Adverse and Advantageous Selection in the Medicare Supplemental Market: A Bayesian Analysis of Prescription drug Expenditure

Identification of Factors Influencing Out-of-county Hospitalizations in the New Cooperative Medical Scheme

Throughout the duration of the New Cooperative Medical Scheme (NCMS), it was found that an increasing number of rural patients were seeking out-of-county medical treatment, which posed a great burden on the NCMS fund. Our study was conducted to examine the prevalence of out-of-county hospitalizations and its related factors, and to provide a scientific basis for follow-up health insurance policies. A total of 215 counties in central and western China from 2008 to 2016 were selected. The total out-of-county hospitalization rate in nine years was 16.95%, which increased from 12.37% in 2008 to 19.21% in 2016 with an average annual growth rate of 5.66%. Its related expenses and compensations were shown to increase each year, with those in the central region being higher than those in the western region. Stepwise logistic regression reveals that the increase in out-of-county hospitalization rate was associated with region (X1), rural population (X2), per capita per year net income (X3), per capita gross domestic product (GDP) (X4), per capita funding amount of NCMS (X5), compensation ratio of out-of-county hospitalization cost (X6), per time average in-county (X7) and out-of-county hospitalization cost (X8). According to Bayesian network (BN), the marginal probability of high out-of-county hospitalization rate was as high as 81.7%. Out-of-county hospitalizations were directly related to X8, X3, X4 and X6. The probability of high out-of-county hospitalization obtained based on hospitalization expenses factors, economy factors, regional characteristics and NCMS policy factors was 95.7%, 91.1%, 93.0% and 88.8%, respectively. And how these factors affect out-of-county hospitalization and their interrelationships were found out. Our findings suggest that more attention should be paid to the influence mechanism of these factors on out-of-county hospitalizations, and the increase of hospitalizations outside the county should be reasonably supervised and controlled and our results will be used to help guide the formulation of proper intervention policies.

Do Robotic Surgical Systems Improve Profit Margins? A Cross-Sectional Analysis of California Hospitals

BACKGROUND

The aim of this study was to examine the association between ownership of robotic surgical systems and hospital profit margins.

METHODS

This study used hospital annual utilization data, annual financial data, and discharge data for year 2011 from the California Office of Statewide Health Planning and Development. We first performed bivariate analysis to compare mean profit margin by hospital and market characteristics and to examine whether these characteristics differed between hospitals that had one or more robotic surgical systems in 2011 and those that did not. We applied the t test and the F test to compare mean profit margin between two groups and among three or more groups, respectively. We then conducted multilevel logistic regression to determine the association between ownership of robotic surgical systems and having a positive profit margin after controlling for other hospital and market characteristics and accounting for possible correlation among hospitals located within the same market.

RESULTS

The study sample included 167 California hospitals with valid financial information. Hospitals with robotic surgical systems tended to report more favorable profit margins. However, multilevel logistic regression showed that this relationship (an association, not causality) became only marginally significant (odds ratio [OR] = 6.2; P = 0.053) after controlling for other hospital characteristics, such as ownership type, teaching status, bed size, and surgical volumes, and market characteristics, such as total number of robotic surgical systems owned by other hospitals in the same market area.

CONCLUSIONS

As robotic surgical systems become widely disseminated, hospital decision makers should carefully evaluate the financial and clinical implications before making a capital investment in this technology.