J R Condon1, J You, J McDonnell. 1. Health Gains Planning, Department of Health, Northern Territory Government, Casuarina, Northern Territory, Australia. john.condon@menzies.edu.au
Abstract
BACKGROUND: Indigenous Australians have higher prevalence of chronic diseases and worse acute care outcomes than other Australians. The extent to which higher chronic disease comorbidity levels are responsible for their worse outcomes is not clear, and the performance of comorbidity indices has not been assessed for this population with very high comorbidity levels. METHODS: Using hospital separations data, the Charlson and Elixhauser comorbidity indices were used to measure chronic disease prevalence in 2035 indigenous and non-indigenous patients hospitalised after their first acute myocardial infarction (AMI) in the Northern Territory of Australia between 1992 and 2004, and to adjust for comorbidity in multivariate analysis of mortality outcomes (in-hospital and long-term deaths from coronary heart disease and all causes). Index performance was assessed by the difference between C statistic, Akaike information criterion statistic and estimate of excess indigenous mortality in models with and without comorbidity adjustment. RESULTS: Comorbidity index scores were higher for indigenous than non-indigenous patients and increased considerably over time, at least partly because of information bias. Indigenous patients' higher risk of in-hospital all-cause death was almost fully explained by their higher comorbidity levels. Their higher risk of long-term coronary heart disease and all-cause death was partially explained by higher comorbidity levels. Charlson and Elixhauser indices performed satisfactorily and similarly in this population. CONCLUSION: Comorbidity indices performed well in a population with very high chronic disease prevalence. After adjusting for comorbidity, short-term outcomes were similar for indigenous and non-indigenous AMI patients, but comorbidity at the time of the acute episode only partly explained the worse long-term outcomes for indigenous patients.
BACKGROUND: Indigenous Australians have higher prevalence of chronic diseases and worse acute care outcomes than other Australians. The extent to which higher chronic disease comorbidity levels are responsible for their worse outcomes is not clear, and the performance of comorbidity indices has not been assessed for this population with very high comorbidity levels. METHODS: Using hospital separations data, the Charlson and Elixhauser comorbidity indices were used to measure chronic disease prevalence in 2035 indigenous and non-indigenous patients hospitalised after their first acute myocardial infarction (AMI) in the Northern Territory of Australia between 1992 and 2004, and to adjust for comorbidity in multivariate analysis of mortality outcomes (in-hospital and long-term deaths from coronary heart disease and all causes). Index performance was assessed by the difference between C statistic, Akaike information criterion statistic and estimate of excess indigenous mortality in models with and without comorbidity adjustment. RESULTS: Comorbidity index scores were higher for indigenous than non-indigenous patients and increased considerably over time, at least partly because of information bias. Indigenous patients' higher risk of in-hospital all-cause death was almost fully explained by their higher comorbidity levels. Their higher risk of long-term coronary heart disease and all-cause death was partially explained by higher comorbidity levels. Charlson and Elixhauser indices performed satisfactorily and similarly in this population. CONCLUSION: Comorbidity indices performed well in a population with very high chronic disease prevalence. After adjusting for comorbidity, short-term outcomes were similar for indigenous and non-indigenous AMI patients, but comorbidity at the time of the acute episode only partly explained the worse long-term outcomes for indigenous patients.
Authors: Chenkai Wu; Ellen Smit; Jason L Sanders; Anne B Newman; Michelle C Odden Journal: J Gerontol A Biol Sci Med Sci Date: 2017-10-01 Impact factor: 6.053
Authors: Abbey Diaz; Peter D Baade; Patricia C Valery; Lisa J Whop; Suzanne P Moore; Joan Cunningham; Gail Garvey; Julia M L Brotherton; Dianne L O'Connell; Karen Canfell; Diana Sarfati; David Roder; Elizabeth Buckley; John R Condon Journal: PLoS One Date: 2018-05-08 Impact factor: 3.240