The Relationship between Costs and Quality in Veterans Health Administration Community Living Centers: An Analysis Using Longitudinal Data

OBJECTIVE

To examine the relationship between cost and quality in Veterans Health Administration (VA) nursing homes (called Community Living Centers, CLCs) using longitudinal data.

DATA SOURCES/STUDY SETTING

One hundred and thirty CLCs over 13 quarters (from FY2009 to FY2012) were studied. Costs, resident days, and resident severity (RUGs score) were obtained from the VA Managerial Cost Accounting System. Clinical quality measures were obtained from the Minimum Data Set, and resident-centered care (RCC) was measured using the Artifacts of Culture Change Tool.

STUDY DESIGN

We used a generalized estimating equation model with facilities included as fixed effects to examine the relationship between total cost and quality after controlling for resident days and severity. The model included linear and squared terms for all independent variables and interactions with resident days.

PRINCIPAL FINDINGS

With the exception of RCC, all other variables had a statistically significant relationship with total costs. For most poorer performing smaller facilities (lower size quartile), improvements in quality were associated with higher costs. For most larger facilities, improvements in quality were associated with lower costs.

CONCLUSIONS

The relationship between cost and quality depends on facility size and current level of performance.

Is higher nursing home quality more costly?

Widespread issues regarding quality in nursing homes call for an improved understanding of the relationship with costs. This relationship may differ in European countries, where care is mainly delivered by nonprofit providers. In accordance with the economic theory of production, we estimate a total cost function for nursing home services using data from 45 nursing homes in Switzerland between 2006 and 2010. Quality is measured by means of clinical indicators regarding process and outcome derived from the minimum data set. We consider both composite and single quality indicators. Contrary to most previous studies, we use panel data and control for omitted variables bias. This allows us to capture features specific to nursing homes that may explain differences in structural quality or cost levels. Additional analysis is provided to address simultaneity bias using an instrumental variable approach. We find evidence that poor levels of quality regarding outcome, as measured by the prevalence of severe pain and weight loss, lead to higher costs. This may have important implications for the design of payment schemes for nursing homes.