334 - Price Variability for Common Hospital Charges and Correlation with Bed Capacity and Local Income Levels in California

Publication Number: 334.6494

Tam Nguyen, Loma Linda University Children's Hospital, Loma Linda, CA, United States; Vallent Lee, Loma Linda University Children's Hospital, Loma Linda, CA, United States

Background: The 2021 Price Transparency Rule mandates that hospitals disclose comprehensive standard charges for all services, aiming to enhance price competition and reduce healthcare costs. Despite this, the factors that underlie cost variability have not been well defined. Understanding these disparities is crucial for promoting equitable, high-value care.

Objective: This study investigates the variability of costs for services such as laboratory tests, imaging, and hospital stays across children’s hospitals in California. We hypothesized that correlations may exist between these costs and hospital-specific factors such as bed capacity or local community income level.

Design/Methods: Pricing data were collected from chargemasters of 21 children’s hospitals across California. We examined charges for 14 common laboratory and imaging studies, as well as for acute (medical-surgical) and intensive levels of care. Bed capacities were available from the California Department of Public Health and the Children’s Hospital Association. Median income estimates based on zip code were obtained from the U.S. Census Bureau. Linear regressions were performed to explore correlations between service costs and hospital characteristics.

Results: Bed prices were significantly correlated with hospitals’ bed capacity (Figure 1; R^2 = 0.35) and with community median income levels (Figure 2; R^2 = 0.28). These relationships were confirmed with multivariable linear regression (Table 1; R^2 = 0.60). In contrast, despite substantial variability in costs of laboratory and imaging tests across hospitals, we did not find consistent correlations to bed capacity or local income levels, especially if adjusting for multiple comparisons.

Conclusion(s): Hospitals with larger bed capacities may have higher overhead costs, leading to more expensive beds. However, the association with income level may indicate that hospitals adjust their prices based on the communities they serve, with possible impact from insurance coverage or perceived willingness to spend. Pricing strategies for laboratory and imaging studies do not align with bed capacity or local income levels. The reasons for disparities in these services remain unclear, but could be related to other factors such as negotiated insurance rates. There are implications for potential advocacy, standardization, or regulatory intervention to reduce arbitrary variations, promote price transparency, and ensure that essential inpatient services are not prohibitive for patients from low-income populations.

Figure 1. Price of acute care beds correlates with number of licensed beds
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Figure 2. Price of acute care beds correlates with local median income levels
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Table 1. Multivariable linear regression for price of acute care bed
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