Evolution of non-fatal burden estimates for cardiovascular disease in Australia: a comparison of national and state-wide methodology of burden of disease
Courtney Weber A B C * , Michelle Hobday C , Wendy Sun C , Laura Kirkland C , Lee Nedkoff A D and Judith M. Katzenellenbogen AA School of Population and Global Health, The University of Western Australia, WA, Australia.
B Centre for Clinical Research in Emergency Medicine, Harry Perkins Institute of Medical Research, Perth, WA, Australia.
C Western Australian Department of Health, East Perth, WA, Australia.
D Cardiology Population Health Laboratory, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia.
Australian Health Review 46(6) 756-764 https://doi.org/10.1071/AH22149
Submitted: 7 June 2022 Accepted: 23 October 2022 Published: 18 November 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of AHHA.
Abstract
Objective Burden of disease studies measure the impact of disease at the population level;however, methods and data sources for estimates of prevalence vary. Using a selection of cardiovascular diseases, we aimed to describe the implications of using different disease models and linked administrative data on prevalence estimation within three Australian burden of disease studies.
Methods Three different methods (A = 2011 Australian Burden of Disease Study; B = 2015 Australian Burden of Disease Study; C = 2015 Western Australian Burden of Disease Study), which used linked data, were used to compare prevalence estimates of stroke, aortic aneurysm, rheumatic valvular heart disease (VHD) and non-rheumatic VHD. We applied these methods to 2015 Western Australian data, and calculated crude overall and age-specific prevalence for each condition.
Results Overall, Method C produced estimates of cardiovascular prevalence that were lower than the other methods, excluding non-rheumatic VHD. Prevalence of acute and chronic stroke was up to one-third higher in Method A and B compared to Method C. Aortic aneurysms had the largest change in prevalence, with Method A producing an eight-fold higher estimate compared to Method C, but Method B was 10–20% lower. Estimates of VHD varied dramatically, with an up to six-fold change in prevalence in Method C due to substantial changes to disease models and the use of linked data.
Conclusions Prevalence estimates require the best available data sources, updated disease models and constant review to inform government policy and health reform. Availability of nation-wide linked data will markedly improve future burden estimates.
Keywords: burden of disease, cardiovascular disease, disease models, hospitalisation, linked data, methodology, population, prevalence.
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