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Recent research findings have augmented the nutrition variable to a higher category of importance than previously appreciated. Inadequate nutrition can impair cognitive development and is associated with educational failure among impoverished children. This suggests that poor nutrition interferes with the formation of human capital, the cornerstone of a nation's social and economic development. Even temporary food shortages can produce adverse outcomes in developed as well as developing countries. The long-held concept of a critical period of brain development has been modified in light of the new understanding that developmental and morphological plasticity are far greater than previously recognized. This knowledge does not mean that there are no lasting adverse outcomes, but that from a policy perspective, intervention and rehabilitation can play crucial roles. This article highlights the relevance of this evidence to social and health programs and policies.

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Background: While numerous studies have identified factors associated with severe COVID-19 outcomes, they have yet to quantify these characteristics. Therefore, our study's purpose is to stratify these risk factors and use them to predict outcomes. Study Design: This is a retrospective review of the CDC COVID-19 Surveillance Data. Logistic regression models calculated risk estimates for independent variables, and random forest models predicted the chance of severe outcomes. Results: Our sample of 3,798,261 patients with COVID-19 consisted mainly of females (51.9%), 10- to 69-year-olds, and White/Non-Hispanics (34.9%). Most were not healthcare workers (90.6%) and did not have preexisting medical conditions (47.1%). Age had an increased risk of severe outcomes that grew every decade of life. White patients had a decreased occurrence of severe outcomes than Non-Whites, except for Pacific Islanders with comparable mortality. The variable selection algorithm detected that three outcomes were more accurate without healthcare worker classification: mechanical ventilation/intubation, pneumonia, and ARDS Acute respiratory distress. However, providers had a decreased risk of severe outcomes overall. Also, patients with preexisting conditions demonstrated an increased risk in all outcomes. Compared to the logistic regressions, the predictive models had a higher performance (AUC>0.8). The death model had the best metrics, followed by hospitalization and ventilation. We amassed these predictive models into the Severe COVID-19 Calculator web application that estimates the probability of severe outcomes. Conclusions: Several patient social and medical demographics recorded by the CDC significantly affect severe COVID-19 outcomes suggesting a multifactorial influence. To account for these variables, a generated Severe Covid-19 Calculator can accurately predict the chance of severe outcomes in citizens that may contract or have COVID-19.