Predicting mortality due to SARS-CoV-2: A mechanistic score relating obesity and diabetes to COVID-19 outcomes in Mexico

Abstract

WORDS)BACKGROUND: The SARS-CoV-2 outbreak poses challenge to healthcare systems due to high complication rates in patients with cardiometabolic diseases. Here, we identify risk factors and propose a clinical score to predict COVID-19 lethality, including specific factors for diabetes and obesity and its role in improving risk prediction. METHODS:We obtained data of confirmed and negative COVID-19 cases and their demographic and health characteristics from the General Directorate of Epidemiology of Mexican Ministry of Health. We investigated specific risk factors associated to COVID-19 positivity and mortality and explored the impact of diabetes and obesity on modifying COVID-19 related lethality. Finally, we built a clinical score to predict COVID-19 lethality. RESULTS:Among 177,133 subjects at May 18 th , 2020, we observed 51,633 subjects with SARS-CoV-2 and 5,332 deaths. Risk factors for lethality in COVID-19 include early-onset diabetes, obesity, COPD, advanced age, hypertension, immunosuppression, and CKD; we observed that obesity mediates 49.5% of the effect of diabetes on COVID-19 lethality. Earlyonset diabetes conferred an increased risk of hospitalization and obesity conferred an increased risk for ICU admission and intubation. Our predictive score for COVID-19 lethality included age ≥ 65 years, diabetes, early-onset diabetes, obesity, age <40 years, CKD, hypertension, and immunosuppression and significantly discriminates lethal from non-lethal COVID-19 cases (c-statistic=0.823). RESULTS:Here, we propose a mechanistic approach to evaluate risk for complications and lethality attributable to COVID-19 considering the effect of obesity and diabetes in Mexico.Our score offers a clinical tool for quick determination of high-risk susceptibility patients in a first contact scenario.

Lockdown exit strategies and risk of a second epidemic peak: a stochastic agent-based model of SARS-CoV-2 epidemic in France

“…Following previously described methods, 14 which have been recently applied to model the COVID-19 epidemic in New York City, 17 we built a stochastic ABM model of the epidemic All rights reserved. No reuse allowed without permission.…”

“…12 The probabilities of ICU admission and death were stratified by age and comorbidities, including hypertension, diabetes, obesity, coronary heart diseases, and chronic pulmonary diseases based on prior work. [2][3][4][5][6]12,14 Delays between infection, symptom onset, hospital admission, ICU admission, death and recovery were based on prior reports 12,13,16,27 and are detailed in eTable 1. Delays were randomly assigned based on the Weibull distribution.…”

“…In this report, we present results of epidemiological modelling that has helped inform policy making in France. Based on a stochastic agent-based microsimulation model 14 of the COVID-19 epidemic in France, we have projected the potential impact of competing NPIs on the disease's cumulative incidence and mortality, and on ICU-bed occupancy. Specifically, we evaluated quarantine extension from 8 to 16 weeks and post-quarantine measures including social distancing, mandatory mask-wearing, and shielding of the population the more vulnerable to severe COVID-19 infection.…”

“…Advantages of ABM over other traditional modelling techniques include a flexible individual-based approach that can capture an emergent phenomenon with complex interactions between individuals in an heterogeneous population, and provide a natural description of a complex system. 14,15 Because of several uncertainties that determine the risk of virus transmission, such as the number of asymptomatic cases and the duration of the contagious period, 12 the present analysis followed recent recommendations for improving predictive mathematical models of the COVID-19 pandemic 16 and was based on a calibration process that accounts for several disease's transmission parameters within constraints defined by the contact matrix and known parameters of the disease.…”

Asthma and COVID-19 in children – a systematic review and call for data

“…None of the BioRxiv posts were relevant to our topic. Of the 86 pre-prints in MedRxiv, 14 non-duplicate studies included information on asthma [27][28][29][30][31][32][33][34][35][36][37][38][39][40], but none of them included specific information in children.…”