A Predictive Model for Severe COVID-19 in the Medicare Population: A Tool for Prioritizing Primary and Booster COVID-19 Vaccination

Experton, Bettina; Tetteh, Hassan A.; Lurie, Nicole; Walker, Peter; Elena, Adrien; Hein, Christopher S.; Schwendiman, Blake; Vincent, Justin L.; Burrow, Christopher R.

doi:10.3390/biology10111185

Cited by 15 publications

(20 citation statements)

References 16 publications

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“…In the random forest model, the HHI is located at the bottom as the next to the least important factor, while it appears to be one of the few significant explanatory variables in the quantile regression and mixed effect models. This similar discrepancy in the results appears in the existing studies and is probably because random forest models assign greater weight to prediction accuracy and the magnitudes of the coefficients instead of the causal relationship and the statistical significance of individual regressors [ 52 ]. This finding is noted as a limitation in the interpretability of this research.…”

Section: Discussionsupporting

confidence: 50%

Demographics, Socioeconomic Context, and the Spread of Infectious Disease: The Case of COVID-19

Ying

Lee

Chi

et al. 2022

IJERPH

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Importance: Due to the evolving variants of coronavirus disease 2019 (COVID-19), it is important to understand the relationship between the disease condition and socioeconomic, demographic, and health indicators across regions. Background: Studies examining the relationships between infectious disease and socioeconomic variables are not yet well established. Design: A total of 3042 counties in the United States are included as the observation unit in the study. Two outcome variables employed in the study are the control of disease spread and infection prevalence rates in each county. Method: Data are submitted to quantile regression, hierarchical regression, and random forest analyses to understand the extent to which health outcomes are affected by demographics, socioeconomics, and health indicators. Results: Counties with better control of the disease spread tend to have lower infection rates, and vice versa. When measuring different outcome variables, the common risk factors for COVID-19 with a 5% level of statistical significance include employment ratio, female labor ratio, young population ratio, and residents’ average health risk factors, while protective factors include land size, housing value, travel time to work, female population ratio, and ratio of residents who identify themselves as mixed race. Conclusions: The implications of the findings are that the ability to maintain social distancing and personal hygiene habits are crucial in deterring disease transmission and lowering incidence rates, especially in the early stage of disease formation. Relevant authorities should identify preventive factors and take early actions to fight infectious diseases in the future.

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Section: Discussionsupporting

confidence: 50%

Demographics, Socioeconomic Context, and the Spread of Infectious Disease: The Case of COVID-19

Ying

Lee

Chi

et al. 2022

IJERPH

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“…The models can also prioritize which populations to vaccinate or to urge to receive booster doses to maximize lives saved and reduce the load on hospitalization facilities. Several attempts have already been made to build predictive models for COVID-19 severity, notably [4,9,19,[35][36][37][38]. Among the five models cited in the preceding sentence, only the models of Iannou and colleagues [4] and of Experton and colleagues [27] predict at least one of risk of hospitalization or risk of death in a newly infected individual.…”

Section: Discussionmentioning

confidence: 99%

“…The variations in vaccination uptake [8] provide an opportunity to assess the beneficial effects of different vaccination doses after accounting for patient risk factors. Among the factors known to affect COVID-19 severity are advanced age [9][10][11][12], type II diabetes [10,[13][14][15][16][17], kidney disease [10,[17][18][19], chronic obstructive pulmonary disease (COPD) [19][20][21][22][23], obesity [10,14,15,24,25], hypertension [26][27][28], and malignancy [29].…”

Section: Introductionmentioning

confidence: 99%

A Calculator for COVID-19 Severity Prediction Based on Patient Risk Factors and Number of Vaccines Received

et al. 2022

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Vaccines have allowed for a significant decrease in COVID-19 risk, and new antiviral medications can prevent disease progression if given early in the course of the disease. The rapid and accurate estimation of the risk of severe disease in new patients is needed to prioritize the treatment of high-risk patients and maximize lives saved. We used electronic health records from 101,039 individuals infected with SARS-CoV-2, since the beginning of the pandemic and until 30 November 2021, in a national healthcare organization in Israel to build logistic models estimating the probability of subsequent hospitalization and death of newly infected patients based on a few major risk factors (age, sex, body mass index, hemoglobin A1C, kidney function, and the presence of hypertension, pulmonary disease, and malignancy) and the number of BNT162b2 mRNA vaccine doses received. The model’s performance was assessed by 10-fold cross-validation: the area under the curve was 0.889 for predicting hospitalization and 0.967 for predicting mortality. A total of 50%, 80%, and 90% of death events could be predicted with respective specificities of 98.6%, 95.2%, and 91.2%. These models enable the rapid identification of individuals at high risk for hospitalization and death when infected, and they can be used to prioritize patients to receive scarce medications or booster vaccination. The calculator is available online.

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“…Male sex, particularly those with androgenetic alopecia (AGA) [ 171 - 175 ], users of anabolic-androgenic steroids (AAS) [ 176 ], hypersensitivity of the androgen receptor (AR) [ 177 , 178 ], and women with hyperandrogenic states are known independent risk factors for COVID-19 [ 179 , 180 ]. In contrast, users of anti-androgen agents, in particular those under androgen deprivation therapy (ADT) for castration-resistant prostate cancer, and males with prostate cancer have lower risk compared to age-adjusted males without prostate cancer [ 181 - 185 ]. These observations are supported by the molecular mechanisms of SARS-CoV-2 cell entry is highly dependent on androgen activity [ 186 , 187 ].…”

Section: Anti-androgen Therapy and Covid-19mentioning

confidence: 99%

“MATH+” Multi-Modal Hospital Treatment Protocol for COVID-19 Infection: Clinical and Scientific Rationale

Kory¹,

Meduri

Iglesias

et al. 2022

J Clin Med Res

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In December 2019, coronavirus disease 2019 (COVID-19), a severe respiratory illness caused by the new coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China. The greatest impact that COVID-19 had was on intensive care units (ICUs), given that approximately 20% of hospitalized cases developed acute respiratory failure (ARF) requiring ICU admission. Based on the assumption that COVID-19 represented a viral pneumonia and no anti-coronaviral therapy existed, nearly all national and international health care societies recommended "supportive care only" avoiding other therapies outside of randomized controlled trials, with a specific prohibition against the use of corticosteroids in treatment. However, early studies of COVID-19-associated ARF reported inexplicably high mortality rates, with frequent prolonged durations of mechanical ventilation (MV), even from centers expert in such supportive care strategies. These reports led the authors to form a clinical expert panel called the Front-Line COVID-19 Critical Care Alliance (www.flccc.net). The panel collaboratively reviewed the emerging clinical, radiographic, and pathological reports of COVID-19 while initiating multiple discussions among a wide clinical network of front-line clinical ICU experts from initial outbreak areas in China, Italy, and New York. Based on the shared early impressions of "what was working and what wasn't working", the increasing medical journal publications and the rapidly accumulating personal clinical experiences with COVID-19 patients, a treatment protocol was created for the hospitalized patients based on the core therapies of methylprednisolone, ascorbic acid, thiamine, heparin and non-antiviral co-interventions (MATH+). This manuscript reviews the scientific and clinical rationale behind MATH+ based on published in-vitro, pre-clinical, and clinical data in support of each medicine, with a special emphasis of studies supporting their use in the treatment of patients with viral syndromes and COVID-19 specifically.

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A Predictive Model for Severe COVID-19 in the Medicare Population: A Tool for Prioritizing Primary and Booster COVID-19 Vaccination

Cited by 15 publications

References 16 publications

Demographics, Socioeconomic Context, and the Spread of Infectious Disease: The Case of COVID-19

Demographics, Socioeconomic Context, and the Spread of Infectious Disease: The Case of COVID-19

A Calculator for COVID-19 Severity Prediction Based on Patient Risk Factors and Number of Vaccines Received

“MATH+” Multi-Modal Hospital Treatment Protocol for COVID-19 Infection: Clinical and Scientific Rationale

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