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HOME > Res Community Public Health Nurs > Volume 35(2); 2024 > Article
Original Article
Prediction model of health checkup and cancer screening experience of elderly population using 2021 Community Health Survey
Myeunghee Hanorcid
Research in Community and Public Health Nursing 2024;35(2):140-155.
DOI: https://doi.org/10.12799/rcphn.2024.00500
Published online: June 28, 2024

Assistant Professor, College of Nursing, Dongyang University, Yeongju, Korea

Corresponding author: Myeunghee Han College of Nursing, Dongyang University, 145, Dongyangdae-ro, Punggi-eup, Yeongju-si, Gyeongsangbuk-do, 36040, Korea Tel: +82-054-630-1279, E-mail: dewdrop54@daum.net
• Received: February 6, 2024   • Revised: April 29, 2024   • Accepted: May 10, 2024

© 2024 Korean Academy of Community Health Nursing

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License. (https://creativecommons.org/licenses/by-nd/4.0) which allows readers to disseminate and reuse the article, as well as share and reuse the scientific material. It does not permit the creation of derivative works without specific permission.

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  • Purpose
    This study was conducted to build a decision tree model composed of factors that predict whether or not the elderly population underwent health or cancer screening using the 2021 community health survey.
  • Methods
    This study included 74,434 and 74,417 subjects who answered for experience of health checkup or cancer screening, respectively. This study used Chi-squared test, independent t-tests, and decision tree analysis to analyze the data.
  • Results
    Regarding the health checkup, 78.1% of women received a health checkup, and among women, those aged 65-74 years who were not recipients of the basic livelihood service showed the highest participation rate of 85.0%. In case of men, those who were married and had no problems in daily activity showed the highest participation rate of 81.4%. In the case of cancer screening, for women, those who were 65-74 years old and were nonrecipients of basic livelihood benefits showed the highest participation rate of 75.3%. For men, the cancer screening rate was the highest at 74.3% in those who had no problems with mobility and were married.
  • Conclusion
    It was found that the general and health characteristics of the elderly had a great influence on the health checkup and cancer screening. In order to develop a more improved screening system, screening rates and effects should be continuously observed and studied.
Background
As life expectancy has been increased with the development of medical technologies and improvements in overall living standards, the elderly population is rapidly growing [1]. In 2000, Korea became an aging society with the elderly population aged 65 or older accounting for 7.3% of the entire population, and it became an ‘aged society’ in 2018, when the proportion of the elderly population exceeded 14%. In addition, Korea is estimated to become a ‘superaged society’ in 2026 with the elderly population exceeding 20% of the total population. With this trend of population aging, Korea is projected to undergo population aging at the fastest pace in the world [2]. What is needed for elderly people living in an era of rapid population aging is not simply extending the lifespan but having a good quality of life and living a happy life by maximally increasing the period of living in good health [3]. However, with a significant increase of the proportion of the elderly population, the incidence and prevalence of chronic conditions have been increasing, leading to a great increase of health expenditure due to chronic diseases [4].
With respect to the representative causes of deaths in the elderly population, chronic conditions, including cardiovascular diseases, respiratory diseases, and cancers, account for a large proportion of causes of deaths, and among chronic diseases, cardiovascular diseases, such as hyperlipidemia, hypertension, angina pectoris, myocardial infarction, and stroke, characteristically show an increasing incidence with age [5]. The most important cause of the deterioration of health and quality of life and deaths in elderly people is reported to be cancer, and according to a statistical report of cancer incidence in 2021, the average lifetime incidence of cancer until the age of 83 is about 35.5%, which means that one out of three people in the elderly population are likely to be diagnosed with a cancer [6]. The prevalence of chronic diseases and the increase of cancer patients among elderly people are directly related to the increase of health expenditure [6]. According to the 2021 statistical data from the National Health Insurance Service, the health expenditure of elderly people aged 65 or older amounts to 40 trillion 612.9 billion won, which accounts for 43.4% of the total health expenditure [7]. More specifically, people aged 65 or older take up 16.5% of the total population, health expenditure per person in the elderly population is 4.8 million won, out-of-pocket health expenditure per person in the elderly population is 1.11 million won, and health expenditure has been gradually increasing [8]. To reduce medical expenses due to geriatric diseases and maintain and enhance the health status of elderly people, it is most important for elderly people to practice health behaviors and get regular health checkups [9].
Health checkups are carried out mainly for early detection of cancers or chronic diseases, and they enable the effective treatment and management of diseases through the early detection of them, and ultimately prevent complications such as early death and disability [5,10]. WHO stated that health checkups and cancer screening are the most effective methods to prevent cancers and reduce premature deaths due to cancers, and recommends the implementation of early screening [11]. According to previous studies, various sociodemographic and health-related factors affect participation in the national health checkup and cancer screening programs, and the influencing factors identified in previous studies include gender [12-14], age [12,15-17], residential area [17], marital status [15,16], education level [15,18,19], subjective health status [15,19], smoking status [15,17], income level [15,18], economic activities [15-17], drinking status [15,19], Body Mass Index[19], physical activity [14,17,20], quality of life (Euro-Quality of Life-5 Dimension, EQ-5D)[20], chronic diseases [16,17], and recipient status for the national basic livelihood security system [17].
Effective and efficient health checkups are an essential method for the early detection of various severe illnesses that cause the greatest damage to health in elderly people and incur considerable medical expenses, and they are also important means for the management of frequently occurring chronic diseases that deteriorate quality of life [9]. In Korea, participation in the ‘old-age life transition period screening program’ for people aged 66 or older was found to be associated with the decrease of the premature death rate and a reduction in health expenditure among elderly people, and these research results emphasize the need for active participation in the national health screening program [21].
However, according to the statistical data from the Ministry of Health and Welfare, the cancer screening rate of the total population has been increasing from 46.1% in 2009 to 55.3% in 2018, but the cancer screening rate in the population aged 60 or older has been rather decreasing, showing that cancer screening for elderly people has not been efficiently carried out [22]. In this regard, a prior study pointed out that the reduction of participation rates and preventive effects can be attributed to the fact that health check-ups and cancer screening for elderly people are carried out in a uniform manner without considering the individual, age-related, and gender-related characteristics of subjects, and thus there is a need to implement national health checkup and cancer screening programs in consideration of individual characteristics [12]. Therefore, in order to increase the participation rates for health checkup and cancer screening programs among elderly people, it is necessary to investigate factors affecting elderly people’s participation in the programs [6]. Actually, there have been few studies on the participation rates of health checkup and cancer screening in elderly people aged 65 or older, and it has been suggested that there is a need to change health-related policies by investigating the causes of differences in participation rates among elderly people through an analysis focused on gender or age groups showing differences in participation rates [13]. Against this backdrop, this study aimed to construct a prediction model composed of factors affecting participation in health checkup and cancer screening in elderly people aged 65 or older to identify high-risk groups that are not likely to participate in health checkup and cancer screening. In addition, the present study also attempted to present basic data for the development of methods to increase the participation rates of health checkup and cancer screening and induce voluntary participation in health checkup and cancer screening.
Objectives
This study aimed to construct a prediction model composed of predictive factors for participation in health checkup and cancer screening among elderly people aged 65 or older by using data from the 2021 Korea Community Health Survey provided by the Korea Disease Control and Prevention Agency in order to present basic data that can be utilized for the establishment of strategies to improve the participation rates of national health and cancer screening programs. The specific objectives of this study are as follows:
1) To examine and compare general characteristics of the participants according to the participation or nonparticipation of health checkup and cancer screening;
2) to examine and compare health behaviors and health status according to the participation or nonparticipation of health checkup and cancer screening;
3) to construct a decision tree model composed of various factors to predict the participation or nonparticipation of health checkup and cancer screening, and verify the validity of the model.
Study design and participants
This study is a descriptive study that performed an empirical analysis using cross-sectional data from the 2021 Korea Community Health Survey, which is secondary data provided by the Korea Disease Control and Prevention Agency. The participants of this study consisted of elderly people aged 65 or older who responded to the questions about the experience of getting a health checkup and cancer screening among the participants of the 2021 Korea Health Community Survey. More specifically, the participants of this research included 74,434 people who answered the question “Have you received a health checkup (except for cancer screening) in the past two years to check your health status although you have not had any particular health problems?” and 74,417 people who responded to the question “Have you received cancer screening in the past two years to check your health status although you have not had any particular health problems?”
Measures
The instruments used in this study were described based on the explanations presented in the guidelines for the use of raw data of the 2021 Korea Community Health Survey [23].

General characteristics

The general characteristics of the participants analyzed in this study were as follows: age (65-74 years (young-old); 75-84 years (old-old); 85 years or older (super-old))[24], gender (male/female), residence location (city/province), household income (monthly, 10,000 won), education level (high school graduation or less, 2-3 year college graduation or more), economic activity (Yes/No), marital status (single/married), basic livelihood recipient status (Yes/No), subjective stress level (Feel stressed very much; Feel stressed a lot; Feel stressed a little; Rarely feel stressed), and Body Mass Index (BMI) (kg/m2). The level of life satisfaction (happiness index) was measured on a 10-point scale from 1 point (=Very much dissatisfied) to 10 points (=Very much satisfied) by using the following question: “Considering the overall aspects of your life, how much are you (generally) satisfied with your recent life?” In this study, scores of 5 points or less were categorized as a ‘low’ level of life satisfaction and scores of 6 points or more were categorized as a ‘high’ level of life satisfaction.

Health behaviors and health characteristics

The items of health behaviors of the participants were as follows: current smoking status (Yes/No), the average amount of smoking of daily smokers (the number of cigarettes), drinking status (Yes/No), drinking frequency in the past year (Never in the past year, less than once per month, once per month, 2-4 times per month, 2-3 times per week, 4 times or more per week), amount of drinking on one occasion (1-2 cups, 3-4 cups, 5-6 cups, 7-9 cups, 10 cups or more), days of strenuous physical activity (physical activity that you performed in the past week and during which you felt much more exhausted or felt much more out of breath than usual), time (hours) of strenuous physical activity, days of moderate physical activity (physical activity other than walking that you performed in the past week and during which you felt a little more tired than usual or felt slightly out of breath), time (hours) of moderate physical activity, days of walking (days when you walked for 10 minutes or more at one time in the past week), time of walking (hours), and the number of days of breakfast consumption per week in the past year (5-7 times per week, 3-4 times per week, 1-2 times per week, hardly (0 times per week)).
With respect to the items of health characteristics of the participants, mental health (depression) was measured using a Korean version of the Patient Health Questionnaire (PHQ-9) used in the Korea Community Health Survey. PHQ-9 [25] contains 9 items, and the total scores range from 0 to 27 points. Higher scores indicate a higher level of depression. Based on the total scores, the severity of depression is classified into four levels as follows: absence of depression (0-4 points), mild depression (5-9 points), moderate depression (10-19 points), and severe depression (20-27 points). In this study, the level of depression was reclassified by categorizing the score of 0-4 points as ‘no depression’ and the score of 5 points or more (mild, moderate, and severe depression) as ‘depression.’ Heath characteristics also included subjective health status (good, fair, poor), presence of hypertension (Yes/No), experience of cognitive impairment (Yes/No), presence of diabetes, and experience of falling down in the past year (Yes/No). The items of EQ-5D included mobility, self-care, daily activity (e.g.: work, studying, house chores, family activity or leisure activity), pain/discomfort, and anxiety/depression. The responses to the items of EQ-5D were divided into ‘No problems’, ‘Some problems’, and ‘A lot of problems.’

Statistical analysis

Data analysis was conducted using SPSS 28.0(IBM, Armonk, NU, USA), and the Chi-squared test and the independent sample t-test were performed to comparatively analyze general characteristics, health characteristics, and health behaviors according to the experience of health checkup and cancer screening of the participants. The decision tree model was used to construct a prediction model for the experience of health checkup and cancer screening. The decision tree model has the advantage that since the process of data analysis is represented in a form of a tree structure, researchers can easily understand and explain an analysis using a decision tree [26]. Since this study included both categorical and continuous variables, analysis was conducted using CHAID (Chi-squared Automatic Interaction Detection), a technique for analysis considering both types of variables, among decision tree algorithms. More specifically, CHAID is a decision tree technique that performs analysis using the Chi-squared test for categorical variables and using the F-test for continuous variables [27]. Regarding the decision tree model, the maximum level was set as 3, and the minimum numbers of records required for the parent node and the child node were set as 100 and 50, respectively. In this study, to construct pathways for the prediction of the experience of heath checkup and cancer screening according to gender, a decision tree analysis was performed by specifying the first branching node (root node) as gender. In this study, to verify the validity of the derived decision tree model, split-sample validation was conducted [19]. The level of significance was set at p<.05.

Ethical considerations

This study was conducted using data from the 2021 Korea Community Health Survey, and since the data did not contain person information, anonymity and confidentiality were guaranteed. Thus, the present study received a review exemption determination from the IRB of Dongyang University, the institution that the researcher is currently affiliated with (IRB No. 10141495-202301-HR-01-01).
General characteristics according to the experience of health checkup
The results of the comparison of general characteristics according to the experience of health checkup are shown in Table 1. A total of 74,434 people answered the question about participation in health checkups, and among the respondents, 59,164 people (79.5%) received a health checkup, and 15,270 people (20.5%) did not. In both the health checkup and non-health checkup groups, the proportion of the 65-94 age group (p<.001) was highest, Also, in both groups, the proportion of females was larger than males (p<.001), and the proportion of people living in a province was higher than those living in a city (p=.007). In terms of household income level, the average monthly household income was 1.8513 million won in the health checkup group and 1.572 million won in the non-health checkup group, so there was a significant difference in income level between the two groups (p<.001). For education level, in both groups, people group with the education level of high school graduation or less accounted for a larger proportion. Regarding economic activity, people not engaged in economic activities took up more than 50% (p<.001). In both groups, married people took up a larger proportion than unmarried people (p<.001), and the proportion of the non-recipients of basic livelihood benefits was higher than that of basic livelihood recipients (p<.001). As for subjective stress level, people that feel a little stressed took up the largest proportion in both groups (p<.001). Also, in both groups, the group with normal BMI took up the largest proportion (p<.001), and the group with a high level of life satisfaction accounted for the largest proportion (p<.001).
General characteristics according to the experience of cancer screening
This study included a total of 74,417 people who answered the question about the participation in cancer screening. Among the respondents, 50,486 people (67.8%) participated in cancer screening, and 23,931 people (32.2%) did not. In both the cancer screening and non-cancer screening groups, people aged 65-74 (p<.001) took up the largest proportion, females took up a larger proportion than males (p<.001), and people living in a province accounted for a larger proportion than those living in a city (p<.001). As for household income level, the average monthly household income was 1.8923 million won in the cancer screening group and 1.5879 million won in the non-cancer screening group, so there was a significant difference between the two groups. Regarding education level, in both groups, the group with the education level of high school graduation or less accounted for 90% or more (p<.001). In both groups, the proportion of people not engaged in economic activities was more than 50% (p<.001), and in terms of marital status, married people accounted for more than 50% (p<.001). In both groups, the proportion of the non-recipients of basic livelihood benefits was closer to 90% (p<.001). For subjective stress level, the group that feel a little stressed took up the largest proportion in both groups (p<.001), and the group with normal BMI made up the largest proportion in both groups (p<.001). Also, in both groups, people with a high level of life satisfaction took up more than 50% (p<.001) (Table 2).
Health behaviors and health status according to the experience of health checkup
In both the health checkup and non-health checkup groups, current non-smokers (p<.001) and current drinkers (p<.001) took up more than 50%. As to drinking frequency, people who have never consumed alcohol in the past year took up the largest proportion (p<.001). With respect to physical activities, the average days per week of strenuous physical activity was 0.37 days in the health checkup group and 0.22 days in the non-health checkup group, showing a significant difference between the two groups (p<.001). The average number of days of moderate physical activity per week was 1.03 days in the health checkup group and 0.71 days in the non-health checkup group (p<.001). The average time of moderate physical activity per day was 2.10 hours in the health checkup group and 1.99 hours in the non-health checkup group, and there was a significant difference between the two groups (p=.014). The average number of days of walking was 3.94 days in the health checkup group and 3.30 days in the non-health checkup group (p<.001), and the average time of walking per day was 0.97 hours in the health checkup group and 0.84 hours in the non-health checkup group, showing that there was a significant difference between the two groups (p<.001). As for the number of days of eating breakfast per week in the past year, in both groups, more than 90% reported that they eat breakfast 5-7 times per week (p<.001).
Regarding the presence of depression, people without depression took up more than 90% in both groups (p<.001). As to subjective health status, people rating their health status as ‘fair’ (41.4%) took up the largest proportion in the health checkup group, while people reporting their health status as ‘poor’ (41.8%) took up the largest proportion in the non-health checkup group (p<.001). As for cognitive impairment and the experience of falling down, people without cognitive impairment took up a larger proportion than those with cognitive impairment (p<.001), and people without the experience of falling down also accounted for a larger proportion than those with the experience of falling down (p<.001). With respect to the specific items of EQ-5D used to assess quality of life, in mobility (p<.001), self-care (p<.001), daily activity (p<.001), and anxiety/depression (p<.001), people with no problems took up the largest proportion in both groups. In the case of pain/discomfort, in the health checkup group, people with no problems in pain/discomfort (48.9%) took up the largest proportion, but in the non-health checkup group, people with some problems in pain/discomfort (50.9%) took up the largest proportion, so there was a significant difference in pain/discomfort between the two groups (p<.001). However, there were no significant differences between the two groups in the presence of hypertension (p=.488), presence of diabetes (p=.124), amount of drinking on one occasion (p=.246), time of strenuous physical activity (p=.121), and average amount of smoking per day (p=.084) (Table 3).
Health behaviors and health status according to the experience of cancer screening
With respect to health behaviors, in both the cancer screening group with the experience of cancer screening and the non-cancer screening group without the experience of cancer screening, nonsmokers took up a larger proportion than smokers (p<.001) and drinkers took up a larger proportion than nondrinkers (p<.001). The average number of days of strenuous physical activity in the past week was 0.38 days in the cancer screening group and 0.27 days in the non-cancer screening group, showing a significant difference between the two groups (p<.001). The average time of strenuous physical activity was 2.29 hours in the cancer screening group and 2.35 hours in the non-cancer screening group, showing a significant difference between the two groups (p=.009). The average number of days of moderate physical activity in the past week was 1.07 days in the cancer screening group and 0.74 days in the non-cancer screening group (p<.001), and the average time of moderate physical activity was 2.11 hours in the cancer screening group and 1.99 hours in the non-cancer screening group, showing a significant difference between the two groups (p<.001). The average number of days of walking in the past week was 3.95 days in the cancer screening group and 3.51 days in the non-cancer screening group (p<.001), and the average time of walking was 0.97 hours in the cancer screening group and 0.89 hours in the non-cancer screening group, showing a significant difference between the two groups (p<.001). In both the screening and non-cancer screening groups, 90% of people reported eating breakfast 5-7 times per week in the past year (p<.001).
In both the screening and non-cancer screening groups, the proportion of people without depression was more than 90% (p<.001). In terms of subjective health status, in the cancer screening group, people with fair health status took up the highest proportion (41.6%), but in the non-cancer screening group, people reporting poor health status made up the highest proportion (39.1%), so there was a significant difference between the two groups (p<.001). In both groups, the proportion of people without cognitive impairment was higher (p<.001). As to the presence of diabetes, the proportion of people without diabetes was more than 70% in both groups (p=.002). As for the experience of falling down in the past year, the proportion of people without the experience of falling down was more than 80% in both groups (p<.001). Regarding the items of EQ-5D for the assessment of quality of life, in the case of mobility (p<.001), self-care (p<.001), daily activity (p<.001), and anxiety/depression (p<.001), the proportion of people with ‘no problems’ was highest in both groups. However, in the responses to the item about pain/discomfort, the proportion of people with ‘no problems’ was highest at 49.5% in the cancer screening group, while the proportion of people with ‘some problems’ was highest at 49.6% in the non-cancer screening group (p<.001). There were no significant differences between the two groups in the average amount of smoking per day of daily smokers (p=.077), the amount of drinking on one occasion (p=.484), and the presence of hypertension (p=.912) (Table 4).
A prediction model for the experience of the health checkup
The prediction model to predict the presence or absence of the experience of a health checkup in the participants was derived as decision tree paths including a total of 14 terminal nodes (Figure 1). There was a gender difference in the health checkup participation rate (△p<.001, F=121.05). For females (Node1), 78.1% of them received a health checkup, and 21.9% did not. Among females (Node1), there was a difference in the health checkup participation rate according to age (△p<.001, F=2122.85). The health checkup participation rate was 84.8% in the 65-74 age group (Node3), 75.6% in the 75-84 age group (Node4), and 54.7% in the ≥85 age group (Node5), showing that the health checkup participation rate was decreased with increasing age. In the 65-74 age group (Node3), basic livelihood recipient status influenced the health checkup participation rate (△p<.001, F=169.22). More specifically, among women aged 65-74 (Node3), the health checkup participation rate was 85.6% in the nonrecipients of basic livelihood benefits (Node9) and 72.5% in the recipients of basic livelihood benefits (Node10). In the 75-84 age group (Node4), the health checkup participation rate was different according to the self-care ability (△p<.001, F=225.98). The health checkup participation rate was 78.0% in women with no problems in self-care (Node11), 68.0% in women with some problems in self-care (Node12), and 51.7% in women with a lot of problems in self-care (Node13). In the ≥85 age group, the health checkup participation rate varied according to daily activity (△p<.001, F=95.19). The health checkup participation rate was 63.1% in women with no problems in daily activity (Node14), 52.2% in women with some problems in daily activity (Node15), and 37.9% in women with a lot of problems in daily activity (Node16).
In the case of males (Node2), there were differences in the health checkup participation rate according to the degree of problems in daily activity (△p<.001, F=638.03). Among males, the health checkup participation rate was 83.6% in the group with no problems in daily activity (Node6), 74.0% in the group with some problems in daily activity (Node7), and 40.2% in the group with a lot of problems in daily activity (Node8).
Among males with no problems in daily activity (Node6), there was a difference in the health checkup participation rate according to marital status (△p<.001, F=252.20). In other words, the health checkup participation rate was 85.0% in the married group (Node17) and 73.8% in the single group (Node18). Among males with some problems in daily activity (Node6), the health checkup participation rate was different according to the presence of economic activity (△p<.001, F=111.08). The health checkup participation rate was 84.3% in males engaged in economic activities (Node19) and 70.1% in males not engaged in economic activities (Node20). Among males with a lot of problems in daily activity (Node6), the health checkup participation rate was different according to marital status (△p=.011, F=6.47), and the health checkup participation rates of the married group (Node21) and the single group (Node22) were 51.8% and 37.5%, respectively.
A prediction model for the experience of cancer screening
The decision tree model for the experience of cancer screening of the participants is shown in Figure 2. The participation rate of cancer screening exhibited a gender difference (△p<.001, F=102.30). In females (Node1), the cancer screening participation rate was 66.4%, and it varied according to age (△p=.011, F=2027.07). Regarding the cancer screening rate according to age among females, the cancer screening participation rate was 74.5% in the 65-74 age group (Node3), 62.3% in the 75-84 age group (Node4), and 41.8% in the ≥85 age group (Node5). Among women aged 65-74, the cancer screening participation rate was different according to basic livelihood recipient status (△p<.001, F=107.08). The cancer screening participation rate was 75.3% in the nonrecipients of the basic livelihood security program (Node9) and 62.6% in the recipients (Node10). Among women aged 75-84 (Node4), the cancer screening participation rate was different according to the degree of self-care (△p<.001, F=164.23). The cancer screening participation rate was 64.5% in the group with no problems in self-care (Node11), 55.4% in the group with some problems in self-care (Node12), and 37.7% in the group with a lot of problems in self-care (Node13). Among women aged 85 or older, the cancer screening participation rate was different according to the degree of daily activity (△p<.001, F=70.05). The cancer screening participation rate was 64.5% in the group with no problems in daily activity (Node14), 55.4% in the group with some problems in daily activity (Node15), and 37.7% in the group with a lot of problems in daily activity (Node16).
In males (Node2), the cancer screening participation rate was different according to the degree of mobility (△p<.001, F=490.91). Among males with no problems in mobility, the cancer screening participation rate was significantly different according to marital status (△p<.001, F=190.74). The cancer screening participation rates of the married and single groups were 74.3% and 62.0%, respectively. Among males with some problems in mobility (Node7), the cancer screening participation rate was different according to the presence of economic activity (△p<.001, F=98.96). The cancer screening participation rate was 70.3% in the group engaged in economic activities and 58.3% in the group not engaged in economic activities. Among males with a lot of problems in mobility (Node8), the cancer screening participation rate was different according to age (△p=.022, F=7.22). The cancer screening participation rate was 48.8% in the 65-74 age group (Node21) and 32.2% in the 75-84 age group and the ≥85 age group (Node22).
Validity analysis of the prediction model for the prediction about the experience of health checkup and cancer screening
Table 5 shows the results of validity analysis for the decision tree model derived from the research results of this study. The results of validity analysis for the decision tree model for the experience of health checkup showed that the risk estimate (RE) of training data was .203, indicating that the probability of accurate classification was 79.7%. In the validity analysis of the decision tree model for the experience of cancer screening, the RE of training data was .309, indicating that the probability of accurate classification was 69.9%.
This study aimed to investigate general characteristics and health-related factors according to the experience of health checkup and cancer screening in elderly people aged 65 or older by using data from the 2021 Community Health Survey, and construct a decision tree model composed of predictive factors for the experience of health checkup and cancer screening. In this study, the participation rates of health checkup and cancer screening among the participants were 79.5% and 67.8%, respectively, showing that the participation rate of cancer screening was relatively lower. In this study, gender was found to have a significant impact on the participation rates of health checkup and cancer screening. For health checkups, the participation rates of females and males were 78.1% and 81.4%, respectively. For cancer screening, the participation rates of females and males were 66.4% and 69.9%, respectively. Actually, in the case of general health checkups, males are reported to show a higher participation rate. In this regard, according to a previous study, because people designated as the targets of the national health screening program are those who are engaged in economic activities or self-employed enrollees of the National Health Insurance Service who are the head of a household, and because males generally participate in social activities for a relatively longer period than females, males tend to show a higher participation rate [13]. These results of a prior study are consistent with the findings of this study. Compared to men, women showed lower participation rates in both health checkups and cancer screening. In particular, women showed a significantly lower cancer screening rate than men. According to a study, attention should be paid to the fact that the characteristics of a specific type of cancer screening for women may reduce the cancer screening rate, and regarding the cervical cancer screening rate, which is actually found to be lower than the breast cancer screening rate, the lower relatively screening rate of cervical cancer can be attributed to the method of cervical cancer screening, which may cause the avoidance of cervical cancer screening [29]. Therefore, it is necessary to improve the cancer screening participation rate for each item of cancer screening rather than simply focusing on the improvement of the cancer screening participation rate for all cancers regardless of types of cancers. To this end, continuous efforts should be made to identify the specific reasons for subjects’ avoidance and properly address the problems.
With respect to the health checkup participation rate, for females, 78.1% received a health checkup. Among females, those who are in the 65-74 age group and are not basic livelihood recipients showed the highest participation rate of 85.0%. On the other hand, among females, those who are in the ≥85 age group and have a lot of problems in daily activity exhibited the lowest participation rate of 37.9%. For males, 81.4% received a health checkup. Among males, those who have no problems in daily activity and are married showed the highest participation rate of 85.0%. However, males who have a lot of problems in daily activity and are single showed the lowest participation rate of 37.5%. With respect to cancer screening, the cancer screening participation rates of females and males were 66.4% and 69.9%, respectively. In the case of females, women who are in the 65-74 age group and are not basic livelihood recipients showed the highest participation rate of 75.3%. Among females, those who are in the ≥85 age group and have a lot of problems in daily activity showed the lowest participation rate of 25.5%. For males, those who have no problems in mobility and are married showed the highest participation rate of 74.3%, and those who have a lot of problems in mobility and are in the 75-84 age group or in the ≥85 age group exhibited the lowest participation rate of 32.2%.
In this study, among females, the participation rates of health checkups and cancer screening varied according to age, basic livelihood recipient status, and daily activity. Among males, daily activity, marital status, and mobility were found to affect the participation rates of health checkups and cancer screening. In a similar vein, the economic situations of households, such as the basic livelihood recipient status, have been reported to have a significant impact on participation in health checkups or cancer screening. In particular, regarding the health checkup participation rate in the past 5 years, only about 1.7% of approximately 500,000 elderly medical aid beneficiaries received health checkups and 98.2% did not get appropriate health checkups, showing the presence of a welfare dead zone in terms of healthcare [30]. Older people, unmarried people, and people with more problems in daily activity and mobility were found to be at higher risk for severe diseases due to the lack of self-care and the lower practice rates of health behaviors, so it is essentially needed to develop and implement national-level measures for the screening and management of the vulnerable groups regarding their lower health checkup and cancer screening participation rates. In addition, sociodemographic factors such as educational level [31] and health behavior factors such as regular exercise and smoking abstinence [32] have been reported to be factors associated with the participation rates of health checkups and cancer screening, but they were not significantly associated with the participation rates of health checkup and cancer in this study, so a follow-up study is needed to clarify their relationships.
With the rapid increase of the elderly population, there is growing social interest in healthy life in old age and quality of life, and these social changes have led to the increasing importance of the management of the health of elderly people and the greater need to closely observe changes in health [3]. To respond to these social needs, the early detection and early treatment of diseases are essentially needed for the improvement of quality of life in elderly people and the reduction of health expenditure [6]. For this reason, health checkups and cancer screening essential for the health management of elderly people should be effectively implemented. However, according to a previous study, because currently implemented health checkup and cancer screening programs provide information about health examination results on a one-time basis, and provide only standardized information that does not reflect the characteristics of elderly people, current health checkup and cancer screening programs are not sufficiently effective, so these aspects may influence the participation rates of health checkups and cancer screening [10]. Therefore, organizations related to health screening, such as the National Health Insurance Service and the Ministry of Health and Welfare, should examine the current status of the participation rates of health checkups and cancer screening, and analyze factors affecting participations in health checkups and cancer screening, and actively implement measures to improve the participation rates. These efforts are expected to contribute to the systematic management of elderly people in a dead zone of health and cancer screening as well as the establishment and implementation of appropriate health policies and health promotion programs [13].
This study has the following limitations. First, some factors affecting health checkups and cancer screening identified in prior studies, such as the degree of knowledge about health and cancer screening, were not included in the analysis. Second, this study did not consider variables about the characteristics of health screening itself which may influence the utilization of health screening programs. Third, although the forms and attributes of health checkups and cancer screening are continuously changing, this study conducted a cross-sectional study to analyze data at a specific time point, so this research could not derive analysis results about changes in the participation rates of health checkup and cancer screening. Lastly, the data of this study included an imbalance in the number of samples between the participation and non-participation groups for health checkup and cancer screening, so this study had limitations in the derivation of accurate analysis results.
The results of this study showed that general characteristics of the participants, such as gender, age, marital status, and basic livelihood recipient status, and health characteristics, such as daily activity and mobility, were found to have a significant impact on the utilization of the national health checkup and cancer screening programs in elderly people aged 65 or older. The establishment and implementation of effective and efficient health checkup and cancer screening programs are essentially required for healthy life in elderly people. Therefore, it is necessary to increase public awareness of the importance of health examinations and cancer screening through active promotion activities, and it is also needed to provide transportation services and activate a home-visiting health screening program for elderly people with low levels of daily activity and mobility. Considering that elderly people often get a health checkup and cancer screening due to the recommendation of the spouse or the family members, it is also considered necessary to perform a family education program about health examination programs.
To provide improved health checkup and cancer screening systems, it is necessary to continuously observe and monitor changes in the participation rates and the effects of the currently implemented health screening programs [10]. Therefore, a time-series analysis should be conducted to examine changes over time in the participation rates and effects of health screening programs that reflect changes in the forms and methods of health checkups and cancer screening rather than analyzing the participation rates and effects of health screening programs at a specific time point as in this study. In addition, given that health and cancer screening activities are affected by various factors such as sociodemographic, health-related, and environmental factors [6], it is necessary to continuously conduct research on a broad range of factors affecting participation in health checkups and cancer screening among elderly people by taking into account various factors mentioned above.

Conflict of interest

The authors declared no conflict of interest.

Funding

This study was supported by a grant from Dong Yang University in 2022.

Authors’ contributions

Myeunghee Han contributed to conceptualization, data curation, formal analysis, methodology, project administration, visualization, writing – original draft, review & editing, investigation, and resources.

Data availability

Please contact the corresponding author for data availability.

None.
Figure 1.
Decision tree of health check-up experience in the last 2 years
rcphn-2024-00500f1.jpg
Figure 2.
Decision tree of cancer screening experience in the last 2 years
rcphn-2024-00500f2.jpg
Table 1.
Comparison of General Characteristics according to Experience of Health Checkup (N=74,434)
Characteristics Categories Health checkup experience in the last 2 years p-value
Yes group (n=59,164, 79.5%) No group (n=15,270, 20.5%)
n (%) or M±SD n (%) or M±SD
Age 65-74 34,404 (57.5) 6,318 (41.4) <.001
75-84 21,237 (35.9) 6,135 (40.2)
≥85 3,923 (6.6) 2,817 (18.4)
Sex Male 25,604 (43.3) 5,855 (38.3) <.001
Female 33,560 (56.7) 9,415 (61.7)
Location City 14,323 (24.2) 3,843 (25.2) .007
Province 44,841 (75.8) 11,427 (74.8)
Household income (month, 10,000won) 185.13±186.84 157.20±176.01 <.001
Education level ≤High school graduation 53,664 (90.8) 14,212 (93.1) <.001
≥College graduation 5,457 (9.2) 1,050 (6.9)
Economic activity Yes 26,000 (43.9) 4,492 (29.4) <.001
No 33,164 (56.1) 10,777 (70.6)
Marital status Married 40,367 (68.2) 8,080 (52.9) <.001
Single 18,796 (31.8) 71,839 (47.1)
Basic livelihood Yes 3,590 (6.1) 1,788 (11.7) <.001
recipients No 55,567 (93.9) 13,478 (88.3)
Subjective stress level Very much 1,084 (1.8) 444 (2.9) <.001
Much 7,612 (12.9) 2,299 (15.1)
Little 27,498 (46.5) 6,262 (41.1)
Rare 22,957 (38.8) 6,249 (41.0)
Body Mass Index (BMI, kg/m2) Underweight 2,536 (4.5) 1,066 (7.4) <.001
Normal 23,060 (40.5) 6,348 (44.4)
Overweight 15,648 (27.5) 3,441 (24.0)
Obesity 15,636 (27.5) 3,458 (24.2)
Life satisfaction Low 16,084 (27.3) 5,576 (36.7) <.001
High 42,880 (72.7) 9,626 (63.3)
Table 2.
Comparison of General Characteristics according to Experience of Cancer Screening (N=74,417)
Characteristics Categories Cancer screening experience in the last 2 years p-value
Yes group (n=50,486, 67.8%) No group (n=23,931, 32.2%)
n (%) or M±SD n (%) or M±SD
Age 65-74 29,803 (59.0) 10,515 (43.9) <.001
75-84 17,653 (35.0) 9,709 (40.6)
≥85 3,030 (6.0) 3,707 (15.5)
Sex Male 21,977 (43.5) 9,479 (39.6) <.001
Female 28,509 (56.5) 14,452 (60.4)
Location City 11,797 (23.4) 6,369 (26.6) <.001
Province 38,689 (76.6) 17,562 (73.4)
Household income (month, 10,000won) 189.23±190.07 158.79±172.22 <.001
Education level ≤High school graduation 45,679 (90.6) 22,181 (92.7) <.001
≥College graduation 4,767 (9.4) 1,739 (7.3)
Economic activity Yes 22,574 (44.7) 7,910 (33.1) <.001
No 27,912 (55.3) 16,020 (66.9)
Marital status Married 34,862 (69.1) 13,572 (56.7) <.001
Single 15,613 (30.9) 10,353 (43.3)
Basic livelihood recipients Yes 2,941 (5.8) 2,436 (10.2) <.001
No 47,540 (94.2) 21,491 (89.8)
Subjective stress level Very much 908 (1.8) 620 (2.6) <.001
Much 6,557 (13.0) 3,354 (14.0)
Little 23,600 (46.8) 10,154 (42.5)
Rare 19,411 (38.5) 9,784 (40.9)
Body Mass Index (BMI, kg/m2) Underweight 2,083 (4.3) 1,519 (6.7) <.001
Normal 1,947 (40.1) 9,930 (43.8)
Overweight 13,468 (27.8) 5,620 (24.8)
Obesity 13,500 (27.8) 591 (24.7)
Life satisfaction Low 13,488 (26.8) 8,166 (34.2) <.001
High 36,818 (73.2) 15,678 (65.8)
Table 3.
Comparison of Health Status and Health Behaviors according to Experience of Health Checkup (N=74,434)
Characteristics Categories Health check-up experience in the last 2 years p-value
Yes group (n=59,164, 79.5%) No group (n=15,270, 20.5%)
n (%) or M±SD n (%) or M±SD
Heath behaviors
Smoking Yes 4,879 (24.8) 1,659 (34.0) <.001
No 14,830 (75.2) 3,224 (66.0)
Amount of smoking 13.84±7.29 13.54±7.29 .084
Drinking Yes 36,789 (62.2) 8,501 (55.7) <.001
No 22,371 (37.8) 6,767 (44.3)
Frequency of drinking None 17,583 (47.8) 4,564 (53.7) <.001
<1/month 5,374 (14.6) 1,065 (12.5)
1/month 2,678 (7.3) 494 (5.8)
2-4/month 4,316 (11.7) 854 (10.0)
2-3/week 3,586 (9.7) 697 (8.2)
≥4/week 3,250 (8.8) 827 (9.7)
Amount of drinking 1-2cups 9,768 (50.9) 2,034 (51.7) .246
3-4cups 4,530 (23.6) 879 (22.3)
5-6cups 1,642 (8.6) 322 (8.2)
7-9cups 2,528 (13.2) 532 (13.5)
≥10cups 736 (3.8) 170 (4.3)
Days of strenuous physical activity 0.37±1.32 0.22±1.04 <.001
Time of strenuous physical activity 2.29±2.12 2.41±2.29 .121
Days of moderate physical activity 1.03±2.09 0.71±1.82 <.001
Time of moderate physical activity 2.10±1.92 1.99±1.92 .014
Days of walking 3.94±2.82 3.30±2.91 <.001
Time of walking 0.97±0.95 0.84±0.90 <.001
Days of breakfast 5-7times/week 5,400 (93.6) 13,859 (90.8) <.001
3-4times/week 1,397 (2.4) 510 (3.3)
1-2times/week 555 (0.9) 223 (1.5)
0/week 1,812 (3.1) 678 (4.4)
Heath status
Patient Health Questionnaire (PHQ-9) Depression 1,738 (2.9) 931 (6.1) <.001
No-depression 57,287 (97.1) 14,264 (93.9)
Subjective health status Good 16,143 (27.3) 3,468 (22.7) <.001
Average 24,481 (41.4) 5,411 (35.4)
Bad 18,539 (31.3) 6,390 (41.8)
Hypertension Yes 32,541 (55.0) 8,350 (54.7) .488
No 26,610 (45.0) 6,916 (45.3)
Experience of cognitive impairment Yes 21,228 (35.9) 6,549 (42.9) <.001
No 37,923 (64.1) 8,714 (57.1)
Diabetes Yes 13,755 (23.3) 3,459 (22.7) .124
No 45,402 (76.7) 11,807 (77.3)
Fall down Yes 9,697 (16.4) 2,924 (19.1) <.001
No 49,464 (83.6) 12,345 (80.9)
EQ-5D Mobility No 39,250 (66.3) 7,754 (50.8) <.001
Little 19,496 (33.0) 7,018 (46.0)
Yes 418 (0.7) 498 (3.3)
EQ-5D Self-care No 52,988 (89.6) 11,715 (76.7) <.001
Little 5,651 (9.6) 2,975 (19.5)
Yes 525 (0.9) 580 (3.8)
EQ-5D Daily activity No 44,820 (75.8) 9,197 (60.2) <.001
Little 13,631 (23.0) 5,298 (34.7)
Yes 712 (1.2) 774 (5.1)
EQ-5D Pain/Discomfort No 28,905 (48.9) 6,013 (39.4) <.001
Little 27,082 (45.8) 7,729 (50.9)
Yes 3,176 (5.4) 1,528 (10.0)
EQ-5D Anxiety/Depression No 49,149 (83.1) 11,544 (75.6) <.001
Little 9,391 (15.9) 6,404 (22.3)
Yes 622 (1.1) 320 (2.1)
Table 4.
Comparison of Health Status and Health Behaviors according to Experience of Cancer Screening (N=74,417)
Characteristics Categories Cancer screening experience in the last 2 years p-value
Yes group (n=50,486, 67.8%) No group (n=23,931, 32.2%)
n (%) or M±SD n (%) or M±SD
Heath behaviors
Smoking Yes 4,906 (24.2) 2,443 (31.9) <.001
No 12,845 (75.8) 5,210 (68.1)
Amount of smoking 13.87±7.27 13.59±7.31 .077
Drinking Yes 31,575 (62.5) 13,706 (57.3) <.001
No 18,910 (37.5) 10,220 (42.7)
Frequency of drinking None 15,047 (47.7) 7,093 (51.8) <.001
<1/month 467 (14.7) 1,783 (13.0)
1/month 2,324 (7.4) 847 (6.2)
2-4/month 3,734 (11.8) 1,433 (10.5)
2-3/week 3,074 (9.7) 1,209 (8.8)
≥4/week 2,738 (8.7) 1,340 (9.8)
Amount of drinking 1-2cups 8,424 (51.0) 3,377 (51.1) .484
3-4cups 3,886 (23.5) 1,522 (23.0)
5-6cups 1,408 (8.5) 556 (8.4)
7-9cups 2,185 (13.2) 875 (13.2)
≥10cups 624 (3.8) 282 (4.3)
Days of strenuous physical activity 0.38±1.32 0.27±1.13 <.001
Time of strenuous physical activity 2.29±2.11 2.35±2.22 .009
Days of moderate physical activity 1.07±2.13 0.74±1.84 <.001
Time of moderate physical activity 2.11±1.94 1.99±1.89 <.001
Days of walking 3.95±2.81 3.51±2.92 <.001
Time of walking 0.97±0.94 0.89±0.93 <.001
Days of breakfast 5-7times/week 47,310 (93.7) 21,932 (91.6) <.001
3-4times/week 1,176 (2.3) 730 (3.1)
1-2times/week 459 (0.9) 320 (1.3)
0/week 1,541 (3.1) 949 (4.0)
Heath status
Patient Health Questionnaire (PHQ-9) Depression 1,378 (2.7) 1,290 (5.4) <.001
No-depression 48,993 (97.3) 22,543 (94.6)
Subjective health status Good 13,932 (27.6) 5,674 (23.7) <.001
Average 20,991 (41.6) 8,895 (37.2)
Bad 15,563 (30.8) 9,360 (39.1)
Hypertension Yes 27,745 (55.0) 13,142 (54.9) .912
No 22,728 (45.0) 10,785 (45.1)
Experience of cognitive impairment Yes 17,862 (35.4) 9,904 (41.4) <.001
No 32,612 (64.6) 14,019 (58.6)
Diabetes Yes 11,846 (23.5) 5,369 (22.4) .002
No 38,632 (76.5) 18,559 (77.6)
Fall down Yes 8,271 (16.4) 4,346 (18.2) <.001
No 42,212 (83.6) 19,584 (81.8)
EQ-5D Mobility No 33,999 (67.3) 12,998 (54.3) <.001
Little 16,154 (32.0) 10,350 (43.2)
Yes 333 (0.7) 583 (2.4)
EQ-5D Self-care No 45,438 (90.0) 19,250 (80.4) <.001
Little 4,654 (9.2) 3,969 (16.6)
Yes 394 (0.8) 712 (3.0)
EQ-5D Daily activity No 38,592 (76.4) 15,412 (64.4) <.001
Little 11,338 (22.5) 7,588 (31.7)
Yes 555 (1.1) 931 (3.9)
EQ-5D Pain/Discomfort No 24,976 (49.5) 9,936 (41.5) <.001
Little 22,918 (45.4) 11,881 (49.6)
Yes 2,591 (5.1) 2,114 (8.8)
EQ-5D Anxiety/Depression No 42,215 (83.6) 18,465 (77.2) <.001
Little 7,770 (15.4) 5,020 (21.0)
Yes 499 (1.0) 444 (1.9)
Table 5.
Risk Chart of Decision Tree
Group Variables Risk estimate Standard Error
Health check-up experience in the last 2 years Training data .203 .002
Test data .205 .002
Cancer screening experience in the last 2 years Training data .309 .002
Test data .307 .002

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