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Spatial patterning of self-harm rates within urban areas.

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Title: Spatial patterning of self-harm rates within urban areas.
Authors: Polling, Catherine, Bakolis, Ioannis, Hotopf, Matthew, Hatch, Stephani L.
Source: Social Psychiatry & Psychiatric Epidemiology. Jan2019, Vol. 54 Issue 1, p69-79. 11p.
Subjects: Metropolitan areas, Social disorganization, Hospital admission & discharge, Population density, Ethnicity
Abstract: Purpose: Urban areas are usually found to have higher rates of self-harm, with deprivation the strongest predictor at area-level. We use a disease mapping approach to examine how self-harm is patterned within an urban area and its associations with deprivation, urbanness and ethnicity.Methods: Data from clinical records on individuals admitted for self-harm for 725 small areas in South East London were included. Bayesian hierarchical models explored the spatio-temporal patterns of self-harm admission rates and potential associations with proximity to city centre, population density, percentage greenspace and non-white ethnic-minority populations. All models were adjusted for area-level deprivation, social fragmentation and hospital of admission.Results: There were 8327 first admissions for self-harm during the study period. Self-harm admission rates varied fourfold across the study area, with lower rates close to the city centre [adjusted standardised admission ratio, closest versus furthest quartile 0.71(95% CrI 0.54-0.96)]. Deprivation was associated with self-harm but partially masked rather than explained the spatial pattern, which strengthened after adjustment. After adjustment for deprivation, hospital of admission and social fragmentation, greenspace, population density and ethnicity were not associated with self-harm rates.Conclusion: Proximity to the city centre was associated with lower rates of self-harm, but the usual operationalisations of urbanness, population density and greenspace, were not. Deprivation did not explain the spatial patterning, nor did ethnicity. While nationally self-harm rates are higher in urban and deprived areas, this cannot be extrapolated to mean that within cities the inner-city is the highest risk area nor that risk will be principally patterned according to deprivation. [ABSTRACT FROM AUTHOR]
Copyright of Social Psychiatry & Psychiatric Epidemiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Spatial patterning of self-harm rates within urban areas.
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  Data: <searchLink fieldCode="AR" term="%22Polling%2C+Catherine%22">Polling, Catherine</searchLink><br /><searchLink fieldCode="AR" term="%22Bakolis%2C+Ioannis%22">Bakolis, Ioannis</searchLink><br /><searchLink fieldCode="AR" term="%22Hotopf%2C+Matthew%22">Hotopf, Matthew</searchLink><br /><searchLink fieldCode="AR" term="%22Hatch%2C+Stephani+L%2E%22">Hatch, Stephani L.</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Social+Psychiatry+%26+Psychiatric+Epidemiology%22">Social Psychiatry & Psychiatric Epidemiology</searchLink>. Jan2019, Vol. 54 Issue 1, p69-79. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Metropolitan+areas%22">Metropolitan areas</searchLink><br /><searchLink fieldCode="DE" term="%22Social+disorganization%22">Social disorganization</searchLink><br /><searchLink fieldCode="DE" term="%22Hospital+admission+%26+discharge%22">Hospital admission & discharge</searchLink><br /><searchLink fieldCode="DE" term="%22Population+density%22">Population density</searchLink><br /><searchLink fieldCode="DE" term="%22Ethnicity%22">Ethnicity</searchLink>
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  Data: <bold>Purpose: </bold>Urban areas are usually found to have higher rates of self-harm, with deprivation the strongest predictor at area-level. We use a disease mapping approach to examine how self-harm is patterned within an urban area and its associations with deprivation, urbanness and ethnicity.<bold>Methods: </bold>Data from clinical records on individuals admitted for self-harm for 725 small areas in South East London were included. Bayesian hierarchical models explored the spatio-temporal patterns of self-harm admission rates and potential associations with proximity to city centre, population density, percentage greenspace and non-white ethnic-minority populations. All models were adjusted for area-level deprivation, social fragmentation and hospital of admission.<bold>Results: </bold>There were 8327 first admissions for self-harm during the study period. Self-harm admission rates varied fourfold across the study area, with lower rates close to the city centre [adjusted standardised admission ratio, closest versus furthest quartile 0.71(95% CrI 0.54-0.96)]. Deprivation was associated with self-harm but partially masked rather than explained the spatial pattern, which strengthened after adjustment. After adjustment for deprivation, hospital of admission and social fragmentation, greenspace, population density and ethnicity were not associated with self-harm rates.<bold>Conclusion: </bold>Proximity to the city centre was associated with lower rates of self-harm, but the usual operationalisations of urbanness, population density and greenspace, were not. Deprivation did not explain the spatial patterning, nor did ethnicity. While nationally self-harm rates are higher in urban and deprived areas, this cannot be extrapolated to mean that within cities the inner-city is the highest risk area nor that risk will be principally patterned according to deprivation. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Social Psychiatry & Psychiatric Epidemiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jan2019
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