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OBJECTIVE: The aim of the study was to evaluate the condition of the cardiovascular system and the effect of the combined preparation of magnesium and vitamin B6 in children with secondary cardiomyopathy on the background of chronic tonsillitis. PATIENTS AND METHODS: Materials and methods: 100 children at the aged of 13-17 years were surveyed, including 60 patients with secondary cardiomyopathy with chronic tonsillitis. And the group I consisted of 45 children receiving standard treatment, group II - 15 patients who, along with standard treatment, received the drug magnesium. The control group consisted of 40 healthy children. To assess the condition of the cardiovascular system and vegetative regulation, we performed electrocardiographic examination (ECG), exercise test and ECG control, cardiointervalography with clinoortostatic test. The concentration of magnesium in the serum was determined by atomic absorption spectrophotometry. Also, we have provided the requirements of the principles of bioethics and drafted a protocol in accordance with the basic principles of the Helsinki Declaration. RESULTS: Results: In addition to the standard treatment of children with magnesium drug, there was a significant decrease in the frequency of sinus arrhythmias, disorders of the intraventricular conduction, and the processes of repolarization, extrasystole, sinus suchchardia were not observed in any of the patients. The normalization of magnesium in the serum of patients was observed. CONCLUSION: Conclusions: Supplementation of combination therapy with magnesium and B6 helped to improve myocardial electrophysiology and cardiac output, as well as to normalize the serum magnesium in children with secondary cardiomyopathy.

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INTRODUCTION: Robust metrics for national-level preparedness are critical for assessing global resilience to epidemic and pandemic outbreaks. However, existing preparedness assessments focus primarily on public health systems or specific legislative frameworks, and do not measure other essential capacities that enable and support public health preparedness and response. METHODS: We developed an Epidemic Preparedness Index (EPI) to assess national-level preparedness. The EPI is global, covering 188 countries. It consists of five subindices measuring each country's economic resources, public health communications, infrastructure, public health systems and institutional capacity. To evaluate the construct validity of the EPI, we tested its correlation with proxy measures for preparedness and response capacity, including the timeliness of outbreak detection and reporting, as well as vaccination rates during the 2009 H1N1 influenza pandemic. RESULTS: The most prepared countries were concentrated in Europe and North America, while the least prepared countries clustered in Central and West Africa and Southeast Asia. Better prepared countries were found to report infectious disease outbreaks more quickly and to have vaccinated a larger proportion of their population during the 2009 pandemic. CONCLUSION: The EPI measures a country's capacity to detect and respond to infectious disease events. Existing tools, such as the Joint External Evaluation (JEE), have been designed to measure preparedness within a country over time. The EPI complements the JEE by providing a holistic view of preparedness and is constructed to support comparative risk assessment between countries. The index can be updated rapidly to generate global estimates of pandemic preparedness that can inform strategy and resource allocation.

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Uncovering the temporal trend in crash counts provides a good understanding of the context for pedestrian safety. With a rareness of pedestrian crashes it is impossible to investigate monthly temporal effects with an individual segment/intersection level data, thus the time dependence should be derived from the aggregated level data. Most previous studies have used annual data to investigate the differences in pedestrian crashes between different regions or countries in a given year, and/or to look at time trends of fatal pedestrian injuries annually. Use of annual data unfortunately does not provide sufficient information on patterns in time trends or seasonal effects. This paper describes statistical methods uncovering patterns in monthly pedestrian crashes aggregated on urban roads in Connecticut from January 1995 to December 2009. We investigate the temporal behavior of injury severity levels, including fatal (K), severe injury (A), evident minor injury (B), and non-evident possible injury and property damage only (C and O), as proportions of all pedestrian crashes in each month, taking into consideration effects of time trend, seasonal variations and VMT (vehicle miles traveled). This type of dependent multivariate data is characterized by positive components which sum to one, and occurs in several applications in science and engineering. We describe a dynamic framework with vector autoregressions (VAR) for modeling and predicting compositional time series. Combining these predictions with predictions from a univariate statistical model for total crash counts will then enable us to predict pedestrian crash counts with the different injury severity levels. We compare these predictions with those obtained from fitting separate univariate models to time series of crash counts at each injury severity level. We also show that the dynamic models perform better than the corresponding static models. We implement the Integrated Nested Laplace Approximation (INLA) approach to enable fast Bayesian posterior computation. Taking CO injury severity level as a baseline for the compositional analysis, we conclude that there was a noticeable shift in the proportion of pedestrian crashes from injury severity A to B, while the increase for injury severity K was extremely small over time. This shift to the less severe injury level (from A to B) suggests that the overall safety on urban roads in Connecticut is improving. In January and February, there was some increase in the proportions for levels A and B over the baseline, indicating a seasonal effect. We found evidence that an increase in VMT would result in a decrease of proportions over the baseline for all injury severity levels. Our dynamic model uncovered a decreasing trend in all pedestrian crash counts before April 2005, followed by a noticeable increase and a flattening out until the end of the fitting period. This appears to be largely due to the behavior of injury severity level A pedestrian crashes.

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