COVID-19 hospitalizations and patients' age at admission: The neglected importance of data variability for containment policies.

Danila Azzolina, Rosanna Comoretto, Corrado Lanera, Paola Berchialla, Ileana Baldi, Dario Gregori
Author Information
  1. Danila Azzolina: Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
  2. Rosanna Comoretto: Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
  3. Corrado Lanera: Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
  4. Paola Berchialla: Department of Clinical and Biological Science, University of Torino, Torino, Italy.
  5. Ileana Baldi: Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
  6. Dario Gregori: Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.

Abstract

Introduction: An excess in the daily fluctuation of COVID-19 in hospital admissions could cause uncertainty and delays in the implementation of care interventions. This study aims to characterize a possible source of extravariability in the number of hospitalizations for COVID-19 by considering age at admission as a potential explanatory factor. Age at hospitalization provides a clear idea of the epidemiological impact of the disease, as the elderly population is more at risk of severe COVID-19 outcomes. Administrative data for the Veneto region, Northern Italy from February 1, 2020, to November 20, 2021, were considered.
Methods: An inferential approach based on quasi-likelihood estimates through the generalized estimation equation (GEE) Poisson link function was used to quantify the overdispersion. The daily variation in the number of hospitalizations in the Veneto region that lagged at 3, 7, 10, and 15 days was associated with the number of news items retrieved from Global Database of Events, Language, and Tone (GDELT) regarding containment interventions to determine whether the magnitude of the past variation in daily hospitalizations could impact the number of preventive policies.
Results: This study demonstrated a significant increase in the pattern of hospitalizations for COVID-19 in Veneto beginning in December 2020. Age at admission affected the excess variability in the number of admissions. This effect increased as age increased. Specifically, the dispersion was significantly lower in people under 30 years of age. From an epidemiological point of view, controlling the overdispersion of hospitalizations and the variables characterizing this phenomenon is crucial. In this context, the policies should prevent the spread of the virus in particular in the elderly, as the uncontrolled diffusion in this age group would result in an extra variability in daily hospitalizations.
Discussion: This study demonstrated that the overdispersion, together with the increase in hospitalizations, results in a lagged inflation of the containment policies. However, all these interventions represent strategies designed to contain a mechanism that has already been triggered. Further efforts should be directed toward preventive policies aimed at protecting the most fragile subjects, such as the elderly. Therefore, it is essential to implement containment strategies before the occurrence of potentially out-of-control situations, resulting in congestion in hospitals and health services.

Keywords

References

  1. Sci Rep. 2020 Dec 28;10(1):22386 [PMID: 33372191]
  2. Comput Hum Behav Rep. 2022 May;6:100179 [PMID: 35233473]
  3. Proc Natl Acad Sci U S A. 2021 Apr 6;118(14): [PMID: 33741734]
  4. Nat Med. 2020 Nov;26(11):1714-1719 [PMID: 32943787]
  5. Semin Immunol. 2018 Dec;40:83-94 [PMID: 30501873]
  6. Clin Drug Investig. 2022 Mar;42(3):237-242 [PMID: 35218000]
  7. PLOS Glob Public Health. 2022 Jan 13;2(1):e0000165 [PMID: 36962166]
  8. Stat Med. 2009 Feb 15;28(4):642-58 [PMID: 19065625]
  9. Nat Commun. 2021 Mar 12;12(1):1655 [PMID: 33712583]
  10. JAMA. 2020 May 12;323(18):1775-1776 [PMID: 32203977]
  11. JAMA. 2020 Jun 9;323(22):2249-2251 [PMID: 32374370]
  12. Lancet Infect Dis. 2021 Sep;21(9):1203-1204 [PMID: 34352224]
  13. Int J Health Geogr. 2020 Mar 11;19(1):8 [PMID: 32160889]
  14. BMC Public Health. 2020 Nov 19;20(1):1742 [PMID: 33213391]
  15. Spat Stat. 2022 Jun;49:100551 [PMID: 34782854]
  16. Biometrics. 1986 Mar;42(1):121-30 [PMID: 3719049]
  17. Lancet Public Health. 2022 Mar;7(3):e259-e273 [PMID: 35180434]
  18. Eur J Intern Med. 2020 Nov;81:100-103 [PMID: 33004264]
  19. Disaster Med Public Health Prep. 2022 Aug;16(4):1355-1361 [PMID: 33750493]
  20. Methods Inf Med. 2010;49(5):421-5; discussion 426-32 [PMID: 20871939]
  21. Int J Environ Res Public Health. 2020 May 15;17(10): [PMID: 32429172]
  22. Euro Surveill. 2020 Apr;25(13): [PMID: 32265003]
  23. Clin Microbiol Infect. 2020 Nov;26(11):1537-1544 [PMID: 32810610]
  24. Math Biosci. 2020 Nov;329:108466 [PMID: 32920095]
  25. Public Health. 2020 Aug;185:127 [PMID: 32622218]
  26. Wellcome Open Res. 2020 Apr 9;5:67 [PMID: 32685698]
  27. Front Cell Infect Microbiol. 2022 Jan 24;11:795026 [PMID: 35141170]
  28. Crit Care. 2020 Apr 28;24(1):175 [PMID: 32345337]
  29. Epidemiol Prev. 2020 Sep-Dec;44(5-6 Suppl 2):252-259 [PMID: 33412817]
  30. Sci Rep. 2020 Nov 26;10(1):20731 [PMID: 33244144]
  31. PLoS One. 2021 Jun 1;16(6):e0252441 [PMID: 34061888]
  32. Healthcare (Basel). 2022 Mar 03;10(3): [PMID: 35326954]
  33. Nat Commun. 2020 Aug 26;11(1):4264 [PMID: 32848152]
  34. JMIR Public Health Surveill. 2020 Aug 25;6(3):e20828 [PMID: 32745013]
  35. PLoS One. 2020 Oct 28;15(10):e0241364 [PMID: 33112926]
  36. Int J Environ Res Public Health. 2020 Jul 21;17(14): [PMID: 32708118]

MeSH Term

Humans
Aged
COVID-19
Hospitalization
Policy
Italy

Word Cloud

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