Determinants of tourists' length of stay.
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ABSTRACT:
SUBMITTER: Oklevik O
PROVIDER: S-EPMC8653829 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and purposeAn ageing population at greater risk of proximal femoral fracture places an additional clinical and financial burden on hospital and community medical services. We analyse the variation in i) length of stay (LoS) in hospital and ii) costs across the acute care pathway for hip fracture from emergency admission, to hospital stay and follow-up outpatient appointments.Patients and methodsWe analyse patient-level data from England for 2009/10 for around 60,000 hip fracture cases in 152 hospitals using a random effects generalized linear multi-level model where the dependent variable is given by the patient's cost or length of stay (LoS). We control for socio-economic characteristics, type of fracture and intervention, co-morbidities, discharge destination of patients, and quality indicators. We also control for provider and social care characteristics.ResultsOlder patients and those from more deprived areas have higher costs and LoS, as do those with specific co-morbidities or that develop pressure ulcers, and those transferred between hospitals or readmitted within 28 days. Costs are also higher for those having a computed tomography (CT) scan or cemented arthroscopy. Costs and LoS are lower for those admitted via a 24h emergency department, receiving surgery on the same day of admission, and discharged to their own homes.InterpretationPatient and treatment characteristics are more important as determinants of cost and LoS than provider or social care factors. A better understanding of the impact of these characteristics can support providers to develop treatment strategies and pathways to better manage this patient population.
| S-EPMC4512684 | biostudies-literature
Project description:The increasing prevalence and high hospitalization rates make atrial fibrillation (AF) a significant healthcare strain. However, there are limited data regarding the length of hospital stay (LOS) of AF patients. Our purpose was to determine the main drivers of extended LOS of AF patients. All AF patients, hospitalized consecutively in a tertiary cardiology center, from January 2018 to February 2020 were included in this retrospective cohort study. Readmissions were excluded. Prolonged LOS was defined as more than seven days (the upper limit of the third quartile). Our study included 949 AF patients, 52.9% females. The mean age was 72.5 ± 10.3 years. The median LOS was 4 days. A total of 28.7% had an extended LOS. Further, 82.9% patients had heart failure (HF). In multivariable analysis, the independent predictors of extended LOS were: acute coronary syndromes (ACS) (HR 4.60, 95% CI 1.66-12.69), infections (HR 2.61, 95% CI 1.44-3.23), NT-proBNP > 1986 ng/mL (HR 1.96, 95% CI 1.37-2.82), acute decompensated HF (ADHF) (HR 1.76, 95% CI 1.23-2.51), HF with reduced ejection fraction (HFrEF) (HR 1.69, 95% CI 1.15-2.47) and the HAS-BLED score (HR 1.42, 95% CI 1.14-1.78). ACS, ADHF, HFrEF, increased NT-proBNP levels, infections and elevated HAS-BLED were independent predictors of extended LOS, while specific clinical or therapeutical AF characteristics were not.
| S-EPMC8396858 | biostudies-literature
Project description:BACKGROUND:Measuring and understanding main determinants of length of stay (LOS) in emergency departments (EDs) is critical from an operations perspective, since LOS is one of the main performance indicators of ED operations. Therefore, this study analyzes both the main and interaction effects of four widely-used independent determinants of ED-LOS. METHODS:The analysis was conducted using secondary data from an ED of a large urban hospital in Izmir, Turkey. Between-subject factorial analysis of variance (ANOVA) was used to test the main and interaction effects of the corresponding factors. P values <.05 were considered statistically significant. RESULTS:While the main effect of gender was insignificant, age, mode of arrival, and clinical acuity had significant effects, whereby ED-LOS was significantly higher for the elderly, those arriving by ambulance, and clinically-categorized high-acuity patients. Additionally, there was an interaction between the age and clinical acuity in that, while ED-LOS increased with age for high acuity patients, the opposite trend occurred for low acuity patients. When ED-LOS was modeled using gender, age, and mode of arrival, there was a significant interaction between age and mode of arrival. However, this interaction was not significant when the model included age, mode of arrival, and clinical acuity. CONCLUSION:Significant interactions exist between commonly used ED-LOS determinants. Therefore, interaction effects should be considered in analyzing and modelling ED-LOS.
| S-EPMC7306116 | biostudies-literature
Project description:Hospital length of stay (LOS) after burn injury is commonly estimated as 1 day per percent burn, but LOS often exceeds that estimate. The purpose of this study is to develop a novel method for estimating burn hospital LOS at any time during hospitalization. The authors used the American Burn Association National Burn Repository from 2000 to 2009 to directly estimate the median residual LOS (MRLOS) of patients hospitalized for burn injuries and who survived to discharge. The MRLOS is the median of how many more days a burn patient will be hospitalized given that the person has been in hospital for a specified time period. The authors also estimated the 25th and 75th percentiles of residual life and quantified the relationship between MRLOS and LOS with ordinary least squares for all burn patients, by burn size and by presence of inhalation injury. MRLOS increased with increasing LOS, confirming that discharge estimates change over time. Patients with inhalation injury had longer MRLOS than patients without inhalation injury in the first 100 hospital days. Patients with large burns (>25%) had large MRLOS consistent with prolonged hospitalization, but patients with small burns (<25%) also had steadily increasing MRLOS during hospitalization (ie, the longer the patient was in the hospital, the longer the predicted LOS regardless of initial LOS estimate). Estimating remaining hospital LOS at any time during admission has been problematic; MRLOS can be used to provide an estimate of remaining hospital LOS and resource utilization to families, administrators, and other medical professionals.
| S-EPMC5014656 | biostudies-literature
Project description:BackgroundEmergency department (ED) overcrowding is a potential threat for patient safety. We searched for independent determinants of prolonged ED length of stay (LOS) with the aim to identify factors which can be targeted to reduce ED LOS, which may help in preventing overcrowding.MethodsThis prospective cohort study included consecutive ED patients in a Dutch tertiary care centre. Multivariable logistic regression analysis was used to identify independent determinants of ED LOS?>?4 h, including patient characteristics (demographics, referral type, acuity, (number of) presenting complaints and comorbidity), treating specialty, diagnostic testing, consultations, number of patients in the ED and disposition. Furthermore, we quantified the absolute time delays (measured in real-time) associated with the most important independent determinants of prolonged ED LOS.ResultsIn 1434 included patients independent determinants of prolonged ED LOS were number and type of presenting complaints, specialty, laboratory/radiology testing and consultations, and ICU admission. Modifiable determinants with the largest impact were blood testing; Adjusted odds ratio (AOR (95%-CI)); 3.45 (1.95-6.11), urine testing; 1.79 (1.21-2.63), radiology imaging; 3.02 (2.13-4.30), and consultation; 5.90 (4.08-8.54). Combined with the laboratory/radiology testing and/or consultations (requested in 1123 (78%) patients) the decision-making and discharge process consumed between 74 (42%) and 117 (66%) minutes of the total ED LOS of 177 (IQR: 129-225) minutes.ConclusionsIn tertiary care EDs, ED LOS can be reduced if the process of laboratory/radiology testing and consulting is optimized and the decision-making and discharge procedures are accelerated.
| S-EPMC6148782 | biostudies-literature
Project description:Although length of stay (LoS) after childbirth has been diminishing in several high-income countries in recent decades, the evidence on the impact of early discharge (ED) on healthy mothers and term newborns after vaginal deliveries (VD) is still inconclusive and little is known on the characteristics of those discharged early. We conducted a population-based study in Friuli Venezia Giulia (FVG) during 2005-2015, to investigate the mean LoS and the percentage of LoS longer than our proposed ED benchmarks following VD: 2 days after spontaneous vaginal deliveries (SVD) and 3 days post instrumental vaginal deliveries (IVD). We employed a multivariable logistic as well as a linear regression model, adjusting for a considerable number of factors pertaining to health-care setting and timeframe, maternal health factors, newborn clinical factors, obstetric history factors, socio-demographic background and present obstetric conditions. Results were expressed as odds ratios (OR) and regression coefficients (RC) with 95% confidence interval (95%CI). The adjusted mean LoS was calculated by level of pregnancy risk (high vs. low). Due to a very high number of multiple tests performed we employed the procedure proposed by Benjamini-Hochberg (BH) as a further selection criterion to calculate the BH p-value for the respective estimates. During 2005-2015, the average LoS in FVG was 2.9 and 3.3 days after SVD and IVD respectively, and the pooled regional proportion of LoS > ED was 64.4% for SVD and 32.0% for IVD. The variation of LoS across calendar years was marginal for both vaginal delivery modes (VDM). The adjusted mean LoS was higher in IVD than SVD, and although a decline of LoS > ED and mean LoS over time was observed for both VDM, there was little variation of the adjusted mean LoS by nationality of the woman and by level of pregnancy risk (high vs. low). By contrast, the adjusted figures for hospitals with shortest (centres A and G) and longest (centre B) mean LoS were 2.3 and 3.4 days respectively, among "low risk" pregnancies. The corresponding figures for "high risk" pregnancies were 2.5 days for centre A/G and 3.6 days for centre B. Therefore, the shift from "low" to "high" risk pregnancies in all three latter centres (A, B and G) increased the mean adjusted LoS just by 0.2 days. By contrast, the discrepancy between maternity centres with highest and lowest adjusted mean LoS post SVD (hospital B vs. A/G) was 1.1 days both among "low risk" (1.1 = 3.4-2.3 days) and "high risk" (1.1 = 3.6-2.5) pregnanices. Similar patterns were obseved also for IVD. Our adjusted regression models confirmed that maternity centres were the main explanatory factor for LoS after childbirth in both VDM. Therefore, health and clinical factors were less influential than practice patterns in determining LoS after VD. Hospitalization and discharge policies following childbirth in FVG should follow standardized guidelines, to be enforced at hospital level. Any prolonged LoS post VD (LoS > ED) should be reviewed and audited if need be. Primary care services within the catchment areas of the maternity centres of FVG should be improved to implement the follow up of puerperae undergoing ED after VD.
| S-EPMC7136236 | biostudies-literature
Project description:BackgroundMachine learning (ML) approaches have been broadly applied to the prediction of length of stay and mortality in hospitalized patients. ML may also reduce societal health burdens, assist in health resources planning and improve health outcomes. However, the fairness of these ML models across ethnoracial or socioeconomic subgroups is rarely assessed or discussed. In this study, we aim (1) to quantify the algorithmic bias of ML models when predicting the probability of long-term hospitalization or in-hospital mortality for different heart failure (HF) subpopulations, and (2) to propose a novel method that can improve the fairness of our models without compromising predictive power.MethodsWe built 5 ML classifiers to predict the composite outcome of hospitalization length-of-stay and in-hospital mortality for 210 368 HF patients extracted from the Get With The Guidelines-Heart Failure registry data set. We integrated 15 social determinants of health variables, including the Social Deprivation Index and the Area Deprivation Index, into the feature space of ML models based on patients' geographies to mitigate the algorithmic bias.ResultsThe best-performing random forest model demonstrated modest predictive power but selectively underdiagnosed underserved subpopulations, for example, female, Black, and socioeconomically disadvantaged patients. The integration of social determinants of health variables can significantly improve fairness without compromising model performance.ConclusionsWe quantified algorithmic bias against underserved subpopulations in the prediction of the composite outcome for HF patients. We provide a potential direction to reduce disparities of ML-based predictive models by integrating social determinants of health variables. We urge fellow researchers to strongly consider ML fairness when developing predictive models for HF patients.
| S-EPMC9673161 | biostudies-literature
Project description:BackgroundEvidence on the association between newborn length of hospital stay (LOS) and risk of readmission is conflicting. We compared methods for modelling this relationship, by gestational age, using population-level hospital data on births in England between 2005-14.MethodsThe association between LOS and unplanned readmission within 30 days of postnatal discharge was explored using four approaches: (i) modelling hospital-level LOS and readmission rates; (ii) comparing trends over time in LOS and readmission; (iii) modelling individual LOS and adjusted risk of readmission; and (iv) instrumental variable analyses (hospital-level mean LOS and number of births on the same day).ResultsOf 4 667 827 babies, 5.2% were readmitted within 30 days. Aggregated data showed hospitals with longer mean LOS were not associated with lower readmission rates for vaginal (adjusted risk ratio (aRR) 0.87, 95% confidence interval (CI) 0.66, 1.13), or caesarean (aRR 0.89, 95% CI 0.72, 1.12) births. LOS fell by an average 2.0% per year for vaginal births and 3.4% for caesarean births, while readmission rates increased by 4.4 and 5.1% per year respectively. Approaches (iii) and (iv) indicated that longer LOS was associated with a reduced risk of readmission, but only for late preterm, vaginal births (34-36 completed weeks' gestation).ConclusionsLonger newborn LOS may benefit late preterm babies, possibly due to increased medical or psychosocial support for those at greater risk of potentially preventable readmissions after birth. Research based on observational data to evaluate relationships between LOS and readmission should use methods to reduce the impact of unmeasured confounding.
| S-EPMC5518288 | biostudies-literature
Project description: Not available
| S-EPMC1184244 | biostudies-literature
Project description:Successful outcomes of outpatient substance use disorder treatment result from many factors for clients-including intersections between individual characteristics, choices made, and social determinants. However, prioritizing which of these and in what combination, to address and provide support for remains an open and complex question. Therefore, we ask: What factors are associated with outpatient substance use disorder clients remaining in treatment for > 90 days and successfully completing treatment? To answer this question, we apply a virtual twins machine learning (ML) model to de-identified data for a census of clients who received outpatient substance use disorder treatment services from 2018 to 2021 from one treatment program in the Southeast U.S. We find that primary predictors of outcome success are: (1) attending self-help groups while in treatment, and (2) setting goals for treatment. Secondary predictors are: (1) being linked to a primary care provider (PCP) during treatment, (2) being linked to supplemental nutrition assistance program (SNAP), and (3) attending 6 or more self-help group sessions during treatment. These findings can help treatment programs guide client choice making and help set priorities for social determinant support. Further, the ML method applied can explain intersections between individual and social predictors, as well as outcome heterogeneity associated with subgroup differences.
| S-EPMC10460408 | biostudies-literature