostate cancer-specific mortality among localized prostate cancer patients. Within the hierarchical-related regression approach, we utilised Bayesian spatial-temporal multi-level models to study individual and area-level predictors with the outcomes, while accounting for geographically structured and unstructured correlation and non-linear trends.
RESULTS
Individual-level data included 10,075 localized prostate cancer cases with RP reported to the Prostate Cancer Outcomes Registry Victoria between 2009 and 2018. Area-level data comprised socio-economic disadvantage and remoteness data at the local government area level in Victoria, Australia. 26 % of patients had PSM, and the rates varied across areas by years. This variation was mainly associated with NCCN risk, followed by RP techniques, surgical institution type, surgeon volume and socio-economic disadvantage. Intermediate (Odds ratio/OR = 1.21,95 % credible interval/Crl = 1.05-1.41), high/very-high risk groups (OR = 2.24,95 % Crl = 1.91-2.64) and public surgical institution (OR = 1.64, 95 % Crl = 1.46-1.84) were independently associated with a higher likelihood of PSM. Robot-assisted (OR = 0.61, 95 % Crl = 0.55-0.68), laparoscopic RP (OR = 0.76, 95 % Crl = 0.62-0.93), high-volume surgeon (OR = 0.84, 95 % Crl = 0.76-0.93) and socio-economically least disadvantaged status (OR = 0.78, 95 % Crl = 0.64-0.94) showed a lower likelihood of PSM. PSM was also independently associated with a higher five-year all-cause and prostate cancer-specific mortality.
CONCLUSION
Aggressive tumour characteristics and RP techniques were the main contributors to the likelihood of PSM following RP. Reducing the prevalence of PSM will generally improve prostate cancer-specific and all-cause mortality.

Identifiants

pubmed: 33862413
pii: S1877-7821(21)00056-4
doi: 10.1016/j.canep.2021.101939
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

101939

Informations de copyright

Copyright © 2021 Elsevier Ltd. All rights reserved.

Auteurs

Win Wah (W)

Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia. Electronic address: win.wah@monash.edu.

Nathan Papa (N)

Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.

Melanie Evans (M)

Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.

Susannah Ahern (S)

Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.

Arul Earnest (A)

Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.

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