Weight of risk factors for mortality and short-term mortality displacement during the COVID-19 pandemic.


COVID-19, SARS-COV-2, “harvesting effect”, “mortality displacement”, comorbidities, “risk factor”


Background: We conducted a population-based cohort study to estimate mortality before, during and after the COVID-19 peak and to compare mortality in 2020 with rates reported in previous years, with a view to helping decision makers to apply containment measures for high-risk groups.

Methods: All deaths were collected between 2015 and 2020 from municipal registry database. In 2020, weeks 1-26 were stratified in three periods: before, during and after the COVID mortality peak. The Poisson Generalized Linear regression Model showed the “harvesting effect”. Three logistic regressions for 8 dependent variables (age and comorbidities) and a t-test of  differences described all-cause mortality risk factors in 2019 and 2020 and differences between COVID and non-COVID patients.

Results: A total of 47,876 deaths were collected. All-cause deaths increased by 38.5% during the COVID peak and decreased by 18% during the post-peak period in comparison with the average registered during the control period (2015-19), with significant mortality displacement in 2020. Except for chronic renal injuries in subjects aged 45-64 years, diabetes and chronic cardiovascular diseases in those aged 65-84 years, and neuropathies in those aged >84 years, the weight of comorbidities in deaths was similar or lower in COVID subjects than in non-COVID subjects.

Discussions: Surprisingly, the weight of comorbidities in death, compared to weight in non-COVID subjects allows you to highlight some surprising results such as COPD, IBD and Cancer. The excess mortality that we observed in the entire period were modest in comparison with initial estimates during the peak, owing to the mild influenza season and the harvesting effect starting from the second half of May.





1. Theodore Lytras , Katerina Pantavou , Elisavet Mouratidou, Sotirios Tsiodras. Mortality attributable to seasonal influenza in Greece, 2013 to 2017: variation by type/subtype and age, and a possible harvesting effect. Eurosurveillance 2019 Volume 24, Issue 14
2. Kinney PL, Schwartz J, et al. Winter Season Mortality: Will Climate Warming Bring Benefits? Environ Res Lett. 2015;10(6):064016. https://doi.org/10.1088/1748-9326/10/6/064016 PMID: 26495037]
3. Baccini M, Kosatsky T, et al. Impact of summer heat on urban population mortality in Europe during the 1990s: an evaluation of years of life lost adjusted for harvesting. PLoS One. 2013;8(7):e69638. Published 2013 Jul 22. doi:10.1371/journal.pone.0069638
4. Hajat S, Armstrong BG, et al. Mortality displacement of heat-related deaths: a comparison of Delhi, São Paulo, and London. Epidemiology. 2005;16(5):613-620. doi:10.1097/01.ede.0000164559.41092.2).
5. Braga A, Zanobetti A, et al. The time course of weather-related deaths. Epidemiology (2001) 12: 662–667.
6. Rocklöv J, Forsberg B et al. The effect of high ambient temperature on the elderly population in three regions of Sweden. Int J Environ Res Public Health. 2010;7: pag.2607–2619.
7. Hajat S, Armstrong B, et al. Impact of high temperatures on mortality: is there an added heat wave effect? Epidemiology (2006) 17: 632–638.
8. Analitis A., Katsouyanni K., et al. Effects of Cold Weather on Mortality: Results From 15 European Cities Within the PHEWE Project . American Journal of Epidemiology, Volume 168, Issue 12, 15 December 2008, Pages 1397–1408, https://doi.org/10.1093/aje/kwn266
9. World of Meters, https://www.worldometers.info/coronavirus/, accessed to 09/07/2020.
10. Epicentro, Italian Superior Institute (ISS), accessed on 09/07/2020
11. Italian National Statistic’s Institute (ISTAT), web archive (https://www.istat.it/it/archivio), accessed on 10/07/2020
12. Banerjee A, Pasea L, et al. Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study. Lancet, Volume 395, issue 10238, p1715-1725, may 30, 2020
13. Spiegelhalter D. How much ‘normal’ risk does Covid represent? Medium, March 21, 2020. https://medium.com/wintoncentre/how-much-normal-risk-does-covid-represent-4539118e1196 (accessed April 16, 2020)
14. Sylvestre E, Bouzillé G, et al. Combining information from a clinical data warehouse and a pharmaceutical database to generate a framework to detect comorbidities in electronic health records. BMC Med Inform Decis Mak. 2018; 18: 9. PMCID: PMC5784648
15. Rattanaumpawan P, Wongkamhla T, et al. Accuracy of ICD-10 Coding System for Identifying Comorbidities and Infectious Conditions Using Data from a Thai University Hospital Administrative Database. J Med Assoc Thai. 2016;99(4):368-373.
16. Chen Y, Zivkovic M, et al. A Systematic Review of Coding Systems Used in Pharmacoepidemiology and Database Research. Methods Inf Med. 2018;57(1):1-42. doi:10.3414/ME17-05-0006
17. Dushoff J., Plotkin JB., et Al. Mortality due to Influenza in the United States—An Annualized Regression Approach Using Multiple-Cause Mortality Data. American Journal of Epidemiology, Volume 163, Issue 2, 15 January 2006, Pages 181–187, https://doi.org/10.1093/aje/kwj024
18. Hajat S., Armstrong BG., et al. Mortality Displacement of Heat-Related Deaths. A Comparison of Delhi, São Paulo, and London. Epidemiology, September 2005 - Volume 16 - Issue 5 - p 613-620 doi: 10.1097/01.ede.0000164559.41092.2a
19. Jørgen G Bramness, Fredrik A Walby et al., Analyzing Seasonal Variations in Suicide With Fourier Poisson Time-Series Regression: A Registry-Based Study From Norway, 1969-2007PMID: 26081677 DOI: 10.1093/aje/kwv064
20. Stephen Politzer-Ahles, The Hong Kong Polytechnic University, Researchgate.net 2 september 2016, https://www.researchgate.net/post/Comparing_two_odds_ratios_for_statistical_significant_difference accessed 10 July 2020
21. WHO: report interactive charts online https://apps.who.int/flumart/Default?ReportNo=10, accessed 30 july 2020
22. EUROMOMO: excess mortality 2016-2020 https://www.euromomo.eu/bulletins/2020-32/
23. Italian Ministry of Health - Mortality surveillance system of Regione Lazio. http://www.salute.gov.it/portale/caldo/SISMG_sintesi_ULTIMO.pdf. SISMG Sistema di sorveglianza della mortalità giornaliera, andamento stagionale della mortalità 2016-2020 https://www.epiprev.it/andamento-della-mortalit%C3%A0-giornaliera-sismg-nelle-citt%C3%A0-italiane-relazione-all%E2%80%99epidemia-di-covid-19. Accessed 10 august 2020
24. Zhou F, Yu T, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020;395:1054e1062
25. [CDC], People with medical condition need extra precaution, https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html, accessed to 30 July 2020.
26. Yichun C, Ran L, et al. “Kidney disease is associated with in-hospital death of patients with COVID-19” Kidney International. Volume 97, issue 5, p829-838, may 01, 2020 Open Access Published: March 19, 2020 DOI:https://doi.org/10.1016/j.kint.2020.03.005
27. Mantovani A., D. Byrne C., et al. “Diabetes as a risk factor for greater COVID-19 severity and in-hospital death: a meta-analysis of observational studies”. Nutrition, Metabolism and Cardiovascular Diseases, Volume 30, Issue 8, 24 July 2020, Pages 1236-1248. Journal Pre-proof https://doi.org/10.1016/j.numecd.2020.05.014
28. Mandeep R. Mehra, Sapan S. Desai, et al. Cardiovascular Disease, Drug Therapy, and Mortality in Covid-19. New England Journal of Medicine. June 18, 2020; 382:e102 DOI: 10.1056/NEJMoa2007621
29. Wang B, Li R, et al. Does comorbidity increase the risk of patients with COVID-19? Evidence from meta-analysis. Aging (Albany NY). 2020; 12:6049-6057. https://doi.org/10.18632/aging.103000
30. Jing Yang, Ya Zheng, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. International Journal of Infectious Diseases, Volume 94, 2020, Pages 91-95,ISSN 1201-9712,https://doi.org/10.1016/j.ijid.2020.03.017.
31. WHO - Risk Communication and Community Engagement (RCCE) https://www.who.int/publications/i/item/risk-communication-and-community-engagement-(rcce)-action-plan-guidance, accessed 10 august 2020
32. Maringe C., Spicer J, et al. The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-based, modelling study. Lancet 2020]
33. Vassilis G., Papoutsi E. and Ilias I. Siempos Effect of Cancer on Clinical Outcomes of Patients with COVID-19: A Meta-Analysis of Patient Data. JCO Global Oncol 6:799-808, 2020
34. Joharatnam-Hogan N, Hochhauser D, et al: Outcomes of the 2019 novel coronavirus in patients with or without a history of cancer - a multi-centre North London experience. medRxiv 10.1101/2020.04.16.20061127
35. Xu Z, Shi L, et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med 2020; published online Feb 18. https://doi.org/10.1016/S2213-2600(20)30076-X.
36. Lin Ling K, Hilmi I et al. Inflammatory Bowel Disease (IBD) Working Party guidelines on IBD management during the COVID-19 pandemic, JGHF, vol. 4/2020 pag.320-323
37. Bezzio C, Saibeni S et al. Outcomes of COVID-19 in 79 patients with IBD in Italy: an IG-IBD study. BMJ journal, 2020;69:1213–1217. doi:10.1136/gutjnl-2020-321411