Skip to main navigation menu Skip to main content Skip to site footer

Original article

Vol. 142 No. 2324 (2012)

Does increased health care spending afford better health care outcomes?

  • Patrick Vavken
  • Geert Pagenstert
  • Christoph Grimm
  • Ronald Dorotka
Cite this as:
Swiss Med Wkly. 2012;142:w13589


AIMS: While it is commonly accepted that health care costs have been rising to unprecedented levels, the question remains whether the increased expenditure actually affords increased health outcomes. It was the objective of this study to search for associations between health care spending and health care outcome, after adjusting for potential confounding variables, using aggregate data collected since the introduction of diagnosis-related groups (DRG) into Austrian health care financing in 1997.

METHODS: Two parameters of health care outcome, mortality and years of life lost (YLL), were regressed on direct and indirect measures of health care spending. We used ordinary least squares, Prais-Winsten, and 2-stage least squares regression in model building to account for autocorrelation and endogeneity.

RESULTS: Our findings showed that health care spending was associated with mortality and YLL reduction. The strongest association among the independent variables was seen for spending for prevention. The strongest association for the dependent variables was seen for cardiovascular disease followed by injuries. Also, socio-economic status (SES) was shown to be an important confounder in all studied associations. Our data suggest that increases in health care spending produce significant increases in health.

CONCLUSION: Health care spending should not be constrained, but instead an optimised resource allocation would afford an increase in health per expenditure. Emphasising spending in prevention and reduction of SES gradients would strengthen this association.


  1. Catalano R. Health, medical care, and economic crisis. N Engl J Med. 2009;360(8):749–51.
  2. Collier R. Recession stresses mental health system. Cmaj. 2009;181(3–4):E48–9.
  3. Janlert U. Economic crisis, unemployment and public health. Scand J Public Health. 2009;37(8):783–4.
  4. Tanne JH. Poor economy means more Americans have trouble paying medical bills. Bmj. 2008;337:a1430.
  5. Martin S, Rice N, Smith PC. Does health care spending improve health outcomes? Evidence from English programme budgeting data. J Health Econ. 2008;27(4):826–42.
  6. Neumann PJ, Tunis SR. Medicare and Medical Technology – The Growing Demand for Relevant Outcomes. N Engl J Med. 2010;362(5):377–9.
  7. Weinstein MC, Skinner JA. Comparative Effectiveness and Health Care Spending – Implications for Reform. N Engl J Med. 2010;362(5):460–5.
  8. Cochrane A, St Leger A, Moore S. Health service “input” and mortality “output” in developed countries. J Epidemiol Community Health. 1978;32:200–2005.
  9. Nolte E, McKee MD. Does health care save lives? The Nuffield Trust, London; 2004.
  10. Nixon J, Ulmann P. The relationship between health care expenditure and health outcomes. European Journal of Health Economics. 2006;7:7–18.
  11. Schuetz P, Albrich WC, Suter I, et al. Quality of care delivered by fee-for-service and DRG hospitals in Switzerland in patients with community-acquired pneumonia. Swiss Med Wkly;141:w13228.
  12. Huitema BE, McKean JW. A simple and powerful test for autocorrelated errors in OLS intervention models. Psychol Rep. 2000;87(1):3–20.
  13. Smith DP. Durbin-Watson statistics for model life tables. Asian Pac Cens Forum. 1983;9(4):7–9.
  14. Dubin J, Watson G. Testing for serial correlation in least square regression. Biometrika. 1950;37:409–28.
  15. Judge G, Griffiths W, Hill R, Lütkepohl H, Lee T. The Theory and Practice of Econometrics. 2nd Ed. New York: Wiley; 1985.
  16. Sargan J. The estimation of economic realtionships using instrumental variables. Econometrica. 1958;26:393–415.
  17. Cragg J, Donald S. Testing identifiability and specification in instrumental variables models. Econometric Theory. 1993;9:222–40.
  18. Peseran M, Taylor L. Diagnostics for IV regresssions. Oxford Bulletin of Economics and Statistics. 1999;61(2):225–81.
  19. Johnston KM, Gustafson P, Levy AR, Grootendorst P. Use of instrumental variables in the analysis of generalized linear models in the presence of unmeasured confounding with applications to epidemiological research. Stat Med. 2008;27(9):1539–56.
  20. Rassen JA, Schneeweiss S, Glynn RJ, Mittleman MA, Brookhart MA. Instrumental variable analysis for estimation of treatment effects with dichotomous outcomes. Am J Epidemiol. 2009;169(3):273–84.
  21. Terza JV, Basu A, Rathouz PJ. Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling. J Health Econ. 2008;27(3):531–43.
  22. Gardner JW, Sanborn JS. Years of potential life lost (YPLL) – what does it measure? Epidemiology. 1990;1(4):322–9.
  23. Mackenbach JP, Bouvier-Colle MH, Jougla E. “Avoidable” mortality and health services: a review of aggregate data studies. J Epidemiol Community Health. 1990;44(2):106–11.
  24. Health at a glance 2009: OECD indicators [database on the Internet]. 2009. Available from:
  25. Edejer TT, Baltussen R, Adam T, et al. WHO GUIDE TO COST-EFFECTIVENESS ANALYSIS. 2003 Nov 5.
  26. Thornton JG, Lilford RJ, Johnson N. Decision analysis in medicine. BMJ1992 Apr 24;304(6834):1099–103.
  27. Furian G, Hnatek-Petrak K. http://wwwkfvat/unfallstatistik/2006;09:427-32.
  28. Probst-Hensch N, Tanner M, Kessler C, Burri C, Kunzli N. Prevention – a cost-effective way to fight the non-communicable disease epidemic: an academic perspective of the United Nations High-level NCD Meeting. Swiss Med Wkly. 2011;141:w13266.
  29. Natterer J, de Buys Roessingh A, Reinberg O, Hohlfeld J. Targeting burn prevention in the paediatric population: a prospective study of children’s burns in the Lausanne area. Swiss Med Wkly. 2009;139(37–38):535–9.