John McCabe

Deprivation status and the hospital costs of an emergency medical admission

BACKGROUND Deprivation has been shown to adversely affect health outcomes. However, whether deprivation increases hospitalisation costs is uncertain. We have examined the relationship between deprivation and the costs of emergency medical admissions in a single centre between 2008-2014. METHODS We calculated the total hospital costs of emergency admissions related to their deprivation status, based on area of residence (Electoral Division - small census area). We used truncated Poisson and quantile regression methods to examine relationships between predictor variables and total hospital episode costs. RESULTS Over the study period, 29,508 episodes were recorded in 15,932 patients. Compared with the least deprived (Q1), the incidence rate ratios (IRR) for annual costs were increased to Q3 1.15 (95% CI: 1.12, 1.19), Q4 2.39 (95% CI: 2.30, 2.49) and Q5 2.76 (95% CI: 2.68, 2.85). The margin statistic cost estimate per thousand population increased from 183.8 K€ in Q1 to 507.9 K€ in Q5. The total bed days/1000 population increased as follows (compared with Q1): Q3 IRR 1.41 (95% CI: 1.37, 1.45), Q4 1.96 (95% CI: 1.89, 2.03) and Q5 3.04 (95% CI: 2.96, 3.12). The margin statistic bed day estimate (/1000 population) increased from 218.7 in Q1 to 664.0 in Q5. CONCLUSION Deprivation status had a profound impact on total hospital costs for emergency medical admissions. This was primarily mediated through a tripling of total bed days in the most deprived groups.

Age and the economics of an emergency medical admission-what factors determine costs?

Background: The ageing of the population may be anticipated to increase demand on hospital resources. We have investigated the relationship between hospital episode costs and age profile in a single centre. Methods: All Emergency Medical admissions (33 732 episodes) to an Irish hospital over a 6-year period, categorized into three age groups, were evaluated against total hospital episode costs. Univariate and adjusted incidence rate ratios (IRRs) were calculated using zero truncated Poisson regression. Results: The total hospital episode cost increased with age (P < 0.001). The multi-variable Poisson regression model demonstrated that the most important drivers of overall costs were Acute Illness Severity—IRR 1.36 (95% CI: 1.30, 1.41), Sepsis Status −1.46 (95% CI: 1.42, 1.51) and Chronic Disabling Disease Score -1.25 (95% CI: 1.22, 1.27) and the Age Group as exemplified for those >85 years IRR 1.23 (95% CI: 1.15, 1.32). Conclusion: Total hospital episode costs are a product of clinical complexity with contributions from the Acute Illness Severity, Co-Morbidity, Chronic Disabling Disease Score and Sepsis Status. However age is also an important contributor and an increasing patient age profile will have a predictable impact on total hospital episode costs.