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.
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.
['Hildon|Zoe J-L|ZJ|', 'Tan|Chuen Seng|CS|', 'Shiraz|Farah|F|', 'Ng|Wai Chong|WC|', 'Deng|Xiaodong|X|', 'Koh|Gerald Choon Huat|GCH|', 'Tan|Kelvin Bryan|KB|', 'Philp|Ian|I|', 'Wiggins|Dick|D|', 'Aw|Su|S|', 'Wu|Treena|T|', 'Vrijhoef|Hubertus J M|HJM|']
D000368:Aged D000369:Aged, 80 and over D003430:Cross-Sectional Studies D061345:Early Medical Intervention D005163:Factor Analysis, Statistical D005260:Female D006304:Health Status D006306:Health Surveys D006801:Humans D008297:Male D008875:Middle Aged D011594:Psychometrics D012307:Risk Factors D012846:Singapore D012946:Social Welfare D011795:Surveys and Questionnaires
Implementation science Integrated care delivery in the community Interdisciplinary theory Measurement study Risk stratification Successful ageing