Willard G. Manning Memorial Award for the Best Research in Health Econometrics

The award was given for their article “Healthcare cost regressions: going beyond the mean to estimate the full distribution”, which was published in Health Economics in 2015. The prize will be awarded at the 6th Biennial Conference at the University of Pennsylvania in June.

The award winning paper is from a set of publications originating in James Lomas’s PhD thesis. This programme of work was inspired by the literature on regression methods for healthcare costs that was an area of particular methodological interest for Professor Manning. Indeed, James was fortunate to benefit from his insights through a brief but important to visit to the University of Chicago where he presented his thesis work and Will provided detailed and constructive feedback both in writing and in person.

The paper in Health Economics examines methods for estimating the full conditional distribution of healthcare costs. Understanding the data generating process behind healthcare costs remains a key empirical issue. Although much research to date has focused on the prediction of the conditional mean cost, this can potentially miss important features of the full conditional distribution such as tail probabilities. The paper reports a quasi-Monte Carlo experiment using English NHS inpatient data to compare 14 approaches to modelling the distribution of healthcare costs: nine of which are parametric, and have commonly been used to fit healthcare costs, and five others designed specifically to construct a counterfactual distribution. The results show that no one method is clearly dominant and that there is a trade-off between bias and precision of tail probability forecasts. Distributional methods demonstrate significant potential, particularly with larger sample sizes where the variability of predictions is reduced. Parametric distributions such as log-normal, generalised gamma and generalised beta of the second kind are found to estimate tail probabilities with high precision, but with varying bias depending upon the cost threshold being considered.