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Dementia across local districts in England 2014 to 2015.

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Title: Dementia across local districts in England 2014 to 2015.
Authors: Tampubolon, Gindo, Nazroo, James, Keady, John, Pendleton, Neil
Source: International Journal of Geriatric Psychiatry. Aug2018, Vol. 33 Issue 8, p1127-1131. 5p. 1 Chart, 1 Map.
Subjects: Dementia, Medical care, Internet users, Physical activity, Mental illness
Abstract: Background: The number of older people needing dementia care is projected to rise rapidly, and local districts are now charged with responding to this need. But evidence on local area factors of dementia is scarce. We studied the odds of dementia prevalence and its individual risk factors enriched with area factors.Materials and Methods: This study analysed objectively assigned dementia prevalence in people aged 60 and over living in community in England, drawing data from the English Longitudinal Study of Ageing 2014 to 2015 and local districts statistics using multilevel logistic models. Dementia status is ascertained using a modified version of the Telephone Interview for Cognitive Status. A number of individual risk factors were considered including social determinants, internet use, social connections, and health behaviours; 2 contextual factors were included: the index of multiple deprivation and land use mix.Results: The prevalence of dementia by this method is 8.8% (95% confidence interval 7.7%-9.2%) in older adults in England. Maps of dementia prevalence across districts showed prevalent areas. In the full model, no area characteristics were significant in predicting dementia prevalence. Education, social connections, internet use, and moderate to vigorous physical activity showed protective associations.Conclusion: Dementia in older adults in England is largely predicted by individual characteristics, although some districts have a large share of their population with dementia. Given the health and social care costs associated with dementia, differential interventions and support to districts and to groups of individuals defined by these characteristics seem warranted. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Geriatric Psychiatry is the property of Wiley-Blackwell 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.)
Database: Psychology and Behavioral Sciences Collection
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PubType: Academic Journal
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Items – Name: Title
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  Data: Dementia across local districts in England 2014 to 2015.
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  Data: <searchLink fieldCode="AR" term="%22Tampubolon%2C+Gindo%22">Tampubolon, Gindo</searchLink><br /><searchLink fieldCode="AR" term="%22Nazroo%2C+James%22">Nazroo, James</searchLink><br /><searchLink fieldCode="AR" term="%22Keady%2C+John%22">Keady, John</searchLink><br /><searchLink fieldCode="AR" term="%22Pendleton%2C+Neil%22">Pendleton, Neil</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geriatric+Psychiatry%22">International Journal of Geriatric Psychiatry</searchLink>. Aug2018, Vol. 33 Issue 8, p1127-1131. 5p. 1 Chart, 1 Map.
– Name: Subject
  Label: Subjects
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  Data: <searchLink fieldCode="DE" term="%22Dementia%22">Dementia</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+care%22">Medical care</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+users%22">Internet users</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+activity%22">Physical activity</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+illness%22">Mental illness</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: <bold>Background: </bold>The number of older people needing dementia care is projected to rise rapidly, and local districts are now charged with responding to this need. But evidence on local area factors of dementia is scarce. We studied the odds of dementia prevalence and its individual risk factors enriched with area factors.<bold>Materials and Methods: </bold>This study analysed objectively assigned dementia prevalence in people aged 60 and over living in community in England, drawing data from the English Longitudinal Study of Ageing 2014 to 2015 and local districts statistics using multilevel logistic models. Dementia status is ascertained using a modified version of the Telephone Interview for Cognitive Status. A number of individual risk factors were considered including social determinants, internet use, social connections, and health behaviours; 2 contextual factors were included: the index of multiple deprivation and land use mix.<bold>Results: </bold>The prevalence of dementia by this method is 8.8% (95% confidence interval 7.7%-9.2%) in older adults in England. Maps of dementia prevalence across districts showed prevalent areas. In the full model, no area characteristics were significant in predicting dementia prevalence. Education, social connections, internet use, and moderate to vigorous physical activity showed protective associations.<bold>Conclusion: </bold>Dementia in older adults in England is largely predicted by individual characteristics, although some districts have a large share of their population with dementia. Given the health and social care costs associated with dementia, differential interventions and support to districts and to groups of individuals defined by these characteristics seem warranted. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Geriatric Psychiatry is the property of Wiley-Blackwell 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=130628202
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      – Type: doi
        Value: 10.1002/gps.4903
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      – Code: eng
        Text: English
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      – SubjectFull: Internet users
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      – SubjectFull: Physical activity
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              M: 08
              Text: Aug2018
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