Investigating local variation in disease rates within high-rate regions identified using smoothing

Submitted: 12 August 2022
Accepted: 20 January 2023
Published: 25 May 2023
Abstract Views: 263
PDF: 173
Supplementary Materials: 37
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.


Exploratory disease maps are designed to identify risk factors of disease and guide appropriate responses to disease and helpseeking behaviour. However, when produced using aggregatelevel administrative units, as is standard practice, disease maps may mislead users due to the Modifiable Areal Unit Problem (MAUP). Smoothed maps of fine-resolution data mitigate the MAUP but may still obscure spatial patterns and features. To investigate these issues, we mapped rates of Mental Health- Related Emergency Department (MHED) presentations in Perth, Western Australia, in 2018/19 using Australian Bureau of Statistics (ABS) Statistical Areas Level 2 (SA2) boundaries and a recent spatial smoothing technique: the Overlay Aggregation Method (OAM). Then, we investigated local variation in rates within high-rate regions delineated using both approaches. The SA2- and OAM-based maps identified two and five high-rate regions, respectively, with the latter not conforming to SA2 boundaries. Meanwhile, both sets of high-rate regions were found to comprise a select number of localised areas with exceptionally high rates. These results demonstrate how, due to the MAUP, disease maps that are produced using aggregate-level administrative units are unreliable as a basis for delineating geographic regions of interest for targeted interventions. Instead, reliance on such maps to guide responses may compromise the efficient and equitable delivery of healthcare. Detailed investigation of local variation in rates within high-rate regions identified using both administrative units and smoothing is required to improve hypothesis generation and the design of healthcare responses.



PlumX Metrics


Download data is not yet available.


Althubaiti A. 2016. Information bias in health research: Definition, pitfalls, and adjustment methods. J Multidiscip Healthc 9:211‐217. DOI:
Australian Bureau of Statistics (ABS). 2016a. Australian Statistical Geography Standard (ASGS) Volume 1 – main structure and greater capital city statistical area. Catalogue number 1270.0.55.001, ABS, Canberra, ACT, Australia.
Australian Bureau of Statistics (ABS). 2016b. Census of population and housing – Counting persons, place of usual residence (SA1), TableBuilder. Findings based on use of ABS TableBuilder Data.
Australian Commission of Safety and Quality in Health Care (ACSQHC). 2017. Australian atlas of healthcare variation series. Available from:
Australian Institute of Health and Welfare (AIHW). 2007. Atlas of avoidable hospitalisations in Australia: Ambulatory care-sensitive conditions. Available from:
Breslow NE, Day NE. 1987. Statistical methods in cancer research. Volume II--The design and analysis of cohort studies. IARC Sci Publ. 82:1-406.
Carlos HA, Shi X, Sargent J, Tanski S, Berke EM. 2010. Density estimation and adaptive bandwidths: A primer for public health practitioners. Int J Health Geogr 9:1–8. DOI:
Catelan D, Biggeri A. 2010. Multiple testing in disease mapping and descriptive epidemiology. Geospat Health 4:219-229. DOI:
Center for the Evaluative Clinical Sciences, Dartmouth Medical School. 1996. The Dartmouth atlas of health care. Chicago, Ill.: American Hospital Publishing. Available from:
Clayton D, Kaldor J, 1987. Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics 43:671–81. DOI:
Cockings S, Harfoot A, Martin D, Hornby D. 2011. Maintaining existing zoning systems using automated zone-design techniques: methods for creating the 2011 Census output geographies for England and Wales. Environ Plan A 43:2399-418. DOI:
Cockings S, Martin D. 2005. Zone design for environment and health studies using pre-aggregated data. Soc Sci Med 60:2729-2742. DOI:
Devine OJ, Louis TA, Halloran ME. 1994. Empirical Bayes methods for stabilizing incidence rates before mapping. Epidemiology 5:622-630. DOI:
Hodge JK, Marshall E, Patterson G. 2010. Gerrymandering and convexity. College Math J 41:312-324. DOI:
Holman CAJ, Bass AJ, Rouse IL, Hobbs MST. 1999. Population-based linkage of health records in Western Australia: development of a health services research linked database. Aust N Z J Public Health 23:453-9. DOI:
Independent Hospital Pricing Authority (IHPA). 2019. Australian Refined Diagnosis Related Groups (AR-DRG) Version 10.0. Available from:
Kamel Boulos MN, Geraghty EM. 2020. Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: How 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. Int J Health Geogr 19:8. DOI:
Latkin CA, Edwards C, Davey-Rothwell MA, Tobin KE. 2017. The relationship between social desirability bias and self-reports of health, substance use, and social network factors among urban substance users in Baltimore, Maryland. Addict Behav 73:133‐136. DOI:
Lessler J, Moore SM, Luquero FJ, McKay HS, Grais R, Henkens M, Mengel M, Dunoyer J, M'bangombe M, Lee EC, Djingarey MH, Sudre B, Bompangue D, Fraser RSM, Abubakar A, Perea W, Legros D, Azman AS. 2018. Mapping the burden of cholera in sub-Saharan Africa and implications for control: An analysis of data across geographical scales. Lancet 391:1908-15. DOI:
Martin D. Extending the automated zoning procedure to reconcile incompatible zoning systems. 2003. Int J Geogr Inf Sci 17:181-96. DOI:
Mooney P, Juhász L. 2020. Mapping COVID-19: How web-based maps contribute to the infodemic. Dialogues in Hum Geogr 10:265-270. DOI:
Moran PAP. 1950. Notes on continuous stochastic phenomena. Biometrika 37:17–23. DOI:
Openshaw S, Taylor PJ. 1979. A million or so correlation coefficients: three experiments on the modifiable areal unit problem. Chapter 5. In: Wrigley N, editor. Statistical applications in the spatial sciences, vol. 127. London: Pion; p. 127–44.
Queensland Clinical Senate. 2018. Dare to compare: reducing unwarranted variation for potentially preventable hospitalisations. Brisbane: Queensland Health. Available from:
R Core Team. 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available from:
St John of God Health Care. 2016. Annual Report 2015–16. Melbourne: St John of God Health Care. Available from:
Talbot TO, Kulldorf M, Forand SP, Haley VB. 2000. Evaluation of spatial filters to create smoothed maps of health data. Stat Med 19:2399-2408. DOI:<2399::AID-SIM577>3.0.CO;2-R
The International statistical classification of diseases and related health problems, tenth revision, Australian modification (ICD-10-AM). 2010. 7th ed. ed. Lidcombe, NSW: Lidcombe, NSW: National Centre for Classification in Health. Available from:
Tobler, WR. 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, Supplement: Proceedings. International Geographical Union. Commission on Quantitative Methods 46:234-240. DOI:
Tuson M, Yap M, Kok MR, Boruff B, Murray K, Vickery A, Turlach BA, Whyatt D. 2020. Overcoming inefficiencies arising due to the impact of the modifiable areal unit problem on single‑aggregation disease maps. Int J Health Geogr 19:40. DOI:
Tuson M, Yap M, Kok MR, Murray K, Turlach B, Whyatt D. 2019. Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem. Int J Health Geogr 18:6. DOI:
Waller LA, Gotway CA. 2004. Chapter 4: Visualizing Spatial Data. In Balding DJ, Cressie NAC, Fisher NI, Johnstone IM, Kadane JB, Molenberghs G, Ryan LM, Scott DW, Smith AFM, Tengels JL, eds. Applied spatial statistics for public health data. John Wiley & Sons, Inc., Hoboken, New Jersey. p. 86-104.
Werner AK, Strosnider HM. 2020. Developing a surveillance system of sub-county data: Finding suitable population thresholds for geographic aggregations. Spat Spatiotemporal Epidemiol 33:100339. DOI:
Wieland SC, Cassa CA, Mandl KD, Berger B. 2008. Revealing the spatial distribution of a disease while preserving privacy. Proc Natl Acad Sci USA 105:17608-17613. DOI:

Supporting Agencies

Government of Western Australia Department of Health

How to Cite

Tuson, M., Yap, M., & Whyatt, D. (2023). Investigating local variation in disease rates within high-rate regions identified using smoothing. Geospatial Health, 18(1).