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Am J Public Health. 2009 July; 99(7): 1308–1314.
PMCID: PMC2696669

Neighborhood Characteristics and Change in Depressive Symptoms Among Older Residents of New York City

John R. Beard, PhD, MBBS,corresponding author Magda Cerdá, DrPH, Shannon Blaney, MPH, Jennifer Ahern, PhD, David Vlahov, PhD, and Sandro Galea, MD, DrPH

Abstract

Objectives. We investigated the relationship between the depressive symptoms of older adults over time and the characteristics of the neighborhoods in which they live.

Methods. We surveyed a random sample of 1325 New York City residents aged 50 years or older in 2005 and conducted 808 follow-up interviews in 2007. We assessed the compositional characteristics of the respondents' neighborhoods at a census-tract level and determined the relationships between these characteristics and changes in respondents' depressive symptoms.

Results. In multivariable models that adjusted for individual-level covariates including income, a range of neighborhood characteristics predicted worsening depressive symptoms. Factor analysis suggested that these characteristics operated in 3 clusters: neighborhood socioeconomic influences, residential stability, and racial/ethnic composition, with positive neighborhood socioeconomic influences being significantly protective against worsening symptoms. Life stressors, personality trait neuroticism, African American race, and daily baseline contact with social networks were also associated with worsening symptoms.

Conclusions. An older adult's neighborhood of residence is an important determinant of his or her mental health. Those making efforts to improve mental health among the elderly need to consider the role of residential context in improving or impairing mental health.

Depression is an important cause of morbidity in the general community.1 The prevalence of depression is high among elderly persons, and longitudinal studies have found modest increases in depressive symptoms with age.24 The incidence of depression peaks in early adult life, but there appears to be a secondary peak in incidence among people in their 50s, suggesting that the transition to older age may present specific risks for depression.5 Depression is associated with significant disability among older adults and may place their functional independence at risk.6 At least 1 longitudinal study has also suggested that older African Americans may be at increased risk of symptoms of depression compared with older White adults.7

Although a number of individual-level factors are known to increase the risk of depression,810 it has long been thought that the physical and social environments in which people live may also influence their mental health.1113 The environment may play a particularly important role in the mental health of older adults, who, compared with younger adults, are more likely to spend time in their neighborhood of residence, more likely to suffer from disabilities that may be exacerbated by their environments,14 and are more vulnerable to threats to their safety.1517

A number of theories have been proposed to explain this association between neighborhood characteristics and depression. Researchers have drawn on systemic theory to propose that neighborhoods characterized by higher levels of poverty and residential instability have lower levels of social cohesion and lower levels of control over deviant social networks.18,19 The concomitant lack of social order may contribute to low levels of trust, which would impede collaborative efforts to control crime and reduce neighborhood disorder.20 High levels of crime may generate higher levels of fear and stress, as could the deteriorating building conditions and high levels of physical disorder associated with disadvantaged neighborhoods.20,21 In contexts of social isolation and limited social organization, residents may not benefit from the social networks necessary to buffer them from the stressors they face on a daily basis.22

These theories about the influence of the neighborhood context on collective and individual sources of stress agree with the “differential vulnerability” hypothesis and with social stress theory, both of which posit that environments can influence health by increasing the likelihood of personal stress events such as unemployment or traumatic events, or by providing resources to cope with such stressors.2326 Studies using multilevel analytic methods that can account for both individual-level and neighborhood-level effects suggest that neighborhood-level characteristics such as affluence, disadvantage, inequality, and residential stability have a significant impact on physical health, even after accounting for individual-level factors.2730 However, research into their possible influence on mental health has been more limited.

Cross-sectional studies using multilevel approaches have suggested that symptoms of depression are more prevalent in residents of disadvantaged neighborhoods3133 and that this association may be stronger in neighborhoods having less residential turnover34,35 or higher population density.36 Similar associations have been observed among older adults, for whom living in a neighborhood that is poor or has few elderly people has been associated with higher levels of depressive symptoms, after accounting for individual vulnerabilities.37 The presence of stress-buffering support systems has been associated with lower levels of depression in cross-sectional research, whereas low levels of social support in neighborhoods with high social isolation were related to higher depression levels.38,39 However, other research has failed to replicate these findings.40 Furthermore, the cross-sectional nature of this research means that even positive studies cannot exclude the possibility that the observed relationships simply reflect a tendency for depressed individuals to become disadvantaged or to live in disadvantaged neighborhoods.

Longitudinal research can better explore the causal mechanisms behind these relationships, but there have been few prospective studies in this field. A study of individuals who were screened for an HIV prevention intervention found that perceptions of neighborhood characteristics predicted change in depressive symptoms 9 months later.41 The Alameda County Study found that living in a high-poverty area was associated with worse health status and more symptoms of depression; however, this association was lost when all individual-level covariates were included in multivariable analysis.42 In previous research conducted by members of our own team, we identified a significant association between incident depression and neighborhoods classified as low socioeconomic status, even after adjusting for individual income, adverse life events, and educational status.43 This kind of prospective research, although suggestive, has often been weakened by reliance on perceived neighborhood characteristics, limitations of the measures used, or absence of information on possible confounders.

To overcome these limitations, we examined the relationship between characteristics of the neighborhood of residence of older adults and symptoms of depression using longitudinal data from the New York City Neighborhood and Mental Health in the Elderly Study (NYCNAMES). We hypothesized that neighborhood socioeconomic status may either exacerbate or ameliorate the stressors confronting participants, thereby influencing levels of depression symptoms over the study period, even after accounting for key individual-level factors. We used information from the 2000 US Census to characterize neighborhoods, and we aggregated these characteristics into dimensions that might shed light on the mechanisms underlying observed relationships.

METHODS

NYCNAMES is a 2-year longitudinal study of older residents of New York City that commenced between June and December of 2005, when 4000 adults were recruited to a random-digit-dial telephone survey asking questions about the social environment of New York City. One randomly selected adult 18 years or older was interviewed in each household. Interviews were conducted in English or Spanish. The cooperation rate was 54%, representing the percentage of those contacted who agreed to participate in the study ([completed + screened out] / [completed + screened out + refused]).

In early 2007, we attempted to reinterview all 1325 survey participants from 2005 who were 50 years or older and who had indicated they would be prepared to be contacted again. A final total of 808 consenting respondents were administered a computer-assisted second telephone interview that included questions on their physical and mental health as well as key individual-level factors that may influence those health outcomes. Interviews were approximately 20 minutes long and were conducted by trained and supervised lay surveyors. Respondents were offered $10 in compensation for their participation.

Interviews included questions on age, race/ethnicity, gender, marital status, place of birth, education, income, employment, and years lived in the current neighborhood.

Depression

Symptoms of depression were measured in both waves with the Patient Health Questionnaire (PHQ), which includes a 9-item symptom severity rating scale for depression with acceptable psychometric properties and which gives a dimensional total score that is sensitive to change.44 Telephone and in-person assessments that used the PHQ brief interview have been shown to yield similar results to one another.45 The PHQ-9 measures a common conception of depression in African American, Chinese American, Hispanic, and non-Hispanic White patient groups.46 We chose this dimensional depression instrument a priori to allow us to focus on symptom changes as the primary outcome, as opposed to focusing on a dichotomous diagnosis.

Individual-Level Covariates

Physical limitation was measured at follow-up with the Functional Status Questionnaire component that measures physical function in activities of daily living.47 Physical activity levels were determined at follow-up with the Physical Activity Scale for the Elderly (PASE).48 PASE was specifically designed for use with older people by mail or telephone.49 PASE scores have been significantly correlated with 3-day accelerometer readings50 and with physiological and performance characteristics.48,51

A list of major life events that could have proven stressful in a person's life was used to measure stressors between each assessment.52 We defined a continuous variable “change in stressor score” as the difference between the number of stressors at follow-up and the number of stressors at baseline. The frequency of each participant's contact with their social networks at baseline was measured by asking how often they were in contact with members of their family or friends who did not live with them.53 We included personality trait neuroticism in our analysis, because numerous longitudinal studies suggest this trait is an individual-level predictor of incident depression.54 This covariate was measured at wave 2 with the short neuroticism arm of the Eysenck Personality Questionnaire Brief Version.55

Neighborhood-Level Measures

Respondents provided information about their residential addresses or nearest cross-streets, allowing 97.6% of our sample to be geocoded to a US Census tract. Data on the socioeconomic characteristics of participants' census tracts of residence at baseline interview were derived from the 2000 US Census.56 Socioeconomic status measures included in our analysis were chosen based on theoretical assumptions about the types of neighborhood socioeconomic constructs that may influence mental disorders (i.e., disadvantage, affluence, inequality), and based on the types of census measures used to operationalize such theoretical constructs in prior work (Table 1).

TABLE 1
Factor Analysis of Socioeconomic Variables: NYCNAMES, 2005–2007

Analysis

Neighborhood measures are highly collinear because they reflect different characteristics that exist concurrently in neighborhoods, such as the proportion of residents living in poverty and the proportion receiving public assistance. To avoid the problem of multi-collinearity, a number of researchers have undertaken factor analysis to derive a parsimonious and uncorrelated set of factors that capture the key neighborhood socioeconomic and sociodemographic measures of interest.57 We adopted a similar approach to determine the specific dimensionality of the individual measures in our study context and time period. We conducted a principal factor analysis with an orthogonal varimax rotation of the measures we derived from the 2000 Census.58 The factor scores were standardized to have a mean of 0 and a standard deviation of 1. Variables included in the factors were those that loaded 0.4 or more onto at least 1 factor. When variables loaded 0.4 or more on more than 1 factor and there was a clear difference in the magnitude of loading between factors, we included it within the factor onto which it loaded the highest. This is standard practice in epidemiological applications of factor analysis.59 Women-headed households loaded almost identically to 2 dimensions and were therefore dropped from analysis.

The factor analysis supported a 3-factor solution with distinct dimensions based on an eigenvalue of 1 and the scree plot (Table 1). The first factor, which we interpret as socioeconomic influences, denotes “high affluence and low disadvantage” neighborhoods characterized by a high concentration of residents who have a high school diploma, earn at least $50 000, work in professional or managerial occupations, and have an undergraduate degree, and a low concentration of residents who are unemployed, on public assistance, or living in poverty. The second factor, interpreted as residential stability, is characterized by a high proportion of residents living in the same house for the previous 5 years and a high proportion of owner-occupancy. The third factor, interpreted as racial/ethnic composition, is denoted by a higher proportion of foreign-born residents and a lower proportion of African Americans.

The first factor, socioeconomic influences, loaded highly on the affluence dimension. To examine the relative influence of affluence and disadvantage, we also constructed 2 composites from the first factor: (1) those measures loading positively onto the first factor and thus denoting affluence, and (2) those measures loading negatively onto the first factor and denoting disadvantage (Table 1).

Analysis of a model including all individual-level variables showed a significant intraclass correlation coefficient (ICC = 0.06). We therefore used generalized estimating equation (GEE) models for both bivariate and multivariate analyses.60

We first examined associations at the bivariate level. Multivariable analysis was then used to build a model with individual-level covariates that had significant bivariate associations with the outcome. We also included gender, age, and household income in these models because theory and previous literature suggest these factors may confound the relationship between neighborhood and depression. We examined the relationship of neighborhood-level factors with change in depression score by separately entering them into multilevel linear GEE models adjusted for individual-level confounders. Neighborhood-level factors were further examined in multivariable models that adjusted for all neighborhood-level and individual-level characteristics. We used a similar approach to examine new onsets of depression using logistic regression in place of linear regression.

RESULTS

A total of 808 participants were reinterviewed, with 13 more reported as deceased. The gender (P = .940) and age (P = .789) structure of this sample was similar to that of New York City residents aged 51 years or older. Hispanic and Asian participants were underrepresented, however (P = .007). The follow-up rate (interviewed / [total – number died]) was 61.6%. Of those not completing the interview, 83 refused. Loss to follow-up was nondifferential on gender or borough of residence, but White, older, and wealthier participants were more likely to be interviewed in both waves (Table 2). Participants lost to follow-up had higher depression scores but were not more likely to have a diagnosis of depression. At baseline, approximately 90% of participants had lived in their neighborhood (defined as “an area within a 20-minute walk of your house”) for at least 5 years. At follow-up, only 18 participants (2.4% of analyzed sample) had moved more than 3 census tracts away from their baseline address.

TABLE 2
Characteristics of Participants Interviewed in Both Study Waves and Those Lost to Follow-up: NYCNAMES, 2005–2007

The results of analyses of the relationship between individual-level variables and changes in symptoms of depression are shown in Table 3. When individual-level factors were considered together in multivariable analysis, high neuroticism score, high stressor score prior to follow-up, worsening of stressor score between baseline and follow-up, African American race, and less frequent contact with social networks at baseline predicted a worsening depression score.

TABLE 3
Unadjusted and Adjusted Associations Between Individual-Level Covariates and Change in Depression Score: NYCNAMES, 2005–2007

When the 3 neighborhood constructs we had previously identified were included in this multivariate model, the socioeconomic influences factor was protective against worsening depression score (b = −0.48; 95% confidence interval [CI] = −0.83, −0.12) after adjusting for individual-level factors (Table 4). Neither residential stability nor ethnicity of neighborhood was associated with symptoms of depression. When the socioeconomic influences factor was replaced by the affluence and disadvantage composites, affluence remained strongly protective against worsening of depression symptoms (b = −0.45; 95% CI = −0.78, −0.12), whereas disadvantage was a marginally significant predictor of a worsening score (b = 0.37; 95% CI = −0.02, 0.75).

TABLE 4
Unadjusted and Adjusted Associations Between Neighborhood-Level Covariates and Change in Depression Score: NYCNAMES, 2005–2007

At the follow-up interview, there were only 26 new cases of depression (as defined by responses to the PHQ) among participants who did not meet these criteria at baseline. This precluded meaningful analysis of the factors associated with the new diagnoses. However, unadjusted GEE showed a pattern of associations broadly similar to that found for changes in symptom levels.

DISCUSSION

This is one of the first longitudinal studies to investigate and demonstrate a significant association between characteristics of the neighborhood in which one lives and the person's subsequent risk of depression after accounting for key individual-level characteristics, including household income. It provides some of the strongest evidence yet that a person's neighborhood of residence contributes to their mental health, at least for older adults.

Most previous research in this field has been cross-sectional, and the longitudinal research that has been done has only been able to take account of limited individual-level determinants of depression. We included in our analysis all widely accepted individual-level confounders. Of these, personality trait neuroticism and worsening of life stressors were significant determinants of worsening symptoms of depression. These findings are broadly consistent with previous research.54

Being of African American race was also associated with an increased risk of worsening depression symptoms in multivariate individual-level analysis (adjusted for individual-level income). At least 1 other study has identified an increased prevalence of depression among older African Americans, with this racial difference increasing slightly over time.7 In our final model, race lost significance when neighborhood-level factors were included. This may be a consequence of sample size limitations. However, this finding also suggests that at least part of the observed association between African American race and increased risk of depression in this sample of older adults may be explained by contextual factors rather than individual-level factors. This would be consistent with previous research that found that the stressors associated with neighborhood disorder and discrimination influenced depressive symptoms among African American women, independent of household income.19 Lack of access to socioeconomic resources has also been suggested as a key driver of racial disparities in health.61,62 Our findings suggest that some of these resources may exist at a neighborhood level rather than an individual level.

We also found some evidence for an association between depression and the contact participants reported having at baseline with family or friends, with everyday contact predicting subsequent worsening of depression score. There are a number of possible explanations for this observation. It may reflect the tendency of individuals who suffer recurring depression symptoms to have a greater need for support even when their symptoms are in abatement. Alternatively, this finding is consistent with the hypothesis that received (as distinct from anticipated) social support for the elderly may reinforce a sense of dependence, rather than buffering the impact of life stressors, leading to feelings of helplessness and exacerbating symptoms of depression.63 Not all social contact is positive, and it has also been suggested that negative interactions may be more predictive of depression than supportive interactions.64 Strain arising from social ties may be particularly important for individuals with fewer resources.65 For example, elderly people in a disadvantaged family may be required to provide a distressing amount of child care.

When we included the 3 neighborhood dimensions in multivariate analysis, socioeconomic influences were protective against worsening symptoms of depression. Of the 2 composites we had constructed denoting positive and negative aspects of this dimension, affluence appeared to exert the strongest effect, although there was also borderline evidence that disadvantage increased the risk of worsening depression. This is an important distinction, because it suggests that the observed relationship between neighborhood socioeconomic status and depression does not simply result from exposure to increased levels of stressors associated with poor neighborhood conditions. Rather, affluent neighborhoods appear to exert a protective effect, presumably through increasing an individual's resilience to the stressors he or she faces.

The protective effect of neighborhood affluence was observed even after accounting for participants' individual socioeconomic status, which suggests that the observed association was not simply the result of participants with recurring depression symptoms having reduced incomes and a greater likelihood of living in a poor area. This conclusion is reinforced by sensitivity analysis that included baseline depression score in our models. The observed protective effect of neighborhood affluence was little changed in this model.

Cross-sectional research has previously suggested that residential stability may be protective against depression.9 Although our findings were not inconsistent with this research, the trend observed in our study was not statistically significant, which may be a reflection of our limited sample size. Racial/ethnic composition of the neighborhood also had little influence on levels of depression after accounting for individual race.

The number of new PHQ-defined depression cases in our study was too small to allow us to investigate the influence of individual-level and neighborhood-level factors on depression incidence. However, the unadjusted multilevel logistic regression we did undertake was broadly consistent with our findings. In any case, there is considerable evidence suggesting that dimensional measures of depression may be a more appropriate outcome measure than diagnoses in population-based research.66

Strengths and Limitations

This study has many strengths. Its longitudinal nature allowed investigation of temporal relationships that could not have been investigated in cross-sectional research. The cohort was nested in a randomly selected sample of the general population. The relationship between neighborhood characteristics and depression was examined using analytic methods that account for clustering and that included key individual-level determinants of depression. Dependent and independent variables were measured using well-validated instruments, and neighborhood characteristics were estimated using census data, which helped avoid problems with recall bias. Neighborhood characteristics were assigned by census tract, a fine-grained spatial level that accounts for socioeconomic heterogeneity better than data aggregated at larger population levels.67

The major weaknesses of this study relate to the sample and the follow-up rate. Although even this small sample was large enough to allow for identifying neighborhood effects, it would have been interesting to explore in more detail whether these effects were consistent for different age and racial groups or gender. Unfortunately, this was not possible. Although neither the baseline sample nor the follow-up sample differed from the broader older population of New York City in terms of age or gender, both were underrepresentative of Hispanics and Asians. Slightly fewer Whites were lost to follow-up than were those from other races. All analyses were adjusted for race, and the small absolute differences resulting from loss to follow-up seem unlikely to bias the overall study findings. However, the limited Hispanic sample means caution should be taken when extrapolating our results to this group. There was also no systematic difference between participants lost to follow-up with respect to the key independent variable: neighborhood of residence. Participants who were lost to follow-up were not more likely to have a diagnosis consistent with depression, but they did have slightly higher baseline depression scores.

Conclusions

Despite their limitations, these findings suggest that the neighborhood in which an older person lives has a significant impact on his or her mental health, even after accounting for individual-level determinants. It is important to identify neighborhood characteristics that make older persons more or less likely to be depressed so that structural interventions may be better targeted toward improving the health of this population.

Acknowledgments

Magda Cerdá was funded under the Robert Wood Johnson Health and Society Scholars Program. This research was funded, in part, by the National Institute on Drug Abuse (grant 1 R21 DA025343).

Human Participant Protection

The protocol for this study was approved by the New York Academy of Medicine institutional review board.

References

1. Kessler RC, McGonagle KA, Zhao S, et al. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey. Arch Gen Psychiatry 1994;51:8–19 [PubMed]
2. Copeland JR, Beekman AT, Dewey ME, et al. Depression in Europe: geographical distribution among older people. Br J Psychiatry 1999;174:312–321 [PubMed]
3. Fiske A, Gatz M, Pedersen NL. Depressive symptoms and aging: the effects of illness and non-health-related events. J Gerontol B Psychol Sci Soc Sci 2003;58(6):P320–328 [PubMed]
4. Davey A, Halverson CF, Jr, Zonderman AB, Costa PT., Jr Change in depressive symptoms in the Baltimore longitudinal study of aging. J Gerontol B Psychol Sci Soc Sci 2004;59(6):P270–277 [PubMed]
5. Eaton WW, Anthony JC, Gallo J, et al. Natural history of Diagnostic Interview Schedule/DSM-IV major depression: the Baltimore Epidemiologic Catchment Area follow-up. Arch Gen Psychiatry 1997;54(11):993–999 [PubMed]
6. Lenze EJ, Rogers JC, Martire LM, et al. The association of late-life depression and anxiety with physical disability: a review of the literature and prospectus for future research. Am J Ger Psychiatry 2001;9:113–135 [PubMed]
7. Skarupski KA, Mendes de Leon CF, Bienias JL, et al. Black-White differences in depressive symptoms among older adults over time. J Gerontol B Psychol Sci Soc Sci 2005;60(3):P136–142 [PubMed]
8. Kendler KS, Gardner CO, Prescott CA. Toward a comprehensive developmental model for major depression in women. Am J Psychiatry 2002;159:1133–1145 [PubMed]
9. De Graaf R, Bijl RV, Ravelli A, Smit F, Vollebergh WA. Predictors of first incidence of DSM-III-R psychiatric disorders in the general population: findings from the Netherlands Mental Health Survey and Incidence Study. Acta Psychiatr Scand 2002;106:303–313 [PubMed]
10. Kendler KS, Kuhn J, Prescott CA. The interrelationship of neuroticism, sex, and stressful life events in the prediction of episodes of major depression. Am J Psychiatry 2004;161:631–636 [PubMed]
11. Faris RE, Dunham H. Mental Disorders in Urban Areas: An Ecological Study of Schizophrenia and Other Psychoses. Chicago, IL: University of Chicago Press, 1939
12. Srole L, Langer T, Michael S, Kirkpatrick P, Opler M, Rennie TA. Mental Health in the Metropolis. New York, NY: McGraw-Hill, 1962
13. White W. The geographical distribution of insanity in the United States. J Nerv Ment Dis 1902;30:257–279
14. Bowling A, Stafford M. How do objective and subjective assessments of neighbourhood influence social and physical functioning in older age? Findings from a British survey of ageing. Soc Sci Med 2007;64:2533–2549 [PubMed]
15. Centers for Disease Control and Prevention Neighborhood safety and the prevalence of physical inactivity—selected states, 1996. MMWR Morb Mort Wkly Rep 1999;48(7):143–146 [PubMed]
16. Whitley R, Prince M. Fear of crime, mobility and mental health in inner-city London, UK. Soc Sci Med 2005;61:1678–1688 [PubMed]
17. LaGrange R, Ferraro K, Supancic M. Perceived risk and fear of crime: role of social and physical incivilities. J Res Crime Delinquency 1992;29:311–334
18. Sampson RJ, Raudenbush SW, Earls F. Neighborhoods and violent crime: a multilevel study of collective efficacy. Science 1997;277:918–924 [PubMed]
19. Sampson RJ, Morenoff JD, Gannon-Rowley T. Assessing “neighborhood effects”: social processes and new directions in research. Annu Rev Sociol 2002;28:443–478
20. Ross CE, Mirowsky J. Neighbourhood disadvantage, disorder, and health. J Health Soc Behav 2001;42:258–276 [PubMed]
21. Schulz AJ, Israel BA, Zenk SN, et al. Psychosocial stress and social support as mediators of relationships between income, length of residence and depressive symptoms among African American women on Detroit's eastside. Soc Sci Med 2006;62:510–522 [PubMed]
22. Lin N, Ye XL, Ensel WM. Social support and depressed mood: a structural analysis. J Health Soc Behav 1999;40:344–359 [PubMed]
23. McLeod JD, Kessler RC. Socio-economic status differences in vulnerability to undesirable life events. J Health Soc Behav 1990;31:162–172 [PubMed]
24. Wheaton B. Stress, personal coping resources, and psychiatric symptoms: an investigation of interactive models. J Health Soc Behav 1983;24:208–229 [PubMed]
25. Fitzpatrick KM, LaGory M. “Placing” health in an urban sociology: cities as mosaics of risk and protection. City Community 2003;2:33–46
26. Wheaton B. Models for the stress-buffering functions of coping resources. J Health Soc Behav 1985;26(4):352–364 [PubMed]
27. Chaix B, Rosvall M, Merlo J. Neighborhood socioeconomic deprivation and residential instability: effects on incidence of ischemic heart disease and survival after myocardial infarction. Epidemiology 2007;18:104–111 [PubMed]
28. Diez Roux AV. Investigating neighborhood and area effects on health. Am J Public Health 2001;91:1783–1789 [PMC free article] [PubMed]
29. Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health 2001;55:111–122 [PMC free article] [PubMed]
30. Subramanian SV, Kubzansky L, Berkman L, Fay M, Kawachi I. Neighborhood effects on the self-rated health of elders: uncovering the relative importance of structural and service-related neighborhood environments. J Gerontol B Psychol Sci Soc Sci 2006;61(3):S153–160 [PubMed]
31. Galea S, Ahern J, Rudenstine S, Wallace Z, Vlahov D. Urban built environment and depression: a multilevel analysis. J Epidemiol Community Health 2005;59:822–827 [PMC free article] [PubMed]
32. Mulvaney C, Kendrick D. Depressive symptoms in mothers of pre-school children—effects of deprivation, social support, stress and neighbourhood social capital. Soc Psychiatry Psychiatr Epidemiol 2005;40:202–208 [PubMed]
33. Ross C. Neighborhood disadvantage and adult depression. J Health Soc Behav 2000;41:177–187
34. Ross C, Rezny L, Geller ALM. The contingent meaning of neighborhood stability for residents' psychological well-being. Am Sociol Rev 2000;65:581–597
35. Silver E, Mulvey EP, Swanson JW. Neighborhood structural characteristics and mental disorder: Faris and Dunham revisited. Soc Sci Med 2002;55:1457–1470 [PubMed]
36. Walters K, Breeze E, Wilkinson P, Price GM, Bulpitt CJ, Fletcher A. Local area deprivation and urban-rural differences in anxiety and depression among people older than 75 years in Britain. Am J Public Health 2004;94:1768–1774 [PMC free article] [PubMed]
37. Kubzansky LD, Subramanian SV, Kawachi I, Fay ME, Soobader MJ, Berkman LF. Neighborhood contextual influences on depressive symptoms in the elderly. Am J Epidemiol 2005;162:253–260 [PubMed]
38. Aneshensel CS, Sucoff CA. The neighborhood context of adolescent mental health. J Health Soc Behav 1996;37:293–310 [PubMed]
39. Stockdale SE, Wells KB, Tang L, Belin TR, Zhang L, Sherbourne CD. The importance of social context: neighborhood stressors, stress-buffering mechanisms, and alcohol, drug, and mental health disorders. Soc Sci Med 2007;65:1867–1881 [PMC free article] [PubMed]
40. Propper C, Jones K, Bolster A, Burgess S, Johnston R, Sarker R. Local neighbourhood and mental health: evidence from the UK. Soc Sci Med 2005;61:2065–83 [PubMed]
41. Latkin CA, Curry AD. Stressful neighborhoods and depression: a prospective study of the impact of neighborhood disorder. J Health Soc Behav 2003;44(1):34–44 [PubMed]
42. Yen IH, Kaplan GA. Poverty area residence and changes in depression and perceived health status: evidence from the Alameda County Study. Int J Epidemiol 1999;28:90–94 [PubMed]
43. Galea S, Ahern J, Nandi A, Tracy M, Beard J, Vlahov D. Urban neighborhood poverty and the incidence of depression in a population based cohort study. Ann Epidemiol 2007;17:171–179 [PMC free article] [PubMed]
44. Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. JAMA 1999;282:1737–1744 [PubMed]
45. Pinto-Meza A, Serrano-Blanco A, Penarrubia MT, Blanco E, Haro JM. Assessing depression in primary care with the PHQ-9: can it be carried out over the telephone? J Gen Intern Med 2005;8:738–742 [PMC free article] [PubMed]
46. Huang FY, Chung H, Kroenke K, Delucchi KL, Spitzer RL. Using the Patient Health Questionnaire-9 to measure depression among racially and ethnically diverse primary care patients. J Gen Intern Med 2006;21:547–552 [PMC free article] [PubMed]
47. Jette AM, Davies AR, Cleary PD, et al. The Functional Status Questionnaire: reliability and validity when used in primary care. J Gen Intern Med 1986;1:143–149 [PubMed]
48. Washburn RA, McAuley E, Katula J, Mihalko SL, Boileau RA. The physical activity scale for the elderly (PASE): evidence for validity. J Clin Epidemiol 1999;52:643–651 [PubMed]
49. Dinger MK, Oman RF, Taylor EL, Vesely SK, Able J. Stability and convergent validity of the physical activity scale for the elderly (PASE). J Sports Med Phys Fitness 2004;44:186–192 [PubMed]
50. Washburn RA, Ficker JL. Physical activity scale for the elderly (PASE): the relationship with activity measured by a portable accelerometer. J Sports Med Phys Fitness 1999;39(4):336–340 [PubMed]
51. Martin KA, Rejeski WJ, Miller ME, James MK, Ettinger WH, Jr, Messier SP. Validation of the PASE in older adults with knee pain and physical disability. Med Sci Sport Exerc 1999;31:627–633 [PubMed]
52. Boardman JD, Finch BK, Ellison CG, Williams DR, Jackson JS. Neighborhood disadvantage, stress, and drug use among adults. J Health Soc Behav 2001;42:151–165 [PubMed]
53. Sherbourne CD, Stewart AL. The MOS social support survey. Soc Sci Med 1991;32:705–714 [PubMed]
54. deGraaf R, Bijl RV, Ravelli A, Smit F, Vollebergh WA. Predictors of first incidence of DSM-III-R psychiatric disorders in the general population: findings from the Netherlands Mental Health Survey and Incidence Study. Acta Psychiatr Scand 2002;106:303–313 [PubMed]
55. Sato T. The Eysenck Personality Questionnaire Brief Version: factor structure and reliability. J Psychol 2005;139:545–552 [PubMed]
56. US Census Bureau 2000 Census. Available at: http://www.census.gov/main/www/cen2000.html. Accessed August 18, 2008
57. Matheson FI, Moineddin R, Dunn JR, Creatore MI, Gozdyra P, Glazier RH. Urban neighborhoods, chronic stress, gender and depression. Soc Sci Med 2006;63:2604–2616 [PubMed]
58. Land K, McCall P, Cohen L. Structural covariates of homicide rates: are there any invariances across time and social space? Am J Sociol 1990;95:922–963
59. Posner S. Use of psychometric techniques in the analysis of epidemiologic data. Ann Epidemiol 2003;13:344–350 [PubMed]
60. Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics 1986;42:121–130 [PubMed]
61. Williams DR. African-American health: the role of the social environment. J Urban Health 1998;75:300–321 [PMC free article] [PubMed]
62. Alegria M, Perez DJ, Williams S. The role of public policies in reducing mental health status disparities for people of color. Health Aff 2003;22:51–64 [PubMed]
63. Krause N. Anticipated support, received support, and economic stress among older adults. J Gerontol B Psychol Sci Soc Sci 1997;52(6):P284–293 [PubMed]
64. Schuster TL, Kessler RC, Aseltine RH., Jr Supportive interactions, negative interactions, and depressed mood. Am J Community Psychol 1990;18:423–438 [PubMed]
65. Kawachi I, Berkman LF. Social ties and mental health. J Urban Health 2001;78:458–467 [PMC free article] [PubMed]
66. Brugha TS. The end of the beginning: a requiem for the categorization of mental disorder? Psychol Med 2002;32:1149–1154 [PubMed]
67. Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter? The Public Health Disparities Geocoding Project. Am J Epidemiol 2002;156:471–482 [PubMed]

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