Usage of unscheduled hospital care by homeless individuals in Dublin, Ireland: a cross-sectional study.

OBJECTIVES: Homeless people lack a secure, stable place to live and experience higher rates of serious illness than the housed population. Studies, mainly from the USA, have reported increased use of unscheduled healthcare by homeless individuals.We sought to compare the use of unscheduled emergency department (ED) and inpatient care between housed and homeless hospital patients in a high-income European setting in Dublin, Ireland. SETTING: A large university teaching hospital serving the south inner city in Dublin, Ireland. Patient data are collected on an electronic patient record within the hospital. PARTICIPANTS: We carried out an observational cross-sectional study using data on all ED visits (n=47 174) and all unscheduled admissions under the general medical take (n=7031) in 2015. PRIMARY AND SECONDARY OUTCOME MEASURES: The address field of the hospital's electronic patient record was used to identify patients living in emergency accommodation or rough sleeping (hereafter referred to as homeless). Data on demographic details, length of stay and diagnoses were extracted. RESULTS: In comparison with housed individuals in the hospital catchment area, homeless individuals had higher rates of ED attendance (0.16 attendances per person/annum vs 3.0 attendances per person/annum, respectively) and inpatient bed days (0.3 vs 4.4 bed days/person/annum). The rate of leaving ED before assessment was higher in homeless individuals (40% of ED attendances vs 15% of ED attendances in housed individuals). The mean age of homeless medical inpatients was 44.19 years (95% CI 42.98 to 45.40), whereas that of housed patients was 61.20 years (95% CI 60.72 to 61.68). Homeless patients were more likely to terminate an inpatient admission against medical advice (15% of admissions vs 2% of admissions in homeless individuals). CONCLUSION: Homeless patients represent a significant proportion of ED attendees and medical inpatients. In contrast to housed patients, the bulk of usage of unscheduled care by homeless people occurs in individuals aged 25-65 years.

Consultant duration of clinical practice as a cost determinant of an emergency medical admission.

BACKGROUND: Little data exists relating years of hospital consultant work experience, from time of consultant certification, and costs incurred for emergency medical patients under their care. We examined the total cost of emergency medical episodes in relation to certified consultant years experience using a database of emergency admissions. METHODS: All emergency admissions (19,295 patients) from January 2008 to December 2012 were studied. Consultants were categorized by total years of certified experience according to four experience categories (< 15, 15-20, > 20 to ≤ 25, and > 25 years). Costs per case calculations included all pay, non-pay, and diagnostic/support infra-structural costs. We used quantile regression analysis to examine the impact of predictor variables on total costs over the predictor distribution and logistic regression on outcomes and costs, adjusting for other major predictors of cost. RESULTS: Major predictors of costs were identified. Quantile regression cost parameter estimates of hospital episode costs decreased with experience; the unit change at the Q25 point of the years experience distribution was - 62 (95 % CI - 87, - 37), - 162 (95 % CI - 203, - 120) at the median, but decreased at the Q75 point to - 340 (95 % CI - 416, - 264). The odds ratio of a hospital episode cost being below the median for each category of consultant experience >15 years qualified were 0.75 (95 % CI 0.68, 0.83), 0.77 (95 % CI 0.70, 0.86), and 0.70 (95 % CI 0.64, 0.78): p < 0.001 for each experience category vs. <15 years qualified. CONCLUSIONS: There appear to be cost advantages to care delivered by certified consultants of >20 years in clinical practice.

Factors predicting the hospital episode costs of emergency medical admissions.

BACKGROUND: Important outcome predictor variables for emergency medical admissions are the Manchester Triage Category, Acute Illness Severity, Chronic Disabling Disease and Sepsis Status. We have examined whether these are also predictors of hospital episode costs. METHODS: All patients admitted as medical emergencies between January 2008 and December 2012 were studied. Costs per case were adjusted by reference to the relative cost weight of each diagnosis related group (DRG) but included all pay costs, non-pay costs and infra-structural costs. We used a multi-variate logistic regression with generalized estimating equations (GEE), adjusted for correlated observations, to model the prediction of outcome (30-day in-hospital mortality) and hospital costs above or below the median. We used quantile regression to model total episode cost prediction over the predictor distribution (quantiles 0.25, 0.5 and 0.75). RESULTS: The multivariate model, using the above predictor variables, was highly predictive of an in-hospital death-AUROC of 0.91 (95% CI: 0.90, 0.92). Variables predicting outcome similarly predicted hospital episode cost; however predicting costs above or below the median yielded a lower AUROC of 0.73 (95% CI: 0.73, 0.74). Quantile regression analysis showed that hospital episode costs increased disproportionately over the predictor distribution; ordinary regression estimates of hospital episode costs over estimated the costs for low risk and under estimated those for high-risk patients. CONCLUSION: Predictors of outcome also predict costs for emergency medical admissions; however, due to costing data heteroskedasticity and the non-linear relationship between dependant and predictor variables, the hospital episode costs are not as easy to predict based on presentation status.