1. { Aging Population in Europe does it mean More Death from Fall? Martin Rusnák Veronika Bučková

2.   Falls are prominent among the external causes of unintentional injury worldwide.   The frequency of falls increases with age and frailty level. Older people who are living in nursing homes fall more often than those who are living in community.   Approximately 28-35% of people aged of 65 and over fall each year.(WHO, 2007) Falls in higher age

3.   Falls occur as a result of a complex interaction of risk factors.   The main risk factors reflect the multitude of health determinants that directly or indirectly affect well-being. Those are categorized into four dimensions:   biological,   behavioral,   environmental and   socioeconomic factors.   Age is the number one biological factor and its importance increases with population aging.   Women are more likely than men to fall and sustain fracture, resulting in twice more hospitalizations and emergency department visits than men. However, fall-related mortality disproportionately affects men. (Tromp et al., 1998) Risk factors

4.   This paper studies the time series by age of deadly injuries resulting from falls in Slovakia, neighboring countries and the European Union.   Using population projection it looks at prospects of the mortality from falls.   Clinical interventions, such as vitamin D supplementation, exercise or physical therapy programmes, and some comprehensive multifactorial fall assessment and management interventions can reduce falls and are safe for community- dwelling older adults.   Fall interventions at different settings, such as nursing homes, community, and hospitals have been developed and proven to be successful. The lack of dissemination of this knowledge into practice may explain the meagre progress that has been observed so far in falls prevention.(Alamgir et al., 2012) Aim

5.  Population data were downloaded from publicly available sources on demography by EUROSTAT;  Population forecasts were taken from the same source;  Mortality data by ICD 10 for Slovakia were obtained from the National Center for Health Information;  Mortality data for Austria, Czech Republic, Hungary and Poland were downloaded from WHO mortality database;  Statistical environment {R} was used to derive forecasts;  Forecasts made using linear regression with CI and moving average with population prediction;  PYLLs were computed using the LE 0 for the last available year; Methodology

6. Results

7. Predicted populations

8. Prediction SMR Falls for SR 201220132014201520162017201820192020 25.3 predikcia regresiou 21.620.519.518.517.616.715.915.2 predikcia s využitím plávajúceho priemeru 56.458.160.362.765.167.469.772.0

9. SR SMR Falls by Age Groups

10. SMR Falls, 65+ of age

11. Slovakia PYLL from Falls, 65+

12. PYLL Falls 65+

13.   While linear regression provides an optimistic forecast of continuous reduction in standardised mortality the moving average using the population forecast reveals a constant increase over coming years;   The increase is more prominent in SR, HU and CZ compared to A;   There is a need to focus on preventing falls by increased provision of services for aging population to reduce the PYLL from falls. Conclusions