Effect of Weight Change on Economic Outcomes Among Persons with Type 2 Diabetes Mellitus in the United States: Beyond Glycemic Control

Treatment-Related Attributes of Diabetes Therapies and How People with Type 2 Diabetes Report Their Impact on Indicators of Medication-Taking Behaviors

Purpose

Understanding the treatment-related attributes influencing medication-taking behaviors in people with type 2 diabetes (T2D) is important for delivery of patient-centered care. This review aimed to identify and summarize studies in which people with T2D (PwD) directly indicated the treatment-related attributes associated with medication-taking behaviors or intentions.

Materials and Methods

EMBASE and PubMed were searched for studies (Jan 2005-May 2021) reporting the link between PwD-expressed diabetes treatment-related attributes and the decision to initiate, adhere to, or discontinue a T2D medication. Eligible studies reported attributes associated with oral antidiabetes drugs or injectables (not insulin). Studies not explicitly exploring the link between attributes and indicators of behaviors (eg most discrete-choice experiments [DCE] and those interrogating electronic medical records or claims databases) were excluded, as were studies where the link between attribute and behavior came from anyone but the PwD.

Results

Of the 6464 studies identified, 16 were included. Studies were conducted across multiple countries; the USA was most represented (n = 8 studies). The impact of treatment attributes was described on indicators of initiation (n = 3), adherence (n = 12), and discontinuation (n = 4). Some studies evaluated multiple behaviors. PwD perspectives were solicited by structured questionnaires (n = 10), qualitative approaches (n = 4), or DCE explicitly exploring the link to medication-taking behaviors (n = 2). Closed- (n = 9) and open-ended questions (n = 7) were employed. Across studies, several factors including glycemic efficacy (n = 9), weight change (n = 9), dosing frequency (n = 9), hypoglycemia (n = 8), gastrointestinal adverse events (n = 8), regimen complexity (n = 6), route of administration (n = 3), and cardiovascular risk (n = 1) were reported as influencing behaviors, being motivators or barriers to initiation, adherence, or discontinuation.

Conclusion

Several attributes influence how PwD take their medications. Insights gained directly from PwD have the potential to assist stakeholders in making more informed, patient-centered, treatment decisions, thus choosing and managing medications that PwD are comfortable initiating and persisting with over the longer term.

Cost-Effectiveness of Point-of-Care A1C Tests in a Primary Care Setting

Objective: We evaluated the cost-effectiveness of the point-of-care A1c (POC-A1c) test device vs. the traditional laboratory dosage in a primary care setting for people living with type 2 diabetes. Materials and Methods: The Markov model with a 10-year time horizon was based on data from the HealthRise project, in which a group of interventions was implemented to improve diabetes and hypertension control in the primary care network of the urban area of a Brazilian municipality. A POC-A1c device was provided to be used directly in a primary care unit, and for a period of 18 months, 288 patients were included in the point-of-care group, and 1,102 were included in the comparison group. Sensitivity analysis was performed via Monte Carlo simulation and tornado diagram. Results: The results indicated that the POC-A1c device used in the primary care unit was a cost-effective alternative, which improved access to A1c tests and resulted in an increased rate of early control of blood glucose. In the 10-year period, POC-A1c group presented a mean cost of US$10,503.48 per patient and an effectiveness of 0.35 vs. US$9,992.35 and 0.09 for the traditional laboratory test, respectively. The incremental cost was US$511.13 and the incremental effectiveness was 0.26, resulting in an incremental cost-effectiveness ratio of 1,947.10. In Monte Carlo simulation, costs and effectiveness ranged between $9,663.20-$10,683.53 and 0.33-0.37 for POC-A1c test group, and $9,288.28-$10,413.99 and 0.08-0.10 for traditional laboratory test group, at 2.5 and 97.5 percentiles. The costs for nephropathy, retinopathy, and cardiovascular disease and the probability of being hospitalized due to diabetes presented the greatest impact on the model's result. Conclusion: This study showed that using POC-A1c devices in primary care settings is a cost-effective alternative for monitoring glycated hemoglobin A1c as a marker of blood glucose control in people living with type 2 diabetes. According to our model, the use of POC-A1c device in a healthcare unit increased the early control of type 2 diabetes and, consequently, reduced the costs of diabetes-related outcomes, in comparison with a centralized laboratory test.

Economic value of nonsurgical weight loss in adults with obesity

BACKGROUND: Obesity imposes a substantial economic burden on the United States. The short-term value of nonsurgical weight loss (WL) and nonsurgical sustained WL (i.e., WL not resulting from bariatric surgery) is poorly understood. OBJECTIVES: To assess short-term (1 year) effect of nonsurgical WL and sustained nonsurgical WL (i.e., approximately 2 years) on per-patient-per-month (PPPM) total all-cause health care costs among adults with obesity in the United States. METHODS: In this retrospective cohort study, we analyzed data from the IBM MarketScan Explorys Claims-EMR Data Set from January 1, 2012, through June 30, 2018. Adults aged 18-64 years with a body mass index (BMI) measurement ≥ 30 kg/m² on the index date and BMI measurements at 12, 24, and 36 months were classified into weight-gain (≥ 3%), no-weight-change (within ± 3%), and WL (≥ 3%-≤ 5%, > 5%-≤ 10%, and > 10%-≤ 20%) cohorts based on the change from first to second BMI measurements (baseline), and sustained nonsurgical WL based on WL during baseline and < 3% weight gain from second to third BMI measurement. PPPM all-cause health care costs were calculated for baseline, first year, and second year of follow-up. Generalized linear models were used to examine if PPPM all-cause health care cost change (ΔPPPM) from baseline to follow-up differed significantly between nonsurgical WL/sustained WL and no-weight-change cohorts. Analyses were stratified by index obesity class (class 1: BMI 30- < 34.9 kg/m², class 2: BMI 35- < 39.9 kg/m², class 3: BMI ≥ 40 kg/m²). Specific nonsurgical WL treatments used by individuals in the study were not studied. RESULTS: The sample included 20,488 adults who were grouped as follows: weight-gain cohort (24.8%), no-weight-change cohort (56.6%), ≥ 3%- ≤ 5% WL cohort (8.2%), > 5%- ≤ 10% WL cohort (7.7%), and > 10%- ≤ 20% WL cohort (2.8%). Compared with the no-weight-change cohort, adjusted mean ΔPPPM all-cause health care cost from baseline to first year of follow-up was lower in all WL cohorts (≥ 3%- ≤ 5% WL: -$57.36, > 5%- ≤ 10% WL: -$135.35 [P < 0.05], > 10%- ≤ 20% WL: -$193.54 [P < 0.05]). In the second year of follow-up (n = 15,307), the cohorts were weight-gain (43.4%), no-weight-change (59.4%), ≥ 3%- ≤ 5% sustained WL (7.3%), ≥ 5%- ≤ 10% sustained WL (6.3%), and > 10%- ≤ 20% sustained WL (1.8%). Adjusted mean ΔPPPM all-cause health care cost was lower in all sustained WL groups (-$26.38, -$157.41 [P < 0.05], and -$185.41 for ≥ 3%- ≤ 5%, ≥ 5%- ≤ 10%, and > 10%- ≤ 20% WL, respectively). Greater nonsurgical WL and sustained nonsurgical WL were generally associated with larger reduction in short-term all-cause health care costs. Results stratified by index obesity class were mixed, due to small samples. CONCLUSIONS: Substantial all-cause health care cost savings were observed 1 year after nonsurgical WL and after sustained (on average for 2 years) nonsurgical WL in adults with obesity, with greater nonsurgical WL and sustained nonsurgical WL associated with greater cost savings. Comprehensive solutions to chronic weight management, including improved access to antiobesity medications in combination with lifestyle interventions, could be valuable to patients, employers, and payers. DISCLOSURES: This study was sponsored by Novo Nordisk, which also purchased the data. Blanchette is an employee of Novo Nordisk. Smolarz and Ramasamy are employees of Novo Nordisk and hold equity in Novo Nordisk. Ding, Fan, and Weng were employees of Novo Nordisk at the time this study was conducted. The findings from this study were previously presented at Obesity Week 2019; November 3-7, 2019; Las Vegas, NV.

Impact of Weight Change in Adults with Type 2 Diabetes Mellitus: A Literature Review and Critical Analysis

Objective

Weight reduction is a key component of diabetes management in adults with type 2 diabetes mellitus (T2DM), yet the benefits of weight loss in T2DM patients have been difficult to quantify. We examined the medical literature regarding the relationships between weight change and 1) glycemic control and 2) cost and resource use.

Methods

Systematic searches were conducted in the electronic databases Embase, MEDLINE, and the Cochrane Database of Systematic Reviews to identify publications regarding the impact of weight change on T2DM outcomes from 2007 onward. Identified publications were screened for relevance against predefined eligibility criteria, and methodological approaches and results were extracted. Evidence for the impact of weight change on outcomes was evaluated and used to identify strengths, limitations, and gaps in the current literature.

Results

The number of studies meeting eligibility criteria for each outcome was: glycemic control (n=38) and cost and resource use (n=11). The relationship between weight change and glycemic control was dependent on the interplay of multiple factors, eg, the weight loss interventions employed, the antidiabetic medication classes used, the time horizon, and baseline BMI and glycemic control. With respect to cost and resource use, the review indicated that savings were associated with weight loss, and increased costs were associated with weight gain.

Conclusion

Studies regarding weight change in T2DM patients demonstrated varying effects on glycemic control and a positive association with costs and resource use, where weight loss was associated with decreased costs and resource use. Future studies may be able to clarify these relationships.

Practical guidance for use of oral semaglutide in primary care: a narrative review

As the cornerstone of type 2 diabetes (T2D) management within the community, primary care providers are now faced with the challenge of not only managing diabetes itself, but also preventing hypoglycemia and weight gain associated with intensive disease management, and reducing cardiovascular risk. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are well established as efficacious treatments for T2D, and the safety/tolerability profile of this drug class is well defined. However, despite their beneficial effects, GLP-1RAs are under-utilized, highlighting the need for novel approaches to increase their use in primary care. Oral semaglutide is the first oral GLP-1RA approved for the treatment of T2D, offering glucose lowering and body weight loss, a low risk of hypoglycemia, and no increase in cardiovascular risk. Oral semaglutide represents an additional treatment option for patients not achieving their glycemic goal despite treatment with metformin, either alone or with other hypoglycemic agents. Oral semaglutide has the potential to increase usage of GLP-1RAs in the primary care setting by addressing clinician and patient concerns about injections, and may facilitate earlier initiation of GLP-1RA therapy in T2D. Due to the formulation of oral semaglutide, clinicians need to be aware of specific considerations in order to ensure optimal use. Such considerations include dosing conditions and use of concomitant medications. This article provides practical guidance on the use of oral semaglutide in the primary care setting, based on evidence from clinical studies, including the phase 3a PIONEER program, and the authors' clinical experience.