According to the relative income hypothesis, the health status of a population is determined by its horizontal social and financial conditions, both mutually interrelated factors. As a former republic of the Soviet Union, Kazakhstan is a particularly interesting case in which to explore the impact of health-related inequalities due to economic, sociodemographic, and institutional changes experienced as the country transitioned to independent status. The goal of this article is to examine the degree to which commonly-used socioeconomic determinants (education, income, living conditions, marital status, occupation) are associated with health inequalities in Kazakhstan. We found significant differences in the health status characteristics among the population. Poor health was found to be significantly associated with living conditions and income level. This article will assist policy makers in developing and improving existing social and health policies to address the apparent lack of health-related equity in Kazakhstan. 

KEYWORDS inequality, health, determinants, Kazakhstan, Central Asia.

Kazakhstan lies in the heart of Central Asia. It is bordered to the north by Russia, to the east by China and Mongolia, to the south by Kyrgyzstan, Uzbekistan, and Turkmenistan, and to the west by the Caspian Sea. The country comprises 14 provinces that are further divided into districts, sub-districts, and villages. Kazakhstan is the world's largest country in terms of total area, with a surface area of approximately 2,724,900 km2 (about the size of 15 states constituting the European Union) and a population of 18.7 million, making Kazakhstan the fourth most populous former Soviet republic (WB, 2020a). While 60% of residents live in cities (Nyussupova & Kalimurzina, 2016), which are hubs of economic opportunity and prosperity, the very high cost of living in Kazakhstan's cities, and the near absence of a rental housing market outside the capital, Nursultan (former Astana), has meant that the pace of urbanization is slow. With an average population density of approximately six people per km2, Kazakhstan is one of the most sparsely populated regions in the world.

Kazakhstan is a newly industrialized country, and its economy has grown dynamically since independence - between 1990 and 2018, GDP increased from 26.93 to 181.2 billion USD (WB, 2020b). The economy of Kazakhstan is export-dependent with exports accounting for 75% of GDP (WB, 2020b), and the oil and gas sector plays an important role rising from 10.9% GDP in 2001 to 58.7% in 2018 (IEC, 2019). It could be argued that Kazakhstan demonstrates classic symptoms of the “Dutch disease”—an appreciating real exchange rate, a shrinking share of the manufacturing sector in the economy, and expanding nontradable sectors such as construction and services (Kutan & Wyzan, 2005; Egert & Leonard, 2008; Hasanov, 2013). Due to the high reliance on income from oil exports, the Kazakhstani economy and its competitiveness are vulnerable to international commodity prices. The Kazakhstani economy was hit in 2015 when oil prices decreased from USD 147 to USD 36 per barrel (Bloomberg, 2016), and forecasts suggest that prices for oil will remain low in the medium term (e.g., EIA, 2016). Thus, the Kazakhstani government expects a low level of GDP growth. According to forecasts made by the Ministry of National Economy in relation to GDP growth rates, the average annual GDP growth rate between 2015 and 2020 rose from 1.5% to 2.7% (COM RK, 2018).

Regarding social development, since Kazakhstan became independent, the Gini measure of inequality decreased dramatically (from 0.366 in 2000 to 0.282 in 2018), and the middle class grew, as more and more people began to enjoy the benefits of inclusive growth (UNDP, 2019). However, the level of poverty remains very high, especially in rural areas (ADB, 2016). Regional differences in income are also common: households in the more affluent southern part of the country earn 3.5 times more than households in central and western Kazakhstan (ADB, 2016). Rising inequality has indirect impacts on various aspects of social life increasing deprivation and injustice and causing conflict (BBC, 2011). Population health generally remains low in Kazakhstan from an international perspective and compared to countries with similar levels of development (McKee & Chenet, 2002; WHO, 2018). Life expectancy at birth trended upward according to the WHO, from 64.5 years in 1993 to 69.5 years in 2018, although male life expectancy remains significantly low in comparison to the OECD average (WHO, 2018). The differences in health outcomes across genders and regions are profound (Katsaga et al., 2012). For example, in the central region of Kazakhstan, life expectancy is 60.3 years (61.6 years for males and 67.5 for females), and in the southern part of Kazakhstan, it is 67.7 years (65.8 years for males and 71.4 for females).

In the literature, empirical studies in a variety of disciplines (economics, sociology, and public health) demonstrate a robust association between income level, lifestyle, environmental conditions, employment, and health (Winkleby et al., 1992; Kawachi et al., 1997; Adler and Ostrove, 1999; Lynch et al., 2000; Wagstaff & van Doorslaer, 2000; Pikhart et al., 2001; Karlsen et al., 2002; Wilkinson & Marmot, 2003; Kunst et al., 2005; Wilkinson & Pickett, 2006; Harper & Lynch, 2007; Cavalieri, 2013). According to these studies, the health status of the population is largely determined by the mutually interrelated factors of living conditions, socioeconomic status, and lifestyle (Zimmer et al., 2000; Wardle & Steptoe, 2003; Aittomäki et al., 2003; Duetz et al., 2003). People in lower socioeconomic groups have higher mortality and more frequent health problems than those of higher socioeconomic status (Smith et al., 1998; Dalstra et al., 2002) and there is a strong correlation between education and employment (Khang et al., 2004; Kondo et al., 2009). The occupational social class is also a stronger predictor of health outcomes than education (Chandola et al., 2003). The issue of environmental pollution and its impact on health has also received increasing attention in recent years (Evans & Kantrowitz, 2002). There is evidence of a strong relationship between health and environmental risks factors, including hazardous waste and other toxins, ambient and indoor air pollution, water quality, ambient noise, residential crowding, housing quality, working environment, and neighborhood conditions. Poor housing conditions are associated with a wide range of health conditions, including respiratory infections, asthma, injuries, and mental diseases.

Several authors have documented income and health inequalities, health concerns, and people's needs, access to, and interactions with health professionals and the healthcare system in post-communist countries; these studies focused on Russia, Ukraine, Estonia, Latvia, Lithuania, Poland, Hungary, Kazakhstan, and Kyrgystan (Bobak et al., 1998; Walberg et al., 1998; Bobak et al., 2000; Medvedev, 2000; Gilmore et al., 2002; Bobak et al., 2007; Carlson, 2005; Abbott & Wallace, 2007; Fotaki, 2009; Abikulova et al., 2013; Cockerham et al., 2014; Cockerham et al., 2017).

In the case of Kazakhstan, to the best of our knowledge, there are two empirical studies of socioeconomic and health inequality, and the findings are mixed (Abikulova et al., 2013; Cockerham et al., 2014). Cockerham et al. (2014) examined health lifestyles in Kazakhstan and Kyrgyzstan. Data were collected by face-to-face interviews conducted by trained interviewers in the homes of respondents in Kazakhstan and Kyrgyzstan. Cockerham et al. (2014) reported that neither education nor disposable income was a strong predictor of health lifestyle patterns; national peculiarities were found to be particularly significant. The study concluded that Kazakhs have a lower level in the social patterning of health-related behaviors than equivalent Kyrgyzs. As shown in the study, Kyrgyz men lived some 5.1 years longer on average in 2000 than Kazakh men (64.9 years versus 59.8 years). The authors also argued (Cockerham et al., 2014) that the key variables underpinning negative health lifestyles of these former socialist countries were gender (male), age (middle age) and class (working class). Not only is male gender the single most powerful predictor of negative health practices, but age is important, as younger middle-age respondents, especially in Kazakhstan, are heavy drinkers and smokers. In both countries, employment comprising manual labor is common. Neither education nor disposable income was a strong predictor of healthy lifestyle patterns, where the average level of schooling is a secondary education and there is little spending power in the general population. The majority of the study cohort had just enough money for food and clothes (Cockerham et al., 2014). However, occupation was a significant variable in Kazakhstan in that persons in lower-status jobs had the heaviest physical labor and least healthy diets.

Abikulova et al. (2013) assessed gender, ethnicity, and social inequalities in self-rated health status. The data were collected from one central district in the former capital of Almaty. Considerable levels of health-related inequalities were reported by age, gender, education, and perceived material deprivation, but not by ethnicity or marital status. However, given the fact that Almaty is the former capital of Kazakhstan and is still the wealthiest city, the findings may not be generalized to the whole of Kazakhstan or other Central Asian countries, where populations tend to be less privileged than some of those in Almaty.

There is a body of literature recognizing the importance of geographic dimensions when considering health, such as disparities between urban and rural areas (Fang et al., 2009; Yang & Kanavos, 2012) and regional differences (Mu, 2014; Wang et al., 2012). Recent studies addressing the spatial dimension of income inequality have concluded that there is huge regional variability (Gustafsson & Shi, 2002; Carlson, 2005). Carlson (2005) found different parts of Russia have been affected differently economically, socially, and in health terms, with some parts of the country managing the transition from communism to post-communism better than others. Kennedy et al. (1998) found an association between indicators of social capital and life expectancy in the Russian regions. Regions with higher mistrust in the government, higher criminality, poor working relations, and lack of engagement in politics also tend to have a lower life expectancy. Regional variations are important factors for health and income inequality. Therefore, it is necessary to take geographic heterogeneity into account when focusing on inequality studies.

We follow the literature and investigate the degree to which commonly used socioeconomic determinants (education, income, living conditions, occupation, etc.) are associated with health inequalities in Kazakhstan. We think Kazakhstan as a former Soviet country is a particularly interesting case due to recent social, economic, environmental, and institutional changes in the country. This article will contribute to the health-related literature in analyzing a developing country within a transitional economy. Although poor health and high inequality are key features of many developing countries, most existing studies focus on developed or market-based countries mainly in the OECD. In addition, we investigate the health-influencing factors on the individual- and regional-level data. We focus on the Karaganda region, which is characterized by a high level of environmental pollution, industrial activity, and dense population. Understanding the key determinants of health status is needed to understand potential causes of the decline in life expectancy and to develop appropriate health and social policy responses within Kazakhstan.

In this study, we examine the relationship between the level of income and health risks in Kazakhstan using the ordinal logit technique. We use the Kazakhstan Household Survey (KHS), collected in 2014 by the Kazakhstan Ministry of Social Policy and Health (KMSPH) and Karaganda State Medical University (KSMU) and conducted according to methodology provided by the World Health Organization (WHO). The Karaganda Administrative Region is located in the central part of Kazakhstan and covers a total area of 427.982 km2 comprising Kazakhstan's largest region. The region has the largest reserves of nonmetallic raw materials for use in metallurgy and construction industries. Due to significant exploited deposits of mineral resources and an abundant availability of fuel (and thermally powered electricity) as well as a plentiful water supply, there has been rapid industrial development in the ferrous and nonferrous metallurgy, coal, energy, chemical, food, and construction sectors. The backbone of the economy of the Karaganda region is the mining and manufacturing industry, which produces more than 62% of growth regional product (ANS, 2019). The region is administratively divided into nine districts; the cities and towns of oblast significance are Karaganda, Balkhash, Zhezkazgan, Karazhal, Saran, Shakhtinsk, and Temirtau. The total population of Karaganda region is 1,358,064, and there are approximately 685,000 residents living in the rural area, accounting for 51% of the total population. The per capita income of the household in the Karaganda region is lower than the national average in 2018 (37590.00 KZT or 89.11 USD). The mortality rate in the Karaganda region is higher than the national average, 27.5 per 100,000 population in 2018 (ANS, 2019). The life expectancy at birth in the region was 61.6 for males and 67.5 for females (UNDP, 2019). Access to primary healthcare was not hampered by the economic reforms, though the costs involved in hospital care brought about a decline in the utilization of tertiary services.

In our research, sample households were randomly drawn from six different provinces in Karaganda Region including the city of Karaganda (administrative center), Balkhash, Temirtau, Saran, Oskarovk, and Zharkent. The data provided by KMSPH include detailed information on household and individual characteristics, as well as health-related information such as a self-reported health status, physical functions, and wellness. They also contain a set of questions on health outcomes and health services utilization, data on insurance coverage, medical providers, and health facilities that the household might use under selected circumstances. Table 1 summarizes the definitions of key variables in our sample. The data include gender, age, education, marital status, family size, occupation, and health and living status. Table 2 shows the main characteristics of the sample.

In total, 3,683 households involving individuals between 18 and over 65 years old were sampled, including 2,829 (76.8%) female and 854 (23.2%) male respondents. More than 25% of the respondents were under 35 years old, 59.1% of the respondents were 36–59 years old, and the rest (15% [552]) were 60 years or older. Marital status was categorized into four groups: 2,546 (69.1%) married, 435 (11.8%) single, 298 (8.1%) divorced, and 376 (10.8%) widowed. 2,976 (80.8%) of those interviewed had been living in the study area for more than 20 years. As education may have a protective impact on health, the educational level of individuals was determined at four levels: higher education, secondary-specialized (i.e., medical, technical, pedagogical college), secondary, and primary. 26.8% of respondents interviewed were highly educated, 35.9% had secondary education, 34.1% vocational, and only 3% just primary education. 93.8% of the respondents had their own apartment or house and only 5.7% had been living in a rented accommodation.

We have distinguished three categories of occupational status: employed, unemployed, and student. Those employed included farmers; a status as employer referred to those employing more than one employee and included self-employed workers. Almost half of all employed respondents were in professional, technical, or managerial occupations, 26% in sales or service occupations, and 10% each in skilled manual and unskilled manual occupations. 1,660 (44.8%) of a total number of the interviewed respondents reported low income, 46.4% average, and only 13.1% higher than average.

We used a subjective measure of health status. During the interview, all participants rated their current status of their health as one of the following categories: 1 – poor; 2 – quite poor; 3 – neither good nor poor; 4 – quite good; 5 – very good. 46% of the interviewees reported good health; most people over 40 years old reported their health as poor, and they were 35.6% of the interviewed. Given that data on income are difficult to obtain in countries of the former Soviet Union, we used the self-reported measure of material status (Bobak et al., 1998; Bobak et al., 2000; Gilmore et al., 2002; Bobak et al., 2007). 22.4% reported not having enough resources, while 19.8% of respondents reported having more than enough. Most of the study participants (42.1%) rated their living conditions as good while about 13.5% reported having bad living conditions.

The logistic model shows that the health status in the Karaganda region is significantly associated with household income, (p < 0.001, 95% CI = −2.054 to −.383) (Table 3). Income level is also an important predictor of self-rated health (p < 0.001, 95% CI = −5.399 to −3.612). Thus, we show that increasing absolute income by economic development and providing employment will improve the health of the population. Similar results have been found for living conditions (p < 0.001, 95% CI = −2.022 to −1.050). People who lived in their own apartment or house reported relatively good health. Marital status was detrimental to health in the study area (p < 0.005, 95% CI = .139 to .763). The association between occupational status and health is less consistent. However, mental health workers appear to be healthier, reporting fewer chronic health problems and less disability; laborers convey the poorest health status and more chronic health problems.

Using sample data, we conducted a chi-square test for independence; we computed the degrees of freedom, the expected frequency counts, and the chi-square test statistic. Based on the chi-square statistic and the degrees of freedom, we determined the P-value. In this study, the chi-square test for logistic regression model is 4955.833; the model is statistically significant because the P-value is less than .000 (Table 4). The chi-square test (according to Pearson) is shown in Table 5. Since the P-value (0.000) is less than the significance level, we conclude that there is a relationship between the level of income and health risks in Kazakhstan. The Model Summary (Table 6) provides the -2LL and pseudo-R2 values for the full model. The -2LL value for this model is 4955.833. Nagelkerke's R2 suggests that the model explains roughly 84% of the variation in the outcome.

This study focused on socioeconomic factors (age, gender, marital status, income status, etc.) using a logistic regression analysis to determine social inequality. The results showed that income played the most important role in determining respondents' health status. The result was consistent with similar studies in former communist countries of the former Soviet Union—Russia, Ukraine, Estonia, Latvia, Lithuania, Poland, and Hungary (Bobak et al., 1998; Bobak et al., 2000; Gilmore et al., 2002; Carlson, 2005; Bobak et al., 2007; Abbott and Wallace, 2007)—and in former communist countries in Europe (Kunst et al., 2005; Mackenbach et al., 2008). Level of education is clearly related to health status in Kazakhstan. Better-educated men and women are less burdened by chronic conditions and disability than are persons of the same age who obtained only elementary education. The impact of education on health status has been documented in other former Soviet republics (Shkolnikov et al., 2001; Bobak et al., 1998; Kalediene & Petrauskiene, 2000). According to Mackenbach et al. (2008), in Europe as a whole, persons with less education have higher rates of death from all causes except breast cancer, as indicated by a negative slope index of inequality for this cause of death. Inequalities in the rate of death from cardiovascular disease account for 34% of education-related inequalities in the rate of death from any cause among men (451 of 1,333 deaths per 100,000 person-years) and 51% of those among women (251 of 492 deaths per 100,000 person-years). The health of married persons was also superior to the health of those who identified as single. Unfortunately, factors such as occupational status and character of work accounted for only a relatively small proportion of the social inequality. Furthermore, the results also showed that residual variable substantially contributed to the social inequality, suggesting that there remains a good deal of unexplained variation in inequality besides the variables examined in this analysis.

In terms of limitations, this study was not able to examine other factors that may contribute to the high level of poor health. Further study should focus on psychosocial, behavioral and objective health measures. Environmental pollution has also been proposed as a cause of the poor health in Kazakhstan. According to Kazakhstan's Hydrometeorological Service (2016), which monitors air quality, five cities in particular in a central part of Kazakhstan exceeded maximum permissible concentrations for particulate matter: sulphur dioxide, nitrogen oxides, and/or carbon monoxide. Only 10% of the urban population breathes air that is not harmful. Indoor air pollution is also a significant concern, as many households in rural areas used low-quality coal for cooking and heating purposes. The quality of drinking water is also a major concern in Kazakhstan. Poor water-management standards have raised health concerns in many cities, and water safety also is unreliable in the countryside, where the population draws water from common wells affected by groundwater pollution (UNFCCC, 2015). There remains a lack of studies researching the relationships between health and environment in Kazakhstan.

In conclusion, the results of this study suggested that there is a strong inequality of population health in Kazakhstan. We found that income, education, and living conditions were the main factors contributing to the inequality of population health. Feasible measures need to be taken immediately to reduce the risk of health inequality. The progress against the social determinants of health will be crucial to a long-term, sustainable reduction in health inequalities. This will require a sustainable, systematic approach with a rigorous focus on reducing health inequalities across all areas of government. However, there is still a long way to go to lessen health inequality in Kazakhstan. Since the data census in 2014, rapid declines in global oil prices have damaged the Kazakhstani economy further exacerbating health inequalities. Thus, the situation in Kazakhstan today is likely to drive further inequality and poorer health outcomes.

The author (MK) would like to thank the Austrian Agency for International Cooperation in Education and Research (OeAD) for the award of the Ernst Mach Grant (IND130547) and, additionally, MK gratefully acknowledges the Kazakhstan Ministry of Education and Science (MES) for the national grant (AP05131186) “Diffusion of innovations, knowledge-flow dynamics, and economic growth of the regions of Kazakhstan: conceptual framework and mechanisms for implementation” coordinated by Professor Lazat Spankulova (Almaty Technological University).

Articolo pubblicato il 01 luglio 2022

Fonte: University of California Press

Autori: Lazat Spankulova; Marat Karatayev; Michèle L.

Articolo originale: https://online.ucpress.edu/cpcs/article/53/2/177/110710/Trends-in-Socioeconomic-Health-Inequalities-in