OBJECTIVE: To analyse the implications of using different methods to predict diabetes prevalence for the future. APPROACH: Different methods used to predict diabetes were compared and recommendations are made. CONCLUSION: We recommend that all projections take a conservative approach to diabetes prevalence prediction and present a 'base case' using the most robust, contemporary data available. We also recommend that uncertainty analyses be included in all analyses. IMPLICATIONS: Despite variation in assumptions and methodology used, all the published predictions demonstrate that diabetes is an escalating problem for Australia. We can safely assume that unless trends in diabetes incidence are reversed there will be at least 2 million Australian adults with diabetes by 2025. If obesity and diabetes incidence trends, continue upwards, and mortality continues to decline, up to 3 million people will have diabetes by 2025, with the figure closer to 3.5 million by 2033. The impact of this for Australia has not been measured.
OBJECTIVE: To analyse the implications of using different methods to predict diabetes prevalence for the future. APPROACH: Different methods used to predict diabetes were compared and recommendations are made. CONCLUSION: We recommend that all projections take a conservative approach to diabetes prevalence prediction and present a 'base case' using the most robust, contemporary data available. We also recommend that uncertainty analyses be included in all analyses. IMPLICATIONS: Despite variation in assumptions and methodology used, all the published predictions demonstrate that diabetes is an escalating problem for Australia. We can safely assume that unless trends in diabetes incidence are reversed there will be at least 2 million Australian adults with diabetes by 2025. If obesity and diabetes incidence trends, continue upwards, and mortality continues to decline, up to 3 million people will have diabetes by 2025, with the figure closer to 3.5 million by 2033. The impact of this for Australia has not been measured.
Authors: Robert J Commons; Claire H Robinson; David Gawler; Joshua S Davis; Ric N Price Journal: Diabetes Res Clin Pract Date: 2015-09-21 Impact factor: 5.602
Authors: Emily D Williams; Dianna J Magliano; Paul Z Zimmet; Anne M Kavanagh; Christopher E Stevenson; Brian F Oldenburg; Jonathan E Shaw Journal: Diabetes Care Date: 2012-05-22 Impact factor: 19.112
Authors: Maria Donald; Jo Dower; Robert Ware; Bryan Mukandi; Sanjoti Parekh; Christopher Bain Journal: BMC Public Health Date: 2012-01-05 Impact factor: 3.295
Authors: Gerard Wong; Christopher K Barlow; Jacquelyn M Weir; Jeremy B M Jowett; Dianna J Magliano; Paul Zimmet; Jonathan Shaw; Peter J Meikle Journal: PLoS One Date: 2013-10-08 Impact factor: 3.240