Evidence map›Paper›PMID 33731076›Full record

ArticleBMC public health2021

Avoidable deaths in Sweden, 1997-2018: temporal trend and the contribution to the gender gap in life expectancy.

Ali Kiadaliri

Abstract read
In one paragraph

Article in BMC public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Trends in Avoidable Mortality in Kazakhstan From 2015 to 2021.International journal of health policy and management · 2024
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  3. Contributions of avoidable mortality to the sex gap in life expectancy and life disparity in Iran.Archives of public health = Archives belges de sante publique · 2023
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4 · The record

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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

1 author.

Ali KiadaliriClinical Epidemiology Unit, Department of Clinical Sciences Lund, Orthopaedics, Lund University, Skåne University Hospital, Remissgatan 4, SE-221 85, Lund, Sweden. ali.kiadaliri@med.lu.se.ORCID 0000-0002-4254-9099

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAvoidable mortality is considered as a potential indicator of the influences of public health policies and healthcare quality on population health. This study aimed to examine the trend in avoidable mortality and its influence on rising life expectancy (LE) and declining gender gap in LE (GGLE) in Sweden.

methodsWe extracted data on causes of death by age, sex, and year from national registry from 1997 to 2018. The UK Office for National Statistics definition was used to divide causes of death into five mutually exclusive categories: amenable, preventable, amenable & preventable, ischemic heart disease (IHD), and non-avoidable causes. We applied Joinpoint regression to analyse temporal trends in age-standardized mortality rates. The Arriaga method was applied to decompose changes in LE and GGLE by age group and causes of death.

resultsAverage annual reductions in avoidable vs. non-avoidable mortality were 2.6% (95% CI:2.5, 2.7) vs. 1.4% (95% CI:1.3, 1.5) in men, and 1.6% (95% CI:1.4, 1.9) vs. 0.9% (95% CI:0.7, 1.0) in women over the study period. LE in men rose by 4.1 years between 1997 and 2018 (from 72.8 to 76.9 years), of which 2.4 years (59.3%) were attributable to reductions in avoidable mortality. Corresponding LE gain was 2.3 years in women (from 78.0 in 1997 to 80.3 in 2018) and avoidable mortality accounted for 1.0 year (45.6%) of this gain. Between 1997 and 2018, the GGLE narrowed by 1.9 years, of which 1.4 years (77.7%) were attributable to avoidable causes. Among avoidable causes, while preventable causes had the largest contribution to the GGLE, IHD had the greatest contributions to LE gains and the narrowing GGLE.

conclusionsOur findings showed that avoidable causes had a substantial contribution to gain in LE with more profound gain in men than in women, resulting in narrowing the GGLE. Lower pace of reductions in preventable than amenable mortality highlights the need for improving the effectiveness of inter-sectoral health policies aimed at behavioural changes.

Indexed as

Health PolicyLife ExpectancyCause of DeathFemaleHumansMaleMortalitySex FactorsSwedenAmenableAvoidableGender gapLife expectancyMortalityPreventableSwedenTemporal trend

Identifiers

PMID33731076
PMCPMC7968161

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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.