Resumo em Inglês:

Abstract INTRODUCTION Antimicrobial drug resistance constitutes a health risk of increasing concern worldwide. One of the most common avenues for the acquisition of clinically-relevant antimicrobial resistance can be traced back to the food supply, where resistance is acquired through the ingestion of antimicrobial resistant microorganisms present in food. Antimicrobial resistance constitutes a health risk, leading to production losses and negative consequences for livelihood and food safety. OBJECTIVE Determine whether resistant bacteria are present in foods in Cuba. METHODS A descriptive observational study was conducted in the Microbiology Laboratory of Cuba’s National Institute of Hygiene, Epidemiology and Microbiology from September 2004 through December 2018. Researchers analyzed 1178 bacterial isolates from food samples. The isolates were identified as Escherichia coli, Salmonella, Vibrio cholerae and coagulase-positive Staphylococcus. The antimicrobial susceptibility study was performed using the Bauer-Kirby disk diffusion method, following procedures outlined by the Clinical and Laboratory Standards Institute. The data were analyzed using WHONET version 5.6. RESULTS Of the total isolates, 62.1% were resistant to at least one antibiotic. Within each group, >50% of isolates showed some type of resistance. E. coli and V. cholerae exceeded 50% resistance to tetracycline and ampicillin, respectively. Staphylococcus showed the highest resistance to penicillin, and Salmonella to tetracycline, nalidixic acid and ampicillin. The highest percentages of non-susceptible microorganisms were identified in meats and meat products. CONCLUSIONS These results serve as an alert to the dangers of acquiring antibiotic-resistant bacteria from food and demonstrate the need to establish a surveillance system and institute measures bacterial control in food products.

Resumo em Inglês:

Abstract INTRODUCTION On March 11, 2020, WHO declared COVID-19 a pandemic and called on governments to impose drastic measures to fight it. It is vitally important for government health authorities and leaders to have reliable estimates of infected cases and deaths in order to apply the necessary measures with the resources at their disposal. OBJECTIVE Test the validity of the logistic regression and Gompertz curve to forecast peaks of confirmed cases and deaths in Cuba, as well as total number of cases. METHODS An inferential, predictive study was conducted using logistic and Gompertz growth curves, adjusted with the least squares method and informatics tools for analysis and prediction of growth in COVID-19 cases and deaths. Italy and Spain—countries that have passed the initial peak of infection rates—were studied, and it was inferred from the results of these countries that their models were applicable to Cuba. This hypothesis was tested by applying goodness-of-fit and significance tests on its parameters. RESULTS Both models showed good fit, low mean square errors, and all parameters were highly significant. CONCLUSIONS The validity of models was confirmed based on logistic regression and the Gompertz curve to forecast the dates of peak infections and deaths, as well as total number of cases in Cuba.

Resumo em Inglês:

Abstract INTRODUCTION The scales available to predict death and complications after acute coronary syndrome include angiographic studies and serum biomarkers that are not within reach of services with limited resources. Such services need specific and sensitive instruments to evaluate risk using accessible resources and information. OBJECTIVE Develop a scale to estimate and stratify the risk of intrahospital death in patients with acute ST-segment elevation myocardial infarction. METHODS An analytical observational study was conducted in a universe of 769 patients with acute ST-segment elevation myocardial infarction who were admitted consecutively to the Camilo Cienfuegos Provincial Hospital in Sancti Spíritus Province, Cuba, from January 2013 to March 2018. The final study cohort included 667 patients, excluding 102 due to branch blocks, atrial fibrillation, drugs that prolong the QT interval, low life expectancy or history of myocardial infarction. The demographic variables of age, sex, skin color, classic cardiovascular risk factors, blood pressure, heart rate, blood glucose level, in addition to duration and dispersion of the QT interval with and without correction, left ventricular ejection fraction, and glomerular filtration rate were included in the analysis. Patients were categorized according to the Killip-Kimball Classification for degree of heart failure. A risk scale was constructed, the predictive ability of which was evaluated using the detectability index associated with an receiver-operator curve. RESULTS Seventy-seven patients died (11.5%). Mean blood glucose levels were higher among the deceased, while their systolic and diastolic blood pressure, left ventricular ejection fraction, and glomerular filtration rate were lower than those participants discharged alive. Relevant variables included in the scale were systolic blood pressure, Killip-Kimball class, cardiorespiratory arrest, glomerular filtration rate, corrected QT interval dispersion, left ventricular ejection fraction, and blood glucose levels. The variable with the best predictive ability was cardiorespiratory arrest, followed by a blood glucose level higher than 11.1 mmol/L. The scale demonstrated a great predictive ability with a detectability index of 0.92. CONCLUSIONS The numeric scale we designed estimates and stratifies risk of death during hospitalization for patients with ST-segment elevation myocardial infarction and has good metric properties for predictive ability and calibration.