Cost-effectiveness of medically assisted reproduction or expectant management for unexplained subfertility: When to start treatment?

R. van Eekelen, M. J. Eijkemans, M. Mochtar, F. Mol, B. W. Mol, H. Groen, M. van Wely

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2 Citations (Scopus)

Abstract

STUDY QUESTION: Over a time period of 3 years, which order of expectant management (EM), IUI with ovarian stimulation (IUI-OS) and IVF is the most cost-effective for couples with unexplained subfertility with the female age below 38 years? SUMMARY ANSWER: If a live birth is considered worth e32 000 or less, 2 years of EM followed by IVF was the most cost-effective, whereas above e32 000 this was 1 year of EM, 1 year of IUI-OS and then 1 year of IVF. WHAT IS KNOWN ALREADY: IUI-OS and IVF are commonly used fertility treatments for unexplained subfertility although many couples can conceive naturally, as no identifiable barrier to conception could be found by definition. Few countries have guidelines on when to proceed with medically assisted reproduction (MAR), mostly based on the expected probability of live birth after treatment, but there is a lack of evidence to support the strategies proposed by these guidelines. The increased uptake of IUI-OS and IVF over the past decades and costs related to reimbursement of these treatments are pressing concerns to health service providers. For MAR to remain affordable, sustainable and a responsible use of public funds, guidance is needed on the cost-effectiveness of treatment strategies for unexplained subfertility, including EM. STUDY DESIGN, SIZE, DURATION: We developed a decision analytic Markov model that follows couples with unexplained subfertility of which the woman is under 38 years of age for a time period of 3 years from completion of the fertility workup onwards. We divided the time axis of 3 years into three separate periods, each comprising 1 year. The model was based on contemporary evidence, most notably the dynamic prediction model for natural conception, which was combined with MAR treatment effects from a network meta-analysis on randomized controlled trials. We changed the order of options for managing unexplained subfertility for the 1 year periods to yield five different treatment policies in total: IVF-EM-EM (immediate IVF), EM-IVF-EM (delayed IVF), EM-EM-IVF (postponed IVF), IUIOS-IVF-EM (immediate IUI-OS) and EM-IUIOS-IVF (delayed IUI-OS). PARTICIPANTS/MATERIALS, SETTING, METHODS: The main outcomes per policy over the 3-year period were the probability of live birth, the average treatment and delivery costs, the probability of multiple pregnancy, the incremental cost-effectiveness ratio (ICER) and finally, which policy yields the highest net benefit in which costs for a policy were deducted from the health effects, i.e. live births gained. We chose the Dutch societal perspective, but the model can be easily modified for other locations or other perspectives. The probability of live birth after EM was taken from the dynamic prediction model for natural conception and updated for Years 2 and 3. The relative effects of IUI-OS and IVF in terms of odds ratios, taken from the network meta-analysis, were applied to the probability of live birth after EM. We applied standard discounting procedures for economic analyses for Years 2 and 3. The uncertainty around effectiveness, costs and other parameters was assessed by probabilistic sensitivity analysis in which we drew values from distributions and repeated this procedure 20 000 times. In addition, we changed model assumptions to assess their influence on our results. MAIN RESULTS AND THE ROLE OF CHANCE: From IVF-EM-EM to EM-IUIOS-IVF, the probability of live birth varied from approximately 54-64% and the average costs from approximately e4000 to e9000. The policies IVF-EM-EM and EM-IVF-EM were dominated by EM-EM-IVF as the latter yielded a higher cumulative probability of live birth at a lower cost. The policy IUIOS-IVF-EM was dominated by EM-IUIOS-IVF as the latter yielded a higher cumulative probability of live birth at a lower cost. After removal of policies that were dominated, the ICER for EM-IUIOS-IVF was approximately e31 000 compared to EM-EM-IVF. The range of ICER values between the lowest 25% and highest 75% of simulation replications was broad. The net benefit curve showed that when we assume a live birth to be worth approximately e20 000 or less, the policy EM-EM-IVF had the highest probability to achieve the highest net benefit. Between e20 000 and e50 000 monetary value per live birth, it was uncertain whether EM-EM-IVF was better than EM-IUIOS-IVF, with the turning point of e32 000. When we assume a monetary value per live birth over e50 000, the policy with the highest probability to achieve the highest net benefit was EM-IUIOS-IVF. Results for subgroups with different baseline prognoses showed the same policies dominated and the same two policies that were the most likely to achieve the highest net benefit but at different threshold values for the assumed monetary value per live birth. LIMITATIONS, REASONS FOR CAUTION: Our model focused on population level and was thus based on average costs for the average number of cycles conducted. We also based the model on a number of key assumptions. We changed model assumptions to assess the influence of these assumptions on our results. The change in relative effectiveness of IVF over time was found to be highly influential on results and their interpretation. WIDER IMPLICATIONS OF THE FINDINGS: EM-EM-IVF and EM-IUIOS-IVF followed by IVF were the most cost-effective policies. The choice depends on the monetary value assigned to a live birth. The results of our study can be used in discussions between clinicians, couples and policy makers to decide on a sustainable treatment protocol based on the probability of live birth, the costs and the limitations of MAR treatment.

Original languageEnglish
Pages (from-to)2037-2046
Number of pages10
JournalHuman Reproduction
Volume35
Issue number9
DOIs
Publication statusPublished - 1 Sep 2020
Externally publishedYes

Keywords

  • Cost-effectiveness
  • Decision analytic model
  • Decision tree
  • Expectant management
  • IUI
  • IVF
  • Markov model
  • Medically assisted reproduction
  • Unexplained subfertility

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