Impact of Environmental Conditions on the Health of the Far Eastern Region Population

Abstract

The population of the Far Eastern region is under stress due to daily exposure to harmful and carcinogenic substances in the air, with average annual levels exceeding health standards. The purpose of this study is to assess the medical and environmental situation in the Far Eastern region and to identify relationships between environmental factors and the incidence in the population. For four years, studies have been conducted to assess the impact of environmental pollution on public health using basic demographic and morbidity indicators for the emissions of harmful substances, according to the data from State reports “On the sanitary-epidemiological situation in the Russian Federation” and statistical data from the Federal Statistics Service of the Russian Federation. In the course of this research, it was found that the increase in the incidence of malignant neoplasms was promoted by an increase in the amount of air-polluting emissions and an increase in the proportion of people employed in heavy work under conditions that do not meet health requirements. Using the regression equation, the impact of the amount of wastewater discharges and the amount of pollutants emitted into the atmosphere demonstrated an increase in respiratory diseases. As a preventive measure against negative environmental factors, the mandatory use of local adaptogens in everyday food products has been proposed.

1. Introduction

The Far Eastern region is the largest of the Russian Federation districts with an area of 6,169,329 km² that includes nine sub-regions. In 2017, the population of the Far Eastern region was 6,165,284 and the population density was 1.2 people/km².

Currently under the influence of harmful substances that have average annual levels exceeding health standards, there is the entire population of the Far Eastern region, whose main representatives are Russians, Ukrainians, Yakuts, Koreans, and small populations of indigenous peoples (Evenks, Nanai, Udege, etc.). The Far Eastern region is characterized by a diversified industrial production, resulting in the presence of a large number of sources of environmental pollution that cause serious hygienic and medico-demographic problems within a territory [1,2].

International scientific results from research in this area show a close relationship between the state of the environment and diseases of various etiologies [3,4,5,6].

The macrostructure of the industrial and economic potential of this area of the Russian Federation is determined by leading industries such as mining, the construction of the Vostochny cosmodrome and the Power of Siberia gas pipeline, and the production and distribution of electricity. These industries form a whole complex of sanitary and hygiene problems which cause direct and indirect effects on the population, including the appearance of cancer. The assessment of the habitat of the Far Eastern region indicates that the amount of pollutants emitted from stationary sources annually into the atmospheric air increased over the period of 2014–2017. In 2014, emissions from stationary sources amounted to 987.96 thousand tons, while in 2017 the volume of emissions amounted to 1102.12 thousand tons. The majority of the total volume of emissions in 2017 was made by the Republic of Sakha (Yakutia) and Primorsky Krai with 50.04 and 64.12 thousand tons, respectively.

Currently, health problems in the population have worsened due to the unfavorable environmental situation. Across the region there is a tendency towards a decrease in the level of public health. It has been proven that the state of health is negatively affected by air, water, and soil pollution. Drinking water supplies and food products containing xenobiotics of various origins are environmentally unsafe. Negative environmental stressors adversely affect the birth rate and the health status of newborns. As the natural population decline increases, the working-age population decreases [7,8,9,10,11,12].

The purpose of this study was to assess the medical and environmental situation in the Far Eastern region and to identify causal relationships between environmental factors and the incidence of diabetes mellitus, hypertension, malignant neoplasms in the population.

To achieve this goal, the following tasks were conducted:

1. Assess the medical and demographic situation in the Far Eastern region.

2. Conduct a correlation-regression analysis of the impact of some environmental factors on socially significant diseases (diabetes, hypertension, malignant neoplasms) and mortality in the Far Eastern Federal district.

2. Materials and Methods

The studies were conducted over four years (2014–2017). For the analysis, the State reports “On the sanitary-epidemiological situation in the Russian Federation” and statistical data from the Federal Statistics Service of the Russian Federation were used [13]. To assess the impact of environmental pollution on health, basic medical and demographic indicators along with information on the incidence and prevalence of diseases among the population using medical care were used [14,15,16,17]. The sources of information were medical records, annual reports from medical institutions, information on the number of diseases registered in patients living in the area of service of the medical institution, as well as information on the annual number of contingents served [18,19,20,21].

To ensure the uniformity of information from the data set, cases were excluded where there was no information on the incidence in the population or concentrations and emissions of pollutants for some years, for any reason [22,23,24,25].

Diabetes mellitus, hypertension, and malignant neoplasms are designated as socially significant diseases. For the analysis, the State reports “On the sanitary-epidemiological situation in the Russian Federation” and statistical data from the Federal Statistics Service of the Russian Federation were used [13].

In the study of the dynamics of morbidity in the population of the Far Eastern region, extrapolation methods were used from the data of State reports on various classes of diseases and certain types of pathologies [26,27,28,29,30,31,32,33,34]. These methods were based on the study of the existing patterns and the development patterns of the studied phenomenon and their spread to future forecasts, i.e., beyond the base period.

For the purpose of a long-term forecast, a detailed study of the variability of morbidity and the effects of emissions and concentrations of pollutants on the health of the population was conducted using correlation and regression analysis based on an array of data for the analyzed period.

Methods of statistical analysis of time series, construction of regression models of processes, and factor analysis allowed the main trends of development to be analyzed based on a set of data on the object of observation, to study the dependencies of some parameters on others. Statistical models are built on the assumption that the simulated process is random and investigated using statistical techniques. To estimate the combined effect of the incidence of diabetes mellitus, hypertension, malignant neoplasms in the population, the stepwise multiple regression equation was used. The Statistica 8.0 automated processing program was used to build the regression equations. The significance of the equation was determined by the coefficient of determination R² and the distribution of residuals.

In order to determine the impact of water and air pollution on life expectancy and the emergence of diseases in the Far Eastern Federal district, a statistical analysis was carried out. The data were sourced from the publications of the Federal State Statistics Service, posted on the website of the service, as well as statistical materials from the Ministry of Health of the Russian Federation. The indicators taken in the district for 2014–2017 were the maximum available. Calculations were carried out by means of Microsoft Excel and the econometric package Statistica 8.0.

The criteria for assessing the state of atmospheric air in the region were indicators such as emissions of pollutants, and those for assessing the state of water resources included the amount of discharged polluted water. The following data were used as control variables that characterize the economic condition of the region and additional factors affecting health: the use of fresh water, population density, and the proportion of employed in heavy work and in conditions that did not meet health requirements.

3. Results

The main demographic indicators of the population of the Far Eastern region from 2014 to 2017 are presented in the

Table 1. Main demographic indicators of the population of the Far Eastern region in 2014–2017.

Indicator	Year
	2014	2015	2016	2017
Average annual population, thousand people	6218.8	6204.6	6183.2	6165.3
Number of deaths per 1000 people	12.6	13.8	13.5	14.0
Number of births per 1000 people	14.0	13.2	13.0	14.2

.

The data in Table 1 show that the population in 2017 compared to 2014 decreased by 53.5 thousand people. The reason for this was migration outflow; however, the problem of natural population decline still persists. The number of births in the Far Eastern Federal district recently increased slightly; however, this was not enough to increase the population overall. If mortality is to be considered, an analysis of the situation requires overall mortality rates, which are calculated as the ratio of the total number of deaths per year to the average annual population. The Far East takes third place in mortality among other areas of the Russian Federation. The highest value of the total mortality rate belonged to the Jewish Autonomous region and the Amur region. The number of deaths due to respiratory diseases increased by 4.06%, and those due to neoplasms increased by 16.01% compared to 2014.

Total indicators of socially significant diseases (diabetes mellitus, hypertension, malignant neoplasms) and mortality in the Far Eastern Federal district for 2014–2017 are presented in Figure 1 [13].

Figure 1 shows that there was a steady direct relationship between the mortality and socially significant diseases in the population. The smaller the number of registered diseases, the lower the mortality.

As a result of the study, the dependence of the main causes of mortality (neoplasm, organs of the circulatory system, respiratory organs, and digestive organs) on the total amount of harmful emissions in the Far Eastern Federal district revealed that environmental pollution had a negative effect on public health.

This hypothesis is supported by the regression equations presented below in

Table 2. Dependence of mortality on emissions of harmful substances.

Correlation	Equation	Coefficient of Determination
Mortality–solids	у = 39047.08 + 0.17х	r2 = 0.79
Mortality–sulphur dioxide	у = 45689.32 + 237х	r2 = 0.66
Mortality from diseases of the respiratory system–the emission of harmful substances	у = −231.44 + 0.33х	r2 = 0.63
Mortality from respiratory diseases–solids	у = −58.31 + 0.38х	r2 = 0.69
Mortality from diseases of the respiratory system–hydrocarbons	у = 82.31−1.08х	r2 = 0.7

.

• M_oncology = −2.26х₁ + 2.27х₂ − 3.02х₃ − 1.58х₄
• M_strokes = −2.71х₁ + 11.12х₂ − 11.66х₃ − 7.57х₄
• M_{diseases of the respiratory system} = −1.66х₁ + 2.02х₂ − 2.49х₃ − 1.23х₄
• M_{diseases of the respiratory system} = 0.0025х₁ + 2.42х₂ − 2.67х₃ − 1.86х₄
• where x₁—the number of emitted hydrocarbons in the atmospheric air (thousand tons);
• x₂—total emissions (thousand tons);
• x₃—the number of emitted sulfur dioxide in the atmospheric air (thousand tons);
• x₄—amount of discharged polluted wastewater (thousand m³).

The urbanization of the population was 75.43% in 2017. Urbanization contributes to an increase in the concentration of anthropogenic load, resulting in a more polluted environment. This, in turn, negatively affects both the physical and mental state of the population. Sometimes the urban environment cannot keep up with the rapid growth, therefore the quality of water and air in many cities in the Far Eastern Federal district do not meet the standards. Therefore, the unsatisfactory state of air and water resources can lead to an increase in the incidence rate.

In 2017, compared to 2015, the number of patients diagnosed for the first time with neoplasm disease increased by 16.8%, with endocrine diseases by 5.4%, and with respiratory diseases by 16.1%. The highest number of cancer diseases in 2017 was recorded in the Chukotka Autonomous district (15.7 cases per 1000 people) and the Sakhalin region (14.5 cases per 1000 people). The lowest rate was established in the Magadan region (6.4 cases per 1000 people). It should be noted that in the Khabarovsk territory and the Magadan region there were slight reductions in the incidence of tumors for the entire period of the study by 21.19% and 2.61%, respectively. In other regions, the incidence of neoplasms increased over 10 years. In the Republic of Sakha (Yakutia), the number of diseases increased by 31.97% from 2014 to 2017, in the Kamchatka territory by 24.85%, in the Primorsky territory by 17.52%, in the Amur region by 46.83%, in the Sakhalin region by 32.75%, in the Jewish Autonomous region by 68.09%, and in the Chukotka Autonomous district by 94.4%.

The Chukotka Autonomous region and the Republic of Sakha Yakutia became record-setting regions for the number of recorded respiratory diseases in 2017.

A linear dependence was revealed as a result of research of the dynamics of primary diseases in the population from the volume of harmful emissions in the Far East Federal district. These relationships are described by the equations presented below in

Table 3. Regression equations of disease dependence by main classes of diseases on emissions of harmful substances.

Correlation	Equation	Coefficient of Determination
Tumors diseases–carbon monoxide	у = −227.54 + 2.33х	r2 = 0.85
Tumors diseases–hydrocarbons	у = 228.43 + 2.71х	r2 = 0.86
Diseases of the circulatory system–sulfur dioxide	у = 42.29 – 0.11х	r2 = 0.67
Diseases of the respiratory system–hydrocarbons	у = 249.89 + 2.45х	r2 = 0.94

.

The significance of the factors was confirmed by high statistically significant coefficients.

Dependent variables Yi in n observations was determined by m explanatory factors X = (X₁, X₂ .., Xm), and the functional relationship between them is as follows:

Y = β₀ + β₁ * X₁ + β₂ * X₂ +…+ β_m * X_m + ε.

(1)

Table 4. Correlation matrix of input data for regression.

	X1	X2	X3	X4	X5	X6
X1	1
X2	0.7597	1
X3	0.6827	0.6020	1
X4	0.3801	0.6784	0.0887	1
X5	−0.8489	−0.8435	−0.8723	−0.31398	1
X6	−0.8394	−0.8164	−0.8893	−0.28774	0.9876	1

presents the correlation matrix of the initial data for the calculation of the regression. Using this matrix, the presence of the multicollinearity problem can be determined, which consists of the fact that regressors can closely correlate with each other, which in turn can lead to a shift in the regression estimates to smaller values.

The independent variables are:

• X₁—volume of air emissions of pollutants from stationary sources, thousand tons;
• X₂—volume of polluted wastewater discharged into surface water bodies, million m³;
• X₃—population density per 1 km²;
• X₄—use of fresh water, million m³;
• X₅—the percentage of employed people with severe and harmful working conditions, %;
• X₆—the share of workers in conditions that do not meet health requirements, %.

An analysis of the data in Table 4 revealed a very high positive relationship between X1 and X2, which was absolutely true since these two signs are dependent on the level of air pollution. An increase in the values of these signs indicates undesirable consequences due to the state of the environment.

Quite a strong relationship was observed between X₁ and X₃, X₂ and X₄, X₂ and X₃. These indicators reflect the concentration of the population and the development of technostructures, which lead to a significant level of environmental pollution and an increase in human impact on the environment.

The construction of regression equations was as follows:

Y₁ = 9.552 + 0. 001X₁ − 0. 009X₂ + 4.694 X₃ + 0. 0007X₄ + 0.267X₅ + 0.0863X₆, (r² = 0.96)

(2)

Y₂ = −47.488 + 0.262X₁ + 0.039X₂ − 71.847X₃ + 0.084X₄ +11.927X₅ − 2.879X₆, (r² = 0.98)

(3)

Y₃ = 70.346 − 0.021X₁ + 0.0049X₂ + 0.4459X₃ + 0.0026X₄ + 0.259X₅ − 0.035X₆, (r² = 0.95)

(4)

• Y₁—incidence of neoplasms (per 1000 people);
• Y₂—respiratory diseases (per 1000 people);
• Y₃—life expectancy.

4. Conclusions

The following conclusions can be drawn from the obtained equations:

• From equation Y₁, it was found that an increase in the incidence of malignant neoplasms contributed to an increase in the amount of air pollution emissions and an increase in the proportion of those employed in heavy work and in conditions that do not meet health requirements;
• Equation Y₂ showed the impact of the amount of wastewater discharge and the amount of pollutants released into the atmosphere on the increase in respiratory diseases;
• It can be concluded from the equations for Y₃ that environmental pollution has a negative impact on the life expectancy of the population.

The quality of the environment is one of the most-discussed issues affecting absolutely all spheres of human activity. Thus, the concept of ecology is inextricably linked with health. Therefore, these studies address the impact of environmental quality on morbidity and life expectancy.

Traditional measures taken to improve the health of the population include: sanitary control over the purity of the environment; banning smoking in public places; the provision of preventive health services; and conditioning of the body [35,36,37]. Due to the difficulty in regulating industrial activities in the region and considering the risk factors, increasing the nonspecific resistance of the human body is only possible through nutrition by creating functional foods containing local adaptogens (e.g., lemongrass, eleutherococcus), which are homeostasis-optimizing substances and contain a unique complex of biologically active substances [38,39,40].