Ingram, Elizabeth;
(2021)
Could linked health and council data advance our understanding of the determinants of multimorbidity and inform service provision? A mixed methods study.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Background: Multimorbidity – the co-occurrence of multiple chronic conditions within a single individual – is a major public health problem influenced by social determinants of health. To meet challenges such as multimorbidity, whose causes and management transcend organisational boundaries, some areas have linked their administrative health and council records. This mixed-methods thesis aimed to investigate how knowledge from the analysis of linked health and council data (‘analytics’) could advance understanding of the determinants of multimorbidity (Aim 1) and inform the equitable provision of services for groups such as those with, or at risk of, multimorbidity (Aim 2). Methods: Findings from my systematic review of literature examining household and area-level social determinants of multimorbidity informed a quantitative study. Using multi-level logistic regression, I analysed a linked health and council dataset to quantify associations between household tenure and multimorbidity amongst working age residents of Barking and Dagenham. Semi-structured interviews were conducted with 20 senior leaders of North London health and care organisations to explore barriers and facilitators of analytics use for strategic and equitable health and care decision-making. Results: The review found that household-level social determinants of multimorbidity are often overlooked despite large effect sizes for household compared to area-level determinants. The quantitative analysis found that risk of multimorbidity was greater for social housing tenants and lower for private renters when both were compared to owner-occupiers. Interview findings indicated that leaders did not uniformly use this type of knowledge generated from analytics to inform decision-making due to barriers spanning their working environments, relationships, and data quality. Conclusions: Linked health and council data can provide novel population health insights for local concerns like multimorbidity. However, improved data linkage alone will unlikely influence the use of these insights for more equitable service provision without efforts to address further barriers to analytics access and use.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Could linked health and council data advance our understanding of the determinants of multimorbidity and inform service provision? A mixed methods study |
| Event: | UCL (University College London) |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2022. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Applied Health Research |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10141143 |
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