Abstract
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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Notes
For a more detailed description of the Swiss nursing home sector, see [2].
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” [55] 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 [58] 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 [59]. 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 [67]. To test for autocorrelation in the panel data set, we use the test developed by Wooldridge [68, 69].
See, for instance, Hahn et al. [70], for a discussion about weak instruments in the econometric literature.
References
Riedel, M., Kraus, M.: The organisation of formal long-term care for the elderly: results from the 21 European country studies in the ANCIEN Project. Social Welfare Policies, ENEPRI Research report (2011)
Di Giorgio, L., Filippini, M., Masiero, G.: Implications of global budget payment system on nursing home costs. Health Policy 115, 237–248 (2014)
Wodchis, W.P., Teare, G.F., Anderson, G.M.: Cost and quality evidence from Ontario long term care hospitals. Med. Care 45, 981–988 (2007)
Lohr, K.N. (ed.): Medicare: A Strategy for Quality Assurance, vol. 1. National Academy Press, Washington, DC (1990)
A First Class Service—Quality in the New NHS. Department of Health, London (1997)
Council of Europe.: Recommendation on Development and Implementation of Quality Improvement Systems (QIS) in Health Care and Explanatory Memorandum (41st Meeting, 24–26 June). Council of Europe, Strasbourg (1997)
WHO.: The World Health Report 2000: health systems: improving performance. World Health Organization, Geneva (2000)
Legido-Quigley, H., McKee, M., Nolte, E., Glinos, I.A.: Assuring the quality of health care in the European Union. European Observatory on Health Systems and Policies: Observatory Studies Series No. 12 (2008)
Donabedian, A.: The quality of care. How can it be assessed? JAMA 260(12), 1743–1748 (1988)
Zimmerman, D.: Development and testing of nursing home quality indicators. Health Care Financ. Rev. 16(4), 107–127 (1995)
Zimmerman, D.: Improving nursing home quality of care through outcomes data: the MDS quality indicators. Int. J. Geriatr. Psychiatry 18, 250–257 (2003)
Berg, K., Mor, V., Morris, J., Murphy, K.M., Moore, T., Harris, Y.: Identification and evaluation of existing nursing homes quality indicators. Health Care Financ. Rev. 23(4), 19–36 (2002)
Li, Y., Cai, X., Ye, Z., Glance, G.G., Harrington, C., Mukamel, D.B.: Satisfaction with Massachusetts nursing home care was generally high during 2005–2009, with some variability across facilities the productive efficiency and clinical quality of institutional long-term care for the elderly. Health Affairs 32(8), 1416–1425 (2013)
Nakrem, S., Vinsnes, A.G., Harkless, G.E., Paulsen, B., Seim, A.: Nursing sensitive quality indicators for nursing home care: International review of literature, policy and practice. Int. J. Nurs. Stud. 46, 848–857 (2009)
Castle, N.G., Ferguson, J.C.: What is nursing home quality and how is it measured? Gerontologist 50(4), 426–442 (2010)
McKay, N.L.: Quality choice in Medicaid markets: the case of nursing homes. Quarter. Rev. Econ. Bus. 29(2), 27–40 (1989)
Farsi, M., Filippini, M., Kuenzle, M.: Unobserved heterogeneity in stochastic cost frontier models: an application to Swiss nursing homes. Appl. Econ. 37, 2127–2141 (2005)
Farsi, M., Filippini, M., Lunati, D.: Economies of scale and efficiency measurement in Switzerland’s nursing homes. Swiss J. Econ. Stat. 144, 359–378 (2008)
Castle, N.G., Engberg, J.: Staff turnover and quality of care in nursing homes. Med. Care 43(6), 616–626 (2005)
Castle, N.G., Engberg, J.: The influence of staffing characteristics on quality of care in nursing homes. Health Res. Educ. Trust 42(5), 1822–1847 (2007)
Dormont, B., Martin, C.: Quality of service and cost-efficiency of French nursing homes. 9th European Conference on Health Economics (ECHE), Zurich, July 18–21 (2012)
Spilsbury, K., Hewitt, C., Stirk, L.: The relationship between nurse staffing and quality of care in nursing homes: a systematic review. Int. J. Nurs. Stud. 48, 732–750 (2011)
Rantz, M.J., Hicks, L., Grando, V., Petroski, G.F., Madsen, R.W., et al.: Nursing home quality, cost, staffing, and staff mix. Gerontologist 44(1), 24–38 (2004)
Bostick, J.E., Rantz, M.J., Flesner, M.K., Riggs, C.J.: Systematic review of studies of staffing and quality in nursing homes. J. Am. Med. Dir. Assoc. 7(6), 366–376 (2006)
Mor, V., Berg, K., Angelelli, J., Gifford, D., Morris, J., Moore, T.: The quality of quality measurement in US nursing homes. Gerontologist 43(2), 37–46 (2003)
Arling, G., Karon, S.L., Sainfort, F., Zimmerman, D.R., Ross, R.: Risk adjustment of nursing home quality indicators. Gerontologist 37(6), 757–766 (1997)
Nyman, J.A.: Improving the quality of nursing home outcomes. Med. Care 26, 1158–1171 (1988)
Zinn, J.S., Aaronson, W.E., Rosko, M.D.: The use of standardized indicators as quality improvement tools: an application in Pennsylvania nursing homes. Am. J. Med. Qual. 8, 72–78 (1993a)
Zinn, J.S., Aaronson, W.E., Rosko, M.D.: Strategic groups, performance, and strategic response in the nursing home industry. Health Service Res. 29, 187–205 (1994)
Mukamel, D.B.: Risk-adjusted outcome measures and quality of care in nursing homes. Med. Care 35(4), 367–385 (1997)
Zinn, J.S., Aaronson, W.E., Rosko, M.D.: Variations in the outcomes of care provided in Pennsylvania nursing homes: facility and environmental correlates. Med. Care 31, 475–487 (1993b)
Karon, S.L., Sainfort, F., Zimmerman, D.R.: Stability of nursing home quality indicators over time. Med. Care 37(6), 570–579 (1999)
Mukamel, D.B., Glance, L.G., Li, Y., Weimer, D.L., Spector, W.D., Zinn, J.S., Mosqueda, L.: Does risk adjustment of the CMS quality measures for nursing homes matter? Med. Care 46(5), 532–541 (2008)
Gertler, P.J., Waldman, D.M.: Quality-adjusted cost functions and policy evaluation in the nursing home industry. J. Polit. Econ. 100, 1232–1256 (1992)
Carey, K.: A panel data design for estimation of hospital cost functions. Rev. Econ. Stat. 79(3), 443–453 (1997)
Konetzka, R.T., Yi, D., Norton, E.C., Kilpatrick, K.E.: Effects of Medicare payment changes on nursing home staffing and deficiencies. Health Services Res. 39, 463–488 (2004)
Harrington, C., Woolhandler, S., Mullan, J., Carrillo, H., Himmelstein, D.U.: Does investor ownership of nursing homes compromise the quality of care? Am. J. Public Health 91(9), 1452–1455 (2001)
Bowblis, J.R., Crystal, S., Intrator, O., Lucas, J.A.: Response to regulatory stringency: the case of antipsychotic medication use in nursing homes. Health Econ. 21, 977–993 (2012)
Bowblis, J.R., Lucas, J.A.: The impact of state regulations on nursing home care practices. J. Regul. Econ. 42, 52–72 (2012)
Grabowski, D.C., Feng, Z., Hirth, R.A., Rahman, M., Mor, V.: Effect of nursing home ownership on the quality of post-acute care: an instrumental variables approach. J. Health Econ. 32, 12–21 (2013)
Spector, W.D., Selden, T.M., Cohen, J.W.: The impact of ownership type on nursing home outcomes. Health Econ. 7, 639–653 (1998)
Castle, N.G., Liu, D., Engberg, J.: The association of nursing home compare quality measures with market competition and occupancy rates. J. Healthcare Qual. 30(2), 4–14 (2008)
Forder, J., Allan, S.: Competition in the English nursing homes market. PSSRU Discussion Paper 2820, University of Kent (2011)
Grabowski, D.: A longitudinal study of medicaid payment, private-pay price and nursing home quality. Int. J. Health Care Financ. Econ. 4(1), 5–26 (2004)
Starkey, K.B., Weech-Maldonado, R., Mor, V.: Market competition and quality of care in the nursing home industry. J. Health Care Financ. 32(1), 67–81 (2005)
Gutacker, N., Bojke, C., Daidone, S., Devlin, N.J., Parkin, D., Street, A.: Truly inefficiency or providing better quality of care? Analysing the relationship between risk-adjusted hospital costs and patients’ health outcomes. Health Econ. 22, 931–947 (2013)
Mukamel, D.B., Spector, W.D.: Nursing home costs and risk-adjusted outcome measures of quality. Med. Care 38(1), 78–89 (2000)
Laine, J., Linna, M., Häkkinen, U., Noro, A.: Measuring the productive efficiency and clinical quality of institutional long-term care for the elderly. Health Econ. 14, 245–256 (2005a)
Laine, J., Linna, M., Noro, A., Häkkinen, U.: The cost efficiency and clinical quality of institutional long-term care for the elderly. Health Care Manag. Sci. 8, 149–156 (2005b)
Weech-Maldonado, R., Shea, D., Mor, V.: The relationship between quality of care and costs in nursing homes. Am. J. Med. Qual. 21(1), 40–48 (2006)
Mor, V., Morris, J., Lipsitz, L., Fogel, B.: Benchmarking quality in nursing homes: the Q-Metrics System. Can. J. Qual. Health Care 14, 12–17 (1998)
Institute of Medicine.: Performance Measurement: Accelerating Improvement. The National Academies Press, Washington, DC (2006)
Abdi, H., Williams, L.J.: Principal component analysis. WIREs Comp. Stat. 2, 433–459 (2010)
Jolliffe, I.: Principal component analysis. Encyclopedia of Statistics in Behavioral Science. Wiley, New York (2005)
Evans, R.: “Behavioural” cost functions for hospitals. Can. J. Econ. 4, 198–215 (1971)
Kendall, M.G.: Rank Correlation Methods. Hafner Publishing Co, New York (1955)
Stock, J.H., Watson, M.W.: Heteroskedasticity-robust standard errors for fixed effects panel data regression. NBER Technical Working Paper 323 (2006)
Kezdi, G.: Robust standard errors estimation in fixed-effects panel models. Hung. Stat. Rev. Spec. 9, 96–116 (2004)
Rogers, W.H.: Regression standard errors in clustered samples. Stata Tech. Bull. 13, 19–23 (1994)
Clark, T.S., Linzer, D.A.: Should I use fixed or random effects? Polit. Sci. Res. Methods 3(02), 399–408 (2015)
Cameron, A.C., Trivedi, P.K.: Microeconometrics Using Stata, Revised edn. Stata Press, College Station (2010)
Cameron, A.C., Trivedi, P.K.: Microeconometrics. Methods and Applications. Cambridge University Press, New York (2005)
Shea, J.: Instrument relevance in multivariate linear models: a simple measure. Rev. Econ. Stat. 79(2), 348–352 (1997)
Hayashi, F.: Econometrics. Princeton University Press, Princeton (2000)
Bowblis, J.R., McHone, H.: An instrumental variables approach to post-acute care nursing home quality: Is there a dime’s worth of evidence that continuing care retirement communities provide higher quality? J. Health Econ. 32(5), 980–996 (2013)
Hirth, R.A., Grabowski, D., Feng, Z., Rahman, M., Mor, V.: Effect of nursing home ownership on hospitalization of long-stay residents: an instrumental variables approach. Int. J. Health Care Financ. Econ. 14(1), 1–18 (2014)
Angrist, J.D., Krueger, A.B.: Instrumental variables and the search for identification: from supply and demand to natural experiments. J. Econ. Perspect. 15(4), 69–85 (2001)
Drukker, D.M.: Testing for serial correlation in linear panel-data models. Stata J. 3, 168–177 (2003)
Wooldridge, J.M.: Econometric Analysis of Cross Section and Panel Data. MIT Press, Cambridge (2002)
Hahn, J., Ham, J.C., Roger Moon, H.: The Hausman test and weak instruments. J. Econom. 160(2), 289–299 (2011)
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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
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DOI: https://doi.org/10.1007/s10198-015-0743-4
Keywords
- Nursing home
- Costs
- Nonprofit
- Single quality indicators
- Composite quality indicators
- Cost-quality tradeoff
- Process quality
- Outcome quality
- Structure quality