When does neighbourhood matter? Multilevel relationships between neighbourhood social fragmentation and mental health

Social Science & Medicine 72 (2011) 1993e2002

Contents lists available at ScienceDirect

Social Science & Medicine journal homepage: www.elsevier.com/locate/socscimed

When does neighbourhood matter? Multilevel relationships between neighbourhood social fragmentation and mental health Vivienne C. Ivory a, *, Sunny C. Collings b, Tony Blakely a, Kevin Dew c a

Health Inequalities Research Programme, School of Medicine and Health Sciences, University of Otago, Mein st, Newtown, Wellington 6242, New Zealand Social Psychiatry and Population Mental Health Research Unit, University of Otago, Mein st, Newtown, Wellington 6242, New Zealand c Sociology Programme, Victoria University of Wellington, New Zealand b

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 17 May 2011

Studies investigating relationships between mental health and residential areas suggest that certain characteristics of neighbourhood environments matter. After developing a conceptual model of neighbourhood social fragmentation and health we examine this relationship (using the New Zealand Index of Neighbourhood Social Fragmentation (NeighFrag)) with self-reported mental health (using SF-36). We used the nationally representative 2002/3 New Zealand Health Survey dataset of urban adults, employing multilevel methods. Results suggest that increasing neighbourhood-level social fragmentation is associated with poorer mental health, when simultaneously accounting for individual-level confounding factors and neighbourhood-level deprivation. The association was modified by sex (stronger association seen for women) and labour force status (unemployed women more sensitive to NeighFrag than those employed or not in labour force). There was limited evidence of any association of fragmentation with non-mental health outcomes, suggesting specificity for mental health. Social fragmentation as a property of neighbourhoods appears to have a specific association with mental health among women, and particularly unemployed women, in our study. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Neighbourhood Social fragmentation Mental health Deprivation New Zealand

Introduction Observing and understanding ‘neighbourhood effects’ on health continue to both intrigue and frustrate researchers. Examining the relationship between the social aspects of residential contexts such as social fragmentation and mental health is challenging when the mediating mechanisms remain unclear, available ‘exposure’ measures are few and often under-theorised (De Silva, McKenzie, Harpham, & Huttly, 2005), and the research contexts themselves vary between studies (Blakely et al., 2006; Ellaway & Macintyre, 2007). Theoretical and analytical advances mean that more is being asked of observational datasets (Diez Roux & Mair, 2010) which requires clearly specified study designs based firmly on theory (Frohlich, Dunn, McLaren, Shiell, Potvin, Hawe et al., 2007; Stafford et al., 2007). Recent research on neighbourhood social fragmentation fits into a wider body of work investigating the material, physical and social properties of neighbourhood environments. Material aspects (such as deprivation or poverty) and physical properties (such as the built

* Corresponding author. Tel.: þ64 4 385 5541. E-mail address: [email protected] (V.C. Ivory). 0277-9536/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2011.04.015

environment and air quality) have demonstrated relationships to a range of health outcomes (Diez Roux & Mair, 2010; Stevenson, Pearce, Blakely, Ivory, & Witten, 2009). Research investigating the social properties of neighbourhoods has included the characteristics of the social environment (such as levels of resources arising through social capital and cohesion) (Kawachi & Berkman, 2000), interactions between individuals (Stafford, De Silva, Stansfeld, & Marmot, 2008), and interaction between the individual and a group (such as integration into that group) (Berkman, Glass, Brissette, & Seeman, 2000; Bernburg & Thorlindsson, 2007). Within this research context we focus on fragmentation as a neighbourhood characteristic and examine its relationship with individual mental health, rather than the nature of the ties within a given group or relationship that individuals have to it. Such studies help illuminate the causal pathways between social networks and health suggested by Berkman et al. (2000). Kawachi, Subramanian, and Kim (2008) describe social networks as the means by which forms of social capital, such as social support, are exchanged within social groups such as neighbourhoods. Importantly, the nature of social networks and therefore the social resources arising will vary across neighbourhoods, in part determined by factors both within the neighbourhood and wider societal processes (Berkman & Glass, 2000; Carpiano, 2006). We define social

1994

V.C. Ivory et al. / Social Science & Medicine 72 (2011) 1993e2002

fragmentation as the neighbourhood-level conditions that fragment the social relationships within a neighbourhood, inhibiting the levels of social cohesion and social capital available to residents (discussed further below). A theoretical basis for a relationship between neighbourhood social fragmentation and individual mental health is firstly established by drawing on recent findings, conceptual developments in the research field and sociological theory. We then operationalize and test this relationship using multilevel analysis in a nationally representative survey in New Zealand (NZ). Background Studies investigating neighbourhood-level fragmentation and health have moved from exploratory ecological analyses where the unit of analysis for both exposure and outcome is at the aggregated or area level, to multilevel studies that simultaneously analyse area and individual-level data (Blakely & Woodward, 2000), and, more recently to a ‘relational’ approach investigating how individuals interact with their neighbourhood (Cummins, Curtis, Diez-Roux, & Macintyre, 2007). Since the mid 1990’s empirical research investigating the construct of area-level social fragmentation has focused mainly on a measure widely known as the Congdon index. Congdon sought to explain geographical variations in suicide and mental illness rates using a summary four variable census-based measure designed to quantify the social, as opposed to material aspects of deprivation, using area proportions of private renting, single person households, mobility and marital status (Congdon,1996, 2004). The Congdon index has been used as a proxy for the social properties of small areas in a number of ecological studies in the United Kingdom (UK) and was found to predict area suicide rates better than deprivation measures, with increasing fragmentation associated with higher suicide rates (Congdon, 1996, 2004; Evans, Middleton, & Gunnell, 2004; Martikainen, Mäki, & Blomgren, 2004; Smith, Whitley, Dorling, & Gunnell, 2001; Whitley, Gunnell, Dorling, & Smith, 1999). Exceptions reported to date highlight difficulties both in the conceptualisation and measurement of ‘exposures’ such as social fragmentation. Hawton, Harriss, Hodder, Simkin, and Gunnell (2001) did not observe an ecological association between fragmentation and suicide rates. This may have been due to relatively small numbers but it is also possible that the omission of areas with high proportions of non-private dwellings and non-permanent populations severely reduced the range of fragmentation observed because of the high proportion of mobile students in the study setting (Oxfordshire) (Hawton et al., 2001). In both ecological and multilevel analyses from other settings the evidence of a fragmentation/mortality association is mixed. In a recent NZ study no clear association was observed between neighbourhood social fragmentation and suicide, both before and after simultaneously adjusting for individual factors, as well as neighbourhood deprivation. This study used national mortality and probalistically linked census data to conduct multilevel analyses that account for individual factors, and tested two neighbourhood measures: the Congdon and an alternative nine variable measure developed in NZ, the Index of Neighbourhood Social Fragmentation (NeighFrag) (details below) (Ivory, 2009). In contrast, an association was found in Northern Ireland between the Congdon index and suicide rates, but researchers found that this was largely accounted for when individual factors were included (O’Reilly, Rosato, Connolly, & Cardwell, 2008). The difference in associations between study settings may be because fragmentation is less relevant to suicide variation in NZ (Collings, Ivory, Blakely, & Atkinson, 2009), or that the range of fragmentation in NZ is narrower than countries such as the UK meaning residents are less exposed to harmful extremes. Importantly, it should be noted that it was not possible to robustly assess sub-group associations

(Collings et al., 2009): social fragmentation may still be an important factor for suicide rates in some population groups. The evidence of an association with social fragmentation and measures of mental illness is mixed in both ecological and multilevel studies. Overall, it seems that deprivation is a bigger factor than fragmentation for deliberate self-harm (Congdon, 1996, 2004; Corcoran, Arensman, & Perry, 2007; Gunnell, Shepherd, & Evans, 2000). There is some evidence of an association between fragmentation and psychiatric admission (Allardyce et al., 2005; Evans et al., 2004). It is likely, for example, that variation in treatment and diagnosis protocols (Gunnell et al., 2000; Hawton et al., 2001), as well as the very specific pathways between each outcome and fragmentation, explain the lack of consistency in findings in this group of research. Physical illness outcomes do not appear to be as clearly related to fragmentation independently of deprivation. This is reported for outcomes such as injury (Reimers, de Leon, & Laflamme, 2008), heart disease (Stjarne, de Leon, & Hallqvist, 2004), and selfreported physical health (Stafford, Gimeno, & Marmot, 2008). This may also apply generally for all cause mortality (Congdon, 2004; Smith et al., 2001), but for specific causes there are exceptions; e.g. cirrhosis mortality where deprivation and fragmentation contributed equally (Smith et al., 2001). The latest shift in fragmentation/health research uses survey data to examine more specific measures of mental health, such as psychological distress (Fagg et al., 2008; Fagg, Curtis, Stansfeld, & Congdon, 2006; Stafford, Gimeno et al., 2008). Other developments include the use of continuous outcome data scores for all participants with increased statistical power to investigate how individual factors interact with neighbourhood characteristics. For example, it is thought that differences in how people source social support may explain why age (Almedom, 2005) and gender (Kavanagh, Bentley, Turrell, Broom, & Subramanian, 2006; Stafford, Cummins, Macintyre, Ellaway, & Marmot, 2005) have been shown to modify the associations between neighbourhood factors and health. Matheson et al. (2006) suggested that gendered roles and patterns of interaction may mean that local networks play a bigger part in women’s lives when compared with men. Individual sensitivity or vulnerability to the neighbourhood setting may also vary because other settings may play a more important role. Access to non-neighbourhood resources, for example through workplace settings (Inagami, Cohen, Brown, & Asch, 2009; Inagami, Cohen, & Finch, 2007), and duration of exposure to the neighbourhood (Stafford, Gimeno et al., 2008) have all been demonstrated to modify associations between neighbourhood characteristics and health measures. In their study of British civil servants, Stafford, Gimeno et al. (2008) found that increased neighbourhood fragmentation was most strongly associated with mental health in those who had not moved residence. Conversely, Fagg et al. (2008) found that whilst social fragmentation and family support both predicted young adults’ mental health status, but there did not appear to be any interaction between the two. That is, family support did not appear to buffer the impact of neighbourhood fragmentation on psychological distress (Fagg et al., 2008). This finding contrasted with an earlier study showing that neither fragmentation nor deprivation were related to mental health for adolescents; differences in geographical scale, and the age of the study population between the two studies may account for the results, but it could also be that the neighbourhood measures and scale used were less relevant to the younger population (Fagg et al., 2006). Informing analysis: neighbourhood social fragmentation and health theory To outline the rationale for the modelling and analyses used in this study, we return to Durkheim’s theory of social order to link

V.C. Ivory et al. / Social Science & Medicine 72 (2011) 1993e2002

compositional indices (i.e. indices created from individual-level data) such as NeighFrag with neighbourhood levels of cohesion, and therefore to individual mental health. The recent interest in neighbourhood social fragmentation and health calls on Durkheim’s proposition that people’s health is influenced by their local social environment (Berkman et al., 2000; Congdon, 1996; Whitley et al., 1999). Durkheim was concerned with how different types of social settings provide a context for balancing the competing needs of a collective and its individual members, captured in his concept of homo duplex (Taylor & Ashworth, 1987). Balance between the two was attained with the dual mechanisms of integration and regulation. Integration provides a binding or ‘glue’ effect (Bearman, 1991; Durkheim, 1951) within the group, while the regulating aspect of collective life provides “normative limits” for individuals (Bearman, 1991; Thorlindsson & Bernburg, 2004). A paucity or excess of either would be detrimental to the wellbeing of groups and individuals, in particular psychological health (Taylor & Ashworth, 1987). This suggests a U-shaped relationship between levels of integration and regulation and individual wellbeing. A highly fragmented group or neighbourhood would leave individuals isolated, with reduced access to supportive levels of integration or regulation. On the other hand, a highly cohesive social setting could potentially ‘subsume’ individuals (Kushner & Sterk, 2005), overwhelming them and leaving them unable to act in their own interests. We use the concept of ‘social topography’ to make the link between Durkheim’s discussion of types or characteristics of social groups (such as neighbourhoods), fragmentation of those groups, and their levels of integration and regulation. ‘Social topography’ in this instance refers to the way in which characteristics of the group act as the foundation upon which the group operates. For example, if group size and composition affects how group members interact with each other, then they effectively set parameters for the level of integration and regulation within the group and therefore the level of social support available to members (probably in a recursive manner). A given social topography may be regarded as socially fragmenting if it inhibits integration and regulation in the social group. The role of neighbourhood characteristics in the production of the social environment has been recognised elsewhere. The terms ‘macro social determinants’ (Gracia, Garcia, & Musitu, 1995) and ‘structural antecedents’ (Carpiano, 2006) have been used to describe how characteristics of areas or social institutions are important for understanding the production of social support and resources arising from it. In a similar way, compositional features such as residential mobility (Sampson & Groves, 1989) or low proportions of children (Sampson, Morenoff, & Earls, 1999; Witten, McCreanor, & Kearns, 2007) have been frequently regarded as predictors of, or antecedents to, the production of collective efficacy, social cohesion or social capital because of their impact on networks and common neighbourhood interests. In the absence of, for example, ecometric measures of actual levels of integration and regulation within the neighbourhood social group (Chaix, Lindstrom, Merlo, & Rosvall, 2008), we propose that aggregate measures such as NeighFrag and the Congdon index, which include variables such as proportions of children and residential mobility, are proxies for the social topography of the neighbourhood environment. We illustrate the hypothesised link between fragmentation of the neighbourhood and health outcomes in Fig. 1. Building on the social ecological model (Dahlgren & Whitehead, 1991), the model focuses on the dynamic between the degree of neighbourhood fragmentation, individual factors and the mental health response, incorporating interaction between the levels. Degrees of neighbourhood fragmentation are illustrated with shading of the

1995

Fig. 1. Conceptual model of the multilevel relationship between neighbourhood social fragmentation, individual factors and mental health outcomes.

neighbourhood layer. At the top of the neighbourhood layer the neighbourhood setting is less visible, or less ‘present’ in residents’ lives, thereby providing weaker integration and regulation. At the bottom, the neighbourhood setting is more present, providing stronger levels of regulation and integration for residents. The model also highlights the importance of individual-level factors that may mediate or moderate the effect of the neighbourhood environment, as seen in the shaded individual health response at the centre of the model. We focus on two relationships suggested by the model in Fig. 1 and the available data. Firstly we are interested in the relative contribution of factors from both neighbourhood and individual levels to the mental health response, that is, the direct association between the level of fragmentation of the neighbourhood and individual mental health while accounting for other pertinent individual factors. We explore the impact of neighbourhood deprivation on the NeighFrag/mental health association by adding area deprivation to the model as well as testing interaction between deprivation and fragmentation (Blakely & Woodward, 2000). We further test the specificity of the fragmentation/health relationship by comparing association of NeighFrag with mental and non-mental health outcomes. Secondly we examine interaction between the neighbourhood and individual levels. Given the limits of the available data, we focus specifically on whether the impact of the neighbourhoodlevel setting on mental health is modified by individual-level factors discussed above such as age (Almedom, 2005), sex (Stafford et al., 2005) or membership of other groups such as the workplace (Andren & Rosenqvist, 1987), all of which may affect whether individuals are more or less dependent on the neighbourhood as a social resource. This could be because individual stressors are moderated by neighbourhood fragmentation (the impact of being unemployed may be exacerbated in a fragmented neighbourhood because there are fewer local sources of social support, as well as having no workplace social support), or individual factors confer resilience to any effects of neighbourhood levels of fragmentation (older people may have accumulated more social resources to call on other than the neighbourhood). Methods Dataset A multilevel dataset was created from the 2002/3 NZ National Health Survey (NZHS) (Ministry of Health, 2004) by linking data

1996

V.C. Ivory et al. / Social Science & Medicine 72 (2011) 1993e2002

on neighbourhood characteristics with individual data. The NZHS is a population based survey designed to provide a comprehensive assessment of the health status of the population and health care utilisation. The dataset was restricted to permanent residents 15 years and over and usually resident in private dwellings, with complete data. It uses a complex survey sampling method, with ori. The reported response rate for eligible oversampling of Ma people approached was 72% (Ministry of Health, 2004). Analyses conducted here are restricted to the urban population: while it was not clear that neighbourhood fragmentation would mean the same in the rural setting, small numbers prevented stratified analyses. Outcome The primary outcome measure was the Mental Health (MH) sub-scale of the Australian and New Zealand version (Scott, Tobias, Sarfati, & Haslett, 1999) of the Short Form 36 (SF-36) (Ware, Snow, Kosinski, & Gandek, 2000). The SF-36 asked participants to rate their health across eight aspects; general health, mental health, vitality, physical functioning, role physical, bodily pain, social functioning, and role emotional (Ware et al., 2000). The sub-scales can be combined to represent two related, but separate constructs: general and mental health. However, concerns have been raised over the validity of the component scores for M aori and Pacific respondents (Scott, Sarfati, Tobias, & Haslett, 2000). Importantly we were also particularly interested in testing the specificity of the relationship between NeighFrag, mental health and other forms of health; therefore we used the mental health sub-scale as the primary outcome. The scales are measured in a 0e100 range and were specified in the regression analyses as untransformed continuous variables. Exposures Neighbourhood fragmentation The NZ Index of Neighbourhood Social Fragmentation (NeighFrag) (Ivory, 2009) was used to measure the neighbourhood social environment. The NeighFrag index was created in New Zealand using rich census data to more fully measure aspects of neighbourhood composition thought to be related to fragmentation. The index captures the neighbourhood composition theorized to be antecedent to three domains of neighbourhood ‘collective social functioning’ (Macintyre, Ellaway, & Cummins, 2002): communication of norms and values across a neighbourhood, levels of attachment to place and people, and resources required for building neighbourhood-wide social networks. Using 2001 census data that included all usual residents except those in hospitals and prisons, the index was developed at the census area unit level (CAU) (approximately 2000 people in each area, with borders based on locally recognisable communities). Fourteen variables were developed to operationalize the neighbourhood social fragmentation construct, with nine selected after an iterative process using Factor Analysis and Principal Components Analysis (PCA). The following were found to load onto a single factor: single person households, non-family households, recent immigrants, non-NZ language speakers, residential mobility (<1 year), fewer school aged children, homeowners, long term (15 years plus) residents and married adults. The proportions of athome parents, caregivers and pensioners and a measure of within-CAU income inequality did not ‘load’ with the other variables and were therefore not included. PCA was used to create a single weighted index reflecting the relative contribution of the nine variables to the overall index (Ivory, 2009).

Covariates Individual-level covariates were included in analyses in two ways, to statistically adjust for factors that may confound the primary association (referred to as confounders), and as factors that may modify that association (that is, an effect modifier). Covariates included individual-level education: ‘no qualifications’, ‘school or, post-school qualifications’, age: categorised to represent lifecourse stages -‘15e24 years’; ‘25e44 years’; ’45e64 years’ (group) and ‘65þ years’, labour force status: ‘employed’ (group), ‘unemployed’, ‘not in the labour force’, sex: ‘male’, ‘female’, and self-identified ethnicity: ‘Mäori’; ‘Pacific People’; Asian; and ‘non-Mäori, nonPacific, non-Asian’ (‘other’) (note that we used standard ethnicity categories known to be relevant to the New Zealand population). Because of the sampling design used in the NZHS, the sampling weights provided were too complex to allow robust multilevel analyses. Therefore, four ‘design’ variables were created to account for the sampling procedure: ethnicity (above); size of primary sampling unit (categorised from one to seven); number of sampling stratum (categorised from one to eight); and number of adults in a household (categorised from one to five) (see Technical Report for further information (Salmond, 2006)). Sampling weights were used for descriptive analyses. All covariates were specified as categorical variables. Neighbourhood deprivation was measured using the NZDep01 index grouped as quintiles (Salmond & Crampton, 2002). NZDep01 was developed with the same methods as NeighFrag using 2001 census data on socioeconomic characteristics (means tested benefits, employment, equivalised household income, access to a telephone, access to a car, single parent family, qualifications, home ownership, household overcrowding). Note that both neighbourhood measures include household tenure, reflecting its importance theoretically and empirically to both indices. Sensitivity analyses conducted with a NeighFrag index created without the household tenure variable did not alter the findings (results available from authors). Because NZDep was normally constructed at the smaller meshblock-level (Salmond & Crampton, 2002) we used population weighted average CAU values of NZDep for comparisons with NeighFrag.

Analysis Bivariate relationships between weighted mental health scores and exposures were examined with summary relationships presented by sex. Random effects models were used to account for potential clustering within neighbourhoods and to examine the effect of neighbourhood characteristics (at level 2) on an individual outcome (at level 1) (Blakely & Subramanian, 2006), whilst taking into account other important sources of error at both individual and neighbourhood levels. NeighFrag was modelled both as a categorical and a continuous variable to assess the degree of linearity in associations, and to allow for parsimonious modelling where appropriate. Dummy variables were created with NeighFrag deciles categorised into 6 levels to provide information across the spectrum (particularly at the extremes), observe non-linearity, and to maximise power; decile 1; deciles 2&3; 4&5; 6&7; 8&9; and decile 10. In keeping with the theory discussed above, we selected the moderate grouping deciles 4&5 as the reference group e that is as ‘unexposed’ to either too little or too much. The continuous variable was created by ranking the deciles and assigning a value between 0.05 and 0.95 to each decile. The resulting regression estimates, therefore, represent the predicted change, or slope, in the SF-36 score from the hypothetical person living in the most fragmented neighbourhood to

V.C. Ivory et al. / Social Science & Medicine 72 (2011) 1993e2002

that for the person living in the least fragmented neighbourhood (i.e. from the 100th to 1st percentile). Direct association between NeighFrag and mental health The direct association between NeighFrag and predicted Mental Health scores was modelled by progressively adding covariates; firstly, the four design variables; secondly, individual confounders; and finally, adding NZDep as a neighbourhood-level confounder. Interaction between neighbourhood factors was firstly examined by stratifying the sample by NZDep quintiles to observe heterogeneity of the stratified adjusted association and then statistical testing of interaction terms. Specificity of the pathway between NeighFrag and mental health was determined by comparing the strength of the fully adjusted association across all eight SF-36 scales. Interaction between individual and neighbourhood factors Separate adjusted analyses were run for each potentially modifying factor. The dataset was first stratified by the levels of the modifier, followed by statistical testing of interaction terms in regression models run on the pooled data where heterogeneity was observed in the stratified analyses. All analyses were conducted using SAS version 9 using PROC Mixed. Results After restricting the data to those with complete data (98.7% of the total dataset), and then to urban dwellers (77.9% of the total dataset), 9759 eligible NZHS respondents remained. Overall, women reported slightly lower MH scores than men (Table 1). Unemployment, having no qualifications, younger age group and living in more fragmented and deprived neighbourhoods all predicted the worst mental health scores for both men and women. For women, identifying as Mäori was associated with the poorest scores, whereas for men those with the poorest scores identified as Pacific people. Adjusted analyses NeighFrag - mental health associations The regression-based estimates for women of the association between NeighFrag and mental health are shown in Table 2 and Fig. 2. Indicators of poorer mental health increased steadily with increasing fragmentation in a ‘linear’ relationship, therefore we report the regression estimates for the continuous measure of neighbourhood fragmentation. Urban women living in the most highly fragmented neighbourhoods reported poorer mental health compared to those living in the least fragmented neighbourhoods, a change in the Mental Health score of 4.70 (95% CI 6.70, 2.70) across the full NeighFrag range (model 1). The association of NeighFrag with Mental Health was reduced by approximately 15% when individual confounders were added, and reduced again by approximately 18% when neighbourhood deprivation was added to the equation, but remained statistically significant. In the fully adjusted model there was suggestive evidence (although not robust) of worsening mental health with increasing deprivation. The magnitude of the association across the range of NZDep was smaller than that across NeighFrag. The value of the partial F statistic obtained for the NeighFrag in the final model was 3.70 (p < 0.01), suggesting that NeighFrag made a statistically significant contribution to the predicted mental health score after accounting for the contribution of the other variables, and that model fit was adequate (Kleinbaum, Kupper, Muller, & Nizam, 1998). There was limited evidence that neighbourhood deprivation modified the NeighFrag/Mental Health association for women. In the

1997

Table 1 The unadjusted, weighted distribution of SF36 Mental Health Scale scores by exposure variables: Women and Men. Exposure

Ethnicity Asian Mäori Pacific non-Asian non-Mäori non-Pacific Labour force status not in labour force unemployed employed Education no qualifications school qualifications post-school qualifications Age groups 15-24 years 25-44 years 45-64 years 65þ years NeighFrag deciles NF 1(least) NF 2,3 NF 4,5 NF 6,7 NF 8,9 NF10 (most) NZDep2001 Quintiles Least deprived Quintile 2 Quintile 3 Quintile 4 Most deprived

Women

Men

SF36 Mental Health Scale

SF36 Mental Health Scale

n

mean (95% CI)

n

mean (95% CI)

668 2648 561 3680

82.3 79.8 81.4 82.5

83.5) 80.4) 82.9) 82.9)

493 1406 328 2593

84.2 83.6 82.6 84.9

3383 330 3844

81.2 (80.7, 81.7) 76.0 (74.2, 77.9) 83.0 (82.6, 83.4)

1567 207 3046

83.2 (82.5, 84.0) 81.8 (79.7, 83.8) 85.2 (84.8, 85.7)

2243 2128 3186

81.5 (80.9, 82.2) 81.5 (80.9, 82.1) 82.9 (82.4, 83.3)

1378 1092 2350

83.8 (83.0, 84.6) 84.3 (83.6, 85.1) 85.1 (84.6, 85.6)

902 3227 2140 1288

78.2 81.8 83.4 84.5

(77.2, (81.4, (82.8, (83.7,

79.3) 82.3) 84.1) 85.2)

630 1757 1544 889

83.1 83.9 85.3 87.1

(82.0, (83.3, (84.7, (86.2,

84.1) 84.5) 86.0) 88.0)

266 1033 1574 1805 2075 804

86.4 82.6 84.1 82.2 80.9 79.8

(85.2, (81.9, (83.5, (81.5, (80.2, (78.7,

87.6) 83.4) 84.7) 82.9) 81.5) 80.9)

181 722 957 1132 1257 571

86.5 85.3 86.0 84.4 84.0 82.8

(84.7, (84.4, (85.3, (83.6, (83.3, (81.7,

88.2) 86.2) 86.8) 85.2) 84.8) 83.9)

962 936 1097 1449 3113

83.9 83.0 82.5 81.1 80.3

(83.1, (82.1, (81.8, (80.4, (79.7,

84.7) 83.9) 83.3) 81.9) 80.9)

731 626 743 907 1813

86.0 84.4 84.4 85.1 83.1

(85.2, (83.3, (83.5, (84.3, (82.5,

86.8) 85.5) 85.3) 86.0) 83.8)

(81.2, (79.2, (80.0, (82.0,

(83.0, (82.8, (81.0, (84.4,

85.4) 84.3) 84.2) 85.4)

stratified results, the associations for NeighFrag with mental health were stronger (and statistically significant) in the least and most deprived neighbourhoods (Table 4). However, the estimates were imprecise with considerable overlap between the confidence intervals of each stratum. Pooled regression analyses modelling interaction between the neighbourhood measures did not support statistically significant variation of the NeighFrag/Mental Health association across quintiles of deprivation (results available from authors). There was little convincing evidence of an association of neighbourhood fragmentation with the self-reported mental health of urban men. Neighbourhood deprivation was a statistically significant factor for men’s mental health but only when comparing the most with the least deprived areas. The fully adjusted NeighFrag and NZDep estimates for the remaining seven SF-36 scores have been shown in Table 3 for women and men. As the purpose of presenting these scales is primarily comparative, the estimates (with p values) are reported for the NeighFrag and NZDep variables only. Estimates of a change of 3 points or more were seen for General Health and Vitality for women only. NZDep estimates were of similar or greater magnitude to NeighFrag. There was little to suggest that NeighFrag was an important predictor of health status for the remaining SF-36 scales in men or women. Individual/neighbourhood-level interactions Evidence of interaction between neighbourhood and individual levels was sought using stratification (Table 4) and statistical interaction methods (reported in text). None of the stratified results

1998

V.C. Ivory et al. / Social Science & Medicine 72 (2011) 1993e2002

Table 2 The effects of adjusting for individual and neighbourhood factors on the association of NeighFrag and SF-36 Mental Health Scale: Women and Men. Change in mental health score (95% CI) Women

Intercept Neighbourhood Fragmentationa Ethnicity Asian Mäori Pacific non-Asian non-Mäori non-Pacific (ref) Education No qualifications

Men

Model 1

Model 2

Model 3

Model 1

Model 2

Model 3

90.26 (82.80, 97.72) L4.70 (L6.70, L2.70)

93.97 (86.48,101.46) L4.01 (L6.01, L2.02)

95.68 (88.06, 103.31) L3.29 (L5.39, L1.19)

91.68 (84.22, 99.15) 2.00 (4.08, 0.09)

93.26 (85.74, 100.79) 1.32 (3.41, 0.76)

94.94 (87.29, 102.60) 0.67 (2.86, 1.51)

0.53 (0.97, 2.04) L1.89 (L2.98, L0.80) 0.44 (2.08, 1.19) 0.00

1.26 (0.25, 2.78) 0.51 (1.63, 0.60) 0.56 (1.07, 2.20) 0.00

1.27 (0.24, 2.78) 0.24 (1.38, 0.90) 0.92 (0.75, 2.59) 0.00

1.07 (0.47, 2.60) L1.27 (L2.51, L0.02) 1.35 (3.11, 0.42) 0.00

1.74 (0.19, 3.28) 0.29 (1.56, 0.99) 0.64 (2.40, 1.13) 0.00

1.78 (0.23, 3.33) 0.14 (1.44, 1.16) 0.34 (2.14, 1.46) 0.00

L2.32 (L3.24, L1.41) 0.00

L2.20 (L3.13, L1.28) 0.00

L1.17 (L2.22, L0.13) 0.00

L1.11 (L2.17, L0.06) 0.00

L4.56 (L5.94, L3.18) L2.02 (L3.00, L1.05) 0.00 2.65 (1.42, 3.89)

L4.52 (L5.90, L3.14) L2.01 (L2.98, L1.03) 0.00 2.67 (1.44, 3.91)

L2.25 (L3.75, L0.76) L1.39 (L2.46, L0.33) 0.00 2.77 (1.51, 4.04)

L2.23 (L3.72, L0.73) L1.38 (L2.45, L0.32) 0.00 2.82 (1.55, 4.08)

School or post-school qualifications (ref) Age groups 15e24 yrs 25e44 yrs 45e64 yrs (ref) 65þ yrs NZDep2001 Quintiles least deprived (ref) Quintile 2

0.00 1.16 (2.83, 0.51) 0.91 (2.53, 0.71) 1.40 (2.98, 0.19) L2.11 (L3.88, L0.34)

Quintile 3 Quintile 4 Most deprived Random var. (s.e of variance) Neighbourhood-level Individual-level ICC

7.83 (1.81) 223.80 (4.31) 0.034

7.71 (1.79) 220.25 (4.24) 0.034

7.71 (1.81) 220.19 (4.24) 0.034

0.00 1.21 (2.92, 0.49) 1.24 (2.89, 0.42) 0.47 (2.09, 1.15) L2.04 (L3.81, L0.26)

5.98 (2.12) 170.75 (4.34) 0.034

5.90 (2.07) 168.64 (4.28) 0.034

5.90 (2.07) 168.51 (4.28) 0.034

Model 1 NeighFrag and design variables (including ethnicity). Model 2 NeighFrag and individual confounders (age, education, and ethnicity & design variables). Model 3 NeighFrag and individual confounders (age, education, and ethnicity & design variables) and neighbourhood deprivation. Bold indicates significance at the 95% confidence level. a NeighFrag estimates represent the average predicted change in the SF-36 score, from the most to the least fragmented neighbourhood.

for men suggested any effect modification and so the following results focus on women. Some individual characteristics modified the relationship between NeighFrag and Mental Health. As already reported above, sex appeared to modify the association of fragmentation with mental health (coefficients for Table 2 reproduced in Table 4). Statistical modelling in pooled analyses using a sex*NeighFrag interaction term confirmed the magnitude and statistical significance of the observed difference in mental health scores: women had a 2.67 (95% CI 5.25, 0.09) greater change or gradient than men in MH score from least to most fragmented neighbourhoods. Women’s labour force status had a modifying effect on the NeighFrag/mental health association. The Mental Health scores by NeighFrag for unemployed women (a much smaller stratum) suggested a more than 20 point decrease in MH scores for unemployed women living in the most compared to least fragmented neighbourhoods. While confidence intervals were very wide in this stratum they did not overlap with other labour force status strata. By

comparison, a much smaller (2.30) difference in mental health scores was seen for employed women, and an even smaller difference (1.87) in mental health for women not in the labour force. Confidence intervals for both the latter strata included the null. Statistical modelling using an interaction term confirmed the magnitude of heterogeneity. Employed women (the reference group) had a 3.19 (95% CI 5.73, 0.65) change in mental health score across NeighFrag, in a model now including an interaction term between labour force and NeighFrag. Relative to employed women, unemployed women had a 16.44 greater decrease in mental health score across the NeighFrag range (i.e. the coefficient of the interaction term was 16.44 (95% CI 25.56, 7.32)). By comparison, relative to employed women, those not in the labour force had a much smaller decrease of 1.22 but the confidence intervals of this interaction term included the null (1.22 (95% CI 2.32, 4.76)). There was inconsistent evidence of a modifying effect of age group on NeighFrag for women. The stratified results suggested no

V.C. Ivory et al. / Social Science & Medicine 72 (2011) 1993e2002

1999

6 5

Mental Health score

4 3

2.33

2 1

0

0 -1 -2

-1.49

-1.78

-3

-2.37

-2.45

-4 -5 1

2,3

4,5(ref)

6,7

8.9

10

NeighFrag deciles Fig. 2. The nature of the association of NeighFrag with SF36 Mental Health Scale: Women (Fully Adjusted, 95% Confidence Intervals).

group (25e44 yrs: 1.88 (95% CI 2.28, 6.04) and 65þ yrs: 3.33 (95% CI 1.66, 8.32)). Again, all confidence intervals included the null and overlapped.

difference by NeighFrag for 65þ year olds whereas younger age strata suggested an approximately four point decrease in score. However the confidence intervals for all strata substantially overlapped. Further, the estimates for each age group once adjusted for interaction between age and NeighFrag provided only limited support for any difference by strata. The mental health scores of the youngest group across the NeighFrag range changed by 1.47 (7.38, 4.44) points (relative to the reference group estimate of 4.56 (95% CI 7.85, 1.27)), while mental health scores of the other ages groups was relatively better than the reference age

Discussion Neighbourhood social fragmentation was inversely associated with the SF-36 Mental Health scale in the New Zealand adult, urban population, as suggested by our conceptual model. The association remained when accounting for known individual factors related to

Table 3 The association of NeighFrag and non-mental health SF-36 scales (fully adjusted): Women and Men. Change in non-mental health score (95% confidence intervals) Exposures Women NeighFraga NZDep01 least deprived Quintile 2 Quintile 3 Quintile 4 Most deprived Men NeighFrag1 NZDep01 least deprived Quintile 2 Quintile 3 Quintile 4 Most deprived

General Health

Physical Functioning

Role Physical

Bodily Pain

Vitality

Social Functioning

Role Emotional

Health Transition

L4.03 (6.82, 1.23)

1.16 (3.91, 1.59)

1.27 (6.36, 3.82)

0.02 (3.79, 3.82)

L3.01 (5.94, 0.07)

0.81 (3.75, 2.13)

2.84 (6.95, 1.26)

0.59 (3.71, 2.53)

0 1.14 (3.34, L2.57 (4.76, L4.24 (6.36, L4.32 (6.70,

0 1.84 (4.04, L2.42 (4.56, L4.28 (6.37, L6.63 (8.97,

0

0 0.52 (3.57, 2.53) 1.15 (4.11, 1.81)

0 0.56 (2.92, 1.79) 0.75 (3.03, 1.54) L2.36 (4.60, 0.12) L2.74 (5.24, 0.24)

0 2.93 (6.22, 0.36) 2.44 (5.64, 0.75) L3.49 (6.61, 0.36) L4.44 (7.93, 0.94)

0 0.16 (2.34, 2.66) 0.16 (2.26, 2.59) 0.64 (1.74, 3.01) 1.25 (1.41, 3.90) 2.13 (5.59, 1.33)

1.14) 0.42) 2.11) 1.94)

0.37) 0.28) 2.18) 4.29)

0.30 (3.78, 4.37) 3.42 (7.38, 0.54) L4.66 (8.53, 0.79) L6.16 (10.48, 1.83)

L3.37 (6.27, 0.48) L4.94 (8.17, 1.70)

0 0.80 (3.15, L2.76 (5.04, L3.23 (5.46, 2.29 (4.79,

1.55) 0.48) 0.99) 0.20)

1.12 (4.31, 2.08)

0.22 (3.05, 2.61)

4.68 (0.79, 10.15)

3.27 (0.97, 7.51)

0.65 (3.85, 2.55)

2.90 (0.18, 5.98)

0.09 (4.40, 4.22)

0 L3.89 (6.39, L5.58 (8.00, L5.34 (7.71, L5.95 (8.56,

0 2.38 (4.60, L3.47 (5.62, L4.96 (7.06, L7.39 (9.70,

0 2.29 (6.57, 1.99) L6.87 (11.01, 2.72) L5.59 (9.65, 1.54) L9.37 (13.84, 4.91)

0 L3.91 (7.23, L3.58 (6.79, L6.21 (9.36, L5.90 (9.36,

0 2.27 (4.77, L3.98 (6.41, L4.27 (6.65, L3.03 (5.64,

0 L3.14 (5.82, 1.00) L3.30 (5.63, 0.96) L2.72 (5.00, 0.43) L5.54 (8.05, 3.02)

L3.49 (6.86, 0.12) L3.34 (6.61, 0.07) L3.24 (6.44, 0.04) L4.59 (8.11, 1.07)

1.39) 3.15) 2.97) 3.34)

0.17) 1.32) 2.86) 5.08)

0.59) 0.36) 3.06) 2.43)

0.24) 1.55) 1.90) 0.41)

0

Fully adjusted model - individual confounders (age, education, and ethnicity & design variables) and neighbourhood deprivation. Bold indicates significance at the 95% confidence level. a NeighFrag estimates represent the average predicted change in the SF-36 score, from the most to the least fragmented neighbourhood.

1.94 (0.77, 0.27 (2.36, 1.88 (0.69, 0.86 (3.68,

4.65) 2.89) 4.45) 1.97)

2000

V.C. Ivory et al. / Social Science & Medicine 72 (2011) 1993e2002

Table 4 Effect modification by individual and neighbourhood characteristics of the fully adjusted NeighFrag/mental health association, Women and Men. Potential modifying factors Change in health score Women

Men a

NeighFrag estimate (95%CI) Sex female male Labour force status not in labour force unemployed employed Age groups 15e24 years 25e44 years 45e64 years 65þ years NZDep2001 quintiles least deprived Quintile 2 Quintile 3 Quintile 4 Most deprived

NeighFraga estimate (95%CI)

L3.29 (L5.39, L1.19) 0.67 (6.01, 1.51) 1.87 (5.08, 1.34) 0.15 (4.32, 4.02) L22.43 (L35.04, L9.83) 0.27 (15.23, 14.68) 2.30 (4.75, 0.15) 0.15 (2.25, 2.54) 4.53 L3.81 L4.04 0.04

(10.78, 1.72) (L6.90, L0.72) (L7.69, L0.39) (4.03, 3.95)

2.84 1.43 0.36 1.73

(9.36, (4.96, (3.26, (2.59,

3.68) 2.11) 3.98) 6.04)

L3.81 (L7.46, L0.17) 1.67 (1.99, 5.34) 2.72 (8.11, 2.67) 1.75 (6.00, 2.50) 0.87 (4.93, 3.19) 0.64 (5.48, 4.21) 3.11 (7.35, 1.12) L5.28 (L10.07, L0.50) L6.27 (L10.91, L1.62) 1.59 (4.17, 7.35)

Fully adjusted model - individual confounders (age, education, and ethnicity & design variables) and neighbourhood deprivation (where applicable). Bold indicates significance at the 95% confidence level. a NeighFrag estimates represent the average predicted change in the SF-36 score, from the most to the least fragmented neighbourhood.

both mental health and fragmentation and when neighbourhood deprivation was included in the model. While deprivation was also associated with mental health, fragmentation was the stronger factor for women. The association between neighbourhood fragmentation and self-reported health was specific to self-reported mental health. There was no convincing association of NeighFrag with the seven other scales, except possibly General Health and Vitality scales, suggesting specificity as hypothesised. By comparison deprivation had a more consistent, statistically significant and stronger contribution to all non-mental health scales. We argue that it is therefore plausible that fragmented neighbourhoods may provide less ‘healthy’ levels of integration and regulation for residents, with consequences for mental health only. Results from the second step of our analysis provide support for interaction between individual and neighbourhood-level factors. Gender mattered for the influence of the social environment on health with a robust association only observed for women. Labour force status was used as a proxy for membership of nonneighbourhood social groups (and therefore alternative sources of integration and regulation). There was general support for an interaction between fragmentation and labour force status with a much stronger association of neighbourhood fragmentation and mental health observed for unemployed women, albeit with wide confidence intervals. On the other hand, evidence of an interaction between age and fragmentation was inconclusive for women. Thus it would seem that individual-level factors may selectively be critical to understanding how neighbourhood-level factors matter for mental health. While the original datasets were large, the study faced a number of challenges. The restriction and stratification processes resulted in some relatively small comparison groups. We took a conservative approach to interpreting the results because of this and the constrained statistical power once interaction terms are introduced (Kirkwood, Sterne, & Kirkwood, 2003). Nevertheless the sample sizes well exceeded those needed to detect a two point difference between a population norm and a group mean (Ware et al., 2000)

and the F statistics suggested that model fit was adequate. Relatively stable confidence intervals between models indicated that collinearity between the NeighFrag and NZDep was not an issue. Selection into the study is a potential source of bias. The NZHS study design reduces bias that might arise from differences in the relationship between NeighFrag and mental health according to whether people participated in the study. Dataset restriction could also have introduced selection bias. Of particular concern is where mental health status predicts participation (Haapea et al., 2008; Lundberg, Damstrom Thakker, Hallstrom, & Forsell, 2005). However, research investigating the relationship between mental illness, risk factors and participation in field-based surveys suggests that selection bias is unlikely to explain observed associations (Lundberg et al., 2005). Similarly we consider it unlikely that the relationship between mental health and neighbourhood fragmentation would be substantively different between those who participated and those who did not. An important strength of this study is that neighbourhood exposures were measured separately from data collected in the NZHS. This ensured that participants’ reports of their mental health status were separate from the way neighbourhoods were categorised by exposure, reducing the opportunity for correlated measurement error giving rise to spurious associations. Neighbourhood exposure misclassification of individuals was reduced by restricting participants to those usually resident in the selected dwelling. The risk of systematic outcome misclassification by socioeconomic and demographic factors was reduced by the use of the SF-36, which has demonstrated validity and reliability for the New Zealand population (Scott et al., 1999). Cross-sectional observational studies of neighbourhoods have well recognised limitations and we recognise that other factors must partly explain the observed associations (Oakes, 2004). For example, we could not account for exposure to other neighbourhoods e across the lifecourse and within daily life. We took a judicious approach to the selection of covariates, focussing on confounding factors that increased the internal validity of the associations. Importantly, we were able to simultaneously adjust for neighbourhood deprivation and fragmentation demonstrating that deprivation contributed to, but did not explain the NeighFrag/ mental health relationship. Developing our theoretical understanding of the social fragmentation construct as measured by a census-based proxy was an important goal of this study. As well as providing support for our conceptual model of reduced social support in fragmented neighbourhood environments, the analyses undertaken here turn our attention to other related factors that shed light on how such a proxy might be important. Unmeasured factors such as lifestyles and career paths are undoubtedly an important part of the picture (Congdon, 1996). For example, people may need to be relatively mobile as they establish their careers, requiring them to live in highly fragmented areas where there are more rental properties. If the early career stage were also associated with high levels of work stress those living in more highly fragmented neighbourhoods would report poorer mental health. To our minds a combination of endogenous processes and current exogenous neighbourhood exposure may best explain the associations. For example, women living in a highly fragmented neighbourhood may report poorer mental health because of a combination of the events that have led them to live there and because of reduced access to supportive integration and regulation available to them once they are residing there. The conceptual model focuses on mental rather than physical distress as the health outcome. The general pattern of no association with scales more closely related to physical health (such as Bodily Pain), and a weak association with those with some

V.C. Ivory et al. / Social Science & Medicine 72 (2011) 1993e2002

relationship with mental health (General Health and Vitality) (Ware et al., 2000) offer support for the theorised relationship between NeighFrag and health. The specificity of the NeighFrag/ health relationship to mental health is consistent with previous research on fragmentation (Smith et al., 2001; Stafford, Gimeno et al., 2008) and social participation (Ellaway & Macintyre, 2007). We investigated, but did not find, any indication of a “u” shaped relationship between NeighFrag and mental health. The index was developed as a proxy for the ‘topographical’ factors thought to inhibit neighbourhood-level cohesion. Identification of any true ‘U’-shaped relationship as suggested by our conceptual model may therefore be hindered by failure to account for other factors not captured in the index that could contribute to “excessive” levels of integration and regulation such as a highly segregated neighbourhood, charismatic individuals, or local norms (for example). The theoretical approach we used played an important role when deciding how to best use the available data (Frohlich et al., 2007), the appropriate interactions to model, and for making inferences from the results. The difference in the association by sex has two potential explanations. It could be argued that if neighbourhoods were social groups, an association would be present for the whole population and that the ‘failure’ to find an association for men contradicted the conceptual model. Alternatively, the differential could be argued as supportive by showing specificity of effect (Riva, Gauvin, & Barnett, 2007). The difference may reveal important ways in which men and women source social resources. If women use neighbourhood-based social networks more than men they may be more exposed to local factors such as residential mobility (Matheson et al., 2006), explaining the greater sensitivity in their mental health outcomes observed here. Andren and Rosenqvist (1987) argue that support can be sought from a variety of settings; if one setting is less able to provide supportive integration then individuals may be able to seek support elsewhere. The smaller estimates for working women support this explanation, with these women able to source support from the workplace, yet the magnitude of the estimates for unemployed women suggest a synergy rather than simply an either/or scenario. Having little access to social support from neither the neighbourhood nor workplace may represent a considerable strain for mental health. One explanation for unemployed women reporting far worse mental health than working women (in fragmented neighbourhoods) is that they are more ‘exposed’ to a stressful environment. If this were the primary mechanism we might also expect to see an association for unemployed men (Kavanagh et al., 2006; Stafford et al., 2005). This was not the case. In fact NZDep had a stronger association with men’s mental health than women’s, indicating that exposure to the material aspect of the neighbourhood was important. It suggests intriguing differences in what matters about the neighbourhood environment between men and women. Thinking of neighbourhoods in this way shifts the focus from regarding them as a social grouping towards considering how they provide settings that allow supportive groups to develop. Aside from the compositional characteristics captured in the NeighFrag index, other features, such as well supported public places (or “opportunity structures” (Baum & Palmer, 2002)), may be an important means by which fragmented neighbourhoods can support their residents as a group, and the more vulnerable individuals among them. Conclusion The ‘snap shot’ approach taken here is one way of looking at how social factors come to impact on mental health. We have used empirical analysis to ‘make visible’ hypothesised relationships between factors at multiple levels (Galea, Riddle, & Kaplan, 2010) e

2001

in order to understand what does (and does not) contribute to the mental health of residents in neighbourhoods. The neighbourhood social environment, as measured by NeighFrag, appears to matter for the mental health of some residents in urban New Zealand e specifically women, and especially unemployed women. By observing the contribution of individual and neighbourhood factors we have been able to consider who might be most vulnerable in a given neighbourhood setting. Framing the neighbourhood in this way can lead to innovative thinking about what can be done to ameliorate the effect of highly fragmented environments on mental health, both by thinking about the places themselves, and those who live in them. Acknowledgements We thank Maria Turley and Kylie Mason of Public Health Intelligence, Ministry of Health for preparing the Health Survey data. The 2002/03 New Zealand Health Survey was funded by the Ministry of Health and the Crown is the owner of the copyright and the data. We would also like to thank three anonymous reviewers for their helpful comments on an earlier draft of this paper. This research was funded by the New Zealand Health Research Council, as part of the Neighbourhoods and Health project within the Health Inequalities Research Programme. References Allardyce, J., Gilmour, H., Atkinson, J., Rapson, T., Bishop, J., & McCreadie, R. G. (2005). Social fragmentation, deprivation and urbanicity: relation to firstadmission rates for psychoses. British Journal of Psychiatry, 187(Nov.), 401e406. Almedom, A. M. (2005). Social capital and mental health: an interdisciplinary review of primary evidence. Social Science and Medicine, 61(5), 943e964. Andren, K. G., & Rosenqvist, U. (1987). An ecological study of the relationship between risk indicators for social disintegration and use of a somatic emergency department. Social Science and Medicine, 25(10), 1121e1127. Baum, F., & Palmer, C. (2002). ‘Opportunity structures’: urban landscape, social capital and health promotion in Australia. Health Promotion International, 17(4), 351e361. Bearman, P. S. (1991). The social structure of suicide. Sociological Forum, 6(3), 501e524. Berkman, L. F., & Glass, T. (2000). Social integration, social networks, social support, and health. In L. F. Berkman, & I. Kawachi (Eds.), Social epidemiology (pp. 137e173). New York: Oxford University Press. Berkman, L. F., Glass, T., Brissette, I., & Seeman, T. E. (2000). From social integration to health: Durkheim in the new millennium. Social Science and Medicine, 51(6), 843e857. Bernburg, J. G., & Thorlindsson, T. (2007). Community structure and adolescent delinquency in Iceland: a contextual analysis. Criminology, 45(2), 415e444. Blakely, T., Atkinson, J., Ivory, V., Collings, S., Wilton, J., & Howden-Chapman, P. (2006). No association of neighbourhood volunteerism with mortality in New Zealand: a national multilevel cohort study. International Journal of Epidemiology, 35(4), 981e989. Blakely, T., & Subramanian, S. (2006). Multilevel studies. In J. M. Oakes, & J. S. Kaufman (Eds.), Methods in social epidemiology (pp. 316e340). San Francisco CA: Jossey-Bass. Blakely, T., & Woodward, A. J. (2000). Ecological effects in multi-level studies. Journal of Epidemiology and Community Health, 54(5), 367e374. Carpiano, R. M. (2006). Toward a neighborhood resource-based theory of social capital for health: can Bourdieu and sociology help? Social Science and Medicine, 62(1), 165e175. Chaix, B., Lindstrom, M., Merlo, J., & Rosvall, M. (2008). Neighbourhood social interactions and risk of acute myocardial infarction. Journal of Epidemiology and Community Health, 62(1), 62e68. Collings, S. C., Ivory, V., Blakely, T., & Atkinson, J. (2009). Are neighbourhood social fragmentation and suicide associated in New Zealand? A national multilevel cohort study. Journal of Epidemiology & Community Health, . Jech.2009.090985. Congdon, P. (1996). Suicide and parasuicide in London: a small-area study. Urban Studies, 33(1), 137e158. Congdon, P. (2004). Commentary: contextual effects: index construction and technique. International Journal of Epidemiology, 33(4), 741e742. Corcoran, P., Arensman, E., & Perry, I. J. (2007). The area-level association between hospital-treated deliberate self-harm, deprivation and social fragmentation in Ireland. Journal of Epidemiology and Community Health, 61(12), 1050e1055. Cummins, S., Curtis, S., Diez-Roux, A. V., & Macintyre, S. (2007). Understanding and representing `place’ in health research: a relational approach. Social Science and Medicine, 65(9), 1825e1838.

2002

V.C. Ivory et al. / Social Science & Medicine 72 (2011) 1993e2002

Dahlgren, G., & Whitehead, M. (1991). Policies and strategies to promote social equity in health. Stockholm: Institute for Futures Studies. De Silva, M. J., McKenzie, K., Harpham, T., & Huttly, S. R. A. (2005). Social capital and mental illness: a systematic review. Journal of Epidemiology and Community Health, 59(8), 619e627. Diez Roux, A. V., & Mair, C. (2010). Neighborhoods and health. Annals of the New York Academy of Sciences, 1186, 125e145. Durkheim, E. (1951). Suicide: A study in sociology. New York: Free Press. Ellaway, A., & Macintyre, S. (2007). Is social participation associated with cardiovascular disease risk factors? Social Science & Medicine, 64(7), 1384e1391. Evans, J., Middleton, N., & Gunnell, D. (2004). Social fragmentation, severe mental illness and suicide. Social Psychiatry and Psychiatric Epidemiology, 39(3), 165e170. Fagg, J., Curtis, S., Stansfeld, S., & Congdon, P. (2006). Psychological distress among adolescents, and its relationship to individual, family and area characteristics in East London. Social Science and Medicine, 63(3), 636e648. Fagg, J., Curtis, S., Stansfeld, S. A., Cattell, V., Tupuola, A. M., & Arephin, M. (2008). Area social fragmentation, social support for individuals and psychosocial health in young adults: evidence from a national survey in England. Social Science and Medicine, 66(2), 242e254. Frohlich, K. L., Dunn, J. R., McLaren, L., Shiell, A., Potvin, L., Hawe, P., et al. (2007). Understanding place and health: a heuristic for using administrative data. Health and Place, 13(2), 299e309. Galea, S., Riddle, M., & Kaplan, G. A. (2010). Causal thinking and complex system approaches in epidemiology. International Journal of Epidemiology, 39(1), 97e106. Gracia, E., Garcia, F., & Musitu, G. (1995). Macrosocial determinants of social Integration - Social-Class and area effect. Journal of Community and Applied Social Psychology, 5(2), 105e119. Gunnell, D., Shepherd, M., & Evans, M. (2000). Are recent increases in deliberate self-harm associated with changes in socio-economic conditions? An ecological analysis of patterns of deliberate self-harm in Bristol 1972e3 and 1995e6. Psychological Medicine, 30(5), 1197e1203. Haapea, M., Miettunen, J., Läärä, E., Joukamaa, M. I., Järvelin, M. R., Isohanni, M. K., et al. (2008). Non-participation in a field survey with respect to psychiatric disorders. Scandinavian Journal of Public Health, 36(7), 728e736. Hawton, K., Harriss, L., Hodder, K., Simkin, S., & Gunnell, D. (2001). The influence of the economic and social environment on deliberate self-harm and suicide: an ecological and person-based study. Psychological Medicine, 31(5), 827e836. Inagami, S., Cohen, D. A., Brown, A. F., & Asch, S. M. (2009). Body mass index, neighborhood fast food and restaurant concentration, and car ownership. Journal of Urban Health, 86(5), 683e695. Inagami, S., Cohen, D. A., & Finch, B. K. (2007). Non-residential neighborhood exposures suppress neighborhood effects on self-rated health. Social Science and Medicine, 65(8), 1779e1791. Ivory, V. C. (2009). Neighbourhood social fragmentation and health. Bringing social epidemiology and social theory together. PhD thesis. Department of Public Health (p. 430). Wellington University of Otago, Wellington. Available from http:// www.wnmeds.ac.nz/academic/dph/research/HIRP/Neighbourhoods/ NeighbourhoodsPubs.html. Kavanagh, A. M., Bentley, R., Turrell, G., Broom, D. H., & Subramanian, S. V. (2006). Does gender modify associations between self rated health and the social and economic characteristics of local environments? Journal of Epidemiology and Community Health, 60(6), 490e495. Kawachi, I., & Berkman, L. (2000). Social cohesion, social capital, and health. In L. Berkman, & I. Kawachi (Eds.), Social epidemiology (pp. 174e190). Oxford: Oxford University Press. Kawachi, I., Subramanian, S. V., & Kim, D. (2008). Social Capital and Health: A decade of progress and beyond. In I. Kawachi, S. V. Subramanian, & D. Kim (Eds.), Social Capital and Health (pp. 1e26). New York: Springer. Kirkwood, B. R., Sterne, J. A. C., & Kirkwood, B. R. (2003). Essential medical statistics. Malden Mass: Blackwell Science. Kleinbaum, D. G., Kupper, L. L., Muller, K. E., & Nizam, A. (1998). Applied regression analysis and other multivariable methods. Pacific Grove: California: Brooks/Cole Publishing. Kushner, H. I., & Sterk, C. E. (2005). The limits of social capital: Durkheim, suicide, and social cohesion. American Journal of Public Health, 95(7), 1139e1143. Lundberg, I., Damstrom Thakker, K., Hallstrom, T., & Forsell, Y. (2005). Determinants of non-participation, and the effects of non-participation on potential causeeffect relationships, in the PART study on mental disorders. Social Psychiatry and Psychiatric Epidemiology, 40(6), 475e483. Macintyre, S., Ellaway, A., & Cummins, S. (2002). Place effects on health: how can we conceptualise, operationalise and measure them? Social Science and Medicine, 55(1), 125e139.

Martikainen, P., Mäki, N., & Blomgren, J. (2004). The effects of area and individual social characteristics on suicide risk: a multilevel study of relative contribution and effect modification. European Journal of Population, 20(4), 323e350. Matheson, F. I., Moineddin, R., Dunn, J. R., Creatore, M. I., Gozdyra, P., & Glazier, R. H. (2006). Urban neighborhoods, chronic stress, gender and depression. Social Science and Medicine, 63(10), 2604e2616. Ministry of Health. (2004). A portrait of health: Key results of the 2002/03 New Zealand health survey. Wellington: Ministry of Health. O’Reilly, D., Rosato, M., Connolly, S., & Cardwell, C. (2008). Area factors and suicide: 5-year follow-up of the Northern Ireland population. British Journal of Psychiatry, 192, 106e111. Oakes, J. M. (2004). The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology. Social Science & Medicine, 58(10), 1929e1952. Reimers, A.-M., de Leon, A., & Laflamme, L. (2008). The area-based social patterning of injuries among 10 to 19 year olds Changes over time in the Stockholm County. BMC Public Health, 8(1), 131. Riva, M., Gauvin, L., & Barnett, T. A. (2007). Toward the next generation of research into small area effects on health: a synthesis of multilevel investigations published since July 1998. Journal of Epidemiology and Community Health, 61(10), 853e861. Salmond, C. (2006). Fitting complex models using health survey data. Technical report. Wellington: University of Otago. Salmond, C., & Crampton, P. (2002). NZDep2001 index of deprivation. Wellington: Ministry of Health. Sampson, R. J., & Groves, W. B. (1989). Community structure and Crime: testing social-Disorganization theory. The American Journal of Sociology, 94(4), 774. Sampson, R. J., Morenoff, J. D., & Earls, F. (1999). Beyond social capital: spatial dynamics of collective efficacy for children. American Sociological Review, 64(5), 633e660. Scott, K. M., Sarfati, D., Tobias, M. I., & Haslett, S. J. (2000). A challenge to the crosscultural validity of the SF-36 health survey: factor structure in Maori, Pacific and New Zealand European ethnic groups. Social Science and Medicine, 51(11), 1655e1664. Scott, K. M., Tobias, M. I., Sarfati, D., & Haslett, S. J. (1999). SF-36 health survey reliability, validity and norms for New Zealand. Australian and New Zealand Journal of Public Health, 23(4), 401e406. Smith, G. D., Whitley, E., Dorling, D., & Gunnell, D. (2001). Area based measures of social and economic circumstances: cause specific mortality patterns depend on the choice of index. Journal of Epidemiology and Community Health, 55(2), 149e150. Stafford, M., Cummins, S., Ellaway, A., Sacker, A., Wiggins, R. D., & Macintyre, S. (2007). Pathways to obesity: identifying local, modifiable determinants of physical activity and diet. Social Science & Medicine, 65(9), 1882e1897. Stafford, M., Cummins, S., Macintyre, S., Ellaway, A., & Marmot, M. (2005). Gender differences in the associations between health and neighbourhood environment. Social Science and Medicine, 60(8), 1681e1692. Stafford, M., De Silva, M., Stansfeld, S., & Marmot, M. (2008). Neighbourhood social capital and common mental disorder: testing the link in a general population sample. Health and Place, 14(3), 394e405. Stafford, M., Gimeno, D., & Marmot, M. G. (2008). Neighbourhood characteristics and trajectories of health functioning: a multilevel prospective analysis. European Journal Public Health, . ckn091. Stevenson, A., Pearce, J., Blakely, T., Ivory, V., & Witten, K. (2009). Neighbourhoods and health: a review of the New Zealand literature. New Zealand Geographer, 65(3), 211e221. Stjarne, M. K., de Leon, A. P., & Hallqvist, J. (2004). Contextual effects of social fragmentation and material deprivation on risk of myocardial infarction Results from the Stockholm Heart Epidemiology Program (SHEEP). International Journal of Epidemiology, 33(4), 732e741. Taylor, S., & Ashworth, C. (1987). Durkheim and social realism: an approach to health and illness. In G. Scrambler (Ed.), Sociological theory and medical sociology. London: Tavistock. Thorlindsson, T., & Bernburg, J. G. (2004). Durkheim’s theory of social order and deviance: a multi-level test. European Sociological Review, 20(4), 271e285. Ware, J. E., Snow, K. K., Kosinski, M., & Gandek, B. (2000). SF-36 health survey manual and interpretation guide. Lincoln, RI: QualityMetric Incorporated. Whitley, E., Gunnell, D., Dorling, D., & Smith, G. D. (1999). Ecological study of social fragmentation, poverty, and suicide. British Medical Journal, 319(7216), 1034e1037. Witten, K., McCreanor, T., & Kearns, R. (2007). The place of schools in parents’ community belonging. New Zealand Geographer, 63(2), 141e148.