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E-learning and health inequality aversion: A questionnaire experiment.

Richard Cookson1, Shehzad Ali2, Aki Tsuchiya3

  • 1Centre for Health Economics, University of York, York, UK.

Health Economics
|July 24, 2018
PubMed
Summary
This summary is machine-generated.

E-learning interventions improved understanding of health trade-off questions, reducing exclusive focus on health inequality. Both video and spreadsheet methods showed effects, though spreadsheets also introduced non-egalitarian views.

Keywords:
distributional cost-effectiveness analysisempirical ethicsempirical social choicehealth inequalityinequality aversion

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Area of Science:

  • Health Economics
  • Public Health Policy
  • Behavioral Science

Background:

  • Questionnaire data on public health trade-offs can inform policy.
  • Understanding public health inequality aversion is crucial for policymakers.
  • Standard trade-off questions may be misunderstood, leading to biased responses.

Purpose of the Study:

  • To develop and test e-learning interventions to improve understanding of health trade-off questions.
  • To assess the impact of interventions on respondents' prioritization of health inequality reduction.
  • To evaluate the effectiveness of video animation and spreadsheet-based feedback.

Main Methods:

  • Two e-learning interventions were developed: a video animation and a spreadsheet-based questionnaire.
  • Interventions aimed to clarify hypothetical trade-offs between total health improvement and health inequality reduction.
  • Respondents' answers to trade-off questions were analyzed before and after interventions.

Main Results:

  • Both interventions significantly reduced the proportion of respondents prioritizing only health inequality.
  • Median responses still indicated a high level of health inequality aversion.
  • The spreadsheet intervention uniquely introduced a notable minority of non-egalitarian responses.

Conclusions:

  • E-learning interventions can mitigate biases in understanding complex health trade-off questions.
  • While effective, e-learning may introduce new biases, warranting further research.
  • These interventions show potential for improving survey data quality in studies of conflicting values.