Soc Psychiatry Psychiatr Epidemiol DOI 10.1007/s00127-013-0814-8
ORIGINAL PAPER
Bouncing back: remission from depression in a 12-year panel study of a representative Canadian community sample Esme Fuller-Thomson • Marla Battiston • Tahany M. Gadalla • Sarah Brennenstuhl
Received: 30 July 2013 / Accepted: 16 December 2013 Springer-Verlag Berlin Heidelberg 2014
Abstract Purpose This study sought to investigate time to remission from depression in a community-based sample of adults followed for 12 years. Methods Data were derived from the National Population Health Survey (1994/5–2006/7 and 1996/7–2008/9). Fully 1,128 adults were included who were depressed at baseline according to DSM-III/CIDI-SF criteria. Kaplan–Meier and Cox proportional hazards procedures were used to determine time to remission and the demographic (e.g., gender and marital status), psychosocial (e.g., social support and adverse childhood experience) and health-related (e.g., pain, health conditions and alcohol use) factors with which it is associated. Results More than three quarters of the sample (77 %) no longer screened positive for depression at 2 years, and nearly the entire sample (94 %) had remitted by 12 years. Adverse childhood experiences (i.e., childhood abuse and parental additions), lack of social support, the presence of pain and health conditions (i.e., migraines, arthritis and back pain) each predicted more time to remission. The only factor associated with time to remission in the multivariate analysis was a history of childhood physical abuse. Conclusions Most community members with depression get better after 2 years and nearly all will have remitted, at least once, by 12 years. The results of this study may help guide the development of interventions for chronic E. Fuller-Thomson (&) M. Battiston T. M. Gadalla Factor-Inwentash Faculty of Social Work, University of Toronto, 246 Bloor St. W, Toronto, ON M5S 1V4, Canada e-mail: [email protected] S. Brennenstuhl Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
depression that focus on early prevention of childhood abuse. Keywords Survival analysis CIDI-SF Childhood abuse Adverse childhood experiences
Introduction More than 350 million people suffer from depression worldwide, making it the leading cause of disability and a key contributor to the global burden of disease [1, 2]. While treatment can be effective [3, 4], incomplete or lack of recovery from depression is associated with reduced functioning, family problems, a poor quality of life and, at worse, suicidal thoughts and behaviors [5–7]. The societal costs of chronic depression are high, driven predominantly by increased health care utilization and reduced worker productivity [8]. Given the substantial impact that depression has on individuals, families and society, remission is considered the optimal treatment goal [9, 10]. Remission requires a clinical decrease in the number and severity of symptoms over a period of time, usually defined as greater than 8 weeks, as well as a return to pre-depressive functioning [4, 9–11]. While the majority of individuals with depression are expected to remit, a significant minority will not, at least over a short period of time [11]. Understanding the factors that predict remission from depression over the longer term is an important public health goal. With greater awareness of risk factors, clinicians are better able to identify those at a higher risk for chronicity and the negative outcomes associated with it. Methodological weaknesses in studies investigating the course of depression, however, make it difficult to
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determine what factors best foretell remission. Such drawbacks include the use of small sample sizes, crosssectional data or longitudinal data with short follow-up times, and highly selective samples that over-represent severe cases of depression [11, 12]. Moreover, because the bulk of studies use clinical samples, less is known about remission in community dwellers. To address these limitations, the current study investigates remission and its predictors over a 12-year period, in a large, communitybased sample of adults. Predictors of depression remission There are a number of factors that appear to have implications for remission from depression. Many of these, however, are clinical in nature and, thus, data of this type are not always necessarily available to public health researchers. Clinical factors include personality factors and coping styles [13–15], and depression-related factors, such as the number and length of previous episodes [12, 13, 16– 19], and severity [15, 17–25]. In the current study, we focus more broadly on non-clinical factors available in many population health surveys, which can be used by public health professionals to identify groups of community members at risk for a prolonged course of depression. These can be organized into demographic, psychosocial and health-related variables. The demographic variables most consistently associated with depression outcomes are marital status and education level. Being married has been shown to predict earlier recovery [20], whereas being divorced, separated or widowed [22, 23] or having fewer years of formal schooling predicts worse depression outcomes [15, 19, 21–23]. Younger age is also related to remission in some studies [14, 17]. The relationship between remission and other demographics is more unclear. For example, while some studies have shown that gender does not play a significant role in remission [19, 22], others find that women experience a more [23] or less [14, 20] chronic course of depression than men. One of the most important psychosocial factors associated with remission is social support. Lack of social support has been shown to predict depression persistence [18, 19] and severity [15], while higher perceived emotional support has been associated with remission [26]. The experience of negative life events are also linked to depression outcomes. For example, individuals with a history of childhood emotional or physical abuse have worse depression outcomes than their counterparts who never experienced abuse [14, 27–29]. Depression outcomes are associated with presence of physical comorbidities and pain. For example, longer-term recovery is more likely among those without a chronic
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physical illness [19, 30], whereas depression persistence and recurrence are predicted by poor physical functioning [13], co-existing physical conditions [18] and pain [22, 31, 32]. Health behaviors are related to the course of depression: higher levels of physical activity are associated with lower relapses rates [33] and being overweight or obese is linked to a poorer treatment response [34]. Finally, depression persistence is related to addictive behaviors, such as alcohol dependence [6, 35]. Building on previous research, the goal of the current study was to investigate remission, and the non-clinical factors associated with it, in a large, community-based sample of Canadian adults followed for 12 years. In particular, we sought to answer two research questions: 1. 2.
What proportion of depressed Canadians are no longer depressed at 2 and 12 years after baseline? What are the non-clinical factors associated with time to remission in bivariate and multivariate analyses?
Methods Data source Data for this study were derived from the National Population Health Survey (NPHS), which is an ongoing panel study of the Canadian population starting in 1994. This nationally representative survey used a health determinants perspective to track the health status, health behaviors, and health care utilization of Canadians aged 12 and older. Participants were sampled using a multistage-stratified sampling design, details of which are provided elsewhere [36]. In the first wave of the survey (1994/1995). the sample included 17,276 Canadians. Attempts were made to re-survey the initial respondents biennially. For the current study, data from waves 1 through 7 (1994–2006) and waves 2 though 8 (1996–2008) were used, each of which spanned a 12-year period. Reasons for using two, overlapping, time periods are provided below. The response rate in wave 1 was 83.6 %. In wave 2, 92.8 % of respondents from wave 1 had responded to the survey again. By wave 7 and 8, these figures were 77.0 and 70.7 %, respectively [37]. Sample The sample is comprised of adults aged 18 and older classified as depressed at baseline, which was defined as wave 1, or wave 2 in the case that individual was not depressed in wave 1. The first wave of the NPHS included 751 depressed respondents. The second wave included another 377 individual who were depressed at wave 2, but not at wave 1. To increase the sample size, data from the
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two waves were pooled together, resulting in sample of 1,128 (women = 801, men = 327). Measures Depression was assessed biennially for 12 years using the Composite International Diagnostic Interview-Short Form (CIDI-SF) developed by Kessler et al. [38]. The CIDI-SF is a shortlist of items from the fully structured CIDI interview that measures a major depressive episode using the definitions and the criteria of the Diagnostic and Statistical Manual III, and the diagnostic criteria of the International Classification of Diseases, version 10. The CIDI-SF was delivered by well-trained lay interviewers during a 10-min interview. Respondents screened positive for depression if they had a probability of 90 % or greater for a major depressive episode over a period of at least 2 weeks in the last year. The CIDI has been shown to have excellent interrater reliability, good test–retest reliability and validity [39]. The total classification accuracy for a major depressive episode of the CIDI-SF in comparison to CIDI is 93.2 %. The sensitivity and specificity of the CIDI-SF is 89.6 and 93.9 %, respectively [39]. Initial selection of potential predictors of depression was guided by a review of the population- and communitybased literatures on depression remission and persistence. Variables included demographic, psychosocial and healthrelated factors measures at baseline (wave 1 for 751 respondents and wave 2 for 377 respondents), the details of which are provided below: •
•
Demographic variables: age group (\55 years or C55 years), gender (woman or man), marital status (married/common law or single/divorced/separated) and highest level of education level (Bhigh school, [high school). Psychosocial variables: Social support and adverse childhood experiences. Social support was measured by an affirmative response to the question: ‘‘Do you have someone you can confide in, or talk to about your private feelings or concerns?’’ Two adverse childhood experiences were investigated in a section of the survey about childhood experiences. The section was preceded by the following instructions: ‘‘The next few questions ask about some things that may have happened to you while you were a child or a teenager, before you moved out of the house. Please tell me if any of these things have happened to you’’. Physical abuse was assessed by the question ‘‘Were you ever physically abused by someone close to you?’’ and parental addictions were determined using the question ‘‘Did either of your parents drink or use drugs so often that it caused problems for the family?’’
•
Health-related variables: presence of pain and physical comorbidities, physical activity level, BMI and alcohol use. Pain was measured using the question ‘‘Are you usually free of pain and discomfort?’’ Three physical comorbidities were included: back problems, arthritis and migraines. These illnesses were ascertained by separate yes/no responses to a list of chronic conditions ‘‘that had lasted or are expected to last 6 months or more’’ and that had been ‘‘diagnosed by a health professional.’’ Physical activity level was based on the respondent’s daily recreational physical activities lasting more than 15 min, coded into active, moderate and inactive categories. Obesity was assessed by selfreported weight and height and coded into obese (BMI C 30 kg/m2), overweight (BMI = 25–29.99 kg/ m2) and average or underweight (BMI [ 25 kg/m2). A missing category was also included. Alcohol use was determined by the respondent’s weekly drinking habits. Those who consumed 12 or more drinks per week were categorized as heavy drinkers. All others were classified as non-heavy drinkers.
Analyses Survival analysis was undertaken to investigate remission, and the factors associated with it, based on a biennial measurement of depression over a 12-year observation period. For the 751 depressed at wave 1, that period was 1994–2006 and for the 377 depressed at wave 2 (but not at wave 1), the period was 1996–2008. An individual was censored if he/she died or was lost to follow-up or was consistently depressed in each wave until the end of the observation period. The analysis proceeded in several steps. First, the cumulative percentage of those no longer screening positive for depression was calculated at 2 and 12 years after baseline for the whole sample. Next, the mean time to remission was generated using the Kaplan– Meier procedure and compared across categories of each of the potential predictors using the Log Rank test. Respondents were defined as ‘‘in remission’’ if their CIDI-SF score was in the ‘‘non-depressed’’ category in at least one wave of data collection. If respondents had not achieved remission status before they missed one or more waves of data collection, they were classified as ‘‘never remitted’’. Time to remission was calculated as the time between the baseline wave of data collection, and the first wave in which the respondent was classified as ‘‘non-depressed’’. The percentage of those no longer depressed at 2 years was also compared across categories of the potential predictors. Finally, the association between the potential predictors and time to remission was determined by Cox proportional Hazard Ratios (HRs) and 95 % confidence intervals (CIs).
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The assumption of proportional hazards was tested by graphical inspection of the log–log survival curves for each potential predictor stratified by its categories. We determined that the proportional hazard assumption was not violated based on the finding of generally parallel survival curves across categories of each of the predictors. Due to the many predictors included in the analysis, the model was generated using a step-wise procedure (i.e., backwards elimination). This modeling strategy is consistent with other research in this area (e.g., [18, 22,]). All p values were two sided, at a significance level of 0.05. Analyses were performed using SPSS Version 18.
Results Focusing on overall time to remission, we found that more than three quarters of the sample (77 %) were no longer depressed at 2 years, and nearly the entire sample (94 %) had remitted by 12 years. Table 1 presents the mean time to remission according to the categories of the potential predictors and the percentage remitted in each category by 2 years. Only a few of the factors were revealed to have a significant influence on time to remission. A history of either type of adverse childhood experience (i.e., physical abuse or parental addictions) was associated with longer time to remission. For example, while 81 % of those without a history of childhood abuse were no longer depressed after 2 years, this was the case for only 67 % of those who were abused in childhood (p \ 0.001). Having no social support was also associated with a longer time to remission. Fully 79 % of those with a confidant were no longer depressed at 2 years compared to 66 % of those without someone with whom they can confide (p \ 0.001). Finally, each of the health conditions significantly influenced time to remission, including the presence of pain, arthritis, migraines and back problems. As for the presence of pain, 81 % of respondents free of pain were no longer depressed after 2 years versus 70 % of those with regular pain (p \ 0.001). None of the demographic variables (i.e., age, gender, education level and marital status) or health behaviors (i.e., alcohol use, physical activity and obesity) was related to remission. The associations between the predictors and time to remission based on the multivariate Cox proportional hazards model are presented in Table 2. As shown, a history of childhood physical abuse is the only significant predictor of remission once all other factors are accounted for (p \ 0.05). Those experiencing this type of abuse have a 15 % lower probability of remitting from depression (HR = 0.85; 95 % CI 0.73, 0.99). There are several factors that marginally predict remission (p \ 0.10): the presence of pain and migraines are both associated with a lower
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chance of remission, while having a confidant is related to a higher probability of recovering from depression in the short term.
Discussion For the large part, this study has a good news message: Community members with depression do get better. We found that three quarters of those with depression at baseline no longer screened positive for depression after 2 years. Moreover, we showed that almost all of those with depression (94 %) had remitted by 12 years. These figures are similar to those reported in a recent review of the literature indicating that 80–81 % of clinical patients had remitted after 2 years, and approximately 93–94 % had remitted at 10–15 years [11]. While the current study did not determine how long each respondent remained free of depression after they had first remitted, it does provide evidence that highly chronic depression occurs in only a minority of cases. This research also provides novel information on the non-clinical factors that predict remission for depression among members of the community. There is a growing body of evidence showing that remission is predicted by personality factors and coping styles [13–15, 40–42], and depression-related factors such as the number and length of previous episodes [12, 13, 16–19] and severity [15, 17–25]. However, many of these variables are not available to public health professionals trying to identify at-risk groups. The current study, therefore, focuses on non-clinical factors that are available in most population health surveys. We found that the single best predictor for depression persistence was a history of childhood physical abuse. This finding is consistent with a recent meta-analysis of 16 epidemiological studies and 10 clinical trials on childhood maltreatment and depression [28]. The analysis found that childhood maltreatment, which included multiple types of abuse and family conflict or violence, was associated with an increased risk of developing persistent or recurrent depression and a lack of response or remission during treatment [28]. Based on these findings, the authors suggested that childhood maltreatment can be thought of as a developmental risk factor for a poor depression course and a moderator of treatment response. A number of researchers speculate that the link between childhood abuse and poor depression outcomes can be explained by an increased vulnerability to stress [43–45]. In particular, adverse childhood experiences are thought to interrupt the normal development of biological systems, including the hypothalamic–pituitary–adrenal (HPA) axis, which affects stress regulation [43]. Depression in adulthood has been characterized by HPA axis hyperactivity,
Soc Psychiatry Psychiatr Epidemiol Table 1 Mean survival time and percent in remission at 2 years compared across categories of potential demographic, psychosocial, health and health-behavioral predictors Mean survival time
Lower CI
Upper CI
Man
2.875
2.622
3.129
Woman
2.865
2.724
3.006
\55 years
2.901
2.765
3.037
C55 years
2.680
2.397
2.963
p value
% in remission at 2 years
Demographics Gender 0.89
78.9 76.8
Age 0.10
76.2 83.5
Education BHigh school
2.872
2.615
3.129
[High school
2.878
2.735
3.020
Single/separated/divorced
2.841
2.681
3.001
Married/common law
2.891
2.700
3.082
0.78
79.7 76.2
Marital status 0.69
77.6 77.0
Psychosocial Social support Yes confidant
2.772
2.648
2.896
No confidant
3.392
2.973
3.811
\0.001
79.4 66.3
Adverse childhood experiences No physical abuse
2.696
2.565
2.826
Physical abuse
3.451
3.099
3.803
No parental addictions
2.758
2.619
2.898
Parental addictions
3.190
2.886
3.494
\0.001
80.5 66.7
0.002
79.3 71.4
Health Pain Usually free of pain
2.660
2.537
2.783
Not usually free of pain
3.334
3.047
3.621
Yes
3.198
2.882
3.513
No
2.774
2.646
2.902
\0.001
81 69.6
Arthritis 0.004
70.8 78.9
Migraines \0.001
Yes
3.395
3.056
3.734
No
2.729
2.605
2.853
67.1
Yes
3.200
2.894
3.505
No
2.759
2.630
2.889
Normal
2.806
2.632
2.980
Overweight
2.922
2.686
3.159
75.4
Obese
2.935
2.574
3.297
77.2
Missing
2.857
2.501
3.212
77.7
Active
2.874
2.588
3.159
Moderate
2.667
2.427
2.906
80.8
Inactive
2.933
2.768
3.098
75.2
79.8
Back problems 0.004
71.9 79.1
Health behaviors Body Mass Index 0.80
78.6
Physical activity level 0.21
80.4
Alcohol use Not heavy drinker
2.883
2.753
3.014
Heavy drinker
2.646
2.331
2.960
0.39
77.3 77.9
CI confidence interval
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Soc Psychiatry Psychiatr Epidemiol Table 2 Multivariate Cox proportional hazards regression predicting time to remission (n = 966) Hazard ratio
95 % CI
p value
History of childhood physical abuse Yes
1.00
No
0.85
0.049 (0.729, 0.999)
Presence of pain Yes
1.00
No
0.88
0.092 (0.760, 1.021)
Presence of migraine Yes 1.00 No
0.86
0.090 (0.722, 1.024)
Social support Yes
1.00
No
1.17
0.093 (0.974, 1.413)
a
Backward elimination was used to select covariates for final model. All p values were two sided, at a significance level of 0.05
which is linked to the increased production of cortisol [43, 44]. There is some evidence that abnormalities in the stress response are even more pronounced among those with depression and a history of childhood abuse compared to those with depression alone [43, 45]. This research underscores the importance of childhood experiences for shaping health trajectories across the life course and reinforces the importance of public health interventions that support healthy child development [45]. The WHO Commission on the Social Determinants of Health suggests that these interventions should include providing adequate social and health protections for women, mothers-to-be and young families and, specifically, implementation of high quality, affordable early years education and childcare systems [46]. Fathers should also be the focus of interventions to prevent child abuse [47]. Other factors marginally related to time to remission in the multivariate analysis included social support and the presence of pain and migraines. Previous research shows that lack of social support predicts depression persistence [18, 19] and severity [15], while higher perceived emotional support is associated with remission [26]. There is some evidence that ‘‘befriending’’ interventions, which provide social support that is non-directive and emotional in nature, have a modest, positive, influence on depression symptoms [48]. Future research should investigate whether this approach can be used preventatively for those at a high risk for a chronic course of depression. A relationship between the presence of pain and remission has also been established in a number of previous studies [22, 31, 32]. Kroenke et al. [32] suggest that better recognition and optimal management of pain are important for enhancing depression treatment. Forthcoming studies
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should determine whether our marginal findings on social support and pain can be replicated in other populationbased samples. A number of factors identified in other (mostly clinical) research as being predictors of remission were not found to be significant in the current study, including education level and marital status. It may be that these factors are less important predictors in the general population than in clinical samples, which can represent more severe cases of depression. This is suggested by the findings of another population-based study showing no associations between either education level or cohabitation status and depression persistence [18]. The current study has several limitations. The sample selected for the NPHS is likely to under-represent those with the most severe cases of depression, who, if institutionalized, would be ineligible for this communitybased study. In addition, severely depressed community dwellers may have been less likely to participate in the survey. This implies that the proportion remitted at 2 and 12 years, and the factors predicting remission, may less accurately reflect individuals with the most challenging cases of depression. Some caution should be exercised when interpreting the results of the multivariate analysis. While childhood abuse was identified as a predictor of time to remission, the finding was of borderline significance (p = 0.049). This may reflect a lack of statistical power due to the low base rate of childhood abuse. Because childhood abuse was measured retrospectively, there is also a possibility of recall bias. Research shows that when measurement error occurs, it is much more likely to result in false negatives than false positives [49]. In the current research, this means that our results are probably conservative, which may also help account for the borderline finding. While it is possible that those who are depressed are more likely to recount negative life events, prior research has shown that impressions of parental bonding experiences remain relatively stable across levels of depressed mood [50]. Thus, our sample was not necessarily more likely to report childhood abuse at baseline (when they were depressed), which supports the case that our results are conservative. Finally, the results of our study must be interpreted in the national context within which they occurred. The 12-month prevalence of major depression in Canada (4.3 %) is higher than in some comparatively wealthy nations, such as Japan (1.2 %), but lower than in others, including Germany (5.2 %), the Netherlands (5.9 %), and the US (10.0 %) [51]. This crossnational variation in depression prevalence may signal differences in genetic or environmental factors [51], which also could play a role in speed of remission. Similar to European countries, but unlike the US, Canada has a universal health care
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program, which includes free access to mental health services. Differences in health care systems may help shape the extent to which those with depressions are able to obtain appropriate treatment and its continuation over time [52]. Other countrylevel factors that may play a role in depression persistence include level of income and gender inequality [53, 54]. Despite the aforementioned limitations, this study’s long follow-up period of 12 years, with biennial measurements of depression, and use of a large, representative community-based sample makes it a unique contribution to the literature. Moreover, its focus on non-clinical variables makes it valuable to health professionals who are interested in tracking and preventing chronic courses of depression. To conclude, this study has a predominantly positive message: Most community members with depression will get better after 2 years and nearly all will have remitted, at least once, by 12 years. When multiple variables are adjusted simultaneously, the only predictor of time to remission among the non-clinical variables selected for this study is a history of childhood abuse. This finding underscores the strong influence that childhood circumstances have on adult health trajectories and suggests that prevention of chronic depression needs to begin upstream by ensuring that every child has a good start to life. Acknowledgments The first author (Esme Fuller-Thomson) would like to gratefully acknowledge support received from the Sandra Rotman Endowed Chair in Social Work. We wish to thank Statistics Canada and the Social Sciences and Humanities Council of Canada for permission to access the longitudinal form of the National Population Health Survey via the Research Data Center (RDC) at the University of Toronto. We are also grateful to the staff at the Toronto RDC for their help in accessing the data. The opinions expressed herein are our own and do not represent the views of Statistics Canada. Conflict of interest
None.
References 1. Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B (2007) Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet 370:851–858 2. World Health Organization (2004) The global burden of disease: 2004 update. http://www.who.int/healthinfo/global_burden_dis ease/2004_report_update/en/index.html. Accessed April 9, 2013 3. Casacalenda N, Perry JC, Looper K (2002) Remission in major depressive disorder: a comparison of pharmacotherapy, psychotherapy, and control conditions. Am J Psychiatry 159:1354–1360 4. Dawson MY, Michalak EE, Waraich P, Anderson JE, Lam RW (2004) Is remission of depressive symptoms in primary care a realistic goal? A meta-analysis. BMC Fam Pract 5:19 5. Trivedi MH, Corey-Lisle PK, Guo Z, Lennox RD, Pikalov A, Kim E (2009) Remission, response without remission, and nonresponse in major depressive disorder: impact on functioning. Int Clin Psychopharmacol 24:133–138 6. Hirschfeld RMA (2000) Psychosocial Predictors of Outcome in Depression. http://www.acnp.org/G4/GN401000107/. Accessed June 20, 2013
7. Tranter R, O’Donovan C, Chandarana P, Kennedy S (2002) Prevalence and outcome of partial remission in depression. J Psychiatry Neurosci 27:241–247 8. Donohue JM, Pincus HA (2007) Reducing the societal burden of depression: a review of economic costs, quality of care and effects of treatment. Pharmacoeconomics 25:7–24 9. Bakish D (2001) New standard of depression treatment: remission and full recovery. J Clin Psychiatry 62(Suppl 26):5–9 10. Keller MB (2003) Past, present, and future directions for defining optimal treatment outcome in depression: remission and beyond. JAMA 289:3152–3160 11. Richards D (2011) Prevalence and clinical course of depression: a review. Clin Psychol Rev 31:1117–1125 12. Holzel L, Harter M, Reese C, Kriston L (2011) Risk factors for chronic depression—a systematic review. J Affect Disord 129:1–13 13. Conradi HJ, de Jonge P, Ormel J (2008) Prediction of the three-year course of recurrent depression in primary care patients: different risk factors for different outcomes. J Affect Disord 105:267–271 14. Rhebergen D, Lamers F, Spijker J, de Graaf R, Beekman AT, Penninx BW (2012) Course trajectories of unipolar depressive disorders identified by latent class growth analysis. Psychol Med 42:1383–1396 15. Szadoczky E, Rozsa S, Zambori J, Furedi J (2004) Predictors for 2-year outcome of major depressive episode. J Affect Disord 83:49–57 16. Bockting CL, Spinhoven P, Koeter MW, Wouters LF, Schene AH, Depression Evaluation Longitudinal Therapy Assessment Study Group (2006) Prediction of recurrence in recurrent depression and the influence of consecutive episodes on vulnerability for depression: a 2-year prospective study. J Clin Psychiatry 67:747–755 17. Nelson JC, Zhang Q, Deberdt W, Marangell LB, Karamustafalioglu O, Lipkovich IA (2012) Predictors of remission with placebo using an integrated study database from patients with major depressive disorder. Curr Med Res Opin 28:325–334 18. Spijker J, de Graaf R, Bijl RV, Beekman AT, Ormel J, Nolen WA (2004) Determinants of persistence of major depressive episodes in the general population. Results from the Netherlands Mental Health Survey and Incidence Study (NEMESIS). J Affect Disord 81:231–240 19. van den Brink RH, Ormel J, Tiemens BG, Smit A, Jenner JA, van der Meer K et al (2002) Predictability of the one-year course of depression and generalized anxiety in primary care. Gen Hosp Psychiatry 24:156–163 20. Meyers B (2002) Predictors of early recovery from major depression among persons admitted to community-based clinics: an observational study. Arch Gen Psychiatry 59:729–735 21. De Almeida Fleck MP, Simon G, Herrman H, Bushnell D, Martin M, Patrick D et al (2005) Major depression and its correlates in primary care settings in six countries. 9-month follow-up study. British J Psychiatry 186:41–47 22. Barkow K, Maier W, Ustun TB, Gansicke M, Wittchen HU, Heun R (2003) Risk factors for depression at 12-month follow-up in adult primary health care patients with major depression: an international prospective study. J Affect Disord 76:157–169 23. Bracke P (2000) The three-year persistence of depressive symptoms in men and women. Soc Sci Med 51:51–64 24. Conradi H, DeJonge P, Ormel J (2008) Prediction of the threeyear course of recurrent depression in primary care patients: different risk factors for different outcomes. J Affect Disord 105:267–271 25. Melartin TK, Rytsala HJ, Leskela US, Lestela-Mielonen PS, Sokero TP, Isometsa ET (2004) Severity and comorbidity predict episode duration and recurrence of DSM-IV major depressive disorder. J Clin Psychiatry 65:810–819
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Soc Psychiatry Psychiatr Epidemiol 26. Nasser EH, Overholser JC (2005) Recovery from major depression: the role of support from family, friends, and spiritual beliefs. Acta Psychiatr Scand 111:125–132 27. Harkness KL, Bagby RM, Kennedy SH (2012) Childhood maltreatment and differential treatment response and recurrence in adult major depressive disorder. J Consult Clin Psychol 80:342–353 28. Nanni V, Uher R, Danese A (2012) Childhood maltreatment predicts unfavorable course of illness and treatment outcome in depression: a meta-analysis. Am J Psychiatry 169:141–151 29. Miniati M, Rucci P, Benvenuti A, Frank E, Buttenfield J, Giorgi G et al (2010) Clinical characteristics and treatment outcome of depression in patients with and without a history of emotional and physical abuse. J Psychiatr Res 44:302–309 30. Ciudad A, Alvarez E, Roca M, Baca E, Caballero L, Garcia de Polavieja P et al (2012) Early response and remission as predictors of a good outcome of a major depressive episode at 12-month follow-up: a prospective, longitudinal, observational study. J Clin Psychiatry 73:185–191 31. Fuller-Thomson E, Battiston M, Gadalla T, Shaked Y, Raza F (in press) Remission from depression among adults with arthritis: a 12-year follow-up of a population-based study. Depress Res Treat. http://www.hindawi.com/journals/drt/aip/ 32. Kroenke K, Shen J, Oxman TE, Williams JW Jr, Dietrich AJ (2008) Impact of pain on the outcomes of depression treatment: results from the RESPECT trial. Pain 134:209–215 33. Babyak M, Blumenthal JA, Herman S, Khatri P, Doraiswamy M, Moore K et al (2000) Exercise treatment for major depression: maintenance of therapeutic benefit at 10 months. Psychosom Med 62:633–638 34. Kloiber S, Ising M, Reppermund S, Horstmann S, Dose T, Majer M et al (2007) Overweight and obesity affect treatment response in major depression. Biol Psychiatry 62:321–326 35. Boschloo L, Vogelzangs N, van den Brink W, Smit JH, Veltman DJ, Beekman AT et al (2012) Alcohol use disorders and the course of depressive and anxiety disorders. Br J Psychiatry 200:476–484 36. Health Statistics Division, Statistics Canada (1995) National Population Health Survey 1994-95: Overview. http://www23. statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SurvId=3225& SurvVer=0&SDDS=3225&InstaId=15280&InstaVer=1&lang=en &db=imdb&adm=8&dis=2. Accessed June 20, 2013 37. Statistics Canada (2012) National Population Health SurveyHousehold Component—Longitudinal (NPHS). http://www23. statcan.gc.ca:81/imdb/p2SV.pl?Function=getSurvey&SDDS=32 25&lang=en&db=imdb&adm=8&dis=2. Accessed March 11, 2013 38. Kessler RC, Andrews G, Mroczek D, Uston B, Wittchen H (1998) The World Health Organization Composite International Diagnostic Interview Short Form (CIDI-SF). Int J Methods Psychiatr Res 7:171–185 39. Andrews G, Peters L (1998) The psychometric properties of the composite international diagnostic interview. Soc Psychiatry Psychiatr Epidemiol 33:80–88
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40. Mulder RT, Joyce PR, Frampton CM, Luty SE, Sullivan PF (2006) Six months of treatment for depression: outcome and predictors of the course of illness. Am J Psychiatry 63:95–100 41. Oldehinkel AJ, Ormel J, Neeleman J (2000) Predictors of time to remission from depression in primary care patients: do some people benefit more from positive life changes than others? J Abnorm Psychol 109:299–307 42. Parker G, Wilhelm K, Mitchell P, Gladstone G (2000) Predictors of 1-year outcome in depression. Aust N Z J Psychiatry 34:56–64 43. Penza KM, Heim C, Nemeroff CB (2003) Neurobiological effects of childhood abuse: implications for the pathophysiology of depression and anxiety. Arch Women’s Ment Health 6:15–22 44. McCrory E, De Brito SA, Viding E (2012) The link between child abuse and psychopathology: a review of neurobiological and genetic research. J R Soc Med 105:151–156 45. Shonkoff JP, Boyce WT, McEwen BS (2009) Neuroscience, molecular biology, and the childhood roots of health disparities: building a new framework for health promotion and disease prevention. JAMA 301:2252–2259 46. Marmot M, Allen J, Bell R, Bloomer E, Goldblatt P (2012) Consortium for the European Review of Social Determinants of Health and the Health Divide. WHO European review of social determinants of health and the health divide. Lancet 380:1011–1029 47. Scott KL, Lishak V (2012) Intervention for maltreating fathers: statistically and clinically significant change. Child Abuse Negl 36:680–684 48. Mead N, Lester H, Chew-Graham C, Gask L, Bower P (2010) Effects of befriending on depressive symptoms and distress: systematic review and meta-analysis. Br J Psychiatr 196:96–101 49. Hardt J, Rutter M (2004) Validity of adult retrospective reports of adverse childhood experiences: review of the evidence. J Child Psychol Psychiatry 45:260–273 50. Lizardi H, Klein DN (2005) Long-term stability of parental representations in depressed outpatients utilizing the parental bonding instrument. J Nerv Ment Dis 193:183–188 51. Andrade L, Caraveo-Anduaga J, Berglund P, Bijl R, De Graaf R, Vollebergh W et al (2003) The epidemiology of major depressive episodes: results from the International Consortium of Psychiatric Epidemiology (ICPE) Surveys. Int J Methods Psychiatr Res 12:3–21 52. Kohn R, Saxena S, Levav I, Saraceno B (2004) The treatment gap in mental health care. Bull World Health Organ 82:858–866 53. Messias E, Eaton WW, Grooms AN (2011) Income inequality and depression prevalence across the United States: an ecological study. Psychiatr Serv 62:710–712 54. Chen YY, Subramanian SV, Acevedo-Garcia D, Kawachi I (2005) Women’s status and depressive symptoms: a multilevel analysis. Soc Sci Med 60:49–60
ORIGINAL PAPER
Bouncing back: remission from depression in a 12-year panel study of a representative Canadian community sample Esme Fuller-Thomson • Marla Battiston • Tahany M. Gadalla • Sarah Brennenstuhl
Received: 30 July 2013 / Accepted: 16 December 2013 Springer-Verlag Berlin Heidelberg 2014
Abstract Purpose This study sought to investigate time to remission from depression in a community-based sample of adults followed for 12 years. Methods Data were derived from the National Population Health Survey (1994/5–2006/7 and 1996/7–2008/9). Fully 1,128 adults were included who were depressed at baseline according to DSM-III/CIDI-SF criteria. Kaplan–Meier and Cox proportional hazards procedures were used to determine time to remission and the demographic (e.g., gender and marital status), psychosocial (e.g., social support and adverse childhood experience) and health-related (e.g., pain, health conditions and alcohol use) factors with which it is associated. Results More than three quarters of the sample (77 %) no longer screened positive for depression at 2 years, and nearly the entire sample (94 %) had remitted by 12 years. Adverse childhood experiences (i.e., childhood abuse and parental additions), lack of social support, the presence of pain and health conditions (i.e., migraines, arthritis and back pain) each predicted more time to remission. The only factor associated with time to remission in the multivariate analysis was a history of childhood physical abuse. Conclusions Most community members with depression get better after 2 years and nearly all will have remitted, at least once, by 12 years. The results of this study may help guide the development of interventions for chronic E. Fuller-Thomson (&) M. Battiston T. M. Gadalla Factor-Inwentash Faculty of Social Work, University of Toronto, 246 Bloor St. W, Toronto, ON M5S 1V4, Canada e-mail: [email protected] S. Brennenstuhl Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
depression that focus on early prevention of childhood abuse. Keywords Survival analysis CIDI-SF Childhood abuse Adverse childhood experiences
Introduction More than 350 million people suffer from depression worldwide, making it the leading cause of disability and a key contributor to the global burden of disease [1, 2]. While treatment can be effective [3, 4], incomplete or lack of recovery from depression is associated with reduced functioning, family problems, a poor quality of life and, at worse, suicidal thoughts and behaviors [5–7]. The societal costs of chronic depression are high, driven predominantly by increased health care utilization and reduced worker productivity [8]. Given the substantial impact that depression has on individuals, families and society, remission is considered the optimal treatment goal [9, 10]. Remission requires a clinical decrease in the number and severity of symptoms over a period of time, usually defined as greater than 8 weeks, as well as a return to pre-depressive functioning [4, 9–11]. While the majority of individuals with depression are expected to remit, a significant minority will not, at least over a short period of time [11]. Understanding the factors that predict remission from depression over the longer term is an important public health goal. With greater awareness of risk factors, clinicians are better able to identify those at a higher risk for chronicity and the negative outcomes associated with it. Methodological weaknesses in studies investigating the course of depression, however, make it difficult to
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determine what factors best foretell remission. Such drawbacks include the use of small sample sizes, crosssectional data or longitudinal data with short follow-up times, and highly selective samples that over-represent severe cases of depression [11, 12]. Moreover, because the bulk of studies use clinical samples, less is known about remission in community dwellers. To address these limitations, the current study investigates remission and its predictors over a 12-year period, in a large, communitybased sample of adults. Predictors of depression remission There are a number of factors that appear to have implications for remission from depression. Many of these, however, are clinical in nature and, thus, data of this type are not always necessarily available to public health researchers. Clinical factors include personality factors and coping styles [13–15], and depression-related factors, such as the number and length of previous episodes [12, 13, 16– 19], and severity [15, 17–25]. In the current study, we focus more broadly on non-clinical factors available in many population health surveys, which can be used by public health professionals to identify groups of community members at risk for a prolonged course of depression. These can be organized into demographic, psychosocial and health-related variables. The demographic variables most consistently associated with depression outcomes are marital status and education level. Being married has been shown to predict earlier recovery [20], whereas being divorced, separated or widowed [22, 23] or having fewer years of formal schooling predicts worse depression outcomes [15, 19, 21–23]. Younger age is also related to remission in some studies [14, 17]. The relationship between remission and other demographics is more unclear. For example, while some studies have shown that gender does not play a significant role in remission [19, 22], others find that women experience a more [23] or less [14, 20] chronic course of depression than men. One of the most important psychosocial factors associated with remission is social support. Lack of social support has been shown to predict depression persistence [18, 19] and severity [15], while higher perceived emotional support has been associated with remission [26]. The experience of negative life events are also linked to depression outcomes. For example, individuals with a history of childhood emotional or physical abuse have worse depression outcomes than their counterparts who never experienced abuse [14, 27–29]. Depression outcomes are associated with presence of physical comorbidities and pain. For example, longer-term recovery is more likely among those without a chronic
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physical illness [19, 30], whereas depression persistence and recurrence are predicted by poor physical functioning [13], co-existing physical conditions [18] and pain [22, 31, 32]. Health behaviors are related to the course of depression: higher levels of physical activity are associated with lower relapses rates [33] and being overweight or obese is linked to a poorer treatment response [34]. Finally, depression persistence is related to addictive behaviors, such as alcohol dependence [6, 35]. Building on previous research, the goal of the current study was to investigate remission, and the non-clinical factors associated with it, in a large, community-based sample of Canadian adults followed for 12 years. In particular, we sought to answer two research questions: 1. 2.
What proportion of depressed Canadians are no longer depressed at 2 and 12 years after baseline? What are the non-clinical factors associated with time to remission in bivariate and multivariate analyses?
Methods Data source Data for this study were derived from the National Population Health Survey (NPHS), which is an ongoing panel study of the Canadian population starting in 1994. This nationally representative survey used a health determinants perspective to track the health status, health behaviors, and health care utilization of Canadians aged 12 and older. Participants were sampled using a multistage-stratified sampling design, details of which are provided elsewhere [36]. In the first wave of the survey (1994/1995). the sample included 17,276 Canadians. Attempts were made to re-survey the initial respondents biennially. For the current study, data from waves 1 through 7 (1994–2006) and waves 2 though 8 (1996–2008) were used, each of which spanned a 12-year period. Reasons for using two, overlapping, time periods are provided below. The response rate in wave 1 was 83.6 %. In wave 2, 92.8 % of respondents from wave 1 had responded to the survey again. By wave 7 and 8, these figures were 77.0 and 70.7 %, respectively [37]. Sample The sample is comprised of adults aged 18 and older classified as depressed at baseline, which was defined as wave 1, or wave 2 in the case that individual was not depressed in wave 1. The first wave of the NPHS included 751 depressed respondents. The second wave included another 377 individual who were depressed at wave 2, but not at wave 1. To increase the sample size, data from the
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two waves were pooled together, resulting in sample of 1,128 (women = 801, men = 327). Measures Depression was assessed biennially for 12 years using the Composite International Diagnostic Interview-Short Form (CIDI-SF) developed by Kessler et al. [38]. The CIDI-SF is a shortlist of items from the fully structured CIDI interview that measures a major depressive episode using the definitions and the criteria of the Diagnostic and Statistical Manual III, and the diagnostic criteria of the International Classification of Diseases, version 10. The CIDI-SF was delivered by well-trained lay interviewers during a 10-min interview. Respondents screened positive for depression if they had a probability of 90 % or greater for a major depressive episode over a period of at least 2 weeks in the last year. The CIDI has been shown to have excellent interrater reliability, good test–retest reliability and validity [39]. The total classification accuracy for a major depressive episode of the CIDI-SF in comparison to CIDI is 93.2 %. The sensitivity and specificity of the CIDI-SF is 89.6 and 93.9 %, respectively [39]. Initial selection of potential predictors of depression was guided by a review of the population- and communitybased literatures on depression remission and persistence. Variables included demographic, psychosocial and healthrelated factors measures at baseline (wave 1 for 751 respondents and wave 2 for 377 respondents), the details of which are provided below: •
•
Demographic variables: age group (\55 years or C55 years), gender (woman or man), marital status (married/common law or single/divorced/separated) and highest level of education level (Bhigh school, [high school). Psychosocial variables: Social support and adverse childhood experiences. Social support was measured by an affirmative response to the question: ‘‘Do you have someone you can confide in, or talk to about your private feelings or concerns?’’ Two adverse childhood experiences were investigated in a section of the survey about childhood experiences. The section was preceded by the following instructions: ‘‘The next few questions ask about some things that may have happened to you while you were a child or a teenager, before you moved out of the house. Please tell me if any of these things have happened to you’’. Physical abuse was assessed by the question ‘‘Were you ever physically abused by someone close to you?’’ and parental addictions were determined using the question ‘‘Did either of your parents drink or use drugs so often that it caused problems for the family?’’
•
Health-related variables: presence of pain and physical comorbidities, physical activity level, BMI and alcohol use. Pain was measured using the question ‘‘Are you usually free of pain and discomfort?’’ Three physical comorbidities were included: back problems, arthritis and migraines. These illnesses were ascertained by separate yes/no responses to a list of chronic conditions ‘‘that had lasted or are expected to last 6 months or more’’ and that had been ‘‘diagnosed by a health professional.’’ Physical activity level was based on the respondent’s daily recreational physical activities lasting more than 15 min, coded into active, moderate and inactive categories. Obesity was assessed by selfreported weight and height and coded into obese (BMI C 30 kg/m2), overweight (BMI = 25–29.99 kg/ m2) and average or underweight (BMI [ 25 kg/m2). A missing category was also included. Alcohol use was determined by the respondent’s weekly drinking habits. Those who consumed 12 or more drinks per week were categorized as heavy drinkers. All others were classified as non-heavy drinkers.
Analyses Survival analysis was undertaken to investigate remission, and the factors associated with it, based on a biennial measurement of depression over a 12-year observation period. For the 751 depressed at wave 1, that period was 1994–2006 and for the 377 depressed at wave 2 (but not at wave 1), the period was 1996–2008. An individual was censored if he/she died or was lost to follow-up or was consistently depressed in each wave until the end of the observation period. The analysis proceeded in several steps. First, the cumulative percentage of those no longer screening positive for depression was calculated at 2 and 12 years after baseline for the whole sample. Next, the mean time to remission was generated using the Kaplan– Meier procedure and compared across categories of each of the potential predictors using the Log Rank test. Respondents were defined as ‘‘in remission’’ if their CIDI-SF score was in the ‘‘non-depressed’’ category in at least one wave of data collection. If respondents had not achieved remission status before they missed one or more waves of data collection, they were classified as ‘‘never remitted’’. Time to remission was calculated as the time between the baseline wave of data collection, and the first wave in which the respondent was classified as ‘‘non-depressed’’. The percentage of those no longer depressed at 2 years was also compared across categories of the potential predictors. Finally, the association between the potential predictors and time to remission was determined by Cox proportional Hazard Ratios (HRs) and 95 % confidence intervals (CIs).
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The assumption of proportional hazards was tested by graphical inspection of the log–log survival curves for each potential predictor stratified by its categories. We determined that the proportional hazard assumption was not violated based on the finding of generally parallel survival curves across categories of each of the predictors. Due to the many predictors included in the analysis, the model was generated using a step-wise procedure (i.e., backwards elimination). This modeling strategy is consistent with other research in this area (e.g., [18, 22,]). All p values were two sided, at a significance level of 0.05. Analyses were performed using SPSS Version 18.
Results Focusing on overall time to remission, we found that more than three quarters of the sample (77 %) were no longer depressed at 2 years, and nearly the entire sample (94 %) had remitted by 12 years. Table 1 presents the mean time to remission according to the categories of the potential predictors and the percentage remitted in each category by 2 years. Only a few of the factors were revealed to have a significant influence on time to remission. A history of either type of adverse childhood experience (i.e., physical abuse or parental addictions) was associated with longer time to remission. For example, while 81 % of those without a history of childhood abuse were no longer depressed after 2 years, this was the case for only 67 % of those who were abused in childhood (p \ 0.001). Having no social support was also associated with a longer time to remission. Fully 79 % of those with a confidant were no longer depressed at 2 years compared to 66 % of those without someone with whom they can confide (p \ 0.001). Finally, each of the health conditions significantly influenced time to remission, including the presence of pain, arthritis, migraines and back problems. As for the presence of pain, 81 % of respondents free of pain were no longer depressed after 2 years versus 70 % of those with regular pain (p \ 0.001). None of the demographic variables (i.e., age, gender, education level and marital status) or health behaviors (i.e., alcohol use, physical activity and obesity) was related to remission. The associations between the predictors and time to remission based on the multivariate Cox proportional hazards model are presented in Table 2. As shown, a history of childhood physical abuse is the only significant predictor of remission once all other factors are accounted for (p \ 0.05). Those experiencing this type of abuse have a 15 % lower probability of remitting from depression (HR = 0.85; 95 % CI 0.73, 0.99). There are several factors that marginally predict remission (p \ 0.10): the presence of pain and migraines are both associated with a lower
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chance of remission, while having a confidant is related to a higher probability of recovering from depression in the short term.
Discussion For the large part, this study has a good news message: Community members with depression do get better. We found that three quarters of those with depression at baseline no longer screened positive for depression after 2 years. Moreover, we showed that almost all of those with depression (94 %) had remitted by 12 years. These figures are similar to those reported in a recent review of the literature indicating that 80–81 % of clinical patients had remitted after 2 years, and approximately 93–94 % had remitted at 10–15 years [11]. While the current study did not determine how long each respondent remained free of depression after they had first remitted, it does provide evidence that highly chronic depression occurs in only a minority of cases. This research also provides novel information on the non-clinical factors that predict remission for depression among members of the community. There is a growing body of evidence showing that remission is predicted by personality factors and coping styles [13–15, 40–42], and depression-related factors such as the number and length of previous episodes [12, 13, 16–19] and severity [15, 17–25]. However, many of these variables are not available to public health professionals trying to identify at-risk groups. The current study, therefore, focuses on non-clinical factors that are available in most population health surveys. We found that the single best predictor for depression persistence was a history of childhood physical abuse. This finding is consistent with a recent meta-analysis of 16 epidemiological studies and 10 clinical trials on childhood maltreatment and depression [28]. The analysis found that childhood maltreatment, which included multiple types of abuse and family conflict or violence, was associated with an increased risk of developing persistent or recurrent depression and a lack of response or remission during treatment [28]. Based on these findings, the authors suggested that childhood maltreatment can be thought of as a developmental risk factor for a poor depression course and a moderator of treatment response. A number of researchers speculate that the link between childhood abuse and poor depression outcomes can be explained by an increased vulnerability to stress [43–45]. In particular, adverse childhood experiences are thought to interrupt the normal development of biological systems, including the hypothalamic–pituitary–adrenal (HPA) axis, which affects stress regulation [43]. Depression in adulthood has been characterized by HPA axis hyperactivity,
Soc Psychiatry Psychiatr Epidemiol Table 1 Mean survival time and percent in remission at 2 years compared across categories of potential demographic, psychosocial, health and health-behavioral predictors Mean survival time
Lower CI
Upper CI
Man
2.875
2.622
3.129
Woman
2.865
2.724
3.006
\55 years
2.901
2.765
3.037
C55 years
2.680
2.397
2.963
p value
% in remission at 2 years
Demographics Gender 0.89
78.9 76.8
Age 0.10
76.2 83.5
Education BHigh school
2.872
2.615
3.129
[High school
2.878
2.735
3.020
Single/separated/divorced
2.841
2.681
3.001
Married/common law
2.891
2.700
3.082
0.78
79.7 76.2
Marital status 0.69
77.6 77.0
Psychosocial Social support Yes confidant
2.772
2.648
2.896
No confidant
3.392
2.973
3.811
\0.001
79.4 66.3
Adverse childhood experiences No physical abuse
2.696
2.565
2.826
Physical abuse
3.451
3.099
3.803
No parental addictions
2.758
2.619
2.898
Parental addictions
3.190
2.886
3.494
\0.001
80.5 66.7
0.002
79.3 71.4
Health Pain Usually free of pain
2.660
2.537
2.783
Not usually free of pain
3.334
3.047
3.621
Yes
3.198
2.882
3.513
No
2.774
2.646
2.902
\0.001
81 69.6
Arthritis 0.004
70.8 78.9
Migraines \0.001
Yes
3.395
3.056
3.734
No
2.729
2.605
2.853
67.1
Yes
3.200
2.894
3.505
No
2.759
2.630
2.889
Normal
2.806
2.632
2.980
Overweight
2.922
2.686
3.159
75.4
Obese
2.935
2.574
3.297
77.2
Missing
2.857
2.501
3.212
77.7
Active
2.874
2.588
3.159
Moderate
2.667
2.427
2.906
80.8
Inactive
2.933
2.768
3.098
75.2
79.8
Back problems 0.004
71.9 79.1
Health behaviors Body Mass Index 0.80
78.6
Physical activity level 0.21
80.4
Alcohol use Not heavy drinker
2.883
2.753
3.014
Heavy drinker
2.646
2.331
2.960
0.39
77.3 77.9
CI confidence interval
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Soc Psychiatry Psychiatr Epidemiol Table 2 Multivariate Cox proportional hazards regression predicting time to remission (n = 966) Hazard ratio
95 % CI
p value
History of childhood physical abuse Yes
1.00
No
0.85
0.049 (0.729, 0.999)
Presence of pain Yes
1.00
No
0.88
0.092 (0.760, 1.021)
Presence of migraine Yes 1.00 No
0.86
0.090 (0.722, 1.024)
Social support Yes
1.00
No
1.17
0.093 (0.974, 1.413)
a
Backward elimination was used to select covariates for final model. All p values were two sided, at a significance level of 0.05
which is linked to the increased production of cortisol [43, 44]. There is some evidence that abnormalities in the stress response are even more pronounced among those with depression and a history of childhood abuse compared to those with depression alone [43, 45]. This research underscores the importance of childhood experiences for shaping health trajectories across the life course and reinforces the importance of public health interventions that support healthy child development [45]. The WHO Commission on the Social Determinants of Health suggests that these interventions should include providing adequate social and health protections for women, mothers-to-be and young families and, specifically, implementation of high quality, affordable early years education and childcare systems [46]. Fathers should also be the focus of interventions to prevent child abuse [47]. Other factors marginally related to time to remission in the multivariate analysis included social support and the presence of pain and migraines. Previous research shows that lack of social support predicts depression persistence [18, 19] and severity [15], while higher perceived emotional support is associated with remission [26]. There is some evidence that ‘‘befriending’’ interventions, which provide social support that is non-directive and emotional in nature, have a modest, positive, influence on depression symptoms [48]. Future research should investigate whether this approach can be used preventatively for those at a high risk for a chronic course of depression. A relationship between the presence of pain and remission has also been established in a number of previous studies [22, 31, 32]. Kroenke et al. [32] suggest that better recognition and optimal management of pain are important for enhancing depression treatment. Forthcoming studies
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should determine whether our marginal findings on social support and pain can be replicated in other populationbased samples. A number of factors identified in other (mostly clinical) research as being predictors of remission were not found to be significant in the current study, including education level and marital status. It may be that these factors are less important predictors in the general population than in clinical samples, which can represent more severe cases of depression. This is suggested by the findings of another population-based study showing no associations between either education level or cohabitation status and depression persistence [18]. The current study has several limitations. The sample selected for the NPHS is likely to under-represent those with the most severe cases of depression, who, if institutionalized, would be ineligible for this communitybased study. In addition, severely depressed community dwellers may have been less likely to participate in the survey. This implies that the proportion remitted at 2 and 12 years, and the factors predicting remission, may less accurately reflect individuals with the most challenging cases of depression. Some caution should be exercised when interpreting the results of the multivariate analysis. While childhood abuse was identified as a predictor of time to remission, the finding was of borderline significance (p = 0.049). This may reflect a lack of statistical power due to the low base rate of childhood abuse. Because childhood abuse was measured retrospectively, there is also a possibility of recall bias. Research shows that when measurement error occurs, it is much more likely to result in false negatives than false positives [49]. In the current research, this means that our results are probably conservative, which may also help account for the borderline finding. While it is possible that those who are depressed are more likely to recount negative life events, prior research has shown that impressions of parental bonding experiences remain relatively stable across levels of depressed mood [50]. Thus, our sample was not necessarily more likely to report childhood abuse at baseline (when they were depressed), which supports the case that our results are conservative. Finally, the results of our study must be interpreted in the national context within which they occurred. The 12-month prevalence of major depression in Canada (4.3 %) is higher than in some comparatively wealthy nations, such as Japan (1.2 %), but lower than in others, including Germany (5.2 %), the Netherlands (5.9 %), and the US (10.0 %) [51]. This crossnational variation in depression prevalence may signal differences in genetic or environmental factors [51], which also could play a role in speed of remission. Similar to European countries, but unlike the US, Canada has a universal health care
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program, which includes free access to mental health services. Differences in health care systems may help shape the extent to which those with depressions are able to obtain appropriate treatment and its continuation over time [52]. Other countrylevel factors that may play a role in depression persistence include level of income and gender inequality [53, 54]. Despite the aforementioned limitations, this study’s long follow-up period of 12 years, with biennial measurements of depression, and use of a large, representative community-based sample makes it a unique contribution to the literature. Moreover, its focus on non-clinical variables makes it valuable to health professionals who are interested in tracking and preventing chronic courses of depression. To conclude, this study has a predominantly positive message: Most community members with depression will get better after 2 years and nearly all will have remitted, at least once, by 12 years. When multiple variables are adjusted simultaneously, the only predictor of time to remission among the non-clinical variables selected for this study is a history of childhood abuse. This finding underscores the strong influence that childhood circumstances have on adult health trajectories and suggests that prevention of chronic depression needs to begin upstream by ensuring that every child has a good start to life. Acknowledgments The first author (Esme Fuller-Thomson) would like to gratefully acknowledge support received from the Sandra Rotman Endowed Chair in Social Work. We wish to thank Statistics Canada and the Social Sciences and Humanities Council of Canada for permission to access the longitudinal form of the National Population Health Survey via the Research Data Center (RDC) at the University of Toronto. We are also grateful to the staff at the Toronto RDC for their help in accessing the data. The opinions expressed herein are our own and do not represent the views of Statistics Canada. Conflict of interest
None.
References 1. Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B (2007) Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet 370:851–858 2. World Health Organization (2004) The global burden of disease: 2004 update. http://www.who.int/healthinfo/global_burden_dis ease/2004_report_update/en/index.html. Accessed April 9, 2013 3. Casacalenda N, Perry JC, Looper K (2002) Remission in major depressive disorder: a comparison of pharmacotherapy, psychotherapy, and control conditions. Am J Psychiatry 159:1354–1360 4. Dawson MY, Michalak EE, Waraich P, Anderson JE, Lam RW (2004) Is remission of depressive symptoms in primary care a realistic goal? A meta-analysis. BMC Fam Pract 5:19 5. Trivedi MH, Corey-Lisle PK, Guo Z, Lennox RD, Pikalov A, Kim E (2009) Remission, response without remission, and nonresponse in major depressive disorder: impact on functioning. Int Clin Psychopharmacol 24:133–138 6. Hirschfeld RMA (2000) Psychosocial Predictors of Outcome in Depression. http://www.acnp.org/G4/GN401000107/. Accessed June 20, 2013
7. Tranter R, O’Donovan C, Chandarana P, Kennedy S (2002) Prevalence and outcome of partial remission in depression. J Psychiatry Neurosci 27:241–247 8. Donohue JM, Pincus HA (2007) Reducing the societal burden of depression: a review of economic costs, quality of care and effects of treatment. Pharmacoeconomics 25:7–24 9. Bakish D (2001) New standard of depression treatment: remission and full recovery. J Clin Psychiatry 62(Suppl 26):5–9 10. Keller MB (2003) Past, present, and future directions for defining optimal treatment outcome in depression: remission and beyond. JAMA 289:3152–3160 11. Richards D (2011) Prevalence and clinical course of depression: a review. Clin Psychol Rev 31:1117–1125 12. Holzel L, Harter M, Reese C, Kriston L (2011) Risk factors for chronic depression—a systematic review. J Affect Disord 129:1–13 13. Conradi HJ, de Jonge P, Ormel J (2008) Prediction of the three-year course of recurrent depression in primary care patients: different risk factors for different outcomes. J Affect Disord 105:267–271 14. Rhebergen D, Lamers F, Spijker J, de Graaf R, Beekman AT, Penninx BW (2012) Course trajectories of unipolar depressive disorders identified by latent class growth analysis. Psychol Med 42:1383–1396 15. Szadoczky E, Rozsa S, Zambori J, Furedi J (2004) Predictors for 2-year outcome of major depressive episode. J Affect Disord 83:49–57 16. Bockting CL, Spinhoven P, Koeter MW, Wouters LF, Schene AH, Depression Evaluation Longitudinal Therapy Assessment Study Group (2006) Prediction of recurrence in recurrent depression and the influence of consecutive episodes on vulnerability for depression: a 2-year prospective study. J Clin Psychiatry 67:747–755 17. Nelson JC, Zhang Q, Deberdt W, Marangell LB, Karamustafalioglu O, Lipkovich IA (2012) Predictors of remission with placebo using an integrated study database from patients with major depressive disorder. Curr Med Res Opin 28:325–334 18. Spijker J, de Graaf R, Bijl RV, Beekman AT, Ormel J, Nolen WA (2004) Determinants of persistence of major depressive episodes in the general population. Results from the Netherlands Mental Health Survey and Incidence Study (NEMESIS). J Affect Disord 81:231–240 19. van den Brink RH, Ormel J, Tiemens BG, Smit A, Jenner JA, van der Meer K et al (2002) Predictability of the one-year course of depression and generalized anxiety in primary care. Gen Hosp Psychiatry 24:156–163 20. Meyers B (2002) Predictors of early recovery from major depression among persons admitted to community-based clinics: an observational study. Arch Gen Psychiatry 59:729–735 21. De Almeida Fleck MP, Simon G, Herrman H, Bushnell D, Martin M, Patrick D et al (2005) Major depression and its correlates in primary care settings in six countries. 9-month follow-up study. British J Psychiatry 186:41–47 22. Barkow K, Maier W, Ustun TB, Gansicke M, Wittchen HU, Heun R (2003) Risk factors for depression at 12-month follow-up in adult primary health care patients with major depression: an international prospective study. J Affect Disord 76:157–169 23. Bracke P (2000) The three-year persistence of depressive symptoms in men and women. Soc Sci Med 51:51–64 24. Conradi H, DeJonge P, Ormel J (2008) Prediction of the threeyear course of recurrent depression in primary care patients: different risk factors for different outcomes. J Affect Disord 105:267–271 25. Melartin TK, Rytsala HJ, Leskela US, Lestela-Mielonen PS, Sokero TP, Isometsa ET (2004) Severity and comorbidity predict episode duration and recurrence of DSM-IV major depressive disorder. J Clin Psychiatry 65:810–819
123
Soc Psychiatry Psychiatr Epidemiol 26. Nasser EH, Overholser JC (2005) Recovery from major depression: the role of support from family, friends, and spiritual beliefs. Acta Psychiatr Scand 111:125–132 27. Harkness KL, Bagby RM, Kennedy SH (2012) Childhood maltreatment and differential treatment response and recurrence in adult major depressive disorder. J Consult Clin Psychol 80:342–353 28. Nanni V, Uher R, Danese A (2012) Childhood maltreatment predicts unfavorable course of illness and treatment outcome in depression: a meta-analysis. Am J Psychiatry 169:141–151 29. Miniati M, Rucci P, Benvenuti A, Frank E, Buttenfield J, Giorgi G et al (2010) Clinical characteristics and treatment outcome of depression in patients with and without a history of emotional and physical abuse. J Psychiatr Res 44:302–309 30. Ciudad A, Alvarez E, Roca M, Baca E, Caballero L, Garcia de Polavieja P et al (2012) Early response and remission as predictors of a good outcome of a major depressive episode at 12-month follow-up: a prospective, longitudinal, observational study. J Clin Psychiatry 73:185–191 31. Fuller-Thomson E, Battiston M, Gadalla T, Shaked Y, Raza F (in press) Remission from depression among adults with arthritis: a 12-year follow-up of a population-based study. Depress Res Treat. http://www.hindawi.com/journals/drt/aip/ 32. Kroenke K, Shen J, Oxman TE, Williams JW Jr, Dietrich AJ (2008) Impact of pain on the outcomes of depression treatment: results from the RESPECT trial. Pain 134:209–215 33. Babyak M, Blumenthal JA, Herman S, Khatri P, Doraiswamy M, Moore K et al (2000) Exercise treatment for major depression: maintenance of therapeutic benefit at 10 months. Psychosom Med 62:633–638 34. Kloiber S, Ising M, Reppermund S, Horstmann S, Dose T, Majer M et al (2007) Overweight and obesity affect treatment response in major depression. Biol Psychiatry 62:321–326 35. Boschloo L, Vogelzangs N, van den Brink W, Smit JH, Veltman DJ, Beekman AT et al (2012) Alcohol use disorders and the course of depressive and anxiety disorders. Br J Psychiatry 200:476–484 36. Health Statistics Division, Statistics Canada (1995) National Population Health Survey 1994-95: Overview. http://www23. statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SurvId=3225& SurvVer=0&SDDS=3225&InstaId=15280&InstaVer=1&lang=en &db=imdb&adm=8&dis=2. Accessed June 20, 2013 37. Statistics Canada (2012) National Population Health SurveyHousehold Component—Longitudinal (NPHS). http://www23. statcan.gc.ca:81/imdb/p2SV.pl?Function=getSurvey&SDDS=32 25&lang=en&db=imdb&adm=8&dis=2. Accessed March 11, 2013 38. Kessler RC, Andrews G, Mroczek D, Uston B, Wittchen H (1998) The World Health Organization Composite International Diagnostic Interview Short Form (CIDI-SF). Int J Methods Psychiatr Res 7:171–185 39. Andrews G, Peters L (1998) The psychometric properties of the composite international diagnostic interview. Soc Psychiatry Psychiatr Epidemiol 33:80–88
123
40. Mulder RT, Joyce PR, Frampton CM, Luty SE, Sullivan PF (2006) Six months of treatment for depression: outcome and predictors of the course of illness. Am J Psychiatry 63:95–100 41. Oldehinkel AJ, Ormel J, Neeleman J (2000) Predictors of time to remission from depression in primary care patients: do some people benefit more from positive life changes than others? J Abnorm Psychol 109:299–307 42. Parker G, Wilhelm K, Mitchell P, Gladstone G (2000) Predictors of 1-year outcome in depression. Aust N Z J Psychiatry 34:56–64 43. Penza KM, Heim C, Nemeroff CB (2003) Neurobiological effects of childhood abuse: implications for the pathophysiology of depression and anxiety. Arch Women’s Ment Health 6:15–22 44. McCrory E, De Brito SA, Viding E (2012) The link between child abuse and psychopathology: a review of neurobiological and genetic research. J R Soc Med 105:151–156 45. Shonkoff JP, Boyce WT, McEwen BS (2009) Neuroscience, molecular biology, and the childhood roots of health disparities: building a new framework for health promotion and disease prevention. JAMA 301:2252–2259 46. Marmot M, Allen J, Bell R, Bloomer E, Goldblatt P (2012) Consortium for the European Review of Social Determinants of Health and the Health Divide. WHO European review of social determinants of health and the health divide. Lancet 380:1011–1029 47. Scott KL, Lishak V (2012) Intervention for maltreating fathers: statistically and clinically significant change. Child Abuse Negl 36:680–684 48. Mead N, Lester H, Chew-Graham C, Gask L, Bower P (2010) Effects of befriending on depressive symptoms and distress: systematic review and meta-analysis. Br J Psychiatr 196:96–101 49. Hardt J, Rutter M (2004) Validity of adult retrospective reports of adverse childhood experiences: review of the evidence. J Child Psychol Psychiatry 45:260–273 50. Lizardi H, Klein DN (2005) Long-term stability of parental representations in depressed outpatients utilizing the parental bonding instrument. J Nerv Ment Dis 193:183–188 51. Andrade L, Caraveo-Anduaga J, Berglund P, Bijl R, De Graaf R, Vollebergh W et al (2003) The epidemiology of major depressive episodes: results from the International Consortium of Psychiatric Epidemiology (ICPE) Surveys. Int J Methods Psychiatr Res 12:3–21 52. Kohn R, Saxena S, Levav I, Saraceno B (2004) The treatment gap in mental health care. Bull World Health Organ 82:858–866 53. Messias E, Eaton WW, Grooms AN (2011) Income inequality and depression prevalence across the United States: an ecological study. Psychiatr Serv 62:710–712 54. Chen YY, Subramanian SV, Acevedo-Garcia D, Kawachi I (2005) Women’s status and depressive symptoms: a multilevel analysis. Soc Sci Med 60:49–60
