BMC Geriatr

Background The type 2 diabetes (T2D) specific dementia-risk score (DSDRS) was developed to evaluate dementia risk in older adults with T2D. T2D-related factors have been shown increase the risk of age-related conditions, which might also increase dementia risk. Here, we investigate the associations of DSDRS with frailty, disability, quality of life (QoL) and cognition in community-dwelling older adults with T2D. Methods We included 257 community-dwelling older adults with T2D to evaluate the association between DSDRS and Mini-mental state examination (MMSE), Isaac’s set-test (IST), clock drawing test (CDT), quality of life (SF-36), risk of malnutrition (Mini-Nutritional Assessment or MNA), as well as frailty, Katz’ and Lawton-Brody scores. We also assessed the phenotype and correlates of high-estimated dementia risk by assessing individuals with DSDRS >75th age-specific percentiles. Results Mean age of participants was 78.0 ± 6.2 years. DSDRS showed a significant correlation with MMSE test, IST, CDT, SF-36, MNA, Lawton-Brody and Katz scores, and an increasing number of frailty components. DSDRS was higher among frail, pre-frail, and subjects with limited ADL and IADL (p 75th age-specific percentiles had lower education, MMSE, IST, SF-36, MNA, Katz, Lawton-Brody, and higher frailty scores. High-estimated 10-year dementia risk was associated with ADL and IADL disability, frailty and risk of malnutrition. When assessing individual components of DSDRS, T2D-related microvascular complications were associated to all outcome measures. Conclusion The DSDRS is associated with frailty, disability, malnutrition and lower cognitive performance. These findings support that T2D-related factors have significant burden on functional status, QoL, disability and dementia risk

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

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

BackgroundWith 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.MethodsWe 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.ResultsWe 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.ConclusionSARS-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

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.  

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