Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Soc Psychiatry Psychiatr Epidemiol. Author manuscript; available in PMC 2010 April 1.
Published in final edited form as:
PMCID: PMC2662042

Socioeconomic status and anxiety as predictors of antidepressant treatment response and suicidal ideation in older adults

Alex Cohen, PhD
Department of Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA 02115
Stephen E. Gilman, ScD
Departments of Society, Human Development and Health and Epidemiology, Harvard School of Public Health, 677 Huntington Ave. Boston, Massachusetts 02115



Independent analyses have shown that socioeconomic status (SES) and anxiety are predictors of response to antidepressant treatment in older adults. SES status has also been demonstrated to be associated with the occurrence of suicidal ideation.


To determine whether SES differences and baseline anxiety independently contribute to poor depression treatment response and the occurrence of suicidality or that higher levels of anxiety in depressed elderly with low SES explain the differences in these outcomes. We hypothesized that neighborhood SES status will predict response to treatment and the occurrence of suicidal ideation even when controlling for baseline levels of comorbid anxiety.


Secondary analyses of data from the Maintenance Treatment for Late-Life Depression Trials


Regression analyses indicate that neighborhood SES remains an independent predictor of response to treatment and the likelihood of experiencing suicidal ideation, while comorbid anxiety remains a predictor of response to treatment.


These findings indicate the importance of treating anxiety symptoms during treatment of late-life depression and, at the same time, addressing barriers of treatment response related to low SES.

Keywords: socioeconomic status, late-life depression, comorbid anxiety, social determinants of health, response to antidepressant treatment


We recently reported that response to treatment for late-life depression varied significantly according to a patient’s pre-treatment socioeconomic status (SES) [1]. In the Maintenance Treatment for Late-Life Depression (MTLD) Trial [2,3], participants who lived in middle- or high-income neighborhoods were significantly more likely to respond to treatment than residents of low-income neighborhoods (e.g., median household incomes <$25,000; hazard ratio=1.61.; 95% CI, 1.07, 2.42). In addition, residents of middle- or high-income neighborhoods were less likely to experience suicidal ideation during the course of treatment (odds ratio=0.46; 95% CI, 0.24, 0.87) than residents of low-income neighborhoods.

Our findings regarding lower SES and higher levels of depressive and suicidal symptoms during treatment, as well as those of other studies showing similar patterns of associations [47], suggest that the social and contextual factors [813] that increase the risk of depression in community samples [1419] also prolong recovery from depression during treatment. Potential implications of these findings for clinical practice are that lower SES patients entering treatment for depression should be considered at higher risk for treatment failure, and may benefit from more intensive or different interventions [20,21].

Since our study, Andreescu et al. [22] analyzed data from MTLD, and reported that higher pre-treatment levels of anxiety – as measured by the Brief Symptom Inventory [23] – were associated with longer time to treatment response. These findings are consistent with prior studies on the effect of baseline levels of anxiety and subsequent response to treatment for late-life depression [2426,7,27]. Andreescu et al. discussed the importance of treating anxiety symptoms alongside treatment for depression, but emphasized the need for future research to determine the optimal treatment strategies for older adults suffering from depression and co-occurring symptoms of anxiety.

Viewed together, results from the MTLD and other trials suggest that there are multiple categories of patient characteristics that are identifiable at the time of treatment initiation and which provide important prognostic value [e.g.,24,28,29,7]. However, not all prognostic factors are equal. The identification of prognostic factors such as SES, which reflects a complex set of attributes of an individual’s social milieu [30,31], is likely to have implications for the treatment of depression that are different from the implications of identifying other factors such as pre-existing symptoms of anxiety. It is therefore important to determine the relative importance, i.e., the independent effects, of pre-treatment sociodemographic and pre-treatment clinical factors on depressive and suicidal symptoms during treatment.

Accordingly, we reanalyzed data from the MTLD trials to determine whether SES and anxiety independently contribute to poor treatment response and a higher probability of experiencing suicidal ideation. If the effects of SES are reduced after adjusting for baseline levels of anxiety symptoms, this would suggest that social inequalities in response to treatment for late-life depression are due partly to the effects of anxiety symptoms among lower SES patients. Such a finding would be consistent with epidemiologic studies that show significant SES gradients in the risk of anxiety [3235]. Alternatively, the previously observed effects of baseline anxiety symptoms might be better attributed to SES, in that it has effects on a wide range of health outcomes, and has been hypothesized as a “fundamental cause” of health disparities [36]. Lastly, it is possible that anxiety and SES jointly influence the course of anti-depressant treatment. If that is the case, it would indicate the importance of future research examining diverse clinical samples to better understand moderators of treatment efficacy and effectiveness [3739], and of integrating information on pre-treatment factors into the design of randomized trials.


The analyses reported here are based on a sample of 248 subjects who participated in the open-label, non-randomized phases of the two MTLD clinical trials. Comprehensive descriptions of the design of the these trials are available elsewhere [2,3]. Severity of participants’ depressive symptoms were measured weekly with the 17- item Hamilton Rating Scale for Depression (HRSD) [40]. Scores on the HRSD were used to generate indicators of treatment efficacy with response defined as HRSD scores of 10 or less for at least 3 consecutive weeks [41]. Suicidal symptoms during the course of treatment were assessed with HRSD item 3. Participants who reported recurrent thoughts of death or wishes to be dead, had active suicidal ideation, or had attempted suicide were classified as experiencing suicidality. We analyzed outcomes during the acute and continuation phases of the trials, which together lasted up to 26 weeks.

SES was measured by tertiles of median census tract annual income based on data from the 2000 U.S. Census [42], and patients’ own educational level (categorized as <12 years, 12 years, 13–15 years, and ≥16 years). Baseline anxiety symptoms were assessed with the Brief Symptom Inventory [23]. We also adjusted for patient demographic factors (age, sex, race/ethnicity, and marital status), and other baseline clinical characteristics (first vs. recurrent episode of depression, age at first onset of depression, duration of current episode, concurrent medical burden, and HRSD score).

Cox proportional hazards regression [43] was used to examine the effects of SES and baseline anxiety on the likelihood of response to antidepressant treatment, which was defined as achieving HRSD scores ≤10 for at least 3 consecutive weeks [41]. Repeated-measures generalized logit regression [44] was used to model the presence of suicidal symptoms at each week during treatment. For both outcomes, we estimated a model for baseline anxiety alone, SES alone, and for all covariates together.


The demographic and clinical characteristics of the MTLD participants, shown in Table 1, are presented separately for each category of census tract income. At the time of the baseline interview, prior to treatment initiation, there was no significant association between anxiety symptoms and SES, measured either by median census tract income or patients’ educational attainment. This provides an initial indication that the prognostic value of SES and of anxiety symptoms at baseline operate independently from one another.

Table 1
Baseline demographic & clinical characteristics of the income groups: mean (SD) or % (n)

Results of proportional hazards models of treatment response are shown in the first two columns of Table 2. In the first column of Table 2, adjusted hazard ratios for SES and anxiety are shown from two separate models, one with the SES variables and another with baseline anxiety. The second column shows the adjusted hazard ratios from a model with SES and baseline anxiety together. Hazard ratios for SES were virtually unchanged after accounting for baseline anxiety in the model. The likelihood of treatment response was significantly higher among residents of middle income neighborhoods (HR, 1.77; 95%CI, 1.16–2.71), and marginally higher among residents of high-income neighborhoods (HR, 1.32; 95%CI, 0.80–2.19). In the aggregate, residents of middle- and high-income census tracts combined were 1.63 (95%CI, 1.08–2.46) times more likely to respond to treatment than residents of low-income census tracts. Baseline anxiety symptoms were also predictive of treatment response, as reported by Andreescu et al. [22], but the reduction in the likelihood of response associated with anxiety symptoms was entirely independent of SES (HR, 0.74 [95%CI, 0.61–0.91] without controlling for SES, and HR, 0.73 [95%CI, 0.60–0.89] controlling for SES).

Effects of SES and comorbid anxiety on treatment response and suicidality

This general pattern holds when examining the effects of SES and baseline anxiety symptoms on suicidal ideation during the course of treatment: SES and anxiety exert independent effects. Repeated-measures generalized logit regression analyses (Table 2, columns 3 and 4) suggest that, compared to residents of low-income neighborhoods, those in middle-income and high-income neighborhoods, independent of comorbid anxiety, were less likely to report suicidal ideation during treatment (OR, 0.48 [95%CI, 0.27–0.94] and OR, 0.39 [95%CI, 0.16–0.94], respectively). When comorbid anxiety is added to the model, the result is virtually unchanged for residents of middle-income neighborhoods (OR, 0.53 [95%CI, 0.29–0.96]) and slightly reduced for residents of high-income neighborhoods (OR, 0.44 [95%CI, 0.19–1.04]). The odds ratio for middle- and high-income groups combined was 0.51 (95%CI, 0.28–0.90). Independent of SES, baseline anxiety symptoms were associated with a marginally higher odds of suicidal ideation during the course of depression treatment (OR,1.45; [95%CI, 0.98– 2.14]).

The presence of independent effects of SES and baseline anxiety symptoms on depressive symptoms during the course of treatment suggests a marked elevation in the persistence of depression among the low SES individuals who were experiencing anxiety symptoms at the time of treatment initiation. To examine the combined influence of these factors, we evaluated the potential interactive effects of residence in a low-income neighborhood and anxiety on the outcomes of interest. There was no evidence of an interaction between SES and anxiety in the model for treatment response. However, there was suggestive evidence of an interaction between SES and anxiety in the model for suicidal ideation (p=0.08). The results of the model for suicidal ideation (Table 2, Column 4) in which the SES*anxiety interaction was added points to a somewhat stronger effect of baseline anxiety symptoms on suicidal ideation among individuals in low-income neighborhoods (OR, 2.10; 95%CI, 1.31–3.35) than among individuals who lived in middle- and high-income (aggregated) neighborhoods (OR, 1.63; 95%CI, 0.52–5.15).

As in our previous research [1], educational status was not a significant predictor of either likelihood of response or odds of suicidal ideation in the regression models that included baseline comorbid anxiety.


The objective of this study was to investigate whether SES has an independent effect (over and above anxiety) in predicting treatment response and suicidal ideation in depressed older adults. Identification of pre-treatment factors that are related to the prognosis of depression would aid clinicians in the recognition of patients at higher risk for treatment failure. However, research to date is inconclusive about whether the clinical implications of SES as a prognostic factor differ from the clinical implications of baseline clinical factors, e.g., severity of depressive symptoms and comorbid anxiety [45,46,26,27]. Research by Areán et al. [47] suggests that low-income older adults respond to collaborative care for depression to the same extent as their higher-income counterparts. Research by Gum et al. [20] suggests that achieving good response to treatment among low-income older adults with psychiatric comorbidity requires clinical case management or cognitive-behavioral group therapy to retain patients in treatment. Work by Miranda et al. [21] is congruent with these findings. It demonstrates that low-income, African-American young women who are depressed respond well to either medication or psychotherapy interventions, but that enrollment and retention in treatment require intensive outreach, reimbursement for transportation, and provisions for child care. Nevertheless, we are left with the question of whether late-life depression among low-income individuals requires more intensive treatments, different treatments, or both. Our previous work [1] does not provide an answer. Although lower-income older adults were less likely to respond to combination (medication plus psychosocial) treatment and more likely to experience suicidal ideation during treatment than higher-income subjects in the MTLD trials, we do not know whether greater intensity of treatment would have eliminated these differences.

Although the secondary analyses reported here do not answers these questions, either, our findings do suggest that the social worlds which put older adults at elevated risk of depression also act to reduce the effectiveness of antidepressant treatments. At the same time, we cannot dismiss the consequences of psychiatric comorbidity. Adding baseline anxiety to the original regression models did not reduce substantially the effects of neighborhood SES (census tract median household income) as a predictor of response to treatment. Nor did neighborhood SES reduce substantially the effects of baseline comorbid anxiety as a predictor. This suggests that neighborhood and clinical characteristics are independent predictors of response to treatment for late-life depression. At the same time, there is intriguing evidence of an interaction effect on the experience of suicidal ideation the negative consequences of comorbid anxiety may be amplified for individuals who live in low SES neighborhoods. This apparent association, which is broadly consistent with other research concerning the interactive effects of risk factors for depression [4854] and suicidal ideation [55], indicates a need for more research on the interactions among the “emotional, physical, and social factors that determine risk for suicide in the older adults” [56].

In conclusion, we suggest that, identifying, disentangling, and addressing social and clinical prognostic factors should become a major focus of clinical research in psychiatry. To that end, we urge clinical trial investigators to follow the advice of Kraemer et al. [37,38] and conduct secondary data analyses to explore the social factors (e.g., income, wealth, education, race/ethnicity, characteristics of neighborhoods) that may act as moderators of treatment effects. However, such analyses will not be possible unless investigators: 1) employ sampling methods that ensure ample representation of individuals from a wide range of social worlds and ensure sufficient power in clinical trials to detect moderators of treatment efficacy [57]; and, 2) collect detailed data on the socioeconomic status of participants in clinical trials [58]. Together, sampling methods, data collection and analytic strategies to explore moderators of treatment effects will make it possible for clinical research to sort out the independent effects of comorbid symptoms and other clinical factors from the effects of SES.


This study was supported in part by grants P30 MH71944, RO1 MH43832, RO1 MH37869, R25 MH60473, P60 MD000207-03, and R03 MH083335 from the National Institute of Mental Health.


Declaration of Interest: Dr. Reynolds receives research support (in the form of drug supplies only) from GlaxoSmithKline, Forest Pharmaceuticals, Pfizer, Eli Lilly & Co., and Bristol-Meyers Squibb for his NIH sponsored activities. The other authors do not have interests that might be affected by the publication of this paper.


1. Cohen A, Houck PR, Szanto K, Dew MA, Gilman SE, Reynolds CF., III Social inequalities in response to antidepressant treatment in older adults. Arch Gen Psychiatry. 2006;63:50–56. [PubMed]
2. Reynolds CF, 3rd, Dew MA, Pollock BG, Mulsant BH, Frank E, Miller MD, Houck PR, Mazumdar S, Butters MA, Stack JA, Schlernitzauer MA, Whyte EM, Gildengers A, Karp J, Lenze E, Szanto K, Bensasi S, Kupfer DJ. Maintenance treatment of major depression in old age. N Engl J Med. 2006;354:1130–1138. [PubMed]
3. Reynolds CF, 3rd, Frank E, Perel JM, Imber SD, Cornes C, Miller MD, Mazumdar S, Houck PR, Dew MA, Stack JA, Pollock BG, Kupfer DJ. Nortriptyline and interpersonal psychotherapy as maintenance therapies for recurrent major depression: A randomized controlled trial in patients older than 59 years. JAMA. 1999;281:39–45. [PubMed]
4. Hirschfeld RM, Russell JM, Delgado PL, Fawcett J, Friedman RA, Harrison WM, Koran LM, Miller IW, Thase ME, Howland RH, Connolly MA, Miceli RJ. Predictors of response to acute treatment of chronic and double depression with sertraline or imipramine. J Clin Psychiatry. 1998;59:669–675. [PubMed]
5. Rickels K, Jenkins BW, Zamostien B, Raab E, Kanther M. Pharmacotherapy in neurotic depression: Differential population responses. J Nerv Ment Dis. 1968;145:475–485. [PubMed]
6. Spillmann M, Borus JS, Davidson KG, Worthington JJ, 3rd, Tedlow JR, Fava M. Sociodemographic predictors of response to antidepressant treatment. Int J Psychiatry Med. 1997;27:129–136. [PubMed]
7. Trivedi MH, Rush AJ, Wisniewski SR, Nierenberg AA, Warden D, Ritz L, Norquist G, Howland RH, Lebowitz B, McGrath PJ, Shores-Wilson K, Biggs MM, Balasubramani GK, Fava M. STAR*D Study Team. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: Implications for clinical practice. Am J Psychiatry. 2006;163:28–40. [PubMed]
8. Brown GW, Bifulco A, Harris TO. Life events, vulnerability and onset of depression: some refinements. Br J Psychiatry. 1987;150:30–42. [PubMed]
9. Brown GW, Harris T. Stressor, vulnerability and depression: a question of replication. Psychol Med. 1986;16:739–744. [PubMed]
10. Brown GW, Harris TO. Social origins of depression: A study of psychiatric disorder in women. Tavistock; London: 1978.
11. Dohrenwend BP. The role of adversity and stress in psychopathology: some evidence and its implications for theory and research. J Health Soc Behav. 2000;41:1–19. [PubMed]
12. Marmot MG, Kogevinas M, Elston MA. Social/economic status and disease. Annu Rev Public Health. 1987;8:111–135. [PubMed]
13. Monroe SM, Simons AD. Diathesis-stress theories in the context of life stress research: Implications for the depressive disorders. Psychol Bull. 1991;110:406–425. [PubMed]
14. Dohrenwend BP, Dohrenwend BS. Social status and psychological disorder; a causal inquiry. Wiley-Interscience; New York: 1969.
15. Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, Rush AJ, Walters EE, Wang PS. National Comorbidity Survey R. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R) JAMA. 2003;289:3095–3105. [PubMed]
16. Lorant V, Croux C, Weich S, Deliege D, Mackenbach J, Ansseau M. Depression and socio-economic risk factors: 7-year longitudinal population study. Br J Psychiatry. 2007;190:293–298. [PubMed]
17. Lorant V, Deliege D, Eaton W, Robert A, Philippot P, Ansseau M. Socioeconomic inequalities in depression: a meta-analysis. Am J Epidemiol. 2003;157:98–112. [PubMed]
18. Roberts RE, Lee ES. Occupation and the prevalence of major depression, alcohol, and drug abuse in the United States. Environ Res. 1993;61:266–278. [PubMed]
19. Wilson KC, Chen R, Taylor S, McCracken CF, Copeland JR. Socio-economic deprivation and the prevalence and prediction of depression in older community residents. The MRC-ALPHA Study. Br J Psychiatry. 1999;175:549–553. [PubMed]
20. Gum AM, Arean PA, Bostrom A. Low-income depressed older adults with psychiatric comorbidity: secondary analyses of response to psychotherapy and case management. Int J Geriatr Psychiatry. 2007;22:124–130. [PubMed]
21. Miranda J, Chung JY, Green BL, Krupnick J, Siddique J, Revicki DA, Belin T. Treating depression in predominantly low-income young minority women. JAMA. 2003;290:57–65. [PubMed]
22. Andreescu C, Lenze EJ, Dew MA, Begley AE, Mulsant BH, Dombrovski AY, Pollock BG, Stack J, Miller MD, Reynolds CF. Effect of comorbid anxiety on treatment response and relapse risk in late-life depression: controlled study. Br J Psychiatry. 2007;190:344–349. [PubMed]
23. Derogatis LR, Melisaratos N. The Brief Symptom Inventory: an introductory report. Psychol Med. 1983;13:595–605. [PubMed]
24. Dew MA, Reynolds CF, 3rd, Houck PR, Hall M, Buysse DJ, Frank E, Kupfer DJ. Temporal profiles of the course of depression during treatment: predictors of pathways toward recovery in the elderly. Arch Gen Psychiatry. 1997;54:1016–1024. [PubMed]
25. Lenze EJ, Mulsant BH, Shear MK, Schulberg HC, Dew MA, Begley AE, Pollock BG, Reynolds CF., 3rd Comorbid anxiety disorders in depressed elderly patients. Am J Psychiatry. 2000;157:722–728. [PubMed]
26. Steffens DC, McQuoid DR. Impact of symptoms of generalized anxiety disorder on the course of late-life depression. Am J Geriatr Psychiatry. 2005;13:40–47. [PubMed]
27. Whyte EM, Dew MA, Gildengers A, Lenze EJ, Bharucha A, Mulsant BH, Reynolds CF. Time course of response to antidepressants in late-life major depression: therapeutic implications. Drugs Aging. 2004;21:531–554. [PubMed]
28. Hybels CF, Blazer DG, Steffens DC. Predictors of partial remission in older patients treated for major depression: the role of comorbid dysthymia. Am J Geriatr Psychiatry. 2005;13:713–721. [PubMed]
29. Mulder RT, Joyce PR, Frampton CM, Luty SE, Sullivan PF. Six months of treatment for depression: outcome and predictors of the course of illness. Am J Psychiatry. 2006;163:95–100. [PubMed]
30. Berkman LF, Macintyre S. The measurement of social class in health studies: old measures and new formulations. IARC Sci Publ. 1997:51–64. [PubMed]
31. Braveman PA, Cubbin C, Egerter S, Chideya S, Marchi KS, Metzler M, Posner S. Socioeconomic status in health research: one size does not fit all. JAMA. 2005;294:2879–2888. [PubMed]
32. Bernal M, Haro JM, Bernert S, Brugha T, de Graaf R, Bruffaerts R, Lepine JP, de Girolamo G, Vilagut G, Gasquet I, Torres JV, Kovess V, Heider D, Neeleman J, Kessler R, Alonso J. Risk factors for suicidality in Europe: results from the ESEMED study. J Affect Disord. 2007;101:27–34. [PubMed]
33. Jeste ND, Hays JC, Steffens DC. Clinical correlates of anxious depression among elderly patients with depression. J Affect Disord. 2006;90:37–41. [PubMed]
34. Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62:617–627. [PMC free article] [PubMed]
35. Muntaner C, Eaton WW, Diala C, Kessler RC, Sorlie PD. Social class, assets, organizational control and the prevalence of common groups of psychiatric disorders. Soc Sci Med. 1998;47:2043–2053. [PubMed]
36. Link BG, Phelan JC. Social conditions as fundamental causes of disease. J Health Soc Behav. 1995;35:80–94. [PubMed]
37. Kraemer HC, Frank E, Kupfer DJ. Moderators of treatment outcomes: clinical, research, and policy importance. JAMA. 2006;296:1286–1289. [PubMed]
38. Kraemer HC, Wilson GT, Fairburn CG, Agras WS. Mediators and moderators of treatment effects in randomized clinical trials. Arch Gen Psychiatry. 2002;59:877–883. [PubMed]
39. Summerfelt WT, Meltzer HY. Efficacy vs. effectiveness in psychiatric research. Psychiatr Serv. 1998;49:834–835. [PubMed]
40. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62. [PMC free article] [PubMed]
41. Frank E, Prien RF, Jarrett RB, Keller MB, Kupfer DJ, Lavori PW, Rush AJ, Weissman MM. Conceptualization and rationale for consensus definitions of terms in major depressive disorder: Remission, recovery, relapse, and recurrence. Arch Gen Psychiatry. 1991;48:851–855. [PubMed]
42. U.S. Census Bureau. State and County QuickFacts. Allegheny County; Pennsylvania: 2004.
43. Cox DR. Regression models and life-tables. Journal of the Royal Statistical Society, Series B (Methodological) 1972;34:187–220.
44. Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42:121–130. [PubMed]
45. Alexopoulos GS, Katz IR, Bruce ML, Heo M, Ten Have T, Raue P, Bogner HR, Schulberg HC, Mulsant BH, Reynolds CF., 3rd Remission in depressed geriatric primary care patients: a report from the PROSPECT study. Am J Psychiatry. 2005;162:718–724. [PMC free article] [PubMed]
46. Lenze EJ. Comorbidity of depression and anxiety in the elderly. Curr Psychiatry Rep. 2003;5:62–67. [PubMed]
47. Arean PA, Gum AM, Tang L, Unutzer J. Service Use and Outcomes Among Elderly Persons With Low Incomes Being Treated for Depression. Psychiatr Serv. 2007;58:1057–1064. [PubMed]
48. Blazer DG. Depression in late life: review and commentary. J Gerontol A Biol Sci Med Sci. 2003;58:249–265. [PubMed]
49. Brugha TS, Bebbington PE, MacCarthy B, Sturt E, Wykes T, Potter J. Gender, social support and recovery from depressive disorders: a prospective clinical study. Psychol Med. 1990;20:147–156. [PubMed]
50. Caspi A, Sugden K, Moffitt TE, Taylor A, Craig IW, Harrington H, McClay J, Mill J, Martin J, Braithwaite A, Poulton R. Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene. Science. 2003;301:386–389. [PubMed]
51. Griffin JM, Fuhrer R, Stansfeld SA, Marmot M. The importance of low control at work and home on depression and anxiety: do these effects vary by gender and social class? Soc Sci Med. 2002;54:783–798. [PubMed]
52. Kendler KS, Karkowski LM, Prescott CA. Stressful life events and major depression: risk period, long-term contextual threat, and diagnostic specificity. Journal of Nervous & Mental Disease. 1998;186:661–669. [PubMed]
53. Kessler RC, Neighbors HW. A new perspective on the relationships among race, social class, and psychological distress. J Health Soc Behav. 1986;27:107–115. [PubMed]
54. Schieman S, Plickert G. Functional limitations and changes in levels of depression among older adults: a multiple-hierarchy stratification perspective. J Gerontol B Psychol Sci Soc Sci. 2007;62:S36–42. [PubMed]
55. Casey PR, Dunn G, Kelly BD, Birkbeck G, Dalgard OS, Lehtinen V, Britta S, Ayuso-Mateos JL, Dowrick C. Factors associated with suicidal ideation in the general population: five-centre analysis from the ODIN study. Br J Psychiatry. 2006;189:410–415. [PubMed]
56. Conwell Y, Duberstein PR, Caine ED. Risk factors for suicide in later life. Biol Psychiatry. 2002;52:193–204. [PubMed]
57. Ross LE, Campbell VLS, Dennis C-L, Blackmore ER. Demographic Characteristics of Participants in Studies of Risk Factors, Prevention, and Treatment of Postpartum Depression. Canadian Journal of Psychiatry. 2006;51:704–710. [PubMed]
58. Isaacs SL, Schroeder SA. Class - the ignored determinant of the nation’s health. N Engl J Med. 2004;351:1137–1142. [PubMed]