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Original Research Article

DTT 2023; 2(2): 103-110

Published online September 30, 2023 https://doi.org/10.58502/DTT.23.0011

Copyright © The Pharmaceutical Society of Korea.

Incidence and Risk Factors of Opioid Use Disorder in New Users of Opioid Analgesics in Korea: Analysis of a National Claims Database

Kyu-Nam Heo1, Won-Bean Jo2, Yoojin Noh3, Ju-Yeun Lee1, Young-Mi Ah2

1College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
2College of Pharmacy, Yeungnam University, Gyeungsan, Republic of Korea
3Pharmacy School, Massachusetts College of Pharmacy and Health Sciences University, Worcester, MA, USA

Correspondence to:Young-Mi Ah, ymah@ynu.ac.kr

Received: April 3, 2023; Revised: June 8, 2023; Accepted: June 11, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ((http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Opioid analgesics are essential medications for severe pain; however, their use can cause various adverse events. Opioid use disorder (OUD) is a serious challenge associated with infectious diseases, injuries, and deaths. This study aimed to identify the incidence of OUD and its associated factors in Korean adults who were new users of non-injectable opioid analgesics (NIOA) using data from the national claims database provided by Health Insurance Review and Assessment Services from 2016 to 2018. Patients who received a new diagnosis of OUD after NIOA initiation, defined by the International Classification of Diseases-10 were classified as the case group. We used the exact match method to select a control group of patients without OUD in a 1:4 ratio. The cumulative incidence of OUD for 2 years after NIOA initiation was 0.006%. Factors associated with OUD occurrence included daily morphine milligram equivalents (≥ 50 MME/day; adjusted odds ratio [aOR] 5.04, 95% confidence interval [CI] 1.92-13.25), number of NIOA prescriptions (≥ 10; aOR 19.05, 95% CI 5.53-65.58), and use of sustained-release formulation (aOR 3.5, 95% CI 1.8-6.78). Benzodiazepine uses and type of insurance were also significantly associated with OUD occurrence. The incidence of OUD in patients using NIOA identified through diagnostic codes in Korea was lower than that in other countries. Close monitoring is recommended for patients with identified risk factors.

Keywordsopioid use disorder, opioid analgesics, risk factors, national claims database, incidence

Opioid analgesics are essential for controlling severe pain; however, they can cause various adverse events, ranging from mild to severe (Benyamin et al. 2008; World Health Organization 2017). With the increase in opioid analgesic use, serious negative outcomes, such as opioid overdose death and dependence, have been raised, especially in North America (Bolash 2015). Despite several efforts to control opioid-related problems, the opioid crisis in North America has continued, and the illicit use of opioid analgesics has also increased in Europe and Africa (United Nations Office on Drugs and Crime 2020).

Opioid use disorder (OUD) refers to a problematic pattern of opioid use, including abuse, dependence, and addiction. It is associated with serious potential consequences, including infectious disease, injury, and death (Degenhardt et al. 2019). Opioid dependence (353 cases/100,000 individuals) was reported as one of the most common drug use disorders in a study using worldwide data. The prevalence of OUD varied according to geographical location, with the highest rate in North America (1,168.3 cases/100,000 individuals) and the lowest in Central Europe (151.3 cases/100,000 individuals) (GBD 2016 Alcohol and Drug Use Collaborators 2018). A systematic review reported that the incidence of de novo OUD in patients administered opioid analgesics was 4.7% (Higgins et al. 2018). To reduce the dilemma between appropriate pain control and minimizing the risk of OUD in patients prescribed opioid analgesics, the risk factors associated with OUD should be identified. Several studies have reported the risk factors and prediction tools for OUD. Age, chronic opioid use, psychiatric disease, other substance abuse, and opioid dose are known to be associated with OUD onset; however, these studies were reported primarily in North America (Webster and Webster 2005; Butler et al. 2008; Rice et al. 2012).

Although the use of opioid analgesics in Korea is lower compared to other developed countries, the recent average annual percentage change of opioid analgesic use has increased in Korea (30.28 morphine milligram equivalent [MME] /1,000 individuals/day), while it has decreased in North America (−12.84 MME/1,000 individuals/day) (Ju et al. 2022). The increase in opioid analgesic use has been reported in several studies using domestic data (Cho et al. 2021; Noh et al. 2022). Additionally, OUD prevalence in East Asia, including Korea, was 285.8 cases per 100,000 individuals, which is not low (GBD 2016 Alcohol and Drug Use Collaborators 2018). However, only a few studies have investigated the incidence of OUD in patients exposed to opioid analgesics and its associated factors in Korea. Therefore, we aimed to identify the incidence of OUD in new opioid analgesic users within 2 years and its associated factors.

Study participants and outcome definition

In this nested case-control study, we used the national claims database provided by the Health Insurance Review and Assessment Services (HIRA) from 2016 to 2018. The National Health Insurance Service is implemented in Korea and covers almost the entire population, and HIRA is the only institution reviewing claims from all healthcare institutes. Basic demographics, diagnoses, healthcare utilization, procedures performed, and prescription information can be identified in this database.

Adult patients who were newly prescribed with non-injectable opioid analgesics (NIOA) (ATC codes: N02A and R05DA04) from 2017 to 2018 were included in the cohort group. Patients who had no NIOA prescription in 2016 were considered new NIOA users. Fixed-dose combination drugs that contain codeine or dihydrocodeine and are only approved for treating coughs were not included in this study. The first date of the NIOA prescription was defined as the cohort entry date. Patients newly diagnosed with OUD after NIOA initiation were classified into the case group, as defined by the International Classification of Diseases (ICD)-10 (F11). The first date of OUD diagnosis was defined as the index date. We selected a control group among patients without OUD in a ratio of 1:4, using the exact match method with the cohort entry date and follow-up period (from the cohort entry date to the last claim date). The exclusion criteria were as follows: 1) patients who had an NIOA prescription in 2016, 2) those who were diagnosed with OUD before the cohort entry date, and 3) those who were prescribed injectable opioids at the index date. This study was approved by the Yeungnam University Institutional Review Board (approval number: YU 2019-01-001).

Variables

We identified the following variables: 1) demographics (age, sex, and type of insurance); 2) comorbidities (mood disorder, anxiety, dementia, and hypertension) and Charlson Comorbidity Index (CCI); 3) co-medications (benzodiazepines, hypnotics, gabapentinoids, tramadol, and other pain medications); 4) healthcare utilization (emergency department [ED] visits and hospitalization); and 5) NIOA use pattern (indication, type of NIOA, daily MME, the number of sustained release NIOA, the number of NIOA prescriptions and prescribers, and opioid use pattern before the index date). A detailed description of the definition of the independent variables was provided in a previous study by our research team (Heo et al. 2022). Additionally, the cut-off for daily MME was established based on the recommendation from the guideline (Dowell et al. 2022). Briefly, demographics were identified at the cohort entry date, and comorbidities and CCI were identified using data from 1 year before the cohort entry date. Additionally, co-medications and most of the NIOA use patterns were evaluated 1 month before the index date. The number of NIOA prescribers and prescriptions was evaluated from the cohort entry date to the index date. Considering the characteristics of the national claims database, variables that were not identified were classified as the absence of a corresponding condition.

Analysis

For descriptive statistics, we utilized percentages, means, and standard deviations. To compare the case and control groups, the chi-square test, and t-test were employed. The cumulative incidence was calculated as the number of OUD incident cases divided by the number of patients at OUD risk who newly used NIOA. The logistic regression analysis was utilized to identify the factors associated with the occurrence of OUD. Variables that had a frequency greater than 1% and a p-value of less than 0.1 in the univariate analysis were included in the multivariable analysis. The significant risk factors associated with OUD occurrence were analyzed using backward elimination in multivariable analysis. The final model was constructed with the factors identified in the multivariable analyses and demographics (age, sex, and type of insurance). Data and statistical analyses were performed using the SAS version 9.4 (SAS Institute, Cary, NC, USA), and statistical significance was set at p < 0.05.

A total of 1,725,380 adult patients were prescribed NIOA between 2017 and 2018. The cumulative incidence of OUD for 2 years after NIOA initiation was 0.006% (108 patients) (Fig. 1).

Figure 1.Patient selection flow. ICD, International Classification of Diseases; NIOA, non-injectable opioid analgesics; OUD, opioid use disorder.

The case group patients were older (61.8 ± 17.8 vs. 57.3 ± 17.7, p = 0.02) and had a higher frequency of medical aid or national meritorious services (NMS) usage (22.2% vs. 8.2%, p < 0.001) compared to the control group patients. The CCI score (4.0 ± 3.0 vs. 2.6 ± 2.7, p < 0.001) and the proportion of patients with a mood disorder, anxiety, dementia, peripheral vascular disease, diabetes mellitus, or cancer in the case group were significantly higher than those in the control group. The number of ED visits (1.0 ± 3.0 vs. 0.3 ± 0.6, p = 0.02) before NIOA use was also higher in the case group than in the control group (Table 1).

Table 1 Baseline characteristics of included patients

VariablesOUD (−) (N = 429)OUD (+) (N = 108)p-value
N (%)N (%)
Sex, male217 (50.6)66 (61.1)0.05
Age (years), mean ± SD57.3 ± 17.761.8 ± 17.80.02
20-44106 (24.7)18 (16.7)0.141
45-64160 (37.3)40 (37.0)
≥ 65163 (38.0)50 (46.3)
Insurance< 0.001
Medical insurance394 (91.8)84 (77.8)
Medical aid or NMS35 (8.2)24 (22.2)
Mood disorders76 (17.7)35 (32.4)< 0.001
Anxiety85 (19.8)35 (32.4)0.005
Schizophrenia6 (1.4)3 (2.8)0.318
Substance use disorders5 (1.2)3 (2.8)0.216
Sleep apnea3 (0.7)1 (0.9)0.807
Dementia35 (8.2)16 (14.8)0.035
Hypertension195 (45.5)60 (55.6)0.06
Chronic heart failure40 (9.3)15 (13.9)0.162
Myocardial infarction5 (1.2)3 (2.8)0.216
Cerebrovascular disease57 (13.3)21 (19.4)0.105
Peripheral vascular disease67 (15.6)26 (24.1)0.038
Diabetes mellitus116 (27)48 (44.4)< 0.001
Respiratory disease188 (43.8)46 (42.6)0.818
Severe renal disease13 (3)2 (1.9)0.507
Moderate to severe liver disease1 (0.2)1 (0.9)0.291
Cancer68 (15.9)40 (37)< 0.001
Charlson comorbidity index, mean ± SD2.6 ± 2.74.0 ± 3.0< 0.001
0-2265 (61.8)40 (37)< 0.001
3-488 (20.5)29 (26.9)
5-638 (8.9)17 (15.7)
≥ 738 (8.9)22 (20.4)
Number of ED visits0.3 ± 0.61.0 ± 3.0< 0.001
0345 (80.4)76 (70.4)< 0.001
1-382 (19.1)24 (22.2)
≥ 42 (0.5)8 (7.4)
Length of hospitalization (days), mean ± SD8.9 ± 39.39.2 ± 17.40.888
0289 (67.4)63 (58.3)< 0.001
1-765 (15.2)9 (8.3)
≥ 875 (17.5)36 (33.3)

ED, emergency department; NMS, national meritorious service; OUD, opioid use disorder; SD, standard deviation.


Benzodiazepines, gabapentinoids, hypnotics, antidepressants, antipsychotics, other antiepileptics, tramadol, and other analgesics were significantly more prescribed in patients with OUD than in those without OUD. Other analgesics and tramadol were the most frequently prescribed. A higher proportion of patients with OUD had indications other than trauma or surgery (74.1% vs. 63.2%) than that of those without OUD. One month before the index date, the most frequently used NIOA was fentanyl (36.1%) in patients with OUD; in contrast, codeine (17.3%) was the most frequently used NIOA in those without OUD. Additionally, approximately 70% of patients with OUD used a sustained-release formulation of NIOA, which was significantly higher than that of those without OUD (66.7% vs. 17.0%, p < 0.001). Patients with OUD were prescribed NIOA more frequently (5.5 ± 5.4 vs. 1.7 ± 1.6, p < 0.001) from more prescribers (1.7 ± 1.3 vs. 1.1 ± 0.3, p < 0.001) since NIOA initiation than those without OUD. The daily NIOA dose as measured by MME (66.6 ± 06.5 vs. 7.2 ± 25.1, p < 0.001) was significantly higher in patients with OUD, and the NIOA use pattern was highly different between the two groups (persistent users; 45.4% vs. 7.0%) (Table 2).

Table 2 NIOA use pattern and co-medication in included patients

VariablesOUD (−) (N = 429)OUD (+) (N = 108)p-value
N (%)N (%)
Benzodiazepine58 (13.5)44 (40.7)< 0.001
Gabapentinoids33 (7.7)24 (22.2)< 0.001
Hypnotics22 (5.1)22 (20.4)< 0.001
Antidepressants45 (10.5)29 (26.9)< 0.001
Other anxiolytics8 (1.9)5 (4.6)0.095
Other antipsychotics16 (3.7)13 (12)< 0.001
Other antiepileptics7 (1.6)6 (5.6)0.018
Tramadol92 (21.5)45 (41.7)< 0.001
Other analgesics255 (59.4)78 (72.2)0.014
Muscle relaxants70 (16.3)22 (20.4)0.318
Indication of opioid initiation
Trauma57 (13.3)8 (7.4)0.082
Surgery101 (23.5)20 (18.5)
Other271 (63.2)80 (74.1)
Type of NIOA
Buprenorphine30 (7)19 (17.6)< 0.001
Codeine74 (17.3)13 (12)0.189
Dihydrocodeine3 (0.7)1 (0.9)0.807
Fentanyl25 (5.8)39 (36.1)< 0.001
Hydrocodone7 (1.6)0.181
Hydromorphone7 (1.6)7 (6.5)0.005
Morphine1 (0.9)0.046
Oxycodone29 (6.8)36 (33.3)< 0.001
Tapentadol2 (0.5)6 (5.6)< 0.001
Number of SR opioid0.2 ± 0.50.8 ± 0.7< 0.001
0356 (83)36 (33.3)< 0.001
≥ 173 (17)72 (66.7)
Opioid use pattern before index date
No242 (56.4)19 (17.6)< 0.001
Past33 (7.7)3 (2.8)
New124 (28.9)37 (34.3)
Persistent30 (7)49 (45.4)
Number of NIOA prescribers1.1 ± 0.31.7 ± 1.3< 0.001
1394 (91.8)66 (61.1)< 0.001
232 (7.5)24 (22.2)
≥ 33 (0.7)18 (16.7)
Number of opioid Rx1.7 ± 1.65.5 ± 5.4< 0.001
1-2373 (87)50 (46.3)< 0.001
3-640 (9.3)20 (18.5)
7-912 (2.8)12 (11.1)
≥ 104 (0.9)26 (24.1)
Daily MME7.2 ± 25.166.6 ± 106.5< 0.001
0-20392 (91.4)47 (43.5)< 0.001
20-4927 (6.3)25 (23.2)
≥ 5010 (2.3)36 (33.3)

MME, morphine milligram equivalent; NIOA, non-injectable opioid analgesics; OUD, opioid use disorder; Rx, prescriptions; SR: sustained release.

No: no active NIOA prescription for 2 months (−60 to −1 days), new: new active NIOA prescription 1 month (−30 to −1 days), past: active NIOA prescription ended before 1 month (−60 to −31 days), and persistent: active NIOA prescription for 2 months (−60 to −1 days) before the index date.


After adjusting for comorbidities, CCI, co-medications, and NIOA use pattern, daily MME, number of NIOA prescriptions, sustained-release formulation, benzodiazepine use, and insurance were significantly associated with OUD occurrence. In particular, frequent NIOA prescriptions (≥ 10; aOR 19.05, 95% CI 5.53-65.58) and higher daily MME increased the risk of OUD occurrence (≥ 50 MME/day; aOR 5.04, 95% CI 1.92-13.25). The use of benzodiazepines (aOR 3.12, 95% CI 1.71-5.68) and sustained-release formulations (aOR 3.5, 95% CI 1.8-6.78) increased the risk of OUD occurrence approximately by threefold. Age and sex were not associated with OUD occurrence (Fig. 2).

Figure 2.Risk factors of opioid use disorder occurrence in naïve patients for NIOA. aOR, adjusted odds ratio; MME, morphine milligram equivalent; NIOA, non-injectable opioid analgesics; NMS, national meritorious service; Rx, prescriptions.

In this study, we identified the cumulative incidence of OUD and its associated risk factors in the population initiating NIOA using the national claims database, covering almost the entire Korean population. To the best of our knowledge, this is the first study to report the incidence and risk factors of OUD in Korean patients who were prescribed with NIOAs based on an ICD-10 code with a high positive predictive value for OUD identification.

The incidence of OUD in this study (0.006%) was lower than that reported in a previous systematic review (SR) reporting a pooled incidence of iatrogenic OUD (4.7%; the event rate of each study included in the SR ranged from 0.5% to 34.2%) (Higgins et al. 2018). This might be associated with the lower use of opioid analgesics in Korea than in other developed countries and a shorter study period than the known mean time to onset of OUD occurrence (3.41 ± 2.39 years) (Ju et al. 2022; Kendler et al. 2022). The incidence and prevalence of opioid abuse in non-cancer Korean patients with chronic opioid analgesic use were reported as 19.93/100 person-years and 5.0%, respectively (Kim and Suh 2023), which was higher than the results of our study. This discrepancy could be associated with differences in OUD definitions and study populations. We defined OUD case based on ICD-10 codes, while the study mentioned earlier used doctors shopping or prescription duplication. The study's populations also consisted of individuals who were chronic users of opioid analgesics rather than new users. Considering an aOR of 19.05 (95% CI 5.53-65.58) for OUD onset in patients who have been prescribed opioid analgesics more than 10 times in our study, the higher incidence of opioid abuse in chronic users of opioid analgesics in the previous study could be explained. Identifying the exact OUD is important to identify the risk factors associated with OUD more accurately. Therefore, to define the OUD case, we used the ICD-10 codes (F11), which had more than 90% positive predictive value (96.3%, 95% CI 90.8-98.6), although the sensitivity was moderate (42.6%, 95% CI 36.3-49.1) (Campanile and Silverman 2022).

Among the known risk factors for OUD, benzodiazepine use, opioid dose, economic status, and frequent NIOA prescriptions were re-confirmed in this study (Rice et al. 2012; Cragg et al. 2019; Kim and Suh 2023). In line with our results, Rice et al. reported that frequent NIOA prescription was the strongest risk factor of OUD occurrence in prescription opioid users (aOR 6.85, 95% CI 5.98-7.85) (Rice et al. 2012). Medical aid (adjusted hazard ratio 1.13, 95% CI 1.07-1.19) was also reported as a significant risk factor in another study in Korea (Kim and Suh 2023). Common medication classes concomitantly misused in patients with OUD are sedatives, hypnotics, or anxiolytics. Furthermore, the risk of serious harm, such as respiratory depression, increases in patients co-misusing other medications (The ASAM National Practice Guideline for the treatment of opioid use disorder 2020). In particular, the concurrent use of opioids and benzodiazepines and high-dose opioid prescriptions have been presented as high-risk prescriptions in terms of opioid overdose or respiratory depression in the Centers for Disease Control and Prevention guidelines (Dowell et al. 2022). The use of sustained release formulation of NIOA was also reported as a significant risk factor (aOR 1.9, 95% CI 1.1-3.2) for opioid poisoning or overdose in a previous study. However, unlike previous studies, other risk factors, such as substance abuse, psychiatric disorders, male sex, and younger age, were not identified in this present study (Rice et al. 2012; Cragg et al. 2019). These differences with previous studies are difficult to explain; however, they might be correlated with the extent of opioid use.

When interpreting the results of the present study, several limitations should be considered. First, the low cumulative incidence of OUD might be associated with the observation period. To confirm this, additional studies with longer durations are required. Second, we could not confirm whether the patients used the prescribed NIOA because of the characteristics of the claims database. However, since claim databases are used for medication adherence measurement, the discrepancy between prescription and medication-taking behavior might be minimal. Additionally, although illegal drug use is a major risk factor for OUD occurrence, we could not consider illegal drug use for OUD occurrence owing to a lack of information in the database. Likewise, we could not consider other patient characteristics such as alcohol use, smoking, education status, and environmental factors. Lastly, given the labeled indication of codeine, some patients prescribed with codeine might have used it as an antitussive rather than as an analgesic.

In conclusion, the incidence of OUD in patients using NIOA in Korea, identified using the diagnostic code, was lower than that in other countries. Moreover, to prevent and detect OUD in Korean patients using NIOA, close monitoring should be considered with any of the following aspects: concurrent use of benzodiazepines, frequent prescription of NIOA, insurance type as medical aid or NMS, high dose of NIOA, and use of sustained-release formulation. Considering the limitations of this study, additional studies using long-term data to evaluate various factors for OUD are needed.

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP; Ministry of Science, ICT, & Future Planning) (No. NRF-2019R1G1A1011055). The funder had no role in the study.

The authors declare that they have no conflict of interest.

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Article

Original Research Article

DTT 2023; 2(2): 103-110

Published online September 30, 2023 https://doi.org/10.58502/DTT.23.0011

Copyright © The Pharmaceutical Society of Korea.

Incidence and Risk Factors of Opioid Use Disorder in New Users of Opioid Analgesics in Korea: Analysis of a National Claims Database

Kyu-Nam Heo1, Won-Bean Jo2, Yoojin Noh3, Ju-Yeun Lee1, Young-Mi Ah2

1College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
2College of Pharmacy, Yeungnam University, Gyeungsan, Republic of Korea
3Pharmacy School, Massachusetts College of Pharmacy and Health Sciences University, Worcester, MA, USA

Correspondence to:Young-Mi Ah, ymah@ynu.ac.kr

Received: April 3, 2023; Revised: June 8, 2023; Accepted: June 11, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ((http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Opioid analgesics are essential medications for severe pain; however, their use can cause various adverse events. Opioid use disorder (OUD) is a serious challenge associated with infectious diseases, injuries, and deaths. This study aimed to identify the incidence of OUD and its associated factors in Korean adults who were new users of non-injectable opioid analgesics (NIOA) using data from the national claims database provided by Health Insurance Review and Assessment Services from 2016 to 2018. Patients who received a new diagnosis of OUD after NIOA initiation, defined by the International Classification of Diseases-10 were classified as the case group. We used the exact match method to select a control group of patients without OUD in a 1:4 ratio. The cumulative incidence of OUD for 2 years after NIOA initiation was 0.006%. Factors associated with OUD occurrence included daily morphine milligram equivalents (≥ 50 MME/day; adjusted odds ratio [aOR] 5.04, 95% confidence interval [CI] 1.92-13.25), number of NIOA prescriptions (≥ 10; aOR 19.05, 95% CI 5.53-65.58), and use of sustained-release formulation (aOR 3.5, 95% CI 1.8-6.78). Benzodiazepine uses and type of insurance were also significantly associated with OUD occurrence. The incidence of OUD in patients using NIOA identified through diagnostic codes in Korea was lower than that in other countries. Close monitoring is recommended for patients with identified risk factors.

Keywords: opioid use disorder, opioid analgesics, risk factors, national claims database, incidence

Introduction

Opioid analgesics are essential for controlling severe pain; however, they can cause various adverse events, ranging from mild to severe (Benyamin et al. 2008; World Health Organization 2017). With the increase in opioid analgesic use, serious negative outcomes, such as opioid overdose death and dependence, have been raised, especially in North America (Bolash 2015). Despite several efforts to control opioid-related problems, the opioid crisis in North America has continued, and the illicit use of opioid analgesics has also increased in Europe and Africa (United Nations Office on Drugs and Crime 2020).

Opioid use disorder (OUD) refers to a problematic pattern of opioid use, including abuse, dependence, and addiction. It is associated with serious potential consequences, including infectious disease, injury, and death (Degenhardt et al. 2019). Opioid dependence (353 cases/100,000 individuals) was reported as one of the most common drug use disorders in a study using worldwide data. The prevalence of OUD varied according to geographical location, with the highest rate in North America (1,168.3 cases/100,000 individuals) and the lowest in Central Europe (151.3 cases/100,000 individuals) (GBD 2016 Alcohol and Drug Use Collaborators 2018). A systematic review reported that the incidence of de novo OUD in patients administered opioid analgesics was 4.7% (Higgins et al. 2018). To reduce the dilemma between appropriate pain control and minimizing the risk of OUD in patients prescribed opioid analgesics, the risk factors associated with OUD should be identified. Several studies have reported the risk factors and prediction tools for OUD. Age, chronic opioid use, psychiatric disease, other substance abuse, and opioid dose are known to be associated with OUD onset; however, these studies were reported primarily in North America (Webster and Webster 2005; Butler et al. 2008; Rice et al. 2012).

Although the use of opioid analgesics in Korea is lower compared to other developed countries, the recent average annual percentage change of opioid analgesic use has increased in Korea (30.28 morphine milligram equivalent [MME] /1,000 individuals/day), while it has decreased in North America (−12.84 MME/1,000 individuals/day) (Ju et al. 2022). The increase in opioid analgesic use has been reported in several studies using domestic data (Cho et al. 2021; Noh et al. 2022). Additionally, OUD prevalence in East Asia, including Korea, was 285.8 cases per 100,000 individuals, which is not low (GBD 2016 Alcohol and Drug Use Collaborators 2018). However, only a few studies have investigated the incidence of OUD in patients exposed to opioid analgesics and its associated factors in Korea. Therefore, we aimed to identify the incidence of OUD in new opioid analgesic users within 2 years and its associated factors.

Materials and Methods

Study participants and outcome definition

In this nested case-control study, we used the national claims database provided by the Health Insurance Review and Assessment Services (HIRA) from 2016 to 2018. The National Health Insurance Service is implemented in Korea and covers almost the entire population, and HIRA is the only institution reviewing claims from all healthcare institutes. Basic demographics, diagnoses, healthcare utilization, procedures performed, and prescription information can be identified in this database.

Adult patients who were newly prescribed with non-injectable opioid analgesics (NIOA) (ATC codes: N02A and R05DA04) from 2017 to 2018 were included in the cohort group. Patients who had no NIOA prescription in 2016 were considered new NIOA users. Fixed-dose combination drugs that contain codeine or dihydrocodeine and are only approved for treating coughs were not included in this study. The first date of the NIOA prescription was defined as the cohort entry date. Patients newly diagnosed with OUD after NIOA initiation were classified into the case group, as defined by the International Classification of Diseases (ICD)-10 (F11). The first date of OUD diagnosis was defined as the index date. We selected a control group among patients without OUD in a ratio of 1:4, using the exact match method with the cohort entry date and follow-up period (from the cohort entry date to the last claim date). The exclusion criteria were as follows: 1) patients who had an NIOA prescription in 2016, 2) those who were diagnosed with OUD before the cohort entry date, and 3) those who were prescribed injectable opioids at the index date. This study was approved by the Yeungnam University Institutional Review Board (approval number: YU 2019-01-001).

Variables

We identified the following variables: 1) demographics (age, sex, and type of insurance); 2) comorbidities (mood disorder, anxiety, dementia, and hypertension) and Charlson Comorbidity Index (CCI); 3) co-medications (benzodiazepines, hypnotics, gabapentinoids, tramadol, and other pain medications); 4) healthcare utilization (emergency department [ED] visits and hospitalization); and 5) NIOA use pattern (indication, type of NIOA, daily MME, the number of sustained release NIOA, the number of NIOA prescriptions and prescribers, and opioid use pattern before the index date). A detailed description of the definition of the independent variables was provided in a previous study by our research team (Heo et al. 2022). Additionally, the cut-off for daily MME was established based on the recommendation from the guideline (Dowell et al. 2022). Briefly, demographics were identified at the cohort entry date, and comorbidities and CCI were identified using data from 1 year before the cohort entry date. Additionally, co-medications and most of the NIOA use patterns were evaluated 1 month before the index date. The number of NIOA prescribers and prescriptions was evaluated from the cohort entry date to the index date. Considering the characteristics of the national claims database, variables that were not identified were classified as the absence of a corresponding condition.

Analysis

For descriptive statistics, we utilized percentages, means, and standard deviations. To compare the case and control groups, the chi-square test, and t-test were employed. The cumulative incidence was calculated as the number of OUD incident cases divided by the number of patients at OUD risk who newly used NIOA. The logistic regression analysis was utilized to identify the factors associated with the occurrence of OUD. Variables that had a frequency greater than 1% and a p-value of less than 0.1 in the univariate analysis were included in the multivariable analysis. The significant risk factors associated with OUD occurrence were analyzed using backward elimination in multivariable analysis. The final model was constructed with the factors identified in the multivariable analyses and demographics (age, sex, and type of insurance). Data and statistical analyses were performed using the SAS version 9.4 (SAS Institute, Cary, NC, USA), and statistical significance was set at p < 0.05.

Results

A total of 1,725,380 adult patients were prescribed NIOA between 2017 and 2018. The cumulative incidence of OUD for 2 years after NIOA initiation was 0.006% (108 patients) (Fig. 1).

Figure 1. Patient selection flow. ICD, International Classification of Diseases; NIOA, non-injectable opioid analgesics; OUD, opioid use disorder.

The case group patients were older (61.8 ± 17.8 vs. 57.3 ± 17.7, p = 0.02) and had a higher frequency of medical aid or national meritorious services (NMS) usage (22.2% vs. 8.2%, p < 0.001) compared to the control group patients. The CCI score (4.0 ± 3.0 vs. 2.6 ± 2.7, p < 0.001) and the proportion of patients with a mood disorder, anxiety, dementia, peripheral vascular disease, diabetes mellitus, or cancer in the case group were significantly higher than those in the control group. The number of ED visits (1.0 ± 3.0 vs. 0.3 ± 0.6, p = 0.02) before NIOA use was also higher in the case group than in the control group (Table 1).

Table 1 . Baseline characteristics of included patients.

VariablesOUD (−) (N = 429)OUD (+) (N = 108)p-value
N (%)N (%)
Sex, male217 (50.6)66 (61.1)0.05
Age (years), mean ± SD57.3 ± 17.761.8 ± 17.80.02
20-44106 (24.7)18 (16.7)0.141
45-64160 (37.3)40 (37.0)
≥ 65163 (38.0)50 (46.3)
Insurance< 0.001
Medical insurance394 (91.8)84 (77.8)
Medical aid or NMS35 (8.2)24 (22.2)
Mood disorders76 (17.7)35 (32.4)< 0.001
Anxiety85 (19.8)35 (32.4)0.005
Schizophrenia6 (1.4)3 (2.8)0.318
Substance use disorders5 (1.2)3 (2.8)0.216
Sleep apnea3 (0.7)1 (0.9)0.807
Dementia35 (8.2)16 (14.8)0.035
Hypertension195 (45.5)60 (55.6)0.06
Chronic heart failure40 (9.3)15 (13.9)0.162
Myocardial infarction5 (1.2)3 (2.8)0.216
Cerebrovascular disease57 (13.3)21 (19.4)0.105
Peripheral vascular disease67 (15.6)26 (24.1)0.038
Diabetes mellitus116 (27)48 (44.4)< 0.001
Respiratory disease188 (43.8)46 (42.6)0.818
Severe renal disease13 (3)2 (1.9)0.507
Moderate to severe liver disease1 (0.2)1 (0.9)0.291
Cancer68 (15.9)40 (37)< 0.001
Charlson comorbidity index, mean ± SD2.6 ± 2.74.0 ± 3.0< 0.001
0-2265 (61.8)40 (37)< 0.001
3-488 (20.5)29 (26.9)
5-638 (8.9)17 (15.7)
≥ 738 (8.9)22 (20.4)
Number of ED visits0.3 ± 0.61.0 ± 3.0< 0.001
0345 (80.4)76 (70.4)< 0.001
1-382 (19.1)24 (22.2)
≥ 42 (0.5)8 (7.4)
Length of hospitalization (days), mean ± SD8.9 ± 39.39.2 ± 17.40.888
0289 (67.4)63 (58.3)< 0.001
1-765 (15.2)9 (8.3)
≥ 875 (17.5)36 (33.3)

ED, emergency department; NMS, national meritorious service; OUD, opioid use disorder; SD, standard deviation..



Benzodiazepines, gabapentinoids, hypnotics, antidepressants, antipsychotics, other antiepileptics, tramadol, and other analgesics were significantly more prescribed in patients with OUD than in those without OUD. Other analgesics and tramadol were the most frequently prescribed. A higher proportion of patients with OUD had indications other than trauma or surgery (74.1% vs. 63.2%) than that of those without OUD. One month before the index date, the most frequently used NIOA was fentanyl (36.1%) in patients with OUD; in contrast, codeine (17.3%) was the most frequently used NIOA in those without OUD. Additionally, approximately 70% of patients with OUD used a sustained-release formulation of NIOA, which was significantly higher than that of those without OUD (66.7% vs. 17.0%, p < 0.001). Patients with OUD were prescribed NIOA more frequently (5.5 ± 5.4 vs. 1.7 ± 1.6, p < 0.001) from more prescribers (1.7 ± 1.3 vs. 1.1 ± 0.3, p < 0.001) since NIOA initiation than those without OUD. The daily NIOA dose as measured by MME (66.6 ± 06.5 vs. 7.2 ± 25.1, p < 0.001) was significantly higher in patients with OUD, and the NIOA use pattern was highly different between the two groups (persistent users; 45.4% vs. 7.0%) (Table 2).

Table 2 . NIOA use pattern and co-medication in included patients.

VariablesOUD (−) (N = 429)OUD (+) (N = 108)p-value
N (%)N (%)
Benzodiazepine58 (13.5)44 (40.7)< 0.001
Gabapentinoids33 (7.7)24 (22.2)< 0.001
Hypnotics22 (5.1)22 (20.4)< 0.001
Antidepressants45 (10.5)29 (26.9)< 0.001
Other anxiolytics8 (1.9)5 (4.6)0.095
Other antipsychotics16 (3.7)13 (12)< 0.001
Other antiepileptics7 (1.6)6 (5.6)0.018
Tramadol92 (21.5)45 (41.7)< 0.001
Other analgesics255 (59.4)78 (72.2)0.014
Muscle relaxants70 (16.3)22 (20.4)0.318
Indication of opioid initiation
Trauma57 (13.3)8 (7.4)0.082
Surgery101 (23.5)20 (18.5)
Other271 (63.2)80 (74.1)
Type of NIOA
Buprenorphine30 (7)19 (17.6)< 0.001
Codeine74 (17.3)13 (12)0.189
Dihydrocodeine3 (0.7)1 (0.9)0.807
Fentanyl25 (5.8)39 (36.1)< 0.001
Hydrocodone7 (1.6)0.181
Hydromorphone7 (1.6)7 (6.5)0.005
Morphine1 (0.9)0.046
Oxycodone29 (6.8)36 (33.3)< 0.001
Tapentadol2 (0.5)6 (5.6)< 0.001
Number of SR opioid0.2 ± 0.50.8 ± 0.7< 0.001
0356 (83)36 (33.3)< 0.001
≥ 173 (17)72 (66.7)
Opioid use pattern before index date
No242 (56.4)19 (17.6)< 0.001
Past33 (7.7)3 (2.8)
New124 (28.9)37 (34.3)
Persistent30 (7)49 (45.4)
Number of NIOA prescribers1.1 ± 0.31.7 ± 1.3< 0.001
1394 (91.8)66 (61.1)< 0.001
232 (7.5)24 (22.2)
≥ 33 (0.7)18 (16.7)
Number of opioid Rx1.7 ± 1.65.5 ± 5.4< 0.001
1-2373 (87)50 (46.3)< 0.001
3-640 (9.3)20 (18.5)
7-912 (2.8)12 (11.1)
≥ 104 (0.9)26 (24.1)
Daily MME7.2 ± 25.166.6 ± 106.5< 0.001
0-20392 (91.4)47 (43.5)< 0.001
20-4927 (6.3)25 (23.2)
≥ 5010 (2.3)36 (33.3)

MME, morphine milligram equivalent; NIOA, non-injectable opioid analgesics; OUD, opioid use disorder; Rx, prescriptions; SR: sustained release..

No: no active NIOA prescription for 2 months (−60 to −1 days), new: new active NIOA prescription 1 month (−30 to −1 days), past: active NIOA prescription ended before 1 month (−60 to −31 days), and persistent: active NIOA prescription for 2 months (−60 to −1 days) before the index date..



After adjusting for comorbidities, CCI, co-medications, and NIOA use pattern, daily MME, number of NIOA prescriptions, sustained-release formulation, benzodiazepine use, and insurance were significantly associated with OUD occurrence. In particular, frequent NIOA prescriptions (≥ 10; aOR 19.05, 95% CI 5.53-65.58) and higher daily MME increased the risk of OUD occurrence (≥ 50 MME/day; aOR 5.04, 95% CI 1.92-13.25). The use of benzodiazepines (aOR 3.12, 95% CI 1.71-5.68) and sustained-release formulations (aOR 3.5, 95% CI 1.8-6.78) increased the risk of OUD occurrence approximately by threefold. Age and sex were not associated with OUD occurrence (Fig. 2).

Figure 2. Risk factors of opioid use disorder occurrence in naïve patients for NIOA. aOR, adjusted odds ratio; MME, morphine milligram equivalent; NIOA, non-injectable opioid analgesics; NMS, national meritorious service; Rx, prescriptions.

Discussion

In this study, we identified the cumulative incidence of OUD and its associated risk factors in the population initiating NIOA using the national claims database, covering almost the entire Korean population. To the best of our knowledge, this is the first study to report the incidence and risk factors of OUD in Korean patients who were prescribed with NIOAs based on an ICD-10 code with a high positive predictive value for OUD identification.

The incidence of OUD in this study (0.006%) was lower than that reported in a previous systematic review (SR) reporting a pooled incidence of iatrogenic OUD (4.7%; the event rate of each study included in the SR ranged from 0.5% to 34.2%) (Higgins et al. 2018). This might be associated with the lower use of opioid analgesics in Korea than in other developed countries and a shorter study period than the known mean time to onset of OUD occurrence (3.41 ± 2.39 years) (Ju et al. 2022; Kendler et al. 2022). The incidence and prevalence of opioid abuse in non-cancer Korean patients with chronic opioid analgesic use were reported as 19.93/100 person-years and 5.0%, respectively (Kim and Suh 2023), which was higher than the results of our study. This discrepancy could be associated with differences in OUD definitions and study populations. We defined OUD case based on ICD-10 codes, while the study mentioned earlier used doctors shopping or prescription duplication. The study's populations also consisted of individuals who were chronic users of opioid analgesics rather than new users. Considering an aOR of 19.05 (95% CI 5.53-65.58) for OUD onset in patients who have been prescribed opioid analgesics more than 10 times in our study, the higher incidence of opioid abuse in chronic users of opioid analgesics in the previous study could be explained. Identifying the exact OUD is important to identify the risk factors associated with OUD more accurately. Therefore, to define the OUD case, we used the ICD-10 codes (F11), which had more than 90% positive predictive value (96.3%, 95% CI 90.8-98.6), although the sensitivity was moderate (42.6%, 95% CI 36.3-49.1) (Campanile and Silverman 2022).

Among the known risk factors for OUD, benzodiazepine use, opioid dose, economic status, and frequent NIOA prescriptions were re-confirmed in this study (Rice et al. 2012; Cragg et al. 2019; Kim and Suh 2023). In line with our results, Rice et al. reported that frequent NIOA prescription was the strongest risk factor of OUD occurrence in prescription opioid users (aOR 6.85, 95% CI 5.98-7.85) (Rice et al. 2012). Medical aid (adjusted hazard ratio 1.13, 95% CI 1.07-1.19) was also reported as a significant risk factor in another study in Korea (Kim and Suh 2023). Common medication classes concomitantly misused in patients with OUD are sedatives, hypnotics, or anxiolytics. Furthermore, the risk of serious harm, such as respiratory depression, increases in patients co-misusing other medications (The ASAM National Practice Guideline for the treatment of opioid use disorder 2020). In particular, the concurrent use of opioids and benzodiazepines and high-dose opioid prescriptions have been presented as high-risk prescriptions in terms of opioid overdose or respiratory depression in the Centers for Disease Control and Prevention guidelines (Dowell et al. 2022). The use of sustained release formulation of NIOA was also reported as a significant risk factor (aOR 1.9, 95% CI 1.1-3.2) for opioid poisoning or overdose in a previous study. However, unlike previous studies, other risk factors, such as substance abuse, psychiatric disorders, male sex, and younger age, were not identified in this present study (Rice et al. 2012; Cragg et al. 2019). These differences with previous studies are difficult to explain; however, they might be correlated with the extent of opioid use.

When interpreting the results of the present study, several limitations should be considered. First, the low cumulative incidence of OUD might be associated with the observation period. To confirm this, additional studies with longer durations are required. Second, we could not confirm whether the patients used the prescribed NIOA because of the characteristics of the claims database. However, since claim databases are used for medication adherence measurement, the discrepancy between prescription and medication-taking behavior might be minimal. Additionally, although illegal drug use is a major risk factor for OUD occurrence, we could not consider illegal drug use for OUD occurrence owing to a lack of information in the database. Likewise, we could not consider other patient characteristics such as alcohol use, smoking, education status, and environmental factors. Lastly, given the labeled indication of codeine, some patients prescribed with codeine might have used it as an antitussive rather than as an analgesic.

In conclusion, the incidence of OUD in patients using NIOA in Korea, identified using the diagnostic code, was lower than that in other countries. Moreover, to prevent and detect OUD in Korean patients using NIOA, close monitoring should be considered with any of the following aspects: concurrent use of benzodiazepines, frequent prescription of NIOA, insurance type as medical aid or NMS, high dose of NIOA, and use of sustained-release formulation. Considering the limitations of this study, additional studies using long-term data to evaluate various factors for OUD are needed.

Acknowledgements

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP; Ministry of Science, ICT, & Future Planning) (No. NRF-2019R1G1A1011055). The funder had no role in the study.

Conflict of interest

The authors declare that they have no conflict of interest.

Fig 1.

Figure 1.Patient selection flow. ICD, International Classification of Diseases; NIOA, non-injectable opioid analgesics; OUD, opioid use disorder.
Drug Targets and Therapeutics 2023; 2: 103-110https://doi.org/10.58502/DTT.23.0011

Fig 2.

Figure 2.Risk factors of opioid use disorder occurrence in naïve patients for NIOA. aOR, adjusted odds ratio; MME, morphine milligram equivalent; NIOA, non-injectable opioid analgesics; NMS, national meritorious service; Rx, prescriptions.
Drug Targets and Therapeutics 2023; 2: 103-110https://doi.org/10.58502/DTT.23.0011

Table 1 Baseline characteristics of included patients

VariablesOUD (−) (N = 429)OUD (+) (N = 108)p-value
N (%)N (%)
Sex, male217 (50.6)66 (61.1)0.05
Age (years), mean ± SD57.3 ± 17.761.8 ± 17.80.02
20-44106 (24.7)18 (16.7)0.141
45-64160 (37.3)40 (37.0)
≥ 65163 (38.0)50 (46.3)
Insurance< 0.001
Medical insurance394 (91.8)84 (77.8)
Medical aid or NMS35 (8.2)24 (22.2)
Mood disorders76 (17.7)35 (32.4)< 0.001
Anxiety85 (19.8)35 (32.4)0.005
Schizophrenia6 (1.4)3 (2.8)0.318
Substance use disorders5 (1.2)3 (2.8)0.216
Sleep apnea3 (0.7)1 (0.9)0.807
Dementia35 (8.2)16 (14.8)0.035
Hypertension195 (45.5)60 (55.6)0.06
Chronic heart failure40 (9.3)15 (13.9)0.162
Myocardial infarction5 (1.2)3 (2.8)0.216
Cerebrovascular disease57 (13.3)21 (19.4)0.105
Peripheral vascular disease67 (15.6)26 (24.1)0.038
Diabetes mellitus116 (27)48 (44.4)< 0.001
Respiratory disease188 (43.8)46 (42.6)0.818
Severe renal disease13 (3)2 (1.9)0.507
Moderate to severe liver disease1 (0.2)1 (0.9)0.291
Cancer68 (15.9)40 (37)< 0.001
Charlson comorbidity index, mean ± SD2.6 ± 2.74.0 ± 3.0< 0.001
0-2265 (61.8)40 (37)< 0.001
3-488 (20.5)29 (26.9)
5-638 (8.9)17 (15.7)
≥ 738 (8.9)22 (20.4)
Number of ED visits0.3 ± 0.61.0 ± 3.0< 0.001
0345 (80.4)76 (70.4)< 0.001
1-382 (19.1)24 (22.2)
≥ 42 (0.5)8 (7.4)
Length of hospitalization (days), mean ± SD8.9 ± 39.39.2 ± 17.40.888
0289 (67.4)63 (58.3)< 0.001
1-765 (15.2)9 (8.3)
≥ 875 (17.5)36 (33.3)

ED, emergency department; NMS, national meritorious service; OUD, opioid use disorder; SD, standard deviation.


Table 2 NIOA use pattern and co-medication in included patients

VariablesOUD (−) (N = 429)OUD (+) (N = 108)p-value
N (%)N (%)
Benzodiazepine58 (13.5)44 (40.7)< 0.001
Gabapentinoids33 (7.7)24 (22.2)< 0.001
Hypnotics22 (5.1)22 (20.4)< 0.001
Antidepressants45 (10.5)29 (26.9)< 0.001
Other anxiolytics8 (1.9)5 (4.6)0.095
Other antipsychotics16 (3.7)13 (12)< 0.001
Other antiepileptics7 (1.6)6 (5.6)0.018
Tramadol92 (21.5)45 (41.7)< 0.001
Other analgesics255 (59.4)78 (72.2)0.014
Muscle relaxants70 (16.3)22 (20.4)0.318
Indication of opioid initiation
Trauma57 (13.3)8 (7.4)0.082
Surgery101 (23.5)20 (18.5)
Other271 (63.2)80 (74.1)
Type of NIOA
Buprenorphine30 (7)19 (17.6)< 0.001
Codeine74 (17.3)13 (12)0.189
Dihydrocodeine3 (0.7)1 (0.9)0.807
Fentanyl25 (5.8)39 (36.1)< 0.001
Hydrocodone7 (1.6)0.181
Hydromorphone7 (1.6)7 (6.5)0.005
Morphine1 (0.9)0.046
Oxycodone29 (6.8)36 (33.3)< 0.001
Tapentadol2 (0.5)6 (5.6)< 0.001
Number of SR opioid0.2 ± 0.50.8 ± 0.7< 0.001
0356 (83)36 (33.3)< 0.001
≥ 173 (17)72 (66.7)
Opioid use pattern before index date
No242 (56.4)19 (17.6)< 0.001
Past33 (7.7)3 (2.8)
New124 (28.9)37 (34.3)
Persistent30 (7)49 (45.4)
Number of NIOA prescribers1.1 ± 0.31.7 ± 1.3< 0.001
1394 (91.8)66 (61.1)< 0.001
232 (7.5)24 (22.2)
≥ 33 (0.7)18 (16.7)
Number of opioid Rx1.7 ± 1.65.5 ± 5.4< 0.001
1-2373 (87)50 (46.3)< 0.001
3-640 (9.3)20 (18.5)
7-912 (2.8)12 (11.1)
≥ 104 (0.9)26 (24.1)
Daily MME7.2 ± 25.166.6 ± 106.5< 0.001
0-20392 (91.4)47 (43.5)< 0.001
20-4927 (6.3)25 (23.2)
≥ 5010 (2.3)36 (33.3)

MME, morphine milligram equivalent; NIOA, non-injectable opioid analgesics; OUD, opioid use disorder; Rx, prescriptions; SR: sustained release.

No: no active NIOA prescription for 2 months (−60 to −1 days), new: new active NIOA prescription 1 month (−30 to −1 days), past: active NIOA prescription ended before 1 month (−60 to −31 days), and persistent: active NIOA prescription for 2 months (−60 to −1 days) before the index date.


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