Age-specific associations between underlying health conditions and hospitalisation, death and in-hospital death among confirmed COVID-19 cases: a multi-country study based on surveillance data, June to December 2020

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.

Prospective Clinical Surveillance for Severe Acute Respiratory Illness in Kenyan Hospitals during the COVID-19 pandemic

“…The value of surveillance systems during the COVID-19 pandemic cannot be overstated (9). Insights from these systems and other hospital-based studies have been useful in identifying those at increased risk for severe outcomes and informing both pharmaceutical and non-pharmaceutical interventions against the disease(6, 25, 26, 27, 28, 29). Our study found that patients admitted between May 2020 and December 2022 with SARI had a higher mortality rate, especially those with COVID-19, compared to their non-SARI counterparts.…”

“…Relying on routine clinical data and documentation practices which differ by individual and by site limited the study’s ability to explore the effect of other known risk factors for death in COVID-19 such as multiple comorbidities at admission, social habits e.g., smoking, HIV infection or exposure to previous medication (6, 10, 29). While completion of demographic parameters was high, poor documentation practices and missing data for clinical parameters and investigations limited the number of patients included in the multivariable analysis.…”

On the Impact of Mass Screening for SARS-CoV-2 through Self-Testing in Greece

“…The probabilities of severe disease , though learned from the fitting process defined below, are constrained to lie within the following intervals which have widths twice the size of the estimates in (18). Similarly, we learn and constrain them to lie in intervals [0, 2 q a ] where q a represents the total deaths divided by the total hospitalizations in age group a in a given wave, obtained from (23).…”

“…Recall that those with severe disease eventually require hospitalization. (18) provide estimates for the probabilities of infected and unvaccinated individuals who are hospitalized (1% for 0 to 18, 7% for 19 to 64, and 30% for 65+). These estimates do not quite correspond to p severe av in our model, which is a conditional probability on symptomatic disease.…”

“…To facilitate the fitting process, we constrain certain model parameters as we discuss next, and summarized in Table 3. The probabilities of severe disease p severe av , though learned from the fitting process defined below, are constrained to lie within the following intervals 0 ≤ p severe 0-18,v ≤ 0.02, 0 ≤ p severe 19-64,v ≤ 0.14, 0 ≤ p severe 65+,v ≤ 0.6, which have widths twice the size of the estimates in (18). Similarly, we learn p die a and constrain them to lie in intervals [0, 2q a ] where q a represents the total deaths divided by the total hospitalizations in age group a in a given wave, obtained from (23).…”