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Aggregation of data: Although more detailed address information is collected by the state- and territory cancer registries, to preserve confidentiality and privacy, the most detailed geographical information able to be provided for the Australian Cancer Atlas was the SA2 classification used by the ABS. However, even with this aggregation, there were often very small numbers of people diagnosed with cancer in many specific SA2s each year. This meant that we needed to aggregate several years of data to calculate estimates with sufficient reliability.

Choice of geographical area: The aggregation of the data into geographical areas and over time does have some limitations, because it covers over any differences within each area and within the combined time period. Therefore, it is possible that if we chose different geographical areas, or a different time period, then the geographical patterns shown in the current Australian Cancer Atlas would be different. This is what is known as the modifiable areal unit problem (MAUP) [Wong (2004)]. While we do not discount the importance of the MAUP in spatial analyses, the estimates supplied are at the smallest level of geography currently possible. This meant we could not check how estimates would differ when using smaller areas.

Accuracy of addresses: The aggregation of address information into SA2 areas was made by the respective state- and territory-based cancer registries. No details on the specific quality of this aggregation was available, with different methods being used by different cancer registries. It remains possible that some misclassification of SA2 has occurred in these cancer data, for example if patients provide a PO Box rather than street address information, or the location of a hospital is provided rather than a residential address.

Areas with extreme estimates: When examining the initial spatial estimates, we identified some problematic areas, where a specific area would have a markedly high estimate, while its neighbouring areas had correspondingly low estimates. When this occurred consistently across multiple sex/cancer combinations, and the ratio of differences was substantial, some modification of the area assignment was performed after consulting with the relevant data custodians. Cases were redistributed on a population-basis, considering broad age categories, sex and cancer type. The areas this impacted on were Charles (NT), Darwin City (NT), Weddell (NT), Mackay (Qld), Bundaberg (Qld), Berserker (Qld), Burpengary East (Qld), Brisbane Port (Qld) and Morphett Vale (SA). Full details of these modifications are available on request.

Exposure differences: Estimates are for the residential address at time of diagnosis. It is unknown how long the patient may have been living there, or whether they continue to live there throughout their cancer treatment. People often commute or travel each day, and where someone lives may not be where they were exposed. Most cancers have a long lag time between exposure to a carcinogen and developing cancer, so these maps cannot be used to identify areas with high risk factor exposure. Exclusion of some cancers: During the 2000s invasive bladder cancer was inconsistently defined between State and Territory cancer registries, so this cancer could not be included in the Australian Cancer Atlas.

Lack of information at individual level: The estimates generated relate to aggregated data and are not applicable to any individual living within an area.

References

• Anselin L, Syabri I, Kho Y. (2005) GeoDa: An Introduction to Spatial Data Analysis. Geographical Analysis 38 (2006) 5–22
• Wong, DWS. (2004). The Modifiable Areal Unit Problem (MAUP). WorldMinds: Geographical Perspectives on 100 Problems: Commemorating the 100th Anniversary of the Association of American Geographers 1904–2004. D. G. Janelle, B. Warf and K. Hansen. Dordrecht, Springer Netherlands: 571-575.