Impact of socioeconomic factors on in-patient length of stay and their consequences in per case hospital payment systems

Abstract

SE factors are a predictor of in-patient LOS and should be taken into account in per case resource allocation among hospitals.

The association between smoking status and hospital length of stay: evidence from a hospital-based cohort

“…These findings are in line with findings of other previous studies. [1,9] Similarly, a study in Germany also reported that older age was associated with increase direct and indirect costs. [4] The adjusted and unadjusted models indicated the LOS for men was 0.05% and 0.04%, respectively.…”

“…[1,2] It accounts for more than 5 million deaths annually and imposes both health and financial burdens on individuals, health systems, and society as a whole. [3,4] In Iran, the prevalence of smoking is about 12.5% (of which 23.4% males and 1.4% females) and on average an individual used to smoke 13.7 cigarettes daily.…”

“…[4,[6][7][8] Hospital length of stay (LOS) of patients is one of the main indicators that can be influenced by smoking status. [1] Identifying factors affecting LOS is the first step toward decreasing LOS and improving quality of inpatient services. Furthermore, understanding LOS of patients has paramount importance for management and financing, because it has a significant impact on hospital costs.…”

The Great Recession in Portugal: Impact on hospital care use

“…The first causal mechanisms refer to income-related demand effects, that is, people modifying their demand for in-patient care because of income variations. The impoverishment has probably not affected the demand for in-patient admissions and days, because of the very low co-payments and exemptions in the public sector in Portugal; international evidence shows for example that low-income people have a higher use of in-patient care [3], and longer in-patient stays [4]. Though, lower incomes and unemployment may have reduced access to private insurance and private hospitals, resulting in a higher demand for public elective in-patient care.…”

New medical diagnoses and length of stay of acutely unwell older patients: Implications for funding models

“…Output‐based funding using the AR‐DRG (henceforth called DRG) does not accurately predict LOS and the costs of care in older populations [12–14], and changes to the DRG have been proposed [13,14]. Older patients with functional impairment and those with low income are particularly disadvantaged [12–15]. Another problem with DRG‐based funding is that it emphasises average patterns of care rather than desirable methods of care.…”