Rights statement: This is the author’s version of a work that was accepted for publication in Spatial and Spatio-temporal Epidemiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial and Spatio-temporal Epidemiology, 23, 2017 DOI: 10.1016/j.sste.2017.09.002
Accepted author manuscript, 702 KB, PDF document
Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
| <mark>Journal publication date</mark> | 11/2017 |
|---|---|
| <mark>Journal</mark> | Spatial and Spatio-temporal Epidemiology |
| Volume | 23 |
| Number of pages | 9 |
| Pages (from-to) | 59-67 |
| Publication Status | Published |
| Early online date | 5/10/17 |
| <mark>Original language</mark> | English |
Interpreting changes over time in small-area variation in cancer survival, in light of changes in cancer incidence, aids understanding progress in cancer control, yet few space-time analyses have considered both measures. Bayesian space-time hierarchical models were applied to Queensland Cancer Registry data to examine geographical changes in cancer incidence and relative survival over time for the five most common cancers (colorectal, melanoma, lung, breast, prostate) diagnosed during 1997-2004 and 2005-2012 across 516 Queensland residential small-areas. Large variation in both cancer incidence and survival was observed. Survival improvements were fairly consistent across the state, although small for lung cancer. Incidence changes varied by location and cancer type, ranging from lung and colorectal cancers remaining relatively constant over time, to prostate cancer dramatically increasing across the entire state. Reducing disparities in cancer-related outcomes remains a health priority, and space-time modelling of different measures provides an important mechanism by which to monitor progress.