Capturing a Complexity of Nutritional, Environmental, and Economic Impacts on Selected Health Parameters in the Russian High North

: The rapid pace of economic exploration of the Arctic against the backdrop of progressing environmental change put a high priority on improving understanding of health impacts in the northern communities. Deﬁciencies in the capability to capture the complexity of health-inﬂuencing parameters along with a lack of observations in circumpolar territories present major challenges to establishing credible projections of disease incidence across varying northern environments. It is thus crucial to reveal the relative contributions of coacting factors to provide a basis for sustainable solutions in the sphere of public health. In order to better understand the adverse e ﬀ ects associated with public health, this study employed six-stage multiple regression analysis of incidence rates of fourteen diseases (International Classiﬁcation of Diseases (ICD-11) codes most widespread in the Russian Arctic) against a set of environmental, nutritional, and economic variables. Variance inﬂationary factor and best-subsets regression methods were used to eliminate collinearity between the parameters of regression models. To address the diversity of health impacts across northern environments, territories of the Arctic zone of Russia were categorized as (1) industrial sites, (2) urban agglomerations, (3) rural inland, and (4) coastline territories. It was suggested that, in Type 1 territories, public health parameters were most negatively a ﬀ ected by air and water pollution, in Type 2 territories—by low-nutrient diets, in Type 3 and Type 4 territories—by economic factors. It was found that in the Western parts of the Russian Arctic, poor quality of running water along with low access to the quality-assured sources of water might increase the exposure to infectious and parasitic diseases and diseases of the circulatory, respiratory, and genitourinary systems. Low living standards across the Russian Arctic challenged the economic accessibility of adequate diets. In the cities, the nutritional transition to low-quality cheap market food correlated with a higher incidence of digestive system disorders, immune diseases, and neoplasms. In indigenous communities, the prevalence of low diversiﬁed diets based on traditional food correlated with the increase in the incidence rates of nutritional and metabolic diseases. Hypothesis 3 (H3): conﬁrmed. Low income, poverty burden, and a high proportion of food expenditures in households’ budgets (economic variables X 13–16 ) along with the prevalence of traditional food in the diet exert a negative inﬂuence on the majority of Y n diseases in Type 3 and Type 4 territories. Low diversiﬁed meat-based and ﬁsh-based diets result in the increase in the incidence rates of diseases of the digestive system, diseases of the skin and subcutaneous tissue, diseases of the musculoskeletal system and connective tissue, and infectious and parasitic diseases. The growth of real value of cash incomes and reduction of the proportion of population living below a minimum subsistence income may allow reducing the incidence rates of the diseases of the circulatory system; congenital malformations, deformations, and chromosomal abnormalities; neoplasms; diseases of the blood and blood-forming organs; certain disorders involving the immune mechanism; endocrine, nutritional, and metabolic diseases.


Introduction
Over the previous decades, many studies, including those conducted in the framework of the Arctic Monitoring and Assessment Program (AMAP), have explored major aspects of public health in circumpolar communities, as well as assessed various stressors on human populations living in the North [1]. The Arctic, however, is changing rapidly in many ways. The once established patterns are transforming and bringing new potential risks to human health, such as contaminants, climate change, industrialization, urbanization, economic disruptions, and nutritional transitions. Among the below the national average), eggs, potatoes, and bread (45% lower each), and meat and meat products (30% lower) [30]. Due to the shortages of milk and dairy products, vegetables, and fruits, there is a shift of macronutrients in the diet towards carbohydrates (an abundance of sugar, confectioneries, bread, pasta, cereals) and, therefore, a lack of almost all types of vitamins, mineral nutrients (particularly calcium, phosphorus, magnesium, potassium, iodine, zinc, fluorine, etc.), and contamination of food by pesticides, metals, antibiotics, nitrates, and biological agents.
Many studies have advocated traditional food as a premier source of healthy diets and improvement of public health parameters in indigenous communities. Kuhnlein et al. [31] and Lambden et al. [32] considered traditional food as critical for providing many essential nutrients in balanced diets and recognized the progressing transition to high-energy market food in circumpolar communities as a basis for obesity and other related health problems. However, due to climate change and environmental pollution, traditional food is becoming a less obvious solution to health problems in the North. Concentrations of some CECs are increasing in Arctic air and wildlife, indicating their potential for bioaccumulation and biomagnification, including in food webs [33]. Climate change acts through alteration of food web pathways for contaminants [34], while pollution increases the risk of disease transfer from animals to humans as a large volume of marine and terrestrial wildlife is consumed by humans in the Arctic, often raw and inadequately frozen [35]. Dudarev et al. [36,37] found that blubber of marine mammals in Chukotka was highly contaminated by POPs and some metals, which was the reason for the high exposure to those contaminants by indigenous people whose diets included marine mammals. The higher temperature of ocean water moves warmer marine species towards the northern latitudes [38]. Along with the change of the polar water habitats and the effect of ocean acidification, such migrations bring new biological threats to the health of the Arctic inhabitants (diseases and microorganisms previously not met in the North).
Along with the environmental and nutritional imbalances, northern territories report higher morbidity and incidence rates of many diseases and health disorders compared to the national average [39,40] (Figure 1).  The major health issues are the diseases of the respiratory, genitourinary, and digestive systems (Table 1); the extremes recorded in the Nenets and Yamal-Nenets autonomous districts. While Schmale et al. [42], Law and Stohl [43], Shindell et al. [44], and Kuhnlein et al. [31], among others, conducted the estimates of Arctic-specific disease incidence through environmental and nutritional impacts, a question remains whether particular public health parameters might experience the effects of economic factors [45]. Chen and Kan [8] recognized the people with low socioeconomic status as high-risk subgroups in terms of proneness to respiratory, cardiovascular, and other health effects. During the times of economic and social transformations in Russia in the 1990-2000s, the environmental situation in the Arctic deteriorated substantially with by-all-means emergence of extractive industries. Larsen and Fondahl [46] expected that the industrialization and urbanization trends in the Arctic accelerate in the future. The emissions of air pollutants and wastewater discharge will increase and mostly be emitted around existing industrial sites and human settlements. Due to the environmental disruptions of traditional sources of food and water, circumpolar communities have become increasingly vulnerable to economic insecurity [47]. Morozova et al. [48], Erokhin [49], and Liefert and Liefert [50] reported degrading purchasing power of population in Russia, which resulted in the redistribution of family means in favor of food, as well as a shift to less expensive food products and more affordable sources of proteins of lower quality and nutrition value [51,52].
Another question that emerges is whether particular circumpolar territories might have health impacts different from other Arctic regions and whether populations in various environmental and economic patterns respond differently to the varying combinations of influence parameters. One of the priorities declared by the AMAP Human Health program is tailoring health-related studies in the Arctic to address local issues [2]. Adlard et al. [3] and Weihe et al. [53] made a similar recommendation to consider local specifics and allowed for better cross-territorial comparisons. Chowdhury and Dey [54] and Schmale et al. [42] found that disease incidence rates varied dramatically between Arctic countries but also between the territories within a country. As the per-territory disruptions of public health are becoming increasingly complex, identifying individual factors that affect them is crucial [55,56]. In this study, an attempt was made to capture overlapping environmental, nutritional, and economic dimensions and understand their impacts on selected diseases in different types of circumpolar territories.

Materials and Methods
Based on the previous discussion of diversified public health impacts in the Arctic, the authors applied multiple regression analysis to reveal the variables X n that affect the incidence rates of health disorders Y n . The six-stage algorithm was employed ( Figure 2). dramatically between Arctic countries but also between the territories within a country. As the perterritory disruptions of public health are becoming increasingly complex, identifying individual factors that affect them is crucial [55,56]. In this study, an attempt was made to capture overlapping environmental, nutritional, and economic dimensions and understand their impacts on selected diseases in different types of circumpolar territories.

Materials and Methods
Based on the previous discussion of diversified public health impacts in the Arctic, the authors applied multiple regression analysis to reveal the variables Xn that affect the incidence rates of health disorders Yn. The six-stage algorithm was employed ( Figure 2). The study started with a selection of Xn regressors to be considered for inclusion in the model and development of the regression model (Stage 1). To avoid redundancy, variance inflationary factor (VIF) was computed for each Xn at Stage 2. Based on the criteria developed by Snee [57] and further applied by Kutner et al. [58], Montgomery et al. [59], and Ermakov et al. [60], that VIF values should be less than 5, those Xn for which VIF > 5 were excluded from the model. At Stage 3, a best-subsets regression was performed with the remaining Xn for all models. To finalize the collinearity test, the parameters of adjusted R 2 [61,62] and Mallows' Cp statistic [63][64][65][66] were computed for each subset. The subsets with Cp > (k + 1) were eliminated; the study proceeded with those "best" subsets for which relative Cp were the lowest and/or adjusted R 2 were high. At Stage 4, multiple regression analysis of the models chosen was performed across Yn regressands and territories. The revealed correlations allowed us to categorize the territories based on several parameters (Stage 5) and discover the effects of Xn regressors on Yn regressands (Stage 6).

Stage 1
The categorization of major types of diseases was made according to the 11th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-11) [67] of the The study started with a selection of X n regressors to be considered for inclusion in the model and development of the regression model (Stage 1). To avoid redundancy, variance inflationary factor (VIF) was computed for each X n at Stage 2. Based on the criteria developed by Snee [57] and further applied by Kutner et al. [58], Montgomery et al. [59], and Ermakov et al. [60], that VIF values should be less than 5, those X n for which VIF > 5 were excluded from the model. At Stage 3, a best-subsets regression was performed with the remaining X n for all models. To finalize the collinearity test, the parameters of adjusted R 2 [61,62] and Mallows' C p statistic [63][64][65][66] were computed for each subset. The subsets with C p > (k + 1) were eliminated; the study proceeded with those "best" subsets for which relative C p were the lowest and/or adjusted R 2 were high. At Stage 4, multiple regression analysis of the models chosen was performed across Y n regressands and territories. The revealed correlations allowed us to categorize the territories based on several parameters (Stage 5) and discover the effects of X n regressors on Y n regressands (Stage 6).

Stage 1
The categorization of major types of diseases was made according to the 11th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-11) [67] of the World Health Organization (WHO). Out of 26 ICD-11 codes, fourteen types of diseases were included in the study as Y n regressands-those repeatedly reported by the WHO and many scholars among the most widespread health problems in both indigenous communities and urban settlements in the Arctic [68][69][70] (Table 2). Diseases of the blood and blood-forming organs and disorders involving the immune mechanism Y 4 Endocrine, nutritional, and metabolic diseases Y 5 Diseases of the nervous system Y 6 Diseases of the eye and adnexa Y 7 Diseases of the ear and mastoid process Y 8 Diseases of the circulatory system Y 9 Diseases of the respiratory system Y 10 Diseases of the digestive system Y 11 Diseases of the skin and subcutaneous tissue Y 12 Diseases of the musculoskeletal system and connective tissue Y 13 Diseases of the genitourinary system Y 14 Congenital malformations, deformations, and chromosomal abnormalities Note: for all Y n , the measure is the incidence rate per 1000 people. Source: authors' development.
An array of X n regressors was established along with three types of variables, reflecting environmental (X 1-6 ), nutritional (X 7-12 ), and economic (X 13-16 ) dimensions of health-related effects (Table 3). Table 3. Regressors included in the model.

Index
Regressors Measure

X 1 Air pollutant emissions
Thousand tons X 2 The capture of air pollutant emissions Thousand tons X 3 Freshwater utilization Mln m 3 X 4 Recycled and reused water Mln m 3 X 5 Wastewater discharge into the surface and underground water sources Mln m 3 X 6 Percentage of households having running water available in their homes % X 7 Per capita consumption of meat products kg/year X 8 Per capita consumption of dairy products kg/year X 9 Per capita consumption of vegetables kg/year X 10 Per capita consumption of bread kg/year X 11 Per capita consumption of fish and marine mammals kg/year X 12 Traditional food proportion in a diet % X 13 The proportion of households with a hunter, a herder, or a fisherman in a family % X 14 The real value of cash incomes Percentage over the previous year X 15 The proportion of the population living below a minimum subsistence income % X 16 The proportion of food expenditures in total household's expenditures % Source: authors' development.
Physical environment, including quality of the air, safe drinking water, and adequate sanitary facilities, is one of the critical parameters of public health in the Arctic [71,72]. Despite the large gaps and significant uncertainties, which exist around quantification of influence of Arctic air pollution on public health [6], emissions can be severe, negatively affecting public health [42], particularly around the Russian cities of Norilsk, Vorkuta, and Monchegorsk, the areas of highest air pollution in the Arctic [5,73]. Nilsson et al. [47], Parkinson and Butler [74], and Thomas et al. [75] reported waterborne infectious diseases among the people living in the circumpolar territories in many Nordic countries.
Nutritional effects on health are measured as per capita consumption of major food products, including meat, fish, dairy, vegetables, and bread [49,76]. A parameter of traditional food proportion in a diet was included in the array like the one relevant in circumpolar and, particularly, indigenous communities. Many authors consider traditional food systems as essential sources of nutrients, n-3 polyunsaturated fatty acids [77], and vitamins C, B2, and B12 [78]. Sheehy et al. [79] reported that more traditional foods in a diet translated into greater dietary adequacy for proteins and a number of vitamins and minerals, including vitamin A, several B-vitamins, iron, zinc, magnesium, potassium, sodium, and selenium. According to Wesche and Chan [80], traditional food reduces the intake of saturated fats, sucrose, and excess carbohydrates that often are found in marketed food. However, while most of the studies report health advantages of traditional food patterns, including a lower incidence of cardiovascular disease [81], stability of gut microbiome [82], sources of bioavailable iron [83], among others, there are alternative findings of adverse health effects of traditional food. For instance, Jeppesen et al. [84] concluded that traditional food was positively associated with type 2 diabetes mellitus, impaired fasting glucose, and fasting plasma glucose. Bjerregaard et al. [85] found that impaired fasting glucose increased among the Inuit in Greenland with the consumption of traditional marine food, which might result in impaired insulin secretion -a link revealed by Faerch et al. [86] and Weyer et al. [87,88]. Jørgensen et al. [89] discovered a strong association between persistent organic pollutants in a traditional seafood and low insulin secretion, while Kuhnlein [90] found higher health risks of traditional food systems containing sea mammals due to environmental pollution and increased organochlorine consumption. Contamination of traditional food sources is one of the reasons for lower β-cell function, an important early stage in the development of type 2 diabetes mellitus.
Among economic variables, the real value of cash incomes is used as one of the parameters of the economic accessibility of adequate healthcare services and nutrition [91]. The proportion of the population living below a minimum subsistence income along with the proportion of food expenditures in total household expenditures is the measures of economic accessibility of a healthy diet, which are commonly used by the Food and Agriculture Organization of the United Nations (FAO) [92]. They were included in the array to reflect the ability of households to generate sufficient income, which, along with their own production, can be used to meet food needs. The selection was also based on the idea that within a monetary dimension, access to food required a steady income to ensure a consistent, year-round supply of high-quality goods in the stores and a ready supply of healthy wildlife to be harvested [93]. Indigenous people do not rely much on marketed food; their food expenditures are low. But they still have to deal with the high cost of many commodities, such as oil, fuel, and transportation, essential for hunting, fishing, or reindeer herding activities [33]. Since the primary means for obtaining and producing food in indigenous communities are provided by hunting, herding, fishing, and gathering activities, a presence of a hunter, a herder, or a fisherman in a family is used as one of the economic regressors.
For all Y n and X n , the data were obtained from the Federal Service of State Statistics of the Russian Federation [41], as well as from the authors' calculations.

Stage 2
A critical issue in building multiple regression models is how to eliminate independent variables with strong correlations between each other, whether positive or negative. Identification of collinear variables involves several approaches, one of the most widely used being the variance inflationary factor (VIF) (Equation (1)). It has been successfully applied for measuring and reduction collinearity, for instance, by Zainodin et al. [94] in an ordinary least squares regression analysis, Bowerman and O'Connell [95] in expressing independent variables in regression models as the functions of the remaining regressors, and Dan and Vallant [96] in the analysis of variances between independent variables in complex survey data.
where VIF = variance inflationary factor; R 2 n = coefficient of multiple determination for a regression model.
According to Snee [57], Kutner et al. [58], Montgomery et al. [59], and Levine et al. [61], collinearity between the variables is considered high when VIF exceeds 5. The approach used at Stage 2 was that if VIF for a particular set of X n regressors was less than 5, these regressors were included in the model. In case it was not, the X n variable was eliminated from a subset. The computation was made across eight subsets of X n variables, one per territory included in the study (see Stage 4 for the list of territories).

Stage 3
Having eliminated the variables with high VIF, we then attempted to determine whether the resulting subsets all yield appropriate models with low redundancy. Most commonly, such a task is solved by employing stepwise regression, which allows revealing the optimal regression model without examining all subsets [97][98][99][100]. For many decades, this approach to regression model building has been extensively used in statistics and econometrics as an appropriate trade-off between time expenditures and model performance [101][102][103]. Nowadays, a stepwise regression model building commonly employs the best subsets approach (BSA) that allows evaluating all possible regression models for a given set of regressors in a timely-effective and accurate manner [104][105][106].
Generally, the BSA-based checking of regression models involves a parameter of adjusted R 2 [107], which adjusts the R 2 of each subset to account for the number of regressors and the sample size [61]. In this study, the employment of adjusted R 2 instead of R 2 was preferable due to the need to compare Stage 2 subsets with different numbers of X n . Among the competing subsets, the study proceeded with the one with the largest adjusted R 2 . In addition to adjusted R 2 , when the goal is to find the most appropriate model involving multitude subsets of regressors, a criterion of Mallows' C p statistic (Equation (2)) is generally applied [60,61]. Examples include checking matchings between the subsets [108], model averaging [109][110][111], measuring the deviations from perfect rankings [112], and model selection [113].
where C p = Mallow's C p statistic; n = number of observations; k = number of regressors; T = total number of variables in the full model, including the intercept; R 2 k = coefficient of multiple determination for a model with k regressors; R 2 T = coefficient of multiple determination for a model with all T variables. In this study, C p was applied as a tool to measure the differences between the models constructed at Stage 2 and optimal (or true) models that best explain the correlations. The idea was that the closer C p to the number of variables included in a subset, the more accurate would be the model (only random differences from the optimal model might occur). Thus, Stage 3 resulted in identifying the subsets whose C p were close to or below (k + 1). In total, eight subsets of independent X n variables were built for eight territories.

Stage 4
At Stage 4, multiple regression analysis was performed for all combinations of the selected non-collinear X n aggregated in fourteen multitudes separately for each Y n . The aim was to reveal the variables with the highest positive, positive, negative, and the most negative effects on respected Y n in the Russian Arctic, in general, as well as separately in eight territories included in the Arctic zone of Russia (Figure 3): in the Russian Arctic, in general, as well as separately in eight territories included in the Arctic zone of Russia (Figure 3):

Stage 5
To reflect the division of regressors into three dimensions and make cross-country comparisons possible, the diversity of effects on public health was addressed by categorizing the territories based on the respective parameters: 1. Type 1: territories adjacent to industrial agglomerations, where a level of air and water pollution was higher compared to the mean of the sample.

Stage 5
To reflect the division of regressors into three dimensions and make cross-country comparisons possible, the diversity of effects on public health was addressed by categorizing the territories based on the respective parameters: 1.
Type 1: territories adjacent to industrial agglomerations, where a level of air and water pollution was higher compared to the mean of the sample.

2.
Type 2: territories adjacent to urban agglomerations, where a share of market food in consumption was higher compared to the mean of the sample. 3.
Type 3: rural inland territories, where the traditional diets of indigenous people were based on meat. 4.
Type 4: rural coastline territories, where the traditional diets of indigenous people were based on fish and marine mammals.
It was supposed that, in different types of territories, the incidence rates of Y n diseases and related health problems were affected by different X n variables, particularly:

Hypothesis 1 (H1):
In Type 1 territories, the strongest influence over Y n is exerted by environmental variables X 1-6 .

Hypothesis 2 (H2):
In Type 2 territories, the strongest influence over Y n is exerted by nutritional variables X 7-12 .

Hypothesis 3 (H3):
In Type 3 and Type 4 territories, the strongest influence over Y n is exerted by economic variables X 13-16 and traditional food proportion in a diet X 12 .

Stage 6
To test the hypotheses, positive and negative impacts of X n variables on the reduction of incidence rates of Y n were revealed separately for the four types of circumpolar territories. Positive effects were differentiated as high positive (HP), positive (P), and moderately positive (MP); the negative ones-extremely negative (EN), negative (N), and moderately negative (MN). To decide on the degree of positive or negative effect, maximum and minimum extremes (X max and X min , respectively) were excluded from the calculation, and then a mean value X med was determined for each of the multitudes (Equation (3)): A degree of the effect of X n on Y n was recognized, when a value of X n fell into one of the intervals (Table 4). Table 4. X n intervals and effects on Y n .

Results
The results are presented across five sub-sections in accordance with stages 2-6 of the study flow algorithm (Figure 2). We first checked the array of X n variables established at stage 1 for collinearity (Section 3.1.), then selected the best subsets from derived multitudes (Section 3.2.). After that, we performed multiple regression analysis in selected subsets and generalized the effects of X n on Y n for the entire Arctic Zone of Russia (Section 3.3.). Based on the identified correlations, we then categorized the territories into types (Section 3.4.), revealed positive and negative determinants of incidence rates across them, and tested out hypotheses (Section 3.5.).

Checking X n for Collinearity
Collinearity checks were performed in 128 multitudes of X 1-16 variables in eight territories included in the study. Regression models were computed with all independent variables to find VIFs. Application of VIF > 5 criteria resulted in the elimination of high-collinear X n variables from the models in respective territories ( Table 5)-some of the water-use and environmental variables in the western and central territories of the Russian Arctic and economic variables in the Far East.

Selection of the Best Subsets
Best-subsets stepwise regression with the remaining X n allowed to identify several more variables with high collinearity: X 13 in territories 1 and 2, X 5 in territory 2, X 3 in territory 3, X 4 in territory 5, and X 4 in territory 6. Based on the parameters of adjusted R 2 and Mallows' C p statistic, the best subsets of variables (one per territory) were chosen out of competing multitudes (Table 6).

Multiple regression
Multiple regression analysis was performed in 112 multitudes (fourteen Y n regressands and eight territories) with respective adjusted arrays of independent variables. High R 2 in individual multitudes and average R 2 demonstrated that all variations were well explained (Table 7). Generalization of X n values for eight territories allowed to reveal the health-related effects of independent variables in the entire Arctic Zone of Russia (Table 8). X 6 , the percentage of households with available sources of running water, posed the most diverse effects on selected health parameters, from the highest positive to the most negative. Air and water pollution massively had a net detrimental effect on the incidence rates of the diseases under study (excluding X 4 eliminated from the subsets in most of the western territories of the Arctic Zone and X 2 not considered in territories 3, 5, and 6). Economic parameters (excluding high-collinear X 15 and X 16 in the eastern areas of the Arctic Zone) made a positive impact on the reduction of the incidence rates. The effects of nutritional variables varied across Y n , the most positive being consumption of fish and marine mammals in case of the diseases of the circulatory and nervous systems. Note: * for particular X n , the generalizations cover only those territories in which the respective X n is included in the per-territorial models; HP-the highest positive impact of X n on the reduction of Y n ; P-positive impact of X n on the reduction of Y n ; N-negative impact of X n on the reduction of Y n ; EN-extremely negative impact of X n on the reduction of Y n . Source: authors' development.

Categorization of the Territories
Categorization based on the level of pollutant emissions, the proportion of market food in the diets, and the per capita consumption of meat and fish allowed to classify four types of territories (Table 9). Type 1 territories were those most intensively explored by Russian hydrocarbon and mineral companies. The group included the territories of Yamal-Nenets Autonomous District and three areas of Krasnoyarsk Krai-Norilsk and Taimyr Dolgan-Nenets municipal areas and Turukhansk district, where the highest level of air pollutant emissions was registered. The percentage of households with access to quality-assured sources of water was low. In territory 6, the volume of wastewater discharge into the surface and underground water sources was the highest in the Russian Arctic.
Type 2 included the territories of Murmansk and Archangelsk oblasts adjacent to the biggest cities and seaports in the Russian North, Murmansk, and Archangelsk, respectively. In Type 2 territories, people had predominantly westernized type of nutrition with a low proportion of traditional foods in their diets. Due to the low standards of living in Type 2 territories (in 2017, the proportion of the population living below a minimum subsistence income was 18.0%, 15.0%, and 13.5% in Komi Republic, Arkhangelsk, and Murmansk regions, respectively), there was registered underconsumption of meat and dairy products and vegetables.
Inland territories of Russian Arctic relatively remote from either urban or industrial agglomerations were recognized as Type 3 (Republic of Sakha) and Type 4 (Chukotka and Nenets autonomous districts). In Type 3 and 4 territories, diets of people were more traditional compared to the western parts of the Russian North, with a predominance of reindeer meat, fish, and marine mammals. The Yakuts are historically semi-nomadic hunters engaged in animal husbandry, focusing on reindeer herding [114], while people in Type 4 territories depended on fishing. In Chukotka, per capita consumption of meat was the lowest in the Russian Arctic-44 kg/year in 2017.
Despite the relatively similar reliance of diets on traditional food in Type 3 and Type 4 territories, the average incidence rates of diseases under study varied widely (Table 10). This finding supported the assumption that in different types of circumpolar territories, public health parameters are affected by different combinations of factors, nutritional ones being but a few of them. Table 10. Incidence rates of Y n diseases and related health problems across the territories of the Russian Arctic, average in 1997-2017, cases per 1000 people.

Revealing the Correlations
To reveal the determinants of varying incidence rates across the four types of territories, the impacts of X n factors were graded on a scale from high positive to extremely negative. Based on the previous results of collinearity checks and categorization of the territories, the following independent variables were eliminated from the models: in Type 1 territories-X 2 and X 4 ; in Type 2 territories-X 4 (X 6 and X 13 were considered in territory 4 only, X 5 -in territory 1); in Type 3 territories-X 15 and X 16 . The highest positive impact on public health was exerted by the quality of nutrition (consumption of fish and marine mammals and vegetables) in Type 1 and Type 3 territories, economic variables of income, poverty, and food expenditures in Type 2 and Type 3 territories, and quality of running water supply and wastewater treatment in Type 3 and Type 4 territories (Table 11). The most negative impact on public health was exerted by low percentage of households with the running water supply in Type 1, 2, and 4 territories, wastewater discharge into surface and underground water reservoirs in Type 4 territories, consumption of meat products in Type 1 and 2 territories and bread in Type 2 territories, and low economic standards of living in Type 3 and 4 territories (Table 12).  [13][14][15][16] ) along with the prevalence of traditional food in the diet exert a negative influence on the majority of Y n diseases in Type 3 and Type 4 territories. Low diversified meat-based and fish-based diets result in the increase in the incidence rates of diseases of the digestive system, diseases of the skin and subcutaneous tissue, diseases of the musculoskeletal system and connective tissue, and infectious and parasitic diseases. The growth of real value of cash incomes and reduction of the proportion of population living below a minimum subsistence income may allow reducing the incidence rates of the diseases of the circulatory system; congenital malformations, deformations, and chromosomal abnormalities; neoplasms; diseases of the blood and blood-forming organs; certain disorders involving the immune mechanism; endocrine, nutritional, and metabolic diseases.

Discussion
Across Arctic communities, public health outcomes are affected by different combinations of environmental, nutritional, and economic factors. Both the modes and degrees of the influence are determined by a location of a territory, level of industrial development, economic and social situation, and patterns of life and food consumption.
Environmental factors, primarily, air pollution, are commonly recognized as the sources of the most serious toxicological impacts on human health, including respiratory and cardiovascular diseases, neuropsychiatric complications, and cancer [116,117]. In the Russian Arctic, previous studies identified such emissions as sulfate aerosols from metal smelting [118] and flaring associated with oil and gas extraction [119] but found no evidence of direct health implications from air pollutant emissions [2,120]. In this study, the negative influence of air pollution on health parameters was revealed across all types of circumpolar territories. Among the most notable consequences were increased respiratory ailments -the incidence rates of Y 9 were unacceptably high across all four territory types (Table 10). Syurin and Burakova [121] found that the development of respiratory pathology patterns (primarily, chronic bronchitis and chronic obstructive pulmonary disease) was closely associated with the locations of harmful industries in the western part of the Russian Arctic. In those Types 2 and 4 territories, people experienced increasing susceptibility to air irritant agents (X 1 and X 2 ) and the quality of water supply systems in the cities (X 5 and X 6 ). This corresponded with the recommendations to improve access to clean water to reduce respiratory morbidity made by Kovesi [122,123]. Miller and Gaudette [124] suggested that a lack of vitamins (particularly, vitamin A) in the diet might be a possible co-factor of higher lung cancer in northern communities, while Tse et al. [125] reported household crowding and living conditions to be significantly associated with respiratory infections among indigenous people. In relation to our findings, it seemed that the adequacy of nutrient intake (X 7 and X 11 ) along with the degree of outdoor physical activities (X 13 , hunting and fishing) had positive effects on the reduction of Y 9 incidence rate. Other health issues for which a correlation with environmental factors was revealed included eye irritation, increased cardiovascular morbidity, and carcinogenic effect of pollutants. This finding supported earlier results of Li and Mallat [126], Vermaelen and Brusselle [127], and Chen and Kan [8].
Among environmental factors, air pollution was recognized as moderately negative, the most negative being the quality of water and the volume of wastewater discharge. This corresponded with Hennessy et al. [17], Thomas et al. [16], and Wenger et al. [128], who all demonstrated a direct correlation between clean water in sufficient quantities and significant reductions in the occurrence of illness and hospitalizations due to infectious disease. Our finding also supported Nilsson et al. [129], who reported that over one-third of the population in the circumpolar territories of Russia used drinking water from non-centralized sources; Bressler and Hennessy [130], who recognized poor access to safe water among the causes of gastrointestinal illness and water-washed infections, such as respiratory tract infections and skin infections; as well as Daley at al. [131], who associated inadequate domestic water quantities with transmissible diseases and bacterium infections in indigenous communities. According to Hennessy and Bressler [132], the burden of inadequate water and sanitation services on public health is higher among rural and indigenous populations in the Arctic. In contrast with this opinion, our study demonstrated extremely negative health effects of poor water supply systems and water pollution not only in the eastern parts of the Russian Arctic but also in urbanized Type 2 territories and industrialized Type 1 territories. In this part, our results corresponded with the data of Dudarev et al. [133], who discovered that 51% and 19% of water samples taken from the centralized water sources in Type 2 territories did not comply with hygienic norms in terms of chemical and biological contamination, respectively. In the industrialized territories of Type 1, the coverage of households by public water supply exceeds 80%, but the majority of water supply facilities have not been properly repaired, cleaned, and disinfected for a long time [133]. Centralized water sources and drinking water are highly contaminated by chemical and biological agents. In Type 1 and Type 2 territories, water of low quality is delivered through the outdated supply system to the majority of households, which results in the growth of incidence rates of endocrine, nutritional, and metabolic diseases; diseases of the circulatory, digestive, and genitourinary systems; diseases of the musculoskeletal system and connective tissue. High pollution load increases the level of contamination of wildlife, a premier source of food for indigenous peoples in the Arctic. According to Vinokurova [134], Greaves [135], and Ignateva [136], pollution destabilizes the ecological base of the High North and threatens food and nutrition security. Previously, it was demonstrated that traditional food consumption patterns might benefit various health parameters in indigenous communities [32,[77][78][79][80][81][82][83]137]. As distinct from these studies, we revealed the negative health impacts of traditional food in some indigenous habitats in Type 3 and 4 territories. This corresponded well with earlier findings of Jeppesen et al. [84], Bjerregaard et al. [85], and Jørgensen et al. [89] that traditional dietary pattern was associated with lower β-cell function and a higher risk of impaired fasting glucose and type 2 diabetes mellitus. It was found that undiversified meat and fish-based diets in Type 3 and Type 4 territories, respectively, correlated with higher incidence rates of endocrine, nutritional, and metabolic diseases and diseases of the circulatory and genitourinary systems.
In the indigenous communities and rural territories of Type 3 and Type 4, diversification of the diets may impact public health in a positive way, but the economic accessibility of market food among rural people is low. Dudarev et al. [36] complained about the prohibitively high cost and limited availability of market food across the Arctic zone of Russia. Wesche and Chan [80], Ford [138], and Guyot et al. [139] found that low levels of income and high food costs resulted in changing diets and neglecting healthy nutrition. Poverty forces people to seek a substitution to the expensive market food in traditional hunting and fishing, but the contribution of traditional economic activities to the improvement of public health is minor. There are economic barriers reported by Lambden et al. [32] and Goldhar et al. [140], such as high costs of hunting and fishing, tightening food sharing networks, and hunting and fishing regulations.
In industrialized and urbanized territories of the Arctic Zone of Russia, on the contrary, we registered the increase in the proportion of market food in the diets, which was in line with the emergence of "nutritional transition" previously conceptualized by Egeland et al. [141] and Kuhnlein et al. [31] in the case of the Canadian Arctic. Young et al. [142] supposed such transition to contribute to an increase in diabetes and other diseases among northerners. Receveur et al. [143] and Nakano et al. [144] recognized an increased consumption of market food as a contributing factor to a higher incidence of overweight and obesity. Our findings demonstrated that in the territories where the proportion of market food in the diets was above the Arctic average, the negative impact of the transition was limited to the increased incidence rates of the diseases of the digestive system, immune diseases, and neoplasms.

Conclusions
This study attempted to convey the existing complexity of public health impacts in the case of the Arctic zone of Russia. It was investigated how various factors were interrelated with the incidence rates of major diseases in different types of circumpolar communities. The establishment of the set of environmental, nutritional, and economic variables allowed for a particularly useful analysis of the variations within the groups of health impacts and thus made the levels of exposure to certain diseases comparable across the territories. The territories were grouped in four types based on the respective levels of influencing groups of factors: (1) industrial sites, the most negative health impacts of air and water pollution; (2) urban agglomerations, the most negative health impacts of nutritional factors; (3) inland and (4) coastline indigenous communities, the most negative health impacts of economic factors.
The testing of the three hypotheses resulted in the identification of positive and negative effects on selected health parameters. The relationships between the regressands and corresponding regressors were discovered individually for eight territories of the Arctic Zone of Russia and generalized for the four types of the territories, given the alternations between the highest positive and most negative influences on the dependent variables. In Type 1 and Type 2 territories, poor quality of running water along with low access to the quality-assured sources of water increased the exposure to infectious and parasitic diseases, neoplasms, diseases of the circulatory, respiratory, genitourinary, and nervous systems, and endocrine, nutritional, and metabolic diseases. In Type 3 and Type 4 territories, low diversified diets based on traditional food correlated with the increase in the incidence rates of nutritional and metabolic diseases. Underconsumption of milk and vegetables resulted in a lower intake of vitamins and mineral nutrients, including calcium, phosphorus, magnesium, and zinc. Declining economic accessibility of adequate diets further exacerbated nutrition-related health problems.
The set of environmental, nutritional, and economic variables applied in this study as regressors was open-ended and discussible. The six-stage regression analysis that involved collinearity checks based on the VIF and BSA methods allowed to build regression models in which regressands' variations were well explained by independent variables. However, due to the ongoing environmental, climate, and economic changes in the Arctic, a further focus on finding the most feasible influencing factors of public health could place the issue in the larger context of social-ecological change that is affecting the resilience of the Arctic and health and well-being of its inhabitants. In such respect, further studies of health impacts in the High North should involve comparisons with other Arctic countries except Russia. Effectively addressing emerging health-related challenges require continued research into health risk factors and trends in order to facilitate the identification of priority areas for policy interventions.
Author Contributions: T.G. designed a research framework; V.E. conceptualized the methods, performed the data collection, analyzed the data, and wrote the paper. All authors have read and agreed to the published version of the manuscript.