Bayesian Spatial Analysis for the Evaluation of Breast Cancer Detection Methods

Abstract

This study investigated the impact of spatial location on the effectiveness of populationbased breast screening in reducing breast cancer mortality compared to other detection methods among Queensland women. The analysis was based on linked population-based datasets from BreastScreen Queensland and the Queensland Cancer Registry for the period of 1997-2008 for women aged less than 90 years at diagnosis. A Bayesian hierarchical regression modelling approach was adopted and posterior estimation was performed using Markov Chain Monte Carlo techniques. This approach accommodated sparse data resulting from rare outcomes in small geographic areas, while allowing for spatial correlation and demographic influences to be included. A relative survival model was chosen to evaluate the relative excess risk for each breast cancer related factor. Several models were fitted to examine the influence of demographic information, cancer stage, geographic information and detection method on women's relative survival. Overall, the study demonstrated that including the detection method and geographic information when assessing the relative survival of breast cancer patients helped capture unexplained and spatial variability. The study also found evidence of better survival among women with breast cancer diagnosed in a screening program than those detected otherwise, as well as lower risk for those residing in a more urban or socio-economically advantaged region, even after adjusting for tumour stage, environmental factors and demographics. However, no evidence of dependency between method of detection and geographic location was found. This project provides a sophisticated approach to examining the benefit of a screening program while considering the influence of geographic factors.

Understanding the effects of socioeconomic status along the breast cancer continuum in Australian women: a systematic review of evidence

“…Women in professional occupations [ 67 , 68 ] and those living in the most advantaged areas [ 68 – 72 ] had higher survival. Two studies also reported this trend but reported non-significance at the individual [ 66 ] and area level [ 66 , 73 ] after statistical attenuation.…”

Are international differences in breast cancer survival between Australia and the UK present amongst both screen‐detected women and non‐screen‐detected women? survival estimates for women diagnosed in West Midlands and New South Wales 1997–2006

“…Other studies have examined the impact of mammographic screening upon individual patient survival. Analyses of survival include examinations of interval cancers (cancers diagnosed following a normal mammogram but prior to the next screening invitation), comparisons of women in dichotomous groups (attenders vs. never‐attenders and those with screen‐detected vs. non‐screen‐detected cancers) and spatial analyses . A review conducted in the UK in 2003 concluded that a better understanding of the effect of screen‐detection required more detailed data.…”

“…Analyses of survival include examinations of interval cancers 4,5 (cancers diagnosed following a normal mammogram but prior to the next screening invitation), comparisons of women in dichotomous groups (attenders vs. never-attenders 6 and those with screen-detected vs. non-screen-detected cancers 7 ) and spatial analyses. 8 A review conducted in the UK in 2003 9 concluded that a better understanding of the effect of screendetection required more detailed data. In particular, the review identified the importance of linkage of mortality data to screening invitations so that the outcome for tumours diagnosed after the introduction of screening might be examined.…”

Spatially Varying Coefficient Inequalities: Evaluating How the Impact of Patient Characteristics on Breast Cancer Survival Varies by Location

“…The total number of subgroups was allowed to range from J = 2 to 6. The upper bound of 6 was chosen based on previous studies that SLAs can be collapsed into 4–5 spatial regions based on their geographic characteristics such as socio-economic status and area remoteness index [ 3 , 17 , 18 ]. Each mixture subgroup of excess hazard was assigned a mixing probability of π j where 0 < π j < 1 and .…”