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The American Journal of Managed Care August 2014
Personalized Preventive Care Reduces Healthcare Expenditures Among Medicare Advantage Beneficiaries
Shirley Musich, PhD; Andrea Klemes, DO, FACE; Michael A. Kubica, MBA, MS; Sara Wang, PhD; and Kevin Hawkins, PhD
Impact of Hypertension on Healthcare Costs Among Children
Todd P. Gilmer, PhD; Patrick J. O'Connor, MD, MPH; Alan R. Sinaiko, MD; Elyse O. Kharbanda, MD, MPH; David J. Magid, MD, MPH; Nancy E. Sherwood, PhD; Kenneth F. Adams, PhD; Emily D. Parker, MD, PhD; and Karen L. Margolis, MD, MPH
Tracking Spending Among Commercially Insured Beneficiaries Using a Distributed Data Model
Carrie H. Colla, PhD; William L. Schpero, MPH; Daniel J. Gottlieb, MS; Asha B. McClurg, BA; Peter G. Albert, MS; Nancy Baum, PhD; Karl Finison, MA; Luisa Franzini, PhD; Gary Kitching, BS; Sue Knudson, MA; Rohan Parikh, MS; Rebecca Symes, BS; and Elliott S. Fisher, MD
Potential Role of Network Meta-Analysis in Value-Based Insurance Design
James D. Chambers, PhD, MPharm, MSc; Aaron Winn, MPP; Yue Zhong, MD, PhD; Natalia Olchanski, MS; and Michael J. Cangelosi, MA, MPH
Massachusetts Health Reform and Veterans Affairs Health System Enrollment
Edwin S. Wong, PhD; Matthew L. Maciejewski, PhD; Paul L. Hebert, PhD; Christopher L. Bryson, MD, MS; and Chuan-Fen Liu, PhD, MPH
Contemporary Use of Dual Antiplatelet Therapy for Preventing Cardiovascular Events
Andrew M. Goldsweig, MD; Kimberly J. Reid, MS; Kensey Gosch, MS; Fengming Tang, MS; Margaret C. Fang, MD, MPH; Thomas M. Maddox, MD, MSc; Paul S. Chan, MD, MSc; David J. Cohen, MD, MSc; and Jersey Chen, MD, MPH
Potential Benefits of Increased Access to Doula Support During Childbirth
Katy B. Kozhimannil, PhD, MPA; Laura B. Attanasio, BA; Judy Jou, MPH; Lauren K. Joarnt; Pamela J. Johnson, PhD; and Dwenda K. Gjerdingen, MD
Synchronization of Coverage, Benefits, and Payment to Drive Innovation
Annemarie V. Wouters, PhD; and Nancy McGee, JD, DrPH
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Arne Beck, PhD; A. Lauren Crain, PhD; Leif I. Solberg, MD; Jürgen Unützer, MD, MPH; Michael V. Maciosek, PhD; Robin R. Whitebird, PhD, MSW; and Rebecca C. Rossom, MD, MSCR
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Economic Implications of Weight Change in Patients With Type 2 Diabetes Mellitus
Kelly Bell, MSPhr; Shreekant Parasuraman, PhD; Manan Shah, PhD; Aditya Raju, MS; John Graham, PharmD; Lois Lamerato, PhD; and Anna D'Souza, PhD
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Economic Implications of Weight Change in Patients With Type 2 Diabetes Mellitus

Kelly Bell, MSPhr; Shreekant Parasuraman, PhD; Manan Shah, PhD; Aditya Raju, MS; John Graham, PharmD; Lois Lamerato, PhD; and Anna D'Souza, PhD
Modest weight loss (>3%) among metformin-treated patients with type 2 diabetes mellitus was associated with decreased costs, lower resource utilization, and lower rates of treatment discontinuation.

Objective

Assess the impact of weight change on costs, resource use, and treatment discontinuation among metformin-treated patients with type 2 diabetes mellitus (T2DM).


Study Design

Observational, retrospective cohort.


Methods

Adults with T2DM who were pre existing metformin-treated patients were included. Insulin users were excluded. Administrative data from January 1, 2000, to December 31, 2010, were linked to clinical data, and patients were placed into cohorts based on relative change in body weight. Three cohorts were created: weight loss (decrease >3%), weight neutral (change ≤3%), and weight gain (increase >3%). Inter-cohort differences in resource utilization, costs (2010 US$), and treatment discontinuation were evaluated using statistical models that adjusted for baseline characteristics.


Results

A total of 2110 patients (weight loss = 967; weight neutral = 970; weight gain = 173) were included; mean age was 59.6 years, 52.2% were women, 64.1% were Caucasian, and average baseline weight was 98.7 kg. The weight-loss cohort incurred significantly lower costs per year compared with the weight-neutral cohort, driven mainly by lower medical costs from reduced utilization. Weight reduction was associated with approximately $2200 and approximately $440 lower annual all-cause and T2DM-specific costs (P <.05), respectively. Patients who lost weight were 21% less likely to discontinue therapy. Weight gain was associated with a significant increase in all-cause costs of $3400 per year compared with the weight-neutral cohort; however, differences in T2DM-specific costs and discontinuation rates did not reach significance levels.


Conclusions
Weight loss (>3%) among patients with T2DM was associated with decreased costs and lower rates of treatment discontinuation. Hence weight-focused treatment approaches can help reduce the economic burden for patients with T2DM.


Am J Manag Care. 2014;20(8):e320-e329

Modest weight loss in patients with type 2 diabetes (T2DM) is associated with lower rates of treatment discontinuation and economic benefits, illustrated through reductions in both diabetes-specific and all-cause medical costs due to fewer hospital and emergency department visits. Weight gain is associated with increased all-cause medical costs but has no statistically significant impact on diabetes-specific costs or treatment discontinuation. Results of this study complement the clinical advantages of weight loss in patients with T2DM by highlighting its economic and treatment persistence benefits, and hence can help guide patients and health plans in making decisions regarding optimal disease management.

Type 2 diabetes mellitus (T2DM) is the most prevalent form of diabetes, accounting for 90% to 95% of cases affecting more than 20 million adults in the United States.1 In 2012, diabetes-related expenditures were estimated to be $176 billion2; nearly half of which, it is reported, go to treating diabetes-related complications such as cardiovascular disease, hypertension, neuropathy, retinopathy, and nephropathy.3,4

One factor that is strongly associated with T2DM risk is excess body weight, with more than 80% of patients with T2DM being either overweight or obese.5-8 Increased weight may impair glycemic control (via increased insulin resistance); elevate the risk of cardiovascular disease; and negatively affect mental health, body image, and persistence with therapy, which, in turn, may increase the risk for diabetes-related complications.8-10 Accordingly, weight gain can potentially impact the high expenditures associated with treatment of diabetes-related complications.

Conversely, weight loss in T2DM is associated with benefits such as better glycemic control, reduction in cardiometabolic risk factors, and prevention of disease progression through decreased diabetes complications.11-15 Although some recent literature has indicated that weight loss from a diet and exercise program (average weight loss of nearly 5% at 4 years) did not reduce cardiovascular events in T2DM patients,16 a large body of evidence suggests positive benefits.11-15 As a result, weight management as a part of lifestyle modification has become a key factor in T2DM treatment.17

Although there is abundant literature regarding the clinical manifestations of weight change in T2DM, evidence of its contribution to the economic burden of T2DM is relatively sparse. Preliminary evidence shows weight loss can significantly reduce diabetes-related costs.11,18 Furthermore, Brandle et al19 reported that a 10 kg/m2 increase in body mass index or presence of diabetes-related complications can increase direct costs by 10% to 30%.

In addition to being a predisposing condition, weight gain among T2DM patients can also be caused by anti-diabetic drugs; contributing to nonpersistence and potentially to subsequent disease progression. Metformin augmentation or alternate anti-diabetic therapies are becoming common treatment regimens as weight-focused treatment approaches gain importance in T2DM management. Newer anti-diabetic agents have similar effects on glycemic control, but differ in their side-effect profiles; some have been shown to possess weight-altering properties.20 To properly factor in these weight-altering properties during treatment selection, it is important to have a comprehensive understanding of the impact of weight change on T2DM outcomes. Hence, the goal of this study was to investigate the implications of real-world change in body weight (both weight gain and weight loss) on healthcare costs, resource utilization, and continuation of anti-diabetic pharmacotherapy among metformin-treated patients with T2DM.

METHODS

Study Design and Sample Selection

Data from January 1, 2000, through December 31, 2010, were utilized in this retrospective cohort study. The administrative databases of the Henry Ford Health System (HFHS), which comprises medical billing, pharmacy records, external claims for care provided outside of HFHS, and clinical data (such as laboratory values and vital signs) from electronic medical records (EMRs) and progress notes for patients receiving care within the HFHS, were employed in this analysis. The study population was identified from enrollees of a system-owned and -operated health maintenance organization (HMO), the Health Alliance Plan (HAP), who received care at HFHS—a vertically integrated healthcare system providing clinical services to the Michigan community, with over 2.5 million patient visits and 65,000 hospital admissions annually. The HAP enrolls more than 500,000 individuals from more than 3000 employers in the Detroit metropolitan area. Approximately 150,000 of these members receive care through HFHS.

The initial study population comprised patients aged ≥18 years with at least 1 non-insulin anti-diabetic (NIAD) therapy (see eAppendix, available at www. ajmc.com) prescription during the patient identification period of July 1, 2000, to July 31, 2009. Index date was defined as the date of the first NIAD prescription during the patient identification period, and this medication was considered as the index medication. Patients were required to have a diagnosis of T2DM (International Classification of Diseases, 9th Revision, Clinical Modification [ICD-9-CM] code 250.x0 or 250.x2) in any field and metformin monotherapy during the 6-month period prior to the index date, continuous health plan eligibility for the 6-month period prior to the index date through the 18-month period after the index date, and at least 2 weight measurements at specific time periods—1 each in the 1- to 6-month periods before and after the index date. The study analytical time frame, therefore, included a 6-month pre-index period to provide a baseline description of the study sample, and an 18-month post index period which included a 6-month period after the index date to measure weight change, followed by a 12-month follow-up period to compute outcomes (Figure 1). The pre-index period was also used to ensure that patients were users of metformin monotherapy (at least 2 prescription fills of metformin or 1 prescription fill of metformin with a supply of 60 or more days), but naïve to all other NIAD therapy. In addition, patients were also required to have at least 2 glycated hemoglobin (A1C) measurements, 1 in each of the 6-month periods before and after the index date. Finally, all patients with a prescription claim of insulin, diagnosis for type 1 diabetes mellitus or gestational diabetes, or evidence of pregnancy or bariatric surgery (see eAppendix for diagnostic codes) during the time frame for analysis were excluded. Insulin users were excluded, because dosing of insulin varies considerably among patients with T2DM, which in turn could render outcome estimates unreliable.

Patients meeting all the study criteria were initially categorized based on relative change in body weight (ie, percentage change from baseline). Using a 3% cut-off (chosen based on a prior study11), the following groups were identified: weight loss (decrease in body weight by >3%), weight gain (increase in body weight by >3%), and weight neutral (increase or decrease in body weight by ≤3%). The study cohorts were then obtained from these groups based on availability of A1C measurements at the specified time periods.

Study Outcomes

The main outcomes of interest were annual all-cause and T2DM-specific costs and resource utilization computed during the follow-up period, and discontinuation rate of index NIAD therapy, which was captured during the 18-month period after the index date. Resource utilizations were calculated as the total number of unique visits and classified according to the place of service (hospitalizations, emergency department [ED] visits, outpatient visits, and other visits). A visit was defined as a unique date of service for all visits except hospitalizations, and as a unique admission and discharge date for a hospitalization. The costs represented the estimated costs to treat the patient based on charges billed for pharmacy and medical services. All-cause pharmacy and medical costs were calculated by summing the charges for all prescriptions and for all medical resource utilization with any diagnosis, respectively. T2DM-specific pharmacy costs were calculated by summing the charges for all NIAD prescriptions. T2DM-specific medical costs and resource utilization were captured by identifying medical records with a primary or secondary diagnosis code of T2DM and hospitalization records with a primary discharge diagnosis of T2DM. Costs were adjusted to 2010 US dollars using the medical component of the Consumer Price Index.

Patients were considered to have discontinued therapy when more than 30 days had elapsed without drugs that belonged to the index NIAD medication class, or they had switched to another anti-diabetic medication class. Time to discontinuation was calculated from the index date to the ending date of the last prescription prior to discontinuing index NIAD medication, or the end of the follow-up period.

Statistical Analyses

The weight-neutral cohort was considered to be the reference cohort for all statistical comparisons. Baseline differences between the weight-neutral and other study cohorts were evaluated using t tests or x2 tests for continuous or categorical data, respectively. Multivariate statistical analyses were employed to assess differences in annual costs, annual resource utilization, and treatment discontinuation rates among study cohorts, while controlling for baseline characteristics (age, gender, race, index month, pre-index weight, pre-index unique medications, pre-index prescriptions, pre-index A1C levels, Charlson Comorbidity Index [CCI] score21,22, number of unique diagnoses, and presence of coronary artery disease, congestive heart failure, hypertension, dyslipidemia, and depression) that were chosen based on clinical and statistical rationales.  Specifically, generalized linear models with a log-link function or semi-log ordinary least-squares regressions (based on variable distributions) were used to assess differences in costs. Negative binomial regression models were used to assess differences in resource utilization. Furthermore, differences in time to treatment discontinuation were evaluated using a Cox-proportional hazards model. Finally, to test whether a reduction in A1C levels modified the association between weight change and costs (all cause and T2DM-specific), an interaction term was constructed. This interaction term was a binary outcome variable that indicated reduction in A1C by ≥0.5%. This interaction term was added to the multivariate regression models for costs to assess the change in costs by A1C reduction status.

All results are presented after adjusting for baseline characteristics using statistical models in SAS version 9.1.3 (SAS Institute, Cary, North Carolina), testing a 2-sided hypothesis at a significance level of .05. All analyses were conducted from a third-party payer and overall society perspective. The study was approved by the HFHS Institutional Review Board.

RESULTS

 
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