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.
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For a more detailed description of the Swiss nursing home sector, see .
Most of our quality indicators include 173 observations. However, for a few of them, information was collected for only two years. To maximize the number of observations used in the following econometric analysis, we dropped four single quality indicators with missing values (see Table 4 for details).
In a non-competitive environment such as the Swiss one, there is no reason to assume that nursing homes minimize costs. In this case, the estimated costs function is a “behavioral cost function”  and can still be used to make a comparison among firms.
Note that this is not the Resource Utilization Group (RUG)’s classification system of residents. As compared to the RUG system, our case-mix measure is not derived from the MDS. The main advantage is that case-mix differences are less likely to reflect quality levels.
In a preliminary analysis, we also estimated: (1) a full translog cost model and (2) a hybrid translog cost model. In the hybrid translog cost function, quality indicators were included only in linear form. The results of the full translog were not satisfactory, probably due to multicollinearity problems and the loss of degrees of freedom. The results of the hybrid cost function were very similar to those obtained with the log-log functional form.
Squared terms for quality indicators were also considered in a separate analysis to test the presence of a non-linear relationship between quality and costs. The results did not show evidence of non-linear relationship.
The cost function is linear homogenous of degree 1 in input prices when a 10 % increase in all input prices leads to a 10 % increase in total cost.
The Durbin–Wu–Hausman test performed using the lagged SR as instrumental variable does not reject exogeneity at the \(99\, \%\) level.
Four of these indicators are risk-adjusted based on the stratification approach. This means that they are calculated separately for high-risk and low-risk patients. In these cases, we use the low-risk indicators.
Kezdi  states that a sample of 50 clusters is close enough to infinity for accurate inference if the number of observations per cluster is not too small. A cluster is considered small if it contains less than five observations . In our case, the significance of the coefficients remains unchanged when standard errors are clustered.
Note that the correlation between outcome quality indicators in Model 3 is relatively low (0.17). Clearly, the correlation between outcome quality indicators obtained using PCA (Model 1) is zero, because different components are orthogonal.
For comparison purposes, we also ran RE regressions without the institutional form (IF). The size of the coefficients remains unchanged (estimates not reported).
The F diagnostic for weak instruments for the joint significance of the instruments in first-stage regressions does not recognize situations in which some instruments are good while others are weak.
The region considered in the analysis is divided into eight districts: Mendrisio, Lugano, Vallemaggia, Locarno, Bellinzona, Riviera, Blenio, and Leventina. Given that only a few nursing homes are located in northern districts, Vallemaggia, Leventina, and Blenio are pooled together.
Lagged values are an attractive instrument due to the high correlation with the endogenous variable. Nevertheless, caution is necessary in the presence of serial correlation in the data, as this may invalidate the instruments . To test for autocorrelation in the panel data set, we use the test developed by Wooldridge [68, 69].
See, for instance, Hahn et al. , for a discussion about weak instruments in the econometric literature.
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We thank Andrew Street for helpful comments and invaluable advice during our stay with the Policy team at the Center for Health Economics at the University of York. Also, we thank the Swiss National Science Foundation for financial support to the project. Any errors are the authors’ responsibility.
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Giorgio, L.D., Filippini, M. & Masiero, G. Is higher nursing home quality more costly?. Eur J Health Econ 17, 1011–1026 (2016). https://doi.org/10.1007/s10198-015-0743-4
- Nursing home
- Single quality indicators
- Composite quality indicators
- Cost-quality tradeoff
- Process quality
- Outcome quality
- Structure quality