Risk of Prescription Opioid Exposure in Children and Adolescents With Attention Deficit Hyperactivity Disorder: A Nationwide Longitudinal Study

Abstract

Background:

US studies suggest a positive association between attention deficit–hyperactivity disorder (ADHD) and the risk of exposure to prescription opioids. However, whether this association holds in Asian countries remains unclear.

Methods:

In total, 91,518 children and adolescents with ADHD and 91,518 age- and sex-matched controls were enrolled for the period of 2001 to 2009; they were followed up until the end of 2011 to determine whether they had used prescription opioids.

Results:

Children (hazard ratio [HR]: 5.83) and adolescents (HR: 3.44) with ADHD had a higher likelihood of receiving opioid prescriptions than did their matched controls. Furthermore, patients with ADHD had greater likelihoods of cumulative exposure to opioids of >14 (HR: 5.93) and >30 days (HR: 6.07).

Discussion:

Owing to the evidence of a significant link between opioid prescription use and eventual abuse, close monitoring for prescription opioid use is necessary for at-risk children and adolescents with ADHD.

Keywords

attention deficit–hyperactivity disorder prescription opioids medical conditions mental disorders

Introduction

The opioid epidemic has been a public health problem in recent decades (Coussens et al., 2019). Opioid prescription use has approximately quintupled over the past 20 years in the United States, resulting in large increases in opioid abuse (Coussens et al., 2019; Stoicea et al., 2019). According to the International Narcotics Control Board’s 2005 to 2007 data set, Taiwan’s opioid consumption increased by 55% between 2002 and 2007, from 362 to 560 defined daily doses per million people per day. Taiwan was thus ranked 56th out of 181 nations and regions in the world for opioid consumption (Pan et al., 2013). The trend of opioid prescriptions continued to rise by 41% between 2002 and 2014 in Taiwan (Kang et al., 2017). Furthermore, a US Medicaid claims analysis discovered that 10.8% of adolescents had at least one opioid prescription filled for them each year between 2016 and 2018; furthermore, half of the children who experienced opioid-related adverse events had opioid prescriptions filled in the previous 6 months (Brown et al., 2020). Studies have reported that prescription opioids are commonly the first opioids a person misuses, with up to 30% of patients prescribed opioids for chronic pain misusing them and approximately 12% of such patients developing opioid use disorder (Cicero et al., 2014; Vowles et al., 2015).

Studies have reported a positive relationship between attention deficit–hyperactivity disorder (ADHD) and prescription opioid use and misuse (Quinn et al., 2018; Wei et al., 2018; Wickens et al., 2020). A cross-sectional study analyzed 1999 to 2010 Medicaid Analytic eXtract data covering 66,406 adults with ADHD and found long-term concurrent use of prescription psychostimulants and opioids in approximately 5% of patients (Quinn et al., 2018). The study suggested that the comorbidities of substance use disorder (prevalence relative ratio [PRR]: 1.04; 95% confidence interval [CI] [1.03, 1.05]), depressive disorder (1.02; [1.01, 1.03]), and chronic pain (1.10; [1.07, 1.13]) were related to long-term use (Quinn et al., 2018). A cohort study of 1,224,520 incident opioid recipients without cancer diagnoses aged 14 to 18 years indicated that adolescent depression (hazard ratio [HR]: 3.01; 95% CI [2.73, 3.31]), nonopioid substance use disorder (4.02; [3.48, 4.65]), and ADHD (1.73; [1.54, 1.95]) were associated with long-term opioid therapy (Quinn et al., 2018). Among the 1,000,453 opioid recipients who had at least 6 months of follow-up, the cumulative incidence of long-term opioid therapy was 3.0 (95% CI [2.8, 3.1]) per 1,000 recipients within 3 years after the first opioid prescription (Quinn et al., 2018). In a cross-sectional cohort of 6074 adults (≥18 years) in Ontario, Canada, Wickens et al. (2020) demonstrated that the combination of ADHD and conduct symptoms (before age 15 years) significantly predicted medical (odds ratio [OR]: 3.27; 95% CI [1.20, 8.91]) and nonmedical (4.73; [1.05, 21.30]) prescription opioid use. However, the aforementioned studies have only been conducted in the United States, and few of them have studied children aged ≤11 years with ADHD.

By analyzing data covering a large sample drawn from the Taiwan National Health Insurance Research Database (NHIRD), this longitudinal study investigated the hypothesis that children and adolescents with ADHD are at a greater risk of exposure to prescription opioids later in life relative to non-ADHD controls. We also hypothesized that the comorbidities of ADHD-related adolescent depression and disruptive behavior disorder increase this risk.

Methods

Data Acquisition

The Taiwan National Health Research Institute (NHRI) manages the entire insurance claims database, namely the NHIRD, which comprises healthcare data from >99.7% of Taiwan’s population. The NHRI audits and releases the NHIRD for scientific studies. The insurance claim information of the subjects is anonymous to maintain privacy. Comprehensive information on insured subjects is included in the database, such as demographic data, clinical visit dates, disease diagnoses, and prescriptions. The diagnostic codes used were based on the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM). The NHIRD has been used extensively in many Taiwanese epidemiologic studies (Chen et al., 2016, 2018, 2019; Zhang et al., 2021). This study protocol was reviewed and accepted by the Institutional Review Board of Taipei Veterans General Hospital.

Inclusion Criteria for Children and Adolescents With ADHD and the Control Group

Children aged 3 to 11 years and adolescents aged 12 to 19 years who had a diagnosis of ADHD (ICD-9-CM code: 314) given by board-certified psychiatrist at least twice between January 1, 2001 and December 31, 2009 based on the comprehensive diagnostic interviews and their clinical judgment and had no history of any exposure to prescription opioids prior to ADHD diagnosis were included as the ADHD cohort. For each ADHD case, we randomly selected a 1:1 matched control after eliminating those individuals with prior diagnosis of ADHD and with prior opioid prescriptions. The ADHD cohort and control cohort were matched with respect to birthdate (±365 days), sex, time of enrollment, medical and mental comorbidities, and residence. ADHD and control cohorts were followed from the enrollment to December 31, 2011 or death for the investigation of the exposure to prescription opioids. In Taiwan, opioid medications, including morphine, meperidine, opium, propoxyphene, tramadol, fentanyl, nalbuphine, buprenorphine, and alfentanil, are strictly controlled and only prescribed by physicians having a license of prescription for controlled medications, such as pain specialists and anesthesiologists. Prescriptions of opioid medications are declared to and supervised by Taiwan Food and Drug Administration. Pain-related medical conditions, including any pain disorders, malignant cancers, major fracture, and osteoarthrosis and arthropathy, were assessed as confounders. Any pain disorders included diagnoses of dorsopathies, migraine, disorders of the peripheral nervous system, postherpetic polyneuropathy and neuralgia, fibromyalgia, and diabetic neuropathy. Major fracture was identified based on the admission diagnosis only. Psychiatric disorders, including disruptive behavior disorder, depressive disorder, alcohol use disorder, and substance use disorder, were also examined. In addition, the use of ADHD medications (methylphenidate and atomoxetine) during the follow-up was also examined respectively, and divided into three subgroups: non-users (cumulative defined daily dose [cDDD] during the follow-up <30), short-term users (cDDD = 30–364), and long-term users (cDDD ≥ 365). Income level (levels 1–3 per month: ≤19,100 NTD (New Taiwanese Dollars), 19,001–42,000 NTD, and >42,000 NTD) and urbanization level of residence (levels 1–5, most to least urbanized) were assessed as the proxy for healthcare availability in Taiwan (Liu et al., 2006). Additionally, Charlson Comorbidity Index (CCI) and all-cause clinical visits were provided for the study and the matched-control cohorts. CCI consisting of 22 physical conditions was also assessed to determine the systemic health conditions of all enrolled subjects (Charlson et al., 1987). Finally, parental depression, parental alcohol use disorder, and parental substance use disorder were also assessed as confounding factors.

Statistical Methods

Chi-square statistics and F-tests were used to compare categorical and continuous variables, respectively, between study and control cohorts. After the adjustment for demographic data (age, sex, residence, and income), medical (any pain disorders, malignant cancers, major fracture, and osteoarthrosis and arthropathy) and mental (disruptive behavior disorder, depressive disorder, alcohol use disorder, and substance use disorder) conditions, CCI scores, and family history of mental disorders (depressive disorder, alcohol use disorder, and substance use disorder), Cox regression models were performed to examine the likelihoods of exposure to prescription opioids, including hospital prescription opioids, outpatient prescription opioids, prescription intravenous or intramuscular opioids, prescription oral or transcutaneous opioids, and cumulative exposure of >14 days and >30 days of prescription opioids between ADHD and control cohorts (Zou, 2004). Additional analyses stratified by sex and age (children and adolescents) were also performed. Finally, we assessed the impacts of ADHD medications (non-users vs. short-term users vs. long-term users) on the likelihoods of exposure to prescription opioids among patients with ADHD. SAS 9.2 (SAS Institute, Cary, NC, USA) was used for all statistical analyses. All tests were two-tailed, and p < .05 was considered statistically significant.

Data Availability

As participants did not consent for their data to be publicly shared, even anonymized, data will be made available to only potential collaborators with ethical approval after they submit a research proposal to Bureau of the NHI (https://nhird.nhri.org.tw/).

Results

In total, 91,518 children and adolescents with ADHD and 91,518 age- and sex-matched controls were enrolled in current study, with a male predominance (77.6%) and a mean age of 8.5 years (Table 1). ADHD cohort had higher incidence rates of being exposed to prescription opioids (4.9 vs. 0.9%, p < .001), including intravenous or intramuscular opioids (4.2 vs. 0.7%, p < .001), oral or transcutaneous opioids (1.1 vs. 0.3%, p < .001), outpatient prescription opioids (1.9 vs. 0.4%, p < .001), and hospital prescription opioids (3.5 vs. 0.6%, p < .001), compared with the non-ADHD controls (Table 1). Patients with ADHD were more likely to have cumulative use of >14 days (0.8 vs. 0.1%, p < .001) and >30 days (0.4 vs. 0.1%, p < .001) than did the control group (Table 1). In addition, prevalence of parental depression (6.1 vs. 3.0%, p < .001), and parental alcohol (2.0 vs. 1.5%, p < .001) and substance (2.2 vs. 1.3%, p < .001) use disorders were greater in ADHD cohort than in control cohort (Table 1). Family income was higher in ADHD group than in control group (p < .001) (Table 1).

Table 1.

Demographic Characteristics and Exposure to Opioid Prescriptions Between ADHD and Control Cohorts.

	ADHD cohort (n = 91,518)	Control cohort (n = 91,518)	p-Value
Age (years, SD)	8.53 (3.16)	8.55 (3.21)	.205
Male (n, %)	70,990 (77.6)	70,990 (77.6)	1.000
Use of ADHD medications (n, %)
Non-user	44,193 (48.3)
Short-term user	32,169 (35.2)
Long-term user	15,156 (16.5)
Exposure to opioid prescriptions (n, %)
Any exposure	4,453 (4.9)	839 (0.9)	<.001
Cumulative exposure > 14 days	692 (0.8)	107 (0.1)	<.001
Cumulative exposure > 30 days	368 (0.4)	54 (0.1)	<.001
Any exposure to i.v. or i.m. prescription	3,829 (4.2)	625 (0.7)	<.001
Any exposure to oral or t.c. prescription	1,029 (1.1)	284 (0.3)	<.001
Any exposure to outpatient prescription	1,701 (1.9)	393 (0.4)	<.001
Any exposure to hospital prescription	3,194 (3.5)	509 (0.6)	<.001
Age at first exposure (years, SD)	13.76 (4.77)	16.78 (4.71)	<.001
Duration between enrollment and first exposure (years, SD)	4.12 (2.82)	5.79 (2.70)	<.001
Medical conditions (n, %)
Any pain disorder	4,688 (5.1)	4,688 (5.1)	1.000
Any osteoarthrosis and arthropathy	802 (0.9)	802 (0.9)	1.000
Any major fracture	1,148 (1.3)	1,148 (1.3)	1.000
CCI scores (SD)	0.49 (0.64)	0.42 (0.60)	<.001
Mental disorders (n, %)
Disruptive behavior disorder	432 (0.5)	432 (0.5)	1.000
Depressive disorder	2,032 (2.2)	2,032 (2.2)	1.000
Alcohol use disorder	39 (0.0)	39 (0.0)	1.000
Substance use disorder	97 (0.1)	97 (0.1)	1.000
Mental disorders of parents (n, %)
Depressive disorder	5,537 (6.1)	2,739 (3.0)	<.001
Alcohol use disorder	1,871 (2.0)	1,399 (1.5)	<.001
Substance use disorder	2,045 (2.2)	1,195 (1.3)	<.001
Level of urbanization (n, %)			1.000
1 (most urbanized)	15,304 (16.7)	15,304 (16.7)
2	27,428 (30.0)	27,428 (30.0)
3	7,995 (8.7)	7,995 (8.7)
4	7,249 (7.9)	7,249 (7.9)
5 (most rural)	33,542 (36.7)	33,542 (36.7)
Income-related insured amount (n, %)			<.001
≤19,100 NTD/month	16,960 (18.5)	17,269 (18.9)
19,001–42,000 NTD/month	34,675 (37.9)	37,986 (41.5)
>42,000 NTD/month	39,883 (43.6)	36,263 (39.6)
All-cause clinical visits (SD)	7.58 (10.78)	3.72 (4.09)	<.001

Note. SD = standard deviation; CCI = Charlson Comorbidity Index; NTD = new Taiwan dollars; i.v. = intravenous; i.m. = intramuscular; t.c. = transcutaneous.

Cox regression analyses with full adjustment of demographic characteristics, medical and mental comorbidities, CCI scores, family history of mental disorders, and all-cause clinical visits demonstrated that children (HR: 5.83; 95% CI [5.32, 6.39]) and adolescents (3.44; [3.03, 3.91]) with ADHD and males (5.20; [4.78, 5.65]) and females (4.04; [3.42, 4.77]) with ADHD had an elevated risk of exposure to prescription opioids later in life compared with the control group (Table 2, Figure 1). Risks of exposure to intravenous or intramuscular opioids (5.71; [5.24, 6.22]), oral or transcutaneous opioids (3.50; [3.06, 4.00]), outpatient prescription opioids (4.13; [3.70, 4.61]), and hospital prescription opioids (5.84; [5.32, 6.42]) were significantly increased in ADHD cohort compared with the control cohort (Table 2, Figure 1). In addition, patients with ADHD had greater likelihoods of cumulative exposure of >14 days (5.93; [4.83, 7.28]) and >30 days (6.07; [4.55, 8.10]) compared with non-ADHD controls (Table 3, Figure 1).

Table 2.

Risk of Exposure to Any Opioid Prescription Between ADHD and Control Cohorts.

	Exposure to opioid medication (HR, 95% CI)^#
	Any exposure	Any exposure to i.v. or i.m. prescription	Any exposure to oral or t.c. prescription	Any exposure to outpatient prescription	Any exposure to hospital prescription
Control cohort	1 (ref.)	1 (ref.)	1 (ref.)	1 (ref.)	1 (ref.)
ADHD cohort	4.99 (4.63–5.38)	5.71 (5.24–6.22)	3.50 (3.06–4.00)	4.13 (3.70–4.61)	5.84 (5.32–6.42)
Control cohort	1 (ref.)	1 (ref.)	1 (ref.)	1 (ref.)	1 (ref.)
Male ADHD cohort	5.20 (4.78–5.65)	5.87 (5.34–6.46)	3.87 (3.32–4.50)	4.55 (4.01–5.17)	5.88 (5.30–6.53)
Female ADHD cohort	4.04 (3.42–4.77)	4.81 (3.96–5.85)	2.39 (1.81–3.15)	2.69 (2.12–3.40)	5.45 (4.36–6.80)
Control cohort	1 (ref.)	1 (ref.)	1 (ref.)	1 (ref.)	1 (ref.)
Children ADHD cohort	5.83 (5.32–6.39)	6.46 (5.82–7.16)	4.30 (3.58–5.16)	4.79 (4.15–5.34)	6.73 (6.00–7.54)
Adolescent ADHD cohort	3.44 (3.03–3.91)	4.07 (3.49–4.74)	2.62 (2.15–3.19)	3.17 (2.66–3.78)	3.69 (3.35–4.69)
ADHD cohort
Non-user	1 (ref.)	1 (ref.)	1 (ref.)	1 (ref.)	1 (ref.)
Short-term user	1.03 (0.97–1.10)	1.02 (0.95–1.09)	1.17 (1.02–1.33)	1.03 (0.93–1.15)	1.04 (0.96–1.13)
Long-term user	0.95 (0.87–1.03)	0.97 (0.89–1.07)	0.87 (0.72–1.05)	0.87 (0.75–1.00) ^a	1.02 (0.92–1.12)

Note. ADHD = attention deficit hyperactivity disorder; HR = hazard ratio; CI = confidence interval; i.v. = intravenous; i.m. = intramuscular; t.c. = transcutaneous; CCI = Charlson Comorbidity Index. Bold type indicates the statistical significance (p < .05).

p = .046.

Adjusting for demographic characteristics, medical and mental conditions, CCI scores, family history of mental disorders, and all-cause clinical visits.

Figure 1.

Risk of exposure to any and long-term opioid prescriptions between ADHD and control cohorts.

Table 3.

Risk of Exposure to Long-Term Opioid Prescriptions Between ADHD and Control Cohorts.

	Exposure to opioid medication (HR, 95% CI)^#
	Cumulative exposure > 14 days	Cumulative exposure > 30 days
Control cohort	1 (ref.)	1 (ref.)
ADHD cohort	5.93 (4.83–7.28)	6.07 (4.55–8.10)
Control cohort	1 (ref.)	1 (ref.)
Male ADHD cohort	5.65 (4.52–7.06)	5.92 (4.32–8.13)
Female ADHD cohort	7.28 (4.32–12.28)	6.72 (3.34–13.53)
Control cohort	1 (ref.)	1 (ref.)
Children ADHD cohort	6.57 (5.07–8.52)	7.45 (5.11–10.84)
Adolescent ADHD cohort	4.92 (3.51–6.90)	4.56 (2.90–7.18)
ADHD cohort
Non-user	1 (ref.)	1 (ref.)
Short-term user	1.05 (0.89–1.24)	1.08 (0.86–1.35)
Long-term user	0.88 (0.70–1.10)	0.94 (0.69–1.27)

Note. ADHD = attention deficit hyperactivity disorder; HR = hazard ratio; CI = confidence interval; CCI = Charlson Comorbidity Index. Bold type indicates the statistical significance (p <.05).

Adjusting for demographic characteristics, medical and mental conditions, CCI scores, family history of mental disorders, and all-cause clinical visits.

Figure 2 showed that comorbidities of depression (HR: 1.29; 95% CI [1.15, 1.44]), alcohol use disorder (2.15; [1.89, 3.58]), substance use disorder (1.97; [1.44, 2.70]), any pain disorders (1.41; [1.30, 1.54]), osteoarthrosis and arthropathy (1.51; [1.28, 1.79]), major facture (3.73; [3.32, 4.19]), and parental substance use disorder (1.19; [1.00, 1.42]) were related to the increased risk of exposure to prescription opioids. Cumulative exposure of >14 days was related to adolescent substance use disorder (2.32; [1.43, 4.39]), and cumulative exposure of >30 days was associated with any pain disorders (1.60; [1.22, 2.08]), osteoarthrosis and arthropathy (2.61; [1.67, 4.08]), and major facture (3.98; [2.72, 5.83]) (Figure 2).

Figure 2.

Risk of exposure to any and long-term opioid prescriptions with various factors.

Finally, we found that short-term use of ADHD medications was associated with the elevated risk of any exposure to oral or transcutaneous prescription (HR: 1.17; 95% CI [1.02, 1.33]) among patients with ADHD; long-term use of ADHD medications was associated with the decreased likelihood of any exposure to outpatient prescription (HR: 0.87; 95% CI [0.75, 1.00]) among patients with ADHD (Tables 2 and 3).

Discussion

Our findings supported the study hypotheses that children and adolescents with ADHD are more likely than those without ADHD to be exposed to prescription opioids later in life. Those with ADHD also had greater risks of cumulative exposure to opioids of >14 and >30 days relative to the control group. The comorbidities of pain disorders, fracture, depressive disorder, substance use disorder, and alcohol use disorder among children or adolescents with ADHD and a parental history of substance use disorder were also related to a greater risk of prescription opioid use.

Previous studies have reported an increased rate of prescription opioid use among adolescents with ADHD (Quinn et al., 2019, 2020). In a study of 73 treatment-seeking adolescents and young adults at an outpatient facility for young substance users, Olsson et al. (2017) reported that tramadol was the most commonly used opioid and that 53% of adolescents had symptoms meeting the criteria for ADHD diagnosis. Quinn et al. (2019) found that individuals who used opioids or other substances, had pre-existing self-injurious behavior, had ADHD, or had depressive disorder were more likely to initiate opioid therapy than were individuals without the respective conditions. They further reported that ADHD (HR: 2.11; 95% CI [1.90, 2.35]), nonopioid substance use disorder (2.20; [2.15, 2.25]), and depression (1.72; [1.68, 1.76]) were related to long-term opioid therapy (Quinn et al., 2019). A US study of commercial health care claims observed that increases in the risk of long-term opioid use ranged from approximately 1.5 times for a prior ADHD diagnosis (1.53; [1.48, 1.58]) to approximately 3 times for prior nonopioid substance use disorder (3.15; [3.06, 3.24]) and nearly 9 times for prior opioid use disorder (8.70; [8.20, 9.24]) (Quinn, Hur, et al., 2017). Our results are consistent with findings from previous studies that children and adolescents with ADHD are more likely to initiate prescription opioid therapy and undergo long-term opioid therapy later in life compared than are individuals without ADHD; the comorbidities of depression, substance use disorder, pain-related disorders, and fracture increased this risk.

Furthermore, our results suggest that parental substance use disorder is related to an increased risk of opioid therapy initiation and long-term opioid therapy with a cumulative exposure of >14 days. Griesler et al. (2021) indicated that parental illicit substance use (OR: 5.52; 95% CI [1.57, 19.36]) was associated with an increased likelihood of their children misusing prescription opioids. A study of a cohort of 1,252 non-Hispanic White and Native American demonstrated that poor parental mental health for a child aged 9 to 16 years was related to nonheroin opioid use by the child at 30 years old (Shanahan et al., 2021). Our findings suggest an intrafamilial coaggregation among parental substance use disorder, offspring ADHD, offspring substance use disorder, and offspring prescription opioid use; these relationships require further validation in future studies.

Finally, evidence has reported a protective effect (OR: 0.65; 95% CI [0.64, 0.67]) of ADHD medications, such as methylphenidate, on the risk of subsequent substance use disorder among adolescent and adult ADHD patients (Quinn, Chang, et al., 2017). However, Quinn, Hur, et al. (2017) revealed that prior ADHD medication prescriptions (HR: 1.53; 95% CI [1.48, 1.58]) were associated with the increased risk for long-term opioid therapy. The contradictory results of earlier studies may echo our findings that long-term ADHD medication use was associated with a decreased risk of exposure to prescription opioids in the outpatient setting while short-term ADHD medication use was associated with an elevated risk of exposure to oral or transcutaneous prescription opioids.

Our study limitations are as follows. First, information on illicit opioid use was not available in the database despite our results indicating a positive relationship between substance use disorder and prescription opioid use. In addition, data on methadone prescriptions (which, in Taiwan, are only indicated to treat heroin dependence) are available only in the Taiwan Illicit Drug Issue Database but not the NHIRD. Second, prescription opioid misuse and abuse could not be identified because information regarding use patterns is not available in the database. Further studies may be required to investigate whether ADHD is also related to prescription opioid abuse. Third, the prevalence of ADHD may be underestimated because only those individuals who sought medical consultation and treatment were included in the NHIRD. However, the subjects enrolled in our study had board-certified psychiatrists, yielding an improved diagnostic validity. Fourth, the NHIRD does not provide information on lifestyle and environmental factors. We were thus unable to examine the influence of these variables.

In conclusion, children and adolescents with ADHD have an increased risk of exposure to prescription opioids (including cumulative exposure of >14 and >30 days) at follow-up relative to controls without ADHD. Comorbid mental illness (i.e., depression and substance use disorder) and medical conditions (i.e., pain disorder and fracture) further increased this risk. Close monitoring for prescription opioid use is necessary for at-risk patients with ADHD, such as those with pain disorder, depression, or substance use disorder. Further studies are necessary to clarify the mechanisms underlying the increased prescription opioid use by children and adolescents with ADHD.

Footnotes

Acknowledgements

The authors thank Mr. I-Fan Hu, MA (Courtauld Institute of Art, University of London; National Taiwan University) for his friendship and support. Mr. Hu declares no conflicts of interest.

Author Contributions

Drs. MHC and JWH designed the study and wrote the manuscript draft; Drs SJT, YMB, KLH, TPS, and TJC performed the literature reviews and revised the manuscript; Dr MHC performed the statistical analysis; all authors reviewed the final manuscript and agreed for the publication.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was supported by grant from Taipei Veterans General Hospital (V111C-010, V111C-040, V111C-029), Yen Tjing Ling Medical Foundation (CI-109-21, CI-109-22, CI-110-30), and Ministry of Science and Technology, Taiwan (MOST110-2314-B-075-026, MOST110-2314-B-075-024 -MY3, MOST 109-2314-B-010-050-MY3, MOST111-2314-B-075 -014 -MY2, MOST 111-2314-B-075 -013). The funding source had no role in any process of our study.

ORCID iD

Mu-Hong Chen

Author Biographies

Ju-Wei Hsu is a senior child and adolescent psychiatrist.

Shih-Jen Tsai is a senior psychiatrist. His research interest includes genetic psychiatry and genetic epidemiology.

Ya-Mei Bai is a senior psychiatrist and President of the Taiwanese Society of Biological Psychiatry and Neuropsychopharmacology.

Kai-Lin Huang is a senior child and adolescent psychiatrist.

Tung-Ping Su is a senior psychiatrist. His research interest includes novel antidepressant clinical trials and epidemiology.

Tzeng-Ji Chen is a senior family medicine physician. His research interest includes big data analysis and AI medicine.

Mu-Hong Chen is a child and adolescent psychiatrist. His study interest includes novel antidepressant clinical trials, big data analysis, and epidemiology.

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