Using a linked dataset to investigate the characteristics of multiple morbidities in a local population and its impact on health service usage
PHE ePoster Library. Wang S. Sep 12, 2019; 274427; 225
Sophia Wang
Sophia Wang
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Abstract
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Abstract Background
In England[1], Multi-morbidity is a growing challenge at whole population level but difficult to define .The whole systems dataset across Tower Hamlets which includes GP, hospital, schools, social care and wider determinants of health is uniquely positioned to gain insight about multi-morbidity. This poster explains how the linking council and healthcare datasets is used to identify the correlation between multi-morbidity and risk factors.
Method
Based on selected conditions from the QOF disease register, a multi-morbidity flag was created at individual level using SQL. The flag denotes whether a patient has 2 or more conditionsOutputs are being used to identify correlations with wider determinants of health, such as household composition and deprivation and risk factor. Multivariate regression models can be further developed to understand variation and demand on health and social services and its associated risk groups.
Results
Preliminary results show that around 7% of residents are living with two or more conditions. Crucially, age and deprivation are key risk factors for multi-morbidity. Over half of adults aged over 65 within the 3 most deprived deciles live with multiple conditions. Further analysis will be conducted to assess the extent of correlation between these risk factors and multi-morbidity as well as the burden placed on health services.
Conclusions
This project is an innovative example of how data linkage at scale aids in the identification of multi-morbidity risk factors and the demand it places on the health and social care system. External funding details
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