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Original article
Incremental cost-effectiveness of algorithm-driven genetic testing versus no testing for Maturity Onset Diabetes of the Young (MODY) in Singapore
1. Hai Van Nguyen1,
2. Eric Andrew Finkelstein2,
3. Shweta Mital2,
4. Daphne Su-Lyn Gardner3
1. 1 School of Pharmacy, Memorial University of Newfoundland, St. John’s, Canada
2. 2 Health Services and Systems Research, Duke NUS Medical School, Singapore
3. 3 Department of Endocrinology, Singapore General Hospital, Singapore
1. Correspondence to Dr Hai Van Nguyen, School of Pharmacy, Memorial University of Newfoundland, St. John’s, A1B 3V6, Canada; hvnguyen{at}mun.ca

Abstract

Background Offering genetic testing for Maturity Onset Diabetes of the Young (MODY) to all young patients with type 2 diabetes has been shown to be not cost-effective. This study tests whether a novel algorithm-driven genetic testing strategy for MODY is incrementally cost-effective relative to the setting of no testing.

Methods A decision tree was constructed to estimate the costs and effectiveness of the algorithm-driven MODY testing strategy and a strategy of no genetic testing over a 30-year time horizon from a payer’s perspective. The algorithm uses glutamic acid decarboxylase (GAD) antibody testing (negative antibodies), age of onset of diabetes (<45 years) and body mass index (<25 kg/m2 if diagnosed >30 years) to stratify the population of patients with diabetes into three subgroups, and testing for MODY only among the subgroup most likely to have the mutation. Singapore-specific costs and prevalence of MODY obtained from local studies and utility values sourced from the literature are used to populate the model.

Effectiveness

Effectiveness is measured in terms of quality-adjusted life years (QALYs), a measure of a person’s length of life weighted by utility, a valuation of their health-related quality of life. A utility value of 1 indicates a year spent in perfect health. In our analysis, patients who have T2D are assigned a utility value of 0.75 if they are on insulin treatment, 0.82 if on oral medications and 0.82 if on diet and exercise only, as obtained from Zhang et al. 22 A utility value of 0.92, which was estimated for a non-insulin diabetes patient without diabetes complications and without risk factors for cardiovascular diseases in Zhang et al 22 is used as a proxy for diabetes patients with no treatment. All utility values are discounted at 3.5% per year.21

Cost-effectiveness analysis

We use the decision tree to estimate the costs and effectiveness of the two strategies. The ICER is the difference between the overall costs of the two strategies divided by the difference in QALYs. To account for uncertainty, one-way and probabilistic sensitivity analyses are conducted. These analyses model the impact of a change in any of the costs or probabilities on the ICER. In the one-way sensitivity analysis, all relevant costs and probabilities are varied over a reasonably large range, namely, ±25%.23 We also conduct threshold analysis to identify the threshold values of prevalence and costs that render the algorithm-driven MODY testing strategy cost-effective. In probabilistic sensitivity analysis, we assign distributions to all input parameters and perform 10 000 Monte Carlo simulations where each simulation generates an ICER of the algorithm-driven genetic testing relative to no testing. We assume beta distributions for all probabilities, normal distributions for utilities and gamma distributions for testing and treatment costs.23 Results are presented graphically showing the probability that genetic testing is cost-effective for a range of cost-effectiveness thresholds that decision makers may consider. Furthermore, to establish the robustness of our results, we vary the utility values used in our analysis by using those reported in other studies.

Results

The base case cost-effectiveness results are presented in table 2. The cost of the genetic test is US$1050. Total cost of the algorithm-driven testing strategy is, on net, higher than that of the no testing strategy by US$462 (US$9888 vs US$9426). The algorithm-driven testing strategy generates slightly more QALYs on average: 15.215 versus 15.210. These additional QALYs result from the reduced need for insulin treatment in the subset of patients with GCK mutation and the switch from insulin to oral sulphonylurea for patients with HNF1A or HNF4A mutations. Combining the cost and effectiveness estimates, the ICER for the algorithm-driven genetic testing strategy relative to no testing is US$93 663/QALY. Table 2 Cost-effectiveness of algorithm-driven MODY testing versus no testing Sensitivity analyses Results of the one-way sensitivity analyses (figure 2) show that the ICER is most sensitive to the prevalence of MODY mutations (ie, HNF1A/HNF4A, GCK and HNF1B) in the target population (ie, those who receive the gene panel), currently estimated to be 3.93% for Singaporean patients whom we identify should get the algorithm. It is also sensitive to the proportion of patients receiving insulin treatment under usual treatment. The cost-effectiveness acceptability curve (figure 3) shows that, for a commonly applied willingness-to-pay threshold of US$50 000/QALY, the algorithm-driven testing strategy is currently cost-effective in 30% of iterations. The threshold analysis further reveals that provided all other factors remain constant, if the prevalence of HNF1A/HNF4A mutation among those undergoing the gene panel test increases to 4.1% (currently 2.3%) or the prevalence of GCK mutation increases to 0.85% (currently 0.43%), or the cost of the genetic test drops below US$530, then the algorithm-driven MODY testing strategy would be cost-effective using the threshold of US$50 000/QALY.

Figure 2

Tornado diagram. ICER, incremental cost-effectiveness ratio.

Figure 3

Cost-effectiveness (CE) acceptability curve. MODY, Maturity Onset Diabetes of the Young.

As Zhang et al’s study22 is based on a sample of patients with diabetes aged 62 years on average while our study population consist of younger people (aged 45 years and below), we conduct two additional sensitivity analyses to examine the robustness of our results with respect to our use of utility values from Zhang et al.22 In the first test, we use utility values by treatment type from another study, namely, Redekop et al. 24 As that study does not provide a utility value for diabetes with no treatment, we assign a utility of 0.84 for no treatment (as reported in Ara and Brazier25). The value of ICER increases only slightly in the sensitivity analysis (ie, US$93 663/QALY to US$99 469/QALY). In the second test, we attempt to estimate the utilities by treatment type for the age group less than 45 years using data from Zhang et al 22 and Redekop et al. 24 To do this, we first use the utility value for age group 45 years and below reported in Zhang et al 22 and assume that to be the utility for oral therapy. (Note that Zhang et al 22 reported utility by age group but not by age-type of treatment needed for our model.) We then assign utility values for insulin, diet and no treatment such that their differences from the utility value for oral therapy match that reported in Redekop et al. 24 Specifically, difference is 0.13 between oral therapy and insulin, 0.03 between oral therapy and diet and 0.08 between oral therapy and no treatment. We rerun the model using these utility values. The results are similar to our base case estimate.

Discussion

This study reported the results of a cost-effectiveness analysis comparing an algorithm-driven testing strategy to detect MODY versus no testing using Singapore as a case study. We found that compared with the no testing strategy, the algorithm-driven MODY test incurred higher costs while generating slightly higher QALYs, resulting in an ICER of US$93 663 per QALY. The cost difference (ie, US$462) results from the GADAb screening costs (US$59) and MODY testing cost (US$1050) offset by savings from (1) patients with MODY mutations switching to cheaper oral medications or no treatment (from more expensive insulin treatment) and (2) the elimination of the need for glucose strips for self-blood glucose monitoring. The greater utility is due to the switch to oral medications/no treatment. This ICER of US$93 663 per QALY is not cost-effective using the conventional threshold of US$50 000/QALY, but our analysis indicates that if the cost of MODY testing were to decrease to US$530, the algorithm-driven MODY testing would become cost-effective. This is likely to happen in the near future as costs of genetic tests continue to fall.26 Our study differs from Naylor et al,5 who found an ICER of US$205 000 per QALY, in two important ways. First, the testing strategy considered in Naylor et al 5 is not algorithm based; all individuals with T2D aged between 25 and 40 years are subjected to MODY testing. Using the algorithm-based approach, we were able to identify a subgroup of patients most likely to test positive for MODY, thus reducing screening and testing costs and increasing the chances of finding MODY cases. This approach generates significant cost savings relative to the strategy of universal MODY testing.

The second difference between our study and Naylor et al 5 concerns the testing and treatment costs. In Naylor et al,5 the cost of conventional Sanger single gene testing for the three most common MODY subtypes (HNF1A, GCK and HNF4A) was reported to be US$2580 per patient. This cost in Singapore is US$1050 due to the use of NGS technology, which allows screening of multiple genes (16 in this panel) simultaneously. Their treatment costs are also higher than those used in our study. Cost of insulin treatment (including self-blood glucose monitoring) is almost twice the cost in Singapore (US$2641 vs US$1528). Costs for oral medications are nearly 12 times greater (US$767 vs US$61), and costs of sulphonylureas are 1.5 times greater (US$96 vs US$61). To estimate the influence of these cost differences on the ICER, we re-estimated our model using the US testing and treatment cost data as well as utility values used in their study. The resulting ICER is US$148 050 per QALY. This ICER is higher than the ICER estimated for Singapore but is still substantially lower than the ICER for the US estimated in Naylor et al. 5 This suggests that the relatively low ICER obtained in our study and the relatively high ICER in Naylor et al 5 are partly due to the higher cost of genetic testing and treatment in the USA compared with Singapore. It also confirms the value of our proposed algorithm in reducing the testing costs, leading to more favourable cost-effectiveness estimates. In fact, using the US costs and utilising our proposed algorithm, the cost of genetic testing would only need to be reduced from US$2580 to US$1066 for the genetic test to become cost-effective in the USA. Without the algorithm, it would have to be reduced further to US$700 for the genetic test to be cost-effective.5

Our study has a number of limitations. First, our algorithm has not been validated in other population groups or ethnicities or in other cohorts in Singapore. Asians tend to have lower BMI values, different distributions of fat mass and different rates of diabetes for any given BMI as compared with Caucasians.27 28 As such, results would not be expected to generalise to Caucasians. Moreover, the prevalence of the MODY gene appears to vary by ethnicity even among Asians. Further analyses on additional cohorts within and beyond Singapore are needed to confirm these results and to test whether the algorithm-driven testing strategy would be cost-effective in other settings. Our model also did not include possible improvement in glycaemic control associated with optimal treatment following a diagnosis of MODY. Although possible, efficacy of sulfonylurea on glycaemic control is mixed. Shepherd et al 4 and Thanabalasingham et al 6 reported an HbA1c fall of 0.8%–1.5% in small numbers of patients (n=6–8 per study) following a switch to sulfonylurea therapy, but follow-up was limited to an average of 3 months in one study6 and a median duration of 6 months in the other.4 Bacon et al 29 did not find a significant fall in HbA1c when switching patients with HNF1A MODY from insulin to sulfonylureas. Therefore, we did not model better glycaemic control as an outcome of MODY testing. We used utility data based on a group of older patients than those in our study population (62 vs 45) due to the lack of utility data for younger diabetes patients by treatment type in the literature. However, our sensitivity analyses show that our base cost-effectiveness estimates change little when we used utility values from other studies as well as when we used our estimated utility values for lower age groups. Finally, overall MODY prevalence in our sample population was estimated to be only 0.58% (27/4630), which is lower than in previous studies.30 If the true prevalence in this or other populations is greater, then MODY testing becomes more cost-effective.

In conclusion, in combination with a simple algorithm to select individuals for targeted screening, NGS panel sequencing could soon be a cost-effective strategy to tailor diabetes treatment based on genetic variants.

View Abstract

Footnotes

• Contributors HVN led the model development and contributed to data analysis; SM contributed to data analysis; EF and DS-LG conceptualised the initial idea; DS-LG conducted clinical investigations that provided model inputs. All authors contributed to manuscript writing and manuscript revisions. HN and DS-LG are the overall guarantors of the manuscript.

• Funding DS-LG and EF received funding from the Stratified Medicine Programme Office, a joint initiative by the Biomedical Research Council and the National Medical Research Council, Singapore.

• Competing interests None declared.

• Provenance and peer review Not commissioned; externally peer reviewed.

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