Does race matter in universal healthcare? Stroke cost and outcomes in US military health care

Kidney transplant cases in US: study of determinants of variance in hospital charges and inpatient care

We investigate the factors that influence the variance in hospital charges and inpatient care for kidney transplant cases in the US. Using the AHRQ's (Agency for Healthcare Research and Quality) HCUP's (Hospital Cost and Utilization Project) NIS (National Inpatient Sample) database, we find that variance in hospital charges and inpatient care is driven by patient demographics and hospital variables. We find that variance in hospital charges and inpatient care is determined by patient-specific factors including age, gender, race, and income, and hospital factors such as size, type, and location. Our results provide a deeper understanding of the non-clinical factors that impact hospital charges and inpatient care for kidney transplant patients.

Racial disparities in access to, and outcomes of, acute ischaemic stroke treatments in the USA

BACKGROUND

Racism contributes to higher comorbid risk factors and barriers to preventive measures for black Americans. Advancements in systems of care, tissue plasminogen activator (tPA) availability and endovascular thrombectomy (ET) have impacted practice and outcomes while outpacing contemporary investigation into acute ischaemic stroke (AIS) care disparities. We examined whether recent data suggest ongoing disparity in AIS interventions and outcomes, and if hospital characteristics affect disparities.

METHODS

We examined 2016-2019 fee-for-service Medicare inpatient data. We ran unadjusted logistic regression models to calculate ORs and 95% CI for two interventions (tPA and ET) and four outcomes (inpatient mortality, 30-day mortality, discharge home and outpatient visit within 30 days), with the main predictor black versus white race, additionally adjusting for demographics, hospital characteristics, stroke severity and comorbidities.

RESULTS

805 181 AIS admissions were analysed (12.4% black, 87.6% white). Compared with white patients, black patients had reduced odds of receiving tPA (OR 0.71, 95% CI 0.69 to 0.74, p<0.0001) and ET (0.69, 95% CI 0.65 to 0.72, p<0.0001). After tPA, black patients had reduced odds of 30-day mortality (0.77, 95% CI 0.72 to 0.82, p<0.0001), discharge home (0.72, 95% CI 0.68 to 0.77, p<0.0001) and outpatient visit within 30 days (0.89, 95% CI 0.84 to 0.95, p=0.0002). After ET, black patients had reduced odds of 30-day mortality (0.71, 95% CI 0.63 to 0.79, p<0.0001) and discharge home (0.75, 95% CI 0.64 to 0.88, p=0.0005). Adjusted models showed little difference in the magnitude, direction or significance of the main effects.

CONCLUSIONS

Black patients were less likely to receive AIS treatments, and if treated had lower likelihood of 30-day mortality, discharge home and outpatient visits. Despite advancements in practice and therapies, racial disparities remain in the modern era of AIS care and are consistent with inequalities previously identified over the last 20 years. The impact of hospital attributes on AIS care disparities warrants further investigation.

Variance in Hospital Charges and in-Patient Care for Liver Transplants: Examining Non-Clinical Predictors and Implications

The significant and apparent variance in hospital charges and inpatient care in the U.S. has perplexed the general public including many stakeholders such as the healthcare regulators and insurers. While the clinical side of inpatient care has been undergoing tremendous progress and standardization, the overall cost of healthcare has been ballooning. The purpose of this research is to conduct statistical analyses that reveal the sources of variance in hospital charges and inpatient care using the annual data from the AHRQ's (Agency for Healthcare Research and Quality) HCUP's (Hospital Cost and Utilization Project) NIS (National Inpatient Sample) database. Our focus is on non-clinical factors such as patient age, gender, income and race and hospital location data as independent variables to investigate their impact on hospital charges and inpatient care. Our research sample is the liver transplant cases in 2019 sampled in the NIS 2019 database. Our regression results show patient age and gender as well as payer affect the number of diagnoses; and hospital charges are affected by age, payer and hospital location. Number of procedures was not affected by any of these non-clinical factors except the hospital location. Implications suggest that there is more room for standardization of the number of diagnoses and procedures across regions in the US. Results also reveal that race and income do not have any effect on hospital charges and inpatient care. Our study contributes to an empirical understanding of non-clinical factors in the explanation of variance in hospital charges and inpatient care.

Patient demographics as determinants of where they go for hospitalization, what inpatient care they get, and what they are charged: A national study

This study focuses on the impact of race, income, age, and gender on hospital charges in the US. The data include 28,133 discharge records for appendectomies from a stratified sample of 4,584 hospitals in the HCUP's (Hospital Cost and Utilization Project) NIS (National Inpatient Sample) database. Results show that race, income, and age were significant determinants of hospital charges. Gender was not significantly related to the variance in hospital charges. Additionally, hospital variables (ownership/control region, teaching status, size, and primary expected payer) had statistically significant effects on hospital charges. We conclude with implications for clinicians, hospital administrators, and policy makers.