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. Early-onset 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.

Background: The impact of the coronavirus disease 2019 (COVID-19) pandemic in Mexico City has been sharp, as several social inequalities at all levels coexist. Here we conducted an in-depth evaluation of the impact of individual and municipal-level social inequalities on the COVID-19 pandemic in Mexico City. Methods: We analyzed suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases, from the Mexico City Epidemiological Surveillance System from 24 February 2020 to 31 March 2021. COVID-19 outcomes included rates of hospitalization, severe COVID-19, invasive mechanical ventilation, and mortality. We evaluated socioeconomic occupation as an individual risk, and social lag, which captures municipal-level social vulnerability, and urban population density as proxies of structural risk factors. Impact of reductions in vehicular mobility on COVID-19 rates and the influence of risk factors were also assessed. Finally, we assessed discrepancies in COVID-19 and non-COVID-19 excess mortality using death certificates from the general civil registry. Results: We detected vulnerable groups who belonged to economically unfavored sectors and experienced increased risk of COVID-19 outcomes. Cases living in marginalized municipalities with high population density experienced greater risk for COVID-19 outcomes. Additionally, policies to reduce vehicular mobility had differential impacts modified by social lag and urban population density. Finally, we report an under-registry of COVID-19 deaths along with an excess mortality closely related to marginalized and densely populated communities in an ambulatory setting. This could be attributable to a negative impact of modified hospital admission criteria during the pandemic. Conclusions: Socioeconomic occupation and municipality-wide factors played a significant role in shaping the course of the COVID-19 pandemic in Mexico City.

Objective: To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. Design: Two stage individual participant data meta-analysis. Setting: Secondary and tertiary care. Participants: 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. Data sources: Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ , and through PROSPERO, reference checking, and expert knowledge. Model selection and eligibility criteria: Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor. Methods: Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. Main outcome measures: 30 day mortality or in-hospital mortality. Results: Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al’s model (0.96, 0.59 to 1.55, 0.21 to 4.28). Conclusion: The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care.

Introduction Diabetes and hyperglycemia are risk factors for critical COVID-19 outcomes; however, the impact of pre-diabetes and previously unidentified cases of diabetes remains undefined. Here, we profiled hospitalized patients with undiagnosed type 2 diabetes and pre-diabetes to evaluate its impact on adverse COVID-19 outcomes. We also explored the role of de novo and intrahospital hyperglycemia in mediating critical COVID-19 outcomes. Research design and methods Prospective cohort of 317 hospitalized COVID-19 cases from a Mexico City reference center. Type 2 diabetes was defined as previous diagnosis or treatment with diabetes medication, undiagnosed diabetes and pre-diabetes using glycosylated hemoglobin (HbA1c) American Diabetes Association (ADA) criteria and de novo or intrahospital hyperglycemia as fasting plasma glucose (FPG) ≥140 mg/dL. Logistic and Cox proportional regression models were used to model risk for COVID-19 outcomes. Results Overall, 159 cases (50.2%) had type 2 diabetes and 125 had pre-diabetes (39.4%), while 31.4% of patients with type 2 diabetes were previously undiagnosed. Among 20.0% of pre-diabetes cases and 6.1% of normal-range HbA1c had de novo hyperglycemia. FPG was the better predictor for critical COVID-19 compared with HbA1c. Undiagnosed type 2 diabetes (OR: 5.76, 95% CI 1.46 to 27.11) and pre-diabetes (OR: 4.15, 95% CI 1.29 to 16.75) conferred increased risk of severe COVID-19. De novo/intrahospital hyperglycemia predicted critical COVID-19 outcomes independent of diabetes status. Conclusions Undiagnosed type 2 diabetes, pre-diabetes and de novo hyperglycemia are risk factors for critical COVID-19. HbA1c must be measured early to adequately assess individual risk considering the large rates of undiagnosed type 2 diabetes in Mexico.

Health-care workers (HCWs) could be at increased occupational risk for SARS-CoV-2 infection. Information regarding prevalence and risk factors for adverse outcomes in HCWs is scarce in Mexico. Here, we aimed to explore prevalence of SARS-CoV-2, symptoms, and risk factors associated with adverse outcomes in HCWs in Mexico City. We explored data collected by the National Epidemiological Surveillance System in Mexico City. All cases underwent real-time RT-PCR test. We explored outcomes related to severe COVID-19 in HCWs and the diagnostic performance of symptoms to detect SARS-CoV-2 infection in HCWs. As of July 5 ^th, 2020, 35,095 HCWs were tested for SARS-CoV-2 and 11,226 were confirmed (31.9%). Overall, 4,322 were nurses (38.5%), 3,324 physicians (29.6%), 131 dentists (1.16%) and 3,449 laboratory personnel and other HCWs (30.8%). After follow-up, 1,009 HCWs required hospitalization (9.00%), 203 developed severe outcomes (1.81%), and 93 required mechanical-ventilatory support (0.82%). Lethality was recorded in 226 (2.01%) cases. Symptoms associated with SARS-CoV-2 positivity were fever, cough, malaise, shivering, myalgias at evaluation but neither had significant predictive value. We also identified 341 asymptomatic SARS-CoV-2 infections (3.04%). Older HCWs with chronic non-communicable diseases, pregnancy, and severe respiratory symptoms were associated with higher risk for adverse outcomes. Physicians had higher risk for hospitalization and for severe outcomes compared with nurses and other HCWs. We report a high prevalence of SARS-CoV-2 infection in HCWs in Mexico City. No symptomatology can accurately discern HCWs with SARS-CoV-2 infection. Particular attention should focus on HCWs with risk factors to prevent adverse outcomes and reduce infection risk.

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. Early-onset 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.