Developing Economic Modelling to Support a Broader Search for Efficiency: A Case Study in Infertility Treatment

In the context of health service resource constraints, investment in new health care treatments requires corresponding disinvestment in existing treatments and services in order to free up the required resources. Ideally, those responsible for the management of treatment services should aim to disinvest in the low value treatments and reinvest in higher value alternatives. This ensures that system level benefits can improve in the face of resource scarcity. However, research suggests that the actual process of disinvestment is less explicit, with treatments and services implicitly displaced without consideration of their comparative value relative to those they are displaced by (Karlsberg Schaffer et al, 2013).

Factors that contribute to this sub-optimal approach include a lack of evidence on the value of all in-use treatments, and shortcomings in the current approach to economic evaluation for informing efficient reallocations of resources (Scotland and Bryan, 2016). One way of using economic evaluation to better support the challenge of balancing investment and disinvestment in health care technology, is to adopt a broader frame to explicitly identify efficient reallocations of resources within disease pathways (Tappenden et al 2012). This entails developing decision models with the capability of evaluating changes at more than one point in a care pathway; for example, different sequences of first, second and third line treatment options. With such models it becomes possible to explicitly identify changes in the mix and sequence of treatment options that can deliver greater system level benefit without increasing (and perhaps even decreasing) the overall resources required for the treatment pathway as a whole.

Using the treatment pathways for infertility as a case study, this PhD programme will support the development of such an economic model. Some couples have absolute barriers to conception and require immediate treatment, but many more, in whom no medical cause for their infertility can be found (unexplained infertility), have reasonable chances of having a baby without treatment. Due to the invasiveness and cost of assisted reproduction (Pandy et al, 2014), clinicians find it difficult to decide when to abandon a wait and see (expectant management) approach and initiate treatment. There is also debate about the sequence in which alternative types of fertility treatment should be offered to couples with different characteristics (Bahadur et al, 2016). The PhD programme will make use of large observational datasets (local and national) to inform probabilities of treatment success (based on individual patient characteristics) with different types of treatment in comparison to expectant management. It will build on existing statistical models that have been developed to predict the chances of having a baby in couples with unexplained infertility at different time-points with and without initialisation of treatment (McLernon et al, 2014; McLernon et al, 2016). These statistical models will be further extended and incorporated in an economic decision model to simulate the impact of marginal changes in the timing and sequence of treatments within the care pathway. The objective is to develop an economic model that can be used to improve the efficiency of the fertility treatment pathway as a whole.

You will join a multidisciplinary collaborative team with an international reputation in the area of fertility research, and expertise in health economic modelling and the creation of innovative prediction models. This project will suit a numerically skilled student who is interested in reproductive health research, and who preferably holds a postgraduate qualification in health economics, medical statistics or a related quantitative discipline.

Prospective students should include a written statement of no more than 1,000 words that outlines their initial ideas about how they would like to focus and conduct this project. Please upload this document with your formal application.