Page 44 - GHES-3-2

Global Health Economics and
Sustainability
Random price generators in health policy

Appendix

Appendix A1. Stated-preference and revealed-preference studies
Contingent valuation methods are also called stated preference studies. They include different methods, such as conjoint
valuation, willingness to pay, or ability to pay (see UN pricing methodologies, 2003). Usually, they are used in health care at
the individual level or for minorities or subgroups of the populations to analyze large heterogeneity in preferences. However,
incorporating such microdata, often based on intentional rather than real or effective data in macroeconomic models,
remains controversial. Fusion studies with both types of types are limited in healthcare (SP-RP modeling). Discrete choice
modeling can use either intentional or real data (when they exist); it is used in healthcare systems to adjust the preferences of
providers and patients or in population studies. The growth of digital markets also generates additional methods to analyze
their impact on medical markets, such as recent algorithmic pricing methods and neural networks used by big tech players
(e.g., Amazon); however, it is out of the scope of this paper
Appendix A2. Data models and econometrics
Economic model development must also incorporate the fast pace of technological changes (e.g., AI and biotech or genomic
revolutions). Such changes may require additional decision points with economic markers for payers, impacting various
cost-sharing mechanisms for patients and their dependents. Moreover, fast scientific discoveries in genomic medicine push
the frontiers of discovery on humans, with changes in coding, decoding, and re-coding DNA, for example, regenerative
medicine or recent R DNA technologies to fight the COVID-19 pandemic (e.g., BioNTech/Pfizer or Moderna Vaccines).
Machine learning techniques also accelerate using DATA models with genetic and biological variables and clinical,
epidemiological, or socioeconomic predictors used by econometricians. Data models introduce other types of uncertainties:
potential biases (e.g., data from geographical sites that do not automatically match people’s ancestry), non-obvious
interpretation, transferability of study results, and uncertain biomarkers in some genetic disease models (issues of reliability
of early detection techniques).
Therefore, a more comprehensive framework for decision-making processing and selection of alternatives is required;
additional theories may be helpful, for instance, the C-K theory proposed by Hatchuel et al. (2012) and Colorni &
Tsoukias (2018)

Volume 3 Issue 2 (2025) 36 https://doi.org/10.36922/ghes.3579