DOI: https://doi.org/10.21203/rs.3.rs-2193405/v1
To determine socio-demographic characteristics, comorbid disorders of intermittent explosive disorder (IED) patients and their medical service use via National Health Insurance Service claim data in South Korea.
Data from the Korean Health Insurance data, a national medical record covering over 99% of the national population, were analyzed. Chi-square analysis was used to determine the distribution of IED patients across socio-demographic factors, such as gender, age, economic status, and others. A correlation test was done to avoid multicollinearity. Finally, a hierarchical multiple regression analysis was done to examine the linear relationship between IED patients' socio-demographic factors and medical service utilization.
First, IED diagnoses have steadily increased over the last 17 years. Second, IED patients are most likely to be men, young adults in their 20s, and polarized in terms of their economic status, either with the highest economic status or the lowest. Finally, the socio-demographic characteristics of patients actively seeking medical treatment were inconsistent with those diagnosed with IED.
Considering that IED patients are steadily increasing, and the discrepancy is found between the most vulnerable population and the medical service-utilizing population, more public awareness and appropriate intervention should be provided.
Aggression is defined as behavior directed toward others to cause harm [1]. Though mild aggressive actions may be normative [2], excessive aggression is related to several psychological disorders [3]. Intermittent Explosive Disorder (IED) is the only psychiatric disorder for which extreme affective aggression is the defining symptom (American Psychological Association [APA], 2013). Although initially thought to be rare, IED is a relatively prevalent and underdiagnosed disorder in about 5% of the U.S. population [4]. Further, IED is associated with considerable impairment, including workplace difficulties, relationship problems, and potential long-term health problems [5]. It is a severe public health issue with potentially destructive consequences not limited to individuals but also an overall burden on society. Therefore, more attention on IEDs is needed to prevent the onset in advance. Especially, early detection of the vulnerable population is crucial to avoid additional population at risk of IEDs. Identifying factors related to medical treatment seeking in IED patients is also needed to prevent further social loss they can bring about.
There is growing evidence of literature to deal with IEDs in epidemiological perspective. In a study of the national population in the United States, Kessler et al. reported that IED is more prevalent in men than women. Also, married, working, and people with low family income were more vulnerable to IEDs. Although IED was more prevalent in people living in metropolitan cities, its difference in geological prevalence was not statistically significant. Another study in Metropolitan São Paulo, Brazil, found that IED is more prevalent in women (57.5%) than men, findings different from previous results from the United States. Also, the married (55.7%), those currently working (60.5%,) and those in their 30s [6], and relatively low level of education, with about half of them completing secondary school were more subject to IED. Also, a tendency towards a low family income was reported, consistent with previous findings by Kessler et al. In a Timor-Leste study with a sample of women affected by conflicts, women were found to have a higher IED prevalence (41%), compared to men (38%). The urban residence was associated with IED, with an odds ratio of 2.03. However, education level showed no statistical significance in the prevalence of IEDs [7]. In the Asia-Pacific region, only one national study exists. A Japanese study with the World Mental Health Survey 2002–2006 found that lifetime and 12-month prevalence of IED were 2.1% and 0.7%, respectively.
In Korea, IED research has been very limited or misguided. There is no epidemiological research dealing with the nation's population. Currently, the discussions involved in these studies are limited; case reports and studies are confined to intervention treatment or specific groups. Also, the inconsistent use of the term "Intermittent Explosive Disorder" is observed. Even though Korea's National Center for Mental Health clarified the diagnostic term "Intermittent Explosive Disorder," IED was frequently used based on subjective observations rather than an official diagnosis term, such as to describe individuals thought to show anger symptoms. Likewise, evidence-based research on IEDs is very limited in Korea. Therefore, identifying the epidemiological characteristics of IED patients is essential and the first step to being taken to provide them with appropriate care. In this regard, the objective of this study was twofold. First, to determine the socio-demographical patterns in IED diagnosis to prevent these populations beforehand, and second, to investigate IED patients' medical utilization patterns for better management and care. The present research aimed to investigate (1) the changes in IED diagnosis in Korea from 2002 to 2017; (2) Socio-demographic characteristics of IED patients along with their comorbid diseases; (3) IED outpatient hospital utilization patterns with individual medical spending as a dependent variable.
In this population-based study, we analyzed the socio-demographic characteristics of IED patients and their outpatient service use with the customized claim database of the NHIS (National Health Insurance Service). This resource is a database of medical records of Korean nationalities. Since registration for national health insurance is mandatory in Korea, the data is considered representative of the whole national population, covering over 97% of the whole population. The data offers medical records of each patient upon request with specified diseases. Due to privacy protection issues, the medical records of mental disease patients can only be obtained through customized claim data upon request under the authorization of the IRB review process. The data comprises separate databases; (1) beneficiary database, (2) medical records database, (3) health checkups database, and (4) institution database. The beneficiary database contains sex, age, residence, beneficiary type, and premium quantile information. The medical records database includes variables such as medical institution code, visit type(inpatient/outpatient), department code, primary diagnosis and sub-diagnoses, date of visit, days of hospitalization/visits, days of prescription, actual individual medical cost (pocket money), and total medical costs.
This study obtained medical records and beneficiary database of IED patients under the guidance of the National Health Insurance Service (NHIS). Diagnostic data of IED patients were collected from 2002 to 2017. Patients with primary disease code for IED according to the Korean Standard Classification of Disease (KICD-10) were selected from the database. The National Health Insurance System (IRB No. REQ0000035411) and the Institutional Review Boards of the Seoul National University (IRB No. E2004/001–001) approved the study. Of the subjects in the acquired customized data, we excluded claims with IED as a comorbid disease or, in other words, secondary diagnosis. Our study focused on patient characteristics and medical utilization of populations visiting hospital mainly to treat IEDs. Therefore, IED patients were defined as people diagnosed with IED as the primary diagnosis. The data were examined by each claim rather than individually.
Gender, age, economic status, disability, medical aid, and residence were indicated as socio-demographic variables and extracted from the beneficiary database. Age was recoded into several groups. People under the 19 years of age were excluded, and the rest were divided into seven range groups (20–29 years old, 30–39 years old, etc.). Economic status was measured using insurance fee variables by dividing the variable into five groups. Insurance fee is a reliable measure to estimate an individual's economic status since South Korea adopts a progressive tax system. The residence variable was recoded into binary variable "capital residence" representing population residing in Seoul, Gyung-gi, and Incheon provinces and "non-capital residence" representing population outside the area. Variables used to measure medical utilization include number of hospital visits, individual medical spending, and treatment duration. The out-of-pocket health expenditures variable was logarithmized.
First, the total number of IED diagnosis in each year from 2002 to 2017 were analyzed and stratified by year according to each socio-demographic factors, gender, age, and economic status. Second, descriptive statistics in the year 2017 were generated to identify comorbid diseases of IED and listed by frequency since the most recent data available at the time of analysis was medical records of the year 2017. Third, Pearson's chi-square analysis was performed to determine the difference in the distribution of IED patients across socio-demographic factors, such as gender, age, economic status, disability, medical aid, residence, and finally hospital level to identify their medical service use characteristics. To investigate if the changes in epidemiological characteristics of IED diagnosis were observed, the analyses were done with data from both years, the year 2002 and the year 2017. Finally, hierarchical multiple regression analysis was performed to examine the linear relationship between IED patients' socio-economic factors and their medical spending. The regression analysis was done with the data obtained in the year 2017 to identify recent leading factors related to medical treatment seeking, in other words, individual medical spending. A correlation test between the variables was also done to avoid multicollinearity. In general, absolute correlation coefficient higher than 0.7 among predictors indicates the presence of multicollinearity and the variable medical aid was excluded from the model due to collinearity, accounting for 96% of the variance in outpatient medical costs. All variables included in the final analyses had correlation less than 0.7. All analysis were conducted using SAS 9.1 and STATA 16.
Figure 1 shows the trend in IED diagnosis across gender, age, and economic status over the last 17 years. Figure 1 shows a steadily increasing pattern, implying that the number of IED diagnosis have gradually grown. Also, the slope was slightly steeper in the year 2014 than previous years. Figure 2 shows the number of people diagnosed by gender and the difference between men and women each year. The difference between men and women is increasing; in the year 2017, the number of men diagnosed with IED was approximately five times more than women. Figure 3 displays the diagnosed is steeply increasing in the age group 20–29 years, followed by the age group 30–39 years. Figure 4 shows that IED diagnosis is more prevalent among people with a higher economic status.
Comorbid mental disorders of IED are presented in Table 1. In the year 2002, attention deficit/hyperactivity disorder, or ADHD, was the most frequent comorbid disease accounting for 6.78%, followed by depressive disorders. In the year 2017, the three most frequent comorbid disorders were consistent with those in the year 2002. Additionally, 49.18% of IED patients visited hospitals to be treated for the disease.
Disease code and names | n | % |
---|---|---|
None | 1,321 | 12.30 |
F329 (Depressive episode, unspecified) | 871 | 8.11 |
F320 (Mild depressive episode) | 634 | 5.90 |
F900 (Attention deficit disorder with hyperactivity) | 577 | 5.37 |
F319 (Bipolar affective disorder unspecified) | 552 | 5.14 |
F321 (Moderate depressive episode) | 497 | 4.63 |
F328 (Other depressive episodes) | 411 | 3.83 |
F510 (Nonorganic insomnia) | 346 | 3.22 |
F318 (Other bipolar affective disorders) | 335 | 3.12 |
F412 (Mixed anxiety and depressive disorder) | 322 | 3.00 |
F102 (Mental and behavioral disorders due to use of alcohol) | 209 | 1.95 |
Characteristics of patients with IED as a primary diagnosis are shown in Table 2. In both years, men accounted for more than 80% of total IED diagnoses and increased to 85.23% in the year 2017. Whereas in the year 2002 people in the age group 30–39 were diagnosed the most with the highest rate of 27.95%, in 2017, the age group 20–29 were diagnosed the most with the rate of 42.1%. People with the highest economic status and those in the fourth quantile reported the highest proportion in 2002 (27.15%). In 2017, while people at the highest economic status was most frequent with IED, the population with the second most frequency was those with the lowest economic status (21.95%). The rate of increase from 2002 to 2017 was the biggest in the lowest economic class, which increased by 7.29%. It is also shown that the proportion of IED patients receiving medical aid was approximately 16.5% in 2017, which increased from 6.3% in 2002, indicating that the proportion of medical aid beneficiaries has risen in IED patients. Regarding hospital level, more than half of the IED patients visited primary hospitals for treatment.
2002 | 2017 | ||||
---|---|---|---|---|---|
n | % | n | % | ||
Sex | |||||
Male | 2,255 | 82.15 | 8,594 | 85.5 | |
Female | 490 | 17.85 | 1,458 | 14.5 | |
Chi-square | p < 0.001 | p = 0.075 | |||
Age | |||||
20–29 | 815 | 30.07 | 3,954 | 41.77 | |
30–39 | 721 | 26.61 | 2,077 | 21.94 | |
40–49 | 538 | 19.85 | 1,587 | 16.76 | |
50–59 | 393 | 14.5 | 1,105 | 11.67 | |
60–69 | 174 | 6.42 | 518 | 5.47 | |
70–79 | 50 | 1.85 | 146 | 1.54 | |
80 + | 19 | 0.7 | 80 | 0.85 | |
Chi-square | p < 0.001 | p < 0.001 | |||
Economic Status | |||||
1st quantile (Lowest) | 352 | 13.83 | 1,820 | 21.97 | |
2nd quantile | 360 | 14.15 | 1,657 | 20.0 | |
3rd quantile | 430 | 16.9 | 1,495 | 18.04 | |
4th quantile | 696 | 27.35 | 1,240 | 14.97 | |
5th quantile (Highest) | 707 | 27.78 | 2,073 | 25.02 | |
Chi-square | p < 0.001 | p < 0.001 | |||
Disability | |||||
Disabled | 241 | 8.78 | 1,207 | 12.01 | |
Not disabled | 2,504 | 91.22 | 8,845 | 87.99 | |
Chi-square | p < 0.001 | p < 0.001 | |||
Medical Aid | |||||
Yes | 122 | 4.44 | 1,564 | 15.56 | |
No | 2,623 | 95.56 | 8,488 | 84.44 | |
Chi-square | p < 0.001 | p < 0.001 | |||
Residence | |||||
Capital Area (Seoul, Incheon, Gyeonggi) | 1,431 | 52.13 | 4,991 | 49.65 | |
Other regions | 1,314 | 47.87 | 5,061 | 50.35 | |
Chi-square | p < 0.001 | p = 0.187 | |||
Hospital-Level | |||||
Primary care (clinic, public health center) | 2,258 | 82.26 | 5,681 | 56.52 | |
Secondary care (hospital, general hospital) | 326 | 11.88 | 3,567 | 35.49 | |
Tertiary care (tertiary hospital) | 161 | 5.87 | 804 | 8.0 | |
Nursing hospital | |||||
Chi-square | p < 0.001 | p < 0.001 | |||
Total | 2,745 | 10,052 |
* p < .05. ** p < .01 *** p < .001
Table 3 shows outpatients with IED as the primary diagnosis. Men accounted for more cases in 2017 than in 2002. The age group 20–29 years accounted the most significant with 41.77%, followed by age group 30–39 years. While the proportion of patients with the highest economic status decreased from 27.78–25.02%, those with the lowest economic status increased from 13.83–21.97%. Also, the proportion of the disabled showed a similar tendency, which increased from 8.78–12.01% in 2017. Medical aid beneficiaries also accounted for 15.56% of outpatient visits in 2017, which have increased compared to 4.44% in 2002. Whereas in 2002, more than three-fourths of people visited primary care institutions to be treated with IED (82.26%), in 2017, only approximately half of patients visited primary care institutions (56.62%), mostly transferred to secondary care institutions, which accounted for 35.49% in 2017 compared to 11.88% in 2002.
2002 | 2017 | ||||
---|---|---|---|---|---|
n | % | n | % | ||
Sex | |||||
Male | 2,255 | 82.15 | 8,594 | 85.5 | |
Female | 490 | 17.85 | 1,458 | 14.5 | |
Chi-square | p < 0.001 | p = 0.075 | |||
Age | |||||
20–29 | 815 | 30.07 | 3,954 | 41.77 | |
30–39 | 721 | 26.61 | 2,077 | 21.94 | |
40–49 | 538 | 19.85 | 1,587 | 16.76 | |
50–59 | 393 | 14.5 | 1,105 | 11.67 | |
60–69 | 174 | 6.42 | 518 | 5.47 | |
70–79 | 50 | 1.85 | 146 | 1.54 | |
80 + | 19 | 0.7 | 80 | 0.85 | |
Chi-square | p < 0.001 | p < 0.001 | |||
Economic Status | |||||
1st quantile (Lowest) | 352 | 13.83 | 1,820 | 21.97 | |
2nd quantile | 360 | 14.15 | 1,657 | 20.0 | |
3rd quantile | 430 | 16.9 | 1,495 | 18.04 | |
4th quantile | 696 | 27.35 | 1,240 | 14.97 | |
5th quantile (Highest) | 707 | 27.78 | 2,073 | 25.02 | |
Chi-square | p < 0.001 | p < 0.001 | |||
Disability | |||||
Disabled | 241 | 8.78 | 1,207 | 12.01 | |
Not disabled | 2,504 | 91.22 | 8,845 | 87.99 | |
Chi-square | p < 0.001 | p < 0.001 | |||
Medical Aid | |||||
Yes | 122 | 4.44 | 1,564 | 15.56 | |
No (national health insurance) | 2,623 | 95.56 | 8,488 | 84.44 | |
Chi-square | p < 0.001 | p < 0.001 | |||
Residence | |||||
Capital Area (Seoul, Incheon, Gyeonggi) | 1,431 | 52.13 | 4,991 | 49.65 | |
Other regions | 1,314 | 47.87 | 5,061 | 50.35 | |
Chi-square | p < 0.001 | p = 0.187 | |||
Hospital-Level | |||||
Primary care (clinic, public health center) | 2,258 | 82.26 | 5,681 | 56.52 | |
Secondary care (hospital, general hospital) | 326 | 11.88 | 3,567 | 35.49 | |
Tertiary care (tertiary hospital) | 161 | 5.87 | 804 | 8.0 | |
Nursing hospital | |||||
Chi-square | p < 0.001 | p < 0.001 | |||
Total | 2,745 | 10,052 |
* p < .05. ** p < .01 *** p < .001
Table 4 shows a multivariate regression analysis of factors related to outpatient medical costs. The first set examined socio-demographic factors (model 1), and the second added other factors related to medical costs other than socio-demographic factors (model 2). The final model(model 2) was selected as the best-fit model, with an Adjusted R square of 29.38%. Medical aid was excluded from the model due to collinearity, accounting for 96% of the variance in outpatient medical costs. All of the independent variables were statistically significant in the final model. Young, non-disabled women of higher economic status living around the capital area were more likely to use outpatient services.
Model 1 | Model 2 | ||||||||
---|---|---|---|---|---|---|---|---|---|
Variables | b | S.E. | b | S.E. | |||||
Sex (ref. women) | -0.757 | ** | 0.231 | -0.132 | *** | 0.02 | |||
Age | -0.005 | *** | 0.001 | -0.005 | *** | 0.001 | |||
Economic Status | 0.064 | 0.005 | 0.062 | *** | 0.062 | ||||
Disability (ref. no disability) | -0.425 | *** | 0.034 | -0.388 | *** | 0.029 | |||
Capital Area (ref. non-capital) | -0.115 | 0.016 | 0.071 | *** | 0.014 | ||||
Number of Hospital Visits | 0.926 | ** | 0.358 | ||||||
Length of Medication | 0.008 | 0.001 | |||||||
Length of Prescription | -0.008 | 0.001 | |||||||
Hospital-Level | 0.535 | *** | 0.011 | ||||||
Constant | 9.662 | *** | 7.896 | *** | 0.36 | ||||
R2 | 5.5 | 29.46 | |||||||
Adjusted R2 | 5.44 | *** | 29.38 | *** | |||||
* p < .05 (two-tailed). ** p < .01 (two-tailed). *** p < .001 (two-tailed) |
Our results show that IED diagnosis in Korea has been increasing since 2002. It showed an even steeper increase in the last decade, especially since 2012. As the number of IED diagnoses as primary diagnosis keeps increasing, more attention should be given, and preventative measures should be taken to avoid the onset at an early stage. Also, the possibility of the underrated number of IED should also be considered. Japan's research may explain the overall low prevalence of IED in South Korea compared to the U.S. and other countries. Yoshimasu et al(2011) revealed that the low prevalence of IED in Japan compared to U.S. studies can be attributed to cross-cultural factors. They reported that Japanese tend to suppress their feelings, especially anger, compared to westerners, due to social norms. This might also be true for South Koreans since collective actions are valued rather than individual or personal opinions. In this social context, showing one's feelings, especially anger, will isolate an individual from other society members. The result also showed that ADHD as a most frequent comorbid disease with IED in Korea. ADHD symptoms are quite different from depression, by which we can infer that the IED vulnerable population in Korea might have different characteristics compared with other countries.
Our findings also reveal that the gender gap in IED diagnosis is increasing and that IED might be a gender-specific disease. The number of diagnoses is higher in men and continues to increase. Although epidemiological evidence is limited, gender difference in IED has also been reported in previous literature. A study in United States also found that IED was about twice as frequent in men than in women [8, 9]. In general, gender disparities are among the most vital indicators of mental health problems [10]. Men and women experience substantially different emotional problems [11–12]. Whereas women experience internalizing feelings, turning problems against the self into depression and anxiety, resulting in more attributions of self-blame and self-reproach [13–15], in contrast, men show higher prevalence of externalizing disorders than women, including antisocial personality disorders and substance abuse or dependence [16–17].
IED prevalence was prevalent in the age group 20–29 years, which is consistent with previous finding tht IED prevalence higher in young individuals under 35 years old [4, 18, 19] than other older age groups. Researchers have speculated that high rates of depression in early adulthood can be attributed to a relative lack of experience in coping with life transitions that occur at that time. In contrast, middle age is associated with greater maturity [10, 20–22]. In this perspective, a high prevalence in the age group 20–29 years may be attributed to the immature and inexperienced self of age group 20–29 years in controlling their feelings.
The gap between the diagnosed and those who seek medical help, especially outpatient visits, was also identified. Whereas men are more likely to be diagnosed than women, women seek more professional help, visiting hospitals frequently than men. Although not IED specific, previous research has reported that women recognize psychological problems more often than men and are more likely to seek treatment for psychological problems or psychiatric disorders [23–26]. Also, women visit psychiatric departments more frequently than men [24, 27–29]. Also, while residing around capital area was not statistically significant in the diagnosis, residence in capital city was significantly related to more individual medical spending. Although further investigation is needed, less medical spending can be attributed to lack of accessible hospitals, considering that issues of hospital deficit in rural areas have been around for several years. The fact that discrepancy between the diagnosed population and people seeking medical help exists should be highlighted, implying that population with high vulnerability is not getting appropriate care. Also, because currently no study can provide information on whether people voluntarily sought medical care or not, more attention should be given to IEDs to identify vulnerable population in need of care, and facilitate early detection, preventing further social burden it can bring about.
Several limitations should be considered interpreting the findings. First, the NHIS data represents medical records rather than a survey. Only nominal reports such as changes in IED diagnosis and their medical service use were available based on the limited variables available. Second, the NHIS data only captures those who visited the medical institution. Therefore, IED patient characteristic before the entry of medical institution could not be determined. Despite these limitations, this study sheds light on IED, an often-neglected disease in South Korea. While aggressive behaviors and failure to control individual’s anger can lead to physical and social adverse outcomes, much of previous studies dealing with mental disorders are focused on depression, schizophrenia, or bipolar disorder. In his study, Coccaro also suggested that IED often precedes the aforementioned diseases and should be examined independently rather than as a comorbid disease [4].
NHIS encompasses over 97% of the Korean’s medical records, a reliable source as a representation for the national population, despite limitation of variables due to its originality as a medical record rather than a survey. This study has value in that it represents preliminary data for further IED research within a nationally representative data. In addition, the records are largely exact, based on physician’s professional diagnosis, not on self-administered responses. Finally, since IED is classified as an acute disease, these data are not open to the public and can only be obtained in claims data.
To our knowledge, no epidemiological evidence regarding IED has been established in Korea. In this regard, this study aimed to fill the gap between social implications and evidence-based data, with analyses on national claim data. Identifying IED vulnerable populations suggests implications for preventive measures. Therefore, this paper first explored the diagnostic trend in IED from the year 2002 to 2017 and evaluated patients' socio-demographic characteristics. Then, the paper further analyzed the IED population's medical utilization, with individual medical spending. The overall findings suggest that, while IED patients are socially considered to be potential criminals with biological defects, socio-demographic factors also contribute to this condition, with discrepancies in the diagnosis and medical treatment-seeking population. Therefore, further studies should be conducted to identify factors contributing to avoidance in medical treatment among population with diagnosis but not seeking medical treatment and develop intervention strategies to motivate them to seek care. Also, early detection of IED and the implementation of adequate treatment should also be highlighted since they can prevent the onset of several other mental disorders individually and the negative outcomes such as harm and violence directed to others socially.
Intermittent Explosive Disorder
National Health Insurance Service
Funding No funding was received to assist with the preparation of this manuscript.
Conflict of interest The authors declare that they have no conflict of interest.
Acknowledgments We would like to thank Professor Soon man Kwon and Sun-Young Kim for their thoughtful advice throughout the research.
Author contributions Y.H. and M.Y. conceptualized the study. Y.H. was responsible for the methodology, formal analysis, and data acquisition. Y.H. wrote the initial draft of the manuscript, and M.Y. assisted with the manuscript's writing, reviewing, and editing. Both authors have read and agreed to the published version of the manuscript.
Ethics approval The National Health Insurance System approved the study (No. REQ0000035411) and the Institutional Review Boards of the Seoul National University (IRB No. E2004/001-001).