Temporal dynamics in total excess mortality and COVID-19 deaths in Italian cities.
COVID-19-related death
Demographic factors
Mortality displacement
Surveillance system
Total excess mortality
Journal
BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562
Informations de publication
Date de publication:
14 Aug 2020
14 Aug 2020
Historique:
received:
04
06
2020
accepted:
03
08
2020
entrez:
16
8
2020
pubmed:
17
8
2020
medline:
20
8
2020
Statut:
epublish
Résumé
Standardized mortality surveillance data, capable of detecting variations in total mortality at population level and not only among the infected, provide an unbiased insight into the impact of epidemics, like COVID-19 (Coronavirus disease). We analysed the temporal trend in total excess mortality and deaths among positive cases of SARS-CoV-2 by geographical area (north and centre-south), age and sex, taking into account the deficit in mortality in previous months. Data from the Italian rapid mortality surveillance system was used to quantify excess deaths during the epidemic, to estimate the mortality deficit during the previous months and to compare total excess mortality with deaths among positive cases of SARS-CoV-2. Data were stratified by geographical area (north vs centre and south), age and sex. COVID-19 had a greater impact in northern Italian cities among subjects aged 75-84 and 85+ years. COVID-19 deaths accounted for half of total excess mortality in both areas, with differences by age: almost all excess deaths were from COVID-19 among adults, while among the elderly only one third of the excess was coded as COVID-19. When taking into account the mortality deficit in the pre-pandemic period, different trends were observed by area: all excess mortality during COVID-19 was explained by deficit mortality in the centre and south, while only a 16% overlap was estimated in northern cities, with quotas decreasing by age, from 67% in the 15-64 years old to 1% only among subjects 85+ years old. An underestimation of COVID-19 deaths is particularly evident among the elderly. When quantifying the burden in mortality related to COVID-19, it is important to consider seasonal dynamics in mortality. Surveillance data provides an impartial indicator for monitoring the following phases of the epidemic, and may help in the evaluation of mitigation measures adopted.
Sections du résumé
BACKGROUND
BACKGROUND
Standardized mortality surveillance data, capable of detecting variations in total mortality at population level and not only among the infected, provide an unbiased insight into the impact of epidemics, like COVID-19 (Coronavirus disease). We analysed the temporal trend in total excess mortality and deaths among positive cases of SARS-CoV-2 by geographical area (north and centre-south), age and sex, taking into account the deficit in mortality in previous months.
METHODS
METHODS
Data from the Italian rapid mortality surveillance system was used to quantify excess deaths during the epidemic, to estimate the mortality deficit during the previous months and to compare total excess mortality with deaths among positive cases of SARS-CoV-2. Data were stratified by geographical area (north vs centre and south), age and sex.
RESULTS
RESULTS
COVID-19 had a greater impact in northern Italian cities among subjects aged 75-84 and 85+ years. COVID-19 deaths accounted for half of total excess mortality in both areas, with differences by age: almost all excess deaths were from COVID-19 among adults, while among the elderly only one third of the excess was coded as COVID-19. When taking into account the mortality deficit in the pre-pandemic period, different trends were observed by area: all excess mortality during COVID-19 was explained by deficit mortality in the centre and south, while only a 16% overlap was estimated in northern cities, with quotas decreasing by age, from 67% in the 15-64 years old to 1% only among subjects 85+ years old.
CONCLUSIONS
CONCLUSIONS
An underestimation of COVID-19 deaths is particularly evident among the elderly. When quantifying the burden in mortality related to COVID-19, it is important to consider seasonal dynamics in mortality. Surveillance data provides an impartial indicator for monitoring the following phases of the epidemic, and may help in the evaluation of mitigation measures adopted.
Identifiants
pubmed: 32795276
doi: 10.1186/s12889-020-09335-8
pii: 10.1186/s12889-020-09335-8
pmc: PMC7426899
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1238Subventions
Organisme : Ministero della Salute
ID : CUP: J85J19000390005
Commentaires et corrections
Type : ErratumIn
Références
Lancet Glob Health. 2020 Aug;8(8):e1003-e1017
pubmed: 32553130
Epidemiology. 2009 Jul;20(4):575-83
pubmed: 19295435
Lancet. 2020 May 2;395(10234):e81
pubmed: 32333839
PLoS One. 2013 Apr 18;8(4):e61720
pubmed: 23637892
Euro Surveill. 2017 Apr 6;22(14):
pubmed: 28424146
MMWR Morb Mortal Wkly Rep. 2020 Jun 26;69(25):795-800
pubmed: 32584802
Int J Environ Res Public Health. 2010 May;7(5):2256-73
pubmed: 20623023
JAMA Intern Med. 2020 Oct 1;180(10):1336-1344
pubmed: 32609310
J Infect. 2020 Aug;81(2):e16-e25
pubmed: 32335169
Cancer Med J. 2021 Aug 1;4(2):44-47
pubmed: 32601624
Euro Surveill. 2020 May;25(19):
pubmed: 32431289
Disaster Med Public Health Prep. 2020 Oct;14(5):e39-e41
pubmed: 32234108
N Engl J Med. 2020 Jul 30;383(5):496-498
pubmed: 32348640
Ann Intern Med. 2020 Sep 1;173(5):385-386
pubmed: 32384135
JAMA Intern Med. 2020 Jul 1;180(7):927-928
pubmed: 32259190
J Infect Dis. 2020 Jun 16;222(1):9-16
pubmed: 32246136
Chest. 2020 Oct;158(4):1364-1375
pubmed: 32533957
Nature. 2020 Aug;584(7821):430-436
pubmed: 32640463
Int Psychogeriatr. 2020 Oct;32(10):1161-1164
pubmed: 32307030
JAMA. 2020 May 12;323(18):1775-1776
pubmed: 32203977
Sci Rep. 2020 Aug 13;10(1):13764
pubmed: 32792591
Euro Surveill. 2020 Jul;25(26):
pubmed: 32643601
JAMA. 2020 Apr 28;323(16):1545-1546
pubmed: 32167538