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Spatial variation in prostate cancer survival in the Northern and Yorkshire region of England using Bayesian relative survival smoothing.

Fairley L, Forman D, West R, Manda S - Br. J. Cancer (2008)

Bottom Line: All covariates had a significant association with excess mortality; men from more deprived areas, older age at diagnosis and diagnosed in 1990-1994 had higher excess mortality.The unadjusted relative excess risks (RER) of death by PCT ranged from 0.75 to 1.66.After adjustment, areas of high and low excess mortality were smoothed towards the mean, and the RERs ranged from 0.74 to 1.49.

View Article: PubMed Central - PubMed

Affiliation: Northern and Yorkshire Cancer Registry and Information Service, St James's Institute of Oncology, St James's University Hospital, Level 6, Bexley Wing, Beckett Street, Leeds LS9 7TF, UK. lesley.fairley@nycris.leedsth.nhs.uk

ABSTRACT
Primary Care Trust (PCT) estimates of survival lack robustness as there are small numbers of deaths per year in each area, even when incidence is high. We assess PCT-level spatial variation in prostate cancer survival using Bayesian spatial models of excess mortality. We extracted data on men diagnosed with prostate cancer between 1990 and 1999 from the Northern and Yorkshire Cancer Registry and Information Service database. Models were adjusted for age at diagnosis, period of diagnosis and deprivation. All covariates had a significant association with excess mortality; men from more deprived areas, older age at diagnosis and diagnosed in 1990-1994 had higher excess mortality. The unadjusted relative excess risks (RER) of death by PCT ranged from 0.75 to 1.66. After adjustment, areas of high and low excess mortality were smoothed towards the mean, and the RERs ranged from 0.74 to 1.49. Using Bayesian smoothing techniques to model cancer survival by geographic area offers many advantages over traditional methods; estimates in areas with small populations or low incidence rates are stabilised and shrunk towards local and global risk estimates improving reliability and precision, complex models are easily handled and adjustment for covariates can be made.

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Related in: MedlinePlus

Relative excess risks (RER) by PCT.
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fig3: Relative excess risks (RER) by PCT.

Mentions: Figure 3 shows the unadjusted and fully adjusted RER for each PCT. As expected many of the PCTs with relative survival rates less than the NYCRIS rate had RERs higher than 1 and PCTs with relative survival rates higher than the NYCRIS rates had RERs less than 1. The unadjusted RER range from 0.76 to 1.66; seven PCTs have significantly higher RERs compared with the overall NYCRIS region and seven PCTs have significantly lower RERs. The fully adjusted RERs range from 0.74 to 1.49, and from the graph we can see that the fully adjusted model shrinks the RERs towards the baseline value of 1. The number of PCTs significantly different from the baseline is reduced to four PCTs with significantly higher RERs and four PCTs with significantly lower RERs.


Spatial variation in prostate cancer survival in the Northern and Yorkshire region of England using Bayesian relative survival smoothing.

Fairley L, Forman D, West R, Manda S - Br. J. Cancer (2008)

Relative excess risks (RER) by PCT.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2600701&req=5

fig3: Relative excess risks (RER) by PCT.
Mentions: Figure 3 shows the unadjusted and fully adjusted RER for each PCT. As expected many of the PCTs with relative survival rates less than the NYCRIS rate had RERs higher than 1 and PCTs with relative survival rates higher than the NYCRIS rates had RERs less than 1. The unadjusted RER range from 0.76 to 1.66; seven PCTs have significantly higher RERs compared with the overall NYCRIS region and seven PCTs have significantly lower RERs. The fully adjusted RERs range from 0.74 to 1.49, and from the graph we can see that the fully adjusted model shrinks the RERs towards the baseline value of 1. The number of PCTs significantly different from the baseline is reduced to four PCTs with significantly higher RERs and four PCTs with significantly lower RERs.

Bottom Line: All covariates had a significant association with excess mortality; men from more deprived areas, older age at diagnosis and diagnosed in 1990-1994 had higher excess mortality.The unadjusted relative excess risks (RER) of death by PCT ranged from 0.75 to 1.66.After adjustment, areas of high and low excess mortality were smoothed towards the mean, and the RERs ranged from 0.74 to 1.49.

View Article: PubMed Central - PubMed

Affiliation: Northern and Yorkshire Cancer Registry and Information Service, St James's Institute of Oncology, St James's University Hospital, Level 6, Bexley Wing, Beckett Street, Leeds LS9 7TF, UK. lesley.fairley@nycris.leedsth.nhs.uk

ABSTRACT
Primary Care Trust (PCT) estimates of survival lack robustness as there are small numbers of deaths per year in each area, even when incidence is high. We assess PCT-level spatial variation in prostate cancer survival using Bayesian spatial models of excess mortality. We extracted data on men diagnosed with prostate cancer between 1990 and 1999 from the Northern and Yorkshire Cancer Registry and Information Service database. Models were adjusted for age at diagnosis, period of diagnosis and deprivation. All covariates had a significant association with excess mortality; men from more deprived areas, older age at diagnosis and diagnosed in 1990-1994 had higher excess mortality. The unadjusted relative excess risks (RER) of death by PCT ranged from 0.75 to 1.66. After adjustment, areas of high and low excess mortality were smoothed towards the mean, and the RERs ranged from 0.74 to 1.49. Using Bayesian smoothing techniques to model cancer survival by geographic area offers many advantages over traditional methods; estimates in areas with small populations or low incidence rates are stabilised and shrunk towards local and global risk estimates improving reliability and precision, complex models are easily handled and adjustment for covariates can be made.

Show MeSH
Related in: MedlinePlus