Purpose: Health policy makers rely on complex information about the efficiency or efficacy of health interventions when making decisions about resource allocation, must ensure that resources are distributed equitably, and that interventions reduce health inequalities. While we generally assume that information about health inequalities is value-neutral, evidence suggests that informational frame can have an influence on decision making. Here, we investigate the impact of informational frames on interpretations of the success of an intervention in reducing health inequalities.
Method: Undergraduate students (n=27) participated in a computer-based experiment, and were presented with scenarios (n=8 fictional diseases) indicating the impact of a health intervention on two populations (e.g. survival of populations before and after intervention). Scenarios differed according to: presentation of data in terms of survival or mortality; health inequalities that increased or decreased; and size of change in inequalities (large or small). Frame was blocked, all other factors were randomized. Participants rated how successful the intervention was in reducing health inequality (7-point scale), how much of a $100 tax increase should be devoted to the continuation of the intervention, and whether the intervention should continue (7-point scale).
Result: Interventions that led to a decrease in inequality were rated as more successful (4.76 compared to 1.78), more deserving of continuation (4.57 to 3.27), and provided more financial support ($33.82 to $19.88; all Fs>13, all p<.001). Participants were more likely to support (4.41 to 3.43), donate to ($34.40 to $19.27), and consider a program successful (3.6 to 2.9; all Fs > 7, all p<.01) if it demonstrated a large (rather than small) change in reducing inequalities. Participants responded consistently, regardless of mortality or survival frame for the majority of scenarios. However, when changes in inequality were large, participants donated more money ($29.70 to $39.10) and were more likely to support the continuation of a program (4.67 to 4.15; all Fs > 5.4, all p<.05) when the scenario was framed in terms of mortality rather than survival.
Conclusion: Participants were sensitive to increases and decreases in health inequalities, and able to distinguish relative size of such changes. Further, mortality or survival frame seemed to influence decision making in the most extreme of scenarios, where framing the success of the intervention in terms of mortality increased the likelihood of support.
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