Difficulties Surrounding Populations in Economic Modelling: STI Case Study

OBJECTIVES: When modelling the impact of an intervention and comparator, they usually have the same population. However, interventions may alter the clinical pathway, which can impact the target population. This can be a challenge to model, for example, the underlying prevalence of the condition explored might be altered in the new population. We present possible approaches to address this using a case study involving the introduction of a home testing kit for sexually transmitted infections (STI).

METHODS: The home testing kit would reach a much larger population with differing risks. An economic model was developed to investigate the health and cost impact of implementing home testing kits compared to standard in-clinic testing for STIs. The key model outcomes were total incremental cost and the number of complications averted from excess cases being detected through home testing.

Data on prevalence was used to inform comparator population prevalence of STIs, test uptake, and positive results. A meta-analysis of 7 studies estimated that home testing increased positive tests by 71%. For the intervention, evidence on increase in positive tests was leveraged to explore scenarios where prevalence estimates were non-equivalent to the comparator population.

RESULTS: The use of the home test is estimated to be cost-effective with an ICER of £3,865 when compared with standard STI testing assuming equivalent prevalence. The inclusion of a differential increase in completed tests and positive diagnoses resulted in a larger ICER (£3,865 and £13,877 for the optimistic scenario and the lower risk scenario, respectively).

CONCLUSIONS: Modelling different populations between the intervention and comparator can be challenging. This case study outlines an approach to addressing this by leveraging evidence to draw assumptions and guide scenario analyses.