It is well documented that comorbidities predispose adults to serious disease. A question that arises is whether the risk factors are the same for young adults as they are for older adults. Molani et al. investigate this and find that risk factors for serious SARS-CoV-2-related disease differ between these two age groups.

The retrospective study done by Molani et al. examines more than 6900 medical records, correlating the effects of age, comorbidities, and the severity of symptoms from contracting SARS-CoV-2.


Participating patients included individuals who were hospitalized after they received a positive test for SARS-CoV-2 between June 31st to November 15th of 2021. Patients receiving mechanical ventilation were excluded from this study. The sample ranged from 51 hospitals and 1081 clinics in five states. Patients were then divided into two subgroups: younger (age≥18 and<50 years with 1,963 patients), and older (≥50 years with 4,943 patients).

Retrospective study

Molani et al. hypothesized that these age-stratified groups would allow for a proper interpretation of mortality due to SARS-CoV-2, solely based on the patient’s medical background. Some of the factors they analyzed in this study include the patient demographic, medical history, vital signs, and laboratory biomarkers. Due to the varying conditions of each patient they analyzed the group as a whole as well as by age group. This would aid in circumventing differences in chronic illness present before infection, or even vaccination status.

Model analysis

The three key findings were: 1) risk models are effective at analyzing clinical data, 2) vital signs and laboratory test results at the time of admission are more important in predicting severe COVID-19 symptoms than the presence of comorbidities, 3) the age-stratified models show that the severity of symptoms between young and older people with COVID-19 are different.

Table 1. Demographics and medical conditions among hospitalized patients with COVID-19 by severity.

Statistical analysis revealed new information on how variables that correlate with severe infection or even death due to SARS-CoV-2, differ between the younger and older age groups. For example, Molani et al. found that younger patients with heart comorbidities and high BMI are more likely to suffer from severe symptoms than older patients. Conversely, older patients with existing dementia or vasopressors are more likely to experience severe symptoms from SARS-CoV-2 compared to younger patients (table 1).

Figure 1. Age-stratified models for severe COVID-19 outcomes in hospitalized patients of ages between (a) 18-50 and (b) 50+. HR = heart rate, RR = respiratory rate, GLU = glucose, BMI = body mass index, CA = calcium, SBP = systolic blood pressure, AST = aspartate aminotransferase, CREA = creatinine.

From the analysis, Molani et al. noted that body mass index is a greater indicator of SARS-Cov-2 severity for young people. In fact, it shows no significant correlation for the older population. Molani et al. note that future investigations may involve BMI-stratified models to determine the risks of being underweight or overweight in young adults.

They also found that many comorbidities such as higher AST which leads to liver damage, higher creatinine which impairs kidney function, lower calcium levels, higher age, and high BMI put the younger population at a greater risk for severe Covid-19 symptoms.

Finally, for both young and older patients, it is more effective to check vital signs and run laboratory tests for predictions most of the time, than to rely on comorbidities and patient demographics. 


This study highlights the need for early risk stratification in patients with SARS-CoV-2 for determining the level of care a patient is likely to need. Molani et al. used readily available data such as demographics, vital signs, laboratory tests, and medical history for predicting the severity of SARS-CoV-2 in a patient. As a result, the age-stratified modeling approach provides us with a more holistic understanding of the patients’ risk factors and how this needs to translate to the health care decisions that are made.