Evidence map›Paper›PMID 33035253›Full record

ArticlePloS one2020

Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic.

Marta Blangiardo, Michela Cameletti, Monica Pirani, Gianni Corsetti, Marco Battaglini, Gianluca Baio

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 3 pooled it
–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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

44 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022
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  18. Assessment of Excess Mortality in Italy in 2020-2021 as a Function of Selected Macro-Factors.International journal of environmental research and public health · 2023
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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

6 authors.

Marta BlangiardoMRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom.ORCID 0000-0002-1621-704X
Michela CamelettiDepartment of Management, Economics and Quantitative Methods, University of Bergamo, Bergamo, Italy.
Monica PiraniMRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom.
Gianni CorsettiIstat, Directorate for Social Statistics and Population Census, Rome, Italy.
Marco BattagliniIstat, Directorate for Social Statistics and Population Census, Rome, Italy.
Gianluca BaioDepartment of Statistical Sciences, University College London, London, United Kingdom.

Funding

Improving statistical methods for small area estimates of public health indicators and demographic characteristicsR01HD092580 · NICHD · EMORY UNIVERSITY · PI Lance A Waller · 2018 to 2026
$2.3M
Medical Research Council MR/S019669/1NICHD NIH HHS R01 HD092580NIH HHS R01HD092580-01A1
6 · The paper itself

Abstract

In this study we present the first comprehensive analysis of the spatio-temporal differences in excess mortality during the COVID-19 pandemic in Italy. We used a population-based design on all-cause mortality data, for the 7,904 Italian municipalities. We estimated sex-specific weekly mortality rates for each municipality, based on the first four months of 2016-2019, while adjusting for age, localised temporal trends and the effect of temperature. Then, we predicted all-cause weekly deaths and mortality rates at municipality level for the same period in 2020, based on the modelled spatio-temporal trends. Lombardia showed higher mortality rates than expected from the end of February, with 23,946 (23,013 to 24,786) total excess deaths. North-West and North-East regions showed one week lag, with higher mortality from the beginning of March and 6,942 (6,142 to 7,667) and 8,033 (7,061 to 9,044) total excess deaths respectively. We observed marked geographical differences also at municipality level. For males, the city of Bergamo (Lombardia) showed the largest percent excess, 88.9% (81.9% to 95.2%), at the peak of the pandemic. An excess of 84.2% (73.8% to 93.4%) was also estimated at the same time for males in the city of Pesaro (Central Italy), in stark contrast with the rest of the region, which does not show evidence of excess deaths. We provided a fully probabilistic analysis of excess mortality during the COVID-19 pandemic at sub-national level, suggesting a differential direct and indirect effect in space and time. Our model can be used to help policy-makers target measures locally to contain the burden on the health-care system as well as reducing social and economic consequences. Additionally, this framework can be used for real-time mortality surveillance, continuous monitoring of local temporal trends and to flag where and when mortality rates deviate from the expected range, which might suggest a second wave of the pandemic.

Indexed as

Databases, FactualBayes TheoremBetacoronavirusCause of DeathCoronavirus InfectionsCOVID-19FemaleHumansItalyMaleModels, TheoreticalPandemicsPneumonia, ViralSARS-CoV-2

Identifiers

PMID33035253
PMCPMC7546500

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.