|Environment & Health||ISSN: 2077-7477 eISSN: 2077-7485
No: 3 (79) - 2016 - Pages: 46-49
Hygienic assessment of the environmental factor effect on the development of pre-school age children in the town of Chernivtsi
1 HSEI ź Bukovinian State Medical University╗
Objective. We studied the environmental factor effects (indices, characterizing a quality of the food intakes: caloric value, protein, carbohydrate, fat, calcium and phosphorus content; level of soil pollution by low doses of heavy metals and radionuclides, hygienic conditions at childrenĺs educational institutions) on the indices of physical development of the childrenĺs population in Chernivtsi.
Materials and methods. We performed a multiple regression analysis to study the effect of the above-mentioned environmental factors on the indices of the physical development of pre-school age children and its harmony.
Besides, we calculated the reliability indices for the regression coefficients.
The comparison of the importance of environmental and nutritional factor effect on the indices of physical development was carried out in terms of β-coefficientsĺ values, and also we determined the percentage of the impact of the studied factors (d).
The findings were processed on the PC using Statistica 7.0 software (StatSoft Inc., USA).
Results. The analysis of multiple regression allowed us to point out the risk groups of pre-school age children which included the boys 3 years age as well as girls and boys at the age of 3.5 with strongly disharmonious physical development due to the body mass deficiency whose physical development was significantly influenced by most of the studied factors.
Use of the multiple regression analysis allowed us to assess and validate the regressive models of the changes of the indices of balanced physical development of pre-school age children which may appear due to the changes of the studied environmental factors from the perspective of variance-and-statistical analysis.
physical development, nutrition, environmental factors, analysis of multiple regression.
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