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10. Муталов А.П Коморбидная патология в практике врача-педиатра. URL: https://www.med-vestnik.ru/content/medarti-cles/Komorbidnaya-patologiya-v-praktike-vracha-pediatra.html.
11. Артамонов Р.П К вопросу о коморбидности в педиатрической практике. Педиатрия. 2012. Т. 91, № 4. С.146-149.
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(in Russian).
Над '1йшла до редакцИ 08.04.2017
Г1ПЕН1ЧНЕ ОБГРУНТУВАННЯ РОЗРАХУНКОВИХ МОДЕЛЕЙ ПРОГНОЗУВАННЯ ТОКСИЧНОСТ1 ФУНГ1ЦИД1В Р1ЗНИХ КЛАС1В ЗАЛЕЖНО В1Д 1ХН1Х Ф1ЗИКО-Х1М1ЧНИХ ВЛАСТИВОСТЕЙ
Вавршевич О.П., Антоненко А.М., Коршун М.М., Омельчук С.Т.
HYGIENIC SUBSTANTIATION OF CALCULATING MODELS FOR FUNGICIDES OF DIFFERENT CLASSES TOXICITY DEPEND ON THEIR PHYSICAL AND CHEMICAL PROPERTIES PROGNOSIS
oreign laboratories and institutes have for some time used calculation models of the toxicometric parameters dependence on the physico-chemical properties of xenobiotics [1, 2].
In Ukraine and a lot of other countries, such models for fungicides and herbicides do not exist today, and there are no legal grounds for using the conclusions and threshold values obtained by European experts. And actual methods for determining the toxi-cological parameters of pesticides are long-term and require significant financial costs, that is why laboratories do not always cope with the increasing flow of chemical plant protection products [3]. In solving this problem, the important role is played by methods of mathematical modeling and pre-
VAVRINEVYCH O.P., ANTONENKO A.M., KORSHUN M.M., OMELCHUK S.T.
Hygiene and Ecology Institute of the O.O. Bogomolets National Medical University, Kyiv
UDK 613:632. 952:632.95.024:57.013
Key words: Fungicides, toxicology, calculation models, regression equations.
ППеНЧНЕ ОБГРУНТУВАННЯ РОЗРАХУНКОВИХ МОДЕЛЕЙ ПРОГНОЗУВАННЯ ТОКСИЧНОСТ1ФУНГ1ЦИД1В Р1ЗНИХ КЛАС1В ЗАЛЕЖНО В1Д 1ХН1Х Ф1ЗИКО-Х1М1ЧНИХ ВЛАСТИВОСТЕЙ Ваврневич О.П., Антоненко А.М., Коршун М.М., Омельчук С.Т.
1нститут ппени та екологн Нащонального медичного унверситету ím. О.О. Богомольця, м. Кшв
В Укра!н1 та багатьох ¡нших кранах на сьогодн не Снуе методв математичного моделювання та прогнозування токсичност для фунпцид/в та гербцидв, як i немае юридичних пдстав для вико-ристання висновюв та порогових величин, отриманих европей-ськими експертами.
Метою роботи було гiгiенiчне обфунтування розрахункових моделей прогнозування токсичност фунгiцидiв рiзних клаав залежно вд iXнiх фiзико-хiмiчних властивостей. Матер!али та методи. Для анал'\зу ми використовували парамет-ри токсикометрп та фiзико-хiмiчнi показники, поширенi у^ свтово-му сльському господарств'1 фунгiцидiв. Для статистичноi обробки результата були використан пакети статистичних програм IBM SPSS StatisticsBase v.22 та MS Ехсel.
Результати i обговорення. Розроблено лнйн та нелiнiйнi регре-с '1йн '1 рiвняння, що враховують кореляцiйнi залежност мiж токсич-ними властивостями фунпцид 'в класу п'1разол-карбоксамид'1в i карбоксамдв, триазол'(в, iм'щазол'в, карбаматв i дит'окарбамат'в, метоксиакрилатв та iхнiми фiзико-хiмiчними властивостями. У бльшост випадюв розрахованi за нашими формулами значення корелюють з експериментально встановленими. Висновок. Доведено, що запропонован розрахунков'1 модел'1 для прогнозування небезпеки вивчених фунпцид1в е адекватними та статистично достов'рними. Розроблений алгоритм дае змогу стотно спро-стити проведення токсиколопчних експерименш та прискорити процедуру реестрацц нових фунпци^в досл'1джуваних клаав за наявност даних про фiзико-хiмiчнi властивост дослджуваних сполук. Ключовi слова: фунпциди, токсиколопя, розрахунковi модел'1, рiвняння регресп.
© Ваврневич О.П., Антоненко А.М., Коршун М.М., Омельчук С.Т. СТАТТЯ, 2017.
№ 4 2017 Environment & Health 52
diction of xenobiotics toxicity, the results of which can be used both for substantiation of toxicological parameters, and at the stage of experiment planning, which will reduce the errors probability and study duration [4].
The actuality of the search for alternative approaches to toxico-logical assessment of pesticides is confirmed by the fact that on May 2, 2017, Ukraine joined the European Convention for the Protection of Vertebrate Animals used for experiments and other scientific purposes of March 18, 1986 [5].
The purpose of work was the hygienic substantiation of calculating models for fungicides of different classes toxicity depend on their physical and chemical properties prognosis.
Materials and methods. In order to develop and substantiate calculation methods in the hygienic assessment of the studied group of pesticides hazards, an array of experimentally established values of LD50 (median death dose) with oral and percutaneous admission, LC50 (median death concentration) with inhalation admission and NO(A)EL (threshold doses) has been used [6]. For analysis we have selected fungicides, which belong to the most widely used in the world agriculture of next chemical classes [6-10]: 18 active ingredients (a.i.) of carboxamide, pyra-zole-carboxamide class (sedax-ane, penthiopyrad, fluopyram, car-boxin, penflufen, benzovindiflupyr, isopyrazam, boscalid, isofetamid, bixafen, flutolanil, fluxapyroxad, mepronil, oxycarboxin, thifluza-mide, furametpyr, fenfuram, benodanil); 26 a.i. of triazole class (fluquinconazole, triadimenol, mibenconazole, bitertanol, fenbu-conazole, flusilazole, diniconazole, triadimefon, difenoconazole, ipco-nazole, tetraconazole, pencona-zole, prothioconazole, bromucona-zole, tebuconazole, metconazole, triticonazole, propiconazole, hexa-conazole, etaconazole, azacona-zole), 5 a.i. of imidazole class (imazalil, triflumizole, oxpocona-zole, fenamidone, prochloraz), 21 a.i. of carbamate class (metiram, zineb, valifenalate, maneb, iprovali-carb, diethofencarb, pyra-clostrobin, fuberidazole, dasomet, propineb, benthiavalicarb, ziram, thiabendazole, prothiocarb, thio-phanate-methyl, thiram, pyrame-tostrobin, mancozeb, carbendaz-im, benomyl, propamocarb).
In the initial stages of the study, we analyzed Pearson's correlation between the toxicological parameters of fungicides and their physical and chemical properties (molecular weight, water solubility, vapor
AÜBKIAnfl: HQBI METO AM Q^HKM —
pressure, melting point, distribution coefficient in the octanol-water system (log Po/w), surface tension). Data on the physico-chemical properties of fungicides are derived from the IUPAC PPDB [6].
Statistical processing of the results was carried out using IBM SPSS StatisticsBase v. 22 and MS Excel statistical program packages. The substantiation of the calculation models for forecasting the hazard of the fungicides studied classes was based on correlation and regression analysis, taking into account the determination coefficient, which most closely approximates the relationship between the selected toxicological parameters and physical and chemical properties. The significance of the obtained regression equations was checked by Fisher's F-criterion, and the individual coefficients in the regression equation (a, b) -according to Student's t-criterion.
Results and discussion. In the first stage, we analyzed Pearson's correlation dependences between the toxic properties of fungicides of the class of pyrazole-carboxam-ides, carboxamides, triazoles, imi-
dazoles, carbamates, dithiocarba-mates, methoxyacrylates and their physico-chemical properties, which statistically significant results are given in table 1.
The results of the correlation analysis on the array of 18 active substances of the class of pyra-zole-carboxamides and carbox-amides showed that there is a definite positive relationship between dermal LD50, oral LD50 and vapor pressure (r=0.53 and 0.70, respectively, p<0, 05). With the determination coefficient (R2), the proportion of the effect of the investigated factor on the parameters of tox-icometry was determined and it was established that the effect fraction of the vapor pressure is 28.5% and 48.3%, respectively. It can be assumed that as lower the vapor pressure, the lower volatility of the substance and its greater amount remains in the stern or on the skin surface and penetrates into the body of warm-blooded animals and humans, causing more damage [11-13], which leads to LD50 decrease.
Also, a positive correlation between NO(A)EL value and water
Table 1
Relationship between toxicological parameters of fungicides and their physical and chemical properties
Chemical class Resulting variable Factorial variable Statistical parameters*
correlation coefficient determination coefficient, % observations number (n)
Carboxamides , pyrazole-car-boxamides LD5Q per os, mg/kg vapour pressure, mPa 0,53 28,5 17
LD5Q per cut, mg/kg 0,70 48,3 17
NO(A)EL, mg/kg water solubility, mg/l 0,62 39,0 15
Triazoles NO(A)EL, mg/kg molecular weight -0,42 17,9 23
water solubility, mg/l 0,52 27,3 23
Carbamates LD5Q per cut, mg/kg surface tension, mN/m 0,89 80,1 7
nK5Q inhal., mg/m3 -0,97 93,5 7
NO(A)EL, mg/kg water solubility, mg/l 0,47 22,5 19
Note: * - statistically significant results are given (р<0,05).
53 Environment & Health №4 2017
solubility was found (r=0,62; p<0,05). The fraction of this index impact is 39% (table 1). The obtained results can be explained by the fact that water-soluble compounds are rapidly metabolized and excreted from the body without a tendency to accumulate [1113], which reduces toxic manifestations and causes increasing of NO(A)EL.
An analysis of the 26 active substances of the triazole class revealed a negative correlation between NO(A)EL and molecular weight (r=-0.42; p<0.05), the fraction of this index influence is 17.9%. There is a significant relationship between NO(A)EL and water solubility (r=0.52; p<0.05) with fraction 27.3%. The revealed dependence is due to the fact that compounds with very high molecular weight form isomers that signif-
icantly increase the specificity of their action and toxicity, in contrast to substances with a low molecular weight that are badly penetrated into the body, and low molecular weight compounds that can penetrate into the blood with inhalation, oral or percutaneous admission, easily passing through histohemic barriers [11-13].
In the analyzed array of carbamates, a positive correlation between LD50 per cut and surface tension was detected (r=0.89; p<0.05); negative correlation between LC50 inhal. and the surface tension (r=-0.97; p<0.05) and correlation between NO(A)EL and water solubility (r=0.47; p<0.05). Fraction of surface tension effect on LD50 per cut and LC50 inhal. amounted to 80.1 and 93.5%, respectively; the water solubility of the studied compounds on
NO(A)EL value - 22.5%. It is known that as higher the surface tension, the faster substance will evaporate from the application surface, and the worse it will penetrate through it. Therefore, probably, with increasing surface tension, the inhalation toxicity of the substance increases (LC50 decreases) and dermal decreases (LD50 increases) [11-13].
At the second stage, we carried out an estimation using regression analysis and on the basis of it, taking into account the determination coefficient, the regression equations, which most closely approximated the connection between the selected physical and chemical properties and the parameters of toxicometry, were selected (table 2). The significance of the obtained regression equations was checked by Fischer's F-creterion,
Table 2
Models of toxicological parameters of different classes fungicides prediction
(linear regression equations)
Chemical class Observa-tions number (n) № of equation Regression equation Indices of model adequacy Coefficients certainty indices
Fischer's F-criterion approximation reliability (R2) a b t **
F F ** t t
Carboxami des, pyra-zole-car-boxamides 17 1 LD50 per os = -2x10+06X12 + 25046^ + 3355 6,39* 4,49 0,285 4,47* 2,53* 2,12
17 2 LD50 per cut = 11515X1 + 2888 14,03* 4,54 0,483 6,39* 3,75* 2,13
15 3 NO(A)EL = 3x10-05 X22 - 0,030X2 + 4,753 8,31* 4,67 0,390 4,51* 2,88* 2,16
Tria-zoles 23 4 NO(A)EL = -6,11ln(X3) + 37,40 4,58* 4,32 0,179 2,84* 2,14* 2,08
23 5 NO(A)EL = 0,552ln(X2) + 0,102 7,89* 4,32 0,273 3,49* 2,81* 2,08
Carba-mates 7 6 LD50 per cut = 24327ln(X4) - 99022 12,07* 10,13 0,801 2,93 3,47* 3,18
7 7 LC50 inhal. = -29,3ln(X4) + 126,1 42,9* 10,13 0,942 6,97* 6,55* 3,18
19 8 NO(A)EL = 2H10-05 X2 + 6,775 4,93* 4,45 0,225 2,95* 2,22* 2,11
Notes: * - significant results; ** - (at p=0,05 and number of freedom degrees k1=1, k2=n-2);
X1 - vapour pressure, mPa; X2 - water solubility, mg/l; X3 - molecular weight; X4 - surface tension, mN/m.
and the individual coefficients in the regression equation (a, b) - by the Studentst-criterion.
Our assessment of the "a" and "b" coefficients adequacy has shown that in all regression equations they are significant for the Student's t-criterion (p<0.05), except for the equation # 6. In this equation, the free coefficient "a" was not reliable, since the absolute value of the criterion ta was less than tcr., which indicates the impossibility of using this regression equation to predict the risk of fungicides of the carbamates group.
Also, exponential, logarithmic, polynomial, and step functions were used to approximate the dependencies of the parameters of toxicometry and non-acting doses on the physical and chemical properties of the substances studied,
№ 4 2017 Environment & Health 54
Table 3
Models of toxicological parameters of different classes fungicides prediction (nonlinear regression equations)
Chemical class Observations number (n) № of equation Regression equation Index of the model adequacy (R2)
Carboxami des, pyra-zole-car-boxamides 17 1 Afl^per os = -2x106 X12 + 25046X1 + 3355 0,308
17 2 Afl50 per cut = 11515X1 + 2888 0,483
15 3 NO(A)EL = 3x10-5 X22 - 0,030X2 + 4,753 0,462
Triazoles 23 4 NO(A)EL = -6,11ln(X3) + 37,40 0,181
23 5 NO(A)EL = 0,552ln(X2) + 0,102 0,338
Carba-mates 7 6 Afl50 per cut = 24327ln(X4) - 99022 0,804
7 7 AK50 inhal. = -29,3ln(X4) + 126,1 0,942
19 8 NO(A)EL = 2x10-5 X2 + 6,775 0,224
Notes: X1 - vapour pressure, mPa; X2- water solubility, mg/l; X3 - molecular weight; X4 - surface tension, mN/m.
HYGIENIC SUBSTANTIATION OF THE CALCULATION MODELS FOR THE TOXICITY FORECAST OF DIFFERENT CLASSES' FUNGICIDES DEPENDING ON THEIR PHYSICAL-AND-CHEMICAL PROPERTIES Vavrinevych O.P., Antonenko A.M., Korshun M.M., Omelchuk S.T. Hygiene and Ecology Institute, O.O. Bohomolets National Medical University
Background. Today the methods of mathematical modeling and the forecast of xenobiotics' toxicity of fungicides and herbicides do not exist in Ukraine and many other countries, and there are no legal basis for the use of the conclusions and threshold values obtained by the European experts as well. Objective. We performed a hygienic substantiation of calculation models for the fungicides of different toxicity classes depending on their physical-and-chemical properties.
Materials and methods. For the analysis we used the parameters of toxicometry ^ and physical-and-chemical indices of the fungicides widely applied in the world agriculture. IBM SPSS Statistics Base v.22
and MS Excel statistical program packages were used for the statistical processing of the results. Results and discussion. The linear and non-linear regression equations, taking into account correlation dependences between the toxic properties of fungicides of the class of pyrazole-carboxamides and car-boxamides, triazoles, imidazoles, carbamates and dithiocarbamates, methoxyacrylates and their physico-chemical properties, were developed. In the most cases the values, calculated by our formulas, correlates with experimentally established ones. Conclusions. It was proved that the proposed calculation models for the forecast of the hazard of studied fungicides were adequate and statistically reliable ones. The developed algorithm simplifies substantially the performance of the toxicological experiments and accelerates the procedure of the registration of new fungicides of studied classes if there are data on physical- and- chemical properties of the studied compounds.
Keywords: fungicides, toxicology, calculation models, regression equations.
except for the linear one. In table 3 the mathematical models with the greatest values of accuracy of approximation (R2) are given.
The verification of the possibility of using the calculation models for predicting the hazard of the studied groups of fungicides was carried out on the basis of comparison of the parameters of toxicometry, obtained experimentally (actual indices) and calculated on the proposed equations (fig. 1-2).
In most of cases, the calculated values correlated with experimentally established (table 3). For all valid pairs of resultant and factorial variables, a reliable correlation relationship was established (ractual > r^ at p=0.05) except LD50 per os and vapor pressure for pyrazolecarbox-amides, carboxamides, and LD50 per cut and LC50 inhal. and surface tension for carbamates. However, for these pairs a reliable tendency of correlation was establish (ractual > rtable at 0.05<p<0.1).
In isolated cases, calculated LD50 values for oral and percutaneous intake were higher than experimentally established, but this is due to the fact that practically all of the experimentally established indices of these parameters are presented in the form of "more than...".
It should be noted that the correlations we received (table 1) between the toxicity criteria of the studied fungicides and their physical and chemical properties, which are confirmed by the inverse calculations (figs 1-2), are similar to those previously proved for neoni-cotinoid insecticides [14].
Similar calculations for methoxyacrylates (dimoxystrobin, trifloxys-trobin, fluoxystrobin, picoxys-
55 Environment & Health №4 2017
trobin, kresoxim-methyl, azoxys-trobin, piraclostrobin) were also carried out by us, but no reliable correlation between their toxico-logical parameters and physical and chemical properties was detected. Taking into account that for most of the active substances of this chemical class, the threshold values for toxic effects were substantiated in the 1990s, often according to outdated approaches, in different species of animals (rats, mice, dogs), this exception only confirms the established correlations for modern fungicide class molecules.
Conclusions
1. It has been established that there is a significant positive correlation between fungicides of pyrazolecarboxamides, carbox-amides class toxicological parameters (LD50 per os, LD50 per cut, NO(A)EL) and vapor pressure, water solubility (r = 0.53; 0.70 and 0.62, respectively, at p<0.05).
2. A significant negative correlation was found between the NO(A)EL values of triazole fungicides and molecular weight; and the positive correlation with water solubility.
Table 4
Correlation between experimentally established and calculated values of toxicological parameters
Chemical class Statistical parameters*
Resulting variable Factorial variable correlation coefficient (ractual) correlation coefficient (rtab|e) at p observations number
0,05 0,1 (n)
in I LD50 per os, mg/kg vapour pressure, mPa 0,438** 0,482 0,412 17
-Q2O LD50 per cut, mg/kg 0,717* 0,482 0,412 17
O ΠNO(A)EL, mg/kg water solubility, mg/l 0,675* 0,514 0,441 15
in w o N NO(A)EL, mg/kg molecular weight 0,426* 0,413 0,352 23
.Ë h= water solubility, mg/l 0,582* 0,413 0,352 23
in w LD50 per cut, mg/kg surface tension, mN/m 0,700** 0,755 0,669 7
E CO .Q nK50 inhal., mg/m3 0,753** 0,755 0,669 7
CO O NO(A)EL, mg/kg water solubility, mg/l 0,474* 0,456 0,389 19
Notes:
* — results are significant at p<0.05; ** — tendency are present at 0.05<p<0.1.
3. There was significant correlation between LD50 per cut and surface tension, a negative correlation between LC50 inhal. and surface tension, between the NO(A)EL value of carbamates class fungicides and water solubility (r=0.89; -0.97 and 0.47, respectively, at p<0.05.
4. It is proved that the proposed calculation models for forecasting the hazard of pyrazolecarboxam-ides, carboxamides, triazoles, carbamates fungicides classes are adequate and significant according to the Fisher test (p<0.05). The developed algorithm makes it possible to substantially simplify the conduction of toxicological experiments provided that there are data
on the physical and chemical properties of the studied compounds and to accelerate the procedure for registration of new fungicides of the studied classes.
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Fig. 1
A comparative analysis of the experimentally established LD50 per cut (A) and NO(A)EL (B) values with calculated for pyrazolecarboxamides, carboxamides class compounds
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A comparative analysis of the experimentally established LC50 inhal. (A) та NO(A)EL values with calculated for carbamate class compounds
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report.pdf
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Надiйшла до редакцИ 21.08.2017
HYGIENIC FACTORS ON THE STATE OF THE ACOUSTIC ANALYZER AND THE MORBIDITY OF THE OPERATORS OF NON-ALCOHOLIC AND LOW-ALCOHOL BEVERAGES' BOTTLING AT OBOLON CORPORATION
Yavorovskyi O.P., Brukhno R.P., Shydlovska T.A., Hrechkivska N.V.
ОСОБЛИВОСТ1 ВПЛИВУ ВИРОБНИЧОГО ШУМУ I СУПУТН1Х Г1ПСН1ЧНИХ ФАКТОР1В НА СТАН СЛУХОВОГО АНАЛ1ЗАТОРА I ЗАХВОРЮВАН1СТЬ ОПЕРАТОР1В З РОЗЛИВУ БЕЗАЛКОГОЛЬНИХ ТА СЛАБОАЛКОГОЛЬНИХ НАПО1В КОРПОРАЦМ «ОБОЛОНЬ»
1 ЯВОРОВСЬКИЙ О.П., 1 БРУХНО Р.П.,
2 ШИДЛОВСЬКА Т.А., з ГРЕЧК1ВСЬКА Н.В.
1 Нацюнальний медичний уыверситет iM. О.О. Богомольця, м. КиТв 2 ДУ «1нститут отоларингологи iM. О.С. Коломмченка НАМН УкраТ'ни», м. КиТв
3 Нацюнальна медична академiя тслядипломно'Т
осв™ iм. П.Л. Шупика, м. КиТв
УДК:613.6:612.85:314.4:6 63.86-058.234.2 K^40Bi слова: ппешч-на оцшка, виробничий шум,сенсоневральна приглухуватють, захворювашсть.
нтенсивний виробничий шум е одним з прюритетних факторiв ризику у розвитку професмних захворювань на сучасному виробниц™ [2]. ВЫ е провщним шкщливим виробничим фактором на пщприемствах вугшьноТ', транспорт-ноТ', машинобу^вноТ', харчовоТ' та iнших гапузей промисловост [2-4]. Крiм того, з року в рк зростае кiпькiсть людей, як працюють в умовах впливу виробничого шуму. За даними бага-тьох авторiв, в окремих кражах св^у до 25% працiвникiв, зайнятих у проми-сповостi, зазнають впливу Ытенсивно-го виробничого шуму [1, 5].
Внаслщок цього зберiгаеться тен-денцiя до зростання числа оаб з про-фесiйною пригпухуватiстю. У рядi краТ'н бвропи сенсоневральна приглу-хуватiсть професiйного генезу посiдае 1 або 2 мюце за поширенютю серед професiйних захворювань [6-8].
ОСОБЕННОСТИ ВОЗДЕЙСТВИЯ ПРОИЗВОДСТВЕННОГО ШУМА И СОПУТСТВУЮЩИХ ГИГИЕНИЧЕСКИХ ФАКТОРОВ НА СОСТОЯНИЕ СЛУХОВОГО АНАЛИЗАТОРА И ЗАБОЛЕВАЕМОСТЬ ОПЕРАТОРОВ РОЗЛИВА БЕЗАЛКОГОЛЬНЫХ И СЛАБОАЛКОГОЛЬНЫХ НАПИТКОВ КОРПОРАЦИИ «ОБОЛОНЬ»
1ЯворовскийА.П., 1 Брухно Р.П., 2Шидловская Т.А., 3ГречковскаяН.В.
1 Национальный медицинский университет им. А.А. Богомольца, 2ГУ «Институт отоларингологии им. А.С. Коломийченко НАМН Украины», 3Национальная медицинская академия последипломного образования им. П.Л. Шупика, г. Киев
Цель: изучение состояния слухового анализатора и заболеваемости работников «шумовых» профессий пищевой промышленности. Материалы и методы. Проведены гигиенические исследования условий труда, анализ заболеваемости, углубленное клиническое обследование состояния слухового анализатора, определен биологический возраст работников «шумовых» профессий ПАО «Оболонь». Результаты. Установлено, что ведущим вредным фактором производственной среды работников «шумовых» профессий ПАО «Оболонь» является шум, уровни которого превышают допустимые величины на 1-11 дБА. У обследованных работников «шумовых» профессий выявлены ухудшения слуховой функции, особенно на тона в расширенном диапазоне частот, и признаки воздействия шума на центральные отделы слухового анализатора, проявляющиеся в нарушении функции его стволовых и корковых структур. Установлено, что частота выявления у операторов розлива напитков сенсоневральной тугоухости и болезней системы кровообращения статистически достоверно выше частоты выявления этих болезней в контрольной группе (р<0,05). Обнаружены ускоренные темпы старения работников "шумовых" профессий. Заключение. Установлена причинно-следственная связь между условиями, характером труда и частотой выявления заболеваний, изменениями на разных уровнях слухового анализатора, темпами старения работников «шумовых» профессий. Ключевые слова: гигиеническая оценка, производственный шум, сенсоневральная тугоухость, заболеваемость.
© Яворовський О.П., Брухно Р.П., Шидловська Т.А., Гречювська Н.В. СТАТТЯ, 2017.
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