INTRODUCTION
Regional disparities refer to marked imbalances or differences across locations or regions that arise when social origins, resources, or power are unevenly distributed [1,2]. In South Korea, development has been concentrated in the metropolitan region, particularly Seoul, and regional gaps have been observed not only between urban and rural areas but also between metropolitan and non-metropolitan regions [3]. Previous studies have reported substantial regional differences in health status, health behaviors, and healthcare utilization between these areas [3,4], and variations in local governments’ financial capacity and healthcare supply may be associated with residents’ health outcomes [5].
The financial independence rate (FIR) measures the proportion of a local government’s total revenue derived from self-generated sources such as local taxes, reflecting its financial capacity and self-reliance [6]. The mean FIR of the 17 regional governments over the past 5 years was 41.7%, ranging from 76.1% in Seoul to 23.5% in Jeonbuk—a gap of over 50 percentage points [5,6]. Such disparities in financial capacity may affect healthcare infrastructure and service provision, contributing to regional health disparities [4,7]. A lower FIR may limit local governments’ capacity to sustain essential health services for vulnerable populations, including older adults, during periods of heightened demand [3,5,7,8]. Therefore, considering regional FIR is necessary when evaluating healthcare utilization and health status within a community [5,7].
To comprehensively assess health status, subjective health status and subjective oral health status—widely used measures of individuals’ perceived general and oral health—along with mental health indicators such as depressive symptoms, are essential components of health status among older adults [2,9]. Health behaviors, including smoking, alcohol consumption, and physical activity, are modifiable lifestyle factors linked to chronic disease prevention and management [10,11], while healthcare utilization, commonly assessed via unmet healthcare needs, reflects access to necessary medical services and healthcare equity [12]. In this study, “metropolitan and non-metropolitan areas” refer to administrative regional classifications based on population concentration and local government structure [3].
Since the Fifth Global Conference on Health Promotion (2000) emphasized social health equity [8], regional health inequalities have been actively researched [5], with growing attention to health disparities among older adults amid rapid population aging [2]. During the COVID-19 pandemic, healthcare utilization decreased by 13.9% [13], while the prevalence of hypertension, diabetes, and mood disorders including depression increased [13-15]. As over 84% of older Koreans have chronic diseases, often multiple [16], the pandemic represents an important contextual condition for older adults’ health given their vulnerability [17]. Previous studies have examined health, healthcare utilization, and health equity among older adults in urban and rural regions in relation to demographic characteristics, socioeconomic status, and social support [10-12], with more recent research focusing on mental health [18], daily living changes [19], and lifestyle changes during the pandemic [20]. However, detailed examination of regional differences—particularly between metropolitan and non-metropolitan regions—in older adults’ health and healthcare utilization across survey years, considering local governments’ FIR, remains lacking.
This study aimed to identify regional differences in older adults’ health status across two survey years (2019 and 2020), considering regional FIR, and to examine its associations with health behaviors and healthcare utilization. By comparing these factors across regions, this study provides empirical evidence of regional health disparities and examines the associations between regional fiscal capacity and older adults’ health status and service use, thereby informing strategies to reduce health inequalities. Furthermore, this study aims to provide implications for community-based and gerontological nursing practice.
METHODS
Ethic statement: This study was exempted from review by the Institutional Review Board (IRB) of Gwangju University (IRB No. 2-1041318-A-N-01-202409-HR-011-01). Anonymized KCHS data were obtained and used in compliance with the Personal Information Protection Act, the Statistics Act, and the regulations for sharing raw data from the Korea Disease Control and Prevention Agency.
1. Study Design
This was a descriptive correlational study based on secondary data analysis of the 2019~2020 Korean Community Health Survey (KCHS), and it was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (https://www.strobe-statement.org). The KCHS is an annual, standardized survey conducted since 2008 by public health centers nationwide to provide regional health statistics, wherein trained surveyors visit sampled households and conduct face-to-face interviews. The 2019 survey included 211 items across 21 domains, and the 2020 survey included 142 items across 18 domains. This study used data from 2019 and 2020, two independent survey years within the KCHS. The two datasets represent independent, repeated cross-sectional samples; therefore, all analyses were conducted separately for each year, focusing on regional differences according to FIR within each survey year rather than on direct pre–post comparisons. Accordingly, no causal inferences or temporal changes attributable to the COVID-19 pandemic were intended in this study.
2. Study Objectives
This study’s objectives were as follows: 1) to compare general characteristics, health status, health behaviors, and healthcare utilization among older adults according to regional characteristics—classified into three groups based on metropolitan status and FIR: the metropolitan region, non-metropolitan regions with above-average FIR, and non-metropolitan regions with below-average FIR—across two survey years (2019 and 2020); 2) to examine differences in the prevalence of depressive symptoms according to general characteristics, health behaviors, and healthcare utilization by region and survey year; and 3) to identify the associations between regional characteristics (the same three-group classification) with health status—including subjective health status, subjective oral health status, and depressive symptoms among older adults.
3. Participants
The participants comprised older adults aged 65 years or older who responded to the KCHS in 2019 and 2020. In 2019, the sample size was 74,547, including 17,423 older adults residing in the metropolitan region, 9,607 in non-metropolitan regions with above-average FIR, and 47,517 in regions with below-average FIR. In 2020, the corresponding numbers were 72,812, 16,976, 9,243, and 46,593, respectively.
4. Instruments
Based on a literature review, variables associated with older adults’ health were identified and extracted from the raw KCHS data. These variables were organized into four domains: 1) general characteristics, 2) health status, 3) health behaviors, and 4) healthcare utilization.
1) General Characteristics
General characteristics included age, sex, education level, household income, and area of residence. The participants were categorized by age into three groups: 65~74 years, 75~84 years, and ≥85 years; by education level into four groups: no formal education or elementary school, middle school, high school, and college or higher; by monthly household income into four groups: less than 1,000,000 KRW, 1,000,000~1,999,000 KRW, 2,000,000~2,999,000 KRW, and 3,000,000 KRW or higher.
Regional characteristics were classified based on local governments’ FIR. Regions were divided into three groups: the metropolitan region, non-metropolitan regions with above-average FIR, and non-metropolitan regions with below-average FIR. The mean FIR of the 17 local governments in South Korea over the past 5 years (2018~2022) was 41.7% [5], which served as the criterion for classification. The metropolitan region included Seoul, Gyeonggi-do, and Incheon. Non-metropolitan regions with above-average FIR included Busan, Daegu, Gwangju, Daejeon, Ulsan, and Sejong, while those with below-average FIR included Gangwon-do, Chungcheongbuk-do, Chungcheongnam-do, Jeollabuk-do, Jeollanam-do, Gyeongsangbuk-do, Gyeongsangnam-do, and Jeju-do.
2) Health Status
Health status was assessed using three sub-variables: subjective health status, subjective oral health status, and mental health status (depressive symptoms). Although mental health status is conceptually one sub-domain of health status, depressive symptoms were additionally examined as a distinct focal outcome, in accordance with Study Objective 2, given their close relevance to older adults’ psychological well-being during the COVID-19 pandemic.
(1) Subjective Health Status and Subjective Oral Health Status
Subjective health status and subjective oral health status were assessed using single-item measures on a 5-point Likert scale (very good, good, fair, poor, very poor). Responses of “very good” and “good” were classified as indicating a healthy status, whereas responses of “fair,” “poor,” and “very poor” were classified as indicating an unhealthy status.
(2) Mental Health Status
Mental health status was assessed based on depressive symptoms in the past 2 weeks, measured using the Patient Health Questionnaire-9 (PHQ-9) [21,22]. The PHQ-9 consists of nine items rated on a 4-point Likert scale ranging from 0 (“never”) to 3 (“almost every day”), with total scores ranging from 0 to 27. A score of 10 or higher was regarded as indicating the presence of depressive symptoms, consistent with previous studies [14,23].
3) Health Behaviors
Health behaviors included smoking, alcohol consumption, and physical activity. The participants were categorized by smoking as current smokers, former smokers, or never smokers. Hazardous drinking was defined as consuming at least seven standard drinks for males or five standard drinks for females on a single occasion, at least 2 days per week. Physical activity was assessed based on walking practice; participants who walked for at least 30 minutes per day on 5 or more days per week were classified as engaging in walking practice, according to the criteria of the Korea Disease Control and Prevention Agency.
5. Data Collection and Analysis
The study was exempted from review by the Institutional Review Board (IRB) at Gwangju University (IRB No. 2-1041318-A-N-01-202409-HR-011-01). The raw KCHS data used in this study were provided in an anonymized format, in adherence with the Personal Information Protection Act and the Statistics Act. After familiarizing ourselves with the “Regulations for the Procedure of Sharing Raw Data from the Korean Disease Control and Prevention Agency” at the KCHS homepage (https://chs.kdca.go.kr/chs/main.do), we submitted a data request form and received approval to use the data. The KCHS uses a two-stage, stratified cluster sampling design. Given this complex sampling structure, SPSS Complex Samples was used to analyze the data, accounting for sampling weights, stratification variables, and clustering variables to produce appropriate estimates, variances, and test statistics. Analyses were performed using SPSS, version 26 (IBM Corp.). The chi-square tests were used to compare general characteristics, health status, health behaviors, and healthcare utilization across regional groups (Study Objective 1). The chi-square tests were also conducted to examine differences in the prevalence of depressive symptoms according to general characteristics, health behaviors, and healthcare utilization within each regional group (Study Objective 2). Multiple logistic regression analyses were conducted to examine the associations of regional characteristics—classified into three groups (the metropolitan region, non-metropolitan regions with above-average FIR, and non-metropolitan regions with below-average FIR)—with health status (subjective health status, subjective oral health status, and depressive symptoms), in accordance with Study Objective 3.
RESULTS
1. General Characteristics, Health Status, Health Behaviors, and Healthcare Utilization by Regional Characteristics
Table 1 presents the comparison of participants’ general characteristics, health status (subjective health status, subjective oral health status, and depressive symptoms), health behaviors, and healthcare utilization across the two survey years (2019 and 2020). In both survey years, significant regional differences were observed in sex (p<.001), age (p<.001), education level (p<.001), and monthly household income (p<.001). Non-metropolitan regions with below-average FIR had a higher proportion of participants with lower educational attainment (elementary school or below) and lower household income. Significant regional differences were also found in high-risk alcohol use (p<.001), walking practice (p<.001), and unmet healthcare needs (p<.001). In addition, non-metropolitan regions with below-average FIR showed higher proportions of older adults reporting “unhealthy” subjective health status (p<.001). Regarding subjective oral health status, a higher proportion of older adults reported “unhealthy” status in the metropolitan region in 2019 (p<.001), whereas in 2020, this proportion was higher in non-metropolitan regions with below-average FIR (p<.001).
2. Prevalence of Depressive Symptoms According to General Characteristics, Health Behaviors, and Healthcare Utilization, by Regional Characteristics
Table 2 presents the prevalence of depressive symptoms according to general characteristics, health behaviors, and healthcare utilization within each of the three regional groups, across the two survey years (2019 and 2020). Higher prevalence of depressive symptoms was observed among females, older age groups, and those with lower household income (p<.001). Regarding health behaviors, significant differences in hazardous alcohol use and walking practice were observed in 2019; however, these differences were not consistently significant across all regions in 2020. Regarding healthcare utilization, depressive symptoms were more prevalent among those with unmet healthcare needs in both survey years (p<.001).
3. Associations of Regional Characteristics With Health Status
Table 3 presents the results of multiple logistic regression analyses examining the associations between regional disparities and health status (subjective health status, subjective oral health status, and depressive symptoms). The associations between regional characteristics and health outcomes were examined after adjusting for sex, age, education level, household income, smoking status, hazardous drinking, walking practice, and unmet healthcare needs.
In 2019, compared with older adults living in the metropolitan region, those in non-metropolitan regions with above-average FIR had higher odds of reporting “unhealthy” subjective oral health status (adjusted odds ratio [AOR]=1.10, 95% confidence interval [CI]=1.04~1.17), depressive symptoms (AOR=1.12, 95% CI=1.04~1.21), and “unhealthy” subjective health status (AOR=1.13, 95% CI=1.06~1.19); and those in non-metropolitan regions with below-average FIR had higher odds of reporting “unhealthy” subjective oral health status (AOR=1.26, 95% CI=1.21~1.32), depressive symptoms (AOR=1.09, 95% CI=1.03~1.15), and “unhealthy” subjective health status (AOR=1.44, 95% CI=1.38~1.51).
In 2020, compared with older adults living in the metropolitan region, those in non-metropolitan regions with above-average FIR had higher odds of reporting “unhealthy” subjective oral health status (AOR=1.18, 95% CI=1.11~1.25) and “unhealthy” subjective health status (AOR=1.18, 95% CI=1.10~1.27), with no statistically significant association observed for depressive symptoms (AOR=0.94, 95% CI=0.87~1.01); and those in non-metropolitan regions with below-average FIR had higher odds of reporting “unhealthy” subjective oral health status (AOR=1.37, 95% CI=1.31~1.44) and “unhealthy” subjective health status (AOR=1.52, 95% CI=1.44~1.60), whereas no statistically significant association was observed for depressive symptoms (AOR=0.95, 95% CI=0.90~1.01).
DISCUSSION
Knowledge about regional health inequality among older adults has been accumulated through numerous previous studies. However, few studies have examined how regional differences in older adults’ health behaviors and health status are patterned across survey years in the context of infectious disease outbreaks, particularly in relation to structural factors such as local governments’ financial capacity.
In this study, differences in older adults’ health behaviors, healthcare utilization, and health status were observed according to regional characteristics across two survey years (2019 and 2020). The co-occurrence of less favorable health behaviors, particularly low walking participation, and unmet healthcare needs was more prevalent in regions with lower financial capacity, suggesting structural constraints in access to preventive and continuous care. In contrast, regional differences in depressive symptoms were not consistent across survey years, becoming statistically non-significant in 2020 after adjustment, suggesting that depressive symptoms may be more influenced by individual-level factors than by regional structural factors. Several explanations may tentatively account for this attenuation. The COVID-19 pandemic imposed widespread social distancing and mobility restrictions across all regions regardless of local fiscal capacity, which may have uniformly heightened psychological distress nationwide, reducing the relative contribution of regional structural factors [23]. In addition, unlike subjective health and oral health status—more directly tied to continuous, region-dependent healthcare infrastructure—depressive symptoms may be more sensitive to acute, universally experienced stressors such as fear of infection and social isolation than to longstanding structural conditions [24]. These findings indicate that regional disparities may be associated with differences in health outcomes during infectious disease outbreaks, highlighting the need for targeted efforts to reduce health inequalities among older adults [23].
From a theoretical perspective, regional health disparities among older adults can be understood within the framework of the social determinants of health, which emphasizes the role of upstream structural conditions—such as local fiscal capacity—in shaping health outcomes [25]. In this study, lower FIR may reflect limited capacity of local governments to sustain public health infrastructure, preventive programs, and essential healthcare services, related to reduced access to care and higher unmet healthcare needs. Health system capacity and resilience theories further suggest that regions with limited fiscal resources may be more vulnerable during public health crises due to challenges in maintaining continuity and adaptability of healthcare services [24]. Notably, regional differences in health behaviors, such as lower walking practice in non-metropolitan areas with below-average FIR, were consistently observed across both survey years, suggesting that these differences reflect the combined influence of individual socioeconomic vulnerability and local government–level structural limitations, rather than a direct causal effect of regional fiscal capacity alone [24-26].
Such structural constraints may also limit engagement in health-promoting behaviors, as reduced availability of community-based services and health promotion programs can restrict opportunities for physical activity and preventive care. Previous studies have shown that nurse-led self-care interventions and community-based nursing approaches improve health outcomes by enhancing self-management, health literacy, and access to care among older adults [27,28]. Humanized and community-based nursing models have also been reported to improve functional independence, psychological well-being, and healthcare accessibility, particularly among vulnerable older populations [29]. Accordingly, from a gerontological nursing perspective, proactive identification of at-risk older adults and community-based nursing interventions—such as care coordination, health education, and support for chronic disease self-management—may be important for improving healthcare access and reducing regional health disparities.
In our study, residence in non-metropolitan areas with below-average FIR, female sex, older age, and lower educational level were associated with poorer health status among older adults, consistent with previous findings [10,11,24], suggesting that structural and individual-level vulnerabilities may interact in shaping health outcomes. Female older adults may be particularly vulnerable due to combined biological, behavioral, and social factors, including lower physical activity, higher risk of social isolation, and limited socioeconomic resources, which may be associated with increased unmet healthcare needs and reduced access to preventive services [23,30]. Older adults with lower socioeconomic status were also more likely to engage in unfavorable health behaviors, including physical inactivity [10,11]; in this study, those in non-metropolitan areas with below-average FIR showed lower walking participation and greater unmet healthcare needs, likely reflecting limited access to community-based health promotion programs and services. Therefore, interventions addressing both structural inequalities and individual health behaviors—such as tailored, community-based, and continuity-focused care—may be particularly beneficial for vulnerable populations. Strengthening healthcare infrastructure and expanding access to essential health services in regions with below-average FIR may help address these disparities [25,27,28].
This study has several limitations. First, as the KCHS is a self-reported survey, variables such as depressive symptoms may be subject to reporting bias. Second, the cross-sectional design limits causal inference between FIR, area of residence, and health outcomes. Third, the use of secondary data limited adjustment for all potential confounders, such as specific health conditions. Fourth, the large sample size may have resulted in statistically significant differences with small effect sizes. Fifth, as FIR is a regional-level variable, the findings may be subject to ecological interpretation. In addition, healthcare utilization was assessed using perceived unmet healthcare needs, without objective indicators such as outpatient visits or hospitalization. Therefore, these findings should be interpreted with caution and confirmed in future longitudinal or replication studies.
CONCLUSION
This study may provide foundational evidence to inform the development of healthcare policies aimed at reducing regional health inequalities among older adults, particularly regarding local governments’ FIR. Within an infectious disease context, older adults in non-metropolitan areas with below-average FIR may be more vulnerable, underscoring the need for equitable resource allocation and stronger chronic disease management infrastructure.
Securing a well-trained healthcare workforce—namely, strengthening nurse-led interventions and community-based nursing approaches—may help promote health behaviors and reduce regional health disparities among older adults, contributing to greater resilience during future public health crises.
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