Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities

Purpose We know little about how community structures influence the risk of common mental illnesses. This study presents a new way to establish links between depression and social fragmentation, thereby identifying pathways to better target mental health services and prevention programs to the right...
Ausführliche Beschreibung

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Autor*in:	Bagheri, Nasser [verfasserIn] Batterham, Philip J. Salvador-Carulla, Luis Chen, Yingxi Page, Andrew Calear, Alison L. Congdon, Peter

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Social fragmentation index Depression Mental disorders Geographic information systems (GIS) Primary care

Anmerkung:	© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Übergeordnetes Werk:	Enthalten in: Social psychiatry and psychiatric epidemiology - Darmstadt : Steinkopff, 1966, 54(2019), 10 vom: 15. Apr., Seite 1189-1198
Übergeordnetes Werk:	volume:54 ; year:2019 ; number:10 ; day:15 ; month:04 ; pages:1189-1198

Links:	Volltext

DOI / URN:	10.1007/s00127-019-01712-y

Katalog-ID:	SPR001061976

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520			\|a Purpose We know little about how community structures influence the risk of common mental illnesses. This study presents a new way to establish links between depression and social fragmentation, thereby identifying pathways to better target mental health services and prevention programs to the right people in the right place. Method A principal components analysis (PCA) was conducted to develop the proposed Australian neighborhood social fragmentation index (ANSFI). General practice clinical data were used to identify cases of diagnosed depression. The association between ANSFI and depression was explored using multilevel logistic regression. Spatial hot spots (clusters) of depression prevalence and social fragmentation at the statistical area level 1 (SA1) were examined. Results Two components of social fragmentation emerged, reflecting fragmentation related to family structure and mobility. Individuals treated for depression in primary care were more likely to live in neighborhoods with lower socioeconomic status and with higher social fragmentation related to family structure. A 1-SD increase in social fragmentation was associated with a 16% higher depression prevalence (95% CI 11%, 20%). However, the association attenuated with adjustment for neighborhood socio-economic status. Considerable spatial variation in social fragmentation and depression patterns across communities was observed. Conclusions Developing a social fragmentation index for the first time in Australia at a small area level generates a new line of knowledge on the impact of community structures on health risks. Findings may extend our understanding of the mechanisms that drive geographical variation in the incidence of common mental disorders and mental health care.
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allfields	10.1007/s00127-019-01712-y doi (DE-627)SPR001061976 (SPR)s00127-019-01712-y-e DE-627 ger DE-627 rakwb eng Bagheri, Nasser verfasserin aut Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Purpose We know little about how community structures influence the risk of common mental illnesses. This study presents a new way to establish links between depression and social fragmentation, thereby identifying pathways to better target mental health services and prevention programs to the right people in the right place. Method A principal components analysis (PCA) was conducted to develop the proposed Australian neighborhood social fragmentation index (ANSFI). General practice clinical data were used to identify cases of diagnosed depression. The association between ANSFI and depression was explored using multilevel logistic regression. Spatial hot spots (clusters) of depression prevalence and social fragmentation at the statistical area level 1 (SA1) were examined. Results Two components of social fragmentation emerged, reflecting fragmentation related to family structure and mobility. Individuals treated for depression in primary care were more likely to live in neighborhoods with lower socioeconomic status and with higher social fragmentation related to family structure. A 1-SD increase in social fragmentation was associated with a 16% higher depression prevalence (95% CI 11%, 20%). However, the association attenuated with adjustment for neighborhood socio-economic status. Considerable spatial variation in social fragmentation and depression patterns across communities was observed. Conclusions Developing a social fragmentation index for the first time in Australia at a small area level generates a new line of knowledge on the impact of community structures on health risks. Findings may extend our understanding of the mechanisms that drive geographical variation in the incidence of common mental disorders and mental health care. Social fragmentation index (dpeaa)DE-He213 Depression (dpeaa)DE-He213 Mental disorders (dpeaa)DE-He213 Geographic information systems (GIS) (dpeaa)DE-He213 Primary care (dpeaa)DE-He213 Batterham, Philip J. aut Salvador-Carulla, Luis aut Chen, Yingxi aut Page, Andrew aut Calear, Alison L. aut Congdon, Peter aut Enthalten in Social psychiatry and psychiatric epidemiology Darmstadt : Steinkopff, 1966 54(2019), 10 vom: 15. Apr., Seite 1189-1198 (DE-627)254909523 (DE-600)1463160-X 1433-9285 nnns volume:54 year:2019 number:10 day:15 month:04 pages:1189-1198 https://dx.doi.org/10.1007/s00127-019-01712-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2019 10 15 04 1189-1198
spelling	10.1007/s00127-019-01712-y doi (DE-627)SPR001061976 (SPR)s00127-019-01712-y-e DE-627 ger DE-627 rakwb eng Bagheri, Nasser verfasserin aut Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Purpose We know little about how community structures influence the risk of common mental illnesses. This study presents a new way to establish links between depression and social fragmentation, thereby identifying pathways to better target mental health services and prevention programs to the right people in the right place. Method A principal components analysis (PCA) was conducted to develop the proposed Australian neighborhood social fragmentation index (ANSFI). General practice clinical data were used to identify cases of diagnosed depression. The association between ANSFI and depression was explored using multilevel logistic regression. Spatial hot spots (clusters) of depression prevalence and social fragmentation at the statistical area level 1 (SA1) were examined. Results Two components of social fragmentation emerged, reflecting fragmentation related to family structure and mobility. Individuals treated for depression in primary care were more likely to live in neighborhoods with lower socioeconomic status and with higher social fragmentation related to family structure. A 1-SD increase in social fragmentation was associated with a 16% higher depression prevalence (95% CI 11%, 20%). However, the association attenuated with adjustment for neighborhood socio-economic status. Considerable spatial variation in social fragmentation and depression patterns across communities was observed. Conclusions Developing a social fragmentation index for the first time in Australia at a small area level generates a new line of knowledge on the impact of community structures on health risks. Findings may extend our understanding of the mechanisms that drive geographical variation in the incidence of common mental disorders and mental health care. Social fragmentation index (dpeaa)DE-He213 Depression (dpeaa)DE-He213 Mental disorders (dpeaa)DE-He213 Geographic information systems (GIS) (dpeaa)DE-He213 Primary care (dpeaa)DE-He213 Batterham, Philip J. aut Salvador-Carulla, Luis aut Chen, Yingxi aut Page, Andrew aut Calear, Alison L. aut Congdon, Peter aut Enthalten in Social psychiatry and psychiatric epidemiology Darmstadt : Steinkopff, 1966 54(2019), 10 vom: 15. Apr., Seite 1189-1198 (DE-627)254909523 (DE-600)1463160-X 1433-9285 nnns volume:54 year:2019 number:10 day:15 month:04 pages:1189-1198 https://dx.doi.org/10.1007/s00127-019-01712-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2019 10 15 04 1189-1198
allfields_unstemmed	10.1007/s00127-019-01712-y doi (DE-627)SPR001061976 (SPR)s00127-019-01712-y-e DE-627 ger DE-627 rakwb eng Bagheri, Nasser verfasserin aut Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Purpose We know little about how community structures influence the risk of common mental illnesses. This study presents a new way to establish links between depression and social fragmentation, thereby identifying pathways to better target mental health services and prevention programs to the right people in the right place. Method A principal components analysis (PCA) was conducted to develop the proposed Australian neighborhood social fragmentation index (ANSFI). General practice clinical data were used to identify cases of diagnosed depression. The association between ANSFI and depression was explored using multilevel logistic regression. Spatial hot spots (clusters) of depression prevalence and social fragmentation at the statistical area level 1 (SA1) were examined. Results Two components of social fragmentation emerged, reflecting fragmentation related to family structure and mobility. Individuals treated for depression in primary care were more likely to live in neighborhoods with lower socioeconomic status and with higher social fragmentation related to family structure. A 1-SD increase in social fragmentation was associated with a 16% higher depression prevalence (95% CI 11%, 20%). However, the association attenuated with adjustment for neighborhood socio-economic status. Considerable spatial variation in social fragmentation and depression patterns across communities was observed. Conclusions Developing a social fragmentation index for the first time in Australia at a small area level generates a new line of knowledge on the impact of community structures on health risks. Findings may extend our understanding of the mechanisms that drive geographical variation in the incidence of common mental disorders and mental health care. Social fragmentation index (dpeaa)DE-He213 Depression (dpeaa)DE-He213 Mental disorders (dpeaa)DE-He213 Geographic information systems (GIS) (dpeaa)DE-He213 Primary care (dpeaa)DE-He213 Batterham, Philip J. aut Salvador-Carulla, Luis aut Chen, Yingxi aut Page, Andrew aut Calear, Alison L. aut Congdon, Peter aut Enthalten in Social psychiatry and psychiatric epidemiology Darmstadt : Steinkopff, 1966 54(2019), 10 vom: 15. Apr., Seite 1189-1198 (DE-627)254909523 (DE-600)1463160-X 1433-9285 nnns volume:54 year:2019 number:10 day:15 month:04 pages:1189-1198 https://dx.doi.org/10.1007/s00127-019-01712-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2019 10 15 04 1189-1198
allfieldsGer	10.1007/s00127-019-01712-y doi (DE-627)SPR001061976 (SPR)s00127-019-01712-y-e DE-627 ger DE-627 rakwb eng Bagheri, Nasser verfasserin aut Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Purpose We know little about how community structures influence the risk of common mental illnesses. This study presents a new way to establish links between depression and social fragmentation, thereby identifying pathways to better target mental health services and prevention programs to the right people in the right place. Method A principal components analysis (PCA) was conducted to develop the proposed Australian neighborhood social fragmentation index (ANSFI). General practice clinical data were used to identify cases of diagnosed depression. The association between ANSFI and depression was explored using multilevel logistic regression. Spatial hot spots (clusters) of depression prevalence and social fragmentation at the statistical area level 1 (SA1) were examined. Results Two components of social fragmentation emerged, reflecting fragmentation related to family structure and mobility. Individuals treated for depression in primary care were more likely to live in neighborhoods with lower socioeconomic status and with higher social fragmentation related to family structure. A 1-SD increase in social fragmentation was associated with a 16% higher depression prevalence (95% CI 11%, 20%). However, the association attenuated with adjustment for neighborhood socio-economic status. Considerable spatial variation in social fragmentation and depression patterns across communities was observed. Conclusions Developing a social fragmentation index for the first time in Australia at a small area level generates a new line of knowledge on the impact of community structures on health risks. Findings may extend our understanding of the mechanisms that drive geographical variation in the incidence of common mental disorders and mental health care. Social fragmentation index (dpeaa)DE-He213 Depression (dpeaa)DE-He213 Mental disorders (dpeaa)DE-He213 Geographic information systems (GIS) (dpeaa)DE-He213 Primary care (dpeaa)DE-He213 Batterham, Philip J. aut Salvador-Carulla, Luis aut Chen, Yingxi aut Page, Andrew aut Calear, Alison L. aut Congdon, Peter aut Enthalten in Social psychiatry and psychiatric epidemiology Darmstadt : Steinkopff, 1966 54(2019), 10 vom: 15. Apr., Seite 1189-1198 (DE-627)254909523 (DE-600)1463160-X 1433-9285 nnns volume:54 year:2019 number:10 day:15 month:04 pages:1189-1198 https://dx.doi.org/10.1007/s00127-019-01712-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2019 10 15 04 1189-1198
allfieldsSound	10.1007/s00127-019-01712-y doi (DE-627)SPR001061976 (SPR)s00127-019-01712-y-e DE-627 ger DE-627 rakwb eng Bagheri, Nasser verfasserin aut Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Purpose We know little about how community structures influence the risk of common mental illnesses. This study presents a new way to establish links between depression and social fragmentation, thereby identifying pathways to better target mental health services and prevention programs to the right people in the right place. Method A principal components analysis (PCA) was conducted to develop the proposed Australian neighborhood social fragmentation index (ANSFI). General practice clinical data were used to identify cases of diagnosed depression. The association between ANSFI and depression was explored using multilevel logistic regression. Spatial hot spots (clusters) of depression prevalence and social fragmentation at the statistical area level 1 (SA1) were examined. Results Two components of social fragmentation emerged, reflecting fragmentation related to family structure and mobility. Individuals treated for depression in primary care were more likely to live in neighborhoods with lower socioeconomic status and with higher social fragmentation related to family structure. A 1-SD increase in social fragmentation was associated with a 16% higher depression prevalence (95% CI 11%, 20%). However, the association attenuated with adjustment for neighborhood socio-economic status. Considerable spatial variation in social fragmentation and depression patterns across communities was observed. Conclusions Developing a social fragmentation index for the first time in Australia at a small area level generates a new line of knowledge on the impact of community structures on health risks. Findings may extend our understanding of the mechanisms that drive geographical variation in the incidence of common mental disorders and mental health care. Social fragmentation index (dpeaa)DE-He213 Depression (dpeaa)DE-He213 Mental disorders (dpeaa)DE-He213 Geographic information systems (GIS) (dpeaa)DE-He213 Primary care (dpeaa)DE-He213 Batterham, Philip J. aut Salvador-Carulla, Luis aut Chen, Yingxi aut Page, Andrew aut Calear, Alison L. aut Congdon, Peter aut Enthalten in Social psychiatry and psychiatric epidemiology Darmstadt : Steinkopff, 1966 54(2019), 10 vom: 15. Apr., Seite 1189-1198 (DE-627)254909523 (DE-600)1463160-X 1433-9285 nnns volume:54 year:2019 number:10 day:15 month:04 pages:1189-1198 https://dx.doi.org/10.1007/s00127-019-01712-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2019 10 15 04 1189-1198
language	English
source	Enthalten in Social psychiatry and psychiatric epidemiology 54(2019), 10 vom: 15. Apr., Seite 1189-1198 volume:54 year:2019 number:10 day:15 month:04 pages:1189-1198
sourceStr	Enthalten in Social psychiatry and psychiatric epidemiology 54(2019), 10 vom: 15. Apr., Seite 1189-1198 volume:54 year:2019 number:10 day:15 month:04 pages:1189-1198
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Social fragmentation index Depression Mental disorders Geographic information systems (GIS) Primary care
isfreeaccess_bool	false
container_title	Social psychiatry and psychiatric epidemiology
authorswithroles_txt_mv	Bagheri, Nasser @@aut@@ Batterham, Philip J. @@aut@@ Salvador-Carulla, Luis @@aut@@ Chen, Yingxi @@aut@@ Page, Andrew @@aut@@ Calear, Alison L. @@aut@@ Congdon, Peter @@aut@@
publishDateDaySort_date	2019-04-15T00:00:00Z
hierarchy_top_id	254909523
id	SPR001061976
language_de	englisch
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A 1-SD increase in social fragmentation was associated with a 16% higher depression prevalence (95% CI 11%, 20%). However, the association attenuated with adjustment for neighborhood socio-economic status. Considerable spatial variation in social fragmentation and depression patterns across communities was observed. Conclusions Developing a social fragmentation index for the first time in Australia at a small area level generates a new line of knowledge on the impact of community structures on health risks. 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author	Bagheri, Nasser
spellingShingle	Bagheri, Nasser misc Social fragmentation index misc Depression misc Mental disorders misc Geographic information systems (GIS) misc Primary care Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities
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topic_title	Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities Social fragmentation index (dpeaa)DE-He213 Depression (dpeaa)DE-He213 Mental disorders (dpeaa)DE-He213 Geographic information systems (GIS) (dpeaa)DE-He213 Primary care (dpeaa)DE-He213
topic	misc Social fragmentation index misc Depression misc Mental disorders misc Geographic information systems (GIS) misc Primary care
topic_unstemmed	misc Social fragmentation index misc Depression misc Mental disorders misc Geographic information systems (GIS) misc Primary care
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title	Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities
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title_full	Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities
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journal	Social psychiatry and psychiatric epidemiology
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author_browse	Bagheri, Nasser Batterham, Philip J. Salvador-Carulla, Luis Chen, Yingxi Page, Andrew Calear, Alison L. Congdon, Peter
container_volume	54
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doi_str_mv	10.1007/s00127-019-01712-y
title_sort	development of the australian neighborhood social fragmentation index and its association with spatial variation in depression across communities
title_auth	Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities
abstract	Purpose We know little about how community structures influence the risk of common mental illnesses. This study presents a new way to establish links between depression and social fragmentation, thereby identifying pathways to better target mental health services and prevention programs to the right people in the right place. Method A principal components analysis (PCA) was conducted to develop the proposed Australian neighborhood social fragmentation index (ANSFI). General practice clinical data were used to identify cases of diagnosed depression. The association between ANSFI and depression was explored using multilevel logistic regression. Spatial hot spots (clusters) of depression prevalence and social fragmentation at the statistical area level 1 (SA1) were examined. Results Two components of social fragmentation emerged, reflecting fragmentation related to family structure and mobility. Individuals treated for depression in primary care were more likely to live in neighborhoods with lower socioeconomic status and with higher social fragmentation related to family structure. A 1-SD increase in social fragmentation was associated with a 16% higher depression prevalence (95% CI 11%, 20%). However, the association attenuated with adjustment for neighborhood socio-economic status. Considerable spatial variation in social fragmentation and depression patterns across communities was observed. Conclusions Developing a social fragmentation index for the first time in Australia at a small area level generates a new line of knowledge on the impact of community structures on health risks. Findings may extend our understanding of the mechanisms that drive geographical variation in the incidence of common mental disorders and mental health care. © Springer-Verlag GmbH Germany, part of Springer Nature 2019
abstractGer	Purpose We know little about how community structures influence the risk of common mental illnesses. This study presents a new way to establish links between depression and social fragmentation, thereby identifying pathways to better target mental health services and prevention programs to the right people in the right place. Method A principal components analysis (PCA) was conducted to develop the proposed Australian neighborhood social fragmentation index (ANSFI). General practice clinical data were used to identify cases of diagnosed depression. The association between ANSFI and depression was explored using multilevel logistic regression. Spatial hot spots (clusters) of depression prevalence and social fragmentation at the statistical area level 1 (SA1) were examined. Results Two components of social fragmentation emerged, reflecting fragmentation related to family structure and mobility. Individuals treated for depression in primary care were more likely to live in neighborhoods with lower socioeconomic status and with higher social fragmentation related to family structure. A 1-SD increase in social fragmentation was associated with a 16% higher depression prevalence (95% CI 11%, 20%). However, the association attenuated with adjustment for neighborhood socio-economic status. Considerable spatial variation in social fragmentation and depression patterns across communities was observed. Conclusions Developing a social fragmentation index for the first time in Australia at a small area level generates a new line of knowledge on the impact of community structures on health risks. Findings may extend our understanding of the mechanisms that drive geographical variation in the incidence of common mental disorders and mental health care. © Springer-Verlag GmbH Germany, part of Springer Nature 2019
abstract_unstemmed	Purpose We know little about how community structures influence the risk of common mental illnesses. This study presents a new way to establish links between depression and social fragmentation, thereby identifying pathways to better target mental health services and prevention programs to the right people in the right place. Method A principal components analysis (PCA) was conducted to develop the proposed Australian neighborhood social fragmentation index (ANSFI). General practice clinical data were used to identify cases of diagnosed depression. The association between ANSFI and depression was explored using multilevel logistic regression. Spatial hot spots (clusters) of depression prevalence and social fragmentation at the statistical area level 1 (SA1) were examined. Results Two components of social fragmentation emerged, reflecting fragmentation related to family structure and mobility. Individuals treated for depression in primary care were more likely to live in neighborhoods with lower socioeconomic status and with higher social fragmentation related to family structure. A 1-SD increase in social fragmentation was associated with a 16% higher depression prevalence (95% CI 11%, 20%). However, the association attenuated with adjustment for neighborhood socio-economic status. Considerable spatial variation in social fragmentation and depression patterns across communities was observed. Conclusions Developing a social fragmentation index for the first time in Australia at a small area level generates a new line of knowledge on the impact of community structures on health risks. Findings may extend our understanding of the mechanisms that drive geographical variation in the incidence of common mental disorders and mental health care. © Springer-Verlag GmbH Germany, part of Springer Nature 2019
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title_short	Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities
url	https://dx.doi.org/10.1007/s00127-019-01712-y
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author2	Batterham, Philip J. Salvador-Carulla, Luis Chen, Yingxi Page, Andrew Calear, Alison L. Congdon, Peter
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This study presents a new way to establish links between depression and social fragmentation, thereby identifying pathways to better target mental health services and prevention programs to the right people in the right place. Method A principal components analysis (PCA) was conducted to develop the proposed Australian neighborhood social fragmentation index (ANSFI). General practice clinical data were used to identify cases of diagnosed depression. The association between ANSFI and depression was explored using multilevel logistic regression. Spatial hot spots (clusters) of depression prevalence and social fragmentation at the statistical area level 1 (SA1) were examined. Results Two components of social fragmentation emerged, reflecting fragmentation related to family structure and mobility. Individuals treated for depression in primary care were more likely to live in neighborhoods with lower socioeconomic status and with higher social fragmentation related to family structure. A 1-SD increase in social fragmentation was associated with a 16% higher depression prevalence (95% CI 11%, 20%). However, the association attenuated with adjustment for neighborhood socio-economic status. Considerable spatial variation in social fragmentation and depression patterns across communities was observed. Conclusions Developing a social fragmentation index for the first time in Australia at a small area level generates a new line of knowledge on the impact of community structures on health risks. 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Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities

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