Examining the Effect of Depicting a Patient Affected by a Negative Reimbursement Decision in Healthcare on Public Disagreement with the Decision

Abstract

Background

The availability of increasingly advanced and expensive new health technologies puts considerable pressure on publicly financed healthcare systems. Decisions to not—or no longer—reimburse a health technology from public funding may become inevitable. Nonetheless, policymakers are often pressured to amend or revoke negative reimbursement decisions due to the public disagreement that typically follows such decisions. Public disagreement may be reinforced by the publication of pictures of individual patients in the media. Our aim was to assess the effect of depicting a patient affected by a negative reimbursement decision on public disagreement with the decision.

Methods

We conducted a discrete choice experiment in a representative sample of the public (n = 1008) in the Netherlands and assessed the likelihood of respondents’ disagreement with policymakers’ decision to not reimburse a new pharmaceutical for one of two patient groups. We presented a picture of one of the patients affected by the decision for one patient group and “no picture available” for the other group. The groups were described on the basis of patients’ age, health-related quality of life (HRQOL) and life expectancy (LE) before treatment, and HRQOL and LE gains from treatment. We applied random-intercept logit regression models to analyze the data.

Results

Our results indicate that respondents were more likely to disagree with the negative reimbursement decision when a picture of an affected patient was presented. Consistent with findings from other empirical studies, respondents were also more likely to disagree with the decision when patients were relatively young, had high levels of HRQOL and LE before treatment, and large LE gains from treatment.

Conclusions

This study provides evidence for the effect of depicting individual, affected patients on public disagreement with negative reimbursement decisions in healthcare. Policymakers would do well to be aware of this effect so that they can anticipate it and implement policies to mitigate associated risks.