Evidence map›Paper›PMID 39868476›Full record

ArticleInternational journal of epidemiology2024

Quantifying years of life lost in Australia: a multiple cause of death analysis.

Grace Joshy, Karen Bishop, Hang Li, Lauren Moran, Michelle Gourley, Jennifer Welsh, Rosemary Korda, Emily Banks, Tim Adair, Chalapati Rao

Abstract read
In one paragraph

Article in International journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Grace JoshyNational Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia.
Karen BishopNational Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia.
Hang LiNossal Institute for Global Health, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
Lauren MoranAustralian Bureau of Statistics, Canberra, Australia.
Michelle GourleyAustralian Institute of Health and Welfare, Canberra, Australia.
Jennifer WelshNational Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia.ORCID 0000-0003-4415-5920
Rosemary KordaNational Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia.
Emily BanksNational Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia.ORCID 0000-0002-4617-1302
Tim AdairNossal Institute for Global Health, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.ORCID 0000-0002-1562-4452
Chalapati RaoNational Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia.ORCID 0000-0002-9554-0581

Funding

National Health and Medical Research Council of Australia 1163214
6 · The paper itself

Abstract

backgroundDeaths in Australia and other high-income countries increasingly involve multiple conditions. However, key burden of disease measures typically only use the underlying cause of death (UC). We quantified sex and cause-specific years of life lost (YLL) based on UC compared with a method integrating multiple causes of death.

methodsCauses of death for all deaths in Australia (2015-17), mapped to 136 groups based on International Classification of Diseases 10th revision (ICD-10), were ascribed using (1) the UC only and (2) a multiple cause weighting (WT) strategy. Applying the Global Burden of Disease 2010 life table, YLLUC and YLLWT rates were calculated for each sex and cause of death and compared using relative and absolute measures.

resultsAll-cause YLL rates were 113.4/1000 for males and 79.9/1000 for females. Cancers, cardiovascular diseases, external causes, respiratory diseases and nervous system diseases were the five biggest contributors to YLL for each method. For the top 20 causes combined, YLLWT rates were 10% lower for males (YLLWT = 74.93/1000 vs YLLUC = 67.38/1000) and 7% lower for females (YLLWT = 51.34/1000; YLLUC = 47.90/1000); YLLWT rates were lower for ischaemic heart disease and all cancers, but higher for diabetes and dementia, and for chronic obstructive pulmonary disease in males. With multiple cause weighting, renal failure emerged among the top 20 causes of YLL, as did atrial fibrillation and hypertension among females. YLLWT rates for substance abuse, mood disorders, hypertension and schizophrenia were relatively high compared with YLLUC.

conclusionThe YLLWT metric highlights epidemiologically important conditions that are less often selected as the UC.

Indexed as

Life ExpectancyAdolescentAdultAgedAged, 80 and overAustraliaCause of DeathChildChild, PreschoolFemaleGlobal Burden of DiseaseHumansInfantLife TablesMaleMiddle AgedAustraliacause of deathfatal burdenMortalityyears of life lost

Identifiers

PMID39868476
PMCPMC11769717

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.