Journal of Affective Disorders 178 (2015) 71–78

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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad

Special review article

Long-term morbidity in bipolar-I, bipolar-II, and unipolar major depressive disorders Alberto Forte a,b,c, Ross J. Baldessarini a,b,n, Leonardo Tondo a,b,d,e, Gustavo H. Vázquez b,,f, Maurizio Pompili c, Paolo Girardi c a

Department of Psychiatry, Harvard Medical School, Boston, MA, United States International Consortium for Mood & Psychotic Disorder Research, Mailman Research Center, Belmont, MA, United States c NESMOS Department of Psychiatry, Sant'Andrea Medical Center, Sapienza University of Rome, Italy d Lucio Bini Mood Disorders Center, Cagliari, Italy e Lucio Bini Mood Disorders Center, Rome, Italy f Department of Neuroscience, Palermo University, Buenos Aires, Argentina b

art ic l e i nf o

a b s t r a c t

Article history: Received 22 December 2014 Received in revised form 9 February 2015 Accepted 12 February 2015 Available online 3 March 2015

Background: Long-term symptomatic status in persons with major depressive and bipolar disorders treated clinically is not well established, although mood disorders are leading causes of disability worldwide. Aims: To pool data on long-term morbidity, by type and as a proportion of time-at-risk, based on published studies and previously unreported data. Methods: We carried out systematic, computerized literature searches for information on percentage of time in specific morbid states in persons treated clinically and diagnosed with recurrent major depressive or bipolar I or II disorders, and incorporated new data from one of our centers. Results: We analyzed data from 25 samples involving 2479 unipolar depressive and 3936 bipolar disorder subjects (total N¼6415) treated clinically for 9.4 years. Proportions of time ill were surprisingly and similarly high across diagnoses: unipolar depressive (46.0%), bipolar I (43.7%), and bipolar II (43.2%) disorders, and morbidity was predominantly depressive: unipolar (100%), bipolar-II (81.2%), bipolar-I (69.6%). Percent-timeill did not differ between UP and BD subjects, but declined significantly with longer exposure times. Conclusions: The findings indicate that depressive components of all major affective disorders accounted for 86% of the 43–46% of time in affective morbidity that occurred despite availability of effective treatments. These results encourage redoubled efforts to improve treatments for depression and adherence to their long-term use. & 2015 Elsevier B.V. All rights reserved.

Keywords: Bipolar I Bipolar II Major depression Morbidity Long-term Percent-time ill

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Eligibility criteria and information sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Search strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Data extraction and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of funding source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interest. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

n

Corresponding author. Tel.: þ 1 617 855 3203; fax: þ 1 617 855 3479. E-mail address: [email protected] (R.J. Baldessarini).

http://dx.doi.org/10.1016/j.jad.2015.02.011 0165-0327/& 2015 Elsevier B.V. All rights reserved.

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A. Forte et al. / Journal of Affective Disorders 178 (2015) 71–78

1. Introduction

2. Methods

Major mood disorders are increasingly recognized as leading causes of the worldwide burden of disease and disability (Ferrari et al., 2013; Murray and Lopez, 1996; Ustun, 2004; World Health Organization, 2012). In addition to morbidity, disability and costs, depression in unipolar major depressive disorder (UD) and as a major component of bipolar disorders (BDs) also increases mortality associated with other, cooccurring medical illnesses—notably including cardiovascular, endocrine and pulmonary diseases—in addition to its major contribution to risk of suicide and high levels of economic costs (Almeida et al., 2014; Fan et al., 2014; Miller et al., 2014; Ng et al., 2007; Osby et al., 2001; Tondo et al., 2007; Van der Kooy et al., 2007; Wulsin and Singal, 2003; Schaffer et al., 2015). Danger from mood disorders owes to their prevalence, high rates of recurrences, and risks of sustained affective morbidity and disability (Hardeveld et al., 2013; Sutin et al., 2013). Identified factors associated with morbidity and disability in both UD and BD include symptomatic severity in acute episodes, younger onset, more recurrences, psychiatric hospitalization, financial and legal problems, co-occurring anxiety, delay of treatment, and older current age (Melartin et al., 2004; Melchior et al., 2010; Montagnier et al., 2006; Seemüller, 2014). In recent decades, significant advances in psychiatric therapeutics, providing innovative and safer as well as effective antidepressant, antimanic, and mood-stabilizing medicines have been incorporated into nearly universally accepted community standards of clinical care (Baldessarini, 2013). However, aside from their statistical superiority to placebos in short-term, controlled clinical trials, and fewer longterm trials, the actual impact of modern treatments on long-term morbidity characteristic of major mood disorder patients is not well established or quantified (Baldessarini, 2013). Potential limitations to long-term treatment effectiveness include the natural history of recurrent or chronic disorders, treatments of imperfect effectiveness, and the major challenge of nonadherence to long-term treatment (Baldessarini, 2013; Lingam and Scott, 2002; Pompili et al., 2013). Most long-term clinical studies of patients diagnosed with UD or BD have analyzed recurrence frequency, episode duration, and predictors of recovery, relapse or recurrence (Goodwin and Jamison, 2007; Hughes and Cohen, 2009; van Weel-Baumgarten et al., 2000). Of these studies, few have focused on week-by-week or month-bymonth morbidity in major mood disorder patients treated by community standards under clinical conditions (Judd et al., 1998; Judd and Akiskal, 2000; Kupka et al., 2007; Parikh et al., 2014). Clinical and evidence-based observations indicate that long-term morbidity in both UD and BD is not fully controlled by available treatments, despite evidence of their short-term efficacy, particularly in acute episodes of depression or mania (Baldessarini, 2013; Judd et al., 2003, 2002; Yildiz et al., 2014). Our preliminary analyses of treated BD patients followed prospectively from first-lifetime episodes found surprisingly high proportions of new or unresolved morbidity in BD (40–50%), nearly three-quarters of which was accounted for by depression or dysthymia (Baldessarini et al., 2010a), and even more if bipolar mixed states are included as closer to depression than to mania (Baldessarini et al., 2010b). Given the lack of systematic compilations of evidence concerning long-term clinical status in both UD and BD patients undergoing treatment, and the pressing public health significance of mood disorders, particularly their depressive components, led us to undertake the present study. Our aim was to review available data on types and estimated proportions of long-term morbidity among UD, and both types I and II BD patients to compare overall proportions of time ill, depressed, or manic. In addition, previously unreported data from one of our mood disorder programs (LT) are incorporated with information extracted from published reports.

2.1. Eligibility criteria and information sources We used computerized literature searching to identify reports in English appearing between 1980 and the end of 2014, involving studies of long-term morbidity of patients diagnosed with unipolar major depressive disorder (UD) or bipolar I or II disorder (BD). We sought specific information on the proportion of time in major or minor states of affective morbidity during at least 12 months of follow up in samples of adult participants. The MEDLINE/PubMeds, PsychINFOs, and EMBASEs research literature databases were searched with the following terms for UD: longterm morbidity AND depressive disorder OR depression; terms for BD were: long-term morbidity AND bipolar disorder OR bipolar. In addition, we hand-searched the bibliographies of screened full reports for additional information. Exclusion criteria applied to potential citations were: failure to report on long-term morbidity and proportions of time spent ill, diagnosis other than UD or BD or without separation from each other or from other diagnostic groups, sampling only from geriatric or pediatric subjects, follow-up for less than 12 months, repeated reports from the same study, and not reporting in English. 2.2. Search strategy Searching yielded an initial total of 3577 records, abstracts of which were initially screened (by AF), leading to exclusion of 3417 publications as not relevant to the present study or as duplicates. The remaining 160 reports were reviewed independently by two investigators (AF, RJB), disagreements were resolved by consensus, and 73 records were excluded. This process led to 87 full texts to be assessed in detail for eligibility and to review their bibliographies; 62 were excluded, most for lacking data on proportions of time ill. Finally, 25 datasets met study criteria and were included for analysis, including previously unreported data from the Lucio Bini Mood Disorder Research Centers in Cagliari, and Rome (provided by Tondo and Baldessarini, 2015), following methods detailed previously (Baldessarini et al., 2010b; Tondo et al., 2007). The search process is summarized in Fig. 1, and references are provided in Tables 1 and 2 below. 2.3. Data extraction and analysis Two reviewers (AF, RJB) independently extracted from data regarding: [a] percent of time ill; [b] types of morbidity; [c] methods and measures to assess morbidity, including timing of assessments; [d] socio-demographic and clinical characteristics of study samples; and [e] study design; we resolved any disagreements by consensus. Morbidity was categorized as mania or hypomania, major depression or minor depression not meeting full diagnostic criteria (DSM-III or -IV used in all reports, or other states, usually anxiety or bipolar mixed-states). Averages are mean 7standard deviation or with 95% confidence intervals (CI). Proportions of time ill and durations of observation are reported as simple means with CI, but were also compared with averages weighted by subjects/study. Comparisons of continuous measures were based on ANOVA (t-test) methods as well as on nonparametric tests (Kruskal–Wallis test [H-statistic]) owing to uncertain normality of the distribution of morbidity measures; similarly, correlations were tested by bivariate linear regression modeling (r) and by nonparametric Spearman rankmethods (rs). In addition, multivariate linear regression modeling tested for significant and independent effects of selected covariates on %-time-ill.

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Fig. 1. Flow-chart of selection of reports for inclusion in study, based PRISMA recommendations (http://www.prisma-statement.org/statement.htm) to yield 28 study reports included in analyses.

3. Results We identified 14 reports of long-term morbidity involving 2479 UD subjects (Table 1), and 15 (including 4 studies that also included UD cases and 8 with data on BD-I and BD-II cases) concerning 3936 cases of BD, including 2760 BD-I subjects; of these studies, 8 included data on 822 BD-II patients (Table 2). In UD samples 70.2% were women and age averaged 42.07 4.45 years; in reports on BDs, 73.1% of participants were women, and age at intake averaged 43.5 710.9 years. Observation times averaged 12.8 years with UD subjects, and 7.27 years for BD, and 9.38 [6.81–12.0] years overall (Tables 1 and 2). Few studies reported total years of illness: for BD-I subjects, illness averaged 12.6 7 7.15 years (3 reports); in BD-II, 16.2 75.97 years (3 reports); and in UD, 18.4 71.27 years (2 reports). The proportion of time ill among UD subjects averaged 46.0% [CI: 34.0–58.0] (Table 1). Reports varied in providing details regarding specific morbid states, but routinely included time in major depressive episodes, sometimes also time in minor episodes or dysthymic states, and not in primarily anxious states. Therefore, virtually all of the reported illness time in UD can be considered to represent depressive morbidity of various levels of severity, but mainly major depressive

illness. The overall proportion of time ill varied substantially among reports, ranging from 15.0% (Yiend et al., 2009) and 17.2% (Tondo [new data, 2015]) to 75.9% (Fekadu et al., 2012) and 79.3% (Brodaty et al., 2001), and may reflect differences in observation time in these episodic disorders, or in illness severity among patient samples. Notably, for example, patients studied by Fekadu et al. (2012), with relatively high %-time-ill (75.9%), were characterized as having treatment-resistant depressive illness. Even if the findings of the four studies with unusually low or high levels of morbidity (Table 1) are omitted in a sensitivity analysis, the overall percentage of time ill remains at 45.7% [CI: 35.6–55.8], compared to 46.0% with all studies included. The overall percentage of time ill among all BD subjects was very similar to that among UD patients, averaging 44.9% [CI: 40.1–49.7] overall, and 43.7% [37.5–49.9] in BD-I (Table 2). In BD-I cases, proportions of this morbidity consisted of: major or minor depression (30.6% [13.9–37.3]) 4mania or hypomania (10.3% Zother states (anxiety or bipolar mixed-states; 5.73% [1.94–9.52]; Table 2). Depressive morbidity in BD-I cases accounted for 69.6% [60.4–78.9] of total reported morbidity. If “other” morbidity is taken to include a high proportion of dysphoric-mixed BD states, the proportion of depressive of total morbidity could be as high as 77.5% ([30.6þ5.73]/46.9).

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A. Forte et al. / Journal of Affective Disorders 178 (2015) 71–78

Table 1 Summary: time ill in unipolar depressive disorder patients. Study

Subjects (N)

Exposure (years)

Proportion of time ill (%)

Angst and Preisig, 1995 Judd et al., 1998 Brodaty et al., 2001 Kennedy, 2004 Holma et al., 2008 Judd et al., 2008 Coryell et al., 2009aa Yiend et al., 2009 Baldessarini et al., 2010cb Fekadu et al., 2012c Judd et al., 2013 Riihimäki et al., 2014 Vergunst et al., 2013 Tondo and Baldessarini, 2015d

186 431 49 69 142 323 220 37 50 77 536 102 84 173

24.0 8.70 25.0 9.40 5.00 14.2 20.0 23.9 3.33 3.25 16.3 5.00 3.00 18.8

20.0 58.5 79.3 48.0 51.3 35.3 29.9 15.0 39.7 75.9 52.8 57.6 64.1 17.2

Total/mean [95% CI](n¼ 14 studies)

2479

12.8 [7.93–17.7]

46.0 [34.0–58.0]

Morbidity includes major and variably reported minor depression; anxiety states are not included. Means are weighted by subject-counts. Reported morbidity (% time depressed) was not significantly associated with exposure time. a

Outcome is disability not specifically depressive morbidity. Initially hospitalized with psychotic features. Treatment-resistant cases. d Previously unpublished data from Tondo and Baldessarini, 2015. b c

Among BD-II subjects, the overall proportion of time ill averaged 43.2% [35.2–51.2] (Table 2). This morbidity consisted of: major syndromal or minor, subsyndromal depression (35.9% [23.1–48.7]) 4hypomania (5.54% [1.57–9.51])Zother states (2.76% [2.14–3.38]; Table 2). That is, depressive morbidity accounted for even more of total time ill in BD-II subjects (82.4% [70.5–94.3]) than in BD-I cases. For BD-I cases, the overall proportion of time ill ranged from 25.2% in an 18.8-year study (Tondo et al. [new data, 2015]) to 84.6% in a study involving 3.36 years of observation (Joffe and MacQueen, 2004). Among BD-II subjects, the range of outcomes was somewhat smaller, from a low of 23.6% of time ill during 18.8 years of exposure (Tondo et al. [new data, 2015]), to a high of 55.8% over 12.9 years (Judd et al., 2003). Such variance is not readily explained, given limited clinical detail provided in most of the reports analyzed. However, exposure time may be a contributing factor, in that across all three diagnoses, there was a significant inverse correlation of lower total percent time ill with longer exposure-time (r¼  0.602, p¼0.0001; nonparametric rs ¼  0.437, p¼0.010). Notably, the total proportion of time ill declined with longer exposure, significantly and independently even when analyzed in linear regression modeling with diagnosis (UD vs. BD) and approximate onset-age as covariates (slope β ¼  0.940 [CI:  1.57 to 0.308]; t¼3.03, p¼0.005), whereas diagnosis not significantly related to the outcome. Total morbidity averaged 46.0% [34.0–58.0] in UD, 44.6% [39.6– 49.6] in all BD cases, and 45.3% [40.3–50.4] overall. In UD, this morbidity was virtually all depressive, in BD, 76.4% [69.4–83.3], and 86.4% [80.7–92.1] overall (Tables 1 and 2; Fig. 2).

4. Discussion The present findings from 25 studies and including new data greatly expand preliminary reviews of long-term studies of time in morbid states among BD-I patients (Baldessarini et al., 2010a; Miller et al., 2014; Tondo and Baldessarini, 2015), including a cohort followed for several years from first episodes of illness (Baldessarini et al., 2010a) and extend the data to BD subtypes and UD patients. The findings are remarkable in indicating very similar levels of observed long-term morbidity (44.4% [CI: 39.6–49.2] overall) in all major mood disorder types. In addition, very high proportions of

total identified morbidity involved depressive states in all three disorders: 70% in BD-I, 81% in BD-II (76% in all BD cases), and as expected, virtually 100% in UD (Tables 1 and 2; Fig. 2), to average 86% overall. The findings underscore limitations of current psychiatric therapeutics in that long-term prevention and control of depressive morbidity in all types of mood disorders were incomplete. This conclusion contrasts sharply with abundant evidence of short-term efficacy of antimanic, antidepressant, and mood-stabilizing medical and psychosocial treatments for mood-disorder patients, and growing evidence of long-term effectiveness as well (Baldessarini, 2013; Cipriani et al., 2009; Garay et al., 2014; Lam et al., 2009; Selle et al., 2014; Tondo et al., 2013; Undurraga and Baldessarini, 2012; Vázquez et al., 2011; Yatham et al., 2013; Yildiz et al., 2014). However, this evidence involves statistical separation of active treatments from placebo or other controls on symptom-rating scales, and does not necessarily indicate highly clinically effective benefits, particularly as regards long-term or prophylactic effects for specific forms of morbidity. Nevertheless, it appears that long-term protection from recurrences of manic, hypomanic, mixed and psychotic phases of BD is more effective than protection from depressive morbidity, although precise impact of treatments on specific dimensions of illness (frequency, severity, duration, and functional impact) remain inadequately studied (Baldessarini, 2013). Factors that may affect assessments of time in depressive versus manic illness include: [a] preferential recall and reporting of depression more, at least, than hypomania; [b] severe affective illnesses that do not respond fully to available long-term treatments, perhaps over-represented in academic and other specialized centers represented in the present database; [c] limited effectiveness, and differential effectiveness on dissimilar morbidity-types, development of tolerance, or adverse effects of stopping or changing available treatments; [d] modification of illness by treatment; [e] prevalent long-term nonadherence to prescribed, potentially effective treatments; [f] limited access to expert clinical programs that specialize in the treatment of mood-disorder patients; and [g] effects of the duration of observations. The present findings may well include possibly unrepresentative, poorly treatment-responsive patients likely to be encountered in the academic or other tertiary-care centers in which most of the studies analyzed here arose. Tending toward lower proportions of time ill,

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Table 2 Summary of percent time ill in bipolar disorder patients. Study

Bipolar I Judd et al., 2002 Judd et al., 2003 Post et al., 2003 Joffe and MacQueen, 2004 Paykel et al., 2006 Kupka et al., 2007 Judd et al., 2008a Coryell et al., 2009bb Baldessarini et al., 2010ac Baldessarini et al., 2010b Parikh et al., 2014d Tondo and Baldessarini, 2015e Total/mean (12 studies) Bipolar II Judd et al., 2003 Post et al., 2003 Joffe and MacQueen, 2004 Kupka et al., 2007 Judd et al., 2008a Baldessarini et al., 2010ac Parikh et al., 2014d Tondo and Baldessarini, 2015e Total/mean (8 studies) Bipolar unspecified DeDios et al. 2010 Fekadu et al., 2012f Vergunst et al. 2013f Total/mean (3 studies) All bipolar Total/mean (23 total, 15 unique studies)

Exposure (years)

Subjects (n)

Morbidity (% time ill) Dd

Mm

Other

Total

%Dd

14.2 12.9 1.00 3.36 1.50 1.00 14.2 9.45 13.6 2.00 1.38 18.8

146 135 196 97 204 405 145 417 250 303 115 347

31.9 30.6 32.8 39.4 47.2 36.0 – 27.8 – 18.1 27.0 15.5

9.30 9.80 11.8 7.20 16.7 12.5 – 6.45 – 14.2 5.00 9.65

5.90 6.00 – – – 3.67 – – – 12.1 1.00 –

47.1 46.4 44.6 46.6 63.9 52.3 42.7 34.3 43.9 44.4 33.0 25.2

67.7 66.0 73.5 84.6 73.9 68.8 – 81.0 – 40.8 81.8 58.2

7.78 [3.53–12.0]

2760

30.6 [23.9–37.3]

10.3 [7.68–12.8]

5.73 [1.94–9.52]

43.7 [37.5–49.9]

69.6 [60.4–78.9]

12.9 1.00 3.36 1.00 14.2 13.6 1.38 18.8

71 53 41 102 126 150 45 234

51.9 33.5 45.4 36.9 – – 31.0 16.7

1.40 7.60 0.40 10.0 – – 7.00 6.85

2.50 – – 2.79 – – 3.00 –

55.8 41.1 45.8 49.7 40.2 48.2 41.0 23.6

93.0 81.5 91.9 74.3 – – 75.6 70.8

8.28 [2.18–14.4]

822

35.9 [23.1–48.7]

5.54 [1.57–9.51]

2.76 [2.14–3.38]

43.2 [35.2–51.1]

81.2 [71.3–91.0]

1.38 3.25 3.00

296 27 31

24.8 74.7 51.8

8.80 – –

3.00 – –

36.6 74.7 51.8

67.8 100.0 100.0

2.54 [0.01–5.07]

354

50.4 [0.00–112]

8.80

3.00

54.4 [6.70–100]

89.3 [0.00–288]

7.27 [4.43–10.1]

3936

35.4 [28.5–42.3]

8.51 [6.31–10.6]

4.44 [1.92–6.96]

44.9 [40.1–49.7]

76.4 [69.4–83.3]

M ¼mania (includes psychosis when reported), m¼ hypomania, D¼ major depression, d ¼ minor depression or dysthymia, other includes mixed-states and anxiety. Totals are means weighted by subjects/study. Total %-time-ill was not correlated with exposure time or subject-number. % Dd indicates proportion of total morbidity accounted for by depression. Note that some studies included data on BD-I, BD-II, or UD (see Table 1). a

Morbidity is percent time disabled. Mostly BD-I. c Followed from first episodes; mania includes psychosis (0.9%); Other includes mixed-states (12.1%). d Subjects were enrolled in an intensive treatment program. e Previously unpublished data from Tondo and Baldessarini, 2015. f Proportion of BD-II not defined; reported is percent time depressed (D or d); subjects had shown treatment-resistance in depressive phases. b

however, were relatively long duration (averaging 9.38 [6.81–12.0] years), which significantly diminished overall estimates of percent-time ill in the episodic disorders studied; in addition, longer observation may increase the relative proportions of time in longer depressive versus shorter manic-hypomanic phases (APA, 2013; Baldessarini et al., 2010a; Goodwin and Jamison, 2007). A more speculative observation is that the decline of percent-time-ill with longer time at risk does not seem consistent with the view that major affective disorders may worsen over time and with more recurrences (Baldessarini et al., 2012; Oepen et al., 2004). An additional speculation is that the lack of difference between UD and BD, especially as regards depressive morbidity, seems inconsistent with the view that depression in BD is less treatment-responsive than in UD (Pacchiarotti et al., 2013). A major limitation of the present analysis is that the reports considered, while involving patient-subjects nominally treated by clinically determined community standards, do not provide detailed information about the nature of treatments prescribed, their intensity, levels of monitoring, or estimates of types or rates of likely nonadherence. Assertive administration and monitoring of intensive pharmacotherapy and psychotherapy in mood

disorder patients, at least for periods as short as one year, may make a significant impact on residual morbidity, as is suggested by the recent findings of Parikh et al. (2014). In that study, treatment was administered systematically and intensively, and the proportions of total time ill in BD-I and BD-II cases were moderate (33% and 41%), although still substantial. They were not the lowest values observed, perhaps in part reflecting the relatively brief exposure involved with less time for “dilution” of episodic morbidity by longer exposures (Table 2). It is also plausible that illnessseverity may contribute to some of the findings. For example, one study that specifically enrolled subjects with treatment-resistant depression, not surprisingly, found an unusually high percentage of long-term follow-up time ill (75%; Fekadu et al., 2012), although another did not (52%; Vergunst et al., 2013). Clearly needed are long-term studies of morbidity in broadly representative clinical samples, and with well characterized treatment. Regardless of the basis of the observed outcomes, the conclusion seems inescapable that depression in UD and depressive phases of BDI and BD-II are not being fully or even satisfactorily controlled with available treatments (Fig. 2), even when applied aggressively and

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65

[Hypo]mania

% of Long - term Follow - up ( ± C I )

60

Depression

Total Morbidity

55 50 45 40 35 30 25 20 15 10 5 0

UP

All-BD

BD-I

BD-II

Fig. 2. Proportion (% 7CI) of time ill for unipolar depression, all cases of bipolar disorder, bipolar-I, and bipolar-II in long-term follow-up for 9.38 [6.81–12.0] years, for or hypomania (striped bars; not present in UD), depression (black bars), and total illness (gray bars) in 25 long-term studies of clinically treated patients (see Tables 1 and 2). The proportion of total time ill accounted for by depressive morbidity is: unipolar (100%)Z bipolar-II (82.4%) Zbipolar-I (69.6%) disorders.

intensively (Parikh et al., 2014). The present findings should stimulate redoubled efforts to develop more effective antidepressant and moodstabilizing treatments, but also to improve means of delivering them, increasing their acceptance, tolerability, and improving adherence to recommended long-term treatments (Arvilommi et al., 2014; Jamison and Akiskal, 1983; Leclerc et al., 2013; Pompili et al., 2013). Ironically, long-term adherence probably is more difficult to sustain to the extent that it is successful, and symptomatic illness is not present to drive acceptance of treatment (Baldessarini, 2013). Several reports have highlighted the importance of more specifically-tailored interventions for mood disorder patients, ideally by experts and with attention to individual and changing needs and responses. In particular, proposed methods of improving long-term treatment-adherence have ranged from use of long-acting injectable medicines (Gigante et al., 2012) to psychoeducation and disorder-specific psychosocial interventions (Berk et al., 2010; Crowe et al., 2012; Vergouwen et al., 2003). Individualized and critically targeted interventions are more likely to be provided in specialized mood-disorder clinics or centers, accessibility to which should be enhanced and their methods made more widely available (Crowe et al., 2011; Frye et al., 2014; Vieta, 2013). Notably, a recent randomized trial found that treatment in a specialized clinic reduced rehospitalization rates by 40% and increased patient-satisfaction in recently diagnosed bipolar disorder patients (Kessing et al., 2013). Moreover, a recent international symposium highlighted the general need to increase efforts and resources directed at depression by governments and other stake-holders due to the severe economic and social as well as clinical burdens of mood disorders (Economist, 2014). Particularly needed are innovation in pharmacological and psychosocial treatment methods and improved means of clinical service delivery, such as in specialized clinical units for the care of patients with unipolar or bipolar depression.

Role of funding source Funding sources had no involvement in the design, conduct, analysis or reporting of this study.

Conflict of interest Dr. Girardi has received research support from Angelini Pharma, and speaker's honoraria from Janssen, Otsuka, Lilly, and Lundbeck Pharmaceutical Corporations, but no other authors or immediate family members have financial, commercial or other potential conflicts of interest related to the material presented.

Acknowledgments Supported by a research fellowship from the Sapienza University of Rome (AF), by a grant from the Bruce J. Anderson Foundation and the McLean Private Donors Research Fund (RJB), and by a grant from the Aretæus Association of Rome and private donors (LT).

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