Skip to main content

Advertisement

SpringerLink
Log in

Evaluating same-source bias in the association between neighbourhood characteristics and depression in a community sample from Toronto, Canada

  • Original Paper
  • Published:
Social Psychiatry and Psychiatric Epidemiology Aims and scope Submit manuscript

Abstract

Background and purpose

It is common in prior studies of the influence of neighbourhood characteristics on mental health to use participant-assessed neighbourhood exposures, which can lead to same-source bias since an individual’s mental health status may influence their judgement of their neighbourhood. To avoid this potential bias, we evaluated the use of individually assessed neighbourhood exposures to understand how they compare to collectively assessed measures (by aggregating multiple responses within the same neighbourhood). This would increase the validity of the measure by decoupling the neighbourhood measure from an individual’s mental health status.

Methods

We conducted a stratified-randomised survey of 2411 adults across 87 census tracts in Toronto, Canada (mean of 28 per census tract) to investigate how self-reported (individually assessed) social environmental neighbourhood measures compared to aggregated, collectively assessed, measures for neighbourhood problems/disorder, safety, service quality, and linking, bonding and bridging social capital. The outcome, experience of major depression in the past 12 months, was measured using the Composite International Diagnostic Studies Depression Scale Short Form.

Results

(1) Individually assessed neighbourhood problems, (2) low (individually assessed) neighbourhood safety, (3) low (individually assessed) neighbourhood service quality, and (4) low (individually assessed) linking social capital were independently associated with depression (all at least p < 0.05). However, when the individually assessed exposures were aggregated over residents in the same neighbourhood, none of them were significantly associated with depression.

Conclusions

Our study provides evidence for same-source bias in studies of social environmental determinants of depression that relies on individually assessed neighbourhood measures. We caution future studies from solely relying on individually assessed neighbourhood exposures especially in the study of social environmental influences on mental health outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Ferrari AJ, Charlson FJ, Norman RE et al (2013) Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med 10:e1001547

    Article  Google Scholar 

  2. Kessler RC, Akiskal HS, Ames M et al (2006) Prevalence and effects of mood disorders on work performance in a nationally representative sample of US workers. Am J Psychiatry 163:1561–1568

    Article  Google Scholar 

  3. Patten SB (2005) An analysis of data from two general health surveys found that increased incidence and duration contributed to elevated prevalence of major depression in persons with chronic medical conditions. J Clin Epidemiol 58:184–189

    Article  Google Scholar 

  4. Kemp AH, Quintana DS, Gray MA et al (2010) Impact of depression and antidepressant treatment on heart rate variability: a review and meta-analysis. Biol Psychiatry 67:1067–1074

    Article  CAS  Google Scholar 

  5. Kunik ME, Roundy K, Veazey C et al (2005) Surprisingly high prevalence of anxiety and depression in chronic breathing disorders. Chest 127:1205–1211

    PubMed  Google Scholar 

  6. Bisschop MI, Kriegsman DMW, Deeg DJH et al (2004) The longitudinal relation between chronic diseases and depression in older persons in the community: the Longitudinal Aging Study Amsterdam. J Clin Epidemiol 57:187–194

    Article  Google Scholar 

  7. Riso LP, Miyatake RK, Thase ME (2002) The search for determinants of chronic depression: a review of six factors. J Affect Disord 70:103–115

    Article  Google Scholar 

  8. Lorant V, Deliège D, Eaton W et al (2003) Socioeconomic inequalities in depression: a meta-analysis. Am J Epidemiol 157:98–112

    Article  CAS  Google Scholar 

  9. Blair A, Ross NA, Gariepy G, Schmitz N (2014) How do neighborhoods affect depression outcomes? A realist review and a call for the examination of causal pathways. Soc Psychiatry Psychiatr Epidemiol 49:873–887. https://doi.org/10.1007/s00127-013-0810-z

    Article  PubMed  Google Scholar 

  10. Latkin CA, Curry AD (2003) Stressful neighborhoods and depression: a prospective study of the impact of neighborhood disorder. J Heal Soc Behav 44:34–44

    Article  Google Scholar 

  11. Matheson FI, Moineddin R, Dunn JR et al (2006) Urban neighborhoods, chronic stress, gender and depression. Soc Sci Med 63:2604–2616. https://doi.org/10.1016/j.socscimed.2006.07.001

    Article  PubMed  Google Scholar 

  12. Miles R, Coutts C, Mohamadi A (2012) Neighborhood urban form, social environment, and depression. J Urban Heal 89:1–18. https://doi.org/10.1007/s11524-011-9621-2

    Article  Google Scholar 

  13. Vega WA, Ang A, Rodriguez MA, Finch BK (2011) Neighborhood protective effects on depression in Latinos. Am J Community Psychol 47:114–126. https://doi.org/10.1007/s10464-010-9370-5

    Article  PubMed  Google Scholar 

  14. Richardson R, Westley T, Gariépy G et al (2015) Neighborhood socioeconomic conditions and depression: a systematic review and meta-analysis. Soc Psychiatry Psychiatr Epidemiol 50:1641–1656. https://doi.org/10.1007/s00127-015-1092-4

    Article  PubMed  Google Scholar 

  15. Sampson RJ, Raudenbush SW (1999) Systematic social observation of public spaces: a new look at disorder in urban neighborhoods. Am J Sociol 105:603–651

    Article  Google Scholar 

  16. Beck AT, Weissman A, Lester D, Trexler L (1974) The measurement of pessimism: the hopelessness scale. J Consult Clin Psychol 42:861–865

    Article  CAS  Google Scholar 

  17. Blackburn IM, Eunson KM (1989) A content analysis of thoughts and emotions elicited from depressed patients during cognitive therapy. Br J Med Psychol 62(Pt 1):23–33

    Article  Google Scholar 

  18. Crane MK, Bogner HR, Brown GK, Gallo JJ (2007) The link between depressive symptoms, negative cognitive bias and memory complaints in older adults. Aging Ment Health 11:708–715. https://doi.org/10.1080/13607860701368497

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Mujahid MS, Diez Roux AV, Morenoff JD, Raghunathan T (2007) Assessing the measurement properties of neighborhood scales: from psychometrics to ecometrics. Am J Epidemiol 165:858–867

    Article  Google Scholar 

  20. Diez Roux A-V (2007) Neighborhoods and health: where are we and were do we go from here? Rev Epidemiol Sante Publique 55:13–21

    Article  CAS  Google Scholar 

  21. Stafford MAI, Mcmunn A, De Vogli R (2011) Neighbourhood social environment and depressive symptoms in mid-life and beyond. Ageing Soc 31:893–910

    Article  Google Scholar 

  22. Ellaway A, Macintyre S, Kearns A (2001) Perceptions of place and health in socially contrasting neighbourhoods. Urban Stud 38:2299–2316

    Article  Google Scholar 

  23. Kim J (2010) Neighborhood disadvantage and mental health: the role of neighborhood disorder and social relationships. Soc Sci Res 39:260–271

    Article  Google Scholar 

  24. Phongsavan P, Chey T, Bauman A et al (2006) Social capital, socio-economic status and psychological distress among Australian adults. Soc Sci Med 63:2546–2561

    Article  Google Scholar 

  25. Ahern J, Galea S (2011) Collective efficacy and major depression in urban neighborhoods. Am J Epidemiol 173:1453–1462. https://doi.org/10.1093/aje/kwr030

    Article  PubMed  PubMed Central  Google Scholar 

  26. Curry A, Latkin C, Davey-Rothwell M (2008) Pathways to depression: the impact of neighborhood violent crime on inner-city residents in Baltimore, Maryland, USA. Soc Sci Med 67:23–30

    Article  Google Scholar 

  27. Lofors J, Sundquist K (2007) Low-linking social capital as a predictor of mental disorders: a cohort study of 4.5 million Swedes. Soc Sci Med 64:21–34

    Article  Google Scholar 

  28. Echeverría S, Diez-Roux AV, Shea S et al (2008) Associations of neighborhood problems and neighborhood social cohesion with mental health and health behaviors: the Multi-Ethnic Study of Atherosclerosis. Health Place 14:853–865

    Article  Google Scholar 

  29. Wight RG, Cummings JR, Karlamangla AS, Aneshensel CS (2009) Urban neighborhood context and change in depressive symptoms in late life. J Gerontol Ser B 64:247–251

    Article  Google Scholar 

  30. Harpham T, Grant E, Rodriguez C (2004) Mental health and social capital in Cali, Colombia. Soc Sci Med 58:2267–2277

    Article  Google Scholar 

  31. Raudenbush SW, Sampson RJ (1999) Ecometrics: toward a science of assessing ecological settings, with application to the systematic social observation of neighborhoods. Sociol Methodol 29:1–41

    Article  Google Scholar 

  32. Beyer KMM, Kaltenbach A, Szabo A et al (2014) Exposure to neighborhood green space and mental health: evidence from the survey of the health of Wisconsin. Int J Environ Res Public Heal 11:3453–3472. https://doi.org/10.3390/ijerph110303453

    Article  Google Scholar 

  33. Cutrona CE, Wallace G, Wesner KA (2006) Neighborhood characteristics and depression: an examination of stress processes. Curr Dir Psychol Sci 15:188–192. https://doi.org/10.1111/j.1467-8721.2006.00433.x

    Article  PubMed  PubMed Central  Google Scholar 

  34. Sallis JF, Saelens BE, Frank LD et al (2009) Neighborhood built environment and income: examining multiple health outcomes. Soc Sci Med 68:1285–1293. https://doi.org/10.1016/j.socscimed.2009.01.017

    Article  PubMed  PubMed Central  Google Scholar 

  35. Galea S, Ahern J, Rudenstine S et al (2005) Urban built environment and depression: a multilevel analysis. J Epidemiol Community Heal 59:822–827. https://doi.org/10.1136/jech.2005.033084

    Article  Google Scholar 

  36. Schootman M, Andresen EM, Wolinsky FD et al (2007) Neighbourhood environment and the incidence of depressive symptoms among middle-aged African Americans. J Epidemiol Community Heal 61:527–532

    Article  Google Scholar 

  37. Araya R, Dunstan F, Playle R et al (2006) Perceptions of social capital and the built environment and mental health. Soc Sci Med 62:3072–3083

    Article  Google Scholar 

  38. Statistics Canada (2011) 2011 Census, Census subdivision of Toronto. In: Focus Geogr. Ser. https://www12.statcan.gc.ca/census-recensement/2011/as-sa/fogs-spg/Facts-csd-eng.cfm?Lang=eng&GK=CSD&GC=3520005. Accessed 10 Mar 2019

  39. O’Campo P, Wheaton B, Nisenbaum R et al (2015) The Neighbourhood Effects on Health and Well-being (NEHW) study. Health Place 31:65–74

    Article  Google Scholar 

  40. Fisher L, Skaff MM, Mullan JT et al (2007) Clinical depression versus distress among patients with type 2 diabetes: not just a question of semantics. Diabetes Care 30:542–548. https://doi.org/10.2337/dc06-1614

    Article  PubMed  Google Scholar 

  41. Kessler RC, Andrews G, Mroczek D et al (1998) The world health organization composite international diagnostic interview short-form (cidi-sf). Int J Methods Psychiatr Res 7:171–185

    Article  Google Scholar 

  42. Ross CE, Mirowsky J (1999) Disorder and decay: the concept and measurement of perceived neighborhood disorder. Urb Aff Rev 34:412–432. https://doi.org/10.1177/107808749903400304

    Article  Google Scholar 

  43. Ross CE (2000) Neighborhood disadvantage and adult depression. J Health Soc Behav 41:177. https://doi.org/10.2307/2676304

    Article  Google Scholar 

  44. Sampson RJ (2012) Great American city: Chicago and the enduring neighborhood effect. The University of Chicago Press, Chicago

    Book  Google Scholar 

  45. Carver A, Timperio A, Crawford D (2008) Playing it safe: the influence of neighbourhood safety on children’s physical activity—a review. Health Place 14:217–227. https://doi.org/10.1016/j.healthplace.2007.06.004

    Article  Google Scholar 

  46. Narayan-Parker D (1999) Bonds and bridges: Social capital and poverty. World Bank Publications

  47. Szreter S, Woolcock M (2004) Health by association? Social capital, social theory, and the political economy of public health. Int J Epidemiol 33:650–667

    Article  Google Scholar 

  48. Sampson RJ, Raudenbush SW, Earls F (1997) Neighborhoods and violent crime: a multilevel study of collective efficacy. Science (80-) 277:918–924. https://doi.org/10.1126/science.277.5328.918

    Article  CAS  Google Scholar 

  49. O’Campo P, Wheaton B, Nisenbaum R et al (2015) The Neighbourhood Effects on Health and Well-being (NEHW) study. Heal Place 31:65–74. https://doi.org/10.1016/j.healthplace.2014.11.001

    Article  Google Scholar 

  50. De Silva MJ, Huttly SR, Harpham T, Kenward MG (2007) Social capital and mental health: a comparative analysis of four low income countries. Soc Sci Med 64:5–20

    Article  Google Scholar 

  51. Grootaert C (2003) Measuring social capital: an integrated questionnaire. World Bank, Washington, DC

    Google Scholar 

  52. Salk RH, Hyde JS, Abramson LY (2017) Gender differences in depression in representative national samples: meta-analyses of diagnoses and symptoms. Psychol Bull 143:783–822. https://doi.org/10.1037/bul0000102

    Article  PubMed  PubMed Central  Google Scholar 

  53. Bulloch AGM, Williams JVA, Lavorato DH, Patten SB (2017) The depression and marital status relationship is modified by both age and gender. J Affect Disord 223:65–68. https://doi.org/10.1016/j.jad.2017.06.007

    Article  PubMed  Google Scholar 

  54. Inaba A, Thoits PA, Ueno K et al (2005) Depression in the United States and Japan: gender, marital status, and SES patterns. Soc Sci Med 61:2280–2292. https://doi.org/10.1016/j.socscimed.2005.07.014

    Article  PubMed  Google Scholar 

  55. Lohr S (1999) Sampling: design and analysis. Duxbury, Pacific Grove

    Google Scholar 

  56. Chum A, Farrell E, Vaivada T et al (2015) The effect of food environments on fruit and vegetable intake as modified by time spent at home: a cross-sectional study. BMJ Open 5:e006200

    Article  Google Scholar 

  57. Chum A, Campo P, Matheson F (2015) The impact of urban land uses on sleep duration and sleep problems. Can Geogr/Le Géographe Can 59:404–418. https://doi.org/10.1111/cag.12202

    Article  Google Scholar 

Download references

Funding

This study was funded by Canadian Institutes of Health Research Grant MOP-84439 and the Social Science and Humanities Research Council Grant 410-2007-1499.

Author information

Authors and Affiliations

  1. Department of Applied Health Sciences, Brock University, St. Catharines, Canada

    Antony Chum

  2. Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada

    Antony Chum, Patricia O’Campo, James Lachaud & Rosane Nisenbaum

  3. Department of Psychology, York University, Toronto, Canada

    Nicolas Fink

  4. Community Psychology, Wilfrid Laurier University, Waterloo, Canada

    Maritt Kirst

Authors
  1. Antony Chum
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patricia O’Campo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. James Lachaud
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nicolas Fink
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maritt Kirst
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rosane Nisenbaum
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antony Chum.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

127_2019_1718_MOESM1_ESM.docx

Supplementary material 1 (DOCX 19 kb)       Funding: This study was funded by Canadian Institutes of Health Research Grant MOP-84439 and the Social Science and Humanities Research Council Grant 410-2007-1499.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chum, A., O’Campo, P., Lachaud, J. et al. Evaluating same-source bias in the association between neighbourhood characteristics and depression in a community sample from Toronto, Canada. Soc Psychiatry Psychiatr Epidemiol 54, 1177–1187 (2019). https://doi.org/10.1007/s00127-019-01718-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00127-019-01718-6

Keywords

Search

Navigation