# Eliciting Distributive Preferences in Health Care Resource Allocation: A Person Trade-Off Study

## TL;DR

This study explores how people in China prefer to distribute health benefits, finding that preferences shift based on the size of health gains and offering a threshold for policy decisions.

## Contribution

The study provides the first quantitative evidence from China on non-linear shifts in distributive preferences based on health benefit magnitude.

## Key findings

- Non-maximizing tendencies were dominant, with 79% of respondents not preferring maximum health gains for a few.
- A threshold of 4.6 years of health gain was identified, below which people prefer diffusion and above which concentration.
- Income and self-reported health status influence distributive preferences.

## Abstract

Background/Objectives: While a preference for an equal distribution of health gains is common, there are situations where individuals may opt to concentrate health gains for a select few. This study investigates how distributive preferences, defined as societal valuations of alternative allocations of fixed total health benefits, vary with the magnitude of individual health gains. Methods: Using the person trade-off (PTO) method, we conducted an online survey with a nationally representative sample of Chinese adults (N = 500). The respondents evaluated five allocation programs differing in both individual health gain magnitude and number of beneficiaries. Distributive preferences are classified into five distinct types: diffusion, concentration, maximization, extreme egalitarianism and extreme inequality seeking. Threshold regression analysis identified critical transition points in preference patterns. Results: Non-maximizing tendencies were dominant (79% of the respondents). The health gain threshold was estimated to be 4.6 years (95% CI: [4.28, 4.85]): below this threshold, respondents tend to allocate smaller benefits to more patients (diffusion preference); above the threshold, people are inclined to allocate larger benefits to fewer patients (concentration preference). The income level and self-reported health status of the participants were identified as potential factors influencing distributive preferences. Conclusions: This study provides the first quantitative evidence from China that distributive preferences exhibit a non-linear shift based on the magnitude of health benefits. The identified 4.6-year threshold provides policymakers with an empirically based instrument to strike a balance between efficiency and the reduction in inequality in resource allocation. These findings advocate for incorporating social value weights into health technology assessments, especially for interventions that offer substantial individual benefits.