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Abstract

Background

Underlying conditions are risk factors for severe COVID-19 outcomes but evidence is limited about how risks differ with age.

Aim

We sought to estimate age-specific associations between underlying conditions and hospitalisation, death and in-hospital death among COVID-19 cases.

Methods

We analysed case-based COVID-19 data submitted to The European Surveillance System between 2 June and 13 December 2020 by nine European countries. Eleven underlying conditions among cases with only one condition and the number of underlying conditions among multimorbid cases were used as exposures. Adjusted odds ratios (aOR) were estimated using 39 different age-adjusted and age-interaction multivariable logistic regression models, with marginal means from the latter used to estimate probabilities of severe outcome for each condition–age group combination.

Results

Cancer, cardiac disorder, diabetes, immunodeficiency, kidney, liver and lung disease, neurological disorders and obesity were associated with elevated risk (aOR: 1.5–5.6) of hospitalisation and death, after controlling for age, sex, reporting period and country. As age increased, age-specific aOR were lower and predicted probabilities higher. However, for some conditions, predicted probabilities were at least as high in younger individuals with the condition as in older cases without it. In multimorbid patients, the aOR for severe disease increased with number of conditions for all outcomes and in all age groups.

Conclusion

While supporting age-based vaccine roll-out, our findings could inform a more nuanced, age- and condition-specific approach to vaccine prioritisation. This is relevant as countries consider vaccination of younger people, boosters and dosing intervals in response to vaccine escape variants.

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/content/10.2807/1560-7917.ES.2022.27.35.2100883
2022-09-01
2022-09-24
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2022.27.35.2100883
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References

  1. Tejpal A, Gianos E, Cerise J, Hirsch JS, Rosen S, Kohn N, et al. Sex-based differences in COVID-19 outcomes. J Womens Health (Larchmt). 2021;30(4):492-501.  https://doi.org/10.1089/jwh.2020.8974  PMID: 33885345 
  2. Li X, Zhong X, Wang Y, Zeng X, Luo T, Liu Q. Clinical determinants of the severity of COVID-19: A systematic review and meta-analysis. PLoS One. 2021;16(5):e0250602.  https://doi.org/10.1371/journal.pone.0250602  PMID: 33939733 
  3. Bennett KE, Mullooly M, O’Loughlin M, Fitzgerald M, O’Donnell J, O’Connor L, et al. Underlying conditions and risk of hospitalisation, ICU admission and mortality among those with COVID-19 in Ireland: A national surveillance study. Lancet Reg Health Eur. 2021;5:100097.  https://doi.org/10.1016/j.lanepe.2021.100097  PMID: 33880459 
  4. Del Sole F, Farcomeni A, Loffredo L, Carnevale R, Menichelli D, Vicario T, et al. Features of severe COVID-19: A systematic review and meta-analysis. Eur J Clin Invest. 2020;50(10):e13378.  https://doi.org/10.1111/eci.13378  PMID: 32860457 
  5. Hajat C, Stein E. The global burden of multiple chronic conditions: A narrative review. Prev Med Rep. 2018;12:284-93.  https://doi.org/10.1016/j.pmedr.2018.10.008  PMID: 30406006 
  6. Bayliss EA, Bayliss MS, Ware JE Jr, Steiner JF. Predicting declines in physical function in persons with multiple chronic medical conditions: what we can learn from the medical problem list. Health Qual Life Outcomes. 2004;2(1):47.  https://doi.org/10.1186/1477-7525-2-47  PMID: 15353000 
  7. Fortin M, Bravo G, Hudon C, Lapointe L, Almirall J, Dubois M-F, et al. Relationship between multimorbidity and health-related quality of life of patients in primary care. Qual Life Res. 2006;15(1):83-91.  https://doi.org/10.1007/s11136-005-8661-z  PMID: 16411033 
  8. European Centre for Disease Prevention and Control (ECDC). Overview of the implementation of COVID-19 vaccination strategies and deployment plans in the EU/EEA. Stockholm: ECDC; 2022. Available from: https://www.ecdc.europa.eu/en/publications-data/overview-implementation-covid-19-vaccination-strategies-and-vaccine-deployment
  9. World Health Organization (WHO). WHO SAGE roadmap for prioritizing the use of COVID-19 vaccines in the context of limited supply: an approach to inform planning and subsequent recommendations based upon epidemiologic setting and vaccine supply scenarios. Version 1.1. Geneva: WHO; 2020. Available from: https://apps.who.int/iris/handle/10665/341448
  10. European Medicines Agency (EMA). Comirnaty COVID-19 vaccine: EMA recommends approval for children aged 5 to 11. Amsterdam: EMA; 2021. Available from: https://www.ema.europa.eu/en/news/comirnaty-covid-19-vaccine-ema-recommends-approval-children-aged-5-11
  11. European Centre for Disease Prevention and Control (ECDC). TESSy - The European Surveillance System. Coronavirus disease 2019 (COVID-19) data. Reporting protocol. Version 5. Stockholm: ECDC; 2021. Available from: https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-Reporting-Protocol-v4.2.pdf
  12. European Centre for Disease Prevention and Control (ECDC). COVID-19 surveillance report. TESSy data quality. Stockholm: ECDC; 2021. Available from: https://covid19-surveillance-report.ecdc.europa.eu/#4_TESSy_data_quality
  13. Lüdecke D. Technical details: difference between ggpredict() and ggemmeans(). Town: Publisher; 2020. Available from: https://cran.microsoft.com/snapshot/2020-04-20/web/packages/ggeffects/vignettes/technical_differencepredictemmeans.html
  14. Kompaniyets L, Agathis NT, Nelson JM, Preston LE, Ko JY, Belay B, et al. Underlying medical conditions associated with severe COVID-19 illness among children. JAMA Netw Open. 2021;4(6):e2111182.  https://doi.org/10.1001/jamanetworkopen.2021.11182  PMID: 34097050 
  15. Graff K, Smith C, Silveira L, Jung S, Curran-Hays S, Jarjour J, et al. Risk factors for severe COVID-19 in children. Pediatr Infect Dis J. 2021;40(4):e137-45.  https://doi.org/10.1097/INF.0000000000003043  PMID: 33538539 
  16. Kim L, Garg S, O’Halloran A, Whitaker M, Pham H, Anderson EJ, et al. Risk factors for intensive care unit admission and in-hospital mortality among hospitalized adults identified through the US coronavirus disease 2019 (COVID-19)-associated hospitalization surveillance network (COVID-NET). Clin Infect Dis. 2021;72(9):e206-14.  https://doi.org/10.1093/cid/ciaa1012  PMID: 32674114 
  17. Kim H-J, Hwang H, Hong H, Yim J-J, Lee J. A systematic review and meta-analysis of regional risk factors for critical outcomes of COVID-19 during early phase of the pandemic. Sci Rep. 2021;11(1):9784.  https://doi.org/10.1038/s41598-021-89182-8  PMID: 33963250 
  18. Christensen DM, Strange JE, Gislason G, Torp-Pedersen C, Gerds T, Fosbøl E, et al. Charlson comorbidity index score and risk of severe outcome and death in Danish COVID-19 patients. J Gen Intern Med. 2020;35(9):2801-3.  https://doi.org/10.1007/s11606-020-05991-z  PMID: 32583345 
  19. Tuty Kuswardhani RA, Henrina J, Pranata R, Anthonius Lim M, Lawrensia S, Suastika K. Charlson comorbidity index and a composite of poor outcomes in COVID-19 patients: A systematic review and meta-analysis. Diabetes Metab Syndr. 2020;14(6):2103-9.  https://doi.org/10.1016/j.dsx.2020.10.022  PMID: 33161221 
  20. Rosenthal N, Cao Z, Gundrum J, Sianis J, Safo S. Risk factors associated with in-hospital mortality in a US national sample of patients with COVID-19. JAMA Netw Open. 2020;3(12):e2029058.  https://doi.org/10.1001/jamanetworkopen.2020.29058  PMID: 33301018 
  21. Figliozzi S, Masci PG, Ahmadi N, Tondi L, Koutli E, Aimo A, et al. Predictors of adverse prognosis in COVID-19: A systematic review and meta-analysis. Eur J Clin Invest. 2020;50(10):e13362.  https://doi.org/10.1111/eci.13362  PMID: 32726868 
  22. Reddy RK, Charles WN, Sklavounos A, Dutt A, Seed PT, Khajuria A. The effect of smoking on COVID-19 severity: A systematic review and meta-analysis. J Med Virol. 2021;93(2):1045-56.  https://doi.org/10.1002/jmv.26389  PMID: 32749705 
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