Unlocking the potential for the quality of life in older adults from Kazakhstan during the COVID-19 pandemic: modeling study on gender and place of residency and associated factors

Background: This study aimed to evaluate sociodemographic and health-related factors associated with quality of life (QoL) in older adults from Kazakhstan during the COVID-19 pandemic, as well as gender- and place-of-residence differences in these factors associated with QoL.

Method: This study included 445 individuals aged 60 years and above from both urban and rural areas in Kazakhstan, between June and July 2022. Sociodemographic and health-related variables were analyzed. QoL was evaluated by the Older People’s Quality of Life (OPQoL) Scale.

Results: The multivariate regression analysis indicated that the entire study population belonging to an ethnic group other than Kazakh (p < 0.001) was associated with lower OPQoL scores. For men, being married (p = 0.001) was significantly associated with the higher OPQoL scores, while belonging to an ethnic group other than Kazakh (p = 0.040) was associated with the lower OPQoL scores. Regarding women, the presence of COPD (p < 0.001) and belonging to an ethnic group other than Kazakh (p < 0.001) were significantly associated with the lower OPQoL scores. For those living in urban areas, better self-reported overall health (p = 0.005) and the absence of chronic heart failure (p = 0.041) were significantly associated with higher OPQoL scores. In comparison, the presence of chronic diseases (p < 0.001) and belonging to an ethnic group other than Kazakh (p < 0.001) were associated with lower OPQoL scores. In the rural area, in univariate regression analysis, only diabetes (p = 0.012) was significantly associated with lower OPQoL scores. General linear model analysis indicated that age, in combination with various health and sociodemographic factors, significantly affected OPQoL scores. In men, significant associations involved age with diabetes (p = 0.024) and marital status (p < 0.001), and in women, age with COPD (p = 0.005), chronic diseases (p = 0.014), and ethnic background (p < 0.001). Among urban residents, age was significantly associated with chronic heart failure (p = 0.021), chronic disease (p = 0.005), and ethnic background (p < 0.001), while among rural residents, age was significantly associated with hypertension (p = 0.024) and chronic diseases (p = 0.043).

Conclusion: Our findings suggested that various sociodemographic and health-related factors influence QoL in older adults. Furthermore, this study showed gender- and place of residence-differentiated predictors of QoL. These results call for gender and place-of-residence-responsive healthcare provision and community support services.

Background

The outbreak of the COVID-19 pandemic affected all spheres of people’s lives globally, with devastating effects on all aspects of population functioning and quality of life. Reports from 03 October 2023 pointed out that there were more than 1.4 million cases with confirmed COVID-19 infection, and 19,071 deaths in Kazakhstan (1). In the study by Marzo et al. (2), it was reported that COVID-19 adversely affected quality of life (QoL) across different age groups and professions. According to the 2021 population and housing census in Kazakhstan, 12.8% of the total population was 60 years and older (3). Previously, it was pointed out that older adults are more susceptible to COVID-19, with a higher prevalence of mortality related to COVID-19 in the age group 60 years and older compared with other population groups (4). During the COVID-19 pandemic, numerous countries have proposed measures to reduce the spread of the virus (2, 5, 6). Among them are social distancing, employment reductions, changes in everyday activities, and medical service restrictions that affect an individual’s psychological wellbeing (2).

Pandemic measures resulted in profound changes in lifestyle in older people, with the potential to affect the physical and mental health of individuals, particularly for those with disabilities, chronic diseases, and geriatric syndromes (6). In a population-based study in Finland, it was observed that during the early phase of the COVID-19 pandemic, the QoL of older people remained stable, even as negative changes in QoL-related factors were reported (7). A possible explanation for this phenomenon is that older people have a greater capacity to cope with environmental stressors than younger people (7).

In previous studies, numerous factors were reported to affect the QoL of older adults during the COVID-19 pandemic (8–10). In a cross-sectional study by Sürmeli et al. (8), it was noted that age, anxiety, fear, and education level were predictors of QoL in older people. In another study, poorer QoL among older adults during the COVID-19 pandemic was found to be associated with older age, lower education level, being single, unemployment, chronic disease, and self-reported functional constipation (9). Additionally, in a cross-sectional study, in Brazilian older adults with long COVID-19 syndrome, significant predictors of QoL were shown to be race, daily screen time, owning a home, musculoskeletal and anxiety symptoms, and working status (10). Differences between sexes and QoL were described in a cross-sectional online survey of community-dwelling older Canadians during the COVID-19 pandemic, in which ethnicity, region of residence, and social contact were associated with QoL in women (11). In contrast, social engagement was associated with QoL in both sexes (11). In the study on global AGEing and adult health, the results from community-dwelling older adults in China, India, Russia, South Africa, and Ghana demonstrated that social cohesion in men was significantly associated with higher QoL, while in women, being married was significantly associated with higher QoL except for women from Ghana (12).

In a population-based study on the elderly from Brazil, the authors reported a higher prevalence of chronic diseases among the elderly, and that type of disease is associated with the degree of impact on health-related quality of life (13). Furthermore, the literature reports that older people aged 65 years and above have a prevalence of multimorbidity of 67.5–87% globally (14). Additionally, female gender was positively associated with multimorbidity (15).

Moreover, it was noticed that COVID-19 reshaped the quality of life in cities, where the role of transport and land use, facilities and services, housing, urban nature, public space, and information and communication technologies (ICT) underwent transformation during COVID-19 in the quality of life in cities (16). Another study from Norway found that during COVID-19, some characteristics of compact cities were negatively associated with wellbeing and health outcomes, including higher neighborhood density, greater reliance on public transport, smaller dwellings, and reduced green space (17). In contrast, others were positively associated with wellbeing and health outcomes, including the presence of numerous local facilities (17). However, individuals from rural areas in Iowa reported lower perceived importance of social distancing and less concern about COVID-19 (18).

The primary aim of this study was to evaluate the sociodemographic and health-related factors associated with quality of life in older adults from Kazakhstan during the COVID-19 pandemic, as well as to examine gender and place of residence differences in these associations.

Methods

Study design and participants

This cross-sectional study included 445 individuals aged 60 years or older from both urban and rural areas in Kazakhstan. The inclusion criterion for age was 60 years or older, as the retirement age in Kazakhstan is 60 for women and 63 for men. A face-to-face survey was conducted between June and July 2022 by trained personnel. The initial study sample was 451, and the response rate was 98.7%.

Sampling methodology and ethical considerations

In this study, a stratified sampling method was used to ensure the representativeness of the study population based on the percentage distribution of older adults aged 60 years and above in the general population of Almaty City and surrounding rural areas, as reported by the Bureau of National Statistics of the Republic of Kazakhstan (19). These areas encompass a broad range of socioeconomic and living conditions, with the highest proportion of older adults in Kazakhstan. To balance the urban and rural representation, 50% of participants were recruited from urban areas and 50% from rural areas. Within each stratum, participants were further stratified by gender, aiming for a target distribution of approximately 60% women and 40% men in the Almaty region (19).

To secure the privacy and protection of participants’ data, confidentiality was a key ethical consideration. Data collection was carried out by a team of trained interviewers. Before participating, all prospective participants were informed about the research objectives, the research team, and their rights, including the option to withdraw or discontinue the survey at any time without any penalty. Oral informed consent was obtained from all participants. To maintain anonymity, data without names were recorded in the dataset. For accuracy and quality control, all interviews were recorded, and responses were securely stored in a cloud-based system. Participants were assigned unique ID numbers in place of names. Access to the dataset was restricted to the core research team members and protected by password security to prevent unauthorized access.

Inclusion and exclusion criteria

Inclusion criteria:

1. Adults aged 60 years and above

2. Residents of Almaty city and Almaty region

Exclusion criteria:

1. Individuals below 60 years of age

2. Individuals who refused to provide oral informed consent

3. Individuals with aphasia or significant hearing loss

Testing instrument

The Older People’s Quality of Life (OPQoL) Scale was used to assess participants’ quality of life. This questionnaire is specifically designed for older populations and consists of 35 items in 8 categories: Life overall (4 items); Health (4 items); Social relationships (5 items); Independence, Control over life, and Freedom (4 items); Home and neighborhood (4 items); Psychological and emotional wellbeing (4 items); Financial circumstances (4 items); and Leisure and activities (6 items) (20). Participants are asked to indicate the extent to which they agree with each statement by choosing one of five options: “strongly disagree”; “disagree”; “neither agree nor disagree”; “agree”; and “strongly agree” (21). Each response was assigned a score from 1 to 5, and scores for negative statements were reversed so that higher scores indicate better QoL (21). The total OPQoL score ranges from 35 (indicating the worst possible QoL) to 175 (indicating the best possible QoL) (20).

Evaluated variables

The information collected in the survey is as follows:

• Sociodemographic information includes age (categorized as 60–64; 65–69; 70–74; 75–79; and 80 years and above), gender (male and female), education level primary school (3–4 grades); incomplete secondary school (8–9 grades); secondary school (10–11 grades); specialized secondary (technical school and college); higher (bachelor and specialist); postgraduate (master, doctor, PhD, professor, candidate of science, doctor of science, and other); marital status (single, married, widowed, divorced, and other), number of children living with the participant (none; 1–3; 4–6; 7–9; 10 and more), ethnic background (Kazakh and other), and place of residence (urban and rural).

• Health-related information includes self-reported overall health (weak; below average; average; good; and very good), hypertension (no; yes), diabetes (no; yes), chronic heart failure (no; yes), cerebrovascular disease (no; yes), cardiovascular disease (no; yes), chronic obstructive pulmonary disease (COPD) (no; yes), and presence of chronic diseases (neurological except cerebrovascular diseases, respiratory except COPD, musculoskeletal, endocrinological except diabetes, gastrointestinal, hepatobiliary, renal, urological, and hematological diseases) (have; do not have).

For this study, we modified further variables into the following categories:

• Age group: 60–69 years, 70–79 years, and 80 years and above

• Education: Primary and secondary (Primary, Incomplete secondary, and Secondary), Specialized secondary, and University (Higher, Postgraduate, and other)

• Marital status: Single (Single, Widowed, Divorced, and other) and Married

• Number of children participants live with was formulated to “live with children”: no, yes (1 or more children).

Statistical analysis

To assess the adequacy of the sample size, a power analysis was conducted assuming a small-to-moderate effect size (f² = 0.10), a significance level of 0.05, and a statistical power of 80%. The results indicated that a minimum of 300 participants would be required to obtain reliable and generalizable findings. Therefore, the sample size in this study was sufficient to detect meaningful associations and to ensure the robustness of the statistical analyses.

We first compared sociodemographic and health-related characteristics between men and women as well as between individuals living in urban and rural places. Subsequently, we examined mean OPQoL scores in relation to sociodemographic and health-related characteristics for the entire sample, as well as separately for men and women and for those living in urban and rural places. Categorical variables were presented as frequencies (N) and percentages (%), while continuous variables were summarized as mean values (MVs) with standard deviations (SDs). The normality of continuous variables was assessed using the Kolmogorov–Smirnov test and graphical methods, including Q–Q plots, histograms, scatterplots, and residual plots. The Mann–Whitney U-test was used to assess statistical significance between two groups, and the Kruskal–Wallis test was applied for comparisons involving three or more groups.

In this study, all participants completed all items in the survey included in the analysis, resulting in 100% item-level completeness. No data were missing, and therefore no imputation or exclusion of responses was necessary. Data collection was conducted in person under supervision, ensuring that all items were answered.

Linear regression analysis was performed in every model to analyze associations between categorical variables and OPQoL. Coefficients were estimated by ordinary least squares (OLS), and 5,000 bootstrap resamples were used to generate robust 95% confidence intervals (CIs). All regression models were checked for standard assumptions, including linearity, normality, and homoscedasticity of residuals, and multicollinearity (VIF). No violations were observed.

Five separate models were run: (Model 1) the entire sample, (Model 2) men, (Model 3) women, (Model 4) those living in an urban setting, and (Model 5) those living in a rural setting. All variables that were significantly associated with OPQoL in the univariate linear regression were included in the multivariate linear regression analysis.

An adjusted General Linear Model (GLM) was applied with all factors included as fixed effects. Two-way interaction effects were systematically assessed by testing each primary factor (age, gender, and place of residence) against all other covariates. Stratified analyses were also conducted by gender and place of residence to explore subgroup-specific patterns. Effect sizes were reported using partial eta-squared (η²) interpreted according to Cohen (22) as small (η² = 0.01), medium (η² = 0.06), and large (η² = 0.14) effects.

Statistical analysis was done using IBM SPSS statistical software (SPSS for Windows, version 26.0, SPSS, Chicago, IL, USA).

Results

Table 1 presents the study group’s sociodemographic and health-related characteristics by gender. Frequencies across different levels of education significantly differed by gender (p = 0.008), presence of hypertension (p = 0.008), presence of chronic diseases (p = 0.001), and marital status (p < 0.001).

Table 1

Table 1. The study group’s sociodemographic and health-related characteristics regarding gender.

In Table 2, the study group’s sociodemographic and health-related characteristics regarding place of residence were presented. Frequencies in different levels of education (p < 0.001), different degrees of self-reported overall health (p < 0.001), presence of cerebrovascular diseases (p = 0.006), cardiovascular diseases (p = 0.001), COPD (p = 0.011), marital status (p < 0.001), living with children (p < 0.001), and ethnic background (p < 0.001) significantly differed between individuals from urban and rural places.

Table 2

Table 2. The study group’s sociodemographic and health-related characteristics regarding place of residence.

Mean OPQoL values were tested across sociodemographic and health-related groups for the entire study sample and for men and women separately, and are presented in Table 3. Among men, OPQoL mean values differed significantly across categories of self-reported overall health (χ² = 14.73; df = 4; p = 0.005), with the highest mean in the “very good” category and the lowest in the “below average” category. Significantly higher OPQoL mean values were observed among men without chronic diseases (U = 3071.50; z = −1.98; p = 0.048), married men (U = 3463.50; z = 3.64; p < 0.001), and those of Kazakh ethnic background (U = 2256.50; z = −2.81; p = 0.005).

Table 3

Table 3. Mean values of OPQoL score for tested variables (total score and regarding gender).

Similarly, among women, OPQoL mean values differed significantly across categories of self-reported overall health (χ² = 30.35; df = 4; p < 0.001), with the highest mean in the “very good” category and the lowest in the “below average” category. Higher OPQoL mean values were noted among women without hypertension (U = 11002.50; z = 2.84; p = 0.004), chronic heart failure (U = 6502.50; z = 3.17; p = 0.002), cardiovascular disease (U = 7788.00; z = 2.72; p = 0.006), and other chronic diseases (U = 5019.00; z = −4.04; p < 0.001), as well as among those of Kazakh ethnic background (U = 4205.00; z = −6.25; p < 0.001) and those living in rural areas (U = 10767.50; z = 2.46; p = 0.014).

For the entire study sample, OPQoL mean values also varied significantly across categories of self-reported overall health (χ² = 42.26; df = 4; p < 0.001), with the highest mean in the “very good” category and the lowest in the “below average” category. Significantly higher OPQoL mean values were observed among participants without hypertension (U = 28119.00; z = 2.83; p = 0.005), chronic heart failure (U = 15486.50; z = 3.65; p < 0.001), cardiovascular disease (U = 20654.00; z = 3.37; p = 0.001), and other chronic diseases (U = 16698.00; z = −4.28; p < 0.001). Higher mean OPQoL values were also noted among married older adults (U = 25843.00; z = 2.72; p = 0.006), those of Kazakh ethnic origin (U = 12676.00; z = −6.67; p < 0.001), and individuals living in rural areas (U = 28612.00; z = 2.85; p = 0.004).

Mean OPQoL values were tested across sociodemographic and health-related groups for individuals residing in urban and rural regions and are presented in Table 4. Among individuals residing in urban regions, OPQoL mean values differed significantly across categories of self-reported overall health (χ² = 44.51; df = 4; p < 0.001). Higher OPQoL mean values were noted among older adults without hypertension (U = 7098.50; z = 2.12; p = 0.034), chronic heart failure (U = 5089.50; z = 4.42; p < 0.001), cardiovascular disease (U = 6269.50; z = 2.92; p = 0.003), and other chronic diseases (U = 2828.50; z = −5.38; p < 0.001), as well as among those of Kazakh ethnic background (U = 3153.00; z = −6.25; p < 0.001).

Table 4

Table 4. Mean values of OPQoL score for tested variables regarding place of residence.

Among individuals residing in rural regions, significantly higher OPQoL mean values were observed among older adults with diabetes (U = 1068.00; z = −2.68; p = 0.007).

The results of the univariate and multivariate linear regression analyses of sociodemographic and health-related factors associated with OPQoL scores for the entire sample are presented in Table 5. In the univariate linear regression analysis, better self-reported overall health (p < 0.001), absence of hypertension (p = 0.007), absence of chronic heart failure (p < 0.001), absence of cardiovascular diseases (p = 0.001), being married (p = 0.003), having children (p = 0.022), and rural place of residence (p = 0.010) were all significantly associated with the higher OPQoL scores, while presence of chronic diseases (p < 0.001) and belonging to the ethnic group other than Kazakh (p < 0.001) were associated with the lower OPQoL scores. In the multivariate linear regression analysis, belonging to an ethnic group other than Kazakh (p < 0.001) was significantly associated with lower OPQoL scores. In the overall multivariate regression model, we accounted for 14% of the variance in OPQoL (R² = 0.141, F = 7.931, p < 0.001).

Table 5

Table 5. Sociodemographic and health-related factors associated with OPQoL in the entire sample.

The results of the univariate and multivariate linear regression investigations of sociodemographic and health-related factors associated with OPQoL scores in men and women are presented in Table 6. In the univariate linear regression analysis, better self-reported overall health (p = 0.001), absence of cardiovascular diseases (p = 0.030), and being married (p < 0.001) were significantly associated with the higher OPQoL scores, while the presence of chronic diseases (p = 0.036) and belonging to an ethnic group other than Kazakh (p = 0.003) were associated with the lower OPQoL scores. In the multivariate linear regression analysis, being married (p = 0.001) was significantly associated with the higher OPQoL scores, while belonging to an ethnic group other than Kazakh (p = 0.040) was associated with the lower OPQoL scores. In the multivariate regression model from the men subgroup, we verified 15% of the variance for OPQoL (R² = 0.151, F = 5.974, p < 0.001).

Table 6

Table 6. Sociodemographic and health-related factors associated with OPQoL in men and women.

In the univariate linear regression analysis, better self-reported overall health (p < 0.001), absence of hypertension (p = 0.012), absence of chronic heart failure (p = 0.003), and absence of cardiovascular diseases (p = 0.004), and rural residence (p = 0.040) were significantly associated with the higher OPQoL scores, while COPD (p = 0.022), presence of chronic diseases (p < 0.001), and belonging to the ethnic group other that Kazakh (p < 0.001) were associated with the lower OPQoL scores. In the multivariate linear regression analysis, the presence of COPD (p < 0.001) and belonging to an ethnic group other than Kazakh (p < 0.001) were significantly associated with the lower OPQoL scores. In the multivariate regression model from the women subgroup, we verified 20% of the variance for OPQoL (R² = 0.201, F = 8.243, p < 0.001).

The results of the univariate and multivariate linear regressions analyzing sociodemographic and health-related factors associated with OPQoL scores for urban and rural residents are presented in Table 7. In the univariate linear regression analysis for urban residents, better self-reported overall health (p < 0.001), absence of hypertension (p = 0.017), absence of chronic heart failure (p < 0.001), and absence of cardiovascular diseases (p = 0.002) were significantly associated with the higher OPQoL scores, while the presence of chronic diseases (p < 0.001) and belonging to the ethnic group other that Kazakh (p < 0.001) were associated with the lower OPQoL scores. In the multivariate linear regression model for urban residents, better self-reported overall health (p = 0.005) and absence of chronic heart failure (p = 0.041) were significantly associated with the higher OPQoL scores. In comparison, the presence of chronic diseases (p < 0.001) and belonging to an ethnic group other than Kazakh (p < 0.001) were associated with the lower OPQoL scores. In the multivariate regression model from the urban subgroup, we verified 31% of the variance for OPQoL (R² = 0.309, F = 16.009, p < 0.001).

Table 7

Table 7. Sociodemographic and health-related factors associated with OPQoL in urban and rural residences.

In the univariate linear regression analysis for rural residents, diabetes (p = 0.012) was significantly associated with the lower OPQoL scores.

Adjusted GLM analyses examining the interaction effects of age, gender, and place of residence with sociodemographic and health-related variables on OPQoL scores are presented in Table 8. This general linear model analysis revealed that age, in interaction with self-reported overall health (p = 0.032), COPD (p = 0.025), chronic diseases (p = 0.042), marital status (p = 0.013), and ethnic background (p < 0.001), had a significant impact on quality of life, as measured by the OPQoL score. In the age interaction model, the age in interaction with self-reported overall health (η² = 0.056) had the largest effect, followed by the interaction with ethnic background (η² = 0.049), marital status (η² = 0.027), COPD (η² = 0.024), and chronic diseases (η² = 0.021).

Table 8

Table 8. Adjusted GLM analyses examining the interaction effects of age, gender, and residence with sociodemographic and health-related variables on OPQoL scores.

Furthermore, gender in interaction with age (p = 0.046), COPD (p = 0.004), marital status (p = 0.001), and ethnic background (p < 0.001) significantly affected OPQoL scores. In the gender interaction model, the gender in interaction with ethnic background (η² = 0.047) had the largest effect, followed by the interaction with marital status (η² = 0.033), COPD (η² = 0.027), and age (η² = 0.024).

Additionally, place of residence in interaction with self-reported overall health (p = 0.018), chronic heart failure (p = 0.006), COPD (p = 0.022), chronic diseases (p = 0.001), marital status (p = 0.041), and ethnic background (p < 0.001) also had a significant impact on OPQoL scores. In the place of residence interaction model, the place of residence in interaction with ethnic background (η² = 0.045) had the largest effect, followed by the interaction with self-reported overall health (η² = 0.044), chronic diseases (η² = 0.034), chronic heart failure (η² = 0.025), COPD (η² = 0.019), and marital status (η² = 0.016).

Tests of between-subjects effects for OPQoL in relation to age across different genders and places of residence are presented in Table 9. The general linear model analysis demonstrated that, in men, age in interaction with diabetes (p = 0.024) and age in interaction with marital status (p < 0.001) had a significant impact on quality of life measured by the OPQoL score. In women, significant interactions were observed between age and COPD (p = 0.005), chronic diseases (p = 0.014), and ethnic background (p < 0.001). In the male gender interaction model, the age in interaction with marital status (η² = 0.123) had the largest effect, followed by the interaction with diabetes (η² = 0.057), while in the female gender interaction model, the age in interaction with ethnic background (η² = 0.111) had the largest effect, followed by the interaction with COPD (η² = 0.056) and chronic diseases (η² = 0.047).

Table 9

Table 9. Tests of between-subjects effects for OPQoL regarding age between different genders and place of residency.

Among participants living in urban areas, age in interaction with chronic heart failure (p = 0.021), chronic diseases (p = 0.005), and ethnic background (p < 0.001) had a significant impact on OPQoL scores. In contrast, among those living in rural areas, age in interaction with hypertension (p = 0.024) was significant, as well as age with chronic diseases (p = 0.043). In the urban interaction model, the age in interaction with ethnic background (η² = 0.097) had the largest effect, followed by the interaction with chronic diseases (η² = 0.059) and chronic heart failure (η² = 0.044), while in the rural interaction model, the age in interaction with hypertension (η² = 0.053) had the largest effect, followed by the interaction with chronic diseases (η² = 0.046).

The R² values for the men and women samples were 0.444 and 0.348, respectively. The R² values for the urban and rural population samples were 0.451 and 0.308, respectively. The adjusted R² values for the men and women samples were 0.248 and 0.192, respectively. The adjusted R² values for the urban and rural population samples were 0.290 and 0.117, respectively.

Discussion

Before COVID, a 2014 study from Kazakhstan reported that circulatory and respiratory diseases were more common among urban than rural residents (23). Our findings during the COVID pandemic were similar to those of a previous report (23), which demonstrated that cardiovascular diseases and COPD were more frequent in urban than in rural areas.

According to the report analyzing results from the Regional Well-Being Survey of Kazakhstan, conducted by the Economic Research Institute between August and November 2022, it was stated that those living in Almaty city were more satisfied with their lives (89%) then those living in the Almaty region (78%) (24). Additionally, 57% of respondents from Almaty city were satisfied with the quality of health care in their region, compared to 41% of respondents from Almaty region (24). Moreover, according to the study by Panzabekova and Digel (25), life expectancy in the City of Almaty increased from 68.41 years in 2001 to 75.54 years in 2018, while in the Almaty region, it increased from 67.42 years in 2001 to 73.44 years in 2018. Additionally, the authors stated that, aside from economic factors, which have the most decisive influence on life expectancy in Kazakhstan, other factors related to life expectancy and quality of life begin to gain prominence after reaching a certain level of socio-economic development (25). These data indicate the need to evaluate other possible factors that may influence the quality of life of people from Kazakhstan.

During the tested time period in 2022, our results demonstrated from the univariate linear regression that better self-reported overall health, absence of hypertension, absence of chronic heart failure, absence of cardiovascular diseases, being married, having children, and rural place of residence were all significantly associated with better quality of life, while the presence of chronic diseases and belonging to ethnic group other that Kazakh were significantly associated with decreased quality of life. However, in the multivariate linear regression model, belonging to an ethnic group other than Kazakh was associated with decreased quality of life.

In older adults aged 75–99 years, specific self-reported health parameters, such as fatigue, pain, and mobility impairment, predicted both low overall and health-related QoL (26). Regarding chronic conditions, it should be noted that their progression is slow and long-lasting, with an increasing number of individuals with such conditions that may adversely affect one’s health-related QoL (27). However, aside from the fact that chronic disease can adversely affect QoL, particularly in older people, QoL is a complex construct with several dimensions, including physical and mental health, personal perspectives, social and economic factors, and environmental factors (28). Additionally, multimorbidity was reported to have the highest prevalence among older adults with an unfavorable consequence such as disability, functional decline, high health care costs, and poor QoL (29).

Previously, in a study of elderly individuals from India, it was stated that living without a spouse affects QoL (30). Moreover, in another study from Brazil on active elderly, it was also stressed that marital status has an impact on one’s QoL (31). However, in another study, living alone was not associated with lower QoL, whereas loneliness, as a subjective measure, was associated with lower QoL (32). Although living alone is not the same as feeling lonely, it should be noted that older age is often marked by loneliness (32). Therefore, the influence of marital status on older adults’ QoL should be considered a complex dimension, with various factors that can affect the QoL outcome.

Considering ethnic background, a previous study that included Chinese, Malay, and Indian individuals in Singapore found that ethnicity and socioeconomic status were associated with health-related quality of life (33). Furthermore, in another study that was performed on White British, Asian, Black Caribbean, Black African, and Chinese people aged 55 years and above living in England and Scotland, differences in health, income, and social support were found among the ethnic groups (34). These findings could suggest that factors from different ethnic groups can have varying degrees of influence on one’s QoL and its perception.

Regarding the influence of the living environment on individuals’ quality of life, our findings indicated that living in a rural area in Kazakhstan has more favorable effects on older adults’ QoL. It has been stated in the literature that living environment, whether rural or urban, affects income levels, access to services, and the attention received, thereby impacting the QoL of older adults (35). In a cross-sectional study of community-dwelling older adults in Mongolia, lower levels of life satisfaction were associated with living in rural areas (36). However, in the Canadian Longitudinal Study on Aging, higher levels of satisfaction were reported in the rural population than in urban areas (37). Moreover, in a Nigerian study of community-dwelling older adults, it was reported that, in the environmental domain, QoL scores were significantly higher among those living in rural areas than among those living in urban areas (38). At the same time, no differences were found for the physical, psychological, and social domains of QoL (38). These findings imply that the residing area (urban or rural) might have a complex influence on individuals’ QoL; thus, specific and targeted interventions should be proposed and implemented, bearing in mind numerous factors that can influence the overall QoL, particularly in older adults, to improve their wellbeing, satisfaction, and achieve optimal functioning.

Regarding gender during the tested time period in 2022, our findings from univariate linear regression showed that better self-reported overall health and the absence of cardiovascular disease in both genders were significantly associated with better quality of life, whereas chronic diseases and belonging to an ethnic group other than Kazakh in both genders were associated with decreased quality of life. In men, being married was significantly associated with a better quality of life. In women, the absence of hypertension and the absence of chronic heart failure, as well as a rural place of residence, were associated with better quality of life, while the presence of COPD was associated with decreased quality of life. Additionally, we have pointed out that in male participants, aging with diabetes as well as being single significantly worsens the quality of life, while in female participants, aging with COPD, chronic diseases, and belonging to an ethnic background other than Kazakh significantly worsens their quality of life.

It was previously reported that women, compared to men, have more chronic conditions, which, despite causing suffering, threaten life less than heart disease and cancer, which are seen more in men (39). Moreover, it has been stated that, in addition to biological differences between genders, cultural and social structures have a greater influence on gender differences in determining quality of life (39). In our study, we demonstrated that the strongest interaction between age and other variables in both genders was with ethnic background for women, where they had a significantly lower quality of life as they aged compared to their male counterparts. Our results imply that different factors might influence the specific degree of quality of life in men and women; thus, an individualized approach, bearing in mind potential gender differences, should be taken into consideration to adequately address potential factors that can affect the quality of life in both men and women to different degrees.

Regarding the place of residence, univariate linear regression, in subjects from the urban region, better self-reported overall health, absence of hypertension, absence of chronic heart failure, and absence of cardiovascular diseases were significantly associated with better quality of life, while the presence of chronic diseases and belonging to an ethnic group other than Kazakh were significantly associated with decreased quality of life, for those from rural regions, diabetes was significantly associated with decreased quality of life. In the multivariate linear regression model, better self-reported overall health and the absence of chronic heart failure were significantly associated with better quality of life, whereas the presence of chronic diseases and belonging to an ethnic group other than Kazakh were associated with decreased quality of life among participants from urban regions.

For participants from urban areas in our study, aging in combination with chronic heart failure, existing chronic diseases, and belonging to ethnic groups other than Kazakh significantly worsened quality of life. In contrast, for participants from rural areas, aging in combination with hypertension, as well as with chronic diseases, significantly worsened quality of life. These findings suggest that people living in urban areas are exposed to more factors that can affect their quality of life.

In a study of older adults in urban communities in Thailand, good family support was associated with better COVID-19 preventive behaviors (40). Additionally, it was reported that face-to-face communication became less frequent and physical activity decreased more often among rural individuals (41). All of this underscores the complexity of the factors affecting QoL among older people living in urban and rural areas.

Limitations

The design of the study is cross-sectional, thus limiting causal analysis evaluation. We evaluated the quality of life of elderly patients in 2022 in the context of the COVID-19 pandemic without the impact of the pandemic. Self-reported data are used in this study; therefore, certain biases should be pointed out, including proposed measurement misunderstanding and social-desirability bias (42). Moreover, only participants from one region of Kazakhstan (Almaty city and Almaty region) were included in the sampling process; therefore, potential sampling limitations and generalizability difficulties can exist. Additionally, small sample sizes of some subcategories for certain variables are present, thus potentially limiting statistical power in selective subgroup analysis. Furthermore, additional variables including overall mortality, prolonged hospitalization when needed, and long COVID should also be included in future studies. Moreover, to evaluate the impact of the pandemic on the quality of life in the elderly, additional variables should be evaluated, such as fear of infection, social effects of the closures and quarantine, and the use of masks.

Policy and practice implications

Healthcare policies must emphasize the prevention and control of chronic illnesses, especially among the elderly. In this case, it includes boosting access to regular screening, low-cost drugs, and patient education programs. Social programs that provide help to reduce loneliness and provide emotional and practical support to single elderly individuals are also essential.

The Republic of Kazakhstan, within the State Program for Healthcare Development 2020–2025, focuses on additional domains, including healthy lifestyle, development of public health services, and improvement of medical care quality and sustainable development of the healthcare system (43, 44).

This study further indicates gender- and place-of-residence-differentiated predictors of QoL. These results call for gender and place-of-residence-responsive healthcare provision and community support services.

Given that ethnic background was significantly associated with quality of life across all interaction models—including age, gender, and place of residence—it can be assumed that special attention should be directed toward better understanding the factors underlying this association. These interaction effects highlight the importance of considering multiple sociodemographic and health-related factors simultaneously, which is a key advantage of the general linear model. In particular, future efforts should consider the roles of health perception, social integration, and socioeconomic resources, as well as propose and implement optimal measures to reduce the impact and burden of these factors.

This finding shows that rural-dwelling older people reported higher QoL than their urban-dwelling counterparts, suggesting the need to study and improve living conditions in urban settings. Urban planning and service delivery need to be better aligned with the needs of aging populations. Healthcare and social services should be more readily available and adaptable to the needs of aging individuals.

Therefore, policymakers and physicians should use collaborative, culturally valid approaches to address the needs of aging people and alleviate declines in quality of life during and well after government health crises, such as the COVID-19 pandemic.

Conclusion

The findings of our study indicate that quality of life in older adults varies according to the level of self-reported overall health, presence of chronic diseases, marital status, ethnicity, and place of residence. Better quality of life was reported in older adults with better self-reported overall health, those without chronic diseases, as well as those without hypertension, chronic heart failure and cardiovascular disease, older adults who are married, belonging to the Kazakh ethnic group, and living in rural areas.

Subgroup analyses indicated that the influence of health-related and sociodemographic factors differed by gender and place of residence in older adults. For men, marital status and ethnic background were identified as factors associated with the quality of life, whereas among women, COPD and ethnic background were pointed out as factors associated with the quality of life. Differences were also observed between urban and rural residents, with self-reported overall health, presence of chronic diseases, chronic heart failure, and ethnic background being more relevant in urban areas and diabetes in rural areas.

These findings should be interpreted with caution, as they reflect associations observed within the context of the study design. Overall, the results suggest the potential need for preventive and interventional strategies in healthcare and social care settings that account for both gender and place of residence.

Data availability statement

The datasets presented in this article are not readily available because data is available upon reasonable request from the author Dinara Sukenova. Requests to access the datasets should be directed to Dinara Sukenova, c3VrZW5vdmEuZEBrYXpubXUua3o=.

Ethics statement

Ethical approval for the study was granted by the Science Ethics Committee of Kenzhegali Sagadiyev University of International Business (No. 1/22) (Date: 25. April 2022). Oral informed consent was obtained from the participants before inclusion in the study.

Author contributions

AsI: Data curation, Formal analysis, Methodology, Investigation, Writing – original draft, Conceptualization. DS: Data curation, Conceptualization, Investigation, Formal analysis, Writing – original draft, Methodology. AiI: Methodology, Data curation, Investigation, Conceptualization, Writing – original draft, Formal analysis. AN: Formal analysis, Writing – original draft, Data curation, Conceptualization, Investigation, Methodology. DN: Formal analysis, Data curation, Writing – original draft, Methodology, Investigation, Conceptualization. JM: Formal analysis, Methodology, Data curation, Writing – original draft, Conceptualization, Investigation. YC: Writing – original draft, Formal analysis, Investigation. MZ: Writing – review & editing, Investigation, Formal analysis, Writing – original draft. MK: Writing – original draft, Formal analysis, Investigation, Writing – review & editing. SM: Formal analysis, Investigation, Writing – original draft. NR: Investigation, Writing – original draft, Formal analysis. SA: Investigation, Writing – original draft, Formal analysis. FS: Writing – original draft, Formal analysis, Investigation. JK: Formal analysis, Methodology, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Acknowledgments

We sincerely appreciate the contributions of the following individuals and institutions to this study. We extend our gratitude to the study participants for their time and willingness to respond to the surveys. We also acknowledge the support of the Asfendiyarov Kazakh National Medical University and the New York State International Training and Research Program (D43 TW010046).

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author DJ declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

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