Advertisement

The European Journal of Health Economics

, Volume 16, Issue 4, pp 365–375 | Cite as

The impact of office-based care on hospitalizations for ambulatory care sensitive conditions

  • Leonie Sundmacher
  • Thomas Kopetsch
Original Paper

Abstract

Objective

The aim of the study was to quantify the impact of specific medical services in the ambulatory sector (SA) on hospitalizations for ambulatory care sensitive conditions (ACSCs), in order to be able to assess whether and under what conditions specific ambulatory treatments could serve to lower the hospitalization rate.

Data source

The analysis is based on administrative data showing the complete provision of medical services in the ambulatory sector in Germany and data from other sources. The data were provided by the National Association of Statutory Health Insurance Physicians, the Federal Statistical Agency, the Federal Office of Construction and Regional Planning, and the Federal Insurance Agency.

Study design

The impact of an increase in specific medical services on hospitalizations for ACSCs was estimated using linear spatial models at the level of the 413 German counties and county boroughs for the years 2007 and 2008. To allow for an undistorted estimation of the coefficients, SA and physician density were instrumented using a two-stage ‘least squares’ approach. The SA and the rate of hospitalizations for ACSCs were age-standardized. In the models, a well-defined set of covariates was controlled for.

Principal findings

According to the models, an additional € spent on ACSC treatment decreases the rate of hospitalizations for ACSCs for women and men up to an annual Uniform Value Scale For Doctors’ Fees point value of approximately 6,891 and 5,735, respectively. The correlation is not linear but, as suspected, exhibits diminishing marginal returns.

Conclusions

Our models suggest that additional medical services reduce the rate of hospitalizations for ACSCs but that this correlation depends on the absolute level of office-based services in a county, all covariates being held equal. Ceteris paribus counties with a very high volume of services exhibit ‘flat-of-the-curve medicine’, while counties with a very low current level of specific medical services benefit most from an increase in those specific services.

Keywords

Ambulatory care sensitive indications (ACSC) Avoidable hospitalizations Outcomes Germany 

JEL Classification

I120 I18 C490 

References

  1. 1.
    Ansari, Z., Laditka, J.N., Laditka, S.B.: Access to health care and hospitalization for ambulatory care sensitive conditions. Med. Care Res. Rev. 63(6), 719–741 (2006)CrossRefPubMedGoogle Scholar
  2. 2.
    Anselin, L.: Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht (1988)CrossRefGoogle Scholar
  3. 3.
    Augurzky, B., Kopetsch, T., Schmitz, H.: What accounts for the regional differences in the utilisation of hospitals in Germany? Eur. J. Health Econ. (2012). doi: 10.1007/s10198-012-0407-6 PubMedGoogle Scholar
  4. 4.
    Billings, J., Zeitel, L., Lukomnik, J., Carey, T.S., Blank, A.E., Newman, L.: Impact of socioeconomic status on hospital use in New York City. Health Aff. 12(1), 162–173 (1993)CrossRefGoogle Scholar
  5. 5.
    Bindman, A.B., Grumbach, K., Osmond, D., Komaromy, M., Vranizan, K., Lurie, N., Billings, J., Stewart, A.: Preventable hospitalizations and access to health care. J. Am. Med. Assoc. 274(4), 305–311 (1995)CrossRefGoogle Scholar
  6. 6.
    Brown, A., Goldacre, M.J., Hicks, N., Rourke, J.T., McMurty, R.Y., Brown, J.D., Anderson, G.M.: Hospitalization for ambulatory care-sensitive conditions: a method for comparative access and quality studies using routinely collected statistics. Can. J. Publ. Health 92(2), 155–159 (2001)Google Scholar
  7. 7.
    Cameron, A.C., Trivedi, P.K.: Microeconometrics: Methods and Applications. Cambridge University Press, New York (2005)CrossRefGoogle Scholar
  8. 8.
    Caminal, J., Starfield, B., Sanchez, E., Casanova, C., Morales, M.: The role of primary care in preventing ambulatory care sensitive conditions. Eur. J. Publ. Health 14(3), 246–251 (2004)CrossRefGoogle Scholar
  9. 9.
    Evans, R.: Supplier-Induced Demand: Some empirical evidence and implications. In: Perlman, M. (ed.) The Economics of Health and Medical Care: Proceedings of a Conference Held by the International Economic Association at Tokyo, pp. 162–173. Palgrave Macmillan, New York (1974)Google Scholar
  10. 10.
    Falik, M., Needleman, J., Well, B.L., Korb, J.: Ambulatory care sensitive hospitalizations and emergency visits: experiences of Medicaid patients using federally qualified health centers. Med. Care 39(6), 551–561 (2001)CrossRefPubMedGoogle Scholar
  11. 11.
    Freund, T., Campbell, S.: Strategies for reducing potentially avoidable hospitalizations for ambulatory care-sensitive conditions. Ann. Fam. Med. 11, 363–370 (2013)CrossRefPubMedCentralPubMedGoogle Scholar
  12. 12.
    Fuchs, V.: More variation in use of care, more flat-of-the-curve medicine. Health Aff. 23, 104–107 (2004) Google Scholar
  13. 13.
    Greenland, S., Robin, J.M.: Empirical-Bayes adjustments for multiple comparisons are sometimes useful. Epidemiology 2(4), 244–251 (1991)CrossRefPubMedGoogle Scholar
  14. 14.
    Greineder, D.K., Loane, K.C., Parks, P.: Reduction in resource utilization by an asthma outreach program. Arch. Pediatr. Adolesc. Med. 149(4), 415–420 (1995)CrossRefPubMedGoogle Scholar
  15. 15.
    Hausman, J.A.: Specification tests in econometrics. Econometrica 46(6), 1251–1271 (1978)CrossRefGoogle Scholar
  16. 16.
    Jordan, S., von der Lippe E.: Angebote der Prävention: Wer nimmt teil? In: GBE-Kompakt 3(5) (ed.) Robert Koch-Institute, Berlin (2012) Google Scholar
  17. 17.
    Labelle, R., Stoddart, G., Rice, T.: A re-examination of the meaning and importance of supplier induced demand. J. Health Econ. 13(3), 347–368 (1994)CrossRefPubMedGoogle Scholar
  18. 18.
    Laditka, J.N., Laditka, S.B., Probst, J.C.: Health care access in rural areas: evidence that hospitalizations for ambulatory care-sensitive conditions in the United States may increase with the level of rurality. Health Place 15(3), 761–770 (2009)CrossRefGoogle Scholar
  19. 19.
    Laditka, J.N.: Physician supply, physician diversity, and outcomes of primary care for older persons in the United States. Health Place 10(3), 231–244 (2004)CrossRefPubMedGoogle Scholar
  20. 20.
    Martikainen, P., Ferrie, J.: Populations at Special Health Risk: Unemployed: Unemployment and Job Insecurity. In: Heggenhougen, H.K., Quah, S.R. (eds.) International Encyclopedia of Public Health, pp. 268–276. Elsevier, San Diego (2008)CrossRefGoogle Scholar
  21. 21.
    Millman, M.: Access to Health Care in America. National Academies Press, Washington, D.C (1993)Google Scholar
  22. 22.
    Purdy, S., Griffin, T., Salisbury, C., Sharp, C.: Ambulatory care sensitive conditions: terminology and disease coding need to be more specific to aid policy makers and clinicians. Publ. Health 123(2), 169–173 (2009)CrossRefGoogle Scholar
  23. 23.
    Rheinisch-Westfälisches Institut für Wirtschaftsforschung (RWI): Mengenentwicklung und Mengensteuerung stationärer Leistungen, Endbericht Forschungsprojekt im Auftrag des GKV-Spitzenverbandes. RWI-Projektbericht. Essen, Rheinisch-Westfälisches Institut für Wirtschaftsforschung (2012)Google Scholar
  24. 24.
    Ricketts, T.C., Randolph, R., Howard, H.H., Pathman, D., Carey, T.: Hospitalization rates as indicators of access to primary care. Health Place 7(1), 27–38 (2001)CrossRefPubMedGoogle Scholar
  25. 25.
    Robert-Koch Institute (RKI): Daten und Fakten: Ergebnisse der Studie “Gesundheit in Deutschland aktuell 2010“. Beiträge zur Gesundheitsberichterstattung des Bundes. Rober-Koch Institute, Berlin (2012)Google Scholar
  26. 26.
    Sachverständigenrat zur Begutachtung der Entwicklung im Gesundheitswesen (SVR). Wettbewerb an der Schnittstelle zwischen ambulanter und stationärer Gesundheitsversorgung, Sondergutachten 2012. Baden–Baden (2012)Google Scholar
  27. 27.
    Sanderson, C., Dixon, J.: Conditions for which onset or hospital admission is potentially preventable by timely and effective ambulatory care. J. Health Serv. Res. Policy 5(4), 222–230 (2000)PubMedGoogle Scholar
  28. 28.
    Solberg, L.I., Peterson, K.E., Ellis, R.W., et al.: The Minnesota project: a focused approach to ambulatory quality assessment. Inquiry 27(4), 359–367 (1990)PubMedGoogle Scholar
  29. 29.
    Staiger, D.O., Stock, J.H.: Instrumental variables regression with weak instruments. Econometrica 65(3), 557–586 (1997)CrossRefGoogle Scholar
  30. 30.
    Sundmacher, L.: Trends and levels of avoidable mortality among districts: healthy benchmarking in Germany. Health Policy 109(3), 281–289 (2012)CrossRefPubMedGoogle Scholar
  31. 31.
    Sundmacher, L., Kimmerle, J., Latzitis, N., Busse, R.: Vermeidbare Sterbefälle in Deutschland: räumliche Verteilung und regionale Konzentrationen. Gesundheitswesen 73(4), 229–237 (2011)CrossRefPubMedGoogle Scholar
  32. 32.
    Sundmacher, L., Busse, R.: The impact of physician supply on avoidable cancer mortality in Germany. Health Policy 103, 53–62 (2011)CrossRefPubMedGoogle Scholar
  33. 33.
    Weissman, J.S., Gatsonis, C., Epstein, A.M.: Rates of avoidable hospitalization by insurance status in Massachusetts and Maryland. J. Am. Med. Assoc. 268(17), 2388–2394 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Health Services ManagementLudwig Maximilians University MunichMunichGermany
  2. 2.National Association of Statutory Health Insurance PhysiciansBerlinGermany

Personalised recommendations