Health and Productivity Benefits with Early Intensified Treatment in Patients with Type 2 Diabetes: Results from Korea
Abstract
The available evidence suggests positive health outcomes associated with early treatment intensification in Type 2 diabetes mellitus (T2DM). Our study estimated the productivity effects arising from improved health correlated with early intensified treatment in T2DM in Korea. Using a recently published methodology and model, we investigated the association between early intensified treatment and the probability of experiencing fewer diabetes-related complication events. Treatment strategies leading to better health outcomes are expected to be associated with social value through increased participation in paid and unpaid work activities. Therefore, we translated the lower incidence of complications into monetary terms related to productivity for the Korean population. We quantified productivity by considering (a) absenteeism, (b) presenteeism, (c) permanent loss of labor force, and (d) activity restriction. Deterministic and probabilistic sensitivity analyses in the base case parameter were performed. Approximately, 1.7 thousand (standard deviation [SD] ±580 events) micro- and macrovascular complication events could potentially be avoided by early treatment intensification. This led to a societal gain attributed to increased productivity of 23 million USD (SD ± $8.2 million). This article demonstrates the likelihood of achieving better health and productivity through early intensified treatment in diabetes.
What do we already know about this topic?
Worldwide, treatment guidelines highlight the importance of maintaining HbA1c levels below target (which may differ between countries) to prevent and/or delay vascular complications.
How does your research contribute to the field?
Lower incidence of diabetes-related vascular events led to a societal gain through reduced productivity and activity impairment in the Korean population, of approximately 23 million USD in Korea.
What are your research’s implications towards theory, practice, or policy?
Priority should be given in optimizing diabetes therapeutic strategy which could potentially lead to a healthier and more productive population.
Introduction
The steady increase of diabetes mellitus (DM) prevalence has triggered an important socioeconomic burden worldwide. In 2019, the age-adjusted prevalence of DM in Korea was estimated at 3.7 million patients.1 Amongst diabetes types, the most common is Type 2 diabetes mellitus (T2DM) which accounts for approximately 90% of all diabetes cases, globally.2,3 Diabetes patients are at high risk of developing diabetes-related vascular complications.4 These complications often lead to productivity losses due to morbidity and mortality and increase the healthcare burden. In 2019, the economic implications of DM in Korea had been reported at 18293 million USD, with an average per capita cost of 4090 USD.5 Total indirect costs accounted for 5141 million USD, out of which, the majority was attributed to caregivers (1860 million USD), followed by productivity losses due to patient morbidity and mortality (1461 USD and 1820 million USD respectively).5 The same study showed that an increase in the number of complications or related comorbidities (1-3 or more) led to an increase of the per capita cost of diabetes treatment, by approximately 66%.5 Thus, diabetes leads to considerable burden for the patients, the healthcare system and society while diabetes-related complications lead to higher healthcare costs.
Access to efficient diabetes care decreases the burden associated with morbidity and mortality. Early and patient-adjusted treatment is a core element of current treatment guidelines.6 Korean guidelines suggest metformin monotherapy for newly diagnosed cases, unless contraindicated.7 Depending on the patient’s clinical course, treatment intensification may be considered.7 Early intensified treatment, an alternative strategy to curb the disease, is likely to lower the incidence of T2DM complications, compared to metformin monotherapy as initial therapy. Korean guidelines6 suggest that early treatment intensification provides durable benefits and sustained glycemic control. Intensive glycemic control has been effective in preventing microvascular complications, as shown in both the Kumamoto study and the UK Prospective Diabetes Study (UKPDS).8,9 During the Kumamoto study, a prospective 6-year study conducted on non-insulin dependent DM Japanese patients, the intensive glycemic control group achieved nerve conduction velocity improvement, as well as 69% and 70% decrease in retinopathy and nephropathy comorbidities, respectively.8 In the VERIFY study (Vildagliptin Efficacy in combination with metformin for early treatment of type 2 diabetes, ClinicalTrials.gov number, NCT01528254) patients treated with early combination treatment had a lower incidence and extended time to treatment failure, compared to sequential intensification in newly diagnosed T2DM patients with mild hyperglycemia.10 Hence, a greater proportion of patients starting early intensified treatment achieved the glycemic target.
Despite current evidence, due to healthcare systems’ budget constraints that may pose restrictions in adopting new treatment strategies, debate revolves around the widespread implementation of alternative treatment strategies.11 Given that evidence has already highlighted the potential health benefits of early intensified treatment, this study primarily intends to showcase the societal value due to better health associated with early intensified treatment in patients with T2DM in Korea.
Methods
Model Structure
Our analysis adopts the methodology conducted in a similar evaluation in Mexico.12 The latter was developed to estimate the number of complication events and the associated effects on productivity. The simulation was based on a survival model to derive the probability of experiencing a first and a second treatment failure while on antidiabetes treatment. According to the VERIFY clinical trial, treatment failure is defined as the incapacity of maintaining HbA1c values below 7%.10 We used the same definition in this work. For the statistical model, data from the VERIFY clinical trial were used.13 Deriving the probabilities was based on the parametrization of Kaplan Meier (KM) curves. The choice of the best fitting parametric model was based on the Akaike and Bayesian Information Criterion (AIC and BIC, respectively) and visual inspection. For the first and second treatment failure in both treatment scenarios, the log-normal distribution was chosen. The AIC and BIC for every distribution are reported in Supplemental material Table 1.
The model consists of 3 treatment-dependent health states (Supplemental material Figure 1). In the first treatment scenario, the hypothetical population receives early intensified treatment (metformin and vildagliptin). The second scenario consists of the stepwise approach (metformin monotherapy as the initial treatment followed by the addition of vildagliptin). If patients in both scenarios experience a treatment failure, they receive combination therapy (metformin and vildagliptin). Thus, patients in the first scenario continue to receive the same treatment regimens. In the third state, all patients receive insulin or sulfonylureas (SUs) reflecting Korean treatment guidelines.7 Based on expert opinion, we assumed that, following treatment with metformin plus Inhibitors of dipeptidyl peptidase 4 (DPP-4i), 70% of the patients will switch to SUs and 20% will initiate insulin (Novartis, Personal communication). The per age and gender all-cause mortality probability were derived from Korean life tables.14 By applying a relative risk of 1.24 to the all-cause mortality we accounted for diabetes related mortality.15 The model’s time horizon covers 10 years with 6-month cycles. We chose this time horizon to provide a realistic assessment considering the sensitive nature of the socioeconomic parameters used.
The model allows for the probability of experiencing the following complication events: stroke, myocardial infarction (MI), heart failure (HF), neuropathy and nephropathy. Patient level data were used to estimate the probabilities of experiencing an event in the no treatment failure and first-treatment failure states. The probabilities of experiencing a complication event after a second treatment failure were obtained from a real-world study.16 Finally, we converted the reported incidence into biannual probabilities to align with the model’s cycle length following current recommendations.17 (Supplemental material Table 2).
Model Population
We simulated the health impact of early intensified treatment using a static cohort model. To estimate the cohort size expected to be treated with early intensified treatment, we first referred to the prevalence of DM in Korea,18 assuming that T2DM cases account for 88% of total cases.5 Furthermore, our T2DM population was defined as follows: we utilized data from diagnosed and treated patients with any antidiabetes treatment in Korea.5,18 Subsequently, we applied the percentage of patients receiving early intensified treatment with metformin plus vildagliptin (8%) (Novartis, Personal communication). The model considered 5 age groups namely 20 to 29, 30 to 39, 40 to 49, 50 to 59, and 60 to 69 years. The specific age subgrouping was selected to better align with the overall ages considered in the VERIFY10 and the available socioeconomic data. With reference to the previous publication12 and for consistency reasons with the data used (ie, complication events), we utilized evidence from the VERIFY study10 to distribute our patient population in the different age and gender groups. Details with regards to the model population are provided in Supplemental material Figure 2.
Socioeconomic Impact Evaluation
The basis of the productivity losses evaluation resides in the macroeconomic consequences of a disease, that is, the socioeconomic implications of a disease due to productivity losses. Thus, the impact of early intensified treatment on the number of avoided complication events is expected to have an impact on paid and unpaid productivity losses attributed to diabetes complications. We defined paid work as the value added by an individual who belongs to a country’s labor force. We quantified productivity losses in paid work by considering three dimensions: (1) absenteeism, (2) presenteeism, and (3) lost labor force associated with each complication event.19 Additionally, we utilized evidence on employment patterns in Korea, such as employment rates and working hours per age and gender, as derived from the Korean statistical office.20 We defined unpaid work as the value added produced by a person through household and community production that have a societal value by contributing to a country’s national wealth.19 For this analysis, we defined unpaid work as (1) household production, eg, food preparation, (2) family member care, and (3) unpaid volunteer, trainee, and other unpaid work.21 To quantify productivity losses resulting from unpaid work, we appraised the percentages of activity impairment associated with each diabetes complication. Comprehensive details concerning the extent of activity restriction attributable to stroke, MI, HF, neuropathy, and nephropathy can be found in Table 3 of the Supplemental material. Further, we utilized age and gender specific data from the Time Use Survey22 to estimate the time spent on unpaid activities in Korea.
Thus, by applying the evidence of disease impairment derived from the literature and societal figures derived from official statistics, we used the following equations to calculate the socioeconomic gains in paid hours.12
(1)
(2)
(3)
To quantify the unpaid hours, the below formula was used:
(4)
Where, a represents age, g represents gender and c represents each complication event. The estimated avoided hours of paid productivity losses were monetized by applying the average wage in Korea in 2019. To value the avoided unpaid activity losses, we used the minimum wage in Korea in the same year. We report the productivity and socioeconomic parameters used in Supplemental material Table 3.
Sensitivity Analysis
We tested the uncertainty of parameters through a deterministic sensitivity analysis (DSA) by assessing each selected input to its lower and upper bound. When possible, we used the reported 95% Confidence Interval (CI) for each input. If not available, a conventional 10% Standard Error (SE) was used to calculate the lower and upper values.23 The DSA allowed us to investigate the influence of each parameter in our socioeconomic evaluation and, thus, identify the variables against which our model was most sensitive. Additionally, we performed a probabilistic sensitivity analysis (PSA) using 1000 iterations. The DSA included essential productivity and socioeconomic parameters, while we assessed the uncertainty around clinical parameters and productivity inputs in the PSA. The parameters used for the PSA and DSA are reported in Supplemental material Tables 2 and 4.
Results
Patient Population
Our analysis followed a hypothetical cohort of 113 633 patients, which was reduced by 4% throughout the 10-year time horizon. Since our subpopulation distribution was based on the VERIFY trial,10 our model had greater proportion of females than males. Figure 3 in Supplemental material presents the proportional distribution of the modeled population.
Health Impact
Our survival model indicated that patients in the early intensified antidiabetes treatment group have a longer time to first and second treatment failure compared to those in the stepwise approach group. As aforementioned, treatment failure was defined as the presence of HbA1c values exceeding 7%, in accordance with the definition established by the VERIFY trial.10 By the end of the 10-year time horizon, approximately 20% of patients remained in the “no treatment failure” health state in the intensified antidiabetes treatment group compared to 8% in the stepwise approach group.
Our estimations showed that introducing combination therapy early in the course of T2DM improves health. Patients in the stepwise strategy accrued approximately 12 169 complication events. Conversely, patients in the early intensified group generated 10 489 complication events. Thus, 1683 complication events could be avoided over the 10-year time horizon. In other words, patients who received combination therapy at early disease stages accrued 14% fewer diabetes-related complication events compared to patients starting with monotherapy, in 10 years. As shown in Figure 1, neuropathy sustained 51% of the total avoided complication events (n = 855), followed by stroke (24%; n = 409), MI (21%, n = 362) and nephropathy (10%, n = 170). However, patients receiving early intensified treatment, experienced 113 additional HF events compared to the stepwise approach group.
Figure 1. Avoided diabetes complication events due to early intensified treatment.
Note. MI = myocardial infarction; HF = heart failure. Neuropathy accounts for approximately half of the avoided complication events (51%), following by stroke (24%), MI (22%) and nephropathy (10%). Patients in early intensified treatment accumulate additional 113 HF events compared to patients in the stepwise approach.
Socioeconomic Impact
Fewer complication events could be translated into avoided productivity losses. More specifically, the introduction of early intensified antidiabetes treatment resulted in 1 569 141 avoided hours lost in terms of paid and unpaid work for the whole population over 10 years. As our model predominantly included females, women accumulated more avoided productivity losses than men (approximately 876 and 694 thousand, respectively). Three fifths (58.6%) of the avoided productivity losses were attributed to unpaid work activities with females experiencing 63% of them. Table 1 presents both genders’ avoided productivity losses per paid and unpaid activities.
| Avoided productivity and activity losses due to early intensified treatment (hours) | ||||
|---|---|---|---|---|
| Paid work | Unpaid activities | |||
| Absenteeism | Presenteeism | Lost labor force | Activity restriction | |
| Females | 94 920 | 136 316 | 61 833 | 582 468 |
| Males | 117 903 | 168 232 | 70 492 | 336 976 |
| Monetized avoided productivity losses (USD) | ||||
| Paid work | Unpaid activities | |||
| Absenteeism and presenteeism | Lost labor force | Activity restriction | ||
| Females | 5 411 155 | 1 446 968 | 4 950 976 | |
| Males | 6 695 879 | 1 649 584 | 2 864 299 | |
| Total | 12 107 034 | 3 096 552 | 7 815 275 | |
| Societal Impact | 23 018 862 | |||
Note. In females, the avoided productivity losses were higher for paid work compared to males (67%vs 49%). In contrast, the impact of paid work was more considerable for males. These differences can be attributed to the employment patterns in Korea. Males spend more time on paid employment as opposed to females. The monetized socioeconomic impact was higher in paid employment for both genders, as paid work was valued with the average wage and unpaid work with the minimum wage. The contribution of the 2 genders was almost identical (51% for females and 49% for males). The main driver for the small difference was our model’s higher number of females.
The monetized socioeconomic gains amounted to 15.2 million USD in paid work and 7.8 million USD in unpaid work. Thus, the estimated health benefit of early intensified treatment led to a gain of 23 million USD in Korea, attributed to increased productivity. Table 1 presents the monetized impact per gender and paid/unpaid work activity.
Sensitivity Analysis
The DSA results showed that the monetized productivity losses could range from 18.6 million USD to 27.5 million USD. Supplemental Table 5 depicts the varied individual parameters and their impact on our analysis. When assessing the sensitivity of our analysis through PSA, the model outcomes were shown to be robust. The mean monetized productivity losses were estimated at 22.8 million USD (Standard Deviation [SD] ± $8.2 million USD), while the mean number of avoided events were at 1665 (SD ± 580 events). In Supplemental material Figure 4 and 5 reports the results of the sensitivity analyses.
Discussion
T2DM complications lead to poor prognosis for diabetes patients. The socioeconomic burden that results from those complications is significant. Current treatment strategies aim to improve clinical outcomes by slowing the progression of T2DM. It is documented that early intensified treatment—compared to metformin monotherapy—delays the progression of TD2M by extending the time to treatment failure and by reducing the risk of microvascular complications.1 In the present study, we examined the socioeconomic effects of early treatment intensification in Korea by quantifying the avoided productivity losses in paid work and unpaid activities due to diabetes complications. Early intensified treatment resulted in socioeconomic benefits of 23 million USD in 10 years.
In the current study, we used a comparable methodology with a previous publication12 to measure and value productivity effects. In Mexico, the authors estimated approximately 13 000 avoided complication events leading to a societal impact of 53.5 million USD.12 Given the methodological similarities, this variation is mainly driven by the difference in population size of T2DM patients in Korea and Mexico but also the difference in GDP per capita as per the World Bank classification.24 Despite the divergence in the absolute number of findings, our results were consistent and confirmed the potential positive impact of early intensified treatment irrespective of the economic status of the country. There was a proportional decrease of complication events in relation to the overall prevalence considered in both studies. In Mexico, early intensified treatment led to approximately 20% fewer complication events. Our present study found that 14% of complications could be avoided. The small difference in the percentages could be attributed to better data availability in Korea for the percentage of patients receiving early intensified treatment with metformin plus vildagliptin, and proportion of patients receiving insulin and SUs. Furthermore, the monetized effects could be analogically comparable between the 2 countries considering the higher wages in Korea. However, the delivery and official recommendations of diabetes management substantially differ between the 2 countries. In Korea, early intensified treatment for high HbA1c levels in early stages is already included as an option in the treatment algorithm in the 2021 guidelines.6 The guidelines use the findings from the VERIFY study to support the recommendation. In contrast to Korea and based on our current knowledge, the guidelines in Mexico do not incorporate a comparable recommendation.25,26 Therefore, as of the present, early intensified treatment does not appear to be a standard practice in early diabetes stages.26,27 Instead, the prevailing recommendation advocates for a combination treatment approach in cases of elevated HbA1c levels.27 Despite this, existing literature discuss the necessity of treatment with metformin and vildagliptin in instances of glycemic dysfunction, particularly in cases where the patient has not undergone prior pharmacological interventions.28 Concurrently, novel interventions have been introduced and evaluated in Mexico to attain metabolic objectives, resulting in preservation of treatment goals.29,30
Additionally, other differences between the populations (eg, obesity and lifestyle) may influence the onset of diabetes and diabetes outcomes. Global trends in obesity highlight that central Latin American nations, such as Mexico, exhibit elevated rates of obesity among adults compared to countries of the East Asia region, such as Korea.31 In particular, Mexico exhibits one of the highest obesity rates globally, with almost 30% adults being classified as obese. In contrast, obesity rates in Korea rank among the lowest; however, there is an upward trend.32,33 Approximately 4% of the adult population in Korea is categorized as obese, while around 30% are classified as overweight,32-34 inclusive of those in the obese category. Consequently, the obesity in Mexico is estimated to be 10 times higher compared to Korea.34 Concerning the lifestyle in Mexico, a substantial proportion, approximately 80% of adults, encounter barriers in the pursuit of a healthy lifestyle, specifically in maintaining a nutritious diet and engaging in physical activity.35 Among the barriers to fostering dietary health are the financial constraints and cultural eating patterns. On the other hand, Koreans are more engaged to healthy eating habits.36
Overall, it is evident that diabetes exhibits a negative association with employment,37 significantly influencing work habits and wages.38 Particularly, individuals diagnosed with T2DM exhibit a higher frequency of absenteeism in comparison to their healthier counterparts, consequently exerting an adverse impact on their remuneration.39 Furthermore, diabetes imposes a substantial burden on the healthcare system. In Mexico, the indirect costs reached 177 million USD, predominantly attributable to permanently disabled patients. Direct costs associated with diabetes are approximately 1164.8 million USD. Additionally, the costs related to diabetes complications are noteworthy, ranging from 82 million USD for nephropathy to 2.8 USD million for neuropathy.40 While our evaluation does not specifically address the direct costs of complications, it is plausible that a substantial decrease in the incidence of macro- and microvascular complications would yield significant cost savings for the healthcare system.
To the best of our knowledge, this investigation represents the first attempt to evaluate the direct implications of early treatment intensification on productivity gains in the context of T2DM in Korea. Recent empirical evidence underscores the potential economic viability of this therapeutic approach. Notably, findings by Chin et al indicate that early treatment intensification demonstrates cost-effectiveness when compared with a delayed strategy within the framework of the Australian healthcare system.11
Although the present study was based on solid health and socioeconomic inputs and the methodology built on a previously published study, there are sources of uncertainty that should be acknowledged. We used evidence from the VERIFY study,10 an international trial on time to treatment failure. The incidence of diabetes complication events while on metformin monotherapy and metformin plus vildagliptin was derived from the same clinical trial.10 To estimate the probability of experiencing micro- and macrovascular events when our patient population receives insulin and SUs, we used a real-world study16 with a small sample size since data on the Korean population was unavailable. Moreover, this was the only identified study in the literature assessing the treatment regimens of interest. The underlying assumption is that the findings of the VERIFY trial10 and the real-world study16 could be extrapolated to the Korean population. Differences in the population, such as education and nutrition, may affect the outcomes of our model. However, the VERIFY also included an Asian population, and 37 patients were from Korea.
Another source of uncertainty is the productivity parameters used to value the losses due to absenteeism, presenteeism, activity restriction and lost labor force. Due to the unavailability of Korean-specific inputs, most evidence came from other high-income countries. The primary aim of this analysis was to identify predominantly country-specific parameters, or evidence from similar countries, for example, Japan. This was possible for only 7 parameters. We utilized evidence from the USA or Europe for all the remaining parameters. We acknowledge that the proxy countries used may significantly differ regarding the social and healthcare system and the population characteristics. However, we assessed the clinical and productivity parameters in sensitivity analyses to understand the impact of base case variations.
The age and gender distribution of our model came from the VERIFY trial.10 Generally, using evidence on the population distribution reported in trials is a common practice. However, in our case, this was preliminarily done to better align with the evidence on the probability of diabetes complication events, as certain age groups have a higher probability of experiencing complication events.
Conclusion
The present study illustrates the potential impact of treatment intensification in early stages of diabetes compared to the stepwise approach in patients with T2DM in Korea. This article demonstrates the likelihood of achieving better health and the associated socioeconomic outcomes through early intensified treatment in T2DM. A comparison between high income and low/middle income countries reveals the potentially positive effects of applying a different and more intense treatment strategy irrespective of the context.
Ethical Approval
Our study did not require an ethical board approval because no humans were involved.
Declaration of Conflicting Interests
The author declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: F. Tsotra, P. Peristeris, I. Athanasiou, M. Müller are employees of WifOR institute. G. Bader, A. Malhotra are employees of Novartis.
Funding
The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by Novartis Pharma AG.
ORCID iD
Foteini Tsotra https://orcid.org/0000-0002-1295-1044
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Supplementary Material
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Article first published online: May 6, 2024
Issue published: January-December 2024
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© The Author(s) 2024.
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PubMed: 38708904
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This article was published in INQUIRY: The Journal of Health Care Organization, Provision, and Financing.
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