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Objective: The aim of the study is to assess the risk of malnutrition among institutionalized elderly individuals in North Bohemia and compare it to the risk of malnutrition in the capital city of Prague, Czech Republic. In the Czech Republic, very few studies have been conducted with the goal of describing and evaluating the nutritional status of the institutionalized elderly., Methods: In this study, data was collected from 254 elderly individuals (aged ≥ 65) residing in retirement homes in North Bohemia and then compared with similar data for the elderly living in retirement homes in Prague (the data from Prague were previously published in 2013). The data included an Mini-Nutritional Assessment (MNA) test; anthropometric measurements - Body Mass Index (BMI), waist circumference, triceps skinfold thickness; and biochemical parameters - albumin, prealbumin, transferrin, urea, and creatinine., Results: Mean BMI values were 27.4 kg/m 2 for females and 26.3 kg/m 2 for males. According to the MNA questionnaire, 10.4% of all tested elderly were malnourished and 50.8% were at risk of malnutrition; lager proportion of females were found to be malnourished than males. Biochemical indicators supported the MNA results. MNA categories correlated positively with independence (r = 0.56; p < 0.001), mental status (r = 0.54; p < 0.001), mobility (r = 0.46; p < 0.001), calf circumference (r = 0.42; p < 0.001), BMI, and the ability to self-feed (both r = 0.37; p < 0.001). The percentage of institutionalized elderly with malnutrition living in North Bohemia and Prague were about the same; however, the percentage of those at risk of malnutrition was higher in North Bohemia (p = 0.006)., Conclusion: Study results confirmed that the institutionalized elderly face issues that lower their nutritional status and put them at increased risk; clearly these issues need urgent attention., (Copyright© by the National Institute of Public Health, Prague 2018.)

In epidemiology, it is very important to estimate the baseline incidence of infectious diseases, but the available data are often subject to outliers due to epidemic outbreaks. Consequently, the estimate of the baseline incidence is biased and so is the predicted epidemic threshold which is a crucial reference indicator used to suspect and detect an epidemic outbreak. Another problem is that the "usual" incidence varies in a season dependent manner, i.e. it may not be constant throughout the year, is often periodic, and may also show a trend between years. To take account of these factors, more complicated models adjusted for outliers are used. If not adjusted for outliers, the baseline incidence estimate is biased. As a result, the epidemic threshold can be overestimated and thus can make the detection of an epidemic outbreak more difficult. Classical Serfling's model is based on the sine function with a phase shift and amplitude. Multiple approaches are applied to model the long-term and seasonal trends. Nevertheless, none of them controls for the effect of epidemic outbreaks. The present article deals with the adjustment of the data biased by epidemic outbreaks. Some models adjusted for outliers, i.e. for the effect of epidemic outbreaks, are presented. A possible option is to remove the epidemic weeks from the analysis, but consequently, in some calendar weeks, data will only be available for a small number of years. Furthermore, the detection of an epidemic outbreak by experts (epidemiologists and microbiologists) will be compared with that in various models., (Copyright© by the National Institute of Public Health, Prague 2016.)

In epidemiology, it is very important to estimate the baseline incidence of infectious diseases. From this baseline, the epidemic threshold can be derived as a clue to recognize an excess incidence, i.e. to detect an epidemic by mathematical methods. Nevertheless, a problem is posed by the fact that the incidence may vary during the year, as a rule, in a season dependent manner. To model the incidence of a disease, some authors use seasonal trend models. For instance, Serfling applies the sine function with a phase shift and amplitude. A similar model based on the analysis of variance with kernel smoothing and Serfling's higher order models, i.e. models composed of multiple sine-cosine function pairs with a variably long period, will be presented below. Serfling's model uses a long-term linear trend, but the linearity may not be always acceptable. Therefore, a more complex, long-term trend estimation will also be addressed, using different smoothing methods. In addition, the issue of the time unit (mostly a week) used in describing the incidence is discussed., (Copyright© by the National Institute of Public Health, Prague 2015.)

Objectives: The main objective of the study is to explore associations between alcohol consumption and marijuana use in young adults and to discuss the opportunities for brief intervention., Methods: Face to face structured interviews were carried out with 2,221 young adult Czechs (mean age 29.9, sd. 5.8 years). 51.4% were males. Alcohol consumption was calculated using beverage specific quantity frequency method. Alcohol-related problems were assessed using the Czech version of the AUDIT. Frequency of marijuana use in the last twelve months was asked as well., Results: The overall alcohol consumption was 9.2 litres of pure alcohol per person and year. The last year prevalence of marijuana use was 21.8%. The use of marijuana positively correlated with the frequency of beer drinking (r = 0.27), frequency of heavy episodic drinking [HED] (r = 0.32) and with the summary score in AUDIT (r = 0.39). Harmful or problem drinkers (AUDIT score > or = 16) reported marijuana use more frequently than moderate drinkers (60% compared to 18.8%; OR = 6.54; 95% CI = 4.7; 9.1). OR for marihuana use in heavy episodic drinkers was 4.3 (95% CI = 3.3; 5.6)., Discussion: The results suggest that frequent HED and harmful drinking are closely associated with marijuana use in younger adults. Since marijuana use (including heavy use) is rather common in the Czech Republic, it would be recommendable to also extend screening and brief intervention to reduce the use of cannabis. The existing guidelines for brief intervention should be modified in order to cover marijuana consumption as well.

Objectives: The main objective of this work is to quantify the number of hospitalizations caused by smoking, estimate the costs of hospital treatment and to estimate contribution of smoking to mortality in the Czech Republic (CR) in 2002., Methods: The estimate of the proportion by which smoking contributed to hospitalizations and to mortality in the CR was computed using the method of smoking-attributable fractions (SAF). The SAF was computed from relative risks established in the American study Cancer Prevention Study II and from estimates of the prevalence of smoking in the CR from a nationwide study conducted in 2002., Results: In 2002, based on data provided by the General Health Insurance Company, there were 145,336 hospitalizations, and the total cost of hospital treatment was estimated as 4.727,612 (in thousands) CZK. The total number of deaths caused by smoking was 20,550 (95% CI: 18,851-22,262), 14,525 in men and 6,025 in women. Deaths caused by smoking represented 19% of the total nationwide mortality for 2002. Earlier estimates were published by Peto and Lopez for 1995 (22,300 deaths caused by smoking) and 2000 (17,746 deaths). The estimate arrived by authors using the SAF method for 2002 corresponds quite well with that by Peto and Lopez for 2000., Conclusions: The high morbidity and mortality rate related to smoking is directly connected to high prevalence of smoking in the Czech Republic. An effective tobacco control policy, including restrictive measures on availability of tobacco products combined with preventive programmes and smoking cessation programmes, could contribute to the reduction of smoking and save lives and treatment costs caused by smoking.