Cost implications to health care payers of improving glucose management among adults with type 2 diabetes

Changes in Glycemic Control Among Individuals With Diabetes Who Used a Personalized Digital Nutrition Platform: Longitudinal Study

BACKGROUND

Diabetes-related costs are the highest across all chronic conditions in the United States, with type 2 diabetes accounting for up to 95% of all cases of diabetes. A healthy diet is strongly associated with lowering glycated hemoglobin A_1c (HbA_1c) levels among individuals with diabetes, which can help curtail other health complications. Digital health platforms can offer critical support for improving diet and glycemic control among individuals with diabetes. Less is known about the characteristics of people with diabetes who use digital health platforms (specifically, a platform that integrates personalized healthy meal plans and food ordering) and changes in their HbA_1c levels.

OBJECTIVE

The aim of this study is to characterize Foodsmart users with diabetes and evaluate the longitudinal impact of Foodsmart-a personalized digital nutrition platform with meal planning, food ordering, and nutrition education features-on changes in HbA_1c levels.

METHODS

We retrospectively analyzed data collected from 643 adults with at least two self-reported HbA_1c entries in the Foodsmart platform between January 2016 and June 2021. Participants self-reported their HbA_1c levels, height, weight, health conditions, and diet in a 53-item food frequency questionnaire. Diabetes was defined as HbA_1c ≥6.5%. We analyzed distributions of characteristics by baseline diabetes status and examined the association of characteristics with the likelihood of having diabetes at baseline. To evaluate the change in HbA_1c levels among Foodsmart users, we calculated mean changes (absolute and percent) in HbA_1c among participants with diabetes and by length of follow-up. We also compared changes in HbA_1c and weight between participants with diabetes at baseline who achieved a normal HbA_1c level and those who did not.

RESULTS

We found that 43.5% (280/643) of the participants with at least two HbA_1c level entries had diabetes at baseline. Participants with diabetes at baseline were more likely to be male, have a higher weight and BMI, report high blood pressure, and have a poorer diet in comparison to participants without diabetes. Using a multivariable logistic regression model, we found that being male and obese were statistically significantly associated with baseline diabetes. Among participants with diabetes at baseline, HbA_1c was reduced, on average, by 0.46%. In addition, 21.4% (60/280) of participants with diabetes achieved a normal HbA_1c level (<6.5%) in their last HbA_1c level entry; this percentage increased with longer follow-up time (39% [7/18] at >24 months). In a sensitivity analysis, users with an HbA_1c ≥7.0% at baseline had an average absolute change of -0.62% and 31.2% (62/199) of these participants achieved HbA_1c levels of less than 7.0%.

CONCLUSIONS

This study assessed characteristics of individuals enrolled on the Foodsmart platform with HbA_1c levels and found that users with diabetes had lower HbA_1c levels over time and a sizable percentage of participants were successful in achieving normal levels.

Cost-Effectiveness of Technology-Assisted Case Management in Low-Income, Rural Adults with Type 2 Diabetes

Objective: The objective of this study was to examine whether delivering technology-assisted case management (TACM) with medication titration by nurses under physician supervision is cost effective compared with usual care (standard office procedures) in low-income rural adults with type 2 diabetes.

Methods: One hundred and thirteen low-income, rural adults with type 2 diabetes and hemoglobin A1c (HbA1c) ≥8%, were randomized to a TACM intervention or usual care. Effectiveness was measured as differences in HbA1c between the TACM and usual care groups at 6 months. Total cost per patient included intervention or usual care cost, medical care cost, and income loss associated with lost workdays. The total cost per patient and HbA1c were used to estimate a joint distribution of incremental cost and incremental effect of TACM compared with usual care. Incremental cost-effectiveness ratios (ICERs) were estimated to summarize the cost-effectiveness of the TACM intervention relative to usual care to decrease HbA1c by 1%.

Results: Costs due to intervention, primary care, other health care, emergency room visits, and workdays missed showed statistically significant differences between the groups (usual care $1,360.49 vs. TACM $5,379.60, p=0.004), with an absolute cost difference of $4,019.11. Based on the intervention cost per patient and the change in HbA1c, the median bootstrapped ICERs was estimated to be $6,299.04 (standard error=731.71) per 1% decrease in HbA1c.

Conclusion: Based on these results, a 1% decrease in HbA1c can be obtained with the TACM intervention at an approximate cost of $6,300; therefore, it is a cost-effective option for treating vulnerable populations of adults with type 2 diabetes.

What is the cost of quality for diabetes care?

Increasing the quality of care and reducing cost growth are core objectives of numerous private- and public-sector performance improvement initiatives. Using a unique panel data set for a commercially insured population and multivariate regression analysis, this study examines the relationship between medical care spending and diabetes-related quality measures, including provider-initiated processes of care and patient-dependent quality activities. Empirical evidence generated from this analysis of the relationship between a comprehensive set of diabetes quality measures and diabetes-related spending does not lend support for the assumption that high-quality preventive and primary care combined with effective patient self-management can lead to lower costs in the near term. Finally, we find no relationship between adjusted spending and intermediate clinical outcomes (e.g., HbA1c level) measured at the clinic level.

The effects of quality of care on costs: a conceptual framework

CONTEXT

The quality of health care and the financial costs affected by receiving care represent two fundamental dimensions for judging health care performance. No existing conceptual framework appears to have described how quality influences costs.

METHODS

We developed the Quality-Cost Framework, drawing from the work of Donabedian, the RAND/UCLA Appropriateness Method, reports by the Institute of Medicine, and other sources.

FINDINGS

The Quality-Cost Framework describes how health-related quality of care (aspects of quality that influence health status) affects health care and other costs. Structure influences process, which, in turn, affects proximate and ultimate outcomes. Within structure, subdomains include general structural characteristics, circumstance-specific (e.g., disease-specific) structural characteristics, and quality-improvement systems. Process subdomains include appropriateness of care and medical errors. Proximate outcomes consist of disease progression, disease complications, and care complications. Each of the preceding subdomains influences health care costs. For example, quality improvement systems often create costs associated with monitoring and feedback. Providing appropriate care frequently requires additional physician visits and medications. Care complications may result in costly hospitalizations or procedures. Ultimate outcomes include functional status as well as length and quality of life; the economic value of these outcomes can be measured in terms of health utility or health-status-related costs. We illustrate our framework using examples related to glycemic control for type 2 diabetes mellitus or the appropriateness of care for low back pain.

CONCLUSIONS

The Quality-Cost Framework describes the mechanisms by which health-related quality of care affects health care and health status-related costs. Additional work will need to validate the framework by applying it to multiple clinical conditions. Applicability could be assessed by using the framework to classify the measures of quality and cost reported in published studies. Usefulness could be demonstrated by employing the framework to identify design flaws in published cost analyses, such as omitting the costs attributable to a relevant subdomain of quality.

Mental comorbidity and quality of diabetes care under Medicaid: a 50-state analysis

BACKGROUND

Patients with comorbid medical and mental conditions are at risk for poor quality of care. With the anticipated expansion of Medicaid under health reform, it is particularly important to develop national estimates of the magnitude and correlates of quality deficits related to mental comorbidity among Medicaid enrollees.

METHODS

For all 657,628 fee-for-service Medicaid enrollees with diabetes during 2003 to 2004, the study compared Healthcare Effectiveness Data and Information Set (HEDIS) diabetes performance measures (hemoglobin A1C, eye examinations, low density lipoproteins screening, and treatment for nephropathy) and admissions for ambulatory care-sensitive conditions (ACSCs) between persons with and without mental comorbidity. Nested hierarchical models included individual, county, and state-level measures.

RESULTS

A total of 17.8% of the diabetic sample had a comorbid mental condition. In adjusted models, presence of a mental condition was associated with a 0.83 (0.82-0.85) odds of obtaining 2 or more HEDIS indicators, and a 1.32 (1.29-1.34) increase in odds of one or more ACSC hospitalization. Among those with diabetes and mental comorbidities, living in a county with a shortage of primary care physicians was associated with reduced performance on HEDIS measures; living in a state with higher Medicaid reimbursement fees and department of mental health expenses per client were associated both with higher quality on HEDIS measures and lower (better) rates of ACSC hospitalizations.

CONCLUSIONS

Among persons with diabetes treated in the Medicaid system, mental comorbidity is an important risk factor for both underuse and overuse of medical care. Modifiable county and state-level factors may mitigate these quality deficits.