The Impact of Socio-Demographic and Clinical Factors on Acute Medical Service Utilisation
Citation:
Byrne, Declan, The Impact of Socio-Demographic and Clinical Factors on Acute Medical Service Utilisation, Trinity College Dublin, School of Medicine, Clinical Medicine, 2023Download Item:
Abstract:
Socio-economic status impacts health throughout the lifecycle. It does this by affecting income, accommodation, access to education and skills, and access to healthcare. This mediates the phenotypic expression of disease and the trajectory of disease progression. When planning services at local, regional and national levels, it is important to account for the potential effect of inequality.
The work for this thesis was completed in a busy urban hospital with a catchment area of 250,000 people spread across 74 electoral districts. There were 106,586 episodes of care evaluated from the period 2002-2016. Aggregated, anonymised, routinely collected clinical and administrative data was used to classify patients according to major disease category, acute illness severity, co-morbidity score, disability status, sepsis status, and electoral division of residence.
Given that admission and readmission rates to hospitals are count statistics, Poisson regression modelling was undertaken to examine the relationships between socio-economic status and multi-morbidity, and to compare the relative impact of the SAHRU and POBAL HP deprivation indices in terms of explaining the effect of electoral divisional social class on hospital admission and readmission rates.
The analysis of the socio-economic status and multi-morbidity indicated that admissions from the low socio-economic group cohort were a decade younger (62.6 years versus 71.4 years) at the time of presentation. The number of co-morbidities was equivalent between the two groups, but the disadvantaged were more likely to have a respiratory diagnosis or diabetes.
There was poor agreement between the SAHRU and POBAL HP Deprivation instruments, with an overall classification agreement of 46% for data derived from the 2006 census and 42% for the 2011 census. The SAHRU deprivation instrument classified 66% and 55% of the subjects studied into the highest deprivation quintile for 2006 and 2011, respectively. In contrast, POBAL HP mapped 32% and 24% into the highest deprivation quintile for 2006 and 2011, respectively. The admission incidence rate was significantly predicted by the area deprivation status in the multiple variable models adjusted for the other predictive variables of acute illness severity, chronic disabling disease score, Charlson co-morbidity index and sepsis status. Utilising SAHRU deprivation rankings, the Poisson Regression returned an incremental rate ratio for deprivation of 1.48 (95% Confidence interval 1.47, 1.49) using 2006 census data and an IRR of 1.47 (95% confidence interval 1,46,1.48) using 2011 data. Modelling with POBAL HP Deprivation index data resulted in an IRR of 1.28 (95% Confidence interval 1.27,1.28) for the census 2006 and an IRR of 1.26 (95% Confidence interval 1.25,1.26) using 2011 data. The same relationship was shown when readmission rates were examined.
Deprivation influences incidence rates for hospital admission and readmission. The classical SAHRU deprivation instrument seems to approximate population risk more powerfully. Earlier census data model are more powerful, suggesting a long latency between social circumstances and the ultimate expression of acute emergency hospital admission
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Author: Byrne, Declan
Advisor:
Browne, JosephPublisher:
Trinity College Dublin. School of Medicine. Discipline of Clinical MedicineType of material:
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