INTRODUCTION

Healthcare policies and medical resources are closely associated with the premature mortality rate at the national level [1]. As socioeconomic disparities between nations continue to widen, those with lower levels of development are experiencing an increasing trend in premature mortality rates [1]. However, differences in the conceptualization and measurement criteria for avoidable mortality (AVM) make objective cross-national comparisons difficult [2]. Consequently, the Organization for Economic Cooperation and Development (OECD) and Eurostat have formally adopted a standardized list of AVM causes to assess AVM across countries and regions [3]. The avoidable mortality rate (AVMR) refers to a set of causes of death that are significantly influenced by healthcare and public health policies [2] and is divided into 2 categories: the preventable mortality rate (PMR; deaths that could have been prevented through effective public health and prevention interventions) and amenable mortality rate (AMR; deaths that could have been treated through effective healthcare services) [3].

As of 2023, Korea’s AVMR was 142.0 per 100 000 people (PMR, 99.0; AMR, 43.0), which is lower than the OECD average of 239.1 and positions it in the lower group of member countries [4]. In Korea, despite studies on the trends and factors influencing AVM at the national level [5], research on AVM at the provincial level remains insufficient. Furthermore, the formulas used to calculate AVM differ among regional medical institutions, thus making it difficult to objectively compare AVM across regions at the provincial level [5,6]. Unlike other major countries, Korea faces significant inter-regional health disparities due to social phenomena like metropolitan concentration. Therefore, understanding these inter-regional health disparities is crucial [7]. Although previous studies on AVMR trends in capital, metropolitan, and non-metropolitan areas have shown that the inter-regional mortality rate gap has been consistently decreasing, some disparities in AVMRs still exist [7]. These disparities are attributed to the more favorable socioeconomic infrastructure and institutional conditions in the capital and metropolitan areas [7]. In addition, since the OECD and Eurostat recently included deaths due to coronavirus disease 2019 (COVID-19) as an AVM cause, disparities in healthcare policies and medical resources may have a considerable impact on premature deaths across regions [8].

Several studies worldwide have investigated the factors influencing AVM [9,10]. A study in Germany examined regional differences in AVMRs and investigated influencing factors such as sex, access to medical facilities, private health insurance, smoking, and alcohol consumption. Previous studies in Korea have identified income level as an influencing factor of AVM and analyzed the association between the two [10]. However, the factors influencing AVM are multifaceted, necessitating the development of a health determinants model to identify them [11]. Although another study in Korea constructed a social determinants of health model to identify the factors influencing AVM, such studies remain insufficient [5].

Therefore, because of the inconsistency of AVM calculation formulas used in previous studies, research comparing AVM and its influencing factors among regions at the provincial level is lacking. In response, the current study utilizes the AVM calculation formula defined by the OECD and Eurostat to examine trends in AVM at both national and provincial levels. Furthermore, the study identifies the factors influencing AVM. Ultimately, we present trends in AVMR, PMR, and AMR across 16 regions from 2013 to 2022 and propose factors that influence AVM based on the health determinants model.

METHODS

Health Determinants Model

Based on the United States Healthy People 2030 study and World Health Organization data, among others, a health determinants model was constructed to establish goals and directions for improving the national AVMR [12]. Ultimately, the indicators were categorized into 4 main domains: social environment, individual behaviors, health services, and economic environment [13-16]. Furthermore, the indicators within the model were identified at the regional level (municipalities), and population standardization was applied to standardize the unit of measurement for each indicator, thereby allowing for inter-regional comparisons in rates or counts per population. The selected indicators for each domain were as follows: (1) Social environment: Older adult population (%), male-to-female ratio, divorce rate, marriage rate; (2) Individual behaviors: Rates of smoking, high-risk alcohol use, weekly walking, perceived stress, positive self-rated health, smoking cessation attempts, weight control attempts; (3) Health services: Local medical utilization, non-local medical utilization, unmet medical needs, influenza vaccination rate, cancer screening rate, medical check-up rate, number of doctors (per 1000 persons), primary care institutions (per 1000 persons); (4) Economic environment: Financial independence rate, share of the health budget.

Data Source and Study Population

Cause-of-death statistics and regional population data categorized by age from 2013 to 2022 were obtained from Statistics Korea and utilized to investigate the AVMR nationwide and across 16 provincial and metropolitan regions. In accordance with the Statistical Act and the Act on the Registration of Family Relations, the cause-of-death statistics published annually are based on death certificates, which are classified according to the Korean Standard Classification of Diseases (KCD). The AVMR, PMR, and AMR were assessed using variables such as age, sex, cause of death (KCD code), and residential address (municipal level) of participants aged <75 years. The list of AVM (preventable mortality [PM], amenable mortality [AM]) also references recent cause-of-death classifications provided by the OECD and Eurostat [3].

Furthermore, to identify the factors influencing the AVMR, indicators included in the health determinants model were sourced from statistical data published by the Ministry of the Interior and Safety [17], Statistics Korea [18], the Korea Disease Control and Preventive Agency [19], and the National Health Insurance Service [20].

Definition of Variables

Dependent variables

The dependent variables (AVMR, PMR, and AMR) were assessed using the List of Avoidable Causes of Death published by the OECD and Eurostat in January 2022 [3]. In accordance with this classification criterion, individuals aged ≥75 years were excluded from the analysis since AVM is considered a premature death [3]. In addition, in line with the understanding that AVM consists of PM and AM, the PMR and AMR were set as dependent variables [3,21].

Meanwhile, as the aforementioned avoidable causes of death are based on the International Statistical Classification of Diseases and Related Health Problems, we matched them with the KCD to calculate the AVMR (PMR, AMR). Furthermore, among the avoidable causes, such as tuberculosis (International Classification of Diseases, 10th revision [ICD-10]: A15-A19, B90) and diabetes (ICD-10: E10-E14), no clear evidence existed to categorize them distinctly as either PM or AM. Therefore, following the 50% allocation method suggested by the OECD and Eurostat, the number of deaths attributed to these causes was calculated by allocating 50% to PM and 50% to AM [3,21].

Independent variables

The indicators categorized within the 4 domains of the health determinants model were independent variables (Table 1).

Statistical Analysis

First, we assessed not only the status of AVMR (PMR, AMR) over the past 10 years (2013 to 2022) but also compared differences among regions and between sexes. In regional health statistics, simply presenting the number of avoidable deaths per region divided by the population can lead to misinterpretations since age was a confounding variable. Therefore, age should be standardized using the standard population as a reference [22]. We utilized Statistics Korea’s standardized mortality rate calculation method, with the 2005 registered population as the standard population [22]. Subsequently, age was standardized for AVMR (PMR, AMR) in 5-year age groups for each region. However, when calculating the regional AVMRs (PMRs, AMRs) there were instances where the number of deaths by age group was ≤30; therefore, the results should be interpreted with caution. The formula for age standardization is as follows:

ASRa=∑i=1A(DRi∗SPi)SP2005

ASR_a, age-standardized death rate; a, region a; i, age; A, maximum age; SP₂₀₀₅, standard population for 2005; DR_i, i crude death rate; SP_i, standard death rate.

Second, to understand the trend of the AVMR by region over the past 10 years, the compound annual growth rate (CAGR) was used to derive the trends in the AVMR nationwide and in the 16 regions. The CAGR is the annual average growth rate over a specified period, thereby making it useful for identifying trends in health outcomes over time. Although the CAGR intuitively reflects changes in results, it is limited in its ability to reflect the intermediate volatility that also exists. The formula for calculating CAGR is as follows:

CAGR(t0,tn)=(V(tn)V(t0))1tn−t0−1

CAGR, compound annual growth rate; V(t₀), beginning value; V(t_n), ending value; t_n−t₀, time in years.

Third, to establish the health determinants model and identify the factors influencing the AVMR at the national level, analysis of variance and a generalized estimating equation (GEE) were used. The GEE was calculated using the GEMOND procedure with a normal distribution [23]. Based on the correlation between the independent and dependent variables, we employed the compound symmetry structure [23]. Considering the correlation within subjects, the GEE is an appropriate analysis method for longitudinally measured data. The GEE is used when individual observations are repeatedly measured or when data are clustered into groups [23]. Furthermore, the model allowed for the correlation of dependent variables over time and was utilized to identify the factors influencing the AVMR as outcomes of health determinants. The criterion for statistical significance for all analyses was a two-tailed p-value <0.05. All analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).

Ethics Statement

The cause of death statistics used in this study are data approved as National Statistics No. 10156 by Statistics Korea. These data are de-identified and anonymized, thereby qualifying as human subject research exempt from institutional review under Article 13 of the Enforcement Decree of the Bioethics and Safety Act.

RESULTS

The age-standardized AVMR, PMR, and AMR in Korea over the past decade are presented in Figure 1. First, the AVMR (number of AVMs per 100 000 people) displayed a continuous decrease from 153.8 in 2013 to 114.5 in 2020. However, this began to increase in 2021 and reached 122.1 in 2022. Second, the PMR exhibited a decreasing trend from 111.3 in 2013 to 80.8 in 2020, but also began to increase in 2021 and reached 86.3 in 2022. Third, the AMR consistently decreased from 42.5 in 2013 to 33.7 in 2020, but increased to 34.2 in 2021 and further to 35.8 in 2022. Meanwhile, the sex-stratified analysis established that the AVMR, PMR, and AMR were significantly higher in males than females.

The results of AVMR, PMR, and AMR analysis for the 16 regions are presented in Figure 2. As of 2022, the AVMR was higher in Jeonnam (181.0 per 100 000 people), Gyeongbuk (164.7), Chungnam (164.6), Gangwon (163.3), and Jeonbuk (150.5). The PMR was highest in Jeonnam (126.5 per 100 000 people), Chungnam (118.4), Gyeongbuk (116.6), Gangwon (115.8), and Gyeongnam (103.6). The AMR was highest in Jeonnam (54.5 per 100 000 people), Jeonbuk (48.9), Gyeongbuk (48.1), Gangwon (47.4), and Chungbuk (46.7). The AVMR for 16 regions by sex is presented in Supplemntal Material 1.

The descriptive statistics of independent variables in the health determinants model from 2013 to 2022 are presented in Table 2. According to the analysis of variance, all variables except for influenza vaccination, cancer screening, local medical utilization, and non-local medical utilization showed significant differences in AVMR across the years. The coefficient of determination for the health determinants model in this study was 92.8%.

Results of the GEE performed to identify the factors influencing AVM through the health determinants model are reported in Table 3. When the proportion of older adults, the male-to-female ratio, and the proportion of annual marriages (in the social environment domain) increased by one unit, the AVMR decreased by 2.24 per 100 000 people (p=0.016), increased by 5.34 (p<0.001), and decreased by 13.28 (p<0.001), respectively. In the individual behavior domain, as the proportions of smoking and perceived stress increased by 1%, the AVMR increased by 0.80 (p=0.004) and 0.76 (p=0.015), respectively. In addition, the level of positive self-rated health decreased by 0.36 (p=0.039). In the health services domain, as local medical utilization increased by 1%, the AVMR decreased by 0.91% (p=0.021), and with each additional doctor per 1000 population, the rate decreased by 14.73% (p=0.007). Conversely, a 1% increase in non-local medical utilization led the AVMR to increase by 1.34% (p=0.010). In addition, the rates of influenza vaccination and cancer screening decreased by 0.54% (p<0.001) and 1.51% (p<0.001), respectively. In the economic environment domain, as financial independence increased by 1%, the AVMR decreased by 1.08% (p<0.001).

DISCUSSION

We investigated the AVM of 16 provincial and metropolitan regions in Korea over 10 years and identified the influencing factors. Our first major finding was that descriptive statistical differences were not found between healthcare service-related variables and AVM. Second, although Korea’s AVMR, PMR, and AMR showed a continuous decreasing trend for both male and female groups over time, since 2021 the AVMR has shown an increasing trend. Third, as of 2022, Jeonnam, Gyeongbuk, Chungnam, Gangwon, Jeonbuk, Gyeongnam, Busan, Jeju, and Daegu regions showed a higher AVMR than the national average. Fourth, distinct factors influencing mortality were identified across different domains of the health determinants.

A significant disparity exists in the AVMR between groups that receive timely healthcare services, such as influenza vaccination, cancer screening, and medical utilization, and those that do not [24]. However, several previous studies reported that, while healthcare services contribute to improving the health of populations, lifestyle habits and socioeconomic conditions collectively influence the determination of AVM [25]. Furthermore, a study across 14 European countries emphasized that simply increasing healthcare expenditure and utilization cannot reduce the AVMR [26]. Therefore, this study, which found no significant difference in the means between medical-related variables and the AVMR, was consistent with previous research.

According to OECD health statistics, Korea’s AVMR displayed a steady decrease after 2000, a trend that aligned with our findings [4]. Furthermore, the AVMR of males was more than twice that of females. Vulnerability in the healthcare sector has previously been reported as a primary cause for higher AVMRs in males than females [22]. In addition, differences in accident and injury rates between the sexes were found to contribute to this disparity [21]. A European study reported that males had a higher AVMR than females because of the higher life expectancy and healthier lifestyle habits of females [27]. Our analysis of the factors influencing AVM revealed that within the health services domain, an increase in local medical utilization and the ratio of physicians led to a decrease in the AVMR of males. This suggests that increased healthcare resources improves accessibility, ultimately resulting in a decreased AVMR among males, who typically have lower healthcare utilization [28]. Furthermore, significant differences were observed between males and females in the mental health domain. A previous study in Korea [29] established that both males and females with negative self-rated health exhibited poor health behaviors. However, males were more likely to develop severe diseases closely related to mortality, while females tended to have milder conditions. This difference accounts for the observed sex disparities in AVM [29].

The AVMR, which had been consistently decreasing, began to increase in 2021. Since 2022, the addition of mortality codes for COVID-19 (ICD-10: U07.1, U07.2) to the list of AVM causes by the OECD/Eurostat indirectly contributed to an increase [8]. According to OECD data, which calculates AVM statistics internationally, the AVMR of most member countries displayed an increasing trend as of 2021 [4]. A study in the United States attributed the increase in the AVMR to a lack of COVID-19 vaccinations during the pandemic [30]. In Japan, excess mortality rapidly increased during the pandemic [31]. However, the main cause was not vaccination status but the paralysis of medical functions, reduction in welfare services, and changes in lifestyle habits. Consequently, the number of deaths due to COVID-19 and the AVM due to pneumonia and other respiratory diseases increased significantly [31]. Similarly, a study in Korea reported that, after the COVID-19 pandemic began, the avoidable hospitalization rate and AVMR increased because of inadequate outpatient treatment [32]. In particular, disparities were noted in the AVMR based on residential area and income level [8].

Influenced by socioeconomic factors like income, education level, healthcare resources, and health insurance systems, AVM data is crucial for understanding regional disparities in health outcomes [8]. The Jeonnam, Jeonbuk, Chungnam, Chungbuk, Gyeongnam, Gyeongbuk, and Gangwon Provinces each presented a higher AVMR, PMR, and AMR than the national average. Regional deprivation indices, which assess the socioeconomic condition of regions, indicated that the level of deprivation in these regions was considerably higher than in other areas, thereby indicating a higher risk of AVM, PM, and AM [33]. Furthermore, among the 16 regions, these regions not only had higher mortality rates due to major emergency conditions, but also reported higher transfer rates to non-local healthcare facilities [33]. Therefore, it is crucial to manage AVM by improving the disparities in healthcare resources and access.

The first major factor influencing the AVMR, based on the social environment domain of the health determinants model, was the proportion of older adults in the population, with the AVMR, PMR, and AMR rapidly increasing after age 60 years [21]. Since older adults exhibit a rapid increase in mortality risk due to sustained health deterioration, this study highlighted the need to target this population in the future management of healthcare resources and systems [34]. In addition, recent studies found an association between decreased sensitivity to healthcare management and utilization and a continuous decrease in marriage rates, whereas high marriage rates can reduce the AVMR by enhancing healthcare management, life satisfaction, and medical utilization [35]. Second, the major factors influencing the AVMR in the individual behavior domain were low stress and positive self-rated health. These factors not only improved mental health but also promoted physical health through increased health screening, thereby reducing mortality rates [35].

Finally, in the health services and economic environment domain, we assumed that local medical utilization was influenced by disparities in the essential medical services and facilities for managing severe illnesses among regions [5]. Non-local medical utilization primarily served as an indicator of low medical accessibility, often presenting higher figures in rural regions [36]. Higher non-local medical utilization rates and AVMRs have been reported in rural areas than in urban areas [36], and the higher the severity of the disease, the more likely that such rates will increase [37]. Consequently, this group can be classified as having a higher risk of mortality, which can be interpreted as an increase in AVM. Influenza vaccination rates and cancer screening rates are considered indicators directly responsible for reducing AVMRs by effectively lowering mortality rates from pneumonia, stroke, diabetes, and cancer. Wang et al. [38] identified these factors as significant influencers. The financial independence rate is a comprehensive indicator of a region’s socioeconomic status including income, education, and employment levels [39]. Previous studies have reported that higher financial independence is associated with increased access to healthcare and leisure time within a region, thus leading to improvements in cognitive and physical abilities and, consequently, a reduction in mortality rates [39]. Therefore, we emphasize the need to consider regional disparities in financial capacity when developing policies aimed at reducing premature mortality [39].

Recently, Korean health authorities have been actively implementing projects to address health disparities among regions and announced the 5th National Health Plan to resolve regional disparities in health indicators, ultimately aiming to reduce premature mortality and extend the healthy life expectancy [40]. Therefore, we examined the nationwide trends in AVMR, PMR, and AMR across 16 provincial and metropolitan regions over the past decade, in line with health policy directions. The findings provide foundational data to identify regional priorities for AVM management policies and enhance their efficiency.

This study had some limitations. First, because the AVMR (PMR, AMR) is a crude value in this study, future research should control for covariates to more accurately assess the AVMR disparities among the 16 regions. Second, while we investigated AVMR, PMR, and AMR based on the health determinants model, not all influencing factors could be included in the model due to segmented regional statistical data. Third, as we focused on all-cause AVM, future research should identify the factors that influence specific-cause AVM. Fourth, this study has the potential for an ecological fallacy because various regional variables were aggregated, which could result in distortion of the actual relationships among regional variables and may not accurately reflect individual-level relationships. In subsequent studies, it is necessary to conduct multilevel analysis or collect additional individual-level data for more precise analysis. Finally, because we identified the factors influencing AVM at the national level, subsequent research analyzing regional influencing factors is necessary, considering the socioeconomic and healthcare environments.

Supplemental Materials

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Author Contributions

Both authors contributed equally to conceiving the study, analyzing the data, and writing this paper.

Acknowledgements

Figure. 1.

(A) Avoidable mortality rate (AVMR), (B) prevntable mortality rate (PMR), and (C) amenable mortality rate (AMR) per 100 000 standard population in Korea.

Figure. 2.

Avoidable, preventable, and amenable mortality rates per 100 000 standard population within 16 provincial and metropolitan regions of Korea.

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Figure & Data

References

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