IntroductionPediatric delirium is a significant problem when encounterd in an intensive care unit (ICU). The pathophysiology of pediatric delirium is complex and the etiology is typically multifactorial. Even though various risk factors associated with pediatric delirium in a pediatric ICU have been identified, there is still a paucity of literature associated with the condition, especially in extremely critically ill children, sedated and mechanically ventilated.Aim of the studyTo identify factors associated with delirium in mechanically ventilated children in an ICU.Material and MethodsThis is a single-center study conducted at a tertiary care pediatric ICU. Patients admitted to the pediatric ICU requiring sedation and mechanical ventilation for >48 hours were included. Cornell Assessment of Pediatric Delirium scale was used to screen patients with delirium. Baseline demographic and clinical factors as well as daily and cumulative doses of medications were compared between patients with and without delirium. Firth’s penalized maximum likelihood logistic regression was used on a priori set of variables to examine the association of potential factors with delirium. Two regression models were created to assess the effect of daily medication doses (Model 1) as well as cumulative medication doses (Model 2) of opioids and benzodiazepines.Results95 patient visits met the inclusion criteria. 19 patients (20%) were diagnosed with delirium. Older patients (>12 years) had higher odds of developing delirium. Every 1mg/kg/day increase in daily doses of opioids was associated with an increased risk of delirium (OR=1.977, p=0.017). Likewise, 1 mg/kg increase in the cumulative opioid dose was associated with a higher odds of developing delirium (OR=1.035, p=0.022). Duration of mechanical ventilation was associated with the development of delirium in Model 1 (p=0.007).ConclusionsAge, daily and cumulative opioid dosage and the duration of mechanical ventilation are associated with the development of delirium in mechanically ventilated children.

Hospital readmission within 30 days of discharge is an important quality measure given that it represents a potentially preventable adverse outcome. Approximately, 20% of Medicare beneficiaries are readmitted within 30 days of discharge. Many strategies such as the hospital readmission reduction program have been proposed and implemented to reduce readmission rates. Prior research has shown that coordination of care could play a significant role in lowering readmissions. Although having a hospital-based skilled nursing facility (HBSNF) in a hospital could help in improving care for patients needing short-term skilled nursing or rehabilitation services, little is known about HBSNFs’ association with hospitals’ readmission rates. This study seeks to examine the association between HBSNFs and hospitals’ readmission rates. Data sources included 2007-2012 American Hospital Association Annual Survey, Area Health Resources Files, the Centers for Medicare and Medicaid Services (CMS) Medicare cost reports, and CMS Hospital Compare. The dependent variables were 30-day risk-adjusted readmission rates for acute myocardial infarction (AMI), congestive heart failure, and pneumonia. The independent variable was the presence of HBSNF in a hospital (1 = yes, 0 = no). Control variables included organizational and market factors that could affect hospitals’ readmission rates. Data were analyzed using generalized estimating equation (GEE) models with state and year fixed effects and standard errors corrected for clustering of hospitals over time. Propensity score weights were used to control for potential selection bias of hospitals having a skilled nursing facility (SNF). GEE models showed that the presence of HBSNFs was associated with lower readmission rates for AMI and pneumonia. Moreover, higher SNFs to hospitals ratio in the county were associated with lower readmission rates. These findings can inform policy makers and hospital administrators in evaluating HBSNFs as a potential strategy to lower hospitals’ readmission rates.

High Medicaid nursing homes (85% and higher of Medicaid residents) operate in resource-constrained environments. High Medicaid nursing homes (on average) have lower quality and poorer financial performance. However, there is significant variation in performance among high Medicaid nursing homes. The purpose of this study is to examine the organizational and market factors that may be associated with better financial performance among high Medicaid nursing homes. Data sources included Long-Term Care Focus (LTCFocus), Centers for Medicare and Medicaid Services’ (CMS) Medicare Cost Reports, CMS Nursing Home Compare, and the Area Health Resource File (AHRF) for 2009-2015. There were approximately 1108 facilities with high Medicaid per year. The dependent variables are nursing homes operating and total margin. The independent variables included size, chain affiliation, occupancy rate, percent Medicare, market competition, and county socioeconomic status. Control variables included staffing variables, resident quality, for-profit status, acuity index, percent minorities in the facility, percent Medicaid residents, metropolitan area, and Medicare Advantage penetration. Data were analyzed using generalized estimating equations with state and year fixed effects. Results suggest that organizational and market slack resources are associated with performance differentials among high Medicaid nursing homes. Higher financial performing facilities are characterized as having nurse practitioners/physician assistants, more beds, higher occupancy rate, higher Medicare and Medicaid census, and being for-profit and located in less competitive markets. Higher levels of Registered Nurse (RN) skill mix result in lower financial performance in high Medicaid nursing homes. Policy and managerial implications of the study are discussed.

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This study examined the effects of public hospitals' privatization on financial performance. We used a sample of nonfederal acute care public hospitals from 1997 to 2013, averaging 434 hospitals per year. Privatization was defined as conversion from public status to either private not-for-profit (NFP) or private for-profit (FP) status. Financial performance was measured by operating margin (OM) and total margin (TM). We used hospital level and year fixed effects linear panel regressions with nonlagged independent and control variables (Model 1), lagged by 1 year (Model 2), and lagged by 2 years (Model 3). Privatization to FP was associated with 17% higher OM (Model 2) and 9% higher OM (Model 3), compared with 3%, 4%, and 6% higher OM for privatization to NFP for all three Models, respectively. Privatization to FP was associated with 7% higher TM (Model 2) and privatization to NFP was associated with 2% higher TM (Model 3).