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

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

Nivel de conocimientos de estudiantes de medicina sobre diagnóstico y manejo del infarto agudo del miocardio

Introduction: acute myocardial infarction is a disease with high morbidity and mortality.Objective: to determine the knowledge level of medical students about the diagnosis and management of acute myocardial infarction.Method: an observational, descriptive and cross-sectional study was carried out between January and February 2022 in medical students from the University of Medical Sciences of Pinar del Río who participated in the provincial update workshop on acute myocardial infarction. Through intentional sampling, a sample of 92 students was selected. To collect the information, a survey was used using Google Forms.Results: the female sex (65,21%), the age group from 21 to 22 years (65,21%) and the fourth-year students (50%) prevailed. Hypertension was the most identified risk factor (97,98%). 97,82% of the students identified precordial pain as the main clinical manifestation. 100% identified the presentation with complications, where sudden death was the most identified (81,52%). 100% point to the electrocardiogram as the main complementary, where ST alterations were the most identified (84,78%). 95,65% of the students indicated constant monitoring of vital parameters and cardiovascular function as the management measure.Conclusions: Medicine students belonging to the clinical area at the University of Medical Sciences of Pinar del Río have an adequate level of knowledge about the diagnosis and management of acute myocardial infarction.Introducción: el infarto agudo del miocardio constituye una enfermedad con elevada morbilidad y mortalidad.Objetivo: determinar el nivel de conocimientos de estudiantes de medicina sobre el diagnóstico y manejo del infarto agudo del miocardioMétodo: se realizó un estudio observacional, descriptivo y transversal entre enero y febrero de 2022 en estudiantes de Medicina de la Universidad de Ciencias Médicas de Pinar del Río del ciclo clínico que participaron en el Taller provincial de actualización sobre infarto agudo de miocardio. Mediante un muestreo intencional se seleccionó una muestra de 92 estudiantes. Para la recolección de la información se empleó una encuesta mediante Google Forms.Resultados: predominó el sexo femenino (65,21 %), el grupo etario de 21 a 22 años (65,21 %) y los estudiantes de cuarto año (50 %). La hipertensión fue el factor de riesgo más identificado (97,98 %). El 97,82 % de los estudiantes identificó el dolor precordial como principal manifestación clínica. El 100 % identificó la presentación con complicaciones, donde la muerte súbita fue la más identificada (81,52 %). El 100 % señala al electrocardiograma como principal complementario, donde las alteraciones del ST fueron las más identificada (84,78 %). El 95,65 % de los estudiantes indicaron la monitorización constante de los parámetros vitales y función cardiovascular como la medida de manejo.Conclusiones: los estudiantes de Medicina pertenecientes al área clínica en la Universidad de Ciencias Médicas de Pinar del Río poseen un adecuado nivel de conocimientos sobre el diagnóstico y manejo del infarto agudo del miocardio.  

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