Economic Effects of Fixed-Dose Versus Loose-Dose Combination Therapy for Type 2 Diabetes Patients.

OBJECTIVE: We examined the effects of fixe-dose combinations (FDCs) versus loose-dose combinations (LDCs) on costs from the payer and patient perspective and investigated potential channels contributing to differences in costs between the two modes of treatment. METHODS: We investigated administrative data from 2017 to 2020 on diabetes patients in Germany. After using prospensity-score matching to remove dissimilarities between FDC and LDC patients, we compared changes in costs with a difference-in-differences approach. We analyzed pharmaceutical costs, inpatient and outpatient costs, other costs and total healthcare costs from the payer perspective, and co-payments from the patient perspective. RESULTS: The sample comprised 1117 FDC and 1272 LDC patients. Regression analysis revealed that FDC therapy significantly increased antidiabetic pharmaceutical spending in the first year by 5.5% (p < 0.01), but decreased co-payments by 33% (p < 0.01) in the first and 44% (p < 0.01) in the second year. We also observed a trend towards higher outpatient spending in the first year. No significant differences were found with respect to inpatient or other costs. The increase in antidiabetic pharmaceutical spending did not contribute to a significant increase in total healthcare expenditure. We identified a shift of co-payments to the payer and higher adherence as possible mechanisms behind the increase in antidiabetic pharmaceutical spending. CONCLUSION: Although FDC therapy increased disease-specific pharmaceutical spending in the short term, this increase did not lead to differences in total healthcare costs from the payer perspective. From the patient perspective, FDC therapy may be the preferred treatment approach, because of significant saving in co-payments, which is likely attributable to the elimination of one co-payment and therefore a shift in costs to the payer.

Market diffusion of biosimilars in off-patent biologic drug markets across Europe.

Biologics are among the most expensive pharmaceuticals but have begun to lose their exclusivity rights over the past 15 years, offering the possibility for biosimilar competition. Therefore, we examine the market diffusion of biosimilars across Europe. Using revenues and sales data from IQVIA, we identified 12 biologic substances facing first biosimilar competition between 2014 and 2020 in 25 European countries. We investigated biosimilar market share depending on product and market characteristics with beta regression. Moreover, we compared market diffusion across countries using multilevel modelling. The average market share of biosimilars at first biosimilar entry was about seven percent in the retail and hospital market and grew to 34.69% and 38.29% after 16 quarters, respectively. Quarters since first biosimilar entry had a positive but decreasing effect on biosimilar market share (p<.001 for both markets). Quarterly growth ranged from 0.006 (Netherlands) to 0.026 (Slovakia) in the retail market and from 0.007 (Hungary) to 0.040 (United Kingdom) in the hospital market. The diffusion increased over time across all European markets, although at different rates. Biosimilar market share was higher in the hospital market. Compared to generics, diffusion of biosimilars is much slower. If policymakers desire to increase biosimilar diffusion, they should aim at policies that increase competition and use countries with the highest diffusion rates as benchmarks.

The Impact of Iron Dyshomeostasis and Anaemia on Long-Term Pulmonary Recovery and Persisting Symptom Burden after COVID-19: A Prospective Observational Cohort Study.

Coronavirus disease 2019 (COVID-19) is frequently associated with iron dyshomeostasis. The latter is related to acute disease severity and COVID-19 convalescence. We herein describe iron dyshomeostasis at COVID-19 follow-up and its association with long-term pulmonary and symptomatic recovery. The prospective, multicentre, observational cohort study "Development of Interstitial Lung Disease (ILD) in Patients With Severe SARS-CoV-2 Infection (CovILD)" encompasses serial extensive clinical, laboratory, functional and imaging evaluations at 60, 100, 180 and 360 days after COVID-19 onset. We included 108 individuals with mild-to-critical acute COVID-19, whereas 75% presented with severe acute disease. At 60 days post-COVID-19 follow-up, hyperferritinaemia (35% of patients), iron deficiency (24% of the cohort) and anaemia (9% of the patients) were frequently found. Anaemia of inflammation (AI) was the predominant feature at early post-acute follow-up, whereas the anaemia phenotype shifted towards iron deficiency anaemia (IDA) and combinations of IDA and AI until the 360 days follow-up. The prevalence of anaemia significantly decreased over time, but iron dyshomeostasis remained a frequent finding throughout the study. Neither iron dyshomeostasis nor anaemia were related to persisting structural lung impairment, but both were associated with impaired stress resilience at long-term COVID-19 follow-up. To conclude, iron dyshomeostasis and anaemia are frequent findings after COVID-19 and may contribute to its long-term symptomatic outcome.

Regimen simplification and medication adherence: Fixed-dose versus loose-dose combination therapy for type 2 diabetes.

BACKGROUND: Suboptimal patient adherence to pharmacological therapy of type 2 diabetes may be due in part to pill burden. One way to reduce pill burden in patients who need multiple medications is to use fixed-dose combinations. Our study aimed to compare the effects of fixed-dose combination versus loose-dose combination therapy on medication adherence and persistence, health care utilization, therapeutic safety, morbidities, and treatment modification in patients with type 2 diabetes over three years. METHODS: Using administrative data, we conducted a retrospective controlled cohort study comparing type 2 diabetes patients who switched from monotherapy to either a fixed-dose combination or a loose-dose combination. Adherence was assessed as the primary endpoint and calculated as the proportion of days covered with medication. After using entropy balancing to eliminate differences in observable baseline characteristics between the two groups, we applied difference-in-difference estimators for each outcome to account for time-invariant unobservable heterogeneity. RESULTS: Of the 990 type 2 diabetes patients included in our analysis, 756 were taking a fixed-dose combination and 234 were taking a loose-dose combination. We observed a statistically significantly higher change in adherence (year one: 0.22, p<0.001, year two: 0.25, p<0.001, and year three: 0.29, p<0.001) as well as higher persistence and a smaller change in the number of drug prescriptions in each of the three years in the fixed-dose combination group compared to the loose-dose combination group. The differences were most pronounced in patients who were poorly adherent, had a high pill burden, or did not have a severe concomitant disease. CONCLUSION: Our results indicate that taking a fixed-dose combination can lead to a significant improvement in adherence to pharmacological therapy of type 2 diabetes compared to a loose-dose combination. In particular, these findings suggest that reducing pill burden may improve disease management among patients with more complex medication demand and patients who have demonstrated poor medication adherence.

Early Glycemic Control Assessment Based on Consensus CGM Metrics.

Continuous glucose monitoring (CGM) has revolutionized the world of diabetes and transformed the approach to diabetes care. In this context, an expert panel has reached consensus on clinical targets for CGM data interpretation based on eight CGM metrics. At least 70% of 14 consecutive CGM days (referred to as a period) are recommended to assess glycemic control based on the metrics. In clinical practice less CGM data may be available. Therefore, the primary aim of this study is to explore the ability to recover the consensus metrics utilizing less than 14 days of CGM data (intra-period). As a secondary aim, we investigate the recovery considering two consecutive periods (inter-period). The analyses are based on real-world CGM data from 484 diabetes users (4726 periods) acquired from the Cornerstones4Care® Powered by Glooko app. Using up to 14 accumulated days, the consensus metrics are calculated for each user and period, and compared to the fully 14 accumulated intra- and inter-period days. Relatively low deviations were observed for time in range (TIR) and average based metrics when using less than 14 days, however, we observed large deviations in metrics characterizing infrequent events such as time below range (TBR). Furthermore, the consensus metrics obtained in two consecutive 14 day periods have clear discrepancies (inter-period). Recovering consensus metrics using less than 14 days might still be valuable in terms of interpreting CGM data in certain clinical contexts. However, caution should be taken if treatment decisions would be made with less than 14 days of data on critical metrics such as TBR, since the metrics characterizing infrequent events deviate substantially when less data are available. Substantial deviation is also seen when comparing across two consecutive periods, which means that care should be taken not to over-generalize consensus metric based glycemic control conclusions from one period to subsequent periods.

Real-World Evidence of User Engagement With Mobile Health for Diabetes Management: Longitudinal Observational Study.

BACKGROUND: Patient support apps have risen in popularity and provide novel opportunities for self-management of diabetes. Such apps offer patients to play an active role in monitoring their condition, thereby increasing their own treatment responsibility. Although many health apps require active user engagement to be effective, there is little evidence exploring engagement with mobile health (mHealth). OBJECTIVE: This study aims to analyze the extent to which users engage with mHealth for diabetes and identify patient characteristics that are associated with engagement. METHODS: The analysis is based on real-world data obtained by Novo Nordisk's Cornerstones4Care Powered by Glooko diabetes support app. User engagement was assessed as the number of active days and using measures expressing the persistence, longevity, and regularity of interaction within the first 180 days of use. Beta regressions were estimated to assess the associations between user characteristics and engagement outcomes for each module of the app. RESULTS: A total of 9051 individuals initiated use after registration and could be observed for 180 days. Among these, 55.39% (5013/9051) used the app for one specific purpose. The average user activity ratio varied from 0.05 (medication and food) to 0.55 (continuous glucose monitoring), depending on the module of the app. Average user engagement was lower if modules required manual data entries, although the initial uptake was higher for these modules. Regression analyses further revealed that although more women used the app (2075/3649, 56.86%), they engaged significantly less with it. Older people and users who were recently diagnosed tended to use the app more actively. CONCLUSIONS: Strategies to increase or sustain the use of apps and availability of health data may target the mode of data collection and content design and should take into account privacy concerns of the users at the same time. Users' engagement was determined by various user characteristics, indicating that particular patient groups should be targeted or assisted when integrating apps into the self-management of their disease.