Health Systems and Policy Research

  • ISSN: 2254-9137
  • Journal h-index: 12
  • Journal CiteScore: 1.81
  • Average acceptance to publication time (5-7 days)
  • Average article processing time (30-45 days) Less than 5 volumes 30 days
    8 - 9 volumes 40 days
    10 and more volumes 45 days
20+ Million Readerbase
Indexed In
  • China National Knowledge Infrastructure (CNKI)
  • Cosmos IF
  • Directory of Research Journal Indexing (DRJI)
  • OCLC- WorldCat
  • Publons
  • Geneva Foundation for Medical Education and Research
  • Euro Pub
  • Google Scholar
Share This Page
Recommended Webinars & Conferences

35th International conference on Mental Health and Psychiatry

Amsterdam, Netherlands

30th Global Nursing and Health Care Conference

Amsterdam, Netherlands

16th World Congress on Nursing & Health care

Amsterdam, Netherlands

Editorial - (2023) Volume 10, Issue 1

Value Judgments of Social, Ethical and Others in Health Economics Modelling

Maarten Siliva*
Department of Geography, University of Texas at Austin, United States
*Correspondence: Maarten Siliva, Department of Geography, University of Texas at Austin, United States, Email:

Received: 02-Jan-2023, Manuscript No. Iphspr-22-13388; Editor assigned: 05-Jan-2023, Pre QC No. Iphspr-22-13388; Reviewed: 19-Jan-2023, QC No. Iphspr-22-13388; Revised: 23-Jan-2023, Manuscript No. Iphspr- 22-13388 (R); Published: 31-Jan-2023, DOI: 10.36648/2254- 9137.23.10.168


In health economics, modelling is a key approach of investigation. Recent literature has highlighted how social and ethical norms impact model building in other modeling-intensive domains, such as climate science. However, no comparable research in health economics has been conducted. Using philosophical theory and qualitative interviews, this study investigated the role of social, ethical, and other values in health economics modelling in British Columbia, Canada. We spoke with 22 experts in the field of health economics modelling. The study's findings support four philosophical reasons that social and ethical values play a crucial part in scientific investigation and show how these arguments might be applied to health economics modelling. This article emphasises how social values influence early modelling decisions, model assumptions, and trade-offs between acceptable model options. Characteristics and establishing evidence criteria these findings highlight a number of modelling choices that should be the subject of future health economics research, especially that which seeks to take patient and societal values into account.


Health Economics; Modelling Patient-Oriented Research; Patient and Public Involvement; Values; Qualitative


Despite decades of discussion, academics from many fields still have varied perspectives on the place of values in science [1]. Certain assertions are unarguable, such as that societal values are reflected in scientific study subjects, ethical restrictions on methodology, and evidence-based policy choices [2]. There is considerable debate over just which judgements, if any, should be made solely based on scientific principles and whether it is always feasible to discern between scientific and social principles [3]. There haven't been many participants from the field of health economics in this argument, which is predominantly held inside philosophy of science [4]. The National Institute for Health and Care Excellence's efficiency and equity judgements are highlighted by Rawlins and Culyer in their description of social value judgements in cost-effectiveness analyses [5].


They claim that "the primary societal value judgements related efficiency" are used in CEA To offer a comprehensive list of social value judgements in CEA, italics have been included that pertain to the health measure employed and the range of costs and benefits considered [6]. However, their claim begs the issue of what such a list may resemble, not just in CEA but also in general health economics modelling [7]. In order to demonstrate where value judgements are made in health economics modelling, this study will analyse philosophical claims that social values have an impact on all areas of scientific investigation [8]. The remainder of this essay is organised as follows: Section 2 covers major ideas in the literature on philosophy's ideals in science, defines terminology, and summarises reasons for a crucial position [9]. In view of endeavours to include patient and public values into health research, Section contends that there is a need for greater clarity on the role of social value judgements in health economics models [10]. In the section, health economists participated in a qualitative research where they were educated on the philosophical underpinnings and encouraged to share their opinions and relevant examples from practise [11]. Section 5 comes to an end [12]. The VFI has been thoroughly explained elsewhere [13]. For the sake of this discussion, it is sufficient to reiterate that supporters of VFI have maintained since Levi's day that only scientific standards (or "canons of scientific inference") should be used to accept a hypothesis or give it a probability [14]. Consequently, the VFI is essentially a two-part normative statement, stating what phases of scientific study can be influenced by values [15].


However, the statement's conventional formulation is lacking, and both of its components are up for discussion. In regard to the VFI, we briefly explore differences in values and stages of scientific investigation below. In a famous speech, Kuhn defined five scientific standards and referred to them as "values," implying that scientists assess and compare ideas based on their correctness, simplicity, internal and external consistency, breadth of scope, and fruitfulness. Predictive accuracy, internal coherence, external consistency, unifying power, fertility, and simplicity were added to the list by McMullin as "epistemic" qualities, desiderata whose pursuit promotes knowledge. McMullin contrasted "nonepistemic" values political, moral, social, religious, and other values related to human beings—with epistemic values. Objectives apart from knowledge. The dichotomy between epistemic and non-epistemic values, or an equivalent distinction, constitutive vs contextual values, has frequently been cited in the McMullin argument about the viability of the VFI. Rawlins and Culyer make a distinction between social and scientific value judgements in health economics ("what is good and bad in the existing research"). We use Winsberg's definition of social values in this essay, which is as follows: "the assessments of any agent or group of agents of what is important and valuable in the typical social and ethical senses and of what is to be avoided, and to what extent noting that some social values are ethical values. We contrast scientific standards, which we view as epistemic values or values that in some way advance knowledge, with societal values.



Conflict of Interest



  1. Kumar S (2013) Health in international development Agenda: Present, past and future. Indian J Community Med 38: 129-131.
  2. Indexed at, Crossref, Google Scholar

  3. Papanicolas I, Kringos D, Klazinga NS (2013) Health system performance comparison: new directions in research and policy. Health Policy 112: 1-3.
  4. Indexed at, Crossref, Google Scholar

  5. Veillard J, Moses McKeag A, Tipper B (2013) Methods to stimulate national and sub-national benchmarking through international health system performance comparisons: a Canadian approach. Health Policy 112: 141-147.
  6. Indexed at, Crossref, Google Scholar

  7. Giuffrida A, Gravelle H, Roland M (1999) Measuring quality of care with routine data: avoiding confusion between performance indicators and health outcomes. Brit Med J 319: 94.
  8. Indexed at, Crossref, Google Scholar

  9. Crampton P, Perera R, Crengle S (2004) What makes a good performance indicator? Devising primary care performance indicators for New Zealand. New Zeal J Med 117: U820.
  10. Indexed at, Crossref, Google Scholar

  11. Arah O, Westert GP, Hurst J (2006)A conceptual framework for the OECD Health Care Quality Indicators Project.Int J Qual Health Care18: 5-13.
  12. Indexed at, Crossref, Google Scholar

  13. Donabedian A (1966) Evaluating the quality of medical care. Milbank 44: 166-206.
  14. Indexed at, Crossref, Google Scholar

  15. Arah OA, Klazinga NS, Delnoij DMJ (2003) Conceptual frameworks for health systems performance: a quest for effectiveness, quality, and improvement. Int J Qual Health Care 15: 377-398.
  16. Indexed at, Crossref, Google Scholar

  17. Arah OA, Westert GP (2005) Correlates of health and healthcare performance: applying the Canadian health indicators framework at the provincial-territorial level. BMC Health Serv Res 5: 76.
  18. Indexed at, Crossref, Google Scholar

  19. Ten Asbroek AHA, Arah OA, Geelhoed J (2004) Developing a national performance indicator framework for the Dutch health system. Int J Qual Health Care 16: 65-71.
  20. Indexed at, Crossref, Google Scholar

  21. Mannion R, Braithwaite J (2012) Unintended consequences of performance measurement in healthcare: 20 salutary lessons from the English National Health Service. Intern Med J 42: 569-574.
  22. Indexed at, Crossref, Google Scholar

  23. Mainz J, Krog BR, Bjørnshave B (2004) Nationwide continuous quality improvement using clinical indicators: the Danish National Indicator Project. Int J Qual Health Care 16: i45-i50.
  24. Indexed at, Crossref, Google Scholar

  25. Berwick D, James B, Coye M (2003) Connections between quality measurement and improvement. Medical Care 41: I30-I38.
  26. Indexed at, Crossref, Google Scholar

  27. Collopy B (2000) Clinical indicators in accreditation: an effective stimulus to improve patient care. Int J Qual Health Care 12: 211-216.
  28. Indexed at, Crossref, Google Scholar

  29. Adair CE, Simpson E, Casebeer AL (2006) Performance management in healthcare: part II – state of the science findings by stage of the performance measurement process. Health Policy 2: 56-78.
  30. Indexed at, Crossref, Google Scholar

Citation: Maarten Siliva (2023) Value Judgments of Social, Ethical and Others in Health Economics Modelling. Health Sys Policy Res Vol. 10 No. Issue 1:168