Evidence map›Paper›PMID 35288728›Full record

ArticleInternational journal of epidemiology2022

Learning from the pandemic: mortality trends and seasonality of deaths in Australia in 2020.

Gabriel Gregory, Lin Zhu, Andrew Hayen, Katy J L Bell

Abstract read
In one paragraph

Article in International journal of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
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  5. Article
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  8. Diabetes as a cause of death across different COVID-19 epidemic waves.Diabetes research and clinical practice · 2022
    Article
  9. Review
  10. Article
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

4 authors.

Gabriel GregorySchool of Public Health, University of Sydney, Sydney, NSW, Australia and.
Lin ZhuSchool of Public Health, University of Sydney, Sydney, NSW, Australia and.
Andrew HayenSchool of Public Health, University of Technology Sydney, Sydney, NSW, Australia.
Katy J L BellSchool of Public Health, University of Sydney, Sydney, NSW, Australia and.ORCID 0000-0002-0137-3218

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo assess whether the observed numbers and seasonality of deaths in Australia during 2020 differed from expected trends based on 2015-19 data.

methodsWe used provisional death data from the Australian Bureau of Statistics, stratified by state, age, sex and cause of death. We compared 2020 deaths with 2015-19 deaths using interrupted time series adjusted for time trend and seasonality. We measured the following outcomes along with 95% confidence intervals: observed/expected deaths (rate ratio: RR), change in seasonal variation in mortality (amplitude ratio: AR) and change in week of peak seasonal mortality (phase difference: PD).

resultsOverall 4% fewer deaths from all causes were registered in Australia than expected in 2020 [RR 0·96 (0·95-0·98)] with reductions across states, ages and sex strata. There were fewer deaths from respiratory illness [RR 0·79 (0·76-0·83)] and dementia [RR 0·95 (0·93-0·98)] but more from diabetes [RR 1·08 (1·04-1·13)]. Seasonal variation was reduced for deaths overall [AR 0·94 (0·92-0·95)], and for deaths due to respiratory illnesses [AR 0·78 (0·74-0·83)], dementia [AR 0.92 (0.89-0.95)] and ischaemic heart disease [0.95 (0.90-0.97)].

conclusionsThe observed reductions in respiratory and dementia deaths and the reduced seasonality in ischaemic heart disease deaths may reflect reductions in circulating respiratory (non-SARS-CoV-2) pathogens resulting from the public health measures taken in 2020. The observed increase in diabetes deaths is unexplained and merits further study.

Indexed as

Coronary Artery DiseaseCOVID-19DementiaAustraliaCause of DeathHumansMortalityPandemicsAustralia/epidemiologycause of death/trendsCOVID-19/epidemiologyinterrupted time seriesmortality*/trendspandemicsSARS-CoV-2

Identifiers

PMID35288728
PMCPMC9189967

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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.