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      Determinants of willingness to pay for health services: a systematic review of contingent valuation studies

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          Abstract

          Introduction

          Stated preference studies are a valuable tool to elicit respondents’ willingness to pay (WTP) for goods or services, especially in situations where no market valuation exists. Contingent valuation (CV) is a widely used approach among stated-preference techniques for eliciting WTP if prices do not exist or do not reflect actual costs, for example, when services are covered by insurance. This review aimed to provide an overview of relevant factors determining WTP for health services to support variable selection.

          Methods

          A comprehensive systematic literature search and review of CV studies assessing determinants of WTP for health services was conducted, including 11 electronic databases. Two of the authors made independent decisions on the eligibility of studies. We extracted all determinants used and related p values for the effect sizes (e.g. reported in regression models with WTP for a health service as outcome variable). Determinants were summarised in systematic evidence tables and structured by thematic domains.

          Results

          We identified 2082 publications, of which 202 full texts were checked for eligibility. We included 62 publications on 61 studies in the review. Across all studies, we identified 22 WTP determinants and other factors from 5 thematic domains: sociodemographic characteristics, perceived threat, perceived benefit, perceived barriers, and other information.

          Conclusion

          Our review provides evidence on 22 relevant determinants of WTP for health services, which may be used for variable selection and as guidance for planning CV surveys. Endogeneity should be carefully considered before interpreting these determinants as causal factors and potential intervention targets.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s10198-022-01437-x.

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          Most cited references89

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          The use of the health belief model to assess predictors of intent to receive the COVID-19 vaccine and willingness to pay

          The development of a vaccine against SARS-CoV-2 infection is on the way. To prepare for public availability, the acceptability of a hypothetical COVID-19 vaccine and willingness to pay (WTP) were assessed to provide insights into future demand forecasts and pricing considerations.
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            Causal diagrams for epidemiologic research.

            Causal diagrams have a long history of informal use and, more recently, have undergone formal development for applications in expert systems and robotics. We provide an introduction to these developments and their use in epidemiologic research. Causal diagrams can provide a starting point for identifying variables that must be measured and controlled to obtain unconfounded effect estimates. They also provide a method for critical evaluation of traditional epidemiologic criteria for confounding. In particular, they reveal certain heretofore unnoticed shortcomings of those criteria when used in considering multiple potential confounders. We show how to modify the traditional criteria to correct those shortcomings.
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              Control of Confounding and Reporting of Results in Causal Inference Studies: Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals

                Author and article information

                Contributors
                caroline.steigenberger@umit-tirol.at
                Journal
                Eur J Health Econ
                Eur J Health Econ
                The European Journal of Health Economics
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1618-7598
                1618-7601
                15 February 2022
                : 1-28
                Affiliations
                [1 ]GRID grid.41719.3a, ISNI 0000 0000 9734 7019, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, , UMIT - University for Health Sciences, Medical Informatics and Technology, ; Hall in Tirol, Austria
                [2 ]Division of Health Technology Assessment, ONCOTYROL, Center for Personalized Cancer Medicine, Innsbruck, Austria
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Institute for Technology Assessment, Department of Radiology, Massachusetts General Hospital, , Harvard Medical School, ; Boston, MA USA
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Center for Health Decision Science, Departments of Epidemiology and Health Policy and Management, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [5 ]GRID grid.5771.4, ISNI 0000 0001 2151 8122, Department of Economics, Faculty of Economics and Statistics, , University of Innsbruck, ; Innsbruck, Austria
                Author information
                http://orcid.org/0000-0003-0713-2623
                http://orcid.org/0000-0001-6425-7671
                http://orcid.org/0000-0002-8628-5072
                Article
                1437
                10.1007/s10198-022-01437-x
                8853086
                35166973
                8acc3adf-1cfc-478e-ae6a-4b1f62e32f04
                © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 28 May 2021
                : 12 January 2022
                Categories
                Original Paper

                Economics of health & social care
                contingent valuation,willingness to pay,public health,sociodemographic determinants,economic valuation,literature review,d61 allocative efficiency,cost–benefit analysis; i11 analysis of health care markets

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