Articlehybrid2022

Quantifying cause-related mortality in Australia, incorporating multiple causes: observed patterns, trends and practical considerations

Karen Bishop · Margarita Moreno‐Betancur · Saliu Adejumobi Balogun · James Eynstone-Hinkins · Lauren L. Moran · Chalapati Rao +4 more

19 citations0/0 files storedInternational Journal of Epidemiology

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Insurance, Mortality, Demography, Risk Management Healthcare Systems and Public Health Chronic Disease Management Strategies

Abstract

BACKGROUND: Mortality statistics using a single underlying cause of death (UC) are key health indicators. Rising multimorbidity and chronic disease mean that deaths increasingly involve multiple conditions. However, additional causes reported on death certificates are rarely integrated into mortality indicators, partly due to complexities in data and methods. This study aimed to assess trends and patterns in cause-related mortality in Australia, integrating multiple causes (MC) of death. METHODS: Deaths (n = 1 773 399) in Australia (2006-17) were mapped to 136 ICD-10-based groups and MC indicators applied. Age-standardized cause-related rates (deaths/100 000) based on the UC (ASRUC) were compared with rates based on any mention of the cause (ASRAM) using rate ratios (RR = ASRAM/ASRUC) and to rates based on weighting multiple contributing causes (ASRW). RESULTS: Deaths involved on average 3.4 causes in 2017; the percentage with >4 causes increased from 20.9 (2006) to 24.4 (2017). Ischaemic heart disease (ASRUC = 73.3, ASRAM = 135.8, ASRW = 63.5), dementia (ASRUC = 51.1, ASRAM = 98.1, ASRW = 52.1) and cerebrovascular diseases (ASRUC = 39.9, ASRAM = 76.7, ASRW = 33.5) ranked as leading causes by all methods. Causes with high RR included hypertension (ASRUC = 2.2, RR = 35.5), atrial fibrillation (ASRUC = 8.0, RR = 6.5) and diabetes (ASRUC = 18.5, RR = 3.5); the corresponding ASRW were 12.5, 12.6 and 24.0, respectively. Renal failure, atrial fibrillation and hypertension ranked among the 10 leading causes by ASRAM and ASRW but not by ASRUC. Practical considerations in working with MC data are discussed. CONCLUSIONS: Despite the similarities in leading causes under the three methods, with integration of MC several preventable diseases emerged as leading causes. MC analyses offer a richer additional perspective for population health monitoring and policy development.

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References

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