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. Author manuscript; available in PMC: 2013 Sep 1.
Published in final edited form as: Drug Alcohol Depend. 2012 Jun 13;125(Suppl 1):S35–S43. doi: 10.1016/j.drugalcdep.2012.05.027

Neighborhood Context and Substance Use Disorders: A Comparative Analysis of Racial and Ethnic Groups in the United States

Kristine M Molina 1,*, Margarita Alegría 2, Chih-Nan Chen 3
PMCID: PMC3488110  NIHMSID: NIHMS386555  PMID: 22699095

Abstract

Background

There is evidence that ethnic/racial minorities are conferred differential risk for substance use problems based on where they live. Despite a burgeoning of research focusing on the role of neighborhood characteristics on health, limited findings are available on substance use. Our study uses nationally representative data (N= 13, 837) to examine: (1) What neighborhood characteristics are associated with risk of substance use disorders?; (2) Do the associations between neighborhood characteristics and substance use disorders remain after adjusting for individual-level factors?; and (3) Do neighborhood characteristics associated with substance use disorders differ by race/ethnicity after adjusting for individual-level factors?

Methods

Data were drawn from the Collaborative Psychiatric Epidemiology Studies (CPES-Geocode file) with 836 Census tracts. Analyses included African Americans, Asians, Caribbean Blacks, Latinos, and non-Latino whites. Separate logistic regression models were fitted for any past-year substance use disorder, alcohol use disorder, and drug use disorder.

Results

Living in more affluent and residentially unstable census tracts was associated with decreased risk of past-year substance use disorder, even after adjusting for individual-level factors. However, when we investigated the interaction of race/ethnicity and census latent factors with past-year substance use disorders, we found different associations for the different racial/ethnic groups. We also found different associations between neighborhood affluence, residential instability and any past-year substance use and alcohol disorders by nativity.

Conclusions

Characteristics of the environment might represent differential risk for substance disorders depending on a person’s ethnicity/race and nativity status.

Keywords: neighborhood context, substance use disorders, alcohol, drugs, racial/ethnic minorities, nativity

1. Introduction

Prior studies find an association between neighborhood characteristics and risk of substance use net of individual-level effects (Galea et al., 2007; Silver et al., 2002). The strain brought on from poverty and its byproducts in disadvantaged communities (e.g., despair, psychological distress; Boardman et al., 2001; Sampson et al., 1997) as well as the erosion of social bonds in economically deprived contexts is argued to be reasons for the positive association found between disadvantaged neighborhood characteristics and increased risk for psychiatric disorders. Residential context appears to influence both individual behaviors and health outcomes (Diez Roux and Mair, 2010; Pickett and Pearl, 2001; Williams and Collins, 2001). Given the significant implications of residential context on risk for illness and resource allocations related to individual behaviors and health outcomes (Robert, 1999), research assessing the role of contextual factors on population health is expanding.

Substance-related morbidity and mortality, for example, are disproportionately higher among poor, ethnic minority populations (cf. Galea and Vlahov, 2002; Jones-Webb et al., 1996), who more often than non-Latino whites reside in neighborhoods characterized by poverty (Casciano and Massey, 2008; Williams and Collins, 2001). As such, the residential environment where an individual resides can contribute to pervasive racial/ethnic health disparities in the US, including the risk for psychiatric disorders (Morenoff and Lynch, 2004; Stockdale et al., 2007). Examining potential ways that residential context influences the risk of illness (Boardman et al., 2001; Buu et al., 2011; Galea et al., 2007; Silver et al., 2002; Stockdale et al., 2007) is critical to understanding environmental factors associated with substance use disorders and substance-related problems for ethnic minority and socioeconomically disadvantaged groups (e.g., DUI, cirrhosis mortality, homelessness; Caetano, 2003; Chartier and Caetano, 2010; Fisher and Breakey, 1991; Galea and Vlahov, 2002; Jones-Webb et al., 1996).

Previous social disorganization and social control theories (Hirschi, 1969; Robert 1999; Sampson et al., 1997) posit that residing in a poor neighborhood, characterized by high levels of social disorganization and residential mobility increases the likelihood that an individual will engage in problematic behaviors. Poverty and disrupted social bonds are also theorized to account for the positive association between disadvantaged neighborhoods and higher rates of substance use among their inhabitants. Yet the prevalence of substance use disorders varies across racial/ethnic groups (Breslau et al., 2006; Grant et al., 2004), raising questions about whether the effects of residential context differ across these groups. While neighborhood affluence is robustly associated with better health outcomes for the general population (Browning and Cagney, 2003; Wen et al., 2003), neighborhood composition, even in disadvantaged neighborhoods, seems to exert an independent influence on health behaviors that diverges by nativity. That is, increased neighborhood concentrations of immigrants or certain ethnic minorities in poor neighborhoods may confer a greater sense of collective identity and efficacy (Kulis et al., 2007; Mazumdar et al., 2000; Vega et al., 2010) that help mitigate the deleterious effects of neighborhood poverty on health behaviors such as substance use.

In this paper, we contribute to the literature in two important ways. First, using data from the Collaborative Psychiatric Epidemiology Studies (CPES), we investigate whether neighborhood characteristics are associated with higher odds of meeting criteria for DSM-IV 12-month substance, alcohol, and drug use disorders, and if so, whether these associations remain after adjusting for individual-level characteristics. Second, we assess how and to what extent neighborhood context is associated with risk of any past-year substance, alcohol, and drug use disorders among racial/ethnic minority groups compared to non-Latino whites, after adjusting for individual-level factors. We hypothesize that 1) neighborhood disadvantage and residential instability will be associated with higher odds of DSM-IV substance use disorders; 2) neighborhood affluence and greater immigrant concentration will be associated with lower odds of DSM-IV substance use disorders and; 3) that neighborhood characteristics will have variable associations with odds for substance use disorders across racial/ethnic groups. To our knowledge, this is one of the first studies to examine whether there is differential racial/ethnic group effect on the role of neighborhood context on substance use disorders employing a population-based sample. As such, no a priori hypotheses are made regarding specific racial/ethnic comparisons for risk of different substance use disorders.

2. Methods

Data were drawn from the Collaborative Psychiatric Epidemiology Studies (CPES-Geocode file), which consists of three cross-sectional, nationally-representative household surveys: the National Comorbidity Survey-Replication (NCS-R; Kessler et al., 2004), the National Latino and Asian American Study (NLAAS; Alegría et al., 2004a), and the National Study of American Life (NSAL; Jackson et al., 2004). The multiple-frame sampling approach and creation of design-based analysis weights in the CPES were able to generate a single, nationally representative sample (see Heeringa and Berglund, 2007), allowing for the comparative analyses proposed in the present study.

The sample design of the three surveys included in the CPES have been previously described in detail elsewhere (see Heeringa et al., 2004 and Pennell et al., 2004), but are briefly described here. The NCS-R is a nationally representative sample of English-speaking adults residing in the coterminous US, administered in two parts: Part I, including core diagnostic assessments, and Part II, assessing service use, impairment, and correlates of psychiatric illness, administered to a subsample of respondents. We include the 4,180 non-Latino white respondents of the final Part II subsample (n = 5,692). The final weighted response rate was 70.9% (Kessler et al., 2004). The NLAAS, a nationally stratified area probability sample of non -institutionalized Latino and Asian persons in the coterminous US, that includes 2,554 Latinos and 2,095 Asians. The overall weighted response rate for the NLAAS was 73.2% (Alegría et al., 2004a). Lastly, the NSAL is a nationally representative sample of English-speaking black Americans of African and Caribbean descent. The NSAL sample included 3,570 African Americans and 1,621 respondents of Caribbean descent. The overall response rate for the total NSAL sample was 71.5% (Jackson et al., 2004). Caribbean Black respondents who self-reported being of Latino ethnicity (n= 183) were excluded, leaving a sample size of 13, 837. We allowed for listwise deletion in all analyses, as missing data constituted less than the 5% recommended for imputation (Tabachnick and Fidell, 2007).

2.1. Procedure

Data collection was conducted between 2001–2003. The primary mode of data collection for all three surveys was by in-person interviewing at the respondent’s home using computer-assisted interviewing. Only the NLAAS was available in languages other than English (e.g., Spanish, Vietnamese; see Alegría et al., 2004b). The institutional review board committees of all participating institutions approved all study procedures (Pennell et al., 2004).

2.2. Measures

2.2.1. Substance Use Disorders

Alcohol and drug use disorders were assessed using the World Mental Health Composite International Diagnostic Interview (WHM-CIDI; Kessler and Üstün, 2004). Diagnoses are based on the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV; American Psychiatric Association, 1994). Responses to the CIDI are used to classify individuals as meeting DSM-IV criteria for a past-year psychiatric disorder or not. We used the following composite diagnostic categories: any substance use disorder (alcohol abuse, alcohol dependence, drug abuse, drug dependence); any alcohol use disorder (alcohol abuse, alcohol dependence); and any drug use disorder (drug abuse, drug dependence).

2.2.2. Individual-Level Variables

Variables include race/ethnicity (non-Latino white, Latino, Asian American, African American, Black Caribbean), age, sex; marital status, education, work status and nativity (US-born or foreign-born). We also included a poverty index, a continuous measure referring to the income-to-needs ratio (the total household income divided by the federal poverty threshold, adjusted for family size and number of children less than 18 years).

2.2.3. Neighborhood-Level Variables

We define neighborhoods per census tract, a measure theorized to closely approximate neighborhoods (Krieger et al., 2002) and designed to represent homogeneous properties in terms of population characteristics, economic status, and living conditions (US Census, 2009). Data were extracted from the 2000 US Decennial Census Summary File 3 and linked to individual respondents in the CPES-Geocode data set file via unique identifiers. We selected fifteen census tract-level variables that tap into different neighborhood characteristics (Lantz and Pritchard, 2010): percent non-Latino black population; percent non-Latino Asian population; percent Latino population; percent of population who lived in a different house five years previously (in 1995); percent of owner-occupied housing units; percent who completed at least a high school degree (≥25 years); percent who completed an undergraduate degree or higher (≥25 years); percent of households with public assistance; percent of female-headed households with own children under age 18; percent of civilians holding management/ professional/and related professions (for employed civilian population ≥16 years); percent of foreign-born population; percent of families with an annual income above $75,000; percent of recent immigrant population (year of entry between 1995- March 2000); percent of civilians unemployed (≥16 years); and percent of population living below the poverty level (for whom poverty status is determined).

2.3. Analytic Approach

2.3.1. Factor Analysis

We constructed characteristics of neighborhoods through exploratory factor analysis (EFA), using orthogonal varimax rotation. Items with factor loadings of .60 or higher were retained (cf. Sampson et al., 1997). For items with high loadings on two or more factors, we retained it in the factor with the highest loading. Items with a negative loading were recoded so all items loaded positively onto the latent factors. Eigen values (cut-off of 1.0), suggested four factors that represented neighborhood characteristics. However, the fourth was not considered legitimate because there was only one item that loaded highly and solely on this factor (i.e., percent living in different house in 1995). Thus, three factors were retained, with percent living in different house in 1995 considered a single-item, neighborhood-level predictor. Table 1 displays factor loadings for each of the retained census tract-level variables. We constructed neighborhood latent factors using confirmatory factor analysis to describe the observed patterns in the census tract-level data.

Table 1.

Factor Loadings of Census Tract-Level Variables

Census Variables Factor Loadings
Concentrated Disadvantage Latino/Immigrant Concentration Affluence Residential Mobility
% Owner-occupied housing (reverse coded) 0.61
% Black 0.71
% Unemployed 0.73
% Public Assistance 0.78
% Below poverty level 0.80
% Female-headed household w/own children under 18 yrs of age 0.84
% Foreign-born 0.82
% Latino 0.84
% High school degree 0.65
% Annual income at or above $75,000 0.78
% Management/ professional/related occupations 0.92
% Undergraduate degree and higher 0.94
% Lived in different house in 1995 0.78
Chronbach’s α 0.74 0.82 0.94 N/A
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Note. Data are from the 2000 Census Summary File 3.

2.3.2. Multilevel Modeling

We employed multilevel analysis for binary outcomes (Guo and Zhao, 2000). Multilevel logistic regression estimated the association between individual-and neighborhood-level characteristics with risk of meeting criteria for DSM-IV 12-month substance use, alcohol, and drug use disorders; separate successive logistic regression models were fitted for each past-year diagnosis. We first estimated a basic model (Model 1) of the association between individual-level characteristics and each past-year diagnosis. Model 2 fitted multilevel logistic regression models that include neighborhood characteristics as explanatory variables of these outcomes only. Another set of multilevel logistic regression models (Model 3) included all individual-level characteristics and all neighborhood characteristics. The full model (Model 4) added cross-product terms for each racial/ethnic group and each neighborhood characteristic, while controlling for individual-level factors. We estimated multivariable logistic regression models within a multilevel framework using the Generalized Linear Latent and Mixed Models (GLLAMM) commands, incorporating survey weights, within Stata version 10.0. We used the GLLAMM procedure because the maximum-likelihood estimation algorithm has been shown to be superior to more conventional multilevel programs (Rodriguez and Goldman, 1995). Since the nesting of the sampling design of the CPES was not conducted by census tract, we were unable to separate level 1(individual) and level 2 (neighborhood) weights from the sampling weights and cluster variables available in the CPES dataset. Instead, we followed procedures by Kovacevic and Rai (2003) and Pfeffermann et al. (1998) by creating pseudo-weights at each of the levels, re-constructing weights by normalizing final CPES weights to the individual and neighborhood levels using 2000 US Census county population data.

3. Results

3.1. Any Past-Year Substance Use Disorder

Table 3 presents results of logistic regression analyses for any past-year substance use disorder. Model 1 shows that African Americans compared to non-Latino whites; women compared to men; and immigrants compared to US-born respondents, are at a decreased risk for any past-year substance use disorder. According to Model 2, individuals who live in affluent neighborhoods and those who live in residentially mobile neighborhoods are at a decreased risk for any past-year substance use disorder. Finally, contrary to our hypothesis, Model 4 shows that for Asians and African Americans, living in affluent neighborhoods is associated with an increased risk for any past-year substance use disorder (OR= 1.16; 95% CI= 1.01, 1.33 and OR= 1.35; 95% CI= 1.14, 1.59, respectively) compared to non-Latino whites (OR= 0.89; 95% CI= 0.82, 0.98), independent of individual-level factors.

Table 3.

Multilevel Logistic Regression Models of Any 12-Month Substance Use Disorder (Weighted)

Model 1 Model 2 Model 3 Model 4
OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Individual Characteristics
Race/Ethnicity
White (ref) 1.00 1.00 1.00
Latino 0.72 (0.46 – 1.12) 1.52 (0.70 – 3.32) 1.59 (0.73 – 3.46)
Asian 0.58† (0.34 – 1.00) 0.57 (0.27 – 1.25) 0.61 (0.28 – 1.32)
African American 0.51*** (0.37 – 0.70) 0.50* (0.28 – 0.87) 0.51** (0.31 – 0.85)
Black Caribbean 0.95 (0.23 – 4.01) 0.50 (0.22 – 1.16) 0.36* (0.16 – 0.79)
Age, years
18–34 (ref) 1.00 1.00 1.00
35–49 0.67** (0.50 – 0.89) 0.64** (0.46 – 0.90) 0.64** (0.45 – 0.89)
50–64 0.16*** (0.09 – 0.27) 0.15*** (0.07 – 0.34) 0.15*** (0.07 – 0.34)
65+ *** (0.00 – 0.01) 0.00*** (0.00 – 0.02) 0.00*** (0.00 – 0.02)
Gender
Male (ref) 1.00 1.00 1.00
Female 0.41*** (0.31 – 0.54) 0.43*** (0.28 – 0.66) 0.42*** (0.27 – 0.65)
Marital Status
Married/cohabitating (ref) 1.00 1.00 1.00
D/S/W 1.86*** (1.37 – 2.53) 2.05*** (1.47 – 2.86) 2.05*** (1.47 – 2.86)
Never married 2.55*** (1.97 – 3.32) 3.68*** (2.29 – 5.91) 3.68*** (2.29 – 5.92)
Education, years
11 years (ref) 1.00 1.00 1.00
12 years 0.66 (0.38 – 1.15) 1.19 (0.39 – 3.67) 1.21 (0.40 – 3.71)
13–15 years 0.53** (0.34 – 0.83) 0.84 (0.39 – 1.81) 0.84 (0.39 – 1.81)
16 years or more 0.33*** (0.19 – 0.57) 0.34 (0.08 – 1.39) 0.34 (0.08 – 1.40)
Poverty 0.88 (0.60 – 1.28) 0.84 (0.59 – 1.19) 0.85 (0.59 – 1.21)
Work Status
Employed (ref) 1.00 1.00 1.00
Unemployment 1.52* (1.01 – 2.28) 1.31 (0.50 – 3.41) 1.28 (0.49 – 3.34)
Out of Labor Force 1.16 (0.74 – 1.82) 0.92 (0.31 – 2.77) 0.92 (0.30 – 2.77)
Nativity
US Born (ref) 1.00 1.00 1.00
Immigrant 0.28*** (0.15 – 0.52) 0.11*** (0.05 – 0.26) 0.11*** (0.05 – 0.26)
Neighborhood Characteristics
Disadvantage 1.00 (0.91 – 1.09) 1.03 (0.96 – 1.10)
Latino/Immigrant Concentration 1.00 (0.95 – 1.06) 1.01 (0.95 – 1.08)
Affluence 0.92* (0.87 – 0.98) 0.92** (0.86 – 0.98)
Residential Mobility 0.91** (0.86 – 0.96) 0.89** (0.82 – 0.97)
Neighborhood Characteristics x Race/Ethnicity
Disadvantage (White) 1.05 (0.89 – 1.24)
Latinoa 0.97 (0.82 – 1.13)
Asiana 0.77† (0.57 – 1.03)
African Americana 0.93 (0.75 – 1.15)
Black Caribbeana 1.25 (0.95 – 1.64)
Latino/Immigrant Concentration (White) 1.02 (0.95 – 1.09)
Latinoa 0.92† (0.84 – 1.01)
Asiana 1.00 (0.83 – 1.20)
African Americana 0.98 (0.87 – 1.12)
Black Caribbeana 0.84 (0.65 – 1.09)
Affluence (White) 0.89* (0.82 – 0.98)
Latinoa 1.00 (0.86 – 1.16)
Asiana 1.16* (1.01 – 1.33)
African Americana 1.35*** (1.14 – 1.59)
Black Caribbeana 1.13 (0.63 – 2.03)
Residential Mobility (White) 0.90† (0.81 – 1.00)
Latinoa 0.86 (0.70 – 1.04)
Asiana 0.92 (0.76 – 1.12)
African Americana 1.02 (0.87 – 1.19)
Black Caribbeana 1.01 (0.67 – 1.50)
Constant 0.12*** (0.08 – 0.20) 0.02*** (0.02 – 0.04) 0.05*** (0.02 – 0.13) 0.05*** (0.02 – 0.13)
Observations 13626 13624 13592 13592
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Note.

a

Reference group is non-Latino whites. D/S/W= Divorced/separated/widowed.

†

p< .10;

*

p< .05;

**

p< .01;

***

p< .001

3.2. Any Past-Year Alcohol Use Disorder

Similar patterns emerged in Models 1 and 2 for any past-year alcohol use or substance use disorder. In Model 3, results mirror those for any past-year substance use disorders, except that Asians are less likely to meet criteria for any past-year alcohol use disorder compared to non-Latino whites. Model 4 suggests that, after adjusting for individual-level characteristics, Latinos living in neighborhoods with higher concentration of Latinos/immigrants are at a decreased risk for past-year alcohol use disorder (OR= 0.89; 95% CI= 0.80, 0.98) compared to non-Latino whites (OR= 1.06; 95% CI= 0.99, 1.14). Further, African Americans and Black Caribbeans residing in affluent neighborhoods are at an increased risk for any past-year alcohol use disorder (OR= 1.31; 95% CI= 1.09, 1.58 and OR= 1.50; 95% CI= 1.18, 1.91, respectively) compared to non-Latino whites (OR= 1.03; 95% CI= .88, 1.19), independent of individual-level characteristics.

3.3. Any Past-Year Drug Use Disorder

Model 1 results parallel those of any past-year substance use disorder, although unemployed individuals are not at higher risk of any past-year drug use disorder. No significant associations were noted in Model 2 or Model 3. Lastly, in the full model, greater neighborhood concentrated disadvantage was associated with decreased odds of any past-year drug disorder for Asians (OR= 0.69; 95% CI= 0.51, 0.95) compared to non-Latino whites (OR= .99; 95% CI= 0.83, 1.19). African Americans living in more affluent neighborhoods were at an increased risk of meeting criteria for any past-year drug disorder (OR = 1.33; 95% CI = 1.10, 1.60) than non-Latino whites (OR= 0.94; 95% CI= 0.83, 1.07).

3.4 Supplementary Analyses

As a sensitivity check, we included nativity as a modifier of the association between neighborhood context and DSM-IV substance disorders, given that it has been found to moderate the relation between neighborhood context and health outcomes (Kimbro, 2009; Lee and Ferraro, 2007; Vega et al., 2010). After adjustment of individual-level variables, significant interactions between nativity and three of our neighborhood context measures were noted (data not shown). Immigrant respondents residing in more affluent neighborhoods were at significantly increased odds of meeting diagnosis for any past-year substance use disorder (OR= 1.15; 95% CI= 1.03, 1.29) and any past-year alcohol use disorder (OR= 1.12; 95% CI= 1.02, 1.23) compared to their U.S.-born counterparts. We did not find that nativity modified the relation between concentrated disadvantage, Latino/immigrant concentration, and substance use disorders.

4. Discussion

Our results show differences between non-Latino whites and racial/ethnic minority groups regarding the association between neighborhood context and risk of past-year substance-related disorders. Additionally, we found differential risk of substance use disorders between immigrant and US-born respondents across different neighborhood contexts. As in many studies comparing diverse ethnic/racial groups, what is found for one group is not necessarily found for another.

4.1. Neighborhood Concentrated Disadvantage

Asians living in more disadvantaged neighborhoods were at a decreased risk for any past-year drug use disorder compared to their non-Latino white counterparts. These results may be attributable to the low prevalence of substance use disorders among Asians as compared to other racial/ethnic groups (Grant et al., 2004). Specifically, rates of illicit drug use among Asian adults living in poverty are lower than rates among the population living in poverty (NSDUH, 2010). Prior research suggests several mechanisms, including those related to sociocultural factors, which may explain reduced rates of substance use disorders among Asian populations. For example, Asians compared to European Americans have been found to report more abstinence-promoting environments such as having fewer family members who use or offer substances (Johnson and Nagoshi, 1990). Family influences, concerns about shame and losing face, and cultural norms against substance use have been shown to account for reduced rates of substance use/abuse (Au and Donaldson, 2000). A prior study on Asian drug users found that participants expressed stigma attached to drug use in their community; and drug users who had good relationships with their families expressed more shame and embarrassment related to their drug use (Nemoto et al., 1999). Our findings provide a basis for future studies with Asian populations to evaluate how environments that have been shown to be detrimental to health for some racial/ethnic groups may interact with broader sociocultural factors to differentially influence substance use outcomes.

4.2. Neighborhood Affluence

Independent of individual-level characteristics, we found an increased risk for any past-year substance use disorder, alcohol use disorder, and any drug use disorder for African Americans; an increased risk for any past-year alcohol use disorder for Black Caribbeans; and an increased risk for any past-year drug use disorder for Asians living in neighborhoods with greater affluence compared to non-Latino whites in these neighborhoods. These findings contradict our hypothesis, but are similar to those of Galea and colleagues’ (2007), who found that higher neighborhood median income was associated with greater likelihood of alcohol use, and among alcohol users, higher neighborhood median income was associated with greater frequency of alcohol use. Affluence may not necessarily deter one from engaging in alcohol or drug use as affluent neighborhoods may be susceptible to lower cohesiveness and collective efficacy (Luthar, 2003; Sampson et al., 1997); lack deep social connections (Myers, 2000); and have physical characteristics that contribute to feelings of isolation and social disconnect from neighbors (Luthar et al., 2003). Framed within the stress and coping framework (Thoits, 1995), these findings might also be attributed to race-related stress associated with being a minority. Pickett and Wilkinson (2008) note that members of ethnic minority groups with material resources may still endure psychosocial stress associated with being a low status minority within a majority white community. Greater neighborhood SES in concert with fewer individuals from one’s racial group is associated with increased reporting of discrimination (Dailey et al., 2010; Hunt et al., 2007), and discrimination has been associated with increased likelihood of using alcohol as a coping mechanism (Chae et al., 2008; Martin et al., 2003). When data were re-analyzed with nativity as a modifier, foreign-born persons residing in more affluent neighborhoods were at an increased risk for past-year alcohol disorder compared to their US-born counterparts. Affluent neighborhoods appear protective for US-born persons, but not for immigrants. Although research on the “immigrant paradox” shows that positive health outcomes for immigrants are partly explained by the protective role of “ethnic enclaves,” few studies have examined the role of affluence on immigrant health behaviors. In our data, immigrants of color were more likely to reside in neighborhoods with higher SES levels than their US-born counterparts, except for Latino immigrants, who were not significantly more likely than their US-born counterparts to live in affluent neighborhoods (data not shown). This may help explain the association we found for most of the racial/ethnic minority groups in the sample, which were more likely to be comprised of immigrants and are at an increased risk of substance use disorders than non-Latino whites. It is unclear from our data however, if differential experiences associated with minority status, either as racial/ethnic minorities or as immigrants, may result in differential outcomes for minority status groups compared to majority groups living in similar social environments. Nonetheless, in the context of neighborhood affluence, both race/ethnicity and nativity may influence risk of substances use disorders. Future research should include measures of discrimination to evaluate whether neighborhood-level characteristics are associated with risk of substance use disorders independent of self-reported discrimination. As the NCS-R was the only survey in the CPES data set that did not include a measure of discrimination, we were unable to account for this in our analyses.

4.3. Neighborhood Latino/Immigrant Concentration

Consistent with previous studies employing Latino samples (Frank et al., 2007; Kulis et al., 2007; Ostir et al., 2003), we found that living in Latino-immigrant dense neighborhoods was associated with lower risk of any past-year alcohol use disorder for Latinos (compared to non-Latino whites). Although Latino/immigrant enclaves may be disproportionately low-income, they may provide advantages, such as family stability, that foster a sense of collectivism and access to social networks (Mazumdar et al., 2000), counterbalancing adverse outcomes associated with living in ethnic enclaves (Jargowsky, 2006). Supplementary analyses revealed that nativity also modified the association between Latino-immigrant concentration and past-year alcohol use disorder, with foreign-born individuals being at a decreased risk compared to their US-born counterparts. A plausible explanation for these findings concerns the “ethnic-enclave hypothesis” which posits that the lower risk of ill-health among immigrants may be a result of the spatial diffusion of protective practices that deter against risky behavior (Eschbach et al., 2004). In our data, US-born and immigrant Latinos resided in neighborhoods with similar levels of Latino/immigrant concentrations, unlike other racial/ethnic groups in the sample. Greater association between US- and immigrant-born Latinos may result in less engagement with problematic behaviors, which in turn may contribute to a lower risk of alcohol use disorders among Latinos in general. It has been suggested that substance use among Latinos be understood as a complex social learning process wherein not only can substance-related behaviors change as a result of one’s social environment, but so can attitudes and expectancies toward certain behaviors (Finch et al., 2000; Marin et al., 1993). Future studies should examine how intra-group relations and attitudes toward alcohol use may mediate the relation between ethnic/immigrant enclaves and risk of alcohol use disorders among immigrant and US-born Latinos.

4.4. Neighborhood Residential Instability

We did not find that neighborhood residential instability moderated the relation between race/ethnicity and any substance use disorders. Contrary to our hypothesis, residential instability was associated with lower risk of any substance use disorder and any alcohol use disorder among the pooled sample, after adjusting for individual-level characteristics. Our supplementary analyses revealed that living in more residentially unstable neighborhoods was protective of any past-year substance use and alcohol use disorders for US-born persons, but not for immigrants. Interestingly, US-born persons from most racial/ethnic minority groups (except Asians and Afro-Caribbeans) tended to live in tracts with greater residential instability. It is plausible that US-born persons are driving the results we found by race/ethnicity. Nonetheless, residential instability was associated with lower risk of past-year substance use and alcohol use disorders.

Although conventional reasoning suggests that residential instability indicates less social cohesiveness and greater social disorganization, factors typically associated with deleterious outcomes (Sampson et al., 1997), perhaps residential instability reflects a more complex relationship with health behaviors than previously thought (Browning and Cagney, 2003). For example, Wilson (1996) argued that residential instability may have beneficial effects in more disadvantaged neighborhoods, given these neighborhoods may actually drive some residents away (Fischer, 2002). Thus, it is plausible that since substance abusers are a more mobile group than non-users (Cahill and La Vigne, 2008), we may be capturing those tracts from which substance abusers moved out, resulting in the negative association found between residential instability and risk of substance use disorders. Future research on substance use may benefit from examining the conditional estimated effects of residential instability.

Divergent findings between our and those of other studies might be explained by methodological and sampling differences. Residential instability has mostly been measured as a composite measure including renters. However, because the tenure variable we used (i.e., % owner-occupied housing) loaded highest and negatively onto the “Disadvantage” latent factor, we did not include it in our measure of residential instability. Further, most other studies have been regionally-based and have not used diagnostic criteria for substance use disorders. Thus, our findings may not be entirely comparable to those of other studies.

4.5. Limitations and Future Directions

There are a few methodological issues that we believe warrant consideration. First, due to the cross-sectional nature of our data, we cannot infer causation or establish the directionality of any of the observed associations. It is probable that individuals with substance abuse disorders are more likely to reside in more disadvantaged neighborhoods, rather than the converse (Buu et al., 2007). Our study is unable to account for social selection, a major limitation in studies of “neighborhood effects” in terms of determining: if the neighborhood influences the individual; if the individual self-selects a type of neighborhood or; if individual changes the make up of the neighborhood in a reciprocal manner (Manski, 1989; Oakes, 2004). Future studies can attempt to account for prior neighborhood health and an individual’s history of alcohol and drug use/abuse prior to residing in their current neighborhood (Browning and Cagney, 2003). Further, retrospective recall of age of onset and the limited number of incident cases does not allow us to determine if associations between neighborhood characteristics and substance use disorders are due to an increase in the incidence rate or not. Longitudinal studies are needed to sort out the temporal sequence of the observed associations and rule out social selection processes.

Increasingly, research is making use of DSM-IV diagnoses for substance use disorders, though not all previous studies have done so. Although frequency of drug/alcohol use and substance-related disorders may represent similar constructs, the factors underlying substance use and abuse/dependency may be different (Galea et al., 2004). Similarly, it seems unlikely that neighborhood characteristics operate similarly for all psychiatric disorders; future studies should examine whether the neighborhood contexts have similar associations with other health outcomes among different racial/ethnic and nativity groups. Furthermore, census tracts may not capture individual perception of a neighborhood, which is associated with health outcomes (Alegría et al., 2007). Future research would benefit from incorporating objective and subjective features of neighborhoods. Moreover, though we only included Latino/immigrant concentration, different ethnic concentrations may have divergent associations with substance use disorders; more conceptual and methodological approaches to the study of ethnic enclaves and other multi-ethnic contexts are needed (Logan et al., 2002). Lastly, we are unable to capture changes in neighborhood characteristics or historical patterns (Logan et al., 2002) using only 2000 Census data. A systematic examination of how neighborhoods change over time and the impact it may have on substance use disorders is clearly warranted (Diez Roux, 2001).

5. Conclusion

Neighborhood context matters for past-year substance use disorders. Different contexts may differentially impact racial/ethnic and nativity groups, independent of individual-level characteristics. Recognizing this implies that strategies to address health inequalities must consider the differential associations of neighborhood context across race/ethnicity and nativity. Moreover, even when the associations between neighborhood characteristics and substance use outcomes might look similar across groups, their underlying dynamics may be different. Elucidating mechanisms through which different neighborhood contexts are associated with specific outcomes for different segments of the population has important public health and policy implications for developing targeted interventions and reducing racial/ethnic disparities (Diez Roux, 2001). Greater attention should be paid to the role of public policy in addressing social determinants of substance use disorders, such as those that consider the psychosocial conditions and stresses tied to minority status (Alegría et al., 2003). While this study provides only a partial picture of individual and neighborhood-level factors that may be associated with risk of substance use disorders, it makes a significant contribution by documenting for whom and how neighborhood social environments may shape risk of substance use disorders.

Table 2.

Distribution of Sociodemographic Characteristics in CPES Pooled Sample and by Race/Ethnicity (Weighted)

All Non-Latino White Latino Asian African American Caribbean Black p
n = 13,837 n = 4,180 n = 2,554 n = 2,095 n = 3,570 n =1,438
% SE % SE % SE % SE % SE % SE
Sex **
Male 47.5% 0.01 47.5% 0.01 51.5% 0.01 47.5% 0.01 44.0% 0.01 50.5% 0.03
Females 52.5% 0.01 52.5% 0.01 48.5% 0.01 52.5% 0.01 56.0% 0.01 49.5% 0.03
Age, years ***
18–34 31.7% 0.01 27.6% 0.01 49.0% 0.02 39.5% 0.02 35.7% 0.01 39.7% 0.02
35–49 30.9% 0.01 30.4% 0.01 30.1% 0.01 32.2% 0.02 34.1% 0.01 32.4% 0.01
50–64 21.0% 0.01 22.9% 0.01 13.4% 0.01 18.0% 0.01 18.6% 0.01 17.0% 0.01
65 and over 16.4% 0.01 19.1% 0.01 7.5% 0.01 10.3% 0.02 11.6% 0.01 10.9% 0.03
Education, years ***
11 or less 18.2% 0.01 13.0% 0.01 44.5% 0.02 15.1% 0.01 24.2% 0.01 20.7% 0.02
12 30.9% 0.01 31.5% 0.02 24.5% 0.01 17.7% 0.01 37.9% 0.01 30.5% 0.02
13–15 27.3% 0.01 29.1% 0.01 20.8% 0.01 25.3% 0.01 23.8% 0.01 27.1% 0.04
16 or more 23.7% 0.01 26.4% 0.01 10.2% 0.01 42.0% 0.02 14.1% 0.01 21.7% 0.01
Work Status ***
Employed 65.3% 0.01 65.3% 0.01 63.5% 0.02 63.9% 0.01 66.8% 0.01 76.2% 0.01
Unemployed 6.0% 0.00 5.0% 0.01 7.4% 0.01 6.4% 0.01 10.1% 0.01 8.2% 0.01
Out of labor force 28.7% 0.01 29.7% 0.01 29.1% 0.02 29.8% 0.02 23.1% 0.01 15.5% 0.02
Household Income ***
$0–14,999 16.2% 0.01 12.8% 0.01 27.5% 0.02 18.3% 0.01 24.7% 0.01 13.7% 0.03
$15,000–34,999 22.4% 0.01 19.9% 0.01 28.7% 0.01 13.2% 0.01 33.9% 0.01 33.3% 0.03
$35,000–74,999 34.2% 0.01 36.1% 0.01 27.7% 0.02 28.5% 0.02 31.3% 0.01 37.0% 0.03
$75,000 or more 27.1% 0.01 31.2% 0.02 16.1% 0.01 40.0% 0.02 10.1% 0.01 15.9% 0.02
Nativity ***
US-Born 86.8% 0.01 96.7% 0.01 41.5% 0.02 23.1% 0.03 97.7% 0.00 32.8% 0.03
Foreign-Born 13.2% 0.01 3.3% 0.01 58.5% 0.02 76.9% 0.03 2.3% 0.00 67.2% 0.03
Open in a new tab

Note. W/D/S= Widowed/Divorced/Separated. P-values are from Rao-Scott statistic for the Pearson chi-square test for contingency tables.

**

p< .01;

***

p < .001.

Acknowledgments

Role of Funding Source

This paper was supported by Grant #P50 MH073469 from the National Institute of Mental Health and Training Grant #68194 from the Robert Wood Johnson Foundation. Kristine Molina, Ph.D. was supported by a postdoctoral fellowship funded through a National Heart, Lung and Blood Institute Training Grant #T32HL007426. The listed institutes had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the manuscript; or in the decision to submit the paper for publication.

The authors would like to thank Xinliang Li for her technical assistance in geocoding the CPES data and creating neighborhood measures; Anne Valentine for editorial assistance; and Jose Szapocznik, Ph.D. for his insightful comments on a previous draft. We are also grateful to the anonymous reviewers for their feedback on our manuscript.

Footnotes

Contributors

Kristine Molina, Ph.D. acquired the neighborhood level data; guided creation of neighborhood measures, geocoding, and data analysis; and took primary lead on drafting the manuscript. Margarita Alegría, Ph.D. conceptualized the study, helped guide data analysis and writing of the manuscript; and had full access to the CPES data. Chih-Nan Chen, Ph.D. undertook the statistical analysis. All authors contributed to and have approved the final manuscript.

Conflict of Interest

All authors declare that they have no conflicts of interest.

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References

  1. Alegría M, Pérez DJ, Williams S. The role of public policies in reducing mental health status disparities for people of color. Health Affairs. 2003;22:51–64. doi: 10.1377/hlthaff.22.5.51. [DOI] [PubMed] [Google Scholar]
  2. Alegría M, Shrout PE, Woo M, Guarnnacia P, Sribney W, Vila Polo A, Cao Z, Mulvaney-Day N, Torres M, Canino G. Understanding differences in past year psychiatric disorders for Latinos living in the U.S. Soc Sci Med. 2007;65:214–230. doi: 10.1016/j.socscimed.2007.03.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alegría M, Takeuchi D, Canino G, Duan H, Shrout P, Meng X, Vega W, Zane N, Vila D, Woo M, Vera M, Guarnaccia P, Aguilar-Gaxiola S, Sue S, Escobar J, Lin KM, Gong F. Considering context, place and culture: the National Latino and Asian American Study. Int J Method Psych. 2004a;13:208–220. doi: 10.1002/mpr.178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Alegría M, Vila D, Woo M, Canino G, Takeuchi D, Vera M, Febo V, Guarnaccia P, Gaxiola-Aguilar S, Shrout P. Cultural relevance and equivalence in the NLAAS instrument: Integrating etic and emic in the development of cross-cultural measures for a psychiatric epidemiology and services study of Latinos. Int J Method Psych. 2004b;13:270–288. doi: 10.1002/mpr.181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 4. American Psychiatric Association; Washington, DC: 1994. [Google Scholar]
  6. Au JG, Donaldson SI. Social influences as explanations for substance use differences among Asian-American and European-American adolescents. J Psychoactive Drugs. 2000;32:15–23. doi: 10.1080/02791072.2000.10400208. [DOI] [PubMed] [Google Scholar]
  7. Boardman JD, Finch BK, Ellison CG, Williams DR, Jackson J. Neighborhood disadvantage, stress, and drug use among adults. J Health Soc Behav. 2001;42:151–165. [PubMed] [Google Scholar]
  8. Breslau J, Aguilar-Gaxiola S, Kendler K, Su M, Williams D, Kessler RC. Specifying race-ethnic differences in risk for psychiatric disorder in a USA national sample. Psychol Med. 2006;36:57–68. doi: 10.1017/S0033291705006161. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Browning CR, Cagney KA. Moving beyond poverty: neighborhood structure, social processes, and health. J Health Soc Behav. 2003;44:552–571. [PubMed] [Google Scholar]
  10. Buu A, Wang W, Wang J, Puttler LI, Fitzgerald HE, Zucker RA. Changes in women’s alcoholic, antisocial, and depressive symptomatology over 12 years: a multilevel network of individual, familial, and neighborhood influences. Dev Psychopathol. 2011;23:325–337. doi: 10.1017/S0954579410000830. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Caetano R. Alcohol-related health disparities and treatment-related epidemiological findings among Whites, Blacks, and Hispanics in the United States. Alcohol Clin Exp Res. 2003;27:1337–1339. doi: 10.1097/01.ALC.0000080342.05229.86. [DOI] [PubMed] [Google Scholar]
  12. Cahill M, La Vigne N. Residential mobility and drug use among parolees in San Diego, California and implications for policy. In: Thomas YF, Richardson D, Cheung I, editors. Geography and Drug Addiction. New York: Springer; 2008. pp. 21–46. [Google Scholar]
  13. Casciano R, Massey DS. Neighborhoods, employment, and welfare use: assessing the influence of neighborhood socioeconomic composition. Soc Sci Res. 2008;37:544–558. doi: 10.1016/j.ssresearch.2007.08.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Chae DH, Takeuchi DT, Barbeau EM, Bennett GG, Lindsey JC, Stoddard AM, Krieger N. Alcohol disorders among Asian Americans: associations with unfair treatment, racial/ethnic discrimination, and ethnic identification (the National Latino and Asian American Study, 2002–2003) J Epidemiol Community Health. 2008;62:973–979. doi: 10.1136/jech.2007.066811. [DOI] [PubMed] [Google Scholar]
  15. Chartier K, Caetano R. Ethnicity and health disparities in alcohol research. Alcohol Res Health. 2010;33:152–160. [PMC free article] [PubMed] [Google Scholar]
  16. Dailey AB, Kasl SV, Holford TR, Lewis TT, Jones BA. Neighborhood-and individual-level socioeconomic variation in perceptions of racial discrimination. Ethnic Health. 2010;15:145–163. doi: 10.1080/13557851003592561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Diez Roux A. Investigating neighborhood and area effects on health. Am J Public Health. 2001;91:1783–1789. doi: 10.2105/ajph.91.11.1783. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Diez Roux A, Mair C. Neighborhoods and health. Ann NY Acad Sci. 2010;1186:125–145. doi: 10.1111/j.1749-6632.2009.05333.x. [DOI] [PubMed] [Google Scholar]
  19. Eschbach K, Ostir GV, Patel KV, Markides KS, Goodwin JS. Neighborhood context and mortality among older Mexican Americans: is there a barrio advantage? Am J Public Health. 2004;94:1807–1812. doi: 10.2105/ajph.94.10.1807. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Finch BK, Boardman JD, Kolody B, Vega WA. Contextual effects of acculturation on perinatal substance exposure among immigrant and native-born Latinas. Soc Sci Q. 2000;81:421–438. [PubMed] [Google Scholar]
  21. Fischer SC. Ever-more rooted Americans. City Community. 2002;1:177–198. [Google Scholar]
  22. Fischer PJ, Breakey WR. The epidemiology of alcohol, drug, and mental disorders among homeless persons. Am Psychol. 1991;46:115–1128. doi: 10.1037//0003-066x.46.11.1115. [DOI] [PubMed] [Google Scholar]
  23. Frank R, Cerda M, Rendon M. Barrios and burbs: Residential context and health-risk behaviors among Angeleno adolescents. J Health Soc Behav. 2007;48:283–300. doi: 10.1177/002214650704800306. [DOI] [PubMed] [Google Scholar]
  24. Galea S, Ahern J, Tracy M, Vlahov D. Neighborhood income and income distribution and the use of cigarettes, alcohol, and marijuana. Am J Prev Med. 2007;32(Suppl 6):S195–S202. doi: 10.1016/j.amepre.2007.04.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Galea S, Nandi A, Vlahov D. The social epidemiologyof substance use. Epidemiol Rev. 2004;26:36–52. doi: 10.1093/epirev/mxh007. [DOI] [PubMed] [Google Scholar]
  26. Galea S, Vlahov D. Social determinants and the health of drug users: socioeconomic status, homelessness, and incarceration. Public Health Rep. 2002;117:135–S145. [PMC free article] [PubMed] [Google Scholar]
  27. Grant BF, Dawson DA, Stinson FS, Chou SP, Dufour MC, Pickering RP. The 12-month prevalence and trends in DSM-IV alcohol abuse and dependence: United States, 1991–1992 and 2001–2002. Drug Alcohol Depend. 2004;74:223–234. doi: 10.1016/j.drugalcdep.2004.02.004. [DOI] [PubMed] [Google Scholar]
  28. Guo G, Zhao H. Multilevel modeling for binary data. Annu Rev Sociol. 2000;26:441–462. [Google Scholar]
  29. Heeringa SG, Berglund P. National Institutes of Mental Health (NIMH) Collaborative Psychiatric Epidemiology Survey Program (CPES) Data Set: Integrated weights and sampling error codes for design-based analysis. University of Michigan Survey Research Center; Ann Arbor: 2007. Available at www.icpsr.umich.edu/cocoon/cpes/using.xml?Section=Weighting. [Google Scholar]
  30. Heeringa SG, Wagner J, Torres M, Duan N, Adams T, Berglund P. Sample designs and sampling methods for the Collaborative Psychiatric Epidemiology Studies (CPES) Int J Method Psych. 2004;13:221–240. doi: 10.1002/mpr.179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Hirschi T. Causes of Delinquency. University of California Press; Berkeley: 1969. [Google Scholar]
  32. Hunt MO, Wise LA, Jipguep MC, Cozier YC, Rosenberg L. Neighborhood racial composition and perceptions of racial discrimination: evidence from the Black Women’s Health Study. Soc Psychol Quart. 2007;70:272–289. [Google Scholar]
  33. Jackson JS, Torres M, Caldwell CH, Neighbors HW, Nesse RM, Taylor RJ, Trierweiler SJ, Williams DR. The National Survey of American Life: a study of racial, ethnic and cultural influences on mental disorders and mental health. Int J Method Psych. 2004;13:196–207. doi: 10.1002/mpr.177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Jargowsky PA. Immigrants and neighborhoods of concentrated poverty: assimilation or stagnation? National Poverty Center Working Paper Series #06–44. 2006 Available at http://www.npc.umich.edu/publications/working_papers/
  35. Johnson RC, Nagoshi CT. Asians, Asian-Americans and alcohol. J Psychoactive Drugs. 1990;22:45–52. doi: 10.1080/02791072.1990.10472196. [DOI] [PubMed] [Google Scholar]
  36. Jones-Webb R, Snowden L, Herd D, Short B, Hannan P. Alcohol-related problems among Black, Hispanic and White men: the contribution of neighborhood poverty. J Stud Alcohol Drugs. 1996;58:539–545. doi: 10.15288/jsa.1997.58.539. [DOI] [PubMed] [Google Scholar]
  37. Kessler RC, Berglund P, Chiu WT, Demler O, Heeringa S, Hiripi E, Jin R, Pennell BE, Walters EE, Zaslavsky A, Zheng H. The US National Comorbidity Survey Replication (NCS-R): design and field procedures. Int J Method Psych. 2004;13:69–92. doi: 10.1002/mpr.167. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Kessler RC, Üstün TB. The World Mental Health (WMH) survey initiative version of the World Health Organization (WHO) Composite Diagnostic Interview (CIDI) Int J Method Psych. 2004;13:93–121. doi: 10.1002/mpr.168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Kimbro RT. Acculturation in context: gender, age at migration, neighborhood ethnicity, and health behaviors. Soc Sci Q. 2009;90:1145–1166. [Google Scholar]
  40. Kovacevic MS, Rai SN. A pseudo maximum likelihood approach to multi-level modeling of survey data. Commun Stat-Theor M. 2003;32:103–121. [Google Scholar]
  41. Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. Geocoding and monitoring socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter? Am J Epidemiol. 2002;156:471–482. doi: 10.1093/aje/kwf068. [DOI] [PubMed] [Google Scholar]
  42. Kulis S, Marsiglia FF, Sicotte D, Nieri T. Neighborhood effects on youth substance use in a Southwestern city. Soc Perspect. 2007;50:273–301. doi: 10.1525/sop.2007.50.2.273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Lantz PM, Pritchard A. Socioeconomic indicators that matter for population health. Prev Chronic Dis. 2010;7 http://www.cdc.gov/pcd/issues/2010/jul/09_0246.htm. [PMC free article] [PubMed] [Google Scholar]
  44. Lee MA, Ferraro KF. Neighborhood residential segregation and physical health among Hispanic Americans: good, bad, or benign? J Health Soc Beh. 2007;48:131–148. doi: 10.1177/002214650704800203. [DOI] [PubMed] [Google Scholar]
  45. Logan JR, Zhang W, Alba RD. Immigrant enclaves and ethnic communities in New York and Los Angeles. Am Sociol Rev. 2002;67:299–322. [Google Scholar]
  46. Luthar S. The culture of affluence: psychological costs of material wealth. Child Dev. 2003;74:1581–1593. doi: 10.1046/j.1467-8624.2003.00625.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Manski CF. Anatomy of the selection problem. J Human Resources. 1989;24:343–360. [Google Scholar]
  48. Marin G, Posner SF, Kinyon JB. Alcohol expectancies among Hispanics and non-Hispanic Whites: role of drinking status and acculturation. Hisp J Behav Sci. 1993;15:373–381. [Google Scholar]
  49. Martin JK, Tuch SA, Roman PM. Problem drinking patterns among African Americans: the impacts of reports of discrimination, perceptions of prejudice, and “risky” coping strategies. J Health Soc Behav. 2003;44:408–425. [PubMed] [Google Scholar]
  50. Mazumdar S, Mazumdar S, Docuyanan D, McLaughlin CM. Creating a sense of place: the Vietnamese-Americans and Little Saigon. J Environ Psychol. 2000;20:319–333. [Google Scholar]
  51. Morenoff J, Lynch W. What makes a place healthy? Neighborhood influences on racial/ethnic disparities in health over the life course. In: Anderson NB, Bulatao RA, Cohen B, editors. Critical Perspectives on Racial and Ethnic Differences in Health in Late Life. National Academy Press; Washington, D.C: 2004. [Google Scholar]
  52. Myers DG. The funds, friends, and faith of happy people. Am Psychol. 2000;55:56–67. doi: 10.1037//0003-066x.55.1.56. [DOI] [PubMed] [Google Scholar]
  53. National Survey on Drug Use and Health. Substance use among Asian adults. The NSDUH Report. 2010 Available at http://www.oas.samhsa.gov/2k10/179/SUAsianAdults.pdf.
  54. Nemoto T, Aoki B, Huang K, Morris A, Nguyen H, Wong W. Drug use behaviors among Asian drug users in San Francisco. Addict Behav. 1999;24:823–838. doi: 10.1016/s0306-4603(99)00020-9. [DOI] [PubMed] [Google Scholar]
  55. Oakes MJ. The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology. Soc Sci Med. 2004;58:1929–1952. doi: 10.1016/j.socscimed.2003.08.004. [DOI] [PubMed] [Google Scholar]
  56. Ostir GV, Eschbach K, Markides KS, Goodwin JS. Neighborhood composition and depressive symptoms among older Mexican Americans. J Epidemiol Commun Health. 2003;57:987–992. doi: 10.1136/jech.57.12.987. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Pennell B, Bowers A, Carr D, Chardoul S, Cheung G, Dinkelmann K, Gebler N, Hansen SE, Pennell S, Torres M. The development and implementation of the National Comorbidity Survey Replication, the National Survey of American Life, and the National Latino and Asian American Survey. Int J Method Psych. 2004;13:241–269. doi: 10.1002/mpr.180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Pfeffermann D, Skinner CJ, Holmes D, Goldstein H, Rasbash J. Weighting for unequal selection probabilities in multilevel models. J Roy Stat Soc B Met. 1998;60:23–40. [Google Scholar]
  59. Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Commun Health. 2001;55:111–122. doi: 10.1136/jech.55.2.111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Pickett KE, Wilkinson RG. People like us: ethnic group density effects on health. Ethnic Health. 2008;13:321–334. doi: 10.1080/13557850701882928. [DOI] [PubMed] [Google Scholar]
  61. Robert SA. Socioeconomic position and health: the independent contribution of community socioeconomic context. Annu Rev Sociol. 1999;25:489–516. [Google Scholar]
  62. Rodriguez B, Goldman N. An assessment of estimation procedures for multilevel models with binary responses. J Roy Stat Soc A Sta. 1995;164:339–355. [Google Scholar]
  63. Sampson RJ, Raudenbush SW, Earls F. Neighborhoods and violent crime: a multilevel study of collective efficacy. Science. 1997;277:918–924. doi: 10.1126/science.277.5328.918. [DOI] [PubMed] [Google Scholar]
  64. Silver E, Mulvey EP, Swanson JW. Neighborhood structural characteristics and mental disorder: Faris and Dunham revisited. Soc Sci Med. 2002;55:1457–1470. doi: 10.1016/s0277-9536(01)00266-0. [DOI] [PubMed] [Google Scholar]
  65. 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;65:867–1881. doi: 10.1016/j.socscimed.2007.05.045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Tabachnick BG, Fidell LS. Using Multivariate Statistics. 5. Allyn and Bacon; Boston, MA: 2007. [Google Scholar]
  67. Thoits PA. Coping, and social support processes: where are we? What next? J Health Soc Beh. 1995;35:53–79. [PubMed] [Google Scholar]
  68. U.S. Census Bureau. [Accessed August 11, 2009];Cartographic Boundary Files. 2009 < http://www.census.gov/geo/www/cob/tr_metadata.html>.
  69. Vega WA, Ang A, Rodriguez MA, Finch BK. Neighborhood protective effects on depression in Latinos. Am J Commun Psychol. 2010;47:114–126. doi: 10.1007/s10464-010-9370-5. [DOI] [PubMed] [Google Scholar]
  70. Wen M, Browning CR, Cagney KA. Poverty, affluence, and income inequality: neighborhood economic structure and its implications for health. Soc Sci Med. 2003;57:843–860. doi: 10.1016/s0277-9536(02)00457-4. [DOI] [PubMed] [Google Scholar]
  71. Williams DR, Collins C. Racial residential segregation: a fundamental cause of racial disparities in health. Public Health Rep. 2001;116:404–416. doi: 10.1093/phr/116.5.404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Wilson WJ. When Work Disappears: The World of the New Urban Poor. Alfred A. Knopf; New York: 1996. [Google Scholar]

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