Diabetes

Nussbaum SR, Carter MJ, Fife CE, DaVanzo J, Haught R, Nusgart M, Cartwright D.
Value in Health. 2018;21(1):27-32..

OBJECTIVES
The aim of this study was to determine the cost of chronic wound care for Medicare beneficiaries in aggregate, by wound type and by setting.

METHODS
This retrospective analysis of the Medicare 5% Limited Data Set for calendar year 2014 included beneficiaries who experienced episodes of care for one or more of the following: arterial ulcers, chronic ulcers, diabetic foot ulcers, diabetic infections, pressure ulcers, skin disorders, skin infections, surgical wounds, surgical infections, traumatic wounds, venous ulcers, or venous infections. The main outcomes were the prevalence of each wound type, Medicare expenditure for each wound type and aggregate, and expenditure by type of service.

RESULTS
Nearly 15% of Medicare beneficiaries (8.2 million) had at least one type of wound or infection (not pneumonia). Surgical infections were the largest prevalence category (4.0%), followed by diabetic infections (3.4%). Total Medicare spending estimates for all wound types ranged from $28.1 to $96.8 billion. Including infection costs, the most expensive estimates were for surgical wounds ($11.7, $13.1, and $38.3 billion), followed by diabetic foot ulcers ($6.2, $6.9, and $18.7 billion,). The highest cost estimates in regard to site of service were for hospital outpatients ($9.9–$35.8 billion), followed by hospital inpatients ($5.0–$24.3 billion).

CONCLUSIONS
Medicare expenditures related to wound care are far greater than previously recognized, with care occurring largely in outpatient settings. The data could be used to develop more appropriate quality measures and reimbursement models, which are needed for better health outcomes and smarter spending for this growing population.

Leung MYM, Carlsson NP, Colditz GA, Chang S-H.
Value in Health. 2017;20(1):77-84.

BACKGROUND
Diabetes is one of the most prevalent and costly chronic diseases in the United States.

OBJECTIVES
To analyze the risk of developing diabetes and the annual cost of diabetes for a US general population.

METHODS
Data from the Medical Expenditure Panel Survey, 2008 to 2012, were used to analyze 1) probabilities of developing diabetes and 2) annual total health care expenditures for diabetics. The age-, sex-, race-, and body mass index (BMI)-specific risks of developing diabetes were estimated by fitting an exponential survival function to age at first diabetes diagnosis. Annual health care expenditures were estimated using a generalized linear model with log-link and gamma variance function. Complex sampling designs in the Medical Expenditure Panel Survey were adjusted for. All dollar values are presented in 2012 US dollars.

RESULTS
We observed a more than 6 times increase in diabetes risks for class III obese (BMI ≥ 40 kg/m) individuals incurred an annual marginal cost of $628 and $756, respectively. The annual health care expenditure differentials between those with and without diabetes of age 50 years were the highest for individuals with class II ($12,907) and class III ($9,703) obesity.

CONCLUSIONS
This article highlights the importance of obesity on diabetes burden. Our results suggested that obesity, in particular, class II and class III (i.e., BMI ≥ 35 kg/m) obesity, is associated with a substantial increase in the risk of developing diabetes and imposes a large economic burden.

Palmer AJ, Si L, Tew M, Hua X, Willis MS, Asseburg C, McEwan P, Leal ., Gray A, Foos V, Lamotte M, Feenstra T, O'Connor PJ, Brandle M, Smolen HJ, Gahn JC, Valentine WJ, Pollock RF, Breeze P, Brennan A, Pollard D, Ye W, Herman WH, Isaman DJ, Kuo S, Laiteerapong N, Tran-Duy A, Clarke PM.
Value in Health. 2018;21(6):724-731.

OBJECTIVES
The Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes.

METHODS
Participating modeling groups were asked to reproduce the results of two published studies using the input data described in those articles. Gaps in input data were filled with assumptions reported by the modeling groups. Goodness of fit between the results reported in the target studies and the groups’ replicated outputs was evaluated using the slope of linear regression line and the coefficient of determination (R ). After a general discussion of the results, a diabetes-specific checklist for the transparency of model input was developed.

RESULTS
Seven groups participated in the transparency challenge. The reporting of key model input parameters in the two studies, including the baseline characteristics of simulated patients, treatment effect and treatment intensification threshold assumptions, treatment effect evolution, prediction of complications and costs data, was inadequately transparent (and often missing altogether). Not surprisingly, goodness of fit was better for the study that reported its input data with more transparency. To improve the transparency in diabetes modeling, the Diabetes Modeling Input Checklist listing the minimal input data required for reproducibility in most diabetes modeling applications was developed.

CONCLUSIONS
Transparency of diabetes model inputs is important to the reproducibility and credibility of simulation results. In the Eighth Mount Hood Challenge, the Diabetes Modeling Input Checklist was developed with the goal of improving the transparency of input data reporting and reproducibility of diabetes simulation model results.

Einarson TR, Acs A, Ludwig C, Panton UH.
Value in Health. 2018;21(7):881-890.

BACKGROUND
Cardiovascular diseases (CVDs) constitute major comorbidities in type 2 diabetes mellitus (T2DM), contributing substantially to treatment costs for T2DM. An updated overview of the economic burden of CVD in T2DM has not been presented to date.

OBJECTIVES
To systematically review published articles describing the costs associated with treating CVD in people with T2DM.

METHODS
Two reviewers searched MEDLINE, Embase, and abstracts from scientific meetings to identify original research published between 2007 and 2017, with no restrictions on language. Studies reporting direct costs at either a macro level (e.g., burden of illness for a country) or a micro level (e.g., cost incurred by one patient) were included. Extracted costs were inflated to 2016 values using local consumer price indexes, converted into US dollars, and presented as cost per patient per year.

RESULTS
Of 81 identified articles, 24 were accepted for analysis, of which 14 were full articles and 10 abstracts. Cardiovascular comorbidities in patients with T2DM incurred a significant burden at both the population and patient levels. From a population level, CVD costs contributed between 20% and 49% of the total direct costs of treating T2DM. The median annual costs per patient for CVD, coronary artery disease, heart failure, and stroke were, respectively, 112%, 107%, 59%, and 322% higher compared with those for T2DM patients without CVD. On average, treating patients with CVD and T2DM resulted in a cost increase ranging from $3418 to $9705 compared with treating patients with T2DM alone.

CONCLUSIONS
Globally, CVD has a substantial impact on direct medical costs of T2DM at both the patient and population levels.

Janssen EM, Hauber AB, Bridges JFP.
Value in Health. 2018;21(1):59-68.

OBJECTIVE
To consolidate and illustrate good research practices in health care to the application and reporting of a study measuring patient preferences for type 2 diabetes mellitus medications, given recent methodological advances in stated-preference methods.

METHODS
The International Society for Pharmacoeconomics and Outcomes Research good research practices and other recommendations were used to conduct a discrete-choice experiment. Members of a US online panel with type 2 diabetes mellitus completed a Web-enabled, self-administered survey that elicited choices between treatment pairs with six attributes at three possible levels each. A D-efficient experimental design blocked 48 choice tasks into three 16-task surveys. Preference estimates were obtained using mixed logit estimation and were used to calculate choice probabilities.

RESULTS
A total of 552 participants (51% males) completed the survey. Avoiding 90 minutes of nausea was valued the highest (mean −10.00; 95% confidence interval [CI] −10.53 to −9.47). Participants wanted to avoid low blood glucose during the day and/or night (mean −3.87; 95% CI −4.32 to −3.42) or one pill and one injection per day (mean −7.04; 95% CI −7.63 to −6.45). Participants preferred stable blood glucose 6 d/wk (mean 4.63; 95% CI 4.15 to 5.12) and a 1% decrease in glycated hemoglobin (mean 5.74; 95% CI 5.22 to 6.25). If cost increased by $1, the probability that a treatment profile would be chosen decreased by 1%.

CONCLUSIONS
These results are consistent with the idea that people have strong preferences for immediate consequences of medication. Despite efforts to produce recommendations, ambiguity surrounding good practices remains and various judgments need to be made when conducting stated-preference studies. To ensure transparency, these judgments should be described and justified.

Hay JW, Lee P-J, Jin H, Guterman JJ, Gross-Schulman S, Ell K, Wu S.
Value in Health. 2018;21(5):561-568.

BACKGROUND
The Diabetes-Depression Care-Management Adoption Trial is a translational study of safety-net primary care predominantly Hispanic/Latino patients with type 2 diabetes in collaboration with the Los Angeles County Department of Health Services.

OBJECTIVE
To evaluate the cost-effectiveness of an information and communication technology (ICT)-facilitated depression care management program.

METHODS
Cost-effectiveness of the ICT-facilitated care (TC) delivery model was evaluated relative to a usual care (UC) and a supported care (SC) model. TC added automated low-intensity periodic depression assessment calls to patients. Patient-reported outcomes included the 12-Item Short Form Health Survey converted into quality-adjusted life-years (QALYs) and the 9-Item Patient Health Questionnaire–calculated depression-free days (DFDs). Costs and outcomes data were collected over a 24-month period (−6 to 0 months baseline, 0 to 18 months study intervention).

RESULTS
A sample of 1406 patients (484 in UC, 480 in SC, and 442 in TC) was enrolled in the nonrandomized trial. TC had a significant improvement in DFDs (17.3; P = 0.011) and significantly greater 12-Item Short Form Health Survey utility improvement (2.1%; P = 0.031) compared with UC. Medical costs were statistically significantly lower for TC (−$2328; P = 0.001) relative to UC but not significantly lower than for SC. TC had more than a 50% probability of being cost-effective relative to SC at willingness-to-pay thresholds of more than $50,000/QALY.

CONCLUSIONS
An ICT-facilitated depression care (TC) delivery model improved QALYs, DFDs, and medical costs. It was cost-effective compared with SC and dominant compared with UC.

Rowen D, Stevens K, Labeit A, Elliott J, Mulhern B, Carlton J, Basarir H, Ratcliffe J, Brazier J.
Value in Health.  2018;21(1):69.77.

OBJECTIVE
To describe the use of a novel approach in health valuation of a discrete-choice experiment (DCE) including a cost attribute to value a recently developed classification system for measuring the quality-of-life impact (both health and treatment experience) of self-management for diabetes.

METHODS
A large online survey was conducted using DCE with cost on UK respondents from the general population (n = 1497) and individuals with diabetes (n = 405). The data were modeled using a conditional logit model with robust standard errors. The marginal rate of substitution was used to generate willingness-to-pay (WTP) estimates for every state defined by the classification system. Robustness of results was assessed by including interaction effects for household income.

RESULTS
There were some logical inconsistencies and insignificant coefficients for the milder levels of some attributes. There were some differences in the rank ordering of different attributes for the general population and diabetic patients. The WTP to avoid the most severe state was £1118.53 per month for the general population and £2356.02 per month for the diabetic patient population. The results were largely robust.

CONCLUSIONS
Health and self-management can be valued in a single classification system using DCE with cost. The marginal rate of substitution for key attributes can be used to inform cost-benefit analysis of self-management interventions in diabetes using results from clinical studies in which this new classification system has been applied. The method shows promise, but found large WTP estimates exceeding the cost levels used in the survey.