Abstract
Aims:
There is limited evidence with respect to the cost-effectiveness of starting insulin in people with diabetes outside the ‘western’ world. The aim of this study was to assess the cost-effectiveness of starting basal insulin treatment with insulin detemir in people with type 2 diabetes (T2D) inadequately controlled on oral glucose-lowering drugs (OGLDs) in Mexico, South Korea, India, Indonesia, and Algeria.
Methods:
The IMS CORE Diabetes Model was used to project clinical and cost outcomes over a 30-year time horizon. Clinical outcomes, baseline characteristics and health state utility data were taken from the A1chieve study. A 1-year analysis was also conducted based on treatment costs and quality-of-life data. Incremental cost-effectiveness ratios (ICERs) were expressed as a fraction of GDP per capita, and WHO-CHOICE recommendations (ICER < 3.0) used to define cost-effectiveness.
Results:
Starting insulin detemir was associated with a projected increase in life expectancy (≥1 year) and was considered cost-effective in all of the studied populations with ICERs of −0.02 (Mexico), 0.00 (South Korea), 0.48 (India), 0.12 (Indonesia), and 0.88 (Algeria) GDP/quality-adjusted life-year. Cost-effectiveness was maintained after conducting sensitivity analyses in the 30-year and 1-year analyses. A projected increase in treatment costs was partially offset by a reduction in complications. The difference in overall costs between insulin detemir and OGLDs alone was USD518, 1431, 3510, 15, and 5219, respectively.
Conclusion:
Changes in clinical outcomes associated with starting insulin detemir in insulin-naïve individuals with T2D resulted in health gains that made the intervention cost-effective in five countries with distinct healthcare resources.
Introduction
Recent estimates from the International Diabetes Federation (IDF) suggest that 382 million people worldwide have diabetes, of whom the majority (80%) live in low- or middle-income countriesCitation1. By 2030, the global prevalence of diabetes is estimated to increase by at least 50%; a corollary of increasing prevalence in obesity, and greater life expectancy due to improving healthcare management and lifestyle changesCitation2. The rising burden of diabetes and scarcity of resources worldwide highlight the need for not only efficacious, but also cost-effective, solutions to improve the quality-of-life of people with diabetes, both in the short-term and by reduction of long-term organ damage.
A goal in diabetes management is, then, to reduce the risk of diabetes-related complications. Clinically, this includes achieving as near-optimal glycemic control as possible without increasing hypoglycemia and other treatment-related complications. However, achieving glycemic targets remains a challenge in many people with type 2 diabetes (T2D)Citation3–6. In most people with T2D, insulin therapy will be required in time to supplement the progressive loss of β-cell function, but starting insulin therapy is often delayed due to barriers, including fear of hypoglycemic events and weight gain, resistance to changes in regimens, and higher treatment costs compared with non-insulin therapyCitation7. However, delaying insulin therapy, as well as failing to ameliorate the risk of diabetes-related complications, may leave the individual with diabetes with poor health-related quality-of-life (HRQoL)Citation8. These factors may result in increased resource utilization and a rise in the overall costs associated with diabetes managementCitation9.
Clinical guidelines support the add-on of basal insulin to existing regimens in people not achieving glycemic goals while receiving oral glucose-lowering drugs (OGLDs)Citation10–12. In randomized clinical trials (RCTs) in insulin-naïve people with T2D, basal insulin analogs have demonstrated better glycemic control with a lower risk of hypoglycemia compared with human neutral protamine Hagedorn (NPH) insulinCitation13–18. Furthermore, the clinical effectiveness of basal insulin analogs in T2D has been demonstrated in real-life observational studiesCitation5,Citation19–23. Observational data are often collected from large heterogeneous populations that help enhance the generalizability of the clinical findings of RCTs. In addition, observational studies often provide a more representative profile of adverse events for people in routine careCitation24.
A1chieve was a 24-week, observational study that assessed the safety and clinical effectiveness of insulin analogs ± OGLDs in 66,726 people with T2D. The study was conducted in 28 countries outside the ‘western’ world with varying healthcare resources and ethnic diversity. The A1chieve study provides the opportunity to conduct cost-effectiveness analyses in several non-western populations based on the type of insulin analog used and/or prior insulin treatment, and in very different clinical environments. The aim of the current analysis was, therefore, to assess the long- and short-term cost-effectiveness of starting basal insulin therapy with insulin detemir in people with T2D inadequately controlled on OGLDs using clinical outcomes and specific HRQoL values from the A1chieve study.
Materials and methods
The A1chieve study
The A1chieve study and its findings have been described in detail elsewhereCitation5,Citation25. In summary, it was an observational 24-week study in insulin-naïve (n = 44,872) and insulin-experienced people (n = 21,854) with T2D starting biphasic insulin aspart 30, insulin detemir or insulin aspart (all Novo Nordisk, Bagsværd, Denmark) alone or in combination. The study showed that, in routine clinical practice in all of the regions studied, people starting an analog experienced clinically useful improvements in blood-glucose control with improved HRQoL without clinically significant problems associated with hypoglycemia or weight gainCitation25. Similar findings were reported with insulin detemir alone in both the insulin-naïve and insulin-experienced groups.
Simulation cohort and assumptions
For the cost-effectiveness analyses, baseline characteristics and the changes associated with starting insulin detemir (including HbA1c, body mass index [BMI], lipid content, systolic blood pressure, hypoglycemia, and EQ-5D-based HRQoL) () were derived from the A1chieve insulin-naïve group (insulin detemir cohort). Our analysis was country-based given that resource use, currency, and healthcare structure differ between countries. A1chieve study populations starting insulin detemir were large enough (n > 100) from Mexico (n = 101), South Korea (n = 487), Indonesia (n = 109), India (n = 1491) and Algeria (n = 473) to be included in the analysis, and only individuals with an HbA1c measurement at baseline and after 24 weeks of insulin detemir therapy were included in each cohort. In the analysis, we used study-specific health state utility data (EQ-5D HRQoL) from baseline and 24-week measurementsCitation25. A participant number of >100 per cohort was chosen to avoid the risk that each analysis was unrepresentative of the underlying population. The clinical and economic impact of starting insulin detemir in each country was projected over a 30-year time horizon for the base-case scenario. We also assumed that patients in the OGLD comparator arm stayed on treatment with no added effect for 30 years in the base case; this assumption was further challenged in the sensitivity analysis (see below). Both costs and health outcomes were discounted at an annual rate of 3.0% throughout the simulated periods.
Table 1. Baseline demographics and the change in clinical outcomes after 24 weeks from the A1chieve study in people with T2D starting insulin detemir.
A short-term analysis was also conducted for the first year after starting insulin detemir. This analysis was based only on the incremental cost of treatment (excluding costs of complications and hospitalizations related to these given that most diabetes-related complications occur in the long-term) and the incremental effect on HRQoL. Other clinical measures such as HbA1c, BMI, and hypoglycemia were not included in this analysis.
For each of the countries included, specific data were collected independently of the investigators regarding costs, as they are required for the analysis model. Costs were defined from a public healthcare perspective in all countries. These costs included those associated with diabetes management (annual costs for other medications and screening tests) and relevant co-morbid medical conditions (cardiovascular and renal complications, eye disease, acute events, neuropathy, foot ulcer and amputation)Citation26–29. These data were taken from the existing literature, supplemented if necessary, and reviewed by clinical experts from the countries concerned. The costs of insulin and OGLDs were used as the treatment costs of the model and were sourced from local Novo Nordisk affiliates. Background mortality rates for each country were taken from World Health Organization (WHO) data tablesCitation30.
CORE diabetes model
The IMS Centre for Outcomes REsearch (CORE) Diabetes Model was used to determine the long-term health and cost outcomes of starting treatment with insulin detemir in people with T2D inadequately controlled on OGLDs. The CORE Diabetes Model is an interactive computer simulation based on a network of Markov sub-models that simulate complications often associated with diabetes (cardiovascular disease, eye disease, hypoglycemia, ulcers, amputation, stroke, lactic acidosis, nephropathy, neuropathy, ketoacidosis, and mortality)Citation31. These sub-models incorporate time, state, time in state, and diabetes type-dependent probabilities derived from published sources. Default probabilities are predominantly based on results from the UKPDS, DCCT and Framingham studiesCitation31. The development and progression of multiple inter-related complications are simulated using Monte Carlo simulation with tracker variables. Cohort outcomes are projected using several sources of information including baseline characteristics, history of complications, diabetes management and screening strategies, concomitant medication, and changes in surrogate outcomes over time. Baseline, clinical and economic data can be defined by the user, thus providing a platform for calculating country-specific long-term outcomes for various clinical settings using the best currently available data.
Statistics
Non-parametric bootstrapping was used in each simulation (1000 people and 1000 bootstraps per country). The incremental cost-effectiveness ratio (ICER) is expressed as cost per quality-adjusted life-year (QALY) (in both local currency and USD, exchange rates as of September 2013), and also presented as a fraction of the gross domestic product (GDP) per capita for each country included in the analysis. GDP data were taken from the World Bank for 2011Citation32. To determine the relative cost-effectiveness of an intervention, the WHO CHOosing Interventions that are Cost Effective (CHOICE) program recommends a threshold based on the GDP per capitaCitation33. This system considers an intervention non-cost-effective at >3-times GDP per capita, cost-effective at 1–3-times GDP per capita, highly cost-effective at less than the GDP per capita or cost-saving (dominant) if estimated overall costs of new treatment are less than comparator and QALY gained ≥zero.
Several sensitivity analyses were conducted; these included: extending the time horizon from 30 years to 50 years; applying no HbA1c deterioration with time compared with the base-case scenario where HbA1c was set to deteriorate by 0.15%-units (1.6 mmol/mol) each year after the first year; using the median HbA1c treatment effect instead of the mean effect; using the first quartile distribution of the HbA1c treatment effect (i.e., HbA1c change in lowest 25% of the study population) instead of the mean effect; incorporating the costs of one self-measured plasma glucose (SMPG) strip per day vs no strips in the base case; adding another four medical consultations in the first year after starting insulin detemir, based on public sector prices and where the first visit reflects the cost of a specialist and subsequent visits that of a general practitioner (GP) (the highest price of a GP/specialist visit for each country was used in order to be conservative); including two GP visits every year after starting insulin detemir, also based on public sector prices, but both visits reflecting the costs of a non-specialist GP visit; using the average EQ-5D change from the A1chieve study (+0.174) rather than country-specific data; and assuming insulin detemir is started anyway 5 years later in the comparator arm. In the 1-year short-term analysis, sensitivity analyses were conducted for the costs of strips and for four medical consultations after starting insulin detemir, compared with the absence of these factors in the base-case analysis.
An analysis was also conducted to project for each country the maximum total costs (assuming the same clinical outcome as measured in A1chieve) that were cost-effective as defined by an ICER of 3.0-times GDP per capita.
Results
Cost-effectiveness, costs, and diabetes-related complications
In the 30-year base case, the change in clinical outcomes reported in A1chieve for insulin-naïve individuals with T2D starting insulin detemir () was associated with a projected increase in life expectancy in Mexico (1.9 years), India (1.6 years), Algeria (0.8 years), Indonesia (1.0 year) and South Korea (1.0 year)Citation34. Starting insulin detemir was consistently associated with projected reductions in the modeled incidence of diabetes-related complications compared with continuing OGLDs alone (). Overall, the reported reduction of cumulative incidence ranged between 25% (Algeria) and 38% (Indonesia) for severe vision loss, 48% (Algeria) and 68% (India) for end-stage renal disease, 2% (Mexico) and 17% (India) for foot ulcers, and 12% (South Korea) and 22% (Indonesia) for myocardial infarction. The time free of any complication was greater with insulin detemir compared with OGLDs alone, with a difference between treatment groups ranging from 0.3 years for South Korea to 1.3 years for India. For individual complications, similar differences were universally observed, typically 1.0–2.0 extra years free of each complication in each country (). The estimated QALY gains were 1.2 for Algeria, 5.0 for India, 2.5 for Mexico, 1.8 for Indonesia, and 1.0 for South Korea.
Table 2. Cumulative incidence and estimated time alive and free of complications (years) over 30 years after starting insulin detemir compared with not starting the insulin in people with T2D inadequately controlled on OGLDs.
Projected treatment costs were 2–5-times greater in all of the studied populations compared with OGLDs alone, reflecting the additional costs of insulin therapy (). However, costs associated with the management of diabetes complications were reduced in the insulin detemir group, and thus the difference in overall costs was smaller than the difference in treatment costs. Indeed, in Mexico, Indonesia, and South Korea, discounted overall costs were negligibly different between treatment groups (differences in overall costs between insulin detemir and OGLDs: USD518, 15, and 1431 per 30 years, respectively), while in India (USD3510) and Algeria (USD5219) the cost increments remained ().
Table 3. Direct costs per patient of diabetes care simulated over 30 years with insulin detemir compared with OGLDs alone.
The ICERs, expressed as incremental cost per QALY gained, are presented in local currency, USD, and as a fraction of GDP per capita in . The increment in costs, QALYs, and life expectancy are also presented. For Algeria and India, the ICERs, expressed as a fraction of GDP per capita, were 0.88 and 0.48, while in Mexico, Indonesia, and South Korea they were close to zero (−0.02, 0.12, and 0.00, respectively), meeting WHO CHOICE guidelines for being highly cost-effective in all countries.
Table 4. Long-term and short-term cost-effectiveness of starting insulin detemir in people with T2D inadequately controlled on OGLDs. Costs expressed per patient.
In the short-term analysis, the 1-year ICER, expressed as GDP fractional cost per QALY gained, was still highly cost-effective in India (0.71), Mexico (0.48), Indonesia (0.68), and South Korea (0.18). In Algeria it was 1.48, meeting WHO Choice guidelines for cost-effectiveness (<3.0) ().
Sensitivity analysis
If the model was run for 30 years and insulin detemir was started in the OGLD reference group 5 years after the treatment group began it, there is little change in the ICERs (small tendency to be lower) compared with the base case (). Similarly, increasing the time horizon to 50 years, using the average global EQ-5D instead of country-specific values, having no HbA1c deterioration with time compared with the base-case scenario, using the median HbA1c treatment effect instead of the mean effect, having two GP visits every year after starting insulin detemir, or including the costs of four additional GP visits in the first year, had very little effect on the ICERs (). However, the results were more sensitive to using the first quartile distribution of the HbA1c treatment effect rather than the mean HbA1c, or including the costs of one SMPG strip per day, but, even with these changes, the ICERs expressed as GDP per capita per QALY were still within the cost-effective range (<3.0). A cost-effectiveness threshold analysis showed that the maximum percentage increase of total costs that would deliver an ICER of 3.0 GDP per capita was 94% for Mexico, 180% for South Korea, 76% for Indonesia, 194% for India, and 79% for Algeria. In the 1-year analysis, the ICER, expressed as a fraction of GDP per QALY, also remained cost-effective (<3.0) after conducting sensitivity analyses for the cost of SMPG strips and the cost of four GP visits after starting insulin detemir ().
Table 5. Sensitivity analysis of starting insulin detemir in people with T2D inadequately controlled on OGLDs.
Discussion
In this analysis, costs and outcomes associated with starting insulin detemir in individuals with T2D on oral agents alone were simulated over a 30-year time horizon using the previously validated CORE Diabetes Model, and were found to be cost-effective in healthcare settings as different as those in Algeria, Mexico, India, Indonesia, and South Korea. To ensure local validity, we used locally specific baseline clinical characteristics, and within-study surrogate and HRQoL outcomes, together with local costs both for current therapies and long-term management of complications. With this approach, longer-term discounted costs were essentially neutral in Mexico, Indonesia, and South Korea, while the incremental costs in Algeria and India, when calculated as the ratio to the modeled QALY gain (ICER, and expressed as a fraction of GDP per capita), were cost-effective based on WHO CHOICES criteria (). These outcomes are likely to reflect the major improvements in metabolic outcomes in routine care in the A1chieve population, together with the useful improvements in measured HRQoL, and the lack of deterioration in tolerability and safety issues such as hypoglycemia and weight gain. Indeed, the difference in QALY gains between countries (e.g., 5.0 for India, 1.0 for South Korea) is likely to be largely driven by the reduction in HbA1c reported in the A1chieve study; the average end-of-study HbA1c for India was 7.2% (change from baseline: −2.1%), whereas the average end-of-study HbA1c for South Korea was 7.9% (change from baseline: −1.5%).
When modeled on a 30-year time horizon, the metabolic changes are associated with a consistent reduction in the incidence of major diabetes-related health outcomes such as visual loss and myocardial infarction. Accordingly, there is also a gain in life expectancy. Findings of improved metabolic outcomes in the short-term have been consistent with insulin detemir in T2D in both RCTs and observational studies, although the extent of the improvement is often not as large in the RCTs as in the studies done in routine clinical practice such as A1chieveCitation5,Citation13–15,Citation17,Citation19,Citation21,Citation23. A 30-year time horizon was judged realistic, as most people in the modeled cohort would be expected to die within that time, and such a time horizon will allow the capturing of the relevant costs and outcomes. Indeed, extending the time horizon to 50 years in the sensitivity analysis made very little difference to the final cost-effectiveness results.
Although data from RCTs are regarded as the ‘gold standard’ with regard to informing policy-makers and technology appraisalsCitation11, the presented analysis is based on observational data. The use of A1chieve observational data has the advantage of reflecting what happens in routine clinical practice. In reality, the short-term metabolic gains found in A1chieve are replicated in observational studies in longer developed (‘western’) economiesCitation35. Thus, while our findings are not based on results from RCTs, they are, in fact, based on clinical practice in the real world.
Cost-effectiveness analyses are inherently comparative, both for costs and outcomes, and a limitation of our study is the assumption in the base case that the comparator group continues current OGLD therapy indefinitely. We address this limitation by estimating cost-effectiveness on a 1-year time horizon, and by sensitivity analyses allowing that insulin detemir is started anyway after 5 years in the OGLD group. In the 1-year scenario, based only on the incremental cost of treatment and the incremental effect on HRQoL, cost-effectiveness is not as good as in the base case, yet starting insulin detemir is still cost-effective using WHO CHOICES criteria and, indeed, in three countries still highly cost-effective. In the later-start sensitivity analysis, insulin detemir is still cost-effective over a 30-year time horizon, even if it is started in the OGLD group 5 years later than in the base case.
Advantages of our approach include the relatively broad population base, and the use of country-specific data to ensure local relevance of the findings. While the findings are specific to the countries concerned, it is clear that their consistency implies that starting insulin detemir is likely to be cost-effective or even highly cost-effective in most other countries. However, because we find broad differences in the gain of life-years free of complications between countries, and because the incremental healthcare costs varied widely between countries, it is not possible to make any approach to specific estimates for any other country. Nevertheless, other studies in different resource environments have been used to evaluate the cost-effectiveness of insulin detemir, in particular using US and German populationsCitation36,Citation37. These studies also used the CORE Diabetes Model and projected, using a 35-year time horizon, improved quality-adjusted life expectancy with insulin detemir compared with OGLDs alone; in the US study, a discounted life expectancy gain of 0.464 years and an ICER of USD7412 per QALY gained were reported in the base caseCitation36, while in the German study, life expectancy gain was 0.28 years with cost-saving in the insulin detemir armCitation37. In agreement with our findings, cost savings in both studies were driven by the projected decrease in diabetes-related complications associated with insulin detemir therapy.
Our study has other limitations. A1chieve was not a randomized study, thus no other comparator than continued baseline treatment was available for modeling. Other insulins were studied in A1chieve, and similar short-term clinical outcomes were reported, but the populations allocated to each insulin would have been likely to differ in unknown ways. It could be argued that we should model against another insulin as a comparator, but no data are available from A1chieve as to what outcomes would have been found with such an insulin in a population similar to that started on insulin detemir. Clearly, being an observational study, we can only assess the cost-effectiveness associated with starting insulin detemir; as we note in our original paperCitation5, the lack of increase in hypoglycemia or body weight, and the improvement in systolic blood pressure, suggests that starting the insulin analog was an opportunity for enhanced lifestyle modification.
As a limitation, there was a lack of published resources specific to the studied populations in terms of costs. This was addressed by obtaining estimates from two or three local experts in each country where data were lacking, and by asking them to comment on the obtained estimates, but the likely validity of this approach is not knownCitation26–29. However, the approach was undertaken in five different countries and, thus, five sets of local experts, and, as the findings of high cost-effectiveness applied to all five countries, and were robust on sensitivity analyses, it would seem that variance in cost estimates were not a problem for the study.
Costs associated with complications comprise a large proportion of diabetes-related healthcare budgets when estimated from countries as diverse as Tanzania and the US, Mexico, and South KoreaCitation38–41. In the present analysis, while projected treatment costs associated with starting insulin detemir were greater in the studied populations compared with OGLDs, and continued to be throughout the 30-year time horizon, they were partially (Algeria and India) or in essence completely (Indonesia, South Korea, Mexico) offset by the observed reduction in the costs associated with the management of diabetes-related complications. A problem for funders and policymakers is that, in diabetes care, costs have to be incurred upfront to gain savings decades later. An Indian study, for example, found that 60% of people had to use personal savings for their diabetes care, while only 2% of people on high incomes had insurance coverage for diabetesCitation42. In these circumstances, our finding that starting insulin detemir was cost-effective according to GDP criteria even over 1 year is potentially helpful: an early investment delivering an early healthcare gain.
A concern here is that we might not have captured all the costs associated with starting the insulin analog. In many of these countries, educational support teams are not available for insulin starters, but, if provided, would then add to costs, possibly for the duration of insulin therapy. However, equally, diabetes nurses/educators may reduce time needed with more expensive medical staff. Here, using sensitivity analyses, we modeled increased doctor support in the first year as being more relevant to the countries studied, but such extra costs made little difference to cost-effectiveness. Similarly, insulin therapy may require further SMPGCitation11, but this did not substantially affect overall costs either. To further address the hypothetical need for additional resources associated with starting insulin detemir, a sensitivity analysis was conducted that determined the maximum potential increase in total costs that would deliver an ICER of 3.0 GDP per capita. Although these values ranged from 76–194% for the five countries, it is clear that the use of additional resources is plausible without compromising cost-effectiveness. In conclusion, the changes in surrogate outcomes associated with starting insulin detemir in insulin-naïve people with T2D resulted in health gains that made the intervention highly cost-effective in five countries with distinct healthcare resources. A range of sensitivity analyses supported this conclusion and, indeed, using a 1-year time horizon, the intervention was still cost-effective compared to the baseline state.
Transparency
Declaration of funding
This research was supported by Novo Nordisk.
Declaration of financial/other relationships
PH, SB, GGG, and RM, for themselves or institutions with which they are connected, have received funding from Novo Nordisk for advisory, speaker, and research activities, including in regard of the A1chieve study. AN is an employee of Novo Nordisk. JME peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Acknowledgments
We thank all the people with diabetes and the local investigators who took part in the study. The authors take full responsibility for the data and analysis supporting this study, and the results and discussion presented, but are grateful to Steven Barberini of Watermeadow for writing assistance, and Last Mile P/S for assistance with analyses, funded by Novo Nordisk.
Previous presentation: Life expectancy values reported here in each of the studied populations have been previously published in abstract form at the ISPOR 18th Annual International Meeting, New Orleans, LA, May 2013Citation34.
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