Protection of hybrid immunity against SARS-CoV-2 reinfection and severe COVID-19 during periods of Omicron variant predominance in Mexico.

Background: With the widespread transmission of the Omicron SARS-CoV-2 variant, reinfections have become increasingly common. Here, we explored the role of immunity, primary infection severity, and variant predominance in the risk of reinfection and severe COVID-19 during Omicron predominance in Mexico. Methods: We analyzed reinfections in Mexico in individuals with a primary infection separated by at least 90 days from reinfection using a national surveillance registry of SARS-CoV-2 cases from March 3rd, 2020, to August 13th, 2022. Immunity-generating events included primary infection, partial or complete vaccination, and booster vaccines. Reinfections were matched by age and sex with controls with primary SARS-CoV-2 infection and negative RT-PCR or antigen test at least 90 days after primary infection to explore reinfection and severe disease risk factors. We also compared the protective efficacy of heterologous and homologous vaccine boosters against reinfection. Results: We detected 231,202 SARS-CoV-2 reinfections in Mexico, most occurring in unvaccinated individuals (41.55%). Over 207,623 reinfections occurred during periods of Omicron (89.8%), BA.1 (36.74%), and BA.5 (33.67%) subvariant predominance and a case-fatality rate of 0.22%. Vaccination protected against reinfection, without significant influence of the order of immunity-generating events and provided >90% protection against severe reinfections. Heterologous booster schedules were associated with ~11% and ~ 54% lower risk for reinfection and reinfection-associated severe COVID-19, respectively, modified by time-elapsed since the last immunity-generating event, when compared against complete primary schedules. Conclusion: SARS-CoV-2 reinfections increased during Omicron predominance. Hybrid immunity provides protection against reinfection and associated severe COVID-19, with potential benefit from heterologous booster schedules.

Effectiveness of a nationwide COVID-19 vaccination program in Mexico against symptomatic COVID-19, hospitalizations, and death: a retrospective analysis of national surveillance data.

OBJECTIVES: Vaccination has been effective in ameliorating the impact of COVID-19. Here, we report vaccine effectiveness (VE) of the nationally available COVID-19 vaccines in Mexico. METHODS: Retrospective analysis of a COVID-19 surveillance system to assess the VE of the BNT162b2, messenger RNA (mRNA)-12732, Gam-COVID-Vac, Ad5-nCoV, Ad26.COV2.S, ChAdOx1, and CoronaVac vaccines against SARS-CoV-2 infection, COVID-19 hospitalization, and death in Mexico. The VE was estimated using time-varying Cox proportional hazard models in vaccinated and unvaccinated adults, adjusted for age, sex, and comorbidities. VE was also estimated for adults with diabetes, aged ≥60 years, and comparing the predominance of SARS-CoV-2 variants B.1.1.519 and B.1.617.2. RESULTS: We assessed 793,487 vaccinated and 4,792,338 unvaccinated adults between December 24, 2020 and September 27, 2021. The VE against SARS-CoV-2 infection was the highest for fully vaccinated individuals with mRNA-12732 (91.5%, 95% confidence interval [CI] 90.3-92.4) and Ad26.COV2.S (82.2%, 95% CI 81.4-82.9); for COVID-19 hospitalization, BNT162b2 (84.3%, 95% CI 83.6-84.9) and Gam-COVID-Vac (81.4% 95% CI 79.5-83.1), and for mortality, BNT162b2 (89.8%, 95% CI 89.2-90.2) and mRNA-12732 (93.5%, 95% CI 86.0-97.0). The VE decreased for all vaccines in adults aged ≥60 years, people with diabetes, and periods of Delta variant predominance. CONCLUSION: All the vaccines implemented in Mexico were effective against SARS-CoV-2 infection, COVID-19 hospitalization, and death. Mass vaccination with multiple vaccines is useful to maximize vaccination coverage.

Diabetes-Related Excess Mortality in Mexico: A Comparative Analysis of National Death Registries Between 2017–2019 and 2020

OBJECTIVE To estimate diabetes-related mortality in Mexico in 2020 compared with 2017–2019 after the onset of the coronavirus disease 2019 (COVID-19) pandemic. RESEARCH DESIGN AND METHODS This retrospective, state-level study used national death registries of Mexican adults aged ≥20 years for the 2017–2020 period. Diabetes-related death was defined using ICD-10 codes listing diabetes as the primary cause of death, excluding certificates with COVID-19 as the primary cause of death. Spatial and negative binomial regression models were used to characterize the geographic distribution and sociodemographic and epidemiologic correlates of diabetes-related excess mortality, estimated as increases in diabetes-related mortality in 2020 compared with average 2017–2019 rates. RESULTS We identified 148,437 diabetes-related deaths in 2020 (177 per 100,000 inhabitants) vs. an average of 101,496 deaths in 2017–2019 (125 per 100,000 inhabitants). In-hospital diabetes-related deaths decreased by 17.8% in 2020 versus 2017–2019, whereas out-of-hospital deaths increased by 89.4%. Most deaths were attributable to type 2 diabetes (130 per 100,000 inhabitants). Compared with 2018–2019 data, hyperglycemic hyperosmolar state and diabetic ketoacidosis were the two contributing causes with the highest increase in mortality (128% and 116% increase, respectively). Diabetes-related excess mortality clustered in southern Mexico and was highest in states with higher social lag, rates of COVID-19 hospitalization, and prevalence of HbA1c ≥7.5%. CONCLUSIONS Diabetes-related deaths increased among Mexican adults by 41.6% in 2020 after the onset of the COVID-19 pandemic, occurred disproportionately outside the hospital, and were largely attributable to type 2 diabetes and hyperglycemic emergencies. Disruptions in diabetes care and strained hospital capacity may have contributed to diabetes-related excess mortality in Mexico during 2020.

Diabetes-Related Excess Mortality in Mexico: A Comparative Analysis of National Death Registries Between 2017-2019 and 2020.

OBJECTIVE: To estimate diabetes-related mortality in Mexico in 2020 compared with 2017-2019 after the onset of the coronavirus disease 2019 (COVID-19) pandemic. RESEARCH DESIGN AND METHODS: This retrospective, state-level study used national death registries of Mexican adults aged ≥20 years for the 2017-2020 period. Diabetes-related death was defined using ICD-10 codes listing diabetes as the primary cause of death, excluding certificates with COVID-19 as the primary cause of death. Spatial and negative binomial regression models were used to characterize the geographic distribution and sociodemographic and epidemiologic correlates of diabetes-related excess mortality, estimated as increases in diabetes-related mortality in 2020 compared with average 2017-2019 rates. RESULTS: We identified 148,437 diabetes-related deaths in 2020 (177 per 100,000 inhabitants) vs. an average of 101,496 deaths in 2017-2019 (125 per 100,000 inhabitants). In-hospital diabetes-related deaths decreased by 17.8% in 2020 versus 2017-2019, whereas out-of-hospital deaths increased by 89.4%. Most deaths were attributable to type 2 diabetes (130 per 100,000 inhabitants). Compared with 2018-2019 data, hyperglycemic hyperosmolar state and diabetic ketoacidosis were the two contributing causes with the highest increase in mortality (128% and 116% increase, respectively). Diabetes-related excess mortality clustered in southern Mexico and was highest in states with higher social lag, rates of COVID-19 hospitalization, and prevalence of HbA1c ≥7.5%. CONCLUSIONS: Diabetes-related deaths increased among Mexican adults by 41.6% in 2020 after the onset of the COVID-19 pandemic, occurred disproportionately outside the hospital, and were largely attributable to type 2 diabetes and hyperglycemic emergencies. Disruptions in diabetes care and strained hospital capacity may have contributed to diabetes-related excess mortality in Mexico during 2020.

Incidence of Guillain-Barré syndrome following SARS-CoV-2 immunization: Analysis of a nationwide registry of recipients of 81 million doses of seven vaccines.

BACKGROUND AND PURPOSE: Information on Guillain-Barré syndrome (GBS) as an adverse event following immunization (AEFI) against SARS-CoV-2 remains scarce. We aimed to report GBS incidence as an AEFI among adult (≥18 years) recipients of 81,842,426 doses of seven anti-SARS-CoV-2 vaccines between December 24, 2020, and October 29, 2021, in Mexico. METHODS: Cases were retrospectively collected through passive epidemiological surveillance. The overall observed incidence was calculated according to the total number of administered doses. Vaccines were analyzed individually and by vector as mRNA-based (mRNA-1273 and BNT162b2), adenovirus-vectored (ChAdOx1 nCov-19, rAd26-rAd5, Ad5-nCoV, and Ad26.COV2-S), and inactivated whole-virion-vectored (CoronaVac) vaccines. RESULTS: We identified 97 patients (52 males [53.6%]; median [interquartile range] age 44 [33-60] years), for an overall observed incidence of 1.19/1,000,000 doses (95% confidence interval [CI] 0.97-1.45), with incidence higher among Ad26.COV2-S (3.86/1,000,000 doses, 95% CI 1.50-9.93) and BNT162b2 recipients (1.92/1,00,000 doses, 95% CI 1.36-2.71). The interval (interquartile range) from vaccination to GBS symptom onset was 10 (3-17) days. Preceding diarrhea was reported in 21 patients (21.6%) and mild COVID-19 in four more (4.1%). Only 18 patients were tested for Campylobacter jejuni (positive in 16 [88.9%]). Electrophysiological examinations were performed in 76 patients (78.4%; axonal in 46 [60.5%] and demyelinating in 25 [32.8%]); variants were similar across the platforms. On admission, 91.8% had a GBS disability score ≥3. Seventy-five patients (77.3%) received intravenous immunoglobulin, received seven plasma exchange (7.2%), and 15 (15.5%) were treated conservatively. Ten patients (10.3%) died, and 79.1% of survivors were unable to walk independently. CONCLUSIONS: Guillain-Barré syndrome was an extremely infrequent AEFI against SARS-CoV-2. The protection provided by these vaccines outweighs the risk of developing GBS.

Socio-demographic inequalities and excess non-COVID-19 mortality during the COVID-19 pandemic: a data-driven analysis of 1 069 174 death certificates in Mexico.

BACKGROUND: In 2020, Mexico experienced one of the highest rates of excess mortality globally. However, the extent of non-COVID deaths on excess mortality, its regional distribution and the association between socio-demographic inequalities have not been characterized. METHODS: We conducted a retrospective municipal and individual-level study using 1 069 174 death certificates to analyse COVID-19 and non-COVID-19 deaths classified by ICD-10 codes. Excess mortality was estimated as the increase in cause-specific mortality in 2020 compared with the average of 2015-2019, disaggregated by primary cause of death, death setting (in-hospital and out-of-hospital) and geographical location. Correlates of individual and municipal non-COVID-19 mortality were assessed using mixed effects logistic regression and negative binomial regression models, respectively. RESULTS: We identified a 51% higher mortality rate (276.11 deaths per 100 000 inhabitants) compared with the 2015-2019 average period, largely attributable to COVID-19. Non-COVID-19 causes comprised one-fifth of excess deaths, with acute myocardial infarction and type 2 diabetes as the two leading non-COVID-19 causes of excess mortality. COVID-19 deaths occurred primarily in-hospital, whereas excess non-COVID-19 deaths occurred in out-of-hospital settings. Municipal-level predictors of non-COVID-19 excess mortality included levels of social security coverage, higher rates of COVID-19 hospitalization and social marginalization. At the individual level, lower educational attainment, blue-collar employment and lack of medical care assistance prior to death were associated with non-COVID-19 deaths. CONCLUSION: Non-COVID-19 causes of death, largely chronic cardiometabolic conditions, comprised up to one-fifth of excess deaths in Mexico during 2020. Non-COVID-19 excess deaths occurred disproportionately out-of-hospital and were associated with both individual- and municipal-level socio-demographic inequalities.

Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis.

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.

Variability in case fatality rate risk due to Covid-19 according to health services provider in Mexico City hospitals.

OBJECTIVE: To describe differences in Case Fatality Rate (CFR) for Covid-19 among healthcare subsystems in Mexico City between March and December 2020. MATERIALS AND METHODS: This is a retrospective secondary data analysis from the National Epidemiological Surveillance System data of Covid-19 cases. Information about health provider institutions was retrieved from the Catalogue of Health Establishments (CLUES). Logistic regressions were fitted to determine the association between health subsystems and mortality associated to Covid-19. The analyses were divided between hospitalized and ambulatory patients. RESULTS: The probability of dying from Covid-19 was higher among those treated at Instituto Mexicano del Seguro Social (IMSS) (Hospitalized:OR=5.11, Ambulatory:OR=36.57), Instituto de Se-guridad y Servicios Sociales de los Trabajadores del Estado (ISSSTE) (Hospitalized:OR=2.10, Ambulatory:OR=9.19), Secretaría de Salud (SS) (Hospitalized:OR=1.94, Ambulatory:OR=5.29) or other public institutions (Hospitalized: OR=1.70, Ambulatory:OR=9.56) than in those treated in private in-stitutions. CONCLUSIONS: Differences in healthcare quality and access between health subsystems are profound. It is imperative to increase the capacity and quality of the different health subsystems to improve health outcomes.

A spatio-temporal study of state-wide case-fatality risks during the first wave of the COVID-19 pandemic in Mexico.

spatio-temporal analysis of the first wave of the coronavirus (COVID-19) pandemic in Mexico (April to September 2020) was performed by state. Descriptive analyses through diagrams, mapping, animations and time series representations were carried out. Greater risks were observed at certain times in specific regions. Various trends and clusters were observed and analysed by fitting linear mixed models and time series clustering. The association of co-morbidities and other variables were studied by fitting a spatial panel data linear model (SPLM). On average, the greatest risks were observed in Baja California Norte, Chiapas and Sonora, while some other densely populated states, e.g., Mexico City, had lower values. The trends varied by state and a four-order polynomial, including fixed and random effects, was necessary to model them. The most common risk development was observed in states belonging to two clusters and consisted of an initial increase followed by a decrease. Some states presented cluster configurations with a retarded risk increase before the decrease, while the risk increased throughout the time of study in others. A cyclic behaviour with a second increasing trend was also observed in some states. The SPLM approach revealed a positive significant association with respect to case fatality risk between certain groups, such as males and individuals aged 50 years and more, and the prevalence of chronic kidney disease, cardiovascular disease, asthma and hypertension. The analysis may provide valuable insight into COVID-19 dynamics applicable in future outbreaks, as well as identify determinants signifying certain trends at the state level. The combination of spatial and temporal information may provide a better understanding of the fatalities due to COVID-19.

Comprehensive Evaluation of the Impact of Sociodemographic Inequalities on Adverse Outcomes and Excess Mortality During the Coronavirus Disease 2019 (COVID-19) Pandemic in Mexico City.

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.

Serum Vitamin D Levels Are Associated With Increased COVID-19 Severity and Mortality Independent of Whole-Body and Visceral Adiposity.

INTRODUCTION: Coronavirus disease (COVID-19) is a global pandemic. Vitamin D deficiency has been associated with susceptibility to infectious disease. In this study, the association between COVID-19 outcomes and vitamin D levels in patients attending a COVID-19 reference center in Mexico City are examined. METHODS: Consecutive patients with confirmed COVID-19 were evaluated. All patients underwent clinical evaluation and follow-up, laboratory measurements and a thoracic computerized tomography, including the measurement of epicardial fat thickness. Low vitamin D was defined as levels <20 ng/ml (<50nmol/L) and deficient Vitamin D as a level ≤12 ng/ml (<30 nmol/L). RESULTS: Of the 551 patients included, low vitamin D levels were present in 45.6% and deficient levels in 10.9%. Deficient Vitamin D levels were associated with mortality (HR 2.11, 95%CI 1.24-3.58, p = 0.006) but not with critical COVID-19, adjusted for age, sex, body-mass index and epicardial fat. Using model-based causal mediation analyses the increased risk of COVID-19 mortality conferred by low vitamin D levels was partly mediated by its effect on D-dimer and cardiac ultrasensitive troponins. Notably, increased risk of COVID-19 mortality conferred by low vitamin D levels was independent of BMI and epicardial fat. CONCLUSION: Vitamin D deficiency (≤12 ng/ml or <30 nmol/L), is independently associated with COVID-19 mortality after adjustment for visceral fat (epicardial fat thickness). Low vitamin D may contribute to a pro-inflammatory and pro-thrombotic state, increasing the risk for adverse COVID-19 outcomes.

Epicardial adipose tissue thickness is associated with increased COVID-19 severity and mortality.

BACKGROUND: Increased adiposity and visceral obesity have been linked to adverse COVID-19 outcomes. The amount of epicardial adipose tissue (EAT) may have relevant implications given its proximity to the heart and lungs. Here, we explored the role of EAT in increasing the risk for COVID-19 adverse outcomes. METHODS: We included 748 patients with COVID-19 attending a reference center in Mexico City. EAT thickness, sub-thoracic and extra-pericardial fat were measured using thoracic CT scans. We explored the association of each thoracic adipose tissue compartment with COVID-19 mortality and severe COVID-19 (defined as mortality and need for invasive mechanical ventilation), according to the presence or absence of obesity. Mediation analyses evaluated the role of EAT in facilitating the effect of age, body mass index and cardiac troponin levels with COVID-19 outcomes. RESULTS: EAT thickness was associated with increased risk of COVID-19 mortality (HR 1.18, 95% CI 1.01-1.39) independent of age, gender, comorbid conditions and BMI. Increased EAT was associated with lower SpO2 and PaFi index and higher levels of cardiac troponins, D-dimer, fibrinogen, C-reactive protein, and 4 C severity score, independent of obesity. EAT mediated 13.1% (95% CI 3.67-28.0%) and 5.1% (95% CI 0.19-14.0%) of the effect of age and 19.4% (95% CI 4.67-63.0%) and 12.8% (95% CI 0.03-46.0%) of the effect of BMI on requirement for intubation and mortality, respectively. EAT also mediated the effect of increased cardiac troponins on myocardial infarction during COVID-19. CONCLUSION: EAT is an independent risk factor for severe COVID-19 and mortality independent of obesity. EAT partly mediates the effect of age and BMI and increased cardiac troponins on adverse COVID-19 outcomes.

Diagnostic performance and clinical implications of rapid SARS-CoV-2 antigen testing in Mexico using real-world nationwide COVID-19 registry data.

BACKGROUND: SARS-CoV-2 testing capacity is important to monitor epidemic dynamics and as a mitigation strategy. Given difficulties of large-scale quantitative reverse transcription polymerase chain reaction (qRT-PCR) implementation, rapid antigen tests (Rapid Ag-T) have been proposed as alternatives in settings like Mexico. Here, we evaluated diagnostic performance of Rapid Ag-T for SARS-CoV-2 infection and its associated clinical implications compared to qRT-PCR testing in Mexico. METHODS: We analyzed data from the COVID-19 registry of the Mexican General Directorate of Epidemiology up to April 30th, 2021 (n = 6,632,938) and cases with both qRT-PCR and Rapid Ag-T (n = 216,388). We evaluated diagnostic performance using accuracy measures and assessed time-dependent changes in the Area Under the Receiver Operating Characteristic curve (AUROC). We also explored test discordances as predictors of hospitalization, intubation, severe COVID-19 and mortality. RESULTS: Rapid Ag-T is primarily used in Mexico City. Rapid Ag-T have low sensitivity 37.6% (95%CI 36.6-38.7), high specificity 95.5% (95%CI 95.1-95.8) and acceptable positive 86.1% (95%CI 85.0-86.6) and negative predictive values 67.2% (95%CI 66.2-69.2). Rapid Ag-T has optimal diagnostic performance up to days 3 after symptom onset, and its performance is modified by testing location, comorbidity, and age. qRT-PCR (-) / Rapid Ag-T (+) cases had higher risk of adverse COVID-19 outcomes (HR 1.54 95% CI 1.41-1.68) and were older, qRT-PCR (+)/ Rapid Ag-T(-) cases had slightly higher risk or adverse outcomes and ≥7 days from symptom onset (HR 1.53 95% CI 1.48-1.59). Cases detected with rapid Ag-T were younger, without comorbidities, and milder COVID-19 course. CONCLUSIONS: Rapid Ag-T could be used as an alternative to qRT-PCR for large scale SARS-CoV-2 testing in Mexico. Interpretation of Rapid Ag-T results should be done with caution to minimize the risk associated with false negative results.

Spatial epidemiological study of the distribution, clustering, and risk factors associated with early COVID-19 mortality in Mexico.

COVID-19 is a respiratory disease caused by SARS-CoV-2, which has significantly impacted economic and public healthcare systems worldwide. SARS-CoV-2 is highly lethal in older adults (>65 years old) and in cases with underlying medical conditions, including chronic respiratory diseases, immunosuppression, and cardio-metabolic diseases, including severe obesity, diabetes, and hypertension. The course of the COVID-19 pandemic in Mexico has led to many fatal cases in younger patients attributable to cardio-metabolic conditions. Thus, in the present study, we aimed to perform an early spatial epidemiological analysis for the COVID-19 outbreak in Mexico. Firstly, to evaluate how mortality risk from COVID-19 among tested individuals (MRt) is geographically distributed and secondly, to analyze the association of spatial predictors of MRt across different states in Mexico, controlling for the severity of the disease. Among health-related variables, diabetes and obesity were positively associated with COVID-19 fatality. When analyzing Mexico as a whole, we identified that both the percentages of external and internal migration had positive associations with early COVID-19 mortality risk with external migration having the second-highest positive association. As an indirect measure of urbanicity, population density, and overcrowding in households, the physicians-to-population ratio has the highest positive association with MRt. In contrast, the percentage of individuals in the age group between 10 to 39 years had a negative association with MRt. Geographically, Quintana Roo, Baja California, Chihuahua, and Tabasco (until April 2020) had higher MRt and standardized mortality ratios, suggesting that risks in these states were above what was nationally expected. Additionally, the strength of the association between some spatial predictors and the COVID-19 fatality risk varied by zone.

Assessing the Burden of Coronavirus Disease 2019 (COVID-19) Among Healthcare Workers in Mexico City: A Data-Driven Call to Action.

BACKGROUND: Healthcare workers (HCWs) could be at increased occupational risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections due to increased exposure. Information regarding the burden of coronavirus disease 2019 (COVID-19) epidemic in HCWs living in Mexico is scarce. Here, we aimed to explore the epidemiology, symptoms, and risk factors associated with adverse outcomes in HCWs in Mexico City. METHODS: We explored data collected by the National Epidemiological Surveillance System in Mexico City, in HCWs who underwent real-time reverse transcription polymerase chain reaction (RT-PCR) test. We explored COVID-19 outcomes in HCWs and the performance of symptoms to detect SARS-CoV-2 infection. RESULTS: As of 20 September 2020, 57 758 HCWs were tested for SARS-CoV-2 and 17 531 were confirmed (30.35%); 6610 were nurses (37.70%), 4910 physicians (28.0%), 267 dentists (1.52%), and 5744 laboratory personnel and other HCWs (32.76%). Overall, 2378 HCWs required hospitalization (4.12%), 2648 developed severe COVID-19 (4.58%), and 336 required mechanical-ventilatory support (.58%). Lethality was recorded in 472 (.82%) cases. We identified 635 asymptomatic SARS-CoV-2 infections (3.62%). Compared with general population, HCWs had higher incidence, testing, asymptomatic cases, and mortality rates. No individual symptom offers adequate performance to detect SARS-CoV2. Older HCWs with chronic noncommunicable diseases and severe respiratory symptoms were associated with higher risk for adverse outcome; physicians were at higher risk compared with nurses and other HCWs. CONCLUSIONS: We report a high prevalence of SARS-CoV-2 infection in HCWs in Mexico City. Symptoms as a screening method are not efficient to discern those HCWs with a positive PCR-RT test. Particular attention should focus on HCWs with risk factors to prevent adverse outcomes.

Profiling Cases With Nonrespiratory Symptoms and Asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 Infections in Mexico City.

We profiled cases with nonrespiratory symptoms (NRS) and asymptomatic severe acute respiratory syndrome coronavirus 2 infections assessed within Mexico City's Epidemiological Surveillance System. Initially asymptomatic or NRS cases have decreased risk of adverse outcomes compared with cases with respiratory symptoms. Comorbidity and age influence symptom development in initially asymptomatic cases.

Adaptive Metabolic and Inflammatory Responses Identified Using Accelerated Aging Metrics Are Linked to Adverse Outcomes in Severe SARS-CoV-2 Infection.

BACKGROUND: Chronological age (CA) is a predictor of adverse coronavirus disease 2019 (COVID-19) outcomes; however, CA alone does not capture individual responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Here, we evaluated the influence of aging metrics PhenoAge and PhenoAgeAccel to predict adverse COVID-19 outcomes. Furthermore, we sought to model adaptive metabolic and inflammatory responses to severe SARS-CoV-2 infection using individual PhenoAge components. METHOD: In this retrospective cohort study, we assessed cases admitted to a COVID-19 reference center in Mexico City. PhenoAge and PhenoAgeAccel were estimated using laboratory values at admission. Cox proportional hazards models were fitted to estimate risk for COVID-19 lethality and adverse outcomes (intensive care unit admission, intubation, or death). To explore reproducible patterns which model adaptive responses to SARS-CoV-2 infection, we used k-means clustering using PhenoAge components. RESULTS: We included 1068 subjects of whom 222 presented critical illness and 218 died. PhenoAge was a better predictor of adverse outcomes and lethality compared to CA and SpO2 and its predictive capacity was sustained for all age groups. Patients with responses associated to PhenoAgeAccel >0 had higher risk of death and critical illness compared to those with lower values (log-rank p < .001). Using unsupervised clustering, we identified 4 adaptive responses to SARS-CoV-2 infection: (i) inflammaging associated with CA, (ii) metabolic dysfunction associated with cardiometabolic comorbidities, (iii) unfavorable hematological response, and (iv) response associated with favorable outcomes. CONCLUSIONS: Adaptive responses related to accelerated aging metrics are linked to adverse COVID-19 outcomes and have unique and distinguishable features. PhenoAge is a better predictor of adverse outcomes compared to CA.

Impact of undiagnosed type 2 diabetes and pre-diabetes on severity and mortality for SARS-CoV-2 infection.

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.

Validation and repurposing of the MSL-COVID-19 score for prediction of severe COVID-19 using simple clinical predictors in a triage setting: The Nutri-CoV score.

BACKGROUND: During the COVID-19 pandemic, risk stratification has been used to decide patient eligibility for inpatient, critical and domiciliary care. Here, we sought to validate the MSL-COVID-19 score, originally developed to predict COVID-19 mortality in Mexicans. Also, an adaptation of the formula is proposed for the prediction of COVID-19 severity in a triage setting (Nutri-CoV). METHODS: We included patients evaluated from March 16th to August 17th, 2020 at the Instituto Nacional de Ciencias Médicas y Nutrición, defining severe COVID-19 as a composite of death, ICU admission or requirement for intubation (n = 3,007). We validated MSL-COVID-19 for prediction of mortality and severe disease. Using Elastic Net Cox regression, we trained (n = 1,831) and validated (n = 1,176) a model for prediction of severe COVID-19 using MSL-COVID-19 along with clinical assessments obtained at a triage setting. RESULTS: The variables included in MSL-COVID-19 are: pneumonia, early onset type 2 diabetes, age > 65 years, chronic kidney disease, any form of immunosuppression, COPD, obesity, diabetes, and age <40 years. MSL-COVID-19 had good performance to predict COVID-19 mortality (c-statistic = 0.722, 95%CI 0.690-0.753) and severity (c-statistic = 0.777, 95%CI 0.753-0.801). The Nutri-CoV score includes the MSL-COVID-19 plus respiratory rate, and pulse oximetry. This tool had better performance in both training (c-statistic = 0.797, 95%CI 0.765-0.826) and validation cohorts (c-statistic = 0.772, 95%CI 0.0.745-0.800) compared to other severity scores. CONCLUSIONS: MSL-COVID-19 predicts inpatient COVID-19 lethality. The Nutri-CoV score is an adaptation of MSL-COVID-19 to be used in a triage environment. Both scores have been deployed as web-based tools for clinical use in a triage setting.

Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level.

The Islamic Republic of Iran reported its first COVID-19 cases by 19th February 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths to this date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. Therefore, in the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how socioeconomic and climatic features of Iranian provinces might predict the number of cases. The analyses are applied to cumulative cases of the disease from February 19th to March 18th. They correspond to obtaining maps associated with quartiles for rates of COVID-19 cases smoothed through a Bayesian technique and relative risks, the calculation of global (Moran's I) and local indicators of spatial autocorrelation (LISA), both univariate and bivariate, to derive significant clustering, and the fit of a multivariate spatial lag model considering a set of variables potentially affecting the presence of the disease. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran (p-value< 0.05), indicating that the COVID-19 spread within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases (p-value < 0.05). Interestingly, literacy is a factor that is associated with a decrease in the number of cases (p-value < 0.05), which might be directly related to health literacy and compliance with public health measures. These features indicate that social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be useful to reduce SARS-CoV-2 spread in Iran. One limitation of our analysis is that the most updated information we found concerning socioeconomic and climatic features is not for 2020, or even for a same year, so that the obtained associations should be interpreted with caution. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran.