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Does having young children affect the severity of COVID infection in their parents?

12th August 2022

Study evaluated risk of severe COVID-19 among adults with and without exposure to young children in a large, integrated healthcare system in the US.

Having young children does not protect the parents against infection with COVID-19 but does result in a significantly lower risk of more severe illness, according to an analysis by US researchers.

Data from China during the early part of the COVID-19 pandemic suggested that while children of all ages appeared susceptible to infection, the clinical manifestations were generally less severe than those of adult patients.

Moreover, several factors have been postulated to account for these differences and include lower levels of the ACE2 receptor in children, cross-reactive humoral immunity and T-cell immunity from the common cold (which is also a coronavirus) and how children generally produce lower levels of inflammatory cytokines. 

In fact, one study identified COVID-19 spike glycoprotein-reactive antibodies among uninfected individuals, which were particularly prevalent in children and adolescents, suggesting a degree of cross-immunity.

This is perhaps no surprise due to the continued exposure to colds during early life, and one study has shown that attendance at a large day care centre was associated with more common colds among pre-school children during the preschool years but that this was protective against the common cold during the early school years, presumably through acquired immunity.

With children therefore likely to have some level of cross-immunity to infection with COVID-19, does having young children affect the severity of infection in their parents? This was the question addressed by the current study.

Researchers examined data in the Kaiser Permanente Northern California database, which provides comprehensive healthcare information and looked for adults with and without children. The children were categorised into different age bands: young children (aged 0–5 years), 6–11 years and 12–18 years.

Those parents with young children were considered to be the study population of interest and the three comparative groups were propensity matched 1:1 based on the risk of infection, age, sex and co-morbidities.

Exposure to young children and COVID-19 outcomes

A total of 3,126,427 adults, of whom 24% had children under 18 years of age were included in the analysis. Within the whole cohort, 274,316 adults (8.8%) with a mean age of 36.2 years (51.4% female) had young children.

When researchers compared the risk of infection with COVID-19 among adults without children to those with young children, there was a significantly reduced risk (incidence risk ratio, IRR = 0.85, 95% CI 0.83 – 0.87, p < 0.0001). But among adults without children, there was a significantly higher risk of hospitalisation for COVID-19 (IRR = 1.27, 95% CI 1.10 – 1.46, p = 0.0014) and for admission to an intensive care unit (IRR = 1.49).

Furthermore, when the researchers re-calculated this risk of severe illness relative to the total population within the different groups, i.e. the risk of a severe COVID-19 outcome among adults who became infected, there was a 49% higher rate of hospitalisation and a 76% higher risk of intensive care admission.

The authors concluded that while having young children did not reduce the risk of becoming infected with COVID-19, it was associated with a far less severe illness and suggested that cross-immunity might play a role in protecting against more severe COVID-19.

Soloman MD et al. Risk of severe COVID-19 infection among adults with prior exposure to children. Proc Natl Acad Sci USA 2022.

Cardiometabolic and social factors combined better for prediction of adverse COVID-19 outcomes

15th July 2022

Combining both cardiometabolic and social determinants of health improves the predictive power of models assessing outcomes in COVID-19

The combination of clinical markers of cardiometabolic disease and social determinants of health provides a better predictive model for adverse outcomes in patients with COVID-19 according to a retrospective analysis by researchers from Alabama, US.

Cardiometabolic diseases such as diabetes have become recognised as a risk factor for both infection and more severe disease in COVID-19. Moreover, it has also become clear that there increased risks of infection and more severe disease based on race, ethnicity and various socioeconomic determinants.

Therefore, combined analyses that incorporate both cardiometabolic and social factors might enable a better understanding of how health disparities impact upon COVID-19 outcomes, yet such analyses are rarely undertaken.

As a result, for the present study, the US team made use of electronic health records incorporating both clinical and social factors and sought to determine the ability of models based on parameters extracted from both factors, were able to predict the subsequent need for hospitalisation, intensive care unit (ICU) admission and mortality after infection with COVID-19.

Using these records, the researchers obtained clinical patient data (e.g., glucose levels, body mass index, blood pressure etc) and to calculate a cardiometabolic disease staging (CMDS) value, used to assign risk levels for diabetes, and all-cause and cardiovascular disease mortality. CMDS values range from 0 to 0.99 with higher values indicating a greater risk of developing diabetes.

Individual-level social determinants of health (SDoH) included factors such as martial, employment and insurance status. A neighbourhood SDoH considered factors such as social vulnerability (an index of poverty) rurality and health care access.

Using regression analysis, the researchers modelled the risk of being hospitalised, admitted to ICU and death, using a CMDS model only and then after addition of both individual and neighbourhood SDoH values to determine whether the predictive power of the model changed and which was assessed by measuring the area under the curve.

Cardiometabolic health and COVID-19-related outcomes

A total of 2,873 patients with a mean age of 58.3 years (40.9% male) were included in the analysis, of whom 13.9% were hospitalised, 13.7% admitted to an ICU and 14.8% who died.

Using the CMDS model, each one standard deviation increase in CMDS score was associated with hospitalisation (odds ratio, OR = 2.0, 95% CI 1.83 – 2.20), ICU admission (OR = 1.88) and death (OR = 1.69).

Based on individual level SDoH, patients with no insurance had a higher odds of being hospitalised (OR = 3.35), an ICU admission (OR = 2.99) and death (OR = 7.27). In addition, the analysis also showed that patients with high social vulnerability were more likely to be hospitalised or admitted to an ICU.

Interestingly, when the CMDS model alone was used to predict hospitalisation, it had an area under the curve (AUC) of 0.776. But when individual level SDoH was added to the model, the predictive power improved and the AUC increased to 0.815 and it increased slightly further when both individual and neighbourhood SDoH were added (AUC = 0.819) and these differences were significant (p < 0.05).

Similar improvements to the predictive power were seen when individual and neighbourhood SDoH were added for both ICU admission and mortality models.

The authors concluded that using both clinical and social factor data improved the predictability of models for determination of the risk of severe outcomes after infection with COVID-19. They added that incorporation of both clinical and social measures could help guide treatment, intervention and prevention efforts to improve both health and inequality.

Howell CR et al. Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID-19 outcomes Obesity (Silver Spring) 2022

mRNA-1273 vaccine associated with lower risk of COVID-19 outcomes than BNT162b

8th December 2021

mRNA-1273 appears to be associated with a lower risk of COVID-19-related outcomes such as infection, hospitalisation and death compared to BNT162b. This was the finding of the first head-to-head vaccine effectiveness analysis undertaken by a group of researchers from the Brigham and Women’s Hospital, Harvard Medical School, Boston, US.

Randomised controlled trials have already demonstrated the effectiveness of the currently available COVID-19 vaccines. For instance, the mRNA-1273 vaccine (Moderna) has an efficacy of 94.1% at preventing COVID-19 illness and that a similar efficacy (95%) has been observed for BNT162b (Pfizer-BioNTech).

However, despite this near identical level of efficacy, other work has suggested that there might be differences between these two vaccines, even though they have the same mode of action. One study, for example, indicated that the mRNA-1273 vaccine produced a higher humoral immunogenicity than BNT162b, while other data show that the Moderna vaccine it is associated with a two-fold reduced risk of breakthrough infections compared to BNT162b and also results in a lower incidence of COVID-19-related hospitalisations.

For the present study, the US team turned to data from the national healthcare databases of the Department of Veterans Affairs, which is the largest US integrated healthcare system. The researchers sought to compare the relative effectiveness of the Moderna and Pfizer-BioNTech vaccines with respect to documented COVID-19 infections, symptomatic COVID-19, hospitalisation, admission to an intensive care unit (ICU) and death. In addition, the team examined the effectiveness of the two vaccines against the delta COVID-19 variant and examined these outcomes after 24 weeks.


During the period of study, there were 367,113 recipients of the BN162b and 397,690 of the mRNA-1273 vaccines. Over a 24-week period, 2016 COVID-19 infections were documented, of which 559 were detected as symptomatic, 411 which led to hospitalisation, 125 ICU admissions and 81 deaths.

The absolute risk of infection was low in both vaccine groups; 5.75 events per 1000 persons with the Pfizer-BioNTech and 4.52 with the Moderna vaccine. The 24 week risk ratio for infections for BNT162b compared to mRAN-1273 was 1.27 (95% CI 1.15 – 1.42), 1.39 for symptomatic infection, 1.70 for hospitalisation, 1.38 for ICU admission and 1.11 for death. Each of these risks was statistically significant apart from the risk of death. The overall risk difference expressed as events over 24 weeks per 1,000 persons was 1.23 for a documented infection.

Using these data, the authors calculated the number needed to vaccinate with mRNA-1273 instead of BN162b to prevent one case of documented infection was 813.

Analysis during the time period characterised by dominance of the delta COVID-19 variant, there was a 58% higher risk of infection in those vaccinated with BNT162b although this difference was non-significant (risk ratio = 1.58, 95% CI 0.85 – 2.33).

Commenting on these findings, the authors highlighted how their study had shown that recipients of the BNT162b vaccine had a 27% higher risk of a documented COVID-19 infection and a 70% increased risk of hospitalisation compared to the Moderna vaccine.

They concluded that the data provided evidence of a lower 24-week risk of COVID-19-related outcomes among those receiving the m-RNA-1273 vaccine compared to BNT162b.


Dickerman BA et al. Comparative Effectiveness of BNT162b2 and mRNA-1273 Vaccines in U.S. Veterans. New Eng J Med 2021