Abstract
Health with disability is directly related not only to an individual’s quality of life but also to national medical finance. This study focuses on trends in BMI and out-of-pocket (OOP) expenditure of both types of indirect cost exclusion and inclusion. Participants were women with disability (n = 3200) and women without disability (n = 53 082) among adults aged 19 and older from Korea Health Panel from 2009 to 2016. Women with disability had a higher BMI (23.9) than women without disability (22.7), and this time series trend was significant for 8 years (P < .0001). Annual OOP expenditures of both types were higher for women with disability than for women without disability (P < .0001): excluding indirect costs, $518.9 versus $649.4; Including indirect costs, $534.5 versus $681.8. The y-intercept of disability itself and slope of one unit of BMI for both types of annual OOP expenditure is significant (P < 0001): excluding indirect cost, $29.0 and $4.4; including indirect cost, $35.2 versus $4.6. In women with disability, annual OOP expenditure for both types were higher when they were physically inactive (P < .05): excluding indirect cost, $714.1 versus $823.1; including indirect cost, $746.2 versus $880.0. When physical inactivity and overweight and obesity interacted, it increased more than normal weight in dose response manner (P < .05): excluding indirect costs, $799.2 < $800.3 < $886.1; Including indirect costs, $860.2 < $845.9 < $927.5. These results suggest that women with disability are in relatively poor health. It is proposed that inequality of BMI for women with disability can be developed as an agenda from health policy.
BMI is not only the easiest when assessing adult obesity, but also the most common indicator when comparing obesity indices between countries and groups. Also, Obesity is a health hazard and increases the amount of medical care. Subjectively, women with disability appear to be more obese than women without disability, but studies that have scientifically compared this are rare as far as the author knows. Also, there are even fewer studies comparing trends in BMI and healthcare cost among women with disability.
BMI is not only the easiest when assessing adult obesity, but also the most common indicator when comparing obesity indices between countries and groups. Obesity is a health hazard and increases the amount of medical care. However, there are even fewer studies comparing trends in BMI and healthcare cost among women with disability.
Most countries value a balance between inclusive and universal health care. It emphasizes the active discovery of the underprivileged, but concerns about the lack of limited medical finance are growing. Sustainable health systems will be possible when factors in preventable healthcare expenditure are identified and intervened. Therefore, this study focused on the BMI and OOP expenditure of women with disability, and in particular, compared them with women without disability to increase the perceived need for policy development.
Introduction
Disability has been prevalent among adults and the number of individuals reporting disability is increasing in most countries.1-3 In all aspects of life, people with disability are underprivileged, and some public has misconceptions as the stigmatized social group. People with disability experience various barriers to disease prevention and health promotion programs. In Korea, people with disability are registered based on medical diagnosis, and they are a representative vulnerable class in our society. Especially, the number of people with disability will powerfully increase with the aging of the population, 4 and an increase in the national healthcare budget for the disabled will be inevitable. Therefore, there is a need for social interest in preventing the transition to severe disability and reducing the number of years lost due to severe functional disability. To this end, producing evidence on the health gap between the non-disabled and the disabled will be an important task for social integration. In particular, various perspectives on the medical cost gap between the disabled and the non-disabled and related factors are effective study topics in terms of the Positive function of health policy.
The increasing prevalence of obesity are social phenomena that are seen all over the world. According to the World Health Organization (WHO), 39% and 13% of adults worldwide in 2014 were overweight and obese. 5 Obesity is a significant risk factor for a variety of medical problems, including cardiovascular disease, cancer, and diabetes, and also increases risk of the disabled.6,7 Obesity-related health care cost were estimated to exceed $200 billion for US adults in 2005. 8 Studies on obesity has led to many medical advances because it could reduce the mortality rate by controlling risk factors in the obese population. Paradoxically, however, the positive effect of medical technology on obesity comes with a longer lifespan and more disability. 9 In addition, obesity accompanied by disability leads to more serious disability. In other words, obesity is a serious problem not only for non-disabled people but also for people with disability, and the prevalence of obesity among people with physical disability is 1.2 to 3.9 times higher. 10 Nevertheless, study on obesity in the disabled is not abundant as far as the author knows, and translational study on obesity strategies for the disabled is not active.
Although there are questions about the criterion for diagnosing obesity, Body Mass Index (BMI, kg/m2) is a simple, inexpensive, and non-invasive surrogate measure of body fat. A high BMI predicts future morbidity and mortality, and regular measurements of BMI can assess health with reasonable accuracy. 11 In addition, the widespread application of BMI from a long time ago makes it easy to compare with a reference criterion for measuring the health level of the population. 11 Higher BMI and waist circumference are associated with all disorders, especially in women,9,10,12 BMI was associated with increased disability or delayed recovery from. 13 However, there are discriminant errors in the quantitative evaluation of BMI. Typically, because BMI is a weight measurement that cannot distinguish between fat free mass and fat mass, caution is needed when using BMI, especially for men with a lot of muscle mass. 14 When analyzing men’s and women’s BMIs together, there is a disadvantage that gender heterogeneity is rather offset. 15 In addition, there are gender differences in the effects of overweight and obesity on social factors. Representatively, the bad effect of obesity on occupational labor is greater for women than for men. 6 Therefore, in studies using BMI, when gender is stratified rather than controlled, the reliability of the results can be increased.
Against this background, the authors focused on people with disability, women, consistently high BMIs, and health status. And then, annual out-of-pocket (OOP) expenditure was used as an outcome indicator of health status. I set the inference that the BMI will have a significant gap between women without and women with disability, and as a result, there will be a difference in annual out-of-OOP expenditure. It also hopes to identify unhealthy behavioral traits that probably require intervention to manage BMI and high OOP expenditure. Therefore, the author set up the following hypotheses. First, there will be a difference in BMI between women with disability and women without disability, and there will be a gap in time series trend for 8 years. Second, there is a difference in out-of-pocket (OOP) medical expenditure for women with and without disability, and disability itself and BMI affect annual OOP. Third, the unhealthy behavioral characteristics of women with disability may be related to OOP expenditure. In this study, a combined study design that applies both cross-sectional and longitudinal analysis was applied. The author hopes that this study can increase policy interest in BMI and medical expenditure, which are continuously increasing in women with disability, and provide some evidence for health and welfare policies for the disabled.
Methods
Design
The design of this study is a combination of cross-sectional and longitudinal analysis using 8-year long-format panel data (2009-2016).
Data and Ethical Considerations
The KHP is representative data for Koreans. 16 The survey on household, household members, and annual OOP expenditure started in 2008, and the additional survey on psychosocial characteristics and health behaviors of adults started in 2009 and has been surveyed every year until now. KHP collects data on co-payments, non-reimbursement, and indirect cost such as transportation and care cost for each individual medical use. For the completeness of the OOP expenditure survey, the KHP operating institution educated the method of filling out the OOP expenditure on the medical account book to participants. In addition, the panel participants kept receipts corresponding to the basis of medical use and expenditure and reviewed them by visiting investigators. This KHP data was approved by the IRB (KIHASA 2022-017).
Participants
The participants of this study were 56 282 people. Among the participants of this study, 53 082 (94.3%) women were non-disabled, and 3200 (5.7%) women were disabled. The reason why only women were included in this study is that it is not possible to give the same interpretation even with the same BMI due to physiological differences from men. For example, there are big differences in physical growth completion time, muscle mass, pregnancy and childbirth, and menopause. In other words, when the physical gender disparity is clear, it is advantageous for decisive decision-making when analyzing men and women separately rather than controlling during statistical analysis.
Variables
Types of disability were classified into 4 according to the medium medical diagnosis categories: Physical disability, brain lesions, and visual, hearing, speech, and facial disorders were classified as an external physical disability; Kidney, heart, liver, respiratory, hepatic, intestinal fistula, urinary fistula, and epilepsy were classified as an internal organ disability; Intellectual disability and autism are classified as a developmental disability; Schizophrenia, affective disorders, etc. were classified as a mental disability. 17 Afterward, these 4 groups were women with a disability and compared with women without disability.
BMI was calculated using height and weight (kg/m2), and the BMI group was underweight (BMI < 18.5), normal (BMI 18.5-22.9), overweight (BMI 23-24.9), and obese (BMI ≥ 25) according to the Asian obesity standard. 18 Smoking, excessive drinking, and physical activity were analyzed as health behavior variables. Smoking “yes” means current smoker, heavy drinking “yes” means drinking 4 cans of beer or more once a month. Physical activity “yes” means moderate or intense activity based on the International Physical Activity Questionnaire (IPAQ) survey. 19
In this study, there are both types of annual OOP expenditure: first, the sum of co-payments and non-reimbursement for an emergency room, hospitalization, outpatient, and drug expenditure (excluding indirect cost); The second is the Type 1 plus indirect cost such as transportation, etc.
Data Analysis
In this study, the differences in sociodemographic characteristics between women without disability and women with disability were confirmed through chi-square and t test on long format data from 2009 to 2016 of KHP. In order to confirm the representativeness of the study participants’ population, homogeneity with the population was confirmed through weighted analysis (Table 1).
Sociodemographic Characteristics of Women Without and With Disability.
Brain lesions, visual, hearing, speech, and facial disability were classified as ‘external physical disability’.
Kidney, heart, liver, respiratory, hepatic, intestinal or urinary fistula, epilepsy was classified as 'internal organ disability.
Intellectual disability and autism are classified as “developmental disability.”
Schizophrenia, affective disorders, etc. were classified as “mental disability.”
Medical aid, rare incurable diseases or the poor below the poorest.
Non-members of health insurance due to foreign nationals, temporary suspension, etc.
Such as high blood pressure, diabetes, and arthritis that were diagnosed by a doctor.
A panel analysis was conducted to test the first hypothesis. The 8-year time series trend gap in BMI of women without disability and women with disability was presented by the main effects model with the smallest AIC in a mixed effect model (Figure 1).
The 8-year time-series trend of BMI of women without and with disability.a,b
For the second hypothesis test, an Analysis of Covariance (ANCOVA) was used to present the annual OOP expenditure excluding or including indirect cost for women without and with disability. Potential confounding factors were entered as covariance variables. Meanwhile, there may be outliers because annual OOP expenditure is the actual cost of the participants. Therefore, to reduce their effects, the influence, BMI slope or y-intercept of disability, in each of both annual OOP expenditure was identified through the Robust Multiple Regression model. 20 (Table 2).
Annual Out-of-Pocket Expenditure of Women Without and With Disability, and the Impact of BMI and Disability on Annual Out-of-Pocket Expenditure for All Participants.
Annual OOP expenditure excluding indirect cost: The sum of copayments and non-reimbursement for an emergency room, hospitalization, outpatient, and drug expenditure.
Annual OOP expenditure including indirect cost: Care person cost, transportation cost, etc.
Age, education, spouse, living alone, economic activity, type of health care system, and chronic disease status was used as potential confounding factors.
Changed according to the US dollar exchange rate as of July 1, 2009 (1$ = 1295 KRW).
ANCOVA model using potential confounding factors as covariance variables.
Robust multiple regression model controlling for potential confounding factors. This is a model for all study participants.
For the third hypothesis test, multinomial or multiple logistic regression was used to measure the adjusted odds ratios (AOR) of unhealthy behaviors of women with disability: current smoking, excessive drinking, and in physical inactivity (Table 3). Afterward, through ANCOVA, OOP expenditure by health behavior of women with disability was confirmed according to whether or not indirect cost were included. In addition, OOP expenditure due to the interactive effect of the BMI group and lack of physical activity was confirmed in the same way (Table 4).
The Adjusted Odds Ratio of Each Health Behaviors on Women With Disability.
Ref: Reference.
Multinomial logistic regression model.
Multiple logistic regression model.
Age group, education, spouse, living alone, economic activity, type of health care system, and chronic disease status were used as potential confounding factors.
BMI was calculated using height and weight (kg/m2), and BMI group were classified according to Asian obesity criteria: underweight (BMI < 18.5), normal (BMI 18.5-22.9), overweight (BMI 23-24.9), Obesity (BMI ≥ 25).
It means that the participant currently smokes occasionally or every day.
The participant has drunk more than 6 glasses or 4 cans of beer at least once in the past month at one drinking party.
It is based on the IPAQ survey (2004) and is divided into “physical activity” and “physical inactivity” based on intermediate strength or more physical activity.
Comparison of Annual OOP Expenditure According to Obesity, Excessive Drinking, and Physical Inactivity in Women With Disability.
P < .05 for Tukey test
Annual OOP expenditure excluding indirect cost: The sum of copayments and non-reimbursement for an emergency room, hospitalization, outpatient, and drug expenditure.
Annual OOP expenditure including indirect cost: plus indirect costs such as transportation, care person cost, and etc.
Changed according to the US dollar exchange rate as of July 1, 2009 (1$ = 1295 KRW).
ANCOVA model using potential confounding factors as covariance variables. Age group, education, spouse, living alone, economic activity, type of health care system, and chronic disease were potentially confounding factors.
BMI group was classified according to Asian obesity criteria.
The participant has drunk more than 6 glasses of soju (4 cans of beer) at least once in the past month at one drinking party. Soju refers to Korean distilled spirits.
It is based on the IPAQ survey (2004) and is divided into “physical activity” and “non-physical activity” based on intermediate strength or more physical activity.
Underweight with a few participants was excluded.
All annual OOP expenditure were presented in US dollars based on the exchange rate ($1 = 1295 KRW) as of July 1, 2009, the start year of this study period. 21 All analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC, USA), and all variables were analyzed after excluding missing values. When the significance probability was less than alpha .05, it was interpreted as statistically significant.
Results
Sociodemographic Characteristics
The characteristics of the population were statistically identical to those of this study (P < .05). Compared to women without disability, women with disability had poorer demographic characteristics. Women with disability were older than women without disability (50.2vs 65.9 years), had fewer college entrances (32.1%vs 2.7%), were economically inactive (50.4%vs 77.3%), and chronic disease prevalence was higher (62.0%vs 93.9%) (Table 1).
Body Mass Index Time Series Trend
The BMI of women without disability was 22.7, whereas the BMI of women with disability was 23.9, a difference (P < .0001). The proportion of women with disability among all study participants increased from 4.8% in 2009 to 6.1% in 2016. The BMI of women with disability continued to decline trend (P < .0001). However, there was a persistent gap in the predicted BMI of women without disability and women with disability (P < .0001) (Figure 1).
Annual Out-of-Pocket Expenditure and the Effects of BMI and Disability Itself
Annual OOP expenditure, excluding indirect cost was $518.9 for women without disability, compared to $649.4 for women with disability (P < .0001). Annual OOP expenditure, including indirect cost was $534.5 for women without disability, but $681.8 for women with disability (P < .0001).
Additionally, disability itself and BMI influenced annual OOP expenditures for both types. In annual OOP expenditure, excluding indirect cost, the y-Intercept of disability or slopes of 1 unit of BMI were $29.0 and$ 4.4, respectively (P < .0001), and including indirect cost, the y-Intercept of disability or slopes of 1 unit of BMI were $35.2 and $4.6, respectively (P < .0001) (Table 3).
Adjusted Odds Ratios on Obesity, Smoking, Excessive Drinking, and Physical Inactivity
After controlling for potential confounding factors, the AORs for each unhealthy behavior in women with disability compared to women without disability were: obesity 1.5 (95% CI, 1.38-1.65); excessive drinking 0.6 (95% CI, 0.52-0.75); physical inactivity 1.7 (95% CI, 1.60-1.86) (Table 3).
Annual Out-of-Pocket Expenditure According to Unhealthy Behaviors With Disability
There were differences in annual OOP expenditure for women with disability according to physical inactivity. The annual OOP expenditure, excluding indirect cost was higher when they were physically inactive than when they were physically active (P < .05): Physical activity, $714.1 versus physical inactivity $823.1. The same result included indirect cost (P < .05): Physical activity, $746.2 versus physical inactivity, 880.0 (Table 4).
On the other hand, there was a difference in annual OOP expenditure between normal weight and obesity in the Tukey test (P < .05), but there was no significant difference in the overall model. So, the authors crossed BMI groups and physical activity characteristics to find annual OOP expenditure related to obesity. As a result, when the physical activity characteristics were the same, the annual OOP expenditure was higher in a dose response manner in the order of normal weight < overweight < obese (P < .05): excluding indirect cost, $799.2 < $800.3 < $886.1; including indirect cost, $860.2 < $845.9 < $927.5 including indirect cost (Table 4).
Discussions
The topic of sustainable medical security continues to be emphasized. In Korea, universal medical care to guarantee the health of all citizens and inclusive medical care to fulfill the national responsibility by integrating all the marginalized classes are emphasized. To this end, health inequalities by gender, age, socioeconomic level, and various factors and the resulting difference in medical expenditure should be identified. In addition, quality management of medical expenditures to reduce the gap should be emphasized. One of the representative groups in need of inclusive health care is the disabled. The increasing population with disability will increase the burden on the national finances, such as increased demand for medical services and increased socioeconomic welfare cost.
Reports on the prevalence of disability range from 20% to 14% in the world, with 12% of working adults and 39% of the elderly. 2 Criteria of disability are usually defined as a medical concept, but as the generations change, social and legal concepts have also been introduced. Different views on disability and differences in the social environment influence the identification of disability, which may lead to different definitions and prevalence of disability in each country.22,23 Korea in 2021, about 5.12% of the population is disabled. 17 In this study, the number of women with disability was 5.7%, which was slightly higher than that of the population, which could be attributed to the higher prevalence of disability among women or because more disabled were included in the sampling. Therefore, in this study, the representativeness of the participants in this study was confirmed by comparing the results of population adjustment.
This study was attempted to find out the BMI time series trend and health level of women with disability, a representative underprivileged class. Health level is a very comprehensive variable and can be measured with all health-related variables, but as a single variable, the most representative proxy variable may be medical expenditure. In particular, OOP expenditure, which is paid directly by the insured, is a good indicator to infer the severity of health through the amount of medical use and its relative level. When looking at disabled people in Korea in everyday life, women are on the fat side, and men are more dwarfed than non-disabled people. Nevertheless, as far as the authors know, there are not many studies on BMI and medical costs for people with disability. The author’s estimate is that women with disability will have high BMI and high medical costs. Administrative activities will also be possible.
In Korea, although efforts are being made to reduce income polarization by discovering the underprivileged in Korea, medical welfare resources are limited. The sociodemographic characteristics of people with disability are generally poor. 1 In this study, sociodemographic characteristics were more vulnerable for women with disability than for women without disability: older age (50.2 years old vs 65.9 years old), college going on or older (32.1%vs 2.7%), no spouse (33.6%vs 46.3%), economically inactive. (50.4%vs 77.3%), and have one or more chronic conditions (62.0%vs 93.9%). In this study, 4.0% of women without disability and 20.6% of women with disability public health assistance was received. People with disability need social support to live a normal life and, when necessary, help remove health barriers to accessing medical services.24,25 Thus, a predictable outcome for people with disability to benefit more from public healthcare. On the other hand, in this study, the rate of living alone was lower in women with disability than in women without disability, indicating that family supporters are important in caring for the disabled. 26
The main results of this study, confirmed high BMI and high OOP expenditure in women with disability. The BMI of women without disability was 22.7, whereas the BMI of women with disability was 23.9. In addition, BMI, measured repeatedly from 2009 to 2016, was higher for women with disability than for women without disability in all 8 years (P < .0001). Therefore, the first hypothesis of this study was supported. Therefore, it is now important to confirm the health status of women with disability whose BMI is higher than that of women without disability. Previous studies have observed a U-shaped association between BMI and all-cause work disability6. BMI increased disability prevalence and negatively impacted disability recovery. 13
On the other hand, future study needs to evaluate the reason why the BMI of disabled women decreases over time. Because why, a slight increase in BMI over time can be expected. However, the BMI of women without disability indicated a rising trend and that of women with disability indicated a decreasing trend (P < .0001). It will be necessary to review whether the reduction in BMI of women with disability is not due to physiological characteristics, but rather to sociological characteristics and human rights issues. It is necessary to find out whether the decrease in BMI in women with disability is related to sociological characteristics such as negative health conditions or discrimination, rather than intentional weight control behavior.
Another hypothesis of this study is that there is a difference in annual OOP expenditure between women with and without disability and that BMI and disability itself will affect annual OOP expenditure. As a result of the study, the OOP expenditure of women with disability was higher regardless of whether or not indirect costs were included (P < .0001): excluding indirect costs, $518.9 versus $649.4; Including indirect costs, $534.5 versus $681.8. Also, In this study, having a disability itself and BMI impacted annual OOP expenditure (P < .0001). From OOP expenditure excluding indirect cost, the y-intercept of disability and 1 unit of BMI slope were $29.0 and $4.4 respectively. From OOP expenditure including indirect cost, the y-intercept of disability and one unit of BMI slope were $35.2 and $4.6, respectively (P < .0001). In previous studies, there was a significant gap in medical use between people with a developmental disability and people without disability. 27 In addition, BMI and disability pension had a J-shaped relationship, and the proportion of disabled pension recipients was higher in underweight, overweight, and obese than in normal weight. 28 In this way, when it is said that the medical use of the disabled is high and the disability pension is high in previous studies, this study found that the high BMI could be the factor.
The last hypotheses of this study is that annual OOP expenditures would be higher for women without disability when they were overweight, obese, or engaged in unhealthy behaviors. As a result of this study, the annual OOP expenditure and the most unfavorable health behavior was physical inactivity. The AOR of physical inactivity for women with disability was 1.7 (95% CI 1.60-1.86). Therefore, the author confirmed the annuua1 OOP expenditure during physical inactivity. Annual OOP expenditure in case of physical inactivity was higher physical activity, regardless of indirect cost and inclusion: excluding indirect cost, $714.1 versus $823.1; including indirect cost, 746.2versus 880.0. In previous studies, physical inactivity was higher in people with intellectual disability. 29 In a previous study of pain, those who were physically inactive had 15% higher annual drug expenditure than those who were not physically active. 30 In other words, increasing physical activity saved annual OOP expenditures. 31
Meanwhile, in this study, when comparing the weight groups of women with disability to women without disability, the AOR for obesity (BMI ≥ 25) in women with disability was 1.5 (95% CI, 1.38-1.65). In previous studies, this is the same result as that people with physical and intellectual disability had a higher obesity rate than people without disability.10,29 However, there was no significant difference in determining annual OOP expenditure when obese. So, the authors crossed BMI groups and physical activity characteristics to find the annual OOP expenditure associated with obesity. As a result, even with physical inactivity, annual OOP expenditure was dose responsively higher in the order of normal weight < overweight < obese: excluding indirect costs, $799.2 < $800.3 < $886.1; including indirect costs, $860.2 < $845.9 < $927.5. Therefore, there is sufficient evidence for increasing the physical activity of women with disability. As an intervention factor for women with high BMI, physical activity and weight management strategies to prevent obesity will need to be more active than at present. Meanwhile, the drinking AOR of disabled women was 0.6 (0.52-0.75), but there was no relationship with annual OOP expenditure.
Considering the above results, it is necessary to develop social movements in various ways as well as individual efforts to strengthen the promotion of physical activity for women with disability. As the population ages, the number of people with disability will continue to increase. For example, in this study, the proportion of women with disability among all participants increased from 4.8% in 2009 to 6.1% in 2016. Therefore, policymakers in the healthcare field will need special insight into the inequality and impact of BMI on people with disability as part of their efforts to alleviate health inequity between socioeconomic classes. Also, awareness of the high BMI of women with disability should be prioritized by experts in the field of public health work. This is because reducing inequality in BMI for the disabled can help realize social equity.
Although this study provides some evidence for the reason to pay attention to the BMI of women with disability, the limitations of this study are as follows: First, the results of this study question the representation of women around the world. This is because medical welfare for women with disability differs from country to country and will have different behavioral characteristics. In particular, in the case of OOP expenditure, there is a big difference depending on the health insurance and medical coverage rate of each country. Second, this study has limitations in representing the entire population of Korea because it targeted only women aged 19 or older. On the other hand, considering the heterogeneous physiological characteristics between genders, it has the advantage of being highly reliable as a result of women. Third, the type of disability in this study was investigated based on the diagnosis of medical disability, and functional limitations or activities of daily living was not considered. Therefore, it is necessary to explore the social impact, such as functional disability, BMI, and co-payment, in the future. Fourth, although the BMI of women with disability was consistently higher than that of women without disability, the gap is gradually narrowing. Therefore, in the future study, it is necessary to explore the difficult life trajectories of people with disability such as social prejudice and discrimination.
Conclusions
Women with disability are representative targets of inclusive medical care, and their health level and medical expenditure need attention. here was a time series trend gap in BMI between women without and with disability, with disability itself and a high BMI contributing to higher annual OOP expenditure in women. This may provide some evidence that disability prevention and weight management are necessary for rational health care finance. On the other hand, annual OOP expenditure was higher in women with disability when they were ph ysically inactive. In particular, when there was an interaction between physical inactivity and overweight or obesity, annual OOP expenditure increased in a dose response manner compared to normal weight. Therefore, it is necessary to increase physical activity in women with disability and social interest in appropriate weight management, and policy development for this should be preceded. In particular, In the future, along with an aging society, the number of disability will continue to increase, and explosive medical expenditure will be inevitable. In order to reduce these foreseeable future threats, not only the prevention of disability but also the improvement of the health of the disabled should be emphasized. The authors hope that this study can provide some evidence for this need.
Footnotes
Acknowledgements
The author thanks Joongbu University for encouraging research. Also, I would like to thank the Institute for Health and Social Affairs and the National Health Insurance Corporation for their efforts in producing KHP data.
Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Ethics Approval and Consent to Participate
The author submitted the study plan to the KHP Data Management Agency, KIHASA, and received the data officially. Institutional Review Board formally approved the KHP data (KIHASA 2022–017).
