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Title: The theoretical and empirical basis of a BioPsychoSocial (BPS) risk screener for detection of older people's health related needs, planning of community programs, and targeted care interventions
Authors: Hildon Z.J.-L. 
Tan C.S. 
Shiraz F. 
Ng W.C.
Deng X. 
Koh G.C.H. 
Tan K.B. 
Philp I.,
Wiggins D.,
Aw S. 
Wu T.
Vrijhoef H.J.M. 
Keywords: aged
cross-sectional study
early intervention
factor analysis
health status
health survey
middle aged
risk factor
social welfare
very elderly
Aged, 80 and over
Cross-Sectional Studies
Early Medical Intervention
Factor Analysis, Statistical
Health Status
Health Surveys
Middle Aged
Risk Factors
Social Welfare
Surveys and Questionnaires
Issue Date: 2018
Citation: Hildon Z.J.-L., Tan C.S., Shiraz F., Ng W.C., Deng X., Koh G.C.H., Tan K.B., Philp I.,, Wiggins D.,, Aw S., Wu T., Vrijhoef H.J.M. (2018). The theoretical and empirical basis of a BioPsychoSocial (BPS) risk screener for detection of older people's health related needs, planning of community programs, and targeted care interventions. BMC Geriatrics 18 (1) : 49. ScholarBank@NUS Repository.
Abstract: Background: This study introduces the conceptual basis and operational measure, of BioPyschoSocial (BPS) health and related risk to better understand how well older people are managing and to screen for risk status. The BPS Risk Screener is constructed to detect vulnerability at older ages, and seeks to measure dynamic processes that place equal emphasis on Psycho-emotional and Socio-interpersonal risks, as Bio-functional ones. We validate the proposed measure and describe its application to programming. Methods: We undertook a quantitative cross-sectional, psychometric study with n = 1325 older Singaporeans, aged 60 and over. We adapted the EASYCare 2010 and Lubben Social Network Scale questionnaires to help determine the BPS domains using factor analysis from which we derive the BPS Risk Screener items. We then confirm its structure, and test the scoring system. The score is initially validated against self-reported general health then modelled against: number of falls; cognitive impairment; longstanding diseases; and further tested against service utilization (linked administrative data). Results: Three B, P and S clusters are defined and identified and a BPS managing score ('doing' well, or 'some', 'many', and 'overwhelming problems') calculated such that the risk of problematic additive BPS effects, what we term health 'loads', are accounted for. Thirty-five items (factor loadings over 0.5) clustered into three distinct B, P, S domains and were found to be independently associated with self-reported health: B: 1.99 (1.64 to 2.41), P: 1.59 (1.28 to 1.98), S: 1.33 (1.10 to 1.60). The fit improved when combined into the managing score 2.33 (1.92 to 2.83, < 0.01). The score was associated with mounting risk for all outcomes. Conclusions: BPS domain structures, and the novel scoring system capturing dynamic BPS additive effects, which can combine to engender vulnerability, are validated through this analysis. The resulting tool helps render clients' risk status and related intervention needs transparent. Given its explicit and empirically supported attention to P and S risks, which have the potential to be more malleable than B ones, especially in the older old, this tool is designed to be change sensitive. © 2018 The Author(s).
Source Title: BMC Geriatrics
ISSN: 1471-2318
DOI: 10.1186/s12877-018-0739-x
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