Psychiatry Investig Search


Psychiatry Investig > Volume 19(6); 2022 > Article
Kim, Shin, and Jeong: Nature and Extent of Physical Comorbidities Among Korean Patients With Mental Illnesses: Pairwise and Network Analysis Based on Health Insurance Claims Data



The nature of physical comorbidities in patients with mental illness may differ according to diagnosis and personal characteristics. We investigated this complexity by conventional logistic regression and network analysis.


A health insurance claims data in Korea was analyzed. For every combination of psychiatric and physical diagnoses, odds ratios were calculated adjusting age and sex. From the patient-diagnosis data, a network of diagnoses was constructed using Jaccard coefficient as the index of comorbidity.


In 1,017,024 individuals, 77,447 (7.6%) were diagnosed with mental illnesses. The number of physical diagnoses among them was 11.2, which was 1.6 times higher than non-psychiatric groups. The most noticeable associations were 1) neurotic illnesses with gastrointestinal/pain disorders and 2) dementia with fracture, Parkinson’s disease, and cerebrovascular accidents. Unexpectedly, the diagnosis of metabolic syndrome was only scarcely found in patients with severe mental illnesses (SMIs). However, implicit associations between metabolic syndrome and SMIs were suggested in comorbidity networks.


Physical comorbidities in patients with mental illnesses were more extensive than those with other disease categories. However, the result raised questions as to whether the medical resources were being diverted to less serious conditions than more urgent conditions in patients with SMIs.


Comorbidity relates to the simultaneous presence of two or more medical conditions at the same time [1]. In psychiatry, comorbidity is pervasive and of great importance in both clinical and theoretical aspects [2]. Clinicians are increasingly aware of the importance of physical comorbidities in the treatment of mental illnesses [1]. A study done in Korea reported that patients with any mental disorder have an increased risk of chronic physical conditions by an odds ratio of 1.5 to 2.8 [3]. A recent comprehensive study in Denmark estimated the hazard ratio for suffering physical illness after a diagnosis of mental illness to be 1.37 [4]. These ratios are expected to be even higher in patients with severe mental illnesses (SMIs) considering that they do not seek medical services on their own [5].
Physical comorbidity impedes recovery and restricts functional independence [6,7]. Patients with SMIs even have a reduced lifespan [8], which is partly attributed to comorbid medical conditions [9]. Therefore, it is clinicians’ responsibility to detect comorbid conditions and effectively manage them [10].
However, the fact that someone has been given multiple diagnostic labels does not entail that he/she is actually suffering from all those illnesses. An understanding of the phenomenon of comorbidity cannot be completed without considering individual health-seeking behavior and regional healthcare systems. Patients with anxiety or depression often seek consultation from medical specialists before receiving proper psychiatric diagnosis. This is one of the main reasons behind treatment delays and inefficient usage of healthcare resources [11]. Patients’ own denial, limited medical information, and the clinicians’ narrow perspective limited to their own specialties could extend the list of unnecessary diagnostic labels.
So far, studies on physical comorbidity have mainly focused on depression and SMIs [12]. They have emphasized the elevated risk of so-called lifestyle diseases, such as metabolic syndrome, cardiovascular diseases, and cerebrovascular accidents. The common data sources for existing studies were cohort studies, epidemiological catchment area survey or review of admission records. These studies were able to calculate relatively accurate comorbidity rates for preselected pairs of diagnoses because they employed well-designed survey tools or laboratory tests [13-15]. Despite this, they failed to address all the combinatorial pairs of diagnoses that were not included in the study design.
Second, comorbidity in a strict biological sense is not the same as comorbidity in the sociomedical dimension. The latter deals with what problems patients with a certain disease mainly complain about, how they interpret them, which medical specialists they consult and what diagnoses they receive in this process [16-18]. Finally, the results obtained from traditional studies only describe a dyadic relationship in which one disease increases the risk of another. However, according to the concept of triadic closure from graph theory, if the connections A-B and B-C exist, there is a tendency for the new connection A-C to be formed. Therefore, to understand the triadic or multi-adic relationship among the assorted set of diagnoses, it is advantageous to borrow the technique of network analysis [19].
To facilitate such research, large-scale epidemiological data and detailed quantitative analysis is mandatory. Health insurance claim database is a useful data source with a long history and a solid theoretical foundation [20,21]. The number of studies utilizing such big data is steadily increasing. Comorbidity rates are being aggregated for millions or tens of millions of individuals with the aid of national registry or claims data. However, since such datasets are not readily accessible in most countries, the published results are confined to some European nations [2,4,22]. Large-scale studies investigating comorbidity patterns are still scarce in Asia [12].
In this study, we sought clues to the following clinically relevant questions by analyzing the health insurance claims dataset in Korea: Is the physical comorbidity in psychiatric patients more extensive than in other disease categories? Which psychiatric diagnoses are associated most closely (or least closely) to physical conditions? What are the physical conditions they have? Are there specific physical conditions associated with each psychiatric diagnosis? Does the extensive physical comorbidity reflect the health-seeking behavior of neurotic patients or the actual health risk of patients with SMIs?
The obtained result will provide supporting data to help understand the complex nature and extent of physical comorbidity in psychiatric patients. In addition, by revealing the personal traits of Korean patients, cultural influence they receive and the limitations of the current healthcare delivery system, it will pose challenges for Korean psychiatrists to design better ways to help patients with mental illnesses.


Data source

In Korea, the National Health Insurance System (NHIS) is a mandatory social insurance service. The health insurance claim data were collected and archived by the Health Insurance Review and Assessment Service (HIRA). A part of this huge dataset is annually published to facilitate the openness of governmental data. This dataset is freely accessible without restriction at a government operated data portal (
This annual data consisted of crude demographic information and the medical claims records for randomly sampled 1 million subscribers. A sufficiently large sample size and the random sampling procedure assure the representativeness of the dataset. It was carefully anonymized, and personal identifiable information had been erased. It contains diagnoses, the start and end date of medical consultation and the number of prescription days. The diagnoses were coded in the Korean Standard Classification of Disease, version 7 (KCD-7), which is an adapted version of the International Classification of Diseases, version 10 (ICD-10). Due to privacy concerns, the detailed code of the psychiatric diagnosis is no longer provided since 2016. Therefore, this study was conducted based on data from 2015.
The study design and the data analysis protocol were reviewed and approved by Institutional Review Board of Eulji University Hospital (EMC 2021-04-025).

Data processing

The raw data was imported into a database management system called Neo4J. Neo4j is a database specifically designed to store and query graph type datasets. Recently, with the emphasis on the graph properties of biological data, Neo4J is being widely used in biomedical research [23-25].
Neo4J provides a structured query language-inspired query language called Cypher to navigate the stored data. Several in-house Cypher queries were written by authors to address the research questions. In the raw data, each record indicated the patient’s single claim for the medical consultation. If the patient had sought treatment for different problems, another record for a separate claim had been added. Each record contained a couple of diagnoses made by the treating physician. Thus, each patient has multiple records of medical claims and each record has multiple diagnoses. These patient-diagnosis links were stored as a bipartite graph with two types of nodes (patient and diagnosis). A comorbidity network was built by projecting the bipartite graph into a unipartite graph with Jaccard similarity coefficients as their link weights.

Calculation of the degree of comorbidity

The degree of comorbidity at the individual level was represented by the number of distinct diagnoses each patient had during the study period. Since this study mainly focused on the physical comorbidity in psychiatric patients, additional psychiatric diagnoses were excluded when counting the comorbid conditions.
All patients were classified into separate disease categories. In Table 1, the KCD-codes were mapped to conveniently chosen disease categories used in the study. As subjects had been diagnosed with one or more conditions, he or she could belong to more than one category. The individual degree of comorbidity was averaged to obtain the degree of comorbidity at the disease category level. After that, the disease categories were ranked according to their degree of comorbidity. Similarly, F-code diagnoses (psychiatric diagnoses) were ranked according to the diagnosis level degree of comorbidity.

Evaluation of disease-disease association

The likelihood of two diagnoses being associated can be affected by many confounding factors including their overall prevalence. Therefore, simply counting the number of comorbid conditions for each psychiatric diagnosis could not provide an unbiased estimate of comorbidity. As the data contained only sex and age as the available demographic variables, pairwise logistic regression was conducted for all possible diagnostic pairs (between psychiatric and physical diagnoses) with sex and age as confounding variables.
The magnitude of the adjusted odds ratios (AORs) and associated p-values were used to select significant associations. Since the number of required pairwise logistic regression was over one million, the level of significance was adjusted by Bonferroni correction (p<10-7 since the usual p-value 0.05/106= 5×10-7) to ensure that spurious associations were not considered significant solely due to the huge sample size [26].
We paid attention not only to the positive comorbidity (AOR>1), but also to the negative comorbidity (AOR<1) [27]. It is unconvincing to argue that the negative comorbidity implies that a certain disease affords protection to another set of diseases. However, the negative comorbidity obtained in health claims data may be indicative of systemic under-recognition or ignorance of a certain set of physical conditions.

Construction of a comorbidity network

A comorbidity network was built by projecting a bipartite network (patient-disease association) into an unipartite network (disease-disease association) [28]. Jaccard coefficient was used for the edge weight which represented the comorbidity (=similarity) of the two diseases. Existing literature disagrees on what indicators are the most appropriate for representing the similarity of a pair of nodes. Recommended indicators include 2×2 contingency table-based measures such as odds ratio, relative risk, or observed-to-expected ratio (O/E ratio) [26,29,30]. However, these indicators have some disadvantages when trying to construct and visualize a network. First of all, they are not bounded and can extend to positive infinity. Although logarithmic transformation could in part remedy this problem, it still is unbounded and permits few large values [31]. In networks, few larger values of similarity may dominate the layout and prevent the global structure from emerging.
Another issue is the lack of information in the data source. In a pre-planned study, rating scales, structured interviews, or laboratory tests would be used to unequivocally determine the presence or absence of a target disease. From these, all four cells of the contingency table and thus the marginal distribution could be known without any uncertainty. On the other hand, claim based data like ours do not provide any information on individuals who did not seek medical consultation. Since not receiving medical treatment and not having a disease are two separate issues, the marginal distribution cannot be known exactly. Therefore, any contingency table-based measures had to suffer from this lack of information. By contrast, overlap-based measures, such as Jaccard coefficient, do not require the marginal distribution, which is why they are commonly used in gene-sharing networks. Such networks have been traditionally called Comorbidity Network, Phenotype Disease Network, Human Disease Network, or Disease Similarity Network [32-36].
The distribution of the obtained Jaccard coefficients was highly right-skewed, such that over 90% of them were almost zero. In addition, the number of edges in the whole network was too large (=381,714), so neither the backbone structure extraction nor the visualization were possible. To make it work, it was arbitrarily decided that the size of the network had to be reduced to less than 1%. So, the weak links with the coefficient less than 1 percentile of the whole distribution (Jaccard coefficient= 0.025) were discarded. The reduced network was visualized by the quadrilateral backbone layout algorithm [37]. It tries to reflect the relative edge strength as much as possible and uncover the potential community structures. Undoubtedly, it is not recommended to regard the 2D layout distance as the literal measure of connectedness, but it is still possible to interpret that closely positioned nodes are more similar and belong to the same community. In this way, the global structure of the network can emerge and provide a birds-eye view of disease-clustering patterns. Diseases within close proximity can potentially share important characteristics such as risk factors, pathophysiology, or treatment strategy [34].
All the statistical analyses were conducted by the open source statistical package R version 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria). Network analysis and visualization were specifically aided by the R packages “tidygraph” and “ggraph.”


Characteristic of the dataset

The dataset contained the health claim data for 1,017,024 individuals (486,725 males and 530,299 females). The total number of medical claims was 11,231,930, such that each individual received, on average, 11 outpatient or inpatient treatment. The sex and age distribution of the subjects were shown in Table 2. The percentage of subjects with one or more psychiatric diagnoses (F00-F99) was 7.6% (77,447). Their age and the proportion of female subjects were greater than the rest. A total of 77 distinct psychiatric diagnoses had been made to these patients. The most prevalent psychiatric diagnoses were depressive episodes (26.4%), other anxiety disorders (25.9%) and sleep disorders (9.2%).

The degree of comorbidity in various disease categories

The included subjects had an average of 7.5 distinct diagnoses (8.2 for females and 6.7 for males) regardless of disease category. For psychiatric patients, the mean number of distinct diagnoses was 11.7 (12.4 for females and 10.6 for males). Apart from the index diagnosis (an arbitrarily chosen single psychiatric diagnosis), additional diagnoses were separated into 1) other psychiatric diagnosis (F-code) and 2) diagnoses related to physical conditions (non-F-code). While the average number of additional F-code diagnosis was only 0.5, that of comorbid physical diagnosis was 11.2 (11.8 for females and 10.1 for males).
In order to determine whether psychiatric patients had a higher number of comorbid diagnoses than the rest, the mean numbers of comorbid diagnoses (including index diagnosis) across different disease categories were compared and displayed in Figure 1.
The category with the largest number of comorbid diagnoses was neurological disease, which was closely followed by psychiatric disease. Compared to them, other disease categories had a relatively fewer number of comorbid diagnoses. To confirm this difference, Poisson regression was performed. The presence of psychiatric diagnosis significantly elevated the number of comorbid diagnoses (b=0.47; t(1,017,019)=229.85; p<10-7; 95% confidence interval, 1.60 to 1.61) adjusted for sex and age. According to this model, having psychiatric diagnoses increases the number of comorbid diagnoses by 1.6 times.

The number of comorbid diagnoses in each psychiatric diagnoses

The average number of comorbid physical diagnoses for each psychiatric diagnosis was displayed in Figure 2. Other mood disorders (F38), other mental disorders due to brain damage (F06), other neurotic disorders (F48), somatoform disorders (F45), unspecified mood disorder (F39), and other anxiety disorders (F41) were the diagnoses with the highest degree of physical comorbidity. In contrast, mental retardation (F70, F71), schizophrenia (F20), habit and impulse disorders (F63), schizoaffective disorders (F25) were the diagnoses with the lowest degree of comorbidity.

Physical diagnoses of which prevalence is increased or decreased by comorbid psychiatric diagnosis

The number of physical conditions which had at least once been co-diagnosed with any psychiatric illness was 820. As there were 77 F-code diagnoses, the total number of unique combinations to be examined was 63,140 (=77×820). For each combination, a logistic regression was performed controlling for the effect of age and sex to obtain AORs. Some of the main findings of the result were listed in Table 3. The leftmost column listed representative psychiatric diagnoses. The middle column listed the physical diagnoses that had a positive comorbidity relationship with the former. Up to 5 diagnoses with the highest odds ratios were selected among the comorbid combinations that had passed statistical significance (p<10-7). The fourth column presented the physical disorders that were less likely to be found with each psychiatric diagnosis (negative comorbidity). As before, up to 5 diagnoses with the smallest odds ratio were selected among the statistically significant connections. Some of the cells contained less than 5 diagnoses or nothing because the majority of the comorbid combinations for that particular diagnosis didn’t pass the statistical tests.

Comorbidity network

The comorbidity network is visualized in Figure 3. According to a visual inspection, two groups of psychiatric diagnoses could be discerned: 1) On the left side of the figure, a group of child/adolescent psychiatric diagnoses was positioned near the perinatal and congenital conditions. In the central area, another group of psychiatric diagnoses was located intermingled with other physical conditions. Closer inspection suggested that they could be divided into two sub-groups. The first one (numbered 2-1) included both SMIs and various types of dementia. These diagnoses displayed close connections with cardiovascular and neurological conditions. The second (numbered 2-2) was mainly composed of depression (F32, F33), anxiety (F41) and organic mental disorder (F06). They were closely linked with musculoskeletal, gastrointestinal (GI) and injury related conditions. Those injuries included minor ones like sprain, falldown and fracture and were not related to suicidal attempts.


In the present study, the comorbidity relations between psychiatric illnesses and physical conditions were explored using large-scale health claims data. Patients with psychiatric diagnoses had the second highest number of comorbid physical conditions, surpassed by neurological diseases. The high comorbidity was mainly driven by mood/anxiety disorders, somatoform disorders, and organic mental disorders. The degree of comorbidity was deeply influenced by age and sex. After adjusting the effect of age and sex, lists of physical conditions with significantly elevated or lowered odds ratio for each psychiatric diagnosis were obtained. Several noticeable associations were 1) neurotic illnesses with GI and pain disorders, 2) dementia with fracture, Parkinson’s disease and cerebrovascular accidents, 3) schizophrenia and bipolar disorder with epilepsy, and 4) alcohol use disorder with liver and pancreatic diseases. As to the negative comorbidity, schizophrenia was associated with a much lower prevalence of type 2 diabetes mellitus, quite contrary to prior expectation.
The phenomenon of comorbidity is often interpreted as comorbid diseases sharing a common pathophysiology or overlapping risk factors [38]. Chronic stress induces proinflammatory state, overactivation of the hypothalamic-pituitary-adrenal axis, or elevation of sympathetic tone, thus causing depression as well as various lifestyle diseases [39-41]. It helps to explain why the most frequent physical diagnosis with depression and neurotic illnesses were common GI troubles and musculoskeletal pain disorders. The connection between neurotic illnesses and GI trouble may be explained by the tight connection between emotion and GI. According to a Swedish study exploring the predictors of GI symptoms in a large sample of young psychiatric patients, the severity of depressive symptoms, trait anxiety and stress susceptibility were the independent predictors of the Gastrointestinal Symptom Rating Scale for Irritable Bowel Symptom [42]. The author attributed this finding to the bidirectional connection between the gut and brain via vagus nerve, enteric nervous system and alteration of gut flora, commonly referred to as gut-brain axis.
Neurotic illnesses can also lower the threshold of pain perception and promote hypochondriacal worries. In a long-term follow-up study of a community sample, depression was found to be the precursor to future back pain diagnoses [43]. Patients with somatoform disorder often exhibit a heightened focus on their own bodies, and catastrophically interpret their bodily sensation as a separate illness requiring urgent intervention [44]. Some neuroimaging studies even provided a neurobiological basis for this somatosensory amplification phenomenon [45,46].
Apart from these biological explanations, it is necessary to consider psychological, social, and cultural factors, especially when interpreting comorbidity in claims data [26]. The claims data kept the record only if the patient was aware of the problem and sought medical consultation. Therefore, it may reflect how the patient perceived and interpreted the problem, and which specialists he/she wanted to consult. It is not uncommon for general practitioners and medical specialists to overlook the possibility of mental illness and diagnose somatic accompaniments of a mental illness as physical diseases [47,48]. It may lead to an inflated rate of comorbidity [49]. The observation that patients with neurotic illnesses had an unusually large number of comorbid diagnoses indicate that they might have erroneously recognized their somatic symptoms as physical problems [50-52]. Some of the comorbid diagnoses might actually be misdiagnosed and contribute to treatment delays or inefficient use of healthcare resources. Underrepresentation of serious conditions like hypertension, metabolic and neoplastic diseases gave weight to this interpretation. Regardless of the specific psychiatric diagnosis, nonspecific GI, respiratory, musculoskeletal and sleep-related symptoms are often the primary cause of medical consultations and also the predominant reason why these patients visit primary care physicians. A lot of these symptoms are medically unexplained and an assortment of diagnostic labels are attached reflecting this inexplicability.
This distorted situation was also evidenced by the finding that the odds ratios of metabolic and cardiovascular diseases were not elevated but even lower than expected (negative comorbidity) in patients with SMIs. Crump et al. [53] reported that patients with schizophrenia were three times more likely to die of ischemic heart disease or cancer, but the diagnosis rate of these conditions was not elevated in nonfatal cases. Even in fatal cases, these patients were less likely to have been previously diagnosed with these conditions. This issue of under-recognition and under-treatment had also been discussed in a study with the Scottish population [5]. Although patients with schizophrenia had a higher number of physical morbidities, the recorded rates of hypertension and cardiovascular diseases in them were lower than the control group. People with mood and anxiety disorders reported significantly more contact with medical specialists for somatic diseases, but patients with schizophrenia did not receive enough care [54]. Notably, it depends much on the culture and healthcare delivery system of each region. Our results indicated that the current status of the medical system in Korea is not ideal for serious conditions that can compromise physical health and life expectancy of patients with SMIs.
Korea provides better access to health care than other countries, because the government-sponsored health insurance system (NHIS) covers most expenses. As the referral process from a primary physician to a large university hospital is neither complicated nor regulated, patients can receive whatever service they desire from any specialist. While such a system may seem ideal, the higher affordability and accessibility can result in overdiagnosis and overtreatment, exemplified by the recent epidemic of thyroid cancer in Korea [55]. Another issue is that in order to be reimbursed by the insurance system, a plethora of ad-hoc diagnoses must be made. Therefore, even if the primary physician notices that it is an incidental symptom accompanying depression, he/she has to make a separate physical diagnosis.
In contrast, Korean patients with SMIs are less likely to use mental health services due to social prejudice, stigma, as well as lack of insight. Korea’s mental health system does not yet offer mature community services, and inpatient treatment is overused, leaving patients with delayed diagnoses hospitalized for an extended period of time and disconnected from society as a result [56]. Even after discharge, they are not seamlessly integrated into the local community, and unless managed by case managers, they are less likely to receive multifaceted treatment other than just antipsychotic treatment. A possible explanation for the unexpectedly low rate of seeking treatment for lifestyle diseases in SMI patients may be related to their being isolated from society and sequestered in a blind spot of the healthcare system.
Mental health professionals working in the community also need to be alert. In particular, middle-aged and older patients with chronic mental illness are at risk of losing the attention of clinicians because their psychiatric symptoms are not prominent. Caretakers are also getting older and cannot afford to provide proper care for them. For example, it was reported that the rate of medical check-ups of patients with SMIs was much lower than that of the healthy control [57]. It may have been due to their low level of health literacy, but not enough recommendations or encouragement from mental health professionals may have also played a part.
We included a comorbidity network for a more advanced analysis of comorbid relations. It showed that there was differential connectedness between diagnoses, resulting in a complex grouping structure. Diagnoses belonging to the same disease category tended to cluster together. However, this was not always the case, and there were many regions where diagnoses belonging to different categories mingled together to form a group of heterogeneous components. The two prominent findings were 1) SMIs and dementia are closely located with cardiovascular and neurological conditions and 2) neurotic conditions such as depression, anxiety, and somatoform disorder were tightly linked with musculoskeletal, GI, and injury related conditions. Also of interest was the finding that these latter diagnoses (musculoskeletal, GI, and injuries) were inseparably linked.
Findings from the network analysis generally corresponded with the results obtained from pairwise comorbidity analysis. Even so, the indirect links between psychiatric and metabolic disorders could be discerned only in network analysis. The sub-group 2-1) had three diabetes diagnoses (E10: type 1 diabetes mellitus, E13: other diabetes mellitus, E14: unspecified diabetes mellitus; Figure 3), while the sub-group 2-2) had two adult-onset metabolic diseases (E11: type 2 diabetes mellitus, E78: dyslipidemia). By any measure, such links with endocrinological diagnoses could not be found in pairwise analysis. Patients’ unwillingness, especially patients with SMIs, to seek treatment on their own may have obscured latent comorbidities in the pairwise analysis.
Examining comorbidity from a network perspective may present a different picture than from a pairwise analysis. One such difference is that the latter cannot not address the indirect comorbidity mediated by intervening associations with other diseases [58]. The comorbidity network can deepen the understanding of multi-dimensional aspects of disease-disease correlation, such as exposure to risk factors, diagnosis and treatment, health psychology, and determinants of better or worse prognosis [34].
The limitation of this study came from the nature of available data. The claim data only recorded the patients’ seeking treatment voluntarily or inevitably. Therefore, omission in the data did not necessarily mean the absence of disease. Although the dataset provided insight into patients’ health-seeking behavior and physicians’ diagnosis practice, it was not suitable for the precise calculation of comorbidity. In addition, the diagnostic label used in this study may not reflect the actual patients’ condition. In Korean medical systems, diagnostic labels are assigned for various purposes. For example, KCD codes for epileptic syndromes may have been given to avoid reduction in insurance reimbursement for mood stabilizers and, likewise, codes for depressive disorders may have been used to justify antidepressant prescriptions.
Another shortcoming was that the data were only a snapshot of a fixed period. Cross-sectionally measured comorbidities may not have much meaning in itself. It cannot answer the questions as to which set of diagnoses in the present lead to which set of diagnoses in the future. Analyzing the temporal order of the diagnoses with longitudinal data may provide evidence for the cause-effect relationship. Several studies addressed this issue and tried to delineate the pattern of disease progression [29,30,59,60]. They are challenging tasks since access to sensitive datasets is restricted and the methodology to analyze dynamic networks is refined. Besides, temporal order does not necessarily guarantee the causal relationship. The authors of a nation-wide longitudinal study, which examined the diagnostic history of patients over many years, could not determine whether mental illness had been the cause of the following physical illnesses [4]. With this indeterminacy of the causal or temporal direction, it was also hard to decide whether the physical conditions accompanied the psychiatric disorders or vice versa. The result in this study could also be interpreted as psychiatric comorbidities in medical patients. Analysis from this alternate perspective would need a separate study.
In this study, we quantitatively analyzed the degree and nature of physical comorbidity in psychiatric patients. With the help of large-scale health claims data and network-based analysis methods, we could derive several insights from the analysis results. The results obtained are expected to remind psychiatrists that patients are receiving treatment for somatic symptoms from various medical specialists with diverse interpretations. In addition, they would help policy makers to formulate the efficient use of medical resources and medical delivery systems.


Availability of Data and Material

The datasets analyzed during the current study are publicly available at a Korean government operated public data portal (

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: Seong Hoon Jeong. Data curation: Seong Hoon Jeong. Formal analysis: Seong Hoon Jeong. Investigation: Seong Hoon Jeong. Methodology: Seong Hoon Jeong. Project administration: Ho Joon Kim. Software: Seong Hoon Jeong. Supervision: Seong Hoon Jeong. Validation: Sam Yi Shin. Visualization: Seong Hoon Jeong. Writing—original draft: Ho Joon Kim. Writing—review & editing: Seong Hoon Jeong, Sam Yi Shin.

Funding Statement


Figure 1.
The average number of comorbid diagnoses in subjects belonging to different disease categories. The largest number of comorbid diagnoses was associated with neurological diseases. Common to all categories (except urologic disease), female subjects had a lot more comorbid diagnosis. ENT, ear, nose and throat.
Figure 2.
The number of comorbid physical diagnoses associated with each psychiatric diagnosis.
Figure 3.
The network structure of comorbidity relationships among the diagnoses belonging to different disease categories. Psychiatric diagnoses were marked with larger violet circles and labeled with corresponding KCD-7 codes. They were also divided into three distinct groups (1, 2-1 and 2-2) according to their comorbid relationship with other diagnoses: 1) Group 1 consisted mainly of child-adolescent diseases, 2) group 2-1 of severe mental illnesses and dementia, and 3) group 2-2 of neurotic illnesses. The insert is a magnified view of the region containing severe mental illnesses (schizophrenia, schizoaffective disorder, and bipolar disorder etc.). In this magnified view, several endocrine diseases can be discerned (E10: type I diabetes mellitus, E14: unspecified diabetes mellitus).
Table 1.
Disease categories used in the study and their corresponding KCD-7 codes
Disease category KCD-code
Infectious disease A00-B99
Neoplastic disease C00-D89
Endocrine disease E00-E89
Psychiatric disease F01-F99
Neurologic disease G00-G99
Ophthalmologic, ENT disease H00-H95
Cardiovascular disease I00-I99
Respiratory disease J00-J99
Gastrointestinal disease K00-K95
Dermatologic disease L00-L99
Musculoskeletal disease M00-M99
Urologic disease N00-N99
Conditions originating in the perinatal period P00-P96
Congenital malformations Q00-Q99
Injury, poisoning S00-T88
Others O00-O9A, R00-R99, U00-Y99

Top level KCD-7 codes (A to Y) are conveniently grouped into 16 disease categories to suit research purpose. KCD-7 codes are essentially the same as those in ICD-10 with only minor exceptions. KCD-7, Korean Standard Classification of Disease, version 7; ENT, ear, nose and throat; ICD-10, International Classification of Diseases, version 10

Table 2.
The demographic profile and age distribution of 1) all the included subjects and 2) subjects with one or more psychiatric diagnoses (F-codes)
All subjects
Subjects with psychiatric diagnoses
Female Male Total Female Male Total
Number 530,299 (52.1) 486,725 (47.9) 1,017,024 (100) 71,200 (61.0) 45,537 (39.0) 116,737 (100)
Age (yr) 41.3±21.8 39.3± 1.3 40.3±21.6 59.2±19.1 53.2±21.5 56.9±20.3
Age group (yr)
 0-20 102,344 (19.3) 108,089 (22.2) 210,433 (20.7) 2,158 (3.0) 4,192 (9.2) 6,350 (5.4)
 21-40 141,749 (26.7) 128,656 (26.4) 270,405 (26.6) 9,861 (13.8) 7,902 (17.4) 17,763 (15.2)
 41-60 171,743 (32.4) 159,214 (32.7) 330,957 (32.5) 21,355 (30.0) 13,360 (29.3) 34,715 (29.7)
 61-100 114,463 (21.6) 90,766 (18.6) 205,229 (20.2) 37,826 (53.1) 20,083 (44.1) 57,909 (49.6)

The average age and the female-to-male ratio of the subjects with psychiatric diagnosis are significantly higher than the rest. Values are presented as number (%) or mean±standard deviation

Table 3.
Specific physical diagnoses in positive or negative comorbidity relationship with each psychiatric diagnosis
Psychiatric diagnosis Physical diagnoses with positive comorbidity AOR Physical diagnoses with negative comorbidity AOR
F00: Dementia in Alzheimer’s disease
Fracture of femur 37.82
Fracture of lumbar spine and pelvis 30.11
Parkinson’s disease 25.63
Cerebral infarction 19.43
Transient cerebral ischemic attacks and related syndromes 18.90
F01: Vascular dementia
Hemiplegia 21.20 Dislocation, sprain and strain of joints and ligaments at ankle and foot level 0.16
Intracerebral hemorrhage 11.69 Chronic rhinitis, nasopharyngitis, and pharyngitis 0.19
Other disorders of fluid, electrolyte, and acid-base 7.13 Nonsuppurative otitis media 0.22
Subarachnoid hemorrhage 5.32 Hordeolum and chalazion 0.22
Other disorders of thyroid 4.82 Dislocation, sprain and strain of joints and 0.23
F02: Dementia in other diseases classified elsewhere
Parkinson’s disease 1.88 Irritable bowel syndrome 0.03
Synovitis and tenosynovitis 0.03
Headache 0.03
Gastro-esophageal reflux disease 0.04
Vasomotor and allergic rhinitis 0.04
F06: Other mental disorders due to brain damage and dysfunction and to physical disease
Other degenerative diseases of nervous system NEC 11.82
Presence of other functional implants 8.00
Other symptoms and signs involving cognitive functions and awareness 7.40
Other peripheral vascular diseases 6.11
Other intervertebral disc disorders 5.95
F10: Mental and behavioral disorders due to use of alcohol
Alcoholic liver disease 21.63 Osteoporosis without pathological fracture 0.10
Acute pancreatitis 5.04 Bacterial pneumonia, NEC 0.18
Fibrosis and cirrhosis of liver 4.66 Disorders of vestibular function 0.19
Chronic hepatitis, NEC 3.97 Migraine 0.19
Fracture of skull and facial bones 3.56 Other headache syndromes 0.20
F20: Schizophrenia
Epilepsy 2.87 Disorders of lacrimal system 0.01
Gonarthrosis [arthrosis of knee] 0.01
Dorsalgia 0.01
Type 2 diabetes mellitus 0.01
Acute upper respiratory infections of multiple and unspecified sites 0.01
F31: Bipolar affective disorder
Epilepsy 4.90 Abnormalities of heartbeat 0.37
Other extrapyramidal and movement disorders 3.17 Other headache syndromes 0.46
Heartburn 2.85 Disorders of vestibular function 0.51
Candidiasis 2.30 Migraine 0.52
Neuromuscular dysfunction of bladder, NEC 2.03 Spondylosis 0.59
F32: Depressive episode
Gastritis and duodenitis 11.83
Gastro-esophageal reflux disease 10.59
Vasomotor and allergic rhinitis 10.58
Acute bronchitis 10.17
Dorsalgia 10.15
F41: Other anxiety disorders
Gonarthrosis [arthrosis of knee] 19.04
Gastritis and duodenitis 18.27
Pain in throat and chest 17.79
Gastro-esophageal reflux disease 17.26
Disorders of vestibular function 17.10
F45: Somatoform disorders
Other intervertebral disc disorders 6.05
Abdominal and pelvic pain 5.86
Candidiasis 5.60
Keratitis 5.52
Other gastroenteritis and colitis of infectious and unspecified origin 5.51
F48: Other neurotic disorders
Dizziness and giddiness 4.77
Peptic ulcer, site unspecified 4.76
Other intervertebral disc disorders 4.59
Other joint disorders, NEC 4.23
Abdominal and pelvic pain 4.20

Positive (negative) comorbidity means that presence of a certain psychiatric diagnosis increases (decrease) the likelihood of receiving another physical diagnosis. NEC, not elsewhere classified; AOR, adjusted odds ratio


1. Sartorius N. Comorbidity of mental and physical disorders: a key problem for medicine in the 21st century. Acta Psychiatr Scand 2018;137:369-370.
crossref pmid pdf
2. Plana-Ripoll O, Pedersen CB, Holtz Y, Benros ME, Dalsgaard S, de Jonge P, et al. Exploring comorbidity within mental disorders among a Danish national population. JAMA Psychiatry 2019;76:259-270.
crossref pmid pmc
3. Kim JH, Chang SM, Bae JN, Cho SJ, Lee JY, Kim BS, et al. Mentalphysical comorbidity in Korean adults: results from a nationwide general population survey in Korea. Psychiatry Investig 2016;13:496-503.
crossref pmid pmc pdf
4. Momen NC, Plana-Ripoll O, Agerbo E, Benros ME, Børglum AD, Christensen MK, et al. Association between mental disorders and subsequent medical conditions. N Engl J Med 2020;382:1721-1731.
crossref pmid pmc
5. Smith DJ, Langan J, McLean G, Guthrie B, Mercer SW. Schizophrenia is associated with excess multiple physical-health comorbidities but low levels of recorded cardiovascular disease in primary care: crosssectional study. BMJ Open 2013;3:e002808
crossref pmid pmc
6. Nelis SM, Wu YT, Matthews FE, Martyr A, Quinn C, Rippon I, et al. The impact of co-morbidity on the quality of life of people with dementia: findings from the IDEAL study. Age Ageing 2019;48:361-367.
crossref pmid pmc
7. Yang C, Zhong X, Zhou H, Wu Z, Zhang M, Ning Y. Physical Comorbidities are independently associated with higher rates of psychiatric readmission in a Chinese Han population. Neuropsychiatr Dis Treat 2020;16:2073-2082.
pmid pmc
8. Walker ER, McGee RE, Druss BG. Mortality in mental disorders and global disease burden implications: a systematic review and meta-analysis. JAMA Psychiatry 2015;72:334-341.
crossref pmid pmc
9. Pan YJ, Yeh LL, Chan HY, Chang CK. Excess mortality and shortened life expectancy in people with major mental illnesses in Taiwan. Epidemiol Psychiatr Sci 2020;29:e156
crossref pmid pmc
10. Firth J, Siddiqi N, Koyanagi A, Siskind D, Rosenbaum S, Galletly C, et al. The Lancet Psychiatry Commission: a blueprint for protecting physical health in people with mental illness. Lancet Psychiatry 2019;6:675-712.
crossref pmid
11. Jansen L, van Schijndel M, van Waarde J, van Busschbach J. Healtheconomic outcomes in hospital patients with medical-psychiatric comorbidity: a systematic review and meta-analysis. PLoS One 2018;13:e0194029
crossref pmid pmc
12. Park S, Kim GU, Kim H. Physical comorbidity according to diagnoses and sex among psychiatric inpatients in South Korea. Int J Environ Res Public Health 2021;18:4187
crossref pmid pmc
13. Carney CP, Jones L, Woolson RF. Medical comorbidity in women and men with schizophrenia: a population-based controlled study. J Gen Intern Med 2006;21:1133-1137.
crossref pmid pmc
14. Crawford MJ, Jayakumar S, Lemmey SJ, Zalewska K, Patel MX, Cooper SJ, et al. Assessment and treatment of physical health problems among people with schizophrenia: national cross-sectional study. Br J Psychiatry 2014;205:473-477.
crossref pmid
15. Scott KM, Lim C, Al-Hamzawi A, Alonso J, Bruffaerts R, Caldas-de-Almeida JM, et al. Association of mental disorders with subsequent chronic physical conditions: world mental health surveys from 17 countries. JAMA Psychiatry 2016;73:150-158.
crossref pmid pmc
16. Katon WJ. Clinical and health services relationships between major depression, depressive symptoms, and general medical illness. Biol Psychiatry 2003;54:216-226.
crossref pmid
17. Vancampfort D, Koyanagi A, Hallgren M, Probst M, Stubbs B. The relationship between chronic physical conditions, multimorbidity and anxiety in the general population: a global perspective across 42 countries. Gen Hosp Psychiatry 2017;45:1-6.
crossref pmid
18. Romain AJ, Marleau J, Baillot A. Impact of obesity and mood disorders on physical comorbidities, psychological well-being, health behaviours and use of health services. J Affect Disord 2018;225:381-388.
crossref pmid
19. Hu JX, Thomas CE, Brunak S. Network biology concepts in complex disease comorbidities. Nat Rev Genet 2016;17:615-629.
crossref pmid pdf
20. Klabunde CN, Warren JL, Legler JM. Assessing comorbidity using claims data: an overview. Med Care 2002;40(8 Suppl):IV-26-35
21. Klabunde CN, Legler JM, Warren JL, Baldwin LM, Schrag D. A refined comorbidity measurement algorithm for claims-based studies of breast, prostate, colorectal, and lung cancer patients. Ann Epidemiol 2007;17:584-590.
crossref pmid
22. Steffen A, Nübel J, Jacobi F, Bätzing J, Holstiege J. Mental and somatic comorbidity of depression: a comprehensive cross-sectional analysis of 202 diagnosis groups using German nationwide ambulatory claims data. BMC Psychiatry 2020;20:142
crossref pmid pmc pdf
23. Summer G, Kelder T, Ono K, Radonjic M, Heymans S, Demchak B. cy-Neo4j: connecting Neo4j and Cytoscape. Bioinformatics 2015;31:3868-3869.
crossref pmid pmc pdf
24. Balaur I, Mazein A, Saqi M, Lysenko A, Rawlings CJ, Auffray C. Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks. Bioinformatics 2017;33:1096-1098.
crossref pmid pmc
25. Shoshi A, Hofestädt R, Zolotareva O, Friedrichs M, Maier A, Ivanisenko VA, et al. GenCoNet-a graph database for the analysis of comorbidities by gene networks. J Integr Bioinform 2018;15:20180049
crossref pmid pmc
26. Kim JH, Son KY, Shin DW, Kim SH, Yun JW, Shin JH, et al. Network analysis of human diseases using Korean nationwide claims data. J Biomed Inform 2016;61:276-282.
crossref pmid
27. Tabarés-Seisdedos R, Gómez-Beneyto M, Haro JM, González-Pinto A, Vieta E. The importance of negative comorbidity. J Clin Psychiatry 2009;70:1191-1192.
crossref pmid
28. Pavlopoulos GA, Kontou PI, Pavlopoulou A, Bouyioukos C, Markou E, Bagos PG. Bipartite graphs in systems biology and medicine: a survey of methods and applications. Gigascience 2018;7:1-31.
crossref pmid pmc
29. Jeong E, Ko K, Oh S, Han HW. Network-based analysis of diagnosis progression patterns using claims data. Sci Rep 2017;7:15561
crossref pmid pmc pdf
30. Fotouhi B, Momeni N, Riolo MA, Buckeridge DL. Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data. Appl Netw Sci 2018;3:46
crossref pmid pmc pdf
31. Cramer AO, Waldorp LJ, van der Maas HL, Borsboom D. Comorbidity: a network perspective. Behav Brain Sci 2010;33:137-150.
crossref pmid
32. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabási AL. The human disease network. Proc Natl Acad Sci U S A 2007;104:8685-8690.
crossref pmid pmc
33. Hidalgo CA, Blumm N, Barabási AL, Christakis NA. A dynamic network approach for the study of human phenotypes. PLoS Comput Biol 2009;5:e1000353
crossref pmid pmc
34. Jiang Y, Ma S, Shia BC, Lee TS. An epidemiological human disease network derived from disease co-occurrence in Taiwan. Sci Rep 2018;8:4557
crossref pmid pmc pdf
35. García Del Valle EP, Lagunes García G, Prieto Santamaría L, Zanin M, Menasalvas Ruiz E, Rodríguez-González A. Disease networks and their contribution to disease understanding: a review of their evolution, techniques and data sources. J Biomed Inform 2019;94:103206
crossref pmid
36. Ni P, Wang J, Zhong P, Li Y, Wu FX, Pan Y. Constructing disease similarity networks based on disease module theory. IEEE/ACM Trans Comput Biol Bioinform 2020;17:906-915.
crossref pmid
37. Nocaj A, Ortmann M, Brandes U. Untangling the hairballs of multicentered, small-world online social media networks. J Graph Algorithms Appl 2015;19:595-618.
38. Ko Y, Cho M, Lee JS, Kim J. Identification of disease comorbidity through hidden molecular mechanisms. Sci Rep 2016;6:39433
crossref pmid pmc pdf
39. Ehlert U, Gaab J, Heinrichs M. Psychoneuroendocrinological contributions to the etiology of depression, posttraumatic stress disorder, and stress-related bodily disorders: the role of the hypothalamus-pituitaryadrenal axis. Biol Psychol 2001;57:141-152.
crossref pmid
40. Cameron OG, Abelson JL, Young EA. Anxious and depressive disorders and their comorbidity: effect on central nervous system noradrenergic function. Biol Psychiatry 2004;56:875-883.
crossref pmid
41. Bremmer MA, Beekman AT, Deeg DJ, Penninx BW, Dik MG, Hack CE, et al. Inflammatory markers in late-life depression: results from a population-based study. J Affect Disord 2008;106:249-255.
crossref pmid
42. Söderquist F, Syk M, Just D, Kurbalija Novicic Z, Rasmusson AJ, Hellström PM, et al. A cross-sectional study of gastrointestinal symptoms, depressive symptoms and trait anxiety in young adults. BMC Psychiatry 2020;20:535
crossref pmid pmc pdf
43. Larson SL, Clark MR, Eaton WW. Depressive disorder as a long-term antecedent risk factor for incident back pain: a 13-year follow-up study from the Baltimore Epidemiological Catchment Area sample. Psychol Med 2004;34:211-219.
crossref pmid
44. Seto H, Nakao M. Relationships between catastrophic thought, bodily sensations and physical symptoms. Biopsychosoc Med 2017;11:28
crossref pmid pmc pdf
45. Boeckle M, Schrimpf M, Liegl G, Pieh C. Neural correlates of somatoform disorders from a meta-analytic perspective on neuroimaging studies. Neuroimage Clin 2016;11:606-613.
crossref pmid pmc
46. Köteles F, Witthöft M. Somatosensory amplification - An old construct from a new perspective. J Psychosom Res 2017;101:1-9.
crossref pmid
47. Wittchen HU, Mühlig S, Beesdo K. Mental disorders in primary care. Dialogues Clin Neurosci 2003;5:115-128.
crossref pmid pmc
48. Maxwell M, Harris F, Hibberd C, Donaghy E, Pratt R, Williams C, et al. A qualitative study of primary care professionals’ views of case finding for depression in patients with diabetes or coronary heart disease in the UK. BMC Fam Pract 2013;14:46
crossref pmid pmc pdf
49. Zimmerman M. A review of 20 years of research on overdiagnosis and underdiagnosis in the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) project. Can J Psychiatry 2016;61:71-79.
crossref pmid pmc pdf
50. Bijl RV, Ravelli A. Psychiatric morbidity, service use, and need for care in the general population: results of The Netherlands Mental Health Survey and Incidence Study. Am J Public Health 2000;90:602-607.
crossref pmid pmc
51. Boerema AM, Kleiboer A, Beekman AT, van Zoonen K, Dijkshoorn H, Cuijpers P. Determinants of help-seeking behavior in depression: a cross-sectional study. BMC Psychiatry 2016;16:78
crossref pmid pmc
52. Heinig I, Wittchen HU, Knappe S. Help-seeking behavior and treatment barriers in anxiety disorders: results from a representative German community survey. Community Ment Health J 2021;57:1505-1517.
crossref pmid pmc pdf
53. Crump C, Winkleby MA, Sundquist K, Sundquist J. Comorbidities and mortality in persons with schizophrenia: a Swedish national cohort study. Am J Psychiatry 2013;170:324-333.
crossref pmid
54. Haussleiter I, Emons B, Hoffmann K, Juckel G. The somatic care situation of people with mental illness. Health Sci Rep 2021;4:e226
crossref pmid pmc pdf
55. Park S, Oh CM, Cho H, Lee JY, Jung KW, Jun JK, et al. Association between screening and the thyroid cancer “epidemic” in South Korea: evidence from a nationwide study. BMJ 2016;355:i5745
crossref pmid pmc
56. Heo YC, Kahng SK, Kim S. Mental health system at the community level in Korea: development, recent reforms and challenges. Int J Ment Health Syst 2019;13:9
crossref pmid pmc pdf
57. Kim SW, Park WY, Jhon M, Kim M, Lee JY, Kim SY, et al. Physical health literacy and health-related behaviors in patients with psychosis. Clin Psychopharmacol Neurosci 2019;17:279-287.
crossref pmid pmc
58. Marx P, Antal P, Bolgar B, Bagdy G, Deakin B, Juhasz G. Comorbidities in the diseasome are more apparent than real: what Bayesian filtering reveals about the comorbidities of depression. PLoS Comput Biol 2017;13:e1005487
crossref pmid pmc
59. Khan A, Uddin S, Srinivasan U. Comorbidity network for chronic disease: a novel approach to understand type 2 diabetes progression. Int J Med Inform 2018;115:1-9.
crossref pmid
60. Aguado A, Moratalla-Navarro F, López-Simarro F, Moreno V. Morbi-Net: multimorbidity networks in adult general population. Analysis of type 2 diabetes mellitus comorbidity. Sci Rep 2020;10:2416
crossref pmid pmc pdf
Facebook Twitter Linked In Google+
METRICS Graph View
  • 1 Web of Science
  • 1 Crossref
  • 1 Scopus
  • 65,535 View
  • 93 Download


Browse all articles >

Editorial Office
#522, 27, Seochojungang-ro 24-gil, Seocho-gu, Seoul 06601, Korea
Tel: +82-2-717-0892    E-mail:                

Copyright © 2024 by Korean Neuropsychiatric Association.

Developed in M2PI

Close layer
prev next