Comparing Neighborhoods of Adults With Serious Mental Illness and of the General Population: Research Implications
Abstract
Objective
The International Classification of Functioning, Disability and Health emphasizes the importance of assessing the impact of environmental factors on functioning and disability. Drawing on this emphasis, this study used a set of objective measures to compare the characteristics of neighborhoods of adults with serious mental illness and of the general population. It also examined the relationship between neighborhood characteristics and neighborhood concentration of persons with serious mental illness.
Methods
The sample comprised 15,246 adults who were treated for serious mental illness in Philadelphia between 1997 and 2000. Principal-components analysis of 22 neighborhood characteristics resulted in an ideal-factor solution of six components. The mean values of each component in neighborhoods of persons with serious mental illness were compared with values in an equally sized group of neighborhoods created by randomly generated addresses representative of the city’s general population. Ordinary least-squares regression was used to assess the association between neighborhood characteristics and neighborhood concentration of persons with serious mental illness.
Results
Neighborhoods in which adults with serious mental illness resided had higher levels of physical and structural inadequacy, drug-related activity, and crime than comparison neighborhoods. Higher levels of physical and structural inadequacy, crime, drug-related activity, social instability, and social isolation were associated with higher concentration of persons with serious mental illness in the neighborhood’s adult population.
Conclusions
The differences in neighborhood characteristics identified in this study point to factors that merit closer attention as potential barriers or facilitators in the functioning, participation, and community integration of persons with serious mental illness.
More than a decade has passed since the World Health Organization redefined its classification of impairment, disability, and health to reflect a biopsychosocial model of disability (1). As a result, the International Classification of Functioning, Disability and Health (ICF) now emphasizes multiple dimensions of functioning and disability as experienced at the body, individual, and societal levels. As part of this framework, the ICF classifies environmental factors in an effort to capture potential barriers and facilitators to social, physical, and institutional functioning and their role in contributing to disability (2). The classification promotes the assessment and documentation of the environment’s positive or negative impact on functioning as well as the need for environmental interventions to remediate restrictions to participation in educational, economic, social, cultural, and political activities. Despite advances in policy and research related to environmental barriers and adaptations, significant barriers and knowledge gaps remain (3).
The paucity of research in this area is likely a contributing factor to the dearth of interventions addressing community participation of persons with serious mental illness. In fact, direct application of environmental factors classified by ICF in mental health research remains limited (4). This may in part be explained by inadequacies in the conceptualization of the ICF environmental factors and difficulties in measuring them (5). Notably, however, the International Mental Health Task Force was actively involved in developing the ICF to ensure that all aspects of the revised classification apply to persons with mental illness (6).
Despite these shortcomings, according to Baumgartner and Susser (7), the ICF provides a useful framework for research on community integration of persons with serious mental illness, and it offers a conceptual basis for the growing body of research on the relationship between neighborhood characteristics and integration. Although the conceptualization of community integration has evolved over time (8), we use a broad definition of community integration as “the opportunity to live in the community and to be valued for one’s uniqueness and abilities like everyone else” (9). Prior studies have shown community integration to be positively associated with working-class neighborhoods and neighborhood racial-ethnic diversity (10), negatively associated with neighborhood disadvantage and crime (11,12), and both positively and negatively associated with immigrant concentration (11,13). These studies included a limited set of modifiable socioeconomic neighborhood indicators, relatively small samples of persons with serious mental illness, and select neighborhoods of an urban area and, therefore, have had little influence on policy or neighborhood improvements. Furthermore, earlier studies defined neighborhoods by zip code rather than by a smaller geographic unit, such as the block group used by the U.S. Census Bureau, which is a more appropriate proxy for a neighborhood.
Studies of the environment in which persons with serious mental illness live need to include a more diverse spectrum of factors and a unit of analysis that will enhance interventions and ultimately promote health rather than disability. Thus the purpose of this study was to establish a broader foundation from which to study and develop interventions that remove barriers and activate facilitators of participation by persons with serious mental illness. Specifically, consistent with the ICF’s emphasis on environment, this study used objective measures of environmental factors to determine whether physical and social characteristics and crime among neighborhoods in Philadelphia where adults with serious mental illness live differ from those of neighborhoods representative of the city’s general population. It also examined the relationship between neighborhood concentration of persons with serious mental illness and these neighborhood characteristics.
Methods
Study groups
Data for mental health consumers with serious mental illnesses were obtained under a business associate agreement with Community Behavioral Health (CBH), a publicly run managed care organization that funds behavioral health services for Medicaid recipients in Philadelphia. The data set consisted of 15,246 individuals, ages 18–64, who were eligible for Medicaid throughout 2000 and who had a claim for at least one inpatient or two outpatient Medicaid-reimbursed services between 1997 and 2000 that were related to diagnoses of affective disorder (ICD−9 code 296) or schizophrenia (code 295). These are common criteria for identifying serious mental illness in Medicaid claims data (14–16), and their accuracy has been validated (17). Geographic information systems (GIS) software was used to match the addresses of individuals with serious mental illness in the CBH data set to the U.S. Census block group in which their residence was located. GIS software was also used to create a comparison group of 15,246 randomly generated address points that were representative of the general Philadelphia population. These points were generated such that the number of points in each block group was proportional to that block group’s share of the overall Philadelphia population. A more detailed description of the procedures used to generate these points is available elsewhere (15). This study was approved by the institutional review board at the University of Pennsylvania.
Neighborhood-level characteristics
U.S. Census block groups were chosen as the unit of analysis for assessing neighborhood-level characteristics, with Census 2000 identifying 1,816 block groups in the City of Philadelphia. Block groups are geographic units comprising clusters of several street blocks. They are the smallest geography for which the Census Bureau makes available aggregated data and have been previously used as appropriate neighborhood proxies (18,19).
Three data sources were used to obtain an array of variables at the block group level. A number of variables were obtained from the Census 2000 data file, and additional variables, also from the year 2000, were extracted from databases that are maintained by the Cartographic Modeling Lab at the University of Pennsylvania. One of these databases provides crime statistics from the Philadelphia Police Department’s incident transmittal system, which includes records of all incidents to which police responded. An additional database integrates data from the Philadelphia Department of Licenses and Inspection, the Philadelphia Revenue Department, the public gas utility (Philadelphia Gas Works), and the Board of Revision of Taxes, the public entity that assesses property values in Philadelphia.
A set of 22 variables measuring the crime, physical and structural, economic, housing market, and social characteristics of neighborhoods was selected from these data sources.
Neighborhood concentration of serious mental illness
The CBH data set was matched with general population data from Census 2000 in order to assess the concentration of persons in the data set who were living in each block group in Philadelphia. This was calculated for each block group by using the number of residents in the CBH data set as the numerator and the Census 2000 estimate of the number of residents ages 18 to 64 as the denominator.
Analysis procedures
Thirty-one block groups had populations of zero and were dropped from subsequent analyses. Consequently, two persons in the serious mental illness group and eight address points in the comparison group erroneously linked to these block groups were excluded from the analysis.
To identify latent neighborhood characteristics, we subjected the 22 neighborhood characteristics to principal-components analysis by using varimax rotation. To identify the ideal-factor solution, we included components that met the following three criteria: they satisfied Cattell’s (20) scree test, they had eigenvalues greater than 1 (the Kaiser [21] criterion), and they were conceptually comprehensible and interpretable.
After we conducted the principal-components analysis, scores for the retained components were computed for each block group. All records of persons with serious mental illness and of the comparison group were then matched with the component scores for their corresponding block groups, such that all records associated with a particular block group were assigned the same scores.
Independent-sample t tests were used to compare the mean values of each of the principal components of persons with serious mental illness and of the general population comparison points. Ordinary least-squares (OLS) regression was used to assess the strength of the association between neighborhood concentration of persons with serious mental illness and the complete set of principal components after analyses controlled for the proportions of residents in the block group who were ages 18–64, nonwhite, and male. Because neighboring block groups are likely to have similar characteristics, the Moran’s I statistic was used to assess whether there was significant spatial autocorrelation in the OLS residuals. A spatial lag regression model using the Queen weight matrix was then estimated to adjust for spatial dependencies. The results of this model and the OLS model were compared to check for any substantive differences when accounting for potential bias introduced by the presence of spatial autocorrelation. All spatial statistical tests were carried out with the Geoda software (22).
Results
Neighborhood characteristics
Table 1 presents information about each variable, including data source, unit of measurement, and means and standard deviations for all block groups in Philadelphia. Table 2 provides the results of the principal-components analysis. By using the criteria established for identifying the ideal-factor solution, a six-component solution explaining 62% of the variance was identified. [A figure illustrating the scree plot of the eigenvalues for all 22 factors is available online as a data supplement to this article.]
| Variable | Unit of measurement per block group | Sourceb | Mean | SD |
|---|---|---|---|---|
| Crime | ||||
| Aggravated assaults | Rate per 1,000 persons | PPD | 9.43 | 21.47 |
| Robberies | Rate per 1,000 persons | PPD | 10.51 | 29.02 |
| Burglaries | Rate per 1,000 persons | PPD | 10.26 | 20.52 |
| Thefts | Rate per 1,000 persons | PPD | 55.95 | 302.01 |
| Car thefts | Number reported | PPD | 8.67 | 7 |
| Graffiti incidents | Number reported | PPD | .42 | 1.11 |
| Vandalism and criminal mischief incidents | Number reported | PPD | 12.17 | 7.89 |
| Narcotics arrests | Number reported | PPD | 6.33 | 19.73 |
| Arsons | Number reported | PPD | .41 | 1.05 |
| Physical and structural | ||||
| Vacant buildings | Proportion of total buildings | L and I | 5.52 | 8.4 |
| Demolished properties | Proportion of total properties | L and I | 24.24 | 93.89 |
| Gas service shutoff | Proportion of total properties | PGW | 8.52 | 10.28 |
| Vacant properties | Proportion of total properties | L and I | 9.11 | 13.13 |
| Economic and housing market | ||||
| Lien sales for delinquent property taxes | Proportion of total properties | PRD | 4.69 | 6.36 |
| Median monthly rent | Amount in dollars | Census 2000 | 547.02 | 199.15 |
| Median home value | Amount in dollars | Census 2000 | 64,689.74 | 63,042.03 |
| Median household income | Amount in dollars | Census 2000 | 31,047.19 | 16,651.11 |
| Social | ||||
| Renter-occupied households | Proportion of total households | Census 2000 | 39.93 | 22.03 |
| Recently moved households (<1 year’s residence) | Proportion of total households | Census 2000 | 15.82 | 12.61 |
| One-person households | Proportion of total households | Census 2000 | 31.22 | 15.22 |
| Commercial properties | Proportion of total properties | BRT | 5.81 | 9.1 |
| Industrial properties | Proportion of total properties | BRT | 1.32 | 4.08 |
a
Neighborhoods were defined as block groups, clusters of several street blocks that are the smallest geographic unit for which the U.S. Census Bureau provides aggregated data.
b
PPD, Philadelphia Police Department; L and I, Philadelphia Department of Licenses and Inspection; PGW, Philadelphia Gas Works; PRD, Philadelphia Revenue Department; BRT, Board of Revision of Taxes
| Variable | Final communalities | Serious crime | Delinquent crime | Drug-related activity | Physical and structural inadequacy | Social instability | Social isolation |
|---|---|---|---|---|---|---|---|
| Aggravated assaults | .62 | .75b | –.02 | .05 | .21 | .07 | .02 |
| Robberies | .87 | .93b | .00 | .03 | .06 | .06 | .03 |
| Burglaries | .71 | .72b | .03 | –.01 | .03 | .00 | .43 |
| Thefts | .81 | .89b | .06 | –.01 | –.07 | .01 | .12 |
| Car thefts | .56 | .05 | .66b | .23 | –.21 | .15 | –.03 |
| Graffiti incidents | .37 | .00 | .59b | –.11 | –.04 | –.02 | .10 |
| Vandalism and criminal mischief incidents | .74 | .02 | .84b | .15 | –.02 | .12 | –.05 |
| Narcotics arrests | .57 | .07 | .04 | .70b | .23 | .01 | –.13 |
| Arsons | .59 | –.02 | .13 | .73b | .06 | –.08 | .15 |
| Vacant buildings | .41 | .03 | –.15 | .10 | .61b | .06 | .00 |
| Demolished properties | .23 | .03 | –.19 | .13 | .39 | .08 | –.14 |
| Gas service shutoff | .61 | .05 | –.22 | .17 | .68b | .23 | .09 |
| Vacant properties | .63 | .14 | –.34 | .10 | .64b | .21 | .19 |
| Lien sales for delinquent property taxes | .46 | .01 | –.18 | .32 | .56b | –.02 | –.03 |
| Median monthly rent | .39 | –.14 | –.03 | .19 | –.55b | .26 | –.08 |
| Median home value | .43 | –.03 | –.24 | .01 | –.68b | .38 | –.03 |
| Median household income | .70 | .02 | –.25 | .02 | –.79b | –.09 | –.06 |
| Renter-occupied households | .78 | .06 | .03 | .02 | .37 | .78b | .16 |
| Recently moved households (<1 year’s residence) | .59 | .04 | .09 | .11 | –.06 | .74b | .11 |
| One-person households | .61 | .05 | .11 | –.31 | –.03 | .70b | –.11 |
| Commercial properties | .77 | .31 | .07 | –.01 | .05 | .37 | .73b |
| Industrial properties | .85 | .14 | .01 | .03 | .07 | –.05 | .91b |
a
The analysis explained 62% of the variance among neighborhoods.
b
Factor loadings were greater than .5.
The first and second components were labeled serious crime and delinquent crime, respectively. Both arson and narcotics arrests loaded on the third component, drug-related activity. Although these two indicators may appear unrelated, there is evidence of a higher incidence of arson in areas with elevated drug activity (23,24). The fourth component, physical and structural inadequacy, described the physical and socioeconomic environment of neighborhoods. The fifth component—which identified block groups with higher proportions of renter-occupied households, households that had recently moved, and one-person households—was interpreted as an indicator of neighborhood social instability. The final component, labeled social isolation, described neighborhoods that were more commercial and industrial and less residential, thereby potentially providing less opportunity for social interaction than might occur in more residential areas.
Comparison of neighborhood characteristics
Table 3 presents a comparison of neighborhoods of persons with serious mental illness and of the comparison group. The neighborhoods in which persons with serious mental illness resided were found to have significantly higher levels of physical and structural inadequacy. Similarly, persons with serious mental illness lived in neighborhoods with more serious crime, higher amounts of delinquent crime, and far more drug-related activity than the neighborhoods of the comparison group. Persons with serious mental illness lived in neighborhoods with higher amounts of social instability and social isolation than the neighborhoods of the comparison group, but the difference between the neighborhoods was not statistically significant.
| Persons with serious mental illness | Comparison group | |||||
|---|---|---|---|---|---|---|
| Characteristic | M | SD | M | SD | tb | p |
| Serious crime | –.07 | .32 | –.13 | .32 | –15.44 | <.001 |
| Delinquent crime | .30 | 1.04 | .25 | 1.04 | –4.40 | <.001 |
| Drug-related activity | .50 | 2.02 | .06 | 1.10 | –23.42 | <.001 |
| Physical and structural inadequacy | .34 | .89 | –.14 | .88 | –48.34 | <.001 |
| Social instability | .02 | .86 | .00 | 1.01 | –.69 | .490 |
| Social isolation | –.10 | .80 | –.11 | .94 | –1.43 | .153 |
a
The data reflect mean values for each characteristic among 15,244 persons with serious mental illness and a comparison group representing 15,238 randomly generated addresses.
b
df=30,480 for all analyses
On closer inspection, the biggest disparity between the neighborhoods of the two groups was in their physical and structural characteristics. Average component scores for both serious crime and drug-related activity were also markedly higher in the neighborhoods of persons with serious mental illness than in the neighborhoods of the comparison group. By contrast, the difference was not nearly as large for the delinquent-crime component.
Neighborhood and residents with serious mental illness
Table 4 presents the results of the regression analysis assessing the association between neighborhood concentration of persons with serious mental illness and neighborhood characteristics. In the OLS model, Moran’s I (.25) was significantly greater than zero, which indicated the presence of spatial autocorrelation. The Moran’s I value (–.04) for the spatial lag model was slightly negative and suggested less spatial autocorrelation, although the finding was still statistically significant.
| Ordinary least-squares model | Spatial lag model | |||||||
|---|---|---|---|---|---|---|---|---|
| Variable | b | SE | t | p | b | SE | t | p |
| Intercept | .028 | .005 | 5.67 | <.001 | .019 | .004 | 4.24 | <.001 |
| Serious crime | .003 | <.001 | 5.81 | <.001 | .002 | <.001 | –3.48 | <.001 |
| Delinquent crime | <–.001 | <.001 | −.89 | .376 | <–.001 | <.001 | −.64 | .531 |
| Drug-related activity | .005 | <.001 | 11.72 | <.001 | .002 | <.001 | 5.45 | <.001 |
| Physical and structural inadequacy | .009 | .001 | 15.57 | <.001 | .004 | .001 | 8.00 | <.001 |
| Social instability | .005 | .001 | 9.32 | <.001 | .004 | <.001 | 8.87 | <.001 |
| Social isolation | .003 | <.001 | 5.46 | <.001 | .002 | <.001 | 3.87 | <.001 |
| Ages 18–64 (%) | –.077 | .006 | –12.37 | <.001 | –.060 | .006 | –10.90 | <.001 |
| Nonwhite (%) | .002 | .002 | 1.20 | .232 | .001 | .001 | 1.05 | .293 |
| Male (%) | .080 | .011 | 7.53 | <.001 | .009 | .009 | 6.09 | <.001 |
| Moran’s I | .245 | .001 | –.041 | .005 | ||||
| Lambda (spatial error term) | .513 | .026 | ||||||
| R2 | .369 | .512 | ||||||
| Fa | 115.77 | <.001 | 128.41 | <.001 | ||||
| Akaike information criterion | –9,208.59 | –9,570.59 | ||||||
a
df=9 and 1,775
There were no other substantive differences between the OLS and the spatial lag models in terms of the relationship between the neighborhood measures and the concentration of persons with serious mental illness. Consistent with the comparison of neighborhoods of persons with serious mental illness and of the general population, both models found that serious crime, drug-related activity, and physical and structural inadequacy were positively associated with neighborhood concentration of persons with serious mental illness, even after the analyses controlled for additional demographic characteristics of the neighborhoods. However, whereas no significant differences were found in social instability and social isolation between neighborhoods of persons with serious mental illness and neighborhoods of the comparison group, the regression analysis showed that higher levels of these indicators were associated with higher neighborhood concentration of persons with serious mental illness.
Discussion
Drawing on the ICF’s emphasis on assessing the impact of environmental factors on functioning and disability, this study found that on the whole, adults with serious mental illness lived in neighborhoods that were more disadvantaged than neighborhoods of a comparison group representing the general population, a finding that is consistent with prior research (25). Specifically, neighborhoods in which adults with serious mental illness resided were found to have higher levels of physical and structural inadequacy, more drug-related activity, and higher levels of crime. Moreover, the regression models indicated that higher levels of physical and structural inadequacy, crime, drug- related activity, social instability, and social isolation were associated with higher neighborhood concentration of persons with serious mental illness. Notably, the associations observed in the regression models between less desirable neighborhood conditions and neighborhood concentration of persons with serious mental illness did not appear to be solely the result of the concentration of persons with serious mental illness in poor or economically disadvantaged neighborhoods. Apart from delinquent crime, all other components were found to have a significant association with neighborhood concentration of serious mental illness, even after controlling for the physical and structural inadequacy component, which included both median income level and median home value.
The divergent results related to social instability and social isolation found by the regression models and the comparison of neighborhoods with or without high concentrations of persons with mental illness bear mentioning. These components retained a significant association with neighborhood concentration of persons with serious mental illness even after the estimation of an additional set of regression models that included a measure of neighborhood population density as a possible confounder. Thus although the study findings provide strong support for the difference in the physical and crime environments of neighborhoods with or without high concentrations of persons with serious mental illness, additional research should examine the social environments of these neighborhoods more closely.
Although this study was unable to directly test the relationship between neighborhood environmental context and the functioning of persons with serious mental illness, its findings are nonetheless concerning in light of emerging evidence that worse neighborhood characteristics impede the community integration of persons with serious mental illness (11). To date, the still nascent body of research in this area has yet to ascertain the extent to which a worse physical or crime environment may serve as an impediment to community integration of persons with serious mental illness. Thus although it is not possible to draw direct implications from the current study about the impact of neighborhood factors on community integration, the findings presented here provide a broader foundation for future research to identify barriers and facilitators to community participation of persons with serious mental illness.
The need for research on the relationship between neighborhood characteristics and community integration is especially important, given that housing interventions for persons with serious mental illness continue their shift away from congregate housing and toward scattered-site independent housing units in the community (26). Despite evidence that persons with serious mental illness who live in independent housing have better community integration outcomes (13), no study to date has examined the interaction between housing type, neighborhood characteristics, and community integration. Additional research in this area could help refine housing approaches to promote better community integration of persons with serious mental illness.
Future studies on the relationship between environmental context and the functioning and participation of persons with serious mental illness could directly test the same set of factors considered in this study. Doing so would result in greater comparability across studies and allow for the identification of concrete factors that could serve as intervention points.
One limitation of this study was its use of randomly distributed geographic points, rather than actual persons, as a comparison group. Although person-level data were available for the serious mental illness group, without knowledge of the demographic characteristics of the comparison group it was not possible to control for person-level factors that might mediate associations between diagnosis of serious mental illness and neighborhood indicators. Moreover, this study did not assess differences between persons with schizophrenia and those with affective disorders. Given that environmental factors may have different influences on these two groups, such a comparison remains an important goal for future research. As an additional limitation, the data used in this study are now more than a decade old. Although we have no reason to believe that the use of more recent data would yield substantively different results, our results should nonetheless be interpreted cautiously in light of the age of the data. Finally, this study did not include a comparison group of adult Medicaid recipients without a serious mental illness diagnosis. Although such a group may have formed an appropriate and important comparison group, the intent of this study was to compare persons with serious mental illness and the normative, general population. We felt that making this comparison was especially important in the context of a community integration framework and the preference for independent, scattered-site housing, insofar as such a comparison might point to opportunities and challenges in the current environment of services for persons with serious mental illness.
Conclusions
This study found differences in physical and crime characteristics among the neighborhoods of a large group of persons with serious mental illness and a comparison group representing the general population. These factors, as well as neighborhood social characteristics, were also associated with neighborhood concentration of persons with serious mental illness. These findings establish the importance of further exploration of the degree to which environmental factors may act as either barriers or facilitators to the functioning and participation of persons with serious mental illness. This is especially true of physical characteristics, for which the largest effects were observed. Studying these factors in the broader context of the ICF could do much to help identify new areas of intervention intended to promote the functioning and integration of persons with serious mental illness.
Acknowledgments and disclosures
The contents of this article were developed under a grant to Temple University from the United States Department of Education (National Institute on Disability and Rehabilitation Research [NIDRR] grant H133B100037, Dr. Salzer, principal investigator). The authors thank the Philadelphia Department of Behavioral Health and Intellectual Disability Services for its support and for data that were integral to this study as well as the Center for Mental Health Policy and Services Research at the University of Pennsylvania for its assistance with this study. The contents of this article do not necessarily represent the policy of the Department of Education or reflect the endorsement of the federal government. NIDRR had no direct involvement in any aspect of conducting, reporting, or disseminating this research.
The authors report no competing interests.
Supplementary Material
Supplemental Material (782_ds001.pdf)
- View/Download
- 22.08 KB
References
1.
International Classification of Functioning, Disability and Handicap. Geneva, World Health Organization, 2001
2.
Towards a Common Language for Functioning, Disability and Health. Geneva, World Health Organization, 2002
3.
Field MJ, Jette A (eds): The Future of Disability in America. Washington, DC, National Academies Press, 2007
4.
Álvarezz AS: The application of the International Classification of Functioning, Disability, and Health in psychiatry: possible reasons for the lack of implementation. American Journal of Physical Medicine and Rehabilitation 91(suppl 1):S69–S73, 2012
5.
Whiteneck G, Dijkers MP: Difficult to measure constructs: conceptual and methodological issues concerning participation and environmental factors. Archives of Physical Medicine and Rehabilitation 90(suppl):S22–S35, 2009
6.
Kennedy C: Functioning and disability associated with mental disorders: the evolution since ICIDH. Disability and Rehabilitation 25:611–619, 2003
7.
Baumgartner JN, Susser E: Social integration in global mental health: what is it and how can it be measured? Epidemiology and Psychiatric Sciences 1:1–9, 2012
8.
Won YL, Solomon PL: Community integration of persons with psychiatric disabilities in supportive independent housing: a conceptual model and methodological considerations. Mental Health Services Research 4:13–28, 2002
9.
Salzer MS (ed): Psychiatric Rehabilitation Skills in Practice: A CPRP Preparation and Skills Workbook. Columbia, Md, US Psychiatric Rehabilitation Association, 2006
10.
Segal SP, Baumohl J, Moyles EW: Neighborhood types and community reaction to the mentally ill: a paradox of intensity. Journal of Health and Social Behavior 21:345–359, 1980
11.
Yanos PT, Stefanic A, Tsemberis S: Psychological community integration among people with psychiatric disabilities and nondisabled community members. Journal of Community Psychology 39:390–401, 2011
12.
Lögdberg B, Nilsson LL, Levander MT, et al.: Schizophrenia, neighbourhood, and crime. Acta Psychiatrica Scandinavica 110:92–97, 2004
13.
Yanos P, Felton B, Tsemberis S, et al.: Exploring the role of housing type, neighborhood characteristics, and lifestyle factors in the community integration of formerly homeless persons diagnosed. Journal of Mental Health 16:703–717, 2007
14.
Blank MB, Mandell DS, Aiken L, et al.: Co-occurrence of HIV and serious mental illness among Medicaid recipients. Psychiatric Services 53:868–873, 2002
15.
Metraux S, Brusilovskiy E, Prvu-Bettger JA, et al.: Geographic access to and availability of community resources for persons diagnosed with severe mental illness in Philadelphia, USA. Health and Place 18:621–629, 2012
16.
Walkup J, Crystal S, Sambamoorthi U: Schizophrenia and major affective disorder among Medicaid recipients with HIV/AIDS in New Jersey. American Journal of Public Health 89:1101–1103, 1999
17.
Lurie N, Popkin M, Dysken M, et al.: Accuracy of diagnoses of schizophrenia in Medicaid claims. Hospital and Community Psychiatry 43:69–71, 1992
18.
Gross KS, McDermott PA: Use of city-archival data to inform dimensional structure of neighborhoods. Journal of Urban Health 86:161–182, 2009
19.
Wong Y-L, Stanhope V: Conceptualizing community: a comparison of neighborhood characteristics of supportive housing for persons with psychiatric and developmental disabilities. Social Science and Medicine 68:1376–1387, 2009
20.
Cattell RB: The scree test for the number of factors. Multivariate Behavioral Research 1:245–276, 1966
21.
Kaiser HF: The application of electronic computers to factor analysis. Educational and Psychological Measurement 20:141–151, 1960
22.
Anselin L: Geoda 0.9.5-I Release Notes. Urbana, Ill, Center for Spatially Integrated Social Science, 2004
23.
Arson in the United States. Washington, DC, Federal Emergency Management Agency, 1997
24.
Towberman TJ, Towberman DB: Drug-Related Fires in the United States: Final Report on the Relationship Between Drug Activity and Arson in Selected US Cities. Washington, DC, Bureau of Alcohol, Tobacco and Firearms, 1994
25.
Newman SJ: The housing and neighborhood conditions of persons with severe mental illness. Hospital and Community Psychiatry 45:338–343, 1994
26.
Yanos PT: Beyond “Landscapes of Despair”: the need for new research on the urban environment, sprawl, and the community integration of persons with severe mental illness. Health and Place 13:672–676, 2007
Information & Authors
Information
Published In
Cover: Summer Afternoon, by William Dean Fausett, 1943. Oil and tempera on masonite, 30 × 38 inches. Collection of the San Antonio Art League and Museum, San Antonio, Texas.
History
Published in print: August 2013
Published online: 15 October 2014
Authors
Metrics & Citations
Metrics
Citations
Export Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
For more information or tips please see 'Downloading to a citation manager' in the Help menu.
View Options
View options
PDF/EPUB
View PDF/EPUBGet Access
Login options
Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.
Personal login Institutional Login Open Athens loginNot a subscriber?
PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.
Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).