- Email: [email protected]
Comparisons of the clinical outcomes between early- and adult-onset bipolar disorders: A prospective cohort analysis
Journal of Affective Disorders 260 (2020) 1–10
Contents lists available at ScienceDirect
Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Comparisons of the clinical outcomes between early- and adult-onset bipolar disorders: A prospective cohort analysis Shi-Kai Liua,b, Jung-Chi Changc, Hui-Ju Tsaid, Chi-Shin Wue,
T
⁎
a
Centre for Addiction and Mental Health, Toronto, ON, Canada Department of Psychiatry, University of Toronto, Toronto, ON, Canada c Department of Psychiatry, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu, Taiwan d Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan e Department of Psychiatry, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei 10002, Taiwan b
A R T I C LE I N FO
A B S T R A C T
Keywords: Early-onset bipolar disorders Clinical outcomes Hospitalization Substance use disorder Self-harm
Background: This study aimed to explore the impact of age-at-onset on clinical outcomes in bipolar-spectrum disorders, including the development of alcohol or substance use disorder, suicidality, and psychiatric hospitalization. Methods: This population-based study enrolled newly-diagnosed bipolar-spectrum patients, including 4,367 patients with early-onset bipolar disorder (EOBD), 64,787 patients with adult-onset bipolar disorder (AOBD), and the same number of age-controlled comparison subjects without bipolar disorder, from Taiwan's National Health Insurance Research Database. Time-dependent covariate Cox regression models were used to estimate the effect of age-at-onset on clinical outcomes with adjustment for pre-existing psychiatric comorbid conditions and pharmacological treatment patterns. Sensitivity analyses using different definitions of study sample and age cutoffs were conducted. Results: The average follow-up duration was 5.7 years. After adjustment with time-dependent covariates and chronological age, there were no significant differences in the risks for developing new-onset alcohol or substance use disorders and psychiatric hospitalization between EOBD and AOBD patients. Although EOBD patients had a higher risk of hospitalization for suicide and self-harm than did AOBD patients in primary analysis, this finding did not replicated in the sensitivity analyses. Limitations: The symptom profile and severity of bipolar disorder was not available in the NHIRD; therefore, surrogate indicators of clinical outcome might not be sensitive enough to detect the subtle differences. Conclusions: EOBD and AOBD patients had similar risks for developing alcohol or substance use disorders. Their risk of psychiatric hospitalization was similar. Whether EOBD patients might have a higher risk of hospitalization for suicide and self-harm warrants further investigations.
1. Introduction Bipolar spectrum disorder (BD) is marked by heterogeneous clinical manifestation, course, and outcome. Early-onset bipolar disorders (EOBD) has been considered as a distinct subtype of bipolar disorders with a particularly ominous outcome (Joslyn et al., 2016). Evidence from previous cross-sectional studies and one recent meta-analysis suggests that EOBD patients exhibit increased psychiatric comorbidities, higher suicidality, increased number of mood episodes, and more severe long-term functional impairment than those with adult-onset bipolar disorder (AOBD) (Azorin et al., 2013; Baldessarini et al., 2012; Bellivier et al., 2001; Benazzi, 2009; Biffin et al., 2009; Birmaher et al.,
⁎
2006; Carter et al., 2003; Ernst and Goldberg, 2004; Etain et al., 2012; Goldstein and Levitt, 2006; Grunebaum et al., 2006; Holtzman et al., 2015; Javaid et al., 2011; Kennedy et al., 2005; Leverich et al., 2007; Lin et al., 2006; Moor et al., 2012; Perlis et al., 2004; Sala et al., 2013; Tozzi et al., 2011). In addition, family studies found a higher rate of BD among the relatives of EOBD patients than those of AOBD patients (Lin et al., 2006). However, there is some controversy about whether EOBD constitutes a subgroup within the bipolar disorders. First, some prospective studies found that the clinical outcomes of EOBD were not as negative as previous studies had suggested (Birmaher et al., 2014; Coryell et al., 2013; Geller et al., 2004). More than 50% of EOBD youths could
Corresponding author. E-mail addresses: [email protected], [email protected] (C.-S. Wu).
https://doi.org/10.1016/j.jad.2019.08.084 Received 22 April 2019; Received in revised form 30 June 2019; Accepted 28 August 2019 Available online 28 August 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
Journal of Affective Disorders 260 (2020) 1–10
S.-K. Liu, et al.
Fig. 1. Flowchart of subject selection.
disorder, psychiatric hospitalization, and hospitalization for suicide and self-harm.
maintain long-term euthymia (Birmaher et al., 2014). Second, one recent large-scale study failed to demonstrate associations between AAO and polygenic risk score, an indication of genetic risk burden (Kalman et al., 2019). Third, previous studies were riddled with methodological caveats. Most studies addressing the impacts of AAO were cross-sectional, hospital-based studies that enrolled adult patients receiving active treatment (Azorin et al., 2013; Baldessarini et al., 2012; Biffin et al., 2009; Carter et al., 2003; Ernst and Goldberg, 2004; Goldstein and Levitt, 2006; Holtzman et al., 2015; Leverich et al., 2007; Manchia et al., 2017; Patel et al., 2006; Perlis et al., 2004; Propper et al., 2015; Sala et al., 2013). By default, only patients with active treatment would be included. Therefore, the enrolled EOBD patients in adulthood would be inherently a subgroup with unremitted course and poorer prognosis. Such selection bias would result in over-estimating the severity and disease course of EOBD. Moreover, the determination of AAO in cross-sectional studies is subjective and susceptible to recall bias, especially for those with severe courses tend to report earlier age at onset. Finally, the clinical manifestations and pattern of comorbidities might vary with the chronological age at assessment—this could account for the differences between EOBD and AOBD. Unfortunately, AAO, chronologic age at assessment, and duration of illness were intricately intertwined. The effect of current age and duration of illness cannot be properly controlled for in cross-sectional design. To avoid such methodological shortcomings, we emulated a prospective cohort study using data from a representative national claims database. We included all patients with newly-diagnosed BD to avoid selection bias; all were followed-up for the same period of time to control for the effect of duration of illness. In addition, we included a comparison group from the general population matched by AAO to control for the effect of chronologic age on clinical outcomes. Using prospective cohort analysis, we explored the impact of AAO on clinical outcomes, including the development of alcohol or substance use
2. Patients and methods 2.1. Data source Taiwan launched a universal, single-payer National Health Insurance program in 1995. The National Health Insurance program covers more than 99% of the 23 million Taiwanese population; its reimbursement claims have been compiled and updated into the National Health Insurance Research Database (NHIRD). The NHIRD includes comprehensive information about patients’ demographics, clinical diagnosis, medical expenditure, and prescription records in both inpatient and ambulatory settings (Lu et al., 2012). 2.2. Study sample We screened all patients diagnosed with bipolar I disorders (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes: 296.0, 296.4–296.7), bipolar II disorder (ICD-9-CM codes: 296.89) and bipolar disorder not otherwise specified (ICD-9-CM code: 296.80–286.82) registered in the NHIRD between January 1, 2002 and December 31, 2011 for those diagnosed by a psychiatrist and who had at least two ambulatory claims or one inpatient discharge diagnosis of bipolar disorder (n = 129,698). Furthermore, we only included “newly diagnosed patients”, which we defined as one who did not have any diagnostic records of mood episodes in the year preceding the cohort entry date. In addition to prevalent cases (n = 26,169), patients with any diagnosis of schizophrenia during the study period (n = 22,927), missing information of gender (n = 95), or whose age at diagnosis was <13 years (n = 806) or > 60 2
Journal of Affective Disorders 260 (2020) 1–10
S.-K. Liu, et al.
2.6. Treatment pattern
years (n = 10,547) were excluded. The AAO was defined as the age at the first claims record with mood episode, no matter it was manic, depressive, mixed or unspecified episode. There is no clear definition of the age cutoffs for EOBD. One meta-analysis showed the age cutoffs ranged from 18 years to 21 years in most studies (Joslyn et al., 2016). In addition, several studies using admixture analysis and divided three group by AAO. The mean AAO of EOBD was 17–18 years (Azorin et al., 2013; Bellivier et al., 2001; Hamshere et al., 2009). Therefore, we defined EOBD patients were those with onset 18 years or less. The final number of bipolar patients included was 69,154. The flowchart of participant enrollment is shown in Fig. 1. We defined the cohort entry date as the date of the first mood episode, including major depressive episodes (ICD-9-CM codes: 296.2, 296.3, 296.5, or 296.82), manic episodes (296.0, 296.4, or 296.81), mixed episodes (296.6), or unspecified mood episodes (296.7, 296.80, or 296.89).
The medication possession ratio (MPR) was used to present the pattern of use of mood stabilizer, antipsychotic, antidepressant, and benzodiazepine during the follow-up period. MPR is a proxy measure that reflects the medication compliance of patients, as well as a clinician's decision to continue using a specific medication. It is calculated by finding the ratio of the sum of the supplied days of a dispensed drug to 365 days. For example, if a patient received 3 prescriptions of mood stabilizer with 28-day supply, the MPR would be 0.23 (3 × 28/365). If the duration of illness was less than 365 days, the denominator was all the period observed. The overall treatment pattern was classified into irregular use (0 < MPR < 0.8) and regular use (MPR ≧ 0.8) (Cramer et al., 2008). Since medication use may change during a study period, MPR may also vary over time. Hence, MPR was treated as a time-dependent variable. The overall period of follow-up was prospectively divided into 30-day intervals. To trace the change in MRP, the MPR were assessed over a 365-day observation period (or shorter period if duration of illness less than one year) prior to each interval.
2.3. Comparison cohort of subjects without bipolar disorder To identify a comparison group without bipolar disorder from the general population, we used the Longitudinal Health Insurance Database 2000 (LHID 2000), which is a subset of the NHIRD. The LHID 2000 contains all the original claims data of a million randomly sampled beneficiaries from the year 2000 Registry for Beneficiaries of the NHIRD. The distribution of gender and age of the sampled subjects in the LHID 2000 did not differ significantly from that of the general population. For each patient with bipolar disorder, we randomly selected one comparison subject without bipolar disorder matched by age (the year of birth) and sex. The cohort entry date of each subject was assigned on the basis of their matching cases. A total of 69,154 comparison subjects were identified. All of the study subjects were tracked until the date of last medical contact recorded in the claims database.
2.7. Statistical analysis Descriptive statistics of the baseline characteristics of EOBD and AOBD patients and comparison subjects are provided in Table 1. Differences in the characteristics among these two-by-two group comparisons were explored using a logistic regression model. The odds ratios of the EOBD and AOBD groups (age at cohort entry was 13–18 years and 19–60 years, respectively) against comparison subjects were calculated separately. In order to adjust for the chronological age at cohort entry and evaluate the genuine differences in characteristics between EOBD and AOBD patients, we conducted twoby-two analysis on the whole study sample, including bipolar disorder (vs. comparison group), age group at cohort entry (aged of 13–18 years vs. 19–60 years), and the interaction between the above-mentioned two variables. If the interaction term was statistically significant, it indicated that the differences between EOBD and AOBD patients were independent and unmediated by the chronological age effect. The likelihood of developing new-onset alcohol or substance use disorder, psychiatric hospitalization, or hospitalization for suicide and self-harm was presented by the hazard ratios from the Cox proportional hazards regression model including time-dependent covariates. The time-invariable covariates included gender and pre-existing psychiatric conditions. Patients’ chronologic age, which increased during the follow-up period, was treated as a time-dependent variable. Moreover, as the effect of chronologic age might not have linear relationships with the outcomes of interest, the quadratic term of chronologic age was included in the regression model. The treatment pattern of medications also varied during the follow-up period; therefore, they were also analyzed as time-dependent variables. Several sensitivity analyses were conducted to test the robustness of our findings. First, we used 1-year washout period to exclude prevalent bipolar patients in the primary analysis; however, it might be not enough because some patients have remote mood episodes more than one year ago. A sensitivity analysis using 4-year washout periods were conducted in order to have a definitely incident sample. The more definitely incident cases could be identified using longer washout period but the sample size and follow-up period would be smaller. Second, EOBD patients were defined as those AAO before or equal to 18 years. However, the age cutoff might be too late. We used the age cutoff of 16 years to explore whether the earlier AAO group has a more significant difference compared to the AOBD group. Finally, although we included a comparison group and used regression model to control the effect of current age, the current age among these two groups was not totally overlapping. During the study period, the current age ranged from 13 to 29 among the EOBD group and ranged from 19 to 71 among the AOBD group. Thus, we restricted the study patients whose current age were
2.4. Main outcomes The mood symptom and severity was not available in the NHIRD, we used psychiatric hospitalization to indicate the occurrence of a severe mood episode. Given alcohol or substance use disorder would complicate the disease course of bipolar disorder (Cassidy et al., 2001), we chose new-onset alcohol or substance use disorder (ICD-9-CM codes: 291.x 292.x, 303.x, 304.x, or 305.x; tobacco use disorder [305.1] was excluded) as an indicator for new-onset psychiatric comorbid conditions. In addition, we used hospitalization for suicide and self-harm (ICD-9-CM E-code: E950-E959) to assess suicidality. For estimating the risk of developing new-onset alcohol or substance use disorder, subjects already diagnosed with alcohol or substance use disorder before the cohort entry date were excluded; however, they were included in the analysis for the other outcome indicators, such as psychiatric hospitalization or hospitalization for suicide and self-harm. 2.5. Pre-existing psychiatric conditions Pre-existing psychiatric conditions were assessed using the claims records from the year preceding the cohort entry date and included mental retardation (ICD-9-CM code: 317, 318, 319), autism spectrum disorder (ICD-9-CM code: 299), specific development delay (ICD-9-CM code: 315), attention-deficit hyperactivity disorder (ADHD; ICD-9-CM code: 314), alcohol use disorder (ICD-9-CM code: 291, 303, 305.0), substance use disorder (ICD-9-CM code: 292, 304, 305.2-305.9), anxiety disorder (ICD-9-CM code: 300; excluding 300.4), sleep disorder (ICD-9-CM code: 307.4, 780.5), and adjustment disorder (ICD-9-CM code: 309). In addition, we assessed the history of psychiatric hospitalization (to either a psychiatric hospital or psychiatric ward in general hospital) and hospitalization for suicide and self-harm (ICD9-code: E950-E959). 3
Mean age ( ± SD) at cohort entry date Gender, female; n (%) Pre-existing psychiatric conditions, n (%) Mental Retardation Autism Spectrum Disorder Specific development delay ADHD Alcohol Substance use disorder Anxiety disorder Sleep disorder Adjustment disorder History of hospitalization for suicide and self-harm History of psychiatric hospitalization First mood episode, n (%) Depressive episode Manic episode Mixed episode Unspecified episode
4
2446 (56.0)
35 (0.8) 10 (0.2) 44 (1.0) 59 (1.4) 1 (0.0) 1 (0.0) 97 (2.2) 22 (0.5) 2 (0.0) 0 (0.0) 2 (0.0)
2446 (56.0)
388 (8.9) 227 (5.2) 256 (5.9) 705 (16.1) 23 (0.5) 55 (1.3) 1324 (30.3) 490 (11.2) 33 (0.8) 15 (0.3)
326 (7.5)
2156 (49.4) 1103 (25.3) 347 (7.9) 761 (17.4)
16.1 ± 1.6
Comparison subjects (n = 4367)
16.1 ± 1.6
Patient with earlyonset bipolar disorder (n = 4367)
Adolescent cohort, aged 13–18 years
Table 1 Baseline characteristics of study sample.
176.0 (43.8,707.2)
12.1 (8.5,17.1) 23.9 (12.7,45.1) 6.1 (4.4,8.4) 9.0 (6.7,12.0) 23.1 (3.1,171.1) 55.7 (7.7,402.5) 19.2 (15.5,23.7) 25.0 (16.2,38.3) 16.6 (4.0,69.3) NA
Crude odds ratios (earlyonset vs. comparison subjects)
39,390 (60.8) 12,754 (19.7) 4820 (7.4) 7823 (12.1)
7138 (11.0)
1136 (1.8) 229 (0.4) 142 (0.2) 506 (0.8) 4237 (6.5) 2390 (3.7) 32,882 (50.8) 21,741 (33.6) 387 (0.6) 552 (0.9)
37,011 (57.1)
36.6 ± 11.5
Patient with adult-onset bipolar disorder (n = 64,787)
88 (0.1)
184 (0.3) 21 (0.0) 48 (0.1) 59 (0.1) 335 (0.5) 164 (0.3) 7728 (11.9) 3008 (4.6) 40 (0.1) 15 (0.0)
37,011 (57.1)
36.6 ± 11.5
Comparison subjects (n = 64,787)
Adult cohort, aged 19–60 years
90.9 (73.7,112.2)
6.3 (5.4,7.3) 10.9 (7.0,17.1) 3.0 (2.1,4.1) 8.6 (6.6,11.3) 13.5 (12.0,15.1) 15.1 (12.9,17.7) 7.6 (7.4,7.8) 10.4 (10.0,10.8) 9.7 (7.0,13.5) 37.1 (22.2,62.0)
Crude odds ratios (adultonset vs. comparison subjects)
0.359
0.001 0.049 0.002 0.012 0.600 0.197 <0.001 <0.001 0.474 NA
p-value for interaction between age group at cohort entry and bipolar diagnoses
S.-K. Liu, et al.
Journal of Affective Disorders 260 (2020) 1–10
Journal of Affective Disorders 260 (2020) 1–10
5
Significant results are in bold. a Patients with alcohol or substance use disorder in baseline were excluded in the analysis for new-onset of alcohol or substance use disorder, respectively.
267 (0.7) 80 (0.2) 15,127 (51.3) 2145 (5.7) 14 (0.6) 2 (0.1) 1015 (52.3) 100 (4.1)
82.9 (48.9, 140.5) 46.7 (11.4–190.7)
557 (1.4) 17 (0.7) 234 (9.9)
6155 (18.7)
5.7 ± 2.9 5.7 ± 2.9
14.3 (8.7, 23.4)
6.1 ± 0.0 6.0 ± 3.0
Patients with adult-onset bipolar disorders, aged 19–60 years (n = 64,787) Crude HR (bipolar vs. comparison group), 95% CI Comparison groups, aged 13–18 years (n = 4367) Patients with early-onset bipolar disorders, aged 13–18 years (n = 4367)
Table 2 Incidence of clinical outcome and crude hazard ratios among patients with early- and adult-onset bipolar disorders and comparison subjects.
Comparison groups, aged 19–60 years (n = 64,787)
The final study sample consisted of 4367 EOBD patients and 64,787 AOBD patients with their respective age-matched control subjects. Approximately 77.9% (n = 3402) of EOBD and 78.8% (n = 51,076) of AOBD were bipolar I disorder. The percentage of bipolar I disorder was not statistically different between two groups (chi-square = 2.136, pvalue=0.144). A depressive episode was the first mood episode in 49.4% of the EOBD patients, compared to 60.8% in the AOBD group. In contrast, the proportion of patients with manic and unspecified episodes as the first mood episode was higher among EOBD patients than among AOBD patients. Notably, both EOBD and AOBD patients had significantly more preexisting psychiatric disorders than their respective control subjects, albeit with different patterns (Table 1). Disorders of neurodevelopmental origin (e.g., mental retardation, autism spectrum disorder, specific developmental delay, and ADHD) and the acquired psychiatric disorders (alcohol or substance use disorder, anxiety disorder and sleep disorder) were highly prevalent among the EOBD patients, compared to AOBD patients. Excessive psychiatric conditions were also prevalent in AOBD patients; however, the odds ratios for EOBD patients were much higher than those for AOBD patients (Table 1). The mean follow-up duration was 5.7 years for the EOBD group and 6.0 years for the AOBD group (Table 2). During the follow-up period, both EOBD and AOBD groups had a significantly higher risk of developing the outcomes of interest than the control subjects. The risk of developing alcohol or substance use disorder in EOBD patients (crude hazard ratio (HR): 14.3 [95% CI: 8.7–23.4]) and AOBD patients (crude HR: 12.9 [95% CI: 11.8–14.1]) was significantly higher than control subjects. Similarly, both EOBD and AOBD patients had a higher chance of being hospitalized for psychiatric disorders (crude HR: 82.9 [95% CI: 48.9–140.5] and 65.3 [95% CI: 57.9–73.7] for EOBD and AOBD, respectively). In terms of hospitalization for suicide and self-harm, the risk was higher in patients with EOBD (crude HR: 46.7 [95% CI: 11.4–190.7]) than in those with AOBD (crude HR: 20.3 [95% CI: 16.1–25.4]). The results of a time-dependent Cox regression model (Table 3) indicated that there were no significant differences between EOBD and AOBD patients when considering the risks of developing newonset alcohol or substance use disorders and psychiatric hospitalization. However, EOBD subjects had a slightly higher risk for hospitalization for suicide and self-harm than AOBD subjects (p-value = 0.043). There were various pre-existing psychiatric conditions, such as history of hospitalization for suicide and self-harm, benzodiazepine use, and mood stabilizer users were both associated with increased risk of developing alcohol or substance use for both EOBD and AOBD patients (Table 3). Several factors, including mental retardation, autism spectrum disorder, use of antidepressants, etc., had similar risk ratios in both groups but only reached a significant level in AOBD patients, which might be due to sample size. One of the most different effect is male, which is associated with a reduced effect in EOBD group but an increased risk in AOBD groups. For risk factors associated with psychiatric and self-harm hospitalization, previous psychiatric hospitalization was the most consistent predictor for both EOBD and AOBD, while previous self-harm predicted significant hospitalization for suicide and self-harm but not psychiatric hospitalization. In addition, pre-existing use of alcohol or substance predicted a higher risk of psychiatric and self-harm hospitalization. Table 4 shows the results of sensitivity analyses. Using 4-year washout period, we found the incidence and were generally consistent;
Duration of follow-up, years Clinical outcome index, number (incidence per 1000 person-year) New-onset alcohol or substance use disordera Psychiatric hospitalization Hospitalization for suicide and selfharm
3. Results
12.9 (11.8, 14.1)
Crude HR (bipolar vs. comparison group), 95% CI
between 19 and 29 during the study period. All statistical analyses were conducted with SAS version 9.4 (SAS Institute Inc., Cary, North Carolina). The statistical significance of relationships was assessed using 95% confidence intervals (CI) or p values <0.05.
65.3 (57.9, 73.7) 20.3 (16.1–25.4)
S.-K. Liu, et al.
6 2.29 (1.98, 2.66) 1.08 (0.95, 1.24) 0.97 (0.85, 1.11) 1.61 (1.41, 1.85)
2.23 0.76 1.25 1.21
3.07 (2.08, 4.53) 0.58 (0.39, 0.86) 1.16 (0.80, 1.69) 1.23 (0.85, 1.79) 2.37) 0.81) 1.32) 1.27)
1.00 (0.82, 1.22) 0.88 (0.67, 1.15) 0.91 (0.68, 1.22) 1.03 (0.86, 1.23) 1.54 (0.96, 2.45) 1.15 (0.79, 1.67) 0.96 (0.85, 1.08) 1.11 (0.95, 1.30) 1.38 (0.82, 2.35) 2.42 (2.10, 2.77) 1.36 (0.70, 2.63)
0.60 (0.48, 0.75) 0.24 (0.11, 0.54) 0.20 (0.05, 0.80) 0.90 (0.66, 1.22) NA NA 0.92 (0.88, 0.96) 1.21 (1.16, 1.27) 0.99 (0.76, 1.29) 1.22 (1.15, 1.30) 1.37 (1.16, 1.62)
0.64 (0.32, 1.26) 0.65 (0.26, 1.64) 0.39 (0.12, 1.29) 1.36 (0.86, 2.16) NA NA 0.91 (0.69, 1.21) 1.03 (0.70, 1.53) 1.78 (0.57, 5.58) 1.10 (0.69, 1.74) 4.08 (1.27, 13.11) (2.09, (0.72, (1.19, (1.15,
42.45 (27.95, 64.46) 1.30 (1.16, 1.45)
7.42 (6.80, 8.09) 1.66 (1.59, 1.73)
11.94 (7.18, 19.84) 0.61 (0.47, 0.80)
1.91 (1.82, 2.00) 1.02 (0.98, 1.07) 1.15 (1.10, 1.20) 1.33 (1.27, 1.38)
0.94 (0.83, 1.06) 1.05 (0.81, 1.37) 1.33 (0.94, 1.89) 0.72 (0.57, 0.90) 1.70 (1.61, 1.80) 1.13 (1.05, 1.21) 0.89 (0.86, 0.92) 0.91 (0.87, 0.94) 0.89 (0.74, 1.09) 3.33 (3.20, 3.47) 1.66 (1.47, 1.88)
36.28 (31.98, 41.15) 1.25 (1.21, 1.29)
Adult-onset
4.05 (2.52, 6.53) 0.83 (0.53, 1.32) 1.77 (1.13, 2.76) 0.90 (0.57, 1.42)
0.39 (0.14, 1.04) 1.03 (0.35, 3.08) 1.36 (0.45, 4.09) 1.00 (0.51, 1.98) 2.66 (0.84, 8.43) 1.29 (0.46, 3.62) 0.76 (0.53, 1.11) 1.38 (0.88, 2.15) 1.80 (0.44, 7.35) 1.97 (1.25, 3.12) 6.63 (2.56, 17.16)
42.02 (10.26, 172.05) 0.82 (0.58, 1.16)
Early-onset
2.73 (2.40, 3.11) 1.00 (0.89, 1.12) 1.58 (1.41, 1.76) 1.21 (1.09, 1.34)
0.91 (0.61, 1.38) 0.33 (0.08, 1.33) 1.18 (0.38, 3.71) 0.82 (0.41, 1.65) 1.96 (1.71, 2.25) 1.48 (1.25, 1.76) 0.95 (0.86, 1.04) 1.22 (1.11, 1.35) 0.74 (0.42, 1.31) 1.69 (1.49, 1.91) 3.77 (3.04, 4.66)
11.07 (8.75, 14.00) 0.68 (0.62, 0.75)
Adult-onset
Hospitalization for suicide and self-harm
Significant results are in bold. a interaction p-value between bipolar disorder and age group at cohort entry: 0.343 for alcohol or substance use disorder; 0.324 for psychiatric hospitalization; 0.043 for hospitalization for suicide and self-harm.
Bipolar disordera Gender (Male vs. Female) Pre-existing psychiatric conditions Mental Retardation Autism Spectrum Disorder Specific development delay ADHD Alcohol Substance use disorder Anxiety disorder Sleep disorder Adjustment disorder History of psychiatric hospitalization History of hospitalization for suicide and self-harm Treatment pattern ≥ Benzodiazepine use (MPR ≥ 0.8vs. MPR <0.8) Mood stabilizer (MPR ≥ 0.8vs. MPR <0.8) Antidepressant (MPR ≥ 0.8vs. MPR <0.8) Antipsychotic (MPR ≥ 0.8vs. MPR <0.8)
Early-onset
Early-onset
Adult-onset
Psychiatric hospitalization
Alcohol or substance use disorder
Table 3 Adjusted hazard ratios for clinical outcomes among patients with early- and adult-onset bipolar disorders.
S.-K. Liu, et al.
Journal of Affective Disorders 260 (2020) 1–10
New-onset alcohol or substance use disorder Primary analysis With 4-year washout period Using the onset age cut-off of 16 years Restricted to current age between 19 and 29 Psychiatric hospitalization Primary analysis With 4-year washout period Using the onset age cut-off of 16 years Restricted to current age between 19 and 29 Hospitalization for suicide and self-harm Primary analysis With 4-year washout period Using the onset age cut-off of 16 years Restricted to current age between 19 and 29
Clinical outcome index, study population
202 (10.0)
1015 (52.3) 617 (55.8) 522 (50.9) 795 (46.5)
100 (4.1) 48 (3.7) 47 (3.8) 84 (4.0)
4367/64,787 3081/41,663 2275/66,879
3045/8221
4367/64,787 3081/41,663 2275/66,879
3045/8221
234 (9.9) 127 (10.1) 104 (8.6)
2999/7772
4290/58,646 3020/37,164 2242/60,694
Patient number, EOBD/AOBD
Patients with earlyonset bipolar disorders number (incidence per 1000 person year)
7 2 (0.1)
2 (0.1) 2 (0.2) 2 (0.2)
12 (0.6)
14 (0.6) 5 (0.4) 5 (0.4)
14 (0.7)
17 (0.7) 8 (0.6) 7 (0.6)
Comparison groups of early-onset bipolar disorders number (incidence per 1000 person year)
27.40 (6.59, 113.98)
42.02 (10.26, 172.05) 12.00 (2.77, 51.95) 20.52 (4.82, 87.33)
47.96 (26.95, 85.35)
42.45 (27.95, 64.46) 74.28 (30.60, 180.32) 69.20 (28.48, 168.14)
12.11 (6.93, 21.19)
11.94 (7.18, 19.84) 13.35 (6.37, 27.95) 14.67 (6.67, 32.25)
Adjusted HR (bipolar vs. comparison group), 95% CI
Table 4 Sensitivity analysis using different washout period, age cutoff, and restricted sample with comparable current age.
178 (5.7)
2145 (5.7) 1101 (6.1) 2198 (5.6)
2073 (84.1)
15,127 (51.3) 8330 (54.6) 15,620 (48.5)
572 (20.2)
6155 (18.7) 3276 (21.3) 6285 (18.4)
Patients with adultonset bipolar disorders number (incidence per 1000 person year)
4 (0.1)
80 (0.2) 33 (0.2) 80 (0.2)
29 (0.9)
267 (0.7) 134 (0.7) 276 (0.7)
34 (1.1)
557 (1.4) 269 (1.5) 567 (1.4)
Comparison groups of adult-onset bipolar disorders number (incidence per 1000 person year)
21.54 (7.81, 59.42)
11.07 (8.75, 14.00) 13.82 (9.62, 19.83) 14.17 (9.88, 20.33)
66.21 (45.55, 96.25)
36.28 (31.98, 41.15) 38.46 (32.29, 45.80) 36.16 (32.02, 40.85)
10.78 (7.49, 15.52)
7.42 (6.80, 8.09) 7.71 (6.73, 8.83) 7.41 (6.74, 8.13)
Adjusted HR (bipolar vs. comparison group), 95% CI
0.772
0.043 0.827 0.607
0.836
0.324 0.053 0.058
0.993
0.343 0.122 0.109
p-value for interaction
S.-K. Liu, et al.
Journal of Affective Disorders 260 (2020) 1–10
Journal of Affective Disorders 260 (2020) 1–10
S.-K. Liu, et al.
4.2. Comparison with previous studies
however, the difference in the risk of hospitalization for suicide and self-harm was not significant in the sample with 4-year washout period. Using the age cutoff of 16 years, we found the incidence for three clinical outcome indices changed slightly among EOBD patients and their corresponding comparison groups; however, the hazard ratios of EOBD increased for the new-onset alcohol or substance use disorder and psychiatric hospitalization but declined for hospitalization for suicide and self-harm. Finally, we restricted our study sample with comparable current age, ranged from 19 to 29 years during the study period. The results showed all the difference in study outcomes between EOBD and AOBD were not significant.
Literature had been inconsistent as to whether earlier age-at-onset was associated with a higher risk for developing alcohol or substance use disorders in bipolar disorders. There were studies showing that EOBD patients had a higher risk of developing alcohol or substance use disorder, Carter et al. (2003), Coryell et al. (2013), Ernst and Goldberg (2004), Holtzman et al. (2015), and Leverich et al. (2007) whereas others found a null association (Grunebaum et al., 2006; Moor et al., 2012; Propper et al., 2015; Sala et al., 2013) or negative association (Patel et al., 2006). One important but not properly addressed factor affecting clinical manifestations and comorbidity patterns was the chronologic age at assessment. Considering that increased age entails evolving developmental stages and bio-psycho-social matrices, age-dependent social context and challenges would impact the development of comorbidities in bipolar disorders as individuals go through various stages. Such impacts of age could best be illustrated by the estimation of comorbid substance use across ages. One study enrolling patients with EOBD and relatively young AOBD patients (mean age at assessment was 17.2 and 26.1 years, respectively) found that patients with AOBD had a higher rate of alcohol or substance use than those with EOBD (Patel et al., 2006). Such findings might be spurious, since alcohol or substance use disorder generally peaks at early adulthood and subsides gradually during middle age or old age (Kessler et al., 2005). In most other studies addressing AOBD, the average age at assessment was around middle age—a time when substance abuse may not be a problem. Alternatively, it could be under-reported due to recall bias. This methodological issue was addressed in our study. When duration of illness and chronological age at assessment were controlled for by study design and statistical analysis, no significant differences in the risk of alcohol or substance use disorder emerged between AOBD and EOBD. Regarding the severity of mood episodes, our results were different from several studies which indicated that patients with EOBD had more mood episodes or were more likely to be hospitalized than those with AOBD (Azorin et al., 2013; Baldessarini et al., 2012; Coryell et al., 2013; Leverich et al., 2007). Such discrepancies could be partially explained by the various methods of sample selection. In cross-sectional studies conducted in clinical settings, enrolled patients were still receiving active treatment and those who had achieved remission were not be included (Azorin et al., 2013; Baldessarini et al., 2012; Leverich et al., 2007). In these studies recruited EOBD patients would be inherently biased towards a subgroup with a longer duration of illness and an unremitted disease course, hence the poorer long term course. Notwithstanding such potential bias by design, there were studies demonstrating that the number of mood episodes in patients with EOBD was similar to those with AOBD (Etain et al., 2012; Manchia et al., 2017; Patel et al., 2006; Sala et al., 2013). The current results were thus in line with those from previous prospective studies (Patel et al., 2006; Sala et al., 2013), which suggested that the clinical outcomes of EOBD were not much poorer than AOBD. In terms of suicidality, our findings in primary analysis showed that EOBD was weakly associated with a higher risk for hospitalization for suicide and self-harm compared to those with AOBD. The results were consistent with previous studies showing that patients with EOBD have higher suicidal risk (Azorin et al., 2013; Biffin et al., 2009; Carter et al., 2003; Holtzman et al., 2015; Moor et al., 2012; Perlis et al., 2004; Propper et al., 2015). High impulsivity among EOBD patients might have contributed to the elevated suicidal risk (Nandagopal et al., 2011). However, the finding was not replicated in our sensitivity analyses using different washout period, age cutoff, and restricting sample with comparable current age. Some studies showed EOBD was not associated with elevated suicidal risk (Baldessarini et al., 2012; Coryell et al., 2013; Ernst and Goldberg, 2004; Manchia et al., 2017). It should be noted that the number of hospitalization for suicide and self-harms among comparison subjects in adolescent cohorts was small (n = 2).
4. Discussion 4.1. Main findings Using a prospective cohort design which included only patients with newly-diagnosed bipolar disorders, the current study found both EOBD and AOBD patients had significantly more previous neurodevelopmental and/or psychiatric conditions but the pattern differed. However, there were no significant differences in the risks for psychiatric hospitalization and the development of alcohol or substance use disorders between the two groups. EOBD was weakly associated with a higher risk for hospitalization for suicide and self-harm than AOBD in primary analysis but this difference was not statistically significant in the sensitivity analyses. The current study used better research methodologies than previous cross-sectional studies. We used a total population database to trace all newly-onset bipolar patients; therefore, the sample size was large, and the average follow-up period was over 5 years. In addition, this study included a comparison group to estimate the effect of chronological age at assessment on clinical outcomes and carefully controlled potential confounding factors, such as comorbid illness and time-variant pharmacological treatment. The most salient difference in the pattern of psychiatric conditions before the bipolar disorder diagnosis between EOBD and AOBD is the predominance of disorders of neurodevelopmental origin (mental retardation, autism, developmental delays, and attention-deficit/hyperactivity disorder). This was not surprising because bipolar disorder has been among the most prevalent major psychiatric disorders in people with developmental disability and/or autism. Nevertheless, the co-existence of developmental pathology might substantially modify the presentation of mood symptoms as to make them “atypical,” as patients could present with increased psychomotor disturbances and/or irritable mood, instead of the classical mood symptoms. This might have accounted for the widely reported contrast in the proportion of atypical, unspecified or mixed episodes as the presenting symptoms in EOBD (Joshi et al., 2013). The finding that EOBD was associated with a higher comorbidity of anxiety and sleep disorder than AOBD needs further scrutiny. Since both anxiety and sleep disturbances could be part of the initial manifestations of a full-blown mood episode, the increased prevalence in both EOBD and AOBD might be an epiphenomenon. In addition, as both anxiety and sleep disturbance might be common manifestations of comorbid psychiatric conditions, the significantly higher risk in EOBD might reflect the higher number of overall pre-bipolar psychiatric conditions in individuals with EOBD. It will be of great clinical significance and interest as to how these comorbidities affect the outcome of bipolar disorder in the long term. Even though most of the pre-bipolar conditions investigated did not appear to affect the primary outcomes, i.e., the development of alcohol or substance use, psychiatric hospitalization, and hospitalization for suicide and self-harm, it was far from conclusive. Many intervening, protective, or aggravating factors could have affected the trajectory of individual clinical pathways.
8
Journal of Affective Disorders 260 (2020) 1–10
S.-K. Liu, et al.
CRediT authorship contribution statement
Hence, the statistical significance should be interpreted more conservatively. The risk factors for study outcome indices were generally consistent with previous literature (Goldstein et al., 2013, 2005; Stephens et al., 2014). However, one caveat finding is that the female was associated with an increased risk for developing substance or abuse among adolescents with bipolar disorders. Although male is a risk factor for developing substance use disorder (Stone et al., 2012), previous studies showed there was no gender effect on the risk of developing substance use disorder among the adolescent with bipolar disorders (Goldstein et al., 2013; Stephens et al., 2014). In Taiwan, the adolescent female with substance use disorder have higher likelihood to seeking treatment (Lin et al., 2004). Given that we identified alcohol or substance use disorder based on health insurance claims records, the adolescent male with substance or alcohol use disorder were less likely to seek treatment, thereby leading to under-estimate the risk of male bipolar patients. The role of gender on developing alcohol or substance use disorder in this study group warrants more investigation.
Shi-Kai Liu: Conceptualization, Methodology, Supervision, Validation, Writing - original draft, Writing - review & editing. JungChi Chang: Conceptualization, Methodology, Writing - original draft, Writing - review & editing. Hui-Ju Tsai: Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing - review & editing. Chi-Shin Wu: Conceptualization, Formal analysis, Funding acquisition, Methodology, Validation, Writing - original draft, Writing - review & editing. Acknowledgements All authors declare no potential conflict of interest. This work was supported by grants from Ministry of Science and Technology (PI: CSW, MOST 107-2314-B-002-216) and National Health Research Institutes, Taiwan(PI: HJT, PH–104-PP-14, PH-104-SP-05 and PH-104-SP-16). The National Taiwan University Hospital and National Health Research Institutes had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.
4.3. Limitations Our study has inherent limitations from using a claims database. Firstly, the details of mood symptoms and the severity of bipolar disorder were not available in the NHIRD. We assessed the clinical outcomes using several surrogate indicators, which might not be sensitive enough to detect the subtle differences. For example, mild mood episodes or suicidal behaviors without the need for hospitalization would not be measured in our study. However, the severe mood episodes or suicidal attempts need to hospitalization could be fully identified in the NHIRD. Secondly, we identified patients with bipolar disorders based on the diagnostic codes in the claims database; however, the validation of diagnosis was not documented in the NHIRD. Accordingly, we used restrictive definitions, diagnosis by a psychiatrist, and the number of records to improve the validations. Third, the diagnosis of bipolar spectrum disorders was based ICD-9-CM system rather than DSM-5. Therefore, cyclothymia, substance-induced bipolar disorders, and bipolar disorder associated with another medical conditions were not included in this analysis. Finally, other potential confounding factors that might influence the manifestations and clinical course of bipolar disorder, such as personality profile, social support system, socioeconomic status, and treatment compliance, were not addressed in the current study. These preliminary findings should be considered as a basis for further research.
Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2019.08.084. Reference Azorin, J.-.M., Bellivier, F., Kaladjian, A., Adida, M., Belzeaux, R., Fakra, E., Hantouche, E., Lancrenon, S., Golmard, J.-.L., 2013. Characteristics and profiles of bipolar I patients according to age-at-onset: findings from an admixture analysis. J. Affect Disord. 150, 993–1000. Baldessarini, R.J., Tondo, L., Vázquez, G.H., Undurraga, J., Bolzani, L., Yildiz, A., KHALSA, H.M.K., Lai, M., Lepri, B., Lolich, M., 2012. Age at onset versus family history and clinical outcomes in 1,665 international bipolar‐I disorder patients. World Psychiatry 11, 40–46. Bellivier, F., Golmard, J.-.L., Henry, C., Leboyer, M., Schürhoff, F., 2001. Admixture analysis of age at onset in bipolar I affective disorder. Arch. Gen. Psychiatry 58, 510–512. Benazzi, F., 2009. Classifying mood disorders by age-at-onset instead of polarity. Prog. Neuropsychopharmacol. Biol. Psychiatry 33, 86–93. Biffin, F., Tahtalian, S., Filia, K., Fitzgerald, P.B., De Castella, A.R., Filia, S., Berk, M., Dodd, S., Callaly, P., Berk, L., 2009. The impact of age at onset of bipolar I disorder on functioning and clinical presentation. Acta Neuropsychiatry 21, 191–196. Birmaher, B., Axelson, D., Strober, M., Gill, M.K., Valeri, S., Chiappetta, L., Ryan, N., Leonard, H., Hunt, J., Iyengar, S., 2006. Clinical course of children and adolescents with bipolar spectrum disorders. Arch. Gen. Psychiatry 63, 175–183. Birmaher, B., Gill, M.K., Axelson, D.A., Goldstein, B.I., Goldstein, T.R., Yu, H., Liao, F., Iyengar, S., Diler, R.S., Strober, M., 2014. Longitudinal trajectories and associated baseline predictors in youths with bipolar spectrum disorders. Am. J. Psychiatry 171, 990–999. Carter, T.D.C., Mundo, E., Parikh, S.V., Kennedy, J.L., 2003. Early age at onset as a risk factor for poor outcome of bipolar disorder. J. Psychiatry Res. 37, 297–303. Cassidy, F., Ahearn, E.P., Carroll, B.J., 2001. Substance abuse in bipolar disorder. Bipolar Disord. 3, 181–188. Coryell, W., Fiedorowicz, J., Leon, A.C., Endicott, J., Keller, M.B., 2013. Age of onset and the prospectively observed course of illness in bipolar disorder. J. Affect Disord. 146, 34–38. Cramer, J.A., Roy, A., Burrell, A., Fairchild, C.J., Fuldeore, M.J., Ollendorf, D.A., Wong, P.K., 2008. Medication compliance and persistence: terminology and definitions. Value Health 11, 44–47. Ernst, C.L., Goldberg, J.F., 2004. Clinical features related to age at onset in bipolar disorder. J. Affect Disord. 82, 21–27. Etain, B., Lajnef, M., Bellivier, F., Mathieu, F., Raust, A., Cochet, B., Gard, S., M'Bailara, K., Kahn, J.-.P., Elgrabli, O., 2012. Clinical expression of bipolar disorder type i as a function of age and polarity at onset: convergent findings in samples from France and the United States. J. Clin. Psychiatry 73, e561–e566. Geller, B., Tillman, R., Craney, J.L., Bolhofner, K., 2004. Four-year prospective outcome and natural history of mania in children with a prepubertal and early adolescent bipolar disorder phenotype. Arch. Gen. Psychiatry 61, 459–467. Goldstein, B.I., Levitt, A.J., 2006. Further evidence for a developmental subtype of bipolar disorder defined by age at onset: results from the national epidemiologic survey on alcohol and related conditions. Am. J. Psychiatry 163, 1633–1636. Goldstein, B.I., Strober, M., Axelson, D., Goldstein, T.R., Gill, M.K., Hower, H., Dickstein, D., Hunt, J., Yen, S., Kim, E., 2013. Predictors of first-onset substance use disorders during the prospective course of bipolar spectrum disorders in adolescents. J. Am.
5. Conclusions There were significant differences among the psychiatric comorbidities before the onset of bipolar disorders between EOBD and AOBD patients, with a predominance of disorders of neurodevelopmental origin in EOBD. However, the role of these comorbidities in predicting the development of the clinical outcomes after onset of bipolar disorder was unclear. After the onset of bipolar disorder, patients with EOBD might have a higher risk for hospitalization for suicide and self-harm than those with AOBD. However, the differences in the development of alcohol or substance use disorder or psychiatric hospitalization were not significant. Overall, the current study did not support that EOBD had poorer outcomes than AOBD when the important confounding variables of age at assessment and duration of illness were properly controlled for. Hence, future studies should control for age at assessment and duration of illness.
Conflict of Interest All the authors have no conflict of interest. 9
Journal of Affective Disorders 260 (2020) 1–10
S.-K. Liu, et al.
comorbidity and gender differences of persons incarcerated for methamphetamine abuse in taiwan. Psychiatry Clin. Neurosci. 58, 206–212. Lu, L.H., Zhou, X.J., Fitzgerald, J., Keedy, S.K., Reilly, J.L., Passarotti, A.M., Sweeney, J.A., Pavuluri, M., 2012. Microstructural abnormalities of white matter differentiate pediatric and adult-onset bipolar disorder. Bipolar. Disord. 14, 597–606. Manchia, M., Maina, G., Carpiniello, B., Pinna, F., Steardo, L., D'ambrosio, V., Salvi, V., Alda, M., Tortorella, A., Albert, U., 2017. Clinical correlates of age at onset distribution in bipolar disorder: a comparison between diagnostic subgroups. Int. J. Bipolar. Disord. 5, 28. Moor, S., Crowe, M., Luty, S., Carter, J., Joyce, P.R., 2012. Effects of comorbidity and early age of onset in young people with bipolar disorder on self harming behaviour and suicide attempts. J. Affect Disorder. 136, 1212–1215. Nandagopal, J.J., Fleck, D.E., Adler, C.M., Mills, N.P., Strakowski, S.M., DelBello, M.P., 2011. Impulsivity in adolescents with bipolar disorder and/or attention-deficit/hyperactivity disorder and healthy controls as measured by the barratt impulsiveness scale. J. Child. Adol. Psychop. 21, 465–468. Patel, N.C., DelBello, M.P., Keck Jr, P.E., Strakowski, S.M., 2006. Phenomenology associated with age at onset in patients with bipolar disorder at their first psychiatric hospitalization. Bipolar. Disord. 8, 91–94. Perlis, R.H., Miyahara, S., Marangell, L.B., Wisniewski, S.R., Ostacher, M., DelBello, M.P., Bowden, C.L., Sachs, G.S., Nierenberg, A.A., Investigators, S.-.B., 2004. Long-term implications of early onset in bipolar disorder: data from the first 1000 participants in the systematic treatment enhancement program for bipolar disorder (STEP-BD). Biol. Psychiatry 55, 875–881. Propper, L., Ortiz, A., Slaney, C., Garnham, J., Ruzickova, M., Calkin, C.V., O'donovan, C., Hajek, T., Alda, M., 2015. Early‐onset and very‐early‐onset bipolar disorder: distinct or similar clinical conditions? Bipolar. Disord. 17, 814–820. Sala, R., Goldstein, B.I., Wang, S., Flórez-Salamanca, L., Iza, M., Blanco, C., 2013. Increased prospective health service use for depression among adults with childhood onset bipolar disorder. J. Pediatr. 163 1454-1457. e1453. Stephens, J.R., Heffner, J.L., Adler, C.M., Blom, T.J., Anthenelli, R.M., Fleck, D.E., Welge, J.A., Strakowski, S.M., DelBello, M.P., 2014. Risk and protective factors associated with substance use disorders in adolescents with first-episode mania. J. Am. Acad. Child Adolesc. Psychiatry 53, 771–779. Stone, A.L., Becker, L.G., Huber, A.M., Catalano, R.F., 2012. Review of risk and protective factors of substance use and problem use in emerging adulthood. Addict. Behav. 37, 747–775. Tozzi, F., Manchia, M., Galwey, N.W., Severino, G., Del Zompo, M., Day, R., Matthews, K., Strauss, J., Kennedy, J.L., McGuffin, P., 2011. Admixture analysis of age at onset in bipolar disorder. Psychiatry Res. 185, 27–32.
Acad. Child Adolesc. Psychiatry 52, 1026–1037. Goldstein, T.R., Birmaher, B., Axelson, D., Ryan, N.D., Strober, M.A., Gill, M.K., Valeri, S., Chiappetta, L., Leonard, H., Hunt, J., 2005. History of suicide attempts in pediatric bipolar disorder: factors associated with increased risk. Bipolar. Disord. 7, 525–535. Grunebaum, M.F., Galfalvy, H.C., Nichols, C.M., Caldeira, N.A., Sher, L., Dervic, K., Burke, A.K., Mann, J.J., Oquendo, M.A., 2006. Aggression and substance abuse in bipolar disorder. Bipolar. Disord. 8, 496–502. Hamshere, M.L., Gordon-Smith, K., Forty, L., Jones, L., Caesar, S., Fraser, C., Hyde, S., Tredget, J., Kirov, G., Jones, I., 2009. Age-at-onset in bipolar-I disorder: mixture analysis of 1369 cases identifies three distinct clinical sub-groups. J. Affect Disord. 116, 23–29. Holtzman, J.N., Miller, S., Hooshmand, F., Wang, P.W., Chang, K.D., Hill, S.J., Rasgon, N.L., Ketter, T.A., 2015. Childhood-compared to adolescent-onset bipolar disorder has more statistically significant clinical correlates. J. Affect Disord. 179, 114–120. Javaid, N., Kennedy, J.L., De Luca, V., 2011. Ethnicity and age at onset in bipolar spectrum disorders. CNS Spectr. 16, 127–134. Joshi, G., Biederman, J., Petty, C., Goldin, R.L., Furtak, S.L., Wozniak, J., 2013. Examining the comorbidity of bipolar disorder and autism spectrum disorders: a large controlled analysis of phenotypic and familial correlates in a referred population of youth with bipolar i disorder with and without autism spectrum disorders. J. Clin. Psychiatry 74, 578–586. Joslyn, C., Hawes, D.J., Hunt, C., Mitchell, P.B., 2016. Is age of onset associated with severity, prognosis, and clinical features in bipolar disorder? A meta‐analytic review. Bipolar. Disord. 18, 389–403. Kalman, J.L., Papiol, S., Forstner, A.J., Heilbronner, U., Degenhardt, F., Strohmaier, J., Adli, M., Adorjan, K., Akula, N., Alda, M., 2019. Investigating polygenic burden in age at disease onset in bipolar disorder: findings from an international multicentric study. Bipolar. Disord. 21, 68–75. Kennedy, N., Everitt, B., Boydell, J., Van Os, J., Jones, P., Murray, R., 2005. Incidence and distribution of first-episode mania by age: results from a 35-year study. Psychol. Med. 35, 855–863. Kessler, R.C., Berglund, P., Demler, O., Jin, R., Merikangas, K., Walters, E.E., 2005. Lifetime prevalence and age-of-onset distributions of dsm-iv disorders in the national comorbidity survey replication. Arch. Gen. Psychiatry 62 (6), 593–602. Leverich, G.S., Post, R.M., Keck Jr, P.E., Altshuler, L.L., Frye, M.A., Kupka, R.W., Nolen, W.A., Suppes, T., McElroy, S.L., Grunze, H., 2007. The poor prognosis of childhoodonset bipolar disorder. J. Pediatr. 150, 485–490. Lin, P.-.I., McInnis, M.G., Potash, J.B., Willour, V., MacKinnon, D.F., DePaulo, J.R., Zandi, P.P., 2006. Clinical correlates and familial aggregation of age at onset in bipolar disorder. Am. J. Psychiatry 163, 240–246. Lin, S.K., Ball, D., Hsiao, C.C., Chiang, Y.-.L., Ree, S.-.C., Chen, C.-.K., 2004. Psychiatric
10
Contents lists available at ScienceDirect
Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Comparisons of the clinical outcomes between early- and adult-onset bipolar disorders: A prospective cohort analysis Shi-Kai Liua,b, Jung-Chi Changc, Hui-Ju Tsaid, Chi-Shin Wue,
T
⁎
a
Centre for Addiction and Mental Health, Toronto, ON, Canada Department of Psychiatry, University of Toronto, Toronto, ON, Canada c Department of Psychiatry, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu, Taiwan d Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan e Department of Psychiatry, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei 10002, Taiwan b
A R T I C LE I N FO
A B S T R A C T
Keywords: Early-onset bipolar disorders Clinical outcomes Hospitalization Substance use disorder Self-harm
Background: This study aimed to explore the impact of age-at-onset on clinical outcomes in bipolar-spectrum disorders, including the development of alcohol or substance use disorder, suicidality, and psychiatric hospitalization. Methods: This population-based study enrolled newly-diagnosed bipolar-spectrum patients, including 4,367 patients with early-onset bipolar disorder (EOBD), 64,787 patients with adult-onset bipolar disorder (AOBD), and the same number of age-controlled comparison subjects without bipolar disorder, from Taiwan's National Health Insurance Research Database. Time-dependent covariate Cox regression models were used to estimate the effect of age-at-onset on clinical outcomes with adjustment for pre-existing psychiatric comorbid conditions and pharmacological treatment patterns. Sensitivity analyses using different definitions of study sample and age cutoffs were conducted. Results: The average follow-up duration was 5.7 years. After adjustment with time-dependent covariates and chronological age, there were no significant differences in the risks for developing new-onset alcohol or substance use disorders and psychiatric hospitalization between EOBD and AOBD patients. Although EOBD patients had a higher risk of hospitalization for suicide and self-harm than did AOBD patients in primary analysis, this finding did not replicated in the sensitivity analyses. Limitations: The symptom profile and severity of bipolar disorder was not available in the NHIRD; therefore, surrogate indicators of clinical outcome might not be sensitive enough to detect the subtle differences. Conclusions: EOBD and AOBD patients had similar risks for developing alcohol or substance use disorders. Their risk of psychiatric hospitalization was similar. Whether EOBD patients might have a higher risk of hospitalization for suicide and self-harm warrants further investigations.
1. Introduction Bipolar spectrum disorder (BD) is marked by heterogeneous clinical manifestation, course, and outcome. Early-onset bipolar disorders (EOBD) has been considered as a distinct subtype of bipolar disorders with a particularly ominous outcome (Joslyn et al., 2016). Evidence from previous cross-sectional studies and one recent meta-analysis suggests that EOBD patients exhibit increased psychiatric comorbidities, higher suicidality, increased number of mood episodes, and more severe long-term functional impairment than those with adult-onset bipolar disorder (AOBD) (Azorin et al., 2013; Baldessarini et al., 2012; Bellivier et al., 2001; Benazzi, 2009; Biffin et al., 2009; Birmaher et al.,
⁎
2006; Carter et al., 2003; Ernst and Goldberg, 2004; Etain et al., 2012; Goldstein and Levitt, 2006; Grunebaum et al., 2006; Holtzman et al., 2015; Javaid et al., 2011; Kennedy et al., 2005; Leverich et al., 2007; Lin et al., 2006; Moor et al., 2012; Perlis et al., 2004; Sala et al., 2013; Tozzi et al., 2011). In addition, family studies found a higher rate of BD among the relatives of EOBD patients than those of AOBD patients (Lin et al., 2006). However, there is some controversy about whether EOBD constitutes a subgroup within the bipolar disorders. First, some prospective studies found that the clinical outcomes of EOBD were not as negative as previous studies had suggested (Birmaher et al., 2014; Coryell et al., 2013; Geller et al., 2004). More than 50% of EOBD youths could
Corresponding author. E-mail addresses: [email protected], [email protected] (C.-S. Wu).
https://doi.org/10.1016/j.jad.2019.08.084 Received 22 April 2019; Received in revised form 30 June 2019; Accepted 28 August 2019 Available online 28 August 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
Journal of Affective Disorders 260 (2020) 1–10
S.-K. Liu, et al.
Fig. 1. Flowchart of subject selection.
disorder, psychiatric hospitalization, and hospitalization for suicide and self-harm.
maintain long-term euthymia (Birmaher et al., 2014). Second, one recent large-scale study failed to demonstrate associations between AAO and polygenic risk score, an indication of genetic risk burden (Kalman et al., 2019). Third, previous studies were riddled with methodological caveats. Most studies addressing the impacts of AAO were cross-sectional, hospital-based studies that enrolled adult patients receiving active treatment (Azorin et al., 2013; Baldessarini et al., 2012; Biffin et al., 2009; Carter et al., 2003; Ernst and Goldberg, 2004; Goldstein and Levitt, 2006; Holtzman et al., 2015; Leverich et al., 2007; Manchia et al., 2017; Patel et al., 2006; Perlis et al., 2004; Propper et al., 2015; Sala et al., 2013). By default, only patients with active treatment would be included. Therefore, the enrolled EOBD patients in adulthood would be inherently a subgroup with unremitted course and poorer prognosis. Such selection bias would result in over-estimating the severity and disease course of EOBD. Moreover, the determination of AAO in cross-sectional studies is subjective and susceptible to recall bias, especially for those with severe courses tend to report earlier age at onset. Finally, the clinical manifestations and pattern of comorbidities might vary with the chronological age at assessment—this could account for the differences between EOBD and AOBD. Unfortunately, AAO, chronologic age at assessment, and duration of illness were intricately intertwined. The effect of current age and duration of illness cannot be properly controlled for in cross-sectional design. To avoid such methodological shortcomings, we emulated a prospective cohort study using data from a representative national claims database. We included all patients with newly-diagnosed BD to avoid selection bias; all were followed-up for the same period of time to control for the effect of duration of illness. In addition, we included a comparison group from the general population matched by AAO to control for the effect of chronologic age on clinical outcomes. Using prospective cohort analysis, we explored the impact of AAO on clinical outcomes, including the development of alcohol or substance use
2. Patients and methods 2.1. Data source Taiwan launched a universal, single-payer National Health Insurance program in 1995. The National Health Insurance program covers more than 99% of the 23 million Taiwanese population; its reimbursement claims have been compiled and updated into the National Health Insurance Research Database (NHIRD). The NHIRD includes comprehensive information about patients’ demographics, clinical diagnosis, medical expenditure, and prescription records in both inpatient and ambulatory settings (Lu et al., 2012). 2.2. Study sample We screened all patients diagnosed with bipolar I disorders (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes: 296.0, 296.4–296.7), bipolar II disorder (ICD-9-CM codes: 296.89) and bipolar disorder not otherwise specified (ICD-9-CM code: 296.80–286.82) registered in the NHIRD between January 1, 2002 and December 31, 2011 for those diagnosed by a psychiatrist and who had at least two ambulatory claims or one inpatient discharge diagnosis of bipolar disorder (n = 129,698). Furthermore, we only included “newly diagnosed patients”, which we defined as one who did not have any diagnostic records of mood episodes in the year preceding the cohort entry date. In addition to prevalent cases (n = 26,169), patients with any diagnosis of schizophrenia during the study period (n = 22,927), missing information of gender (n = 95), or whose age at diagnosis was <13 years (n = 806) or > 60 2
Journal of Affective Disorders 260 (2020) 1–10
S.-K. Liu, et al.
2.6. Treatment pattern
years (n = 10,547) were excluded. The AAO was defined as the age at the first claims record with mood episode, no matter it was manic, depressive, mixed or unspecified episode. There is no clear definition of the age cutoffs for EOBD. One meta-analysis showed the age cutoffs ranged from 18 years to 21 years in most studies (Joslyn et al., 2016). In addition, several studies using admixture analysis and divided three group by AAO. The mean AAO of EOBD was 17–18 years (Azorin et al., 2013; Bellivier et al., 2001; Hamshere et al., 2009). Therefore, we defined EOBD patients were those with onset 18 years or less. The final number of bipolar patients included was 69,154. The flowchart of participant enrollment is shown in Fig. 1. We defined the cohort entry date as the date of the first mood episode, including major depressive episodes (ICD-9-CM codes: 296.2, 296.3, 296.5, or 296.82), manic episodes (296.0, 296.4, or 296.81), mixed episodes (296.6), or unspecified mood episodes (296.7, 296.80, or 296.89).
The medication possession ratio (MPR) was used to present the pattern of use of mood stabilizer, antipsychotic, antidepressant, and benzodiazepine during the follow-up period. MPR is a proxy measure that reflects the medication compliance of patients, as well as a clinician's decision to continue using a specific medication. It is calculated by finding the ratio of the sum of the supplied days of a dispensed drug to 365 days. For example, if a patient received 3 prescriptions of mood stabilizer with 28-day supply, the MPR would be 0.23 (3 × 28/365). If the duration of illness was less than 365 days, the denominator was all the period observed. The overall treatment pattern was classified into irregular use (0 < MPR < 0.8) and regular use (MPR ≧ 0.8) (Cramer et al., 2008). Since medication use may change during a study period, MPR may also vary over time. Hence, MPR was treated as a time-dependent variable. The overall period of follow-up was prospectively divided into 30-day intervals. To trace the change in MRP, the MPR were assessed over a 365-day observation period (or shorter period if duration of illness less than one year) prior to each interval.
2.3. Comparison cohort of subjects without bipolar disorder To identify a comparison group without bipolar disorder from the general population, we used the Longitudinal Health Insurance Database 2000 (LHID 2000), which is a subset of the NHIRD. The LHID 2000 contains all the original claims data of a million randomly sampled beneficiaries from the year 2000 Registry for Beneficiaries of the NHIRD. The distribution of gender and age of the sampled subjects in the LHID 2000 did not differ significantly from that of the general population. For each patient with bipolar disorder, we randomly selected one comparison subject without bipolar disorder matched by age (the year of birth) and sex. The cohort entry date of each subject was assigned on the basis of their matching cases. A total of 69,154 comparison subjects were identified. All of the study subjects were tracked until the date of last medical contact recorded in the claims database.
2.7. Statistical analysis Descriptive statistics of the baseline characteristics of EOBD and AOBD patients and comparison subjects are provided in Table 1. Differences in the characteristics among these two-by-two group comparisons were explored using a logistic regression model. The odds ratios of the EOBD and AOBD groups (age at cohort entry was 13–18 years and 19–60 years, respectively) against comparison subjects were calculated separately. In order to adjust for the chronological age at cohort entry and evaluate the genuine differences in characteristics between EOBD and AOBD patients, we conducted twoby-two analysis on the whole study sample, including bipolar disorder (vs. comparison group), age group at cohort entry (aged of 13–18 years vs. 19–60 years), and the interaction between the above-mentioned two variables. If the interaction term was statistically significant, it indicated that the differences between EOBD and AOBD patients were independent and unmediated by the chronological age effect. The likelihood of developing new-onset alcohol or substance use disorder, psychiatric hospitalization, or hospitalization for suicide and self-harm was presented by the hazard ratios from the Cox proportional hazards regression model including time-dependent covariates. The time-invariable covariates included gender and pre-existing psychiatric conditions. Patients’ chronologic age, which increased during the follow-up period, was treated as a time-dependent variable. Moreover, as the effect of chronologic age might not have linear relationships with the outcomes of interest, the quadratic term of chronologic age was included in the regression model. The treatment pattern of medications also varied during the follow-up period; therefore, they were also analyzed as time-dependent variables. Several sensitivity analyses were conducted to test the robustness of our findings. First, we used 1-year washout period to exclude prevalent bipolar patients in the primary analysis; however, it might be not enough because some patients have remote mood episodes more than one year ago. A sensitivity analysis using 4-year washout periods were conducted in order to have a definitely incident sample. The more definitely incident cases could be identified using longer washout period but the sample size and follow-up period would be smaller. Second, EOBD patients were defined as those AAO before or equal to 18 years. However, the age cutoff might be too late. We used the age cutoff of 16 years to explore whether the earlier AAO group has a more significant difference compared to the AOBD group. Finally, although we included a comparison group and used regression model to control the effect of current age, the current age among these two groups was not totally overlapping. During the study period, the current age ranged from 13 to 29 among the EOBD group and ranged from 19 to 71 among the AOBD group. Thus, we restricted the study patients whose current age were
2.4. Main outcomes The mood symptom and severity was not available in the NHIRD, we used psychiatric hospitalization to indicate the occurrence of a severe mood episode. Given alcohol or substance use disorder would complicate the disease course of bipolar disorder (Cassidy et al., 2001), we chose new-onset alcohol or substance use disorder (ICD-9-CM codes: 291.x 292.x, 303.x, 304.x, or 305.x; tobacco use disorder [305.1] was excluded) as an indicator for new-onset psychiatric comorbid conditions. In addition, we used hospitalization for suicide and self-harm (ICD-9-CM E-code: E950-E959) to assess suicidality. For estimating the risk of developing new-onset alcohol or substance use disorder, subjects already diagnosed with alcohol or substance use disorder before the cohort entry date were excluded; however, they were included in the analysis for the other outcome indicators, such as psychiatric hospitalization or hospitalization for suicide and self-harm. 2.5. Pre-existing psychiatric conditions Pre-existing psychiatric conditions were assessed using the claims records from the year preceding the cohort entry date and included mental retardation (ICD-9-CM code: 317, 318, 319), autism spectrum disorder (ICD-9-CM code: 299), specific development delay (ICD-9-CM code: 315), attention-deficit hyperactivity disorder (ADHD; ICD-9-CM code: 314), alcohol use disorder (ICD-9-CM code: 291, 303, 305.0), substance use disorder (ICD-9-CM code: 292, 304, 305.2-305.9), anxiety disorder (ICD-9-CM code: 300; excluding 300.4), sleep disorder (ICD-9-CM code: 307.4, 780.5), and adjustment disorder (ICD-9-CM code: 309). In addition, we assessed the history of psychiatric hospitalization (to either a psychiatric hospital or psychiatric ward in general hospital) and hospitalization for suicide and self-harm (ICD9-code: E950-E959). 3
Mean age ( ± SD) at cohort entry date Gender, female; n (%) Pre-existing psychiatric conditions, n (%) Mental Retardation Autism Spectrum Disorder Specific development delay ADHD Alcohol Substance use disorder Anxiety disorder Sleep disorder Adjustment disorder History of hospitalization for suicide and self-harm History of psychiatric hospitalization First mood episode, n (%) Depressive episode Manic episode Mixed episode Unspecified episode
4
2446 (56.0)
35 (0.8) 10 (0.2) 44 (1.0) 59 (1.4) 1 (0.0) 1 (0.0) 97 (2.2) 22 (0.5) 2 (0.0) 0 (0.0) 2 (0.0)
2446 (56.0)
388 (8.9) 227 (5.2) 256 (5.9) 705 (16.1) 23 (0.5) 55 (1.3) 1324 (30.3) 490 (11.2) 33 (0.8) 15 (0.3)
326 (7.5)
2156 (49.4) 1103 (25.3) 347 (7.9) 761 (17.4)
16.1 ± 1.6
Comparison subjects (n = 4367)
16.1 ± 1.6
Patient with earlyonset bipolar disorder (n = 4367)
Adolescent cohort, aged 13–18 years
Table 1 Baseline characteristics of study sample.
176.0 (43.8,707.2)
12.1 (8.5,17.1) 23.9 (12.7,45.1) 6.1 (4.4,8.4) 9.0 (6.7,12.0) 23.1 (3.1,171.1) 55.7 (7.7,402.5) 19.2 (15.5,23.7) 25.0 (16.2,38.3) 16.6 (4.0,69.3) NA
Crude odds ratios (earlyonset vs. comparison subjects)
39,390 (60.8) 12,754 (19.7) 4820 (7.4) 7823 (12.1)
7138 (11.0)
1136 (1.8) 229 (0.4) 142 (0.2) 506 (0.8) 4237 (6.5) 2390 (3.7) 32,882 (50.8) 21,741 (33.6) 387 (0.6) 552 (0.9)
37,011 (57.1)
36.6 ± 11.5
Patient with adult-onset bipolar disorder (n = 64,787)
88 (0.1)
184 (0.3) 21 (0.0) 48 (0.1) 59 (0.1) 335 (0.5) 164 (0.3) 7728 (11.9) 3008 (4.6) 40 (0.1) 15 (0.0)
37,011 (57.1)
36.6 ± 11.5
Comparison subjects (n = 64,787)
Adult cohort, aged 19–60 years
90.9 (73.7,112.2)
6.3 (5.4,7.3) 10.9 (7.0,17.1) 3.0 (2.1,4.1) 8.6 (6.6,11.3) 13.5 (12.0,15.1) 15.1 (12.9,17.7) 7.6 (7.4,7.8) 10.4 (10.0,10.8) 9.7 (7.0,13.5) 37.1 (22.2,62.0)
Crude odds ratios (adultonset vs. comparison subjects)
0.359
0.001 0.049 0.002 0.012 0.600 0.197 <0.001 <0.001 0.474 NA
p-value for interaction between age group at cohort entry and bipolar diagnoses
S.-K. Liu, et al.
Journal of Affective Disorders 260 (2020) 1–10
Journal of Affective Disorders 260 (2020) 1–10
5
Significant results are in bold. a Patients with alcohol or substance use disorder in baseline were excluded in the analysis for new-onset of alcohol or substance use disorder, respectively.
267 (0.7) 80 (0.2) 15,127 (51.3) 2145 (5.7) 14 (0.6) 2 (0.1) 1015 (52.3) 100 (4.1)
82.9 (48.9, 140.5) 46.7 (11.4–190.7)
557 (1.4) 17 (0.7) 234 (9.9)
6155 (18.7)
5.7 ± 2.9 5.7 ± 2.9
14.3 (8.7, 23.4)
6.1 ± 0.0 6.0 ± 3.0
Patients with adult-onset bipolar disorders, aged 19–60 years (n = 64,787) Crude HR (bipolar vs. comparison group), 95% CI Comparison groups, aged 13–18 years (n = 4367) Patients with early-onset bipolar disorders, aged 13–18 years (n = 4367)
Table 2 Incidence of clinical outcome and crude hazard ratios among patients with early- and adult-onset bipolar disorders and comparison subjects.
Comparison groups, aged 19–60 years (n = 64,787)
The final study sample consisted of 4367 EOBD patients and 64,787 AOBD patients with their respective age-matched control subjects. Approximately 77.9% (n = 3402) of EOBD and 78.8% (n = 51,076) of AOBD were bipolar I disorder. The percentage of bipolar I disorder was not statistically different between two groups (chi-square = 2.136, pvalue=0.144). A depressive episode was the first mood episode in 49.4% of the EOBD patients, compared to 60.8% in the AOBD group. In contrast, the proportion of patients with manic and unspecified episodes as the first mood episode was higher among EOBD patients than among AOBD patients. Notably, both EOBD and AOBD patients had significantly more preexisting psychiatric disorders than their respective control subjects, albeit with different patterns (Table 1). Disorders of neurodevelopmental origin (e.g., mental retardation, autism spectrum disorder, specific developmental delay, and ADHD) and the acquired psychiatric disorders (alcohol or substance use disorder, anxiety disorder and sleep disorder) were highly prevalent among the EOBD patients, compared to AOBD patients. Excessive psychiatric conditions were also prevalent in AOBD patients; however, the odds ratios for EOBD patients were much higher than those for AOBD patients (Table 1). The mean follow-up duration was 5.7 years for the EOBD group and 6.0 years for the AOBD group (Table 2). During the follow-up period, both EOBD and AOBD groups had a significantly higher risk of developing the outcomes of interest than the control subjects. The risk of developing alcohol or substance use disorder in EOBD patients (crude hazard ratio (HR): 14.3 [95% CI: 8.7–23.4]) and AOBD patients (crude HR: 12.9 [95% CI: 11.8–14.1]) was significantly higher than control subjects. Similarly, both EOBD and AOBD patients had a higher chance of being hospitalized for psychiatric disorders (crude HR: 82.9 [95% CI: 48.9–140.5] and 65.3 [95% CI: 57.9–73.7] for EOBD and AOBD, respectively). In terms of hospitalization for suicide and self-harm, the risk was higher in patients with EOBD (crude HR: 46.7 [95% CI: 11.4–190.7]) than in those with AOBD (crude HR: 20.3 [95% CI: 16.1–25.4]). The results of a time-dependent Cox regression model (Table 3) indicated that there were no significant differences between EOBD and AOBD patients when considering the risks of developing newonset alcohol or substance use disorders and psychiatric hospitalization. However, EOBD subjects had a slightly higher risk for hospitalization for suicide and self-harm than AOBD subjects (p-value = 0.043). There were various pre-existing psychiatric conditions, such as history of hospitalization for suicide and self-harm, benzodiazepine use, and mood stabilizer users were both associated with increased risk of developing alcohol or substance use for both EOBD and AOBD patients (Table 3). Several factors, including mental retardation, autism spectrum disorder, use of antidepressants, etc., had similar risk ratios in both groups but only reached a significant level in AOBD patients, which might be due to sample size. One of the most different effect is male, which is associated with a reduced effect in EOBD group but an increased risk in AOBD groups. For risk factors associated with psychiatric and self-harm hospitalization, previous psychiatric hospitalization was the most consistent predictor for both EOBD and AOBD, while previous self-harm predicted significant hospitalization for suicide and self-harm but not psychiatric hospitalization. In addition, pre-existing use of alcohol or substance predicted a higher risk of psychiatric and self-harm hospitalization. Table 4 shows the results of sensitivity analyses. Using 4-year washout period, we found the incidence and were generally consistent;
Duration of follow-up, years Clinical outcome index, number (incidence per 1000 person-year) New-onset alcohol or substance use disordera Psychiatric hospitalization Hospitalization for suicide and selfharm
3. Results
12.9 (11.8, 14.1)
Crude HR (bipolar vs. comparison group), 95% CI
between 19 and 29 during the study period. All statistical analyses were conducted with SAS version 9.4 (SAS Institute Inc., Cary, North Carolina). The statistical significance of relationships was assessed using 95% confidence intervals (CI) or p values <0.05.
65.3 (57.9, 73.7) 20.3 (16.1–25.4)
S.-K. Liu, et al.
6 2.29 (1.98, 2.66) 1.08 (0.95, 1.24) 0.97 (0.85, 1.11) 1.61 (1.41, 1.85)
2.23 0.76 1.25 1.21
3.07 (2.08, 4.53) 0.58 (0.39, 0.86) 1.16 (0.80, 1.69) 1.23 (0.85, 1.79) 2.37) 0.81) 1.32) 1.27)
1.00 (0.82, 1.22) 0.88 (0.67, 1.15) 0.91 (0.68, 1.22) 1.03 (0.86, 1.23) 1.54 (0.96, 2.45) 1.15 (0.79, 1.67) 0.96 (0.85, 1.08) 1.11 (0.95, 1.30) 1.38 (0.82, 2.35) 2.42 (2.10, 2.77) 1.36 (0.70, 2.63)
0.60 (0.48, 0.75) 0.24 (0.11, 0.54) 0.20 (0.05, 0.80) 0.90 (0.66, 1.22) NA NA 0.92 (0.88, 0.96) 1.21 (1.16, 1.27) 0.99 (0.76, 1.29) 1.22 (1.15, 1.30) 1.37 (1.16, 1.62)
0.64 (0.32, 1.26) 0.65 (0.26, 1.64) 0.39 (0.12, 1.29) 1.36 (0.86, 2.16) NA NA 0.91 (0.69, 1.21) 1.03 (0.70, 1.53) 1.78 (0.57, 5.58) 1.10 (0.69, 1.74) 4.08 (1.27, 13.11) (2.09, (0.72, (1.19, (1.15,
42.45 (27.95, 64.46) 1.30 (1.16, 1.45)
7.42 (6.80, 8.09) 1.66 (1.59, 1.73)
11.94 (7.18, 19.84) 0.61 (0.47, 0.80)
1.91 (1.82, 2.00) 1.02 (0.98, 1.07) 1.15 (1.10, 1.20) 1.33 (1.27, 1.38)
0.94 (0.83, 1.06) 1.05 (0.81, 1.37) 1.33 (0.94, 1.89) 0.72 (0.57, 0.90) 1.70 (1.61, 1.80) 1.13 (1.05, 1.21) 0.89 (0.86, 0.92) 0.91 (0.87, 0.94) 0.89 (0.74, 1.09) 3.33 (3.20, 3.47) 1.66 (1.47, 1.88)
36.28 (31.98, 41.15) 1.25 (1.21, 1.29)
Adult-onset
4.05 (2.52, 6.53) 0.83 (0.53, 1.32) 1.77 (1.13, 2.76) 0.90 (0.57, 1.42)
0.39 (0.14, 1.04) 1.03 (0.35, 3.08) 1.36 (0.45, 4.09) 1.00 (0.51, 1.98) 2.66 (0.84, 8.43) 1.29 (0.46, 3.62) 0.76 (0.53, 1.11) 1.38 (0.88, 2.15) 1.80 (0.44, 7.35) 1.97 (1.25, 3.12) 6.63 (2.56, 17.16)
42.02 (10.26, 172.05) 0.82 (0.58, 1.16)
Early-onset
2.73 (2.40, 3.11) 1.00 (0.89, 1.12) 1.58 (1.41, 1.76) 1.21 (1.09, 1.34)
0.91 (0.61, 1.38) 0.33 (0.08, 1.33) 1.18 (0.38, 3.71) 0.82 (0.41, 1.65) 1.96 (1.71, 2.25) 1.48 (1.25, 1.76) 0.95 (0.86, 1.04) 1.22 (1.11, 1.35) 0.74 (0.42, 1.31) 1.69 (1.49, 1.91) 3.77 (3.04, 4.66)
11.07 (8.75, 14.00) 0.68 (0.62, 0.75)
Adult-onset
Hospitalization for suicide and self-harm
Significant results are in bold. a interaction p-value between bipolar disorder and age group at cohort entry: 0.343 for alcohol or substance use disorder; 0.324 for psychiatric hospitalization; 0.043 for hospitalization for suicide and self-harm.
Bipolar disordera Gender (Male vs. Female) Pre-existing psychiatric conditions Mental Retardation Autism Spectrum Disorder Specific development delay ADHD Alcohol Substance use disorder Anxiety disorder Sleep disorder Adjustment disorder History of psychiatric hospitalization History of hospitalization for suicide and self-harm Treatment pattern ≥ Benzodiazepine use (MPR ≥ 0.8vs. MPR <0.8) Mood stabilizer (MPR ≥ 0.8vs. MPR <0.8) Antidepressant (MPR ≥ 0.8vs. MPR <0.8) Antipsychotic (MPR ≥ 0.8vs. MPR <0.8)
Early-onset
Early-onset
Adult-onset
Psychiatric hospitalization
Alcohol or substance use disorder
Table 3 Adjusted hazard ratios for clinical outcomes among patients with early- and adult-onset bipolar disorders.
S.-K. Liu, et al.
Journal of Affective Disorders 260 (2020) 1–10
New-onset alcohol or substance use disorder Primary analysis With 4-year washout period Using the onset age cut-off of 16 years Restricted to current age between 19 and 29 Psychiatric hospitalization Primary analysis With 4-year washout period Using the onset age cut-off of 16 years Restricted to current age between 19 and 29 Hospitalization for suicide and self-harm Primary analysis With 4-year washout period Using the onset age cut-off of 16 years Restricted to current age between 19 and 29
Clinical outcome index, study population
202 (10.0)
1015 (52.3) 617 (55.8) 522 (50.9) 795 (46.5)
100 (4.1) 48 (3.7) 47 (3.8) 84 (4.0)
4367/64,787 3081/41,663 2275/66,879
3045/8221
4367/64,787 3081/41,663 2275/66,879
3045/8221
234 (9.9) 127 (10.1) 104 (8.6)
2999/7772
4290/58,646 3020/37,164 2242/60,694
Patient number, EOBD/AOBD
Patients with earlyonset bipolar disorders number (incidence per 1000 person year)
7 2 (0.1)
2 (0.1) 2 (0.2) 2 (0.2)
12 (0.6)
14 (0.6) 5 (0.4) 5 (0.4)
14 (0.7)
17 (0.7) 8 (0.6) 7 (0.6)
Comparison groups of early-onset bipolar disorders number (incidence per 1000 person year)
27.40 (6.59, 113.98)
42.02 (10.26, 172.05) 12.00 (2.77, 51.95) 20.52 (4.82, 87.33)
47.96 (26.95, 85.35)
42.45 (27.95, 64.46) 74.28 (30.60, 180.32) 69.20 (28.48, 168.14)
12.11 (6.93, 21.19)
11.94 (7.18, 19.84) 13.35 (6.37, 27.95) 14.67 (6.67, 32.25)
Adjusted HR (bipolar vs. comparison group), 95% CI
Table 4 Sensitivity analysis using different washout period, age cutoff, and restricted sample with comparable current age.
178 (5.7)
2145 (5.7) 1101 (6.1) 2198 (5.6)
2073 (84.1)
15,127 (51.3) 8330 (54.6) 15,620 (48.5)
572 (20.2)
6155 (18.7) 3276 (21.3) 6285 (18.4)
Patients with adultonset bipolar disorders number (incidence per 1000 person year)
4 (0.1)
80 (0.2) 33 (0.2) 80 (0.2)
29 (0.9)
267 (0.7) 134 (0.7) 276 (0.7)
34 (1.1)
557 (1.4) 269 (1.5) 567 (1.4)
Comparison groups of adult-onset bipolar disorders number (incidence per 1000 person year)
21.54 (7.81, 59.42)
11.07 (8.75, 14.00) 13.82 (9.62, 19.83) 14.17 (9.88, 20.33)
66.21 (45.55, 96.25)
36.28 (31.98, 41.15) 38.46 (32.29, 45.80) 36.16 (32.02, 40.85)
10.78 (7.49, 15.52)
7.42 (6.80, 8.09) 7.71 (6.73, 8.83) 7.41 (6.74, 8.13)
Adjusted HR (bipolar vs. comparison group), 95% CI
0.772
0.043 0.827 0.607
0.836
0.324 0.053 0.058
0.993
0.343 0.122 0.109
p-value for interaction
S.-K. Liu, et al.
Journal of Affective Disorders 260 (2020) 1–10
Journal of Affective Disorders 260 (2020) 1–10
S.-K. Liu, et al.
4.2. Comparison with previous studies
however, the difference in the risk of hospitalization for suicide and self-harm was not significant in the sample with 4-year washout period. Using the age cutoff of 16 years, we found the incidence for three clinical outcome indices changed slightly among EOBD patients and their corresponding comparison groups; however, the hazard ratios of EOBD increased for the new-onset alcohol or substance use disorder and psychiatric hospitalization but declined for hospitalization for suicide and self-harm. Finally, we restricted our study sample with comparable current age, ranged from 19 to 29 years during the study period. The results showed all the difference in study outcomes between EOBD and AOBD were not significant.
Literature had been inconsistent as to whether earlier age-at-onset was associated with a higher risk for developing alcohol or substance use disorders in bipolar disorders. There were studies showing that EOBD patients had a higher risk of developing alcohol or substance use disorder, Carter et al. (2003), Coryell et al. (2013), Ernst and Goldberg (2004), Holtzman et al. (2015), and Leverich et al. (2007) whereas others found a null association (Grunebaum et al., 2006; Moor et al., 2012; Propper et al., 2015; Sala et al., 2013) or negative association (Patel et al., 2006). One important but not properly addressed factor affecting clinical manifestations and comorbidity patterns was the chronologic age at assessment. Considering that increased age entails evolving developmental stages and bio-psycho-social matrices, age-dependent social context and challenges would impact the development of comorbidities in bipolar disorders as individuals go through various stages. Such impacts of age could best be illustrated by the estimation of comorbid substance use across ages. One study enrolling patients with EOBD and relatively young AOBD patients (mean age at assessment was 17.2 and 26.1 years, respectively) found that patients with AOBD had a higher rate of alcohol or substance use than those with EOBD (Patel et al., 2006). Such findings might be spurious, since alcohol or substance use disorder generally peaks at early adulthood and subsides gradually during middle age or old age (Kessler et al., 2005). In most other studies addressing AOBD, the average age at assessment was around middle age—a time when substance abuse may not be a problem. Alternatively, it could be under-reported due to recall bias. This methodological issue was addressed in our study. When duration of illness and chronological age at assessment were controlled for by study design and statistical analysis, no significant differences in the risk of alcohol or substance use disorder emerged between AOBD and EOBD. Regarding the severity of mood episodes, our results were different from several studies which indicated that patients with EOBD had more mood episodes or were more likely to be hospitalized than those with AOBD (Azorin et al., 2013; Baldessarini et al., 2012; Coryell et al., 2013; Leverich et al., 2007). Such discrepancies could be partially explained by the various methods of sample selection. In cross-sectional studies conducted in clinical settings, enrolled patients were still receiving active treatment and those who had achieved remission were not be included (Azorin et al., 2013; Baldessarini et al., 2012; Leverich et al., 2007). In these studies recruited EOBD patients would be inherently biased towards a subgroup with a longer duration of illness and an unremitted disease course, hence the poorer long term course. Notwithstanding such potential bias by design, there were studies demonstrating that the number of mood episodes in patients with EOBD was similar to those with AOBD (Etain et al., 2012; Manchia et al., 2017; Patel et al., 2006; Sala et al., 2013). The current results were thus in line with those from previous prospective studies (Patel et al., 2006; Sala et al., 2013), which suggested that the clinical outcomes of EOBD were not much poorer than AOBD. In terms of suicidality, our findings in primary analysis showed that EOBD was weakly associated with a higher risk for hospitalization for suicide and self-harm compared to those with AOBD. The results were consistent with previous studies showing that patients with EOBD have higher suicidal risk (Azorin et al., 2013; Biffin et al., 2009; Carter et al., 2003; Holtzman et al., 2015; Moor et al., 2012; Perlis et al., 2004; Propper et al., 2015). High impulsivity among EOBD patients might have contributed to the elevated suicidal risk (Nandagopal et al., 2011). However, the finding was not replicated in our sensitivity analyses using different washout period, age cutoff, and restricting sample with comparable current age. Some studies showed EOBD was not associated with elevated suicidal risk (Baldessarini et al., 2012; Coryell et al., 2013; Ernst and Goldberg, 2004; Manchia et al., 2017). It should be noted that the number of hospitalization for suicide and self-harms among comparison subjects in adolescent cohorts was small (n = 2).
4. Discussion 4.1. Main findings Using a prospective cohort design which included only patients with newly-diagnosed bipolar disorders, the current study found both EOBD and AOBD patients had significantly more previous neurodevelopmental and/or psychiatric conditions but the pattern differed. However, there were no significant differences in the risks for psychiatric hospitalization and the development of alcohol or substance use disorders between the two groups. EOBD was weakly associated with a higher risk for hospitalization for suicide and self-harm than AOBD in primary analysis but this difference was not statistically significant in the sensitivity analyses. The current study used better research methodologies than previous cross-sectional studies. We used a total population database to trace all newly-onset bipolar patients; therefore, the sample size was large, and the average follow-up period was over 5 years. In addition, this study included a comparison group to estimate the effect of chronological age at assessment on clinical outcomes and carefully controlled potential confounding factors, such as comorbid illness and time-variant pharmacological treatment. The most salient difference in the pattern of psychiatric conditions before the bipolar disorder diagnosis between EOBD and AOBD is the predominance of disorders of neurodevelopmental origin (mental retardation, autism, developmental delays, and attention-deficit/hyperactivity disorder). This was not surprising because bipolar disorder has been among the most prevalent major psychiatric disorders in people with developmental disability and/or autism. Nevertheless, the co-existence of developmental pathology might substantially modify the presentation of mood symptoms as to make them “atypical,” as patients could present with increased psychomotor disturbances and/or irritable mood, instead of the classical mood symptoms. This might have accounted for the widely reported contrast in the proportion of atypical, unspecified or mixed episodes as the presenting symptoms in EOBD (Joshi et al., 2013). The finding that EOBD was associated with a higher comorbidity of anxiety and sleep disorder than AOBD needs further scrutiny. Since both anxiety and sleep disturbances could be part of the initial manifestations of a full-blown mood episode, the increased prevalence in both EOBD and AOBD might be an epiphenomenon. In addition, as both anxiety and sleep disturbance might be common manifestations of comorbid psychiatric conditions, the significantly higher risk in EOBD might reflect the higher number of overall pre-bipolar psychiatric conditions in individuals with EOBD. It will be of great clinical significance and interest as to how these comorbidities affect the outcome of bipolar disorder in the long term. Even though most of the pre-bipolar conditions investigated did not appear to affect the primary outcomes, i.e., the development of alcohol or substance use, psychiatric hospitalization, and hospitalization for suicide and self-harm, it was far from conclusive. Many intervening, protective, or aggravating factors could have affected the trajectory of individual clinical pathways.
8
Journal of Affective Disorders 260 (2020) 1–10
S.-K. Liu, et al.
CRediT authorship contribution statement
Hence, the statistical significance should be interpreted more conservatively. The risk factors for study outcome indices were generally consistent with previous literature (Goldstein et al., 2013, 2005; Stephens et al., 2014). However, one caveat finding is that the female was associated with an increased risk for developing substance or abuse among adolescents with bipolar disorders. Although male is a risk factor for developing substance use disorder (Stone et al., 2012), previous studies showed there was no gender effect on the risk of developing substance use disorder among the adolescent with bipolar disorders (Goldstein et al., 2013; Stephens et al., 2014). In Taiwan, the adolescent female with substance use disorder have higher likelihood to seeking treatment (Lin et al., 2004). Given that we identified alcohol or substance use disorder based on health insurance claims records, the adolescent male with substance or alcohol use disorder were less likely to seek treatment, thereby leading to under-estimate the risk of male bipolar patients. The role of gender on developing alcohol or substance use disorder in this study group warrants more investigation.
Shi-Kai Liu: Conceptualization, Methodology, Supervision, Validation, Writing - original draft, Writing - review & editing. JungChi Chang: Conceptualization, Methodology, Writing - original draft, Writing - review & editing. Hui-Ju Tsai: Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing - review & editing. Chi-Shin Wu: Conceptualization, Formal analysis, Funding acquisition, Methodology, Validation, Writing - original draft, Writing - review & editing. Acknowledgements All authors declare no potential conflict of interest. This work was supported by grants from Ministry of Science and Technology (PI: CSW, MOST 107-2314-B-002-216) and National Health Research Institutes, Taiwan(PI: HJT, PH–104-PP-14, PH-104-SP-05 and PH-104-SP-16). The National Taiwan University Hospital and National Health Research Institutes had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.
4.3. Limitations Our study has inherent limitations from using a claims database. Firstly, the details of mood symptoms and the severity of bipolar disorder were not available in the NHIRD. We assessed the clinical outcomes using several surrogate indicators, which might not be sensitive enough to detect the subtle differences. For example, mild mood episodes or suicidal behaviors without the need for hospitalization would not be measured in our study. However, the severe mood episodes or suicidal attempts need to hospitalization could be fully identified in the NHIRD. Secondly, we identified patients with bipolar disorders based on the diagnostic codes in the claims database; however, the validation of diagnosis was not documented in the NHIRD. Accordingly, we used restrictive definitions, diagnosis by a psychiatrist, and the number of records to improve the validations. Third, the diagnosis of bipolar spectrum disorders was based ICD-9-CM system rather than DSM-5. Therefore, cyclothymia, substance-induced bipolar disorders, and bipolar disorder associated with another medical conditions were not included in this analysis. Finally, other potential confounding factors that might influence the manifestations and clinical course of bipolar disorder, such as personality profile, social support system, socioeconomic status, and treatment compliance, were not addressed in the current study. These preliminary findings should be considered as a basis for further research.
Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2019.08.084. Reference Azorin, J.-.M., Bellivier, F., Kaladjian, A., Adida, M., Belzeaux, R., Fakra, E., Hantouche, E., Lancrenon, S., Golmard, J.-.L., 2013. Characteristics and profiles of bipolar I patients according to age-at-onset: findings from an admixture analysis. J. Affect Disord. 150, 993–1000. Baldessarini, R.J., Tondo, L., Vázquez, G.H., Undurraga, J., Bolzani, L., Yildiz, A., KHALSA, H.M.K., Lai, M., Lepri, B., Lolich, M., 2012. Age at onset versus family history and clinical outcomes in 1,665 international bipolar‐I disorder patients. World Psychiatry 11, 40–46. Bellivier, F., Golmard, J.-.L., Henry, C., Leboyer, M., Schürhoff, F., 2001. Admixture analysis of age at onset in bipolar I affective disorder. Arch. Gen. Psychiatry 58, 510–512. Benazzi, F., 2009. Classifying mood disorders by age-at-onset instead of polarity. Prog. Neuropsychopharmacol. Biol. Psychiatry 33, 86–93. Biffin, F., Tahtalian, S., Filia, K., Fitzgerald, P.B., De Castella, A.R., Filia, S., Berk, M., Dodd, S., Callaly, P., Berk, L., 2009. The impact of age at onset of bipolar I disorder on functioning and clinical presentation. Acta Neuropsychiatry 21, 191–196. Birmaher, B., Axelson, D., Strober, M., Gill, M.K., Valeri, S., Chiappetta, L., Ryan, N., Leonard, H., Hunt, J., Iyengar, S., 2006. Clinical course of children and adolescents with bipolar spectrum disorders. Arch. Gen. Psychiatry 63, 175–183. Birmaher, B., Gill, M.K., Axelson, D.A., Goldstein, B.I., Goldstein, T.R., Yu, H., Liao, F., Iyengar, S., Diler, R.S., Strober, M., 2014. Longitudinal trajectories and associated baseline predictors in youths with bipolar spectrum disorders. Am. J. Psychiatry 171, 990–999. Carter, T.D.C., Mundo, E., Parikh, S.V., Kennedy, J.L., 2003. Early age at onset as a risk factor for poor outcome of bipolar disorder. J. Psychiatry Res. 37, 297–303. Cassidy, F., Ahearn, E.P., Carroll, B.J., 2001. Substance abuse in bipolar disorder. Bipolar Disord. 3, 181–188. Coryell, W., Fiedorowicz, J., Leon, A.C., Endicott, J., Keller, M.B., 2013. Age of onset and the prospectively observed course of illness in bipolar disorder. J. Affect Disord. 146, 34–38. Cramer, J.A., Roy, A., Burrell, A., Fairchild, C.J., Fuldeore, M.J., Ollendorf, D.A., Wong, P.K., 2008. Medication compliance and persistence: terminology and definitions. Value Health 11, 44–47. Ernst, C.L., Goldberg, J.F., 2004. Clinical features related to age at onset in bipolar disorder. J. Affect Disord. 82, 21–27. Etain, B., Lajnef, M., Bellivier, F., Mathieu, F., Raust, A., Cochet, B., Gard, S., M'Bailara, K., Kahn, J.-.P., Elgrabli, O., 2012. Clinical expression of bipolar disorder type i as a function of age and polarity at onset: convergent findings in samples from France and the United States. J. Clin. Psychiatry 73, e561–e566. Geller, B., Tillman, R., Craney, J.L., Bolhofner, K., 2004. Four-year prospective outcome and natural history of mania in children with a prepubertal and early adolescent bipolar disorder phenotype. Arch. Gen. Psychiatry 61, 459–467. Goldstein, B.I., Levitt, A.J., 2006. Further evidence for a developmental subtype of bipolar disorder defined by age at onset: results from the national epidemiologic survey on alcohol and related conditions. Am. J. Psychiatry 163, 1633–1636. Goldstein, B.I., Strober, M., Axelson, D., Goldstein, T.R., Gill, M.K., Hower, H., Dickstein, D., Hunt, J., Yen, S., Kim, E., 2013. Predictors of first-onset substance use disorders during the prospective course of bipolar spectrum disorders in adolescents. J. Am.
5. Conclusions There were significant differences among the psychiatric comorbidities before the onset of bipolar disorders between EOBD and AOBD patients, with a predominance of disorders of neurodevelopmental origin in EOBD. However, the role of these comorbidities in predicting the development of the clinical outcomes after onset of bipolar disorder was unclear. After the onset of bipolar disorder, patients with EOBD might have a higher risk for hospitalization for suicide and self-harm than those with AOBD. However, the differences in the development of alcohol or substance use disorder or psychiatric hospitalization were not significant. Overall, the current study did not support that EOBD had poorer outcomes than AOBD when the important confounding variables of age at assessment and duration of illness were properly controlled for. Hence, future studies should control for age at assessment and duration of illness.
Conflict of Interest All the authors have no conflict of interest. 9
Journal of Affective Disorders 260 (2020) 1–10
S.-K. Liu, et al.
comorbidity and gender differences of persons incarcerated for methamphetamine abuse in taiwan. Psychiatry Clin. Neurosci. 58, 206–212. Lu, L.H., Zhou, X.J., Fitzgerald, J., Keedy, S.K., Reilly, J.L., Passarotti, A.M., Sweeney, J.A., Pavuluri, M., 2012. Microstructural abnormalities of white matter differentiate pediatric and adult-onset bipolar disorder. Bipolar. Disord. 14, 597–606. Manchia, M., Maina, G., Carpiniello, B., Pinna, F., Steardo, L., D'ambrosio, V., Salvi, V., Alda, M., Tortorella, A., Albert, U., 2017. Clinical correlates of age at onset distribution in bipolar disorder: a comparison between diagnostic subgroups. Int. J. Bipolar. Disord. 5, 28. Moor, S., Crowe, M., Luty, S., Carter, J., Joyce, P.R., 2012. Effects of comorbidity and early age of onset in young people with bipolar disorder on self harming behaviour and suicide attempts. J. Affect Disorder. 136, 1212–1215. Nandagopal, J.J., Fleck, D.E., Adler, C.M., Mills, N.P., Strakowski, S.M., DelBello, M.P., 2011. Impulsivity in adolescents with bipolar disorder and/or attention-deficit/hyperactivity disorder and healthy controls as measured by the barratt impulsiveness scale. J. Child. Adol. Psychop. 21, 465–468. Patel, N.C., DelBello, M.P., Keck Jr, P.E., Strakowski, S.M., 2006. Phenomenology associated with age at onset in patients with bipolar disorder at their first psychiatric hospitalization. Bipolar. Disord. 8, 91–94. Perlis, R.H., Miyahara, S., Marangell, L.B., Wisniewski, S.R., Ostacher, M., DelBello, M.P., Bowden, C.L., Sachs, G.S., Nierenberg, A.A., Investigators, S.-.B., 2004. Long-term implications of early onset in bipolar disorder: data from the first 1000 participants in the systematic treatment enhancement program for bipolar disorder (STEP-BD). Biol. Psychiatry 55, 875–881. Propper, L., Ortiz, A., Slaney, C., Garnham, J., Ruzickova, M., Calkin, C.V., O'donovan, C., Hajek, T., Alda, M., 2015. Early‐onset and very‐early‐onset bipolar disorder: distinct or similar clinical conditions? Bipolar. Disord. 17, 814–820. Sala, R., Goldstein, B.I., Wang, S., Flórez-Salamanca, L., Iza, M., Blanco, C., 2013. Increased prospective health service use for depression among adults with childhood onset bipolar disorder. J. Pediatr. 163 1454-1457. e1453. Stephens, J.R., Heffner, J.L., Adler, C.M., Blom, T.J., Anthenelli, R.M., Fleck, D.E., Welge, J.A., Strakowski, S.M., DelBello, M.P., 2014. Risk and protective factors associated with substance use disorders in adolescents with first-episode mania. J. Am. Acad. Child Adolesc. Psychiatry 53, 771–779. Stone, A.L., Becker, L.G., Huber, A.M., Catalano, R.F., 2012. Review of risk and protective factors of substance use and problem use in emerging adulthood. Addict. Behav. 37, 747–775. Tozzi, F., Manchia, M., Galwey, N.W., Severino, G., Del Zompo, M., Day, R., Matthews, K., Strauss, J., Kennedy, J.L., McGuffin, P., 2011. Admixture analysis of age at onset in bipolar disorder. Psychiatry Res. 185, 27–32.
Acad. Child Adolesc. Psychiatry 52, 1026–1037. Goldstein, T.R., Birmaher, B., Axelson, D., Ryan, N.D., Strober, M.A., Gill, M.K., Valeri, S., Chiappetta, L., Leonard, H., Hunt, J., 2005. History of suicide attempts in pediatric bipolar disorder: factors associated with increased risk. Bipolar. Disord. 7, 525–535. Grunebaum, M.F., Galfalvy, H.C., Nichols, C.M., Caldeira, N.A., Sher, L., Dervic, K., Burke, A.K., Mann, J.J., Oquendo, M.A., 2006. Aggression and substance abuse in bipolar disorder. Bipolar. Disord. 8, 496–502. Hamshere, M.L., Gordon-Smith, K., Forty, L., Jones, L., Caesar, S., Fraser, C., Hyde, S., Tredget, J., Kirov, G., Jones, I., 2009. Age-at-onset in bipolar-I disorder: mixture analysis of 1369 cases identifies three distinct clinical sub-groups. J. Affect Disord. 116, 23–29. Holtzman, J.N., Miller, S., Hooshmand, F., Wang, P.W., Chang, K.D., Hill, S.J., Rasgon, N.L., Ketter, T.A., 2015. Childhood-compared to adolescent-onset bipolar disorder has more statistically significant clinical correlates. J. Affect Disord. 179, 114–120. Javaid, N., Kennedy, J.L., De Luca, V., 2011. Ethnicity and age at onset in bipolar spectrum disorders. CNS Spectr. 16, 127–134. Joshi, G., Biederman, J., Petty, C., Goldin, R.L., Furtak, S.L., Wozniak, J., 2013. Examining the comorbidity of bipolar disorder and autism spectrum disorders: a large controlled analysis of phenotypic and familial correlates in a referred population of youth with bipolar i disorder with and without autism spectrum disorders. J. Clin. Psychiatry 74, 578–586. Joslyn, C., Hawes, D.J., Hunt, C., Mitchell, P.B., 2016. Is age of onset associated with severity, prognosis, and clinical features in bipolar disorder? A meta‐analytic review. Bipolar. Disord. 18, 389–403. Kalman, J.L., Papiol, S., Forstner, A.J., Heilbronner, U., Degenhardt, F., Strohmaier, J., Adli, M., Adorjan, K., Akula, N., Alda, M., 2019. Investigating polygenic burden in age at disease onset in bipolar disorder: findings from an international multicentric study. Bipolar. Disord. 21, 68–75. Kennedy, N., Everitt, B., Boydell, J., Van Os, J., Jones, P., Murray, R., 2005. Incidence and distribution of first-episode mania by age: results from a 35-year study. Psychol. Med. 35, 855–863. Kessler, R.C., Berglund, P., Demler, O., Jin, R., Merikangas, K., Walters, E.E., 2005. Lifetime prevalence and age-of-onset distributions of dsm-iv disorders in the national comorbidity survey replication. Arch. Gen. Psychiatry 62 (6), 593–602. Leverich, G.S., Post, R.M., Keck Jr, P.E., Altshuler, L.L., Frye, M.A., Kupka, R.W., Nolen, W.A., Suppes, T., McElroy, S.L., Grunze, H., 2007. The poor prognosis of childhoodonset bipolar disorder. J. Pediatr. 150, 485–490. Lin, P.-.I., McInnis, M.G., Potash, J.B., Willour, V., MacKinnon, D.F., DePaulo, J.R., Zandi, P.P., 2006. Clinical correlates and familial aggregation of age at onset in bipolar disorder. Am. J. Psychiatry 163, 240–246. Lin, S.K., Ball, D., Hsiao, C.C., Chiang, Y.-.L., Ree, S.-.C., Chen, C.-.K., 2004. Psychiatric
10