The cost-effectiveness of a theory-based online health behaviour intervention for new university students: an economic evaluation

Abstract

Background: Too many young people engage in unhealthy behaviours such as eating unhealthily, being physically inactive, binge drinking and smoking. This study aimed to estimate the short-term and long-term cost-effectiveness of a theory-based online health behaviour intervention ("U@Uni") in comparison with control in young people starting university.

Methods: A costing analysis was conducted to estimate the full cost of U@Uni and the cost of U@Uni roll-out. The short-term cost-effectiveness of U@Uni was estimated using statistical analysis of 6-month cost and health-related quality of life data from the U@Uni randomised controlled trial. An economic modelling analysis combined evidence from the trial with published evidence of the effect of health behaviours on mortality risk and general population data on health behaviours, to estimate the lifetime cost-effectiveness of U@Uni in terms of incremental cost per QALY. Costs and effects were discounted at 1.5% per annum. A full probabilistic sensitivity analysis was conducted to account for uncertainty in model inputs and provide an estimate of the value of information for groups of important parameters.

Results: To implement U@Uni for the randomised controlled trial was estimated to cost £292 per participant, whereas roll-out to another university was estimated to cost £19.71, both giving a QALY gain of 0.0128 per participant. The short-term (6-month) analysis suggested that U@Uni would not be cost-effective at a willingness-to-pay threshold of £20,000 per QALY (incremental cost per QALY gained = £243,926). When a lifetime horizon was adopted the results suggest that the full implementation of U@Uni is unlikely to be cost-effective, whereas the roll-out of U@Uni to another university is extremely likely to be cost-effective. The value of information analysis suggests that the most important drivers of decision uncertainty are uncertainties in the effect of U@Uni on health behaviours.

Conclusions: The study provides the first estimate of the costs and cost-effectiveness of an online health behaviour intervention targeted at new university students. The results suggest that the roll-out, but not the full implementation, of U@Uni would be a cost-effective decision for the UK Department of Health, given a lifetime perspective and a willingness-to pay threshold of £20,000 per QALY.

Trial registration: Current Controlled Trials ISRCTN67684181.

Figure 1

The U@Uni long-term cost-effectiveness model - a two states Markov model. p(dead|alive) = probability of dying if alive = a function of age, gender, fruit and vegetable consumption, alcohol consumption, physical activity and smoking status. p(alive|alive) = probability of staying alive if alive = 1 - p(dead|alive). p(dead|dead) = probability of staying dead if dead = 1.

Figure 2

Within-trial 6-month cost-effectiveness planes and cost-effectiveness acceptability curves for U@Uni compared to do nothing. Presents the individual-level cost-effectiveness plane and cost-effectiveness acceptability curve resulting from the 5,000 bootstrap replicates in the within-trial cost-effectiveness analysis. A) Cost-effectiveness plane showing the per-person incremental 6-month within-trial costs and incremental 6-month within-trial QALYs for full development and implementation of U@Uni compared to do nothing. B) Cost-effectiveness acceptability curve showing the probability (out of 5,000 bootstrap replicates) of full development and implementation of U@Uni being cost-effective compared to do nothing at different willingness-to-pay thresholds.

Figure 3

Long-term cost-effectiveness planes and cost-effectiveness acceptability curves for U@Uni compared to do nothing. PSA = Probabilistic sensitivity analysis. A) Cost-effectiveness plane showing the per-person incremental discounted lifetime costs and incremental discounted lifetime QALYs for full development and implementation of U@Uni compared to do nothing. B) Cost-effectiveness plane showing the per-person incremental discounted lifetime costs and incremental discounted lifetime QALYs for roll-out of U@Uni compared to do nothing. C) Cost-effectiveness acceptability curve showing the probability (out of 5,000 PSA runs) of full development and implementation of U@Uni being cost-effective compared to do nothing at different willingness-to-pay thresholds. D) Cost-effectiveness acceptability curve showing the probability (out of 5,000 PSA runs) of U@Uni roll-out being cost-effective compared to do nothing at different willingness-to-pay thresholds. E) A line chart showing the ICER and the probability (out of 5,000 PSA runs) of full development and implementation of U@Uni being cost-effective compared to do nothing at different intervention effect durations. F) A line chart showing the ICER and the probability (out of 5,000 PSA runs) of U@Uni roll-out being cost-effective compared to do nothing at different intervention effect durations.

Figure 4

Expected value of overall and parameter perfect information per person. Expected value of overall perfect information per person and expected value of perfect information for individual parameters per person at a willingness-to-pay threshold of £20,000 per QALY. A) Full development and implementation of U@Uni. B) Roll-out of U@Uni to another university.