Exploring Health System Responsiveness in Ambulatory Care and Disease Management and its Relation to Other Dimensions of Health System Performance (RAC) – Study Design and Methodology

Document Type: Study Protocol


1 Department of Health Care Management, Berlin Centre for Health Economics Research, Technische Universität Berlin, Berlin, Germany

2 Wissenschaftliches Institut der TK für Nutzen und Effizienz im Gesundheitswesen (WINEG), Hamburg, Germany


The responsiveness of a health system is considered to be an intrinsic goal of  health systems and an essential aspect in performance assessment. Numerous studies have analysed health system responsiveness and related concepts, especially across different countries and health systems. However, fewer studies have applied the concept for the evaluation of specific healthcare delivery structures and thoroughly analysed its determinants within one country. The aims of this study are to assess the level of perceived health system responsiveness to patients with chronic diseases in ambulatory care in Germany and to analyse the determinants of health system responsiveness as well as its distribution across different population groups.
Methods and Analysis
The target population consists of chronically ill people in Germany, with a focus on patients suffering from type 2 diabetes and/or from coronary heart disease (CHD). Data comes from two different sources: (i) cross-sectional survey data from a postal survey and (ii) claims data from a German sickness fund. Data from both sources will be linked at an individual-level. The postal survey has the purpose of measuring perceived health system responsiveness, health related quality of life, experiences with disease management programmes (DMPs) and (subjective) socioeconomic background. The claims data consists of information on (co)morbidities, service utilization, enrolment within a DMP and sociodemographic characteristics, including the type of residential area.
RAC is one of the first projects linking survey data on health system responsiveness at individual level with claims data. With this unique database, it will be possible to comprehensively analyse determinants of health system responsiveness and its relation to other aspects of health system performance assessment. The results of the project will allow German health system decision-makers to assess the performance of nonclinical aspects of healthcare delivery and their determinants in two important areas of health policy: in ambulatory and chronic disease care.


Main Subjects

  1. Murray CJ, Frenk J. A framework for assessing the performance of health systems. Bull World Health Organ. 2000;78:717-731. doi:10.1590/S0042-96862000000600004
  2. Üstün, TB, Chatterji S, Villanueva M, et al. WHO Multi-country Survey Study on Health and Responsiveness 2000-2001. In: Murray CJ, Evans DB, eds. Health System Performance Assessment: Debates, Methods and Empiricism. Geneva: World Health Organization; 2003:761-796.
  3. Üstün TB, Sonmath C, Abdelhay M, Murray CJL, WHS Collaborating Group. The World Health Surveys. In: Murray CJ, Evans DB, eds. Health System Performance Assessment: Debates, Methods and Empiricism. Geneva: World Health Organization; 2003:797-808.
  4. Sirven N, Santos-Eggimann B, Spagnoli J. Comparability of health care responsiveness in europe. Soc Indic Res. 2011;105:255-271. doi:10.1007/s11205-011-9880-z
  5. Rockers PC, Kruk ME, Laugesen MJ. Perceptions of the health system and public trust in government in low- and middle-income countries: evidence from the World Health Surveys. J Health Polit Policy Law. 2012;37:405-437. doi:10.1215/03616878-1573076
  6. Bramesfeld A, Wedegärtner F, Elgeti H, Bisson S. How does mental health care perform in respect to service users' expectations? Evaluating inpatient and outpatient care in Germany with the WHO responsiveness concept. BMC Health Serv Res. 2007;7:99. doi:10.1186/1472-6963-7-99
  7. Liabsuetrakul T, Petmanee P, Sanguanchua S, Oumudee N. Health system responsiveness for delivery care in southern Thailand. Int J Qual Health Care. 2012;24:169-175. doi:10.1093/intqhc/mzr085
  8. Peltzer K, Phaswana-Mafuya N. Patient experiences and health system responsiveness among older adults in South Africa. Glob Health Action. 2012;5:1-11. doi:10.3402/gha.v5i0.18545
  9. Busse R, Siegel M, Sundmacher L. Gesundheitsökonomisches Zentrum Berlin (Berlin HECOR). Public Health Forum. 2013;21:22.e1-22.e3. doi:10.1016/j.phf.2013.09.009
  10. Busse R, Blümel M. Germany: health system review. Health Systems in Transition. 2014;16:1–296.
  11. Bundesversicherungsamt. Zulassung der strukturierten Behandlungsprogramme (Disease Management Programme – DMP) durch das Bundesversicherungsamt (BVA). http://www.bundesversicherungsamt.de/druckversion/weitere-themen/disease-management-programme/zulassung-disease-management-programme-dmp.html#c204. Updated December 2014. Accessed Jan 4, 2015.
  12. Röttger J, Blümel M, Fuchs S, Busse R. Assessing the responsiveness of chronic disease care - Is the World Health Organization's concept of health system responsiveness applicable? Soc Sci Med. 2014;113:87-94. doi:10.1016/j.socscimed.2014.05.009
  13. Hak T, van der Veer K, Jansen H. The Three-Step Test-Interview (TSTI): an observation-based method for pretesting self-completion questionnaires. Survey Research Methods. 2008;2:143-150.
  14. King G, Murray CJL, Salomon JA, Tandon A. Enhancing the validity and cross-cultural comparability of measurement in survey research. Am Pol Sci Rev. 2004;98:191-207. doi:10.1017/S000305540400108X
  15. Murray CJ, Özaltin E, Tandon A, Salomon JA, Sadana R, Chatterji S. Empirical evaluation of the anchoring vignette approach in health surveys. In: Murray CJ, Evans DB, eds. Health System Performance Assessment: Debates, Methods and Empiricism. Geneva: World Health Organization; 2003:369-399.
  16. Agborsangaya CB, Lahtinen M, Cooke T, Johnson JA. Comparing the EQ-5D 3L and 5L: measurement properties and association with chronic conditions and multimorbidity in the general population. Health Qual Life Outcomes. 2014;12:74. doi:10.1186/1477-7525-12-74
  17. Kind P, Brooks R, Rabin R. EQ-5D concepts and methods: a developmental history. Dordrecht: Springer; 2005.
  18. Rabin R, de Charro F. EQ-5D: a measure of health status from the EuroQol Group. Ann Med. 2001;33:337-343.
  19. Hegar R, Mielck A. Subjektiver sozialer Status. Prävention und Gesundheitsförderung 2010;5:389-400. doi:10.1007/s11553-010-0261-2
  20. Hegar R, Döring A, Mielck A. Relevance of 'subjective social status' for health risks and health status - results from the KORA-F4-study [in German]. Gesundheitswesen. 2012;74:306-314. doi:10.1055/s-0031-1283551
  21. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis. 1987;40:373-383. doi:10.1016/0021-9681(87)90171-8
  22. Charlson ME, Charlson RE, Peterson JC, Marinopoulos SS, Briggs WM, Hollenberg JP. The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients. J Clin Epidemiol. 2008;61:1234-1240. doi:10.1016/j.jclinepi.2008.01.006