Measuring the Value of Better Diabetes Management
Published Online: April 12, 2013
Darius N. Lakdawalla, PhD; Michael R. Eber, BSE; Felicia M. Forma, BSc; Jeffrey Sullivan, MS; Pierre-Carl Michaud, PhD; Lily A. Bradley, MBA; and Dana P. Goldman, PhD
The growing burden of type 2 diabetes mellitus has outpaced the modest progress in the efficacy of diabetes medications. However, it is unclear whether we are using our existing medications optimally. This article quantifies the value of addressing underuse of existing diabetes medications in the United States. Interventions—new technologies, public policies, or clinical approaches—that double the rate of initiation of insulin generate a value on average of more than $15,000 over the lifetime of a patient developing diabetes between ages 51 and 60 years, or $12.6 billion in the aggregate. Interventions that improve adherence would generate a value of more than $13,000 on average for the same patients, or $10.7 billion in the aggregate. The value of such interventions is on par with highly optimistic projections of technological progress in medication efficacy.
Type 2 diabetes mellitus (T2DM) continues to grow at a remarkable rate. Diabetes incidence in the United States rose by more than 45% from 2002 to 2010, and its substantial costs make this trend particularly troubling. Diabetes decreases quality of life, increases morbidity, reduces life expectancy by approximately 3 years in near-elderly cohorts, and imposes additional lifetime healthcare costs of roughly $200,000.1
Prevention is a natural response to the growing epidemic, and its social benefits appear to be quite large.1 Yet the policy question of how to improve and extend the lives of the tens of millions of patients who already have or will soon develop the disease remains. Technological progress in treatment is one possibility. Indeed, the discovery of 8 new drug classes in the past 15 years has expanded the arsenal of medications for management of T2DM. Most of the benefits have accrued to patients who do not respond adequately or durably to older therapies (eg, metformin, sulfonylureas). As David Nathan observes, drug classes “that have been developed recently are generally no more potent, and often less effective in lowering glycemia, than the 3 oldest classes.”2 In other words, the newer drugs have benefited patients who are inappropriate for or unresponsive to established treatments by preventing longer-term macrovascular and microvascular complications.3
Since a substantial segment of patients have not enjoyed major benefits from new treatments, it is worth asking whether we are using existing therapies to the greatest benefit. In fact, evidence suggests underuse along at least 2 dimensions: 1) imperfect medication adherence4,5 and 2) delays in or lack of insulin initiation.6,7
First, it is widely understood that patients with chronic illness do not always adhere to their providers’ instructions. Patient adherence to oral blood glucose– lowering medication and insulin regimens generally ranges from 60 to 85 percent, with even lower adherence reported for certain populations, such as those covered by Medicaid.5 A number of earlier researchers have documented the impact of poor adherence on diabetes-related outcomes. 8-10
Second, concerns arise about underuse of diabetes treatments. Many center on insulin because the injection regimen imposes inconvenience and other personal costs.7 This is unfortunate because compared with prevailing standards, earlier use of insulin may be warranted, particularly in patients with poor glycemic control (ie, glycated hemoglobin [A1C] >9).11,12 Clinicians may be reluctant to initiate patients on insulin because it is taken as a sign of “defeat.”13
The healthcare delivery system also can delay insulin initiation because of the need for referral to an endocrinologist in complex cases.
In light of these issues, we study whether and how poor adherence to and lower uptake of insulin therapy quantitatively contribute to the costs of diabetes on patients and the healthcare system. Both the expansion of insulin use and the reduction of poor adherence are likely to require the investment of resources to develop new policies, clinical approaches, and technologies. We are interested in the potential return on such investments.
To investigate these questions, we calculate and compare the lifetime benefits to patients of scenarios that improve adherence and increase insulin use. We then compare these benefits with the value of a scenario generating further improvements in the efficacy of diabetes medicines. In particular, we calculate the future technological progress that would be required in this scenario to match the value of expanding insulin therapy.
For all such scenarios, we simulate outcomes for a cohort of newly diagnosed US diabetes patients who are aged 51 years in either 2003 or 2004 and who acquire diabetes between ages 51 and 60 years. We then examine the costs and benefits of each of the above scenarios, compared with current diabetes management.
Overview of the Model
We use a well-established dynamic microsimulation model, the Future Elderly Model (FEM),14-17 to model the lifetime consequences of expanded insulin use, improved patient adherence, and future progress in the efficacy of diabetes medicines. The model simulates health status, health spending, and mortality experience of the US population with diabetes over the age of 50 years. The FEM is based on data from the Health and Retirement Study (HRS), a biennial survey of Americans 51 years and older that has been ongoing since 1992. We supplement the HRS with medical spending data from the Medicare Current Beneficiary Survey and Medical Expenditure Panel Study for persons who would not be eligible for Medicare. The FEM is well suited for this study, because it allows the simulation of alternative lifetimes for patients aged 51+ years in different treatment environments. Further detail on the mechanics and implementation of the model are provided in the eAppendix (available at www.ajmc.com).
Figure 1 gives a schematic view of how patients progress in the model. We model 3 steps in the management of diabetes. First, the model assigns an initial A1C level. Second, it assigns a first-line oral drug treatment regimen. Finally, the model predicts whether insulin therapy is initiated and whether oral drug therapy is modified.
Oral medications are categorized in 3 drug classes: metformin (biguanides), sulfonylureas, and thiazolidinediones (TZDs). They represent more than 90% of the oral diabetes medications taken by HRS respondents.
We allow for addition of insulin therapy as a second method of treatment starting in the second year of the simulation, because few T2DM patients are treated with insulin as a first-line agent. Upon initiating insulin, patients face a one-time decision whether to keep taking their oral medication or to depend solely on insulin.
For effects of drug treatment on health transitions, we focused on A1C, weight, and mortality, end points that were consistently reported in the literature and could be mapped to the health conditions in the model.
Table 1 presents estimates for clinical effects of oral diabetes medications used in the model by drug class and outcome. Since evidence on the effects of combination therapy is not universally available, we conservatively assumed that the effect of a combination therapy equals that of its most efficacious component therapy, relative to placebo.
For insulin therapy, clinical effects used in the model are summarized in Table 2. Because we focus on the effect of adding insulin to existing treatment regimens, we combined evidence of the effect of adding insulin treatment on A1C reduction with epidemiological evidence linking A1C to mortality, since the evidence on the direct effect of adding insulin is sparse.
In both cases, evidence of the effect of glycemic control from treatment on mortality is taken from the recent 10-year follow-up study of the United Kingdom Prospective Diabetes Study trial.18
Because poor adherence to diabetes medications decreases the effectiveness of treatment,5 we include adherence measures in the model. We map adherence to effectiveness, using estimates from the literature (see eAppendix).
Monetizing Lifetime Consequences
Finally, it is necessary to monetize effects on life expectancy, in order to compare them with the financial costs of increased treatment. The monetary value of longevity extensions is controversial. Viscusi and Aldy estimated based on a review of the literature that the value of a statistical life ranges primarily from $5 million to $12 million.19 Assuming a 3% real rate of interest and a constant flow of value implies figures between $150,000 and $360,000 for each statistical life-year. We chose a value of statistical life-year of $200,000 for our calculations, which is inside but toward the lower end of this range. Note that we conservatively assume that the value of morbidity reductions, independent of mortality, is zero. Future benefits and costs were discounted at 3% to compare scenario outcomes.
For the improved medication adherence scenario, we simulated the effects of bringing all patients to full adherence with their diabetes treatment regimens. Within the model, better adherence increases the effectiveness of diabetes medications, according to estimated adherence effects in the literature.
For the increased insulin initiation scenario, we doubled the rate of insulin initiation in the model, derived from predicted probabilities of insulin initiation based on HRS data. Effectively, current insulin users initiate therapy earlier, and the current nonusers who are predicted to be most likely to use insulin are “treated” with insulin by the simulation.
Finally, we estimated the increase in medication efficacy that would be required in order to match the value per patient of the insulin scenario. We measure efficacy as improvements in the relative risk of mortality and in A1C reductions. For simplicity, we considered a single uniform percentage improvement along both dimensions at once. The results of this scenario provide a benchmark to compare the value of increased insulin use with the value of technological innovation in treatment.
Lifetime Costs and Benefits
In Table 3, we show life expectancy as well as discounted lifetime medical costs for each scenario for the average patient newly diagnosed with T2DM between ages 51 and 60 years. In the baseline scenario, remaining life expectancy at 51 is 30.2 years. On average, 27.2 years are spent with no limitations on activities of daily living (ADL), while 3 years (10% of remaining life expectancy) are spent with at least 1 ADL limitation.
Both the increased insulin initiation and increased adherence scenarios provide an increase in life expectancy of 0.2 years, all of it disability free. Discounting the life expectancy gains and valuing a statistical life-year at $200,000, we obtain average individual benefits from life extension of $17,812 and $14,922 for the increased insulin initiation and improved adherence scenarios, respectively.
Both scenarios increase medical spending. The discounted average lifetime increase associated with the adherence scenario is $1529, and the increase associated with the insulin scenario is $3582, relative to baseline spending of $281,408. These increased expenditures are due to the additional cost of treatments for all conditions during patients’ lifetimes, which are, on average, extended compared with the baseline scenario.
Computing the net value of each scenario as the difference between the longevity benefits and the increase in costs, we obtain a net value of $15,779 in 2004 dollars per newly diagnosed patient with diabetes for the increased insulin initiation scenario, and $13,394 for the increased adherence scenario.
Improvements in Medication Efficacy
We compared our scenarios with improvements in the efficacy of medicines through technological innovation. Specifically, we calculated the across- the-board improvement in the average efficacy of diabetes medicines that would match the net value per individual achieved by expanding the rate of insulin initiation. Efficacy would need to rise by about 6% to match the value of expanded insulin use, which is similar to the value of eliminating poor adherence. Below, we discuss how to interpret the magnitude of this effect and how to compare it with the values of reducing underuse.
Robustness of Estimates
The sensitivity analyses for both scenarios show varied but uniformly positive effects of intervention. Since our estimates of benefits are conservative (no impact on microor macrovascular outcomes), even if the efficacy of early glycemic control is at the lowest range supported by the evidence, increasing adherence or increasing insulin uptake should have positive real-world benefits. Further detail on sensitivity analyses is provided in the eAppendix, as well as a discussion of model limitations.
Discussion and Policy Implications
The underuse of diabetes medications is costly to patients. Interventions, policies, and new technologies that permit the expansion of insulin use when appropriate or the reduction of poor adherence would generate substantial value. Expanding insulin use among T2DM patients would generate net social value in excess of $15,000 to the average 51-year-old who will acquire diabetes in the next 9 years, a total value of $12.6 billion in the United States. Eliminating poor adherence to insulin and oral medicines would generate over $13,000 on average to each newly diagnosed patient or $10.7 billion in the aggregate. Underlying uncertainty about the clinical benefits of various diabetes medications leads to some uncertainty around these estimates, but alternative scenarios all showed at least some positive benefit from improving adherence, increasing insulin uptake, or improving non-insulin efficacy.
The value of adherence and underuse interventions is comparable to optimistic projections of technological progress in the efficacy of diabetes medications. We projected a 6% increase in the efficacy of non-insulin antidiabetic medications for the average p atient that would be required to match the value of increasing insulin uptake. Historical experience suggests this would be an ambitious goal for the coming decades. For example, recently discovered classes of oral medicines have improved outcomes for particular subgroups that failed on existing medications, but have not generated impacts large enough to impact outcomes for most patients. The Agency for Healthcare Research and Quality concluded that none of the major classes of new diabetes treatments (eg, TZDs, DPP-4 inhibitors, meglitinides, and alpha-glucosidase inhibitors) improved on earlier treatments for the average patient.3 This finding appears consistent for monotherapies or dual combinations, where the newer agents were relatively similar to established alternatives for the average patient.3 The value of newer agents lies in second- and later-line alternatives for patients who fail to respond to established therapies. They also may generate benefits not captured by the traditional end point of A1C, including reduced macrovascular and microvascular complications, lower risks of hypoglycemia, and weight loss.3
Our results, coupled with the challenge of sustaining continued growth in the efficacy of diabetes medications, suggest the need to expand the view of what constitutes “innovation” in the treatment of diabetes and possibly other chronic conditions. Traditionally, discussions of innovation have centered on the discovery of new therapies. Attention also must be paid to incentives for the discovery of new approaches to improve the use of existing medications. These include strategies to improve adherence and enhance the patient experience of inconvenient medication regimens such as injectable insulin.8
From a policy perspective, this suggests the need to strengthen incentives to invest and innovate in promoting adherence and improving patients’ insulin experiences. In the current reimbursement environment, innovators cannot be confident of generous reimbursement for new approaches that focus primarily on improving the convenience and patient experience of therapy. The single-minded focus on efficacy of new treatments, as measured in randomized controlled trials, contributes to the problem. It is well understood that efficacy estimates from trials reflect an artificially adherent patient population. Therapies that are no more efficacious in trials but significantly enhance patient convenience may improve clinical outcomes in the real world. Our results suggest that this could be valuable to patients with diabetes and should be reimbursed accordingly.
This logic applies equally to nonpharmaceutical interventions. New approaches for monitoring poor adherence and encouraging compliance should be rewarded. Innovators—whether in pharmaceuticals, healthcare delivery, or proinsurance—will invest more resources in discovery when they expect greater rewards.20 The most efficient mix of policies to encourage innovation is beyond the scope of this study, but it is notable that policy makers have a number of different levers to choose from.
Improving the use of existing diabetes medications will be just as challenging as discovering new ones. Yet, our study suggests that it might be just as rewarding. Long cycles of clinical research and development are necessary before new treatments come to market. Society may need to embrace an equally forward-thinking and long-term proinsurance diagram of research on improving the use of existing diabetes medications. EBDM
Author Affiliation: From University of Southern California (DNL, DPG), Los Angeles, CA; Precision Health Economics (MRE, JS, LAB), Los Angeles, CA; sanofiaventis US (FMF), Bridgewater, NJ; University of Québec (P-CM), Montreal, Quebec, Canada.
Funding Source: None.
Author Disclosures: Dr Lakdawalla is a partner with Precision Health Economics, which receives consultancies from life science firms. Mr Eber, Mr Sullivan, and Ms Bradley report employment with Precision Health Economics, which receives consultancies from life science firms. Ms Forma reports employment with sanofi- aventis US. Dr Michaud reports receiving payment from Precision Health Economics for involvement in the preparation of this manuscript. Dr Goldman reportsreceiving consultancies from sanofi, Bristol-Myers Squibb, and Pfizer.
Authorship Information: Concept and design (DNK, FMF, JS, P-CM, LAB, DPG); acquisition of data (DNK, JS, P-CM, DPG); analysis and interpretation of data (DNK, MRE, JS, P-CM); drafting of the manuscript (DNK, MRE); critical revision of the manuscript for important intellectual content (DNK, FMF, MRE, LAB, DPG); statistical analysis (DNK, MRE, JS, P-CM); provision of study materials or patients (LAB); obtaining funding (DNK, FMF, LAB, DPG); administrative, technical, or logistic support (MRE, LAB); and supervision (DNK, FMF, DPG).
Address correspondence to: Darius N. Lakdawalla, PhD, Schaeffer Center for Health Policy and Economics, University of Southern California, 3335 S Figueroa St, Unit A, Los Angeles, CA 90089. E-mail: email@example.com.
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