To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
With persistent incidence, incomplete vaccination rates, confounding respiratory illnesses, and few therapeutic interventions available, COVID-19 continues to be a burden on the pediatric population. During a surge, it is difficult for hospitals to direct limited healthcare resources effectively. While the overwhelming majority of pediatric infections are mild, there have been life-threatening exceptions that illuminated the need to proactively identify pediatric patients at risk of severe COVID-19 and other respiratory infectious diseases. However, a nationwide capability for developing validated computational tools to identify pediatric patients at risk using real-world data does not exist.
HHS ASPR BARDA sought, through the power of competition in a challenge, to create computational models to address two clinically important questions using the National COVID Cohort Collaborative: (1) Of pediatric patients who test positive for COVID-19 in an outpatient setting, who are at risk for hospitalization? (2) Of pediatric patients who test positive for COVID-19 and are hospitalized, who are at risk for needing mechanical ventilation or cardiovascular interventions?
This challenge was the first, multi-agency, coordinated computational challenge carried out by the federal government as a response to a public health emergency. Fifty-five computational models were evaluated across both tasks and two winners and three honorable mentions were selected.
This challenge serves as a framework for how the government, research communities, and large data repositories can be brought together to source solutions when resources are strapped during a pandemic.
To examine the association between adherence to the Dutch Guidelines for a Healthy Diet created by the Dutch Health Council in 2006 and overall and smoking-related cancer incidence.
Prospective cohort study.
Adherence to the guidelines, which includes one recommendation on physical activity and nine on diet, was measured using an adapted version of the Dutch Healthy Diet (DHD) index. The score ranged from 0 to 90 with a higher score indicating greater adherence to the guidelines. We estimated the hazard ratios (HR) and 95 % confidence intervals for the association between the DHD index (in tertiles and per 20-point increment) at baseline and cancer incidence at follow-up.
We studied 35 608 men and women aged 20–70 years recruited into the European Prospective Investigation into Cancer and Nutrition–Netherlands (EPIC-NL) study during 1993–1997.
After an average follow-up of 12·7 years, 3027 cancer cases were documented. We found no significant association between the DHD index (tertile 3 v. tertile 1) and overall (HR = 0·97; 95 % CI 0·88, 1·07) and smoking-related cancer incidence (HR = 0·89; 95 % CI 0·76, 1·06) after adjustment for relevant confounders. Excluding the components physical activity or alcohol from the score did not change the results. None of the individual components of the DHD index was significantly associated with cancer incidence.
In the present study, participants with a high adherence to the Dutch Guidelines for a Healthy Diet were not at lower risk of overall or smoking-related cancer. This does not exclude that other components not included in the DHD index may be associated with overall cancer risk.
Fish consumption is the major dietary source of EPA and DHA, which according to rodent experiments may reduce body fat mass and prevent obesity. Only a few human studies have investigated the association between fish consumption and body-weight gain. We investigated the association between fish consumption and subsequent change in body weight. Women and men (n 344 757) participating in the European Prospective Investigation into Cancer and Nutrition were followed for a median of 5·0 years. Linear and logistic regression were used to investigate the associations between fish consumption and subsequent change in body weight. Among women, the annual weight change was 5·70 (95 % CI 4·35, 7·06), 2·23 (95 % CI 0·16, 4·31) and 11·12 (95 % CI 8·17, 14·08) g/10 g higher total, lean and fatty fish consumption per d, respectively. The OR of becoming overweight in 5 years among women who were normal weight at enrolment was 1·02 (95 % CI 1·01, 1·02), 1·01 (95 % CI 1·00, 1·02) and 1·02 (95 % CI 1·01, 1·04) g/10 g higher total, lean and fatty consumption per d, respectively. Among men, fish consumption was not statistically significantly associated with weight change. Adjustment for potential over- or underestimation of fish consumption did not systematically change the observed associations, but the 95 % CI became wider. The results in subgroups from analyses stratified by age or BMI at enrolment were not systematically different. In conclusion, the present study suggests that fish consumption has no appreciable association with body-weight gain.
Whether there are differences between countries in the validity of self-reported diet in relation to BMI, as evaluated using recovery biomarkers, is not well understood. We aimed to evaluate BMI-related reporting errors on 24 h dietary recalls (24-HDR) and on dietary questionnaires (DQ) using biomarkers for protein and K intake and whether the BMI effect differs between six European countries. Between 1995 and 1999, 1086 men and women participating in the European Prospective Investigation into Cancer and Nutrition completed a single 24-HDR, a DQ and one 24 h urine collection. In regression analysis, controlling for age, sex, education and country, each unit (1 kg/m2) increase in BMI predicted an approximately 1·7 and 1·3 % increase in protein under-reporting on 24-HDR and DQ, respectively (both P < 0·0001). Exclusion of individuals who probably misreported energy intake attenuated BMI-related bias on both instruments. The BMI effect on protein under-reporting did not differ for men and women and neither between countries on both instruments as tested by interaction (all P>0·15). In women, but not in men, the DQ yielded higher mean intakes of protein that were closer to the biomarker-based measurements across BMI groups when compared with 24-HDR. Results for K were similar to those of protein, although BMI-related under-reporting of K was of a smaller magnitude, suggesting differential misreporting of foods. Under-reporting of protein and K appears to be predicted by BMI, but this effect may be driven by ‘low-energy reporters’. The BMI effect on under-reporting seems to be the same across countries.