1000Quantifying multiple causes of death: A systematic review and audit of methods and practice

Background Mortality reporting and research are typically focused on a single underlying cause of death (UCoD) selected from multiple reported causes. The need to incorporate multiple causes of death (MCoD) in mortality statistics is now recognised internationally, but there is scant methodological work to guide analytical approaches. This review aims to identify and appraise current methods and practices used to analyse MCoD data. Methods The Web of Science, Medline, Pubmed and Scopus (from inception to December 2019) were queried. Studies reporting MCoD alone or in comparison with single UCoD were included. The review is supplemented by qualitative interview with international experts. Results 3491 studies were identified; 141 full texts were included in the review. The measures usually estimated when analysing MCoD can be broadly categorised into descriptive measures (n = 93 studies), measures of associations between diseases (n = 46 studies) and advanced statistical methods (n = 11 studies). Descriptive statistics commonly used include standardized ratio of multiple to underlying cause (SRMU) and mortality rates based on any mention of a disease. Approaches used to assess measures of associations between diseases include the Cause-of-Death Association Indicator (CDAI) and social network analysis. The advanced statistical methods include weighting MCoD and lethal defect-wear model of mortality. Audit results will be discussed. Conclusions This review provides a comprehensive and updated summary of methodological approaches used to analyse MCoD data. The merit of each analytical framework is discussed. Key messages More work is needed to develop methodological frameworks that could be used to support routine consideration of MCoD in practice.

923Quantifying multiple causes of death: Observed patterns in Australia, 2006–2017

Background Four fifths of deaths in Australia involve multiple causes, but statistics typically use a single underlying cause of death (UC). The UC approach alone is insufficient for understanding the impact of non-underlying causes and identifying comorbid disease associations at death. Analysis of multiple causes of death (MC) is needed to measure the impact of all causes. We described MC patterns, considering cause-of-death coding and certification practices in Australia. Methods Using deaths registered in Australia from 2006 to 2017 (n = 1773525) coded to the International Classification of Diseases (ICD) and an extended classification (n = 136 causes) based on a World Health Organization short list, we described MCoD data by cause. Age-standardised rates based on UC and MC were compared using the standardised ratio of multiple to underlying causes (SRMU) to estimate the contribution of the cause to mortality compared to using the UC approach. Comorbidity was explored using the cause of death association indicator (CDAI) to compare the observed joint frequency of a contributory-underlying cause combined with expected frequency of the contributory cause (with any UC). Results On average 3.4 conditions caused each death and 24.4% of deaths had 5 plus causes. Largest SRMUs were for genitourinary diseases (8.0), blood diseases (7.8) and musculoskeletal conditions (6.7). CDAIs showed high associations between, for example, accidental alcohol and opioid poisoning, septicaemia and skin infections, and traumatic brain injury and falls. Conclusions MC indicators enhance measures of mortality and reassess the role of causes of death for descriptive and analytical epidemiology. Key messages This research demonstrates the value of MC analysis for Australian mortality data.

892Quantification of mortality incorporating multiple causes of death: Application of weighting strategies to Australian data

Background Mortality statistics are typically based on a single underlying cause of death (UCoD). Although UCoD provides a useful construct, the relevance of assuming that a single disease caused the death is diminishing, especially with increased life expectancy and high proportions of deaths in older ages from chronic/degenerative diseases. Focussing on common underlying causes of death in Australia, we quantified mortality incorporating weighting strategies for multiple causes of death (MCoD). Methods All deaths registered in Australia from 2015-2017 (478,396 deaths) and coded using International Classification of Diseases Version 10 were classified using an extended cause list (n = 136 causes) based on a World Health Organization short list. Age-standardised rates (ASR) were estimated using three weighting methods: (1) traditional approach using UCoD alone; (2) UCoD and associated causes of death (ACoDs) equally weighted and (3) UCoD weighted 0.5 arbitrarily and remaining 0.5 apportioned to the remaining ACoDs. Results Common UCoD were ischaemic heart diseases, cerebrovascular diseases, dementia; 57671, 31515 and 27377 deaths respectively. There were substantial changes in ASR depending on the weighting method used. Variation in mortality patterns estimated using the three weighting methods and challenges to further refinement of the weighting strategy will be discussed. Conclusions Mortality indicators incorporating MCoD enhance traditional measures of mortality and provide a means to reassess the role of diseases in causing death. Further disease specific methods are required to refine current weighting strategies. Key messages Weighting strategies for are useful for quantifying mortality incorporating MCoD, but methodological challenges exist.

Causes of Death at Very Old Ages, Including for Supercentenarians

The causes of death reported on the death certificates of the oldest old are generally seen as unreliable, and as thus providing little useful information on the process leading to death. However, in advanced countries, a majority of the people who die each year are relatively old, and the level of detail provided on medical certificates about the causes of death among this older population is improving. At the same time, scholars are becoming increasingly interested in studying not just the initial cause of death, but multiple causes of death, thereby taking all of the information reported on the certificate into account. This study demonstrates that in a country like France, the cause-of-death pattern evolves regularly until around age 105. The share of people dying of circulatory diseases tends to be quite stable over the age range, while the share of individuals dying of cancer is declining, and the share of people dying of respiratory/infectious diseases is rising. Furthermore, among people who die at very old ages, a typology of multiple causes of death highlights the growing importance of ill-defined causes, while opening the door to an interesting discussion about the concept of cause of death in the supercentenarian population. Instead of representing an ill-defined cause, senility could be considered an actual cause of death. This suggests that daily care is more crucial to the survival of the oldest old than any conventional medical care or treatment. Supercentenarians tend to be so frail that any minor health event or brief lapse of attention on the part of their caregivers can be lethal.

Redistribution of heart failure deaths using two methods: linkage of hospital records with death certificate data and multiple causes of death data

Heart failure is considered a garbage code when assigned as the underlying cause of death. Reassigning garbage codes to plausible causes reduces bias and increases comparability of mortality data. Two redistribution methods were applied to Brazilian data, from 2008 to 2012, for decedents aged 55 years and older. In the multiple causes of death method, heart failure deaths were redistributed based on the proportion of underlying causes found in matched deaths that had heart failure listed as an intermediate cause. In the hospitalization data method, heart failure deaths were redistributed based on data from the decedents’ corresponding hospitalization record. There were 123,269 (3.7%) heart failure deaths. The method with multiple causes of death redistributed 25.3% to hypertensive heart and kidney diseases, 22.6% to coronary heart diseases and 9.6% to diabetes. The total of 41,324 heart failure deaths were linked to hospitalization records. Heart failure was listed as the principal diagnosis in 45.8% of the corresponding hospitalization records. For those, no redistribution occurred. For the remaining ones, the hospitalization data method redistributed 21.2% to a group with other (non-cardiac) diseases, 6.5% to lower respiratory infections and 9.3% to other garbage codes. Heart failure is a frequently used garbage code in Brazil. We used two redistribution methods, which were straightforwardly applied but led to different results. These methods need to be validated, which can be done in the wake of a recent national study that will investigate a big sample of hospital deaths with garbage codes listed as underlying causes.