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Institutions, culture, and chronically ill patients’ willingness to pay for medical treatment: a meta-regression analysis

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Abstract

Aim

The aim of this study is to provide a synthesis of prior research of estimates of the willingness to pay (WTP) for chronic disease treatment and examines the impact of economic and political institutions, cultures, and other important factors on WTP.

Subject and method

The paper applies meta-regression analysis to 1053 estimates of WTP from 188 medical treatment studies from 40 countries.

Results

The results suggest that patients assign higher values for medical treatment in more capitalistic and more democratic countries. Cultural traits also appear to matter, with higher WTP in societies characterized by individualism and indulgence. Further, disease types matter. Compared to cancer, WTP is lower for diabetes, obesity, asthma, and heart disease treatment, but higher for amyotrophic lateral sclerosis therapy. GDP per capita is positively associated with WTP. The higher public health expenditure is in a country, the higher citizens value health benefits. If individuals expect to live longer, then they will spend less on their health. The published studies estimate a lower value than unpublished studies.

Conclusion

This paper presents a comprehensive synthesis of estimates of chronically ill patients’ WTP for medical treatment and identifies the key factors determining WTP through a meta-regression analysis. The main finding is that economic and political institutions, cultural traits, the types of disease, socio-economic characteristics, and valuation methods all influence WTP estimates.

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Notes

  1. Following Roland (2004), this paper categorizes culture as an institution.

  2. Hofstede (1991) defined culture as “a shared set of values, beliefs or lifestyles that differentiate one society from another”.

  3. Referring to doctors (suppliers) that know much more regarding sickness and therapy than their patients whose treatment procedures and practices are determined by the doctors. Thus, the doctor is in a position to determine demand for medical care. This can result in market failure (Mwabu 1997)

  4. North (1990) defined institutions as “the rules of the game in a society”.

  5. Vatn, 2005a, b defined institutions as “the conventions, norms and formally sanctioned rules of society. They provide expectations, stability and meaning essential to human existence and coordination”.

  6. Mwabu (1997) noted that “a cure from such diseases after receiving medical treatment is a public good whose benefits cannot be excluded from persons unwilling to pay curative health services”.

  7. Jacobs (1997) defined VAIs as “a set of rules about who participates and on what premises, what form the data is selected, and how the data is produced and processed”.

  8. List of chronic diseases based on the Council for Medical Schemes (CMS) and the Ministry of Public Health of Thailand

  9. Four studies dropped out because insufficient information on governance data were not available for time periods.

  10. See Appendix 1 and 2

  11. See the detail in Appendix 3

  12. See the detail in Appendix 4

  13. In this dataset, payment vehicles were out-of-pocket costs with 163 studies, co-payment with ten studies, income tax with nine studies, and insurance with six studies.

  14. The coefficients on paralysis, Parkinson’s, and tuberculosis are not robust because they are based on one observation.

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Acknowledgements

The author would like to thank seminar participants at the 2018 Melbourne MAER-net Colloquium, and Prof. Chris Doucouliagos and Prof. Helen Scarborough for helpful comments and suggestions on this work.

Funding

This research was supported by grants funded by the Research and Development Institute, Kasetsart University, Sakon Nakhon Province Campus, Thailand.

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  1. Faculty of Economics, Chiang Mai University, 239 HuayKaew Road, Suthep, Muang, Chiang Mai, 50200, Thailand

    Mayula Chaikumbung

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Chaikumbung, M. Institutions, culture, and chronically ill patients’ willingness to pay for medical treatment: a meta-regression analysis. J Public Health (Berl.) 30, 959–971 (2022). https://doi.org/10.1007/s10389-020-01372-2

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