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Title: Towards integrated care : using linked data to explore health and social care utilisation for adult residents of Barking and Dagenham in 2016/17
Author: Shand, Jenny
ISNI:       0000 0004 9359 8549
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2020
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Background Better integration across all settings of care is a core ambition of the NHS to address the changing needs of an ageing population and achieve efficiency gains. This study created a novel individual level data resource to explore the use of health and social care services across five care settings, to evaluate the factors associated with this use, and the extent to which these factors vary by setting of care for residents of Barking and Dagenham in 2016/17. Methodology Mixed methods including: semi-structured interviews in Barking and Dagenham, Havering and Redbridge to understand system leaders’ perspectives on working across organisation and sector boundaries; descriptive analysis of service utilisation across five settings of care (hospital, primary, community, mental health and social care) to compare population and cost volumes for 32 possible combinations of service use; a two-part regression model to calculate the combined effect of the probability of service use and the level of service use if there was one across a wide range of co-variates overall and by setting of care; matched analysis of the impact having an informal carer has on cost-weighted utilisation. Results Improving system finances was a key motivator for integration. The 2016/17 total costs for the cohort (114,393 residents) was £180.1 million. Two combinations of service use dominated cost and volume for the system, with a further eight combinations dominant for an individual setting. The relative impact of socio-demographic, health and economic factors differed according to setting of care. Health and care costs were £2,662 higher for people registered in their primary care records to have a carer compared to those who did not. Conclusions Large, linked datasets, such as the one considered in this thesis, provide extensive opportunities to improve our understanding of service user patterns and the wider determinants of health.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available