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For What Illnesses Is a Disease Management Program Most Effective? Eric Jutkowitz, BA, John A. Nyman, PhD, Tzeyu L. Michaud, BA, Jean M. Abraham, PhD, and Bryan Dowd, PhD

Objective: We examined the impact of a disease management (DM) program offered at the University of Minnesota for those with various chronic diseases. Methods: Differences-in-differences regression equations were estimated to determine the effect of DM participation by chronic condition on expenditures, absenteeism, hospitalizations, and avoidable hospitalizations. Results: Disease management reduced health care expenditures for individuals with asthma, cardiovascular disease, congestive heart failure, depression, musculoskeletal problems, low back pain, and migraines. Disease management reduced hospitalizations for those same conditions except for congestive heart failure and reduced avoidable hospitalizations for individuals with asthma, depression, and low back pain. Disease management did not have any effect for individuals with diabetes, arthritis, or osteoporosis, nor did DM have any effect on absenteeism. Conclusions: Employers should focus on those conditions that generate savings when purchasing DM programs. Clinical Significance: This study suggests that the University of Minnesota’s DM program reduces hospitalizations for individuals with asthma, cardiovascular disease, depression, musculoskeletal problems, low back pain, and migraines. The program also reduced avoidable hospitalizations for individuals with asthma, depression, and low back pain.

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early 133 million Americans suffer from at least one chronic condition. As the population ages, the number of Americans with chronic conditions is expected to increase.1,2 In addition to negative health effects, chronic conditions are costly to the health care system, representing 78% of total health care spending.2,3 Because of the health and economic burdens associated with chronic diseases, there is significant interest in finding ways to manage them more effectively.4,5 Disease management (DM) programs represent a broad array of interventions (eg, coaching) designed to improve the management of chronic conditions through better coordination and continuation of care.4,6,7 A significant amount of research has been devoted to evaluating the health and economic impacts of DM programs on different diseases.6,8–11 Nevertheless, because there is significant variability in the DM programs of different vendors and in the characteristics of the firms in which a DM program is applied, it is difficult to evaluate the effectiveness of DM programs in treating different diseases across these studies.12 From the Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis. This study was funded by Employee Benefits, Office of the Vice President for Human Resources, University of Minnesota. Authors Jutkowitz, Nyman, Michaud, Abraham, and Dowd have no relationships/ conditions/circumstances that present potential conflict of interest. The JOEM editorial board and planners have no financial interest related to this research. This study is part of a series of analyses that were reviewed for human subjects content by the University of Minnesota’s Institutional Review Board as application number 08805E32782 and found to be exempt from review under guidelines 45 CFR Part 46.101(b) category 4, EXISTING DATA; RECORDS REVIEW; PATHOLOGICAL SPECIMENS. Address correspondence to: Eric Jutkowitz, BA, Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, 15-219 Philips Wangensteen Building, 420 Delaware St SE, MMC 729 Minneapolis, MN 55455 ([email protected]). C 2015 by American College of Occupational and Environmental Copyright  Medicine DOI: 10.1097/JOM.0000000000000385

Learning Objectives

r Discuss the challenges of assessing the effectiveness and cost r r

effects of disease management (DM) programs for various diseases. Describe the findings on the effectiveness of the University of Minnesota DM program—including specific conditions for which DM did and did not affect hospitalizations, costs, or absenteeism. Summarize the study’s strengths and weaknesses and its implications for employers purchasing DM programs.

Mattke and colleagues4 conducted a review of reviews to evaluate the evidence for the effect of DM on quality of care, disease control, and cost. The review focused on the most common DM programs, those aimed at congestive heart failure, coronary artery disease, diabetes, asthma, chronic obstructive pulmonary disease, and depression.4 The effectiveness of DM programs depended, in part, on the target population (ie, does the DM program target all patients with a condition or just those with a given level of severity) and intensity of the intervention (ie, does the DM program consist of mailings, telephone calls, or face-to-face meetings). As a whole, Mattke and colleagues concluded that there is inconsistent and inclusive evidence on the effect of DM on cost. The lack of conclusive evidence is due in part to a large number of industrysponsored studies with potentially biased results. Results of Mattke and colleagues are supported by results from a large-scale evaluation of a DM Medicare pilot program. An evaluation of the Medicare DM pilot program found that DM did not reduce the cost of care.13 Nevertheless, Ronald Goetzel and colleagues14 conducted a review of specific studies and found that DM programs aimed at congestive heart failure and multiple disease categories resulted in a positive return on investment but that those aimed at diabetes and depression did not.15,16 All these analyses compared the effectiveness of DM programs across different firms and vendors and, therefore, may provide insight into the generalizability of DM. Nevertheless, these analyses do not control for firm and vendor-specific unobservable characteristics. Comparing the effectiveness of DM across chronic conditions in a single setting and using the same DM program from the same vendor limits the generalizability of results but holds constant many of the firm- and vendor-specific unobserved factors. This is important because the method of delivery can have a significant impact on the effectiveness of a DM program. Few studies have attempted to control for firm- and vendorspecific unobservable factors in comparing the relative effectiveness of a DM program across multiple conditions. In a previous study, using University of Minnesota data on the annual health expenditures by employees, Nyman and his colleagues17 estimated the average effect of participation in DM for any disease. The study also evaluated whether participation in the DM program for each disease was differentially effective compared to the overall effect. This analysis found that participation in health promotion by individuals with asthma,

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cardiovascular, and musculoskeletal disease had significantly lower savings than the average effect of the DM program, and those employees with congestive heart failure had significantly higher savings than average. This study, however, did not estimate the average savings associated with participation for a given condition.17 Such an analysis would better be able to identify the diseases for which DM was effective and the savings associated with each. Accordingly, this study builds on this earlier work by using 6 years of data from the University of Minnesota to decompose the savings from the University’s DM program into the savings generated by those with each of 11 chronic conditions who participated in the DM program. In addition, this analysis evaluates whether DM participation has an effect on absenteeism by each of the diseases. Finally, this study evaluates the impact of DM on hospitalizations and avoidable hospitalizations by disease.

METHODS Study Setting The University of Minnesota is a large public institution with campuses across the State of Minnesota. The largest campuses are in the Twin Cities (Minneapolis and St. Paul) and Duluth. Starting in 2006, in an effort to combat rising costs and improve the health of employees, spouses, and dependents, the University implemented a suite of health promotion programs. These programs consisted of a DM program for those with a series of 11 chronic diseases, a health risk assessment, lifestyle management for behavioral risks (eg, alcohol use), online self-help programs for those with specific health risk issues referred to as Miavita, a daily walking program referred to as 10,000 Steps, a nurse call-in line, and a Web-based library of informal materials on various health issues. Over time, the health promotion initiative has continued to add additional programs. In 2008, an exercise incentive program was incorporated and in 2010, a weight management, a stress management, and a smallgroup exercise program led by a personal trainer were also added. This analysis focuses on the effectiveness of the DM program. From 2006 through 2009, the University of Minnesota’s DM vendor identified employees, spouses, and dependents with chronic diseases through a review of claims data and self-administered health risk assessments. Individuals who were enrolled in the University’s selfinsured employee medical plan were invited to participate in the DM program if they had any of the following 11 chronic conditions: diabetes, asthma, cardiovascular problems, congestive heart failure, arthritis, depression, osteoporosis, musculoskeletal problems, lowback pain, migraines, or gastrointestinal problems. Individuals could be invited to participate for multiple conditions and over multiple years. Individuals could only participate in DM if they were invited to do so in a given year (individuals could participate for multiple conditions in a given year), or in the case of a very few participants, if they called in to request participation. All those who participated in DM received telephonic coaching that was specific to their disease, but that was provided by the same vendor under an approach that was consistent across diseases and used the same set of health coaching personnel (a more detailed description of the DM intervention is available by e-mail request from the corresponding author).

Data Administrative claims data of employees, spouses, and dependents covered by the University of Minnesota’s plan were obtained for years 2004 through 2009. The claims data contained detailed records of health care expenditures for all individuals covered by one of the health plans offered to University employees. All expenditure data were adjusted by the medical care portion of the Consumer Price Index to reflect 2010 prices. The data also contained information on the individual’s age, sex, in which of the five health plans they were 118

enrolled, and the chronic condition for which they were invited to participate in DM for a given year. Absenteeism data, measured as hours absent from work due to illness, were obtained from the University of Minnesota Employee Benefits office from 2004 through 2009. Unlike expenditure data, absenteeism data were available only for employees who used time cards. Time card employees are civil service or labor-represented employees. To ensure that expenditures in the pre-period were representative, we required individuals to have 1.5 years of expenditure data before being invited to participate in DM. In addition, individuals were required to have at least one full year of expenditure data for the year they were invited to participate (regardless of whether they actually participated). The participant group consisted of individuals who participated in DM for a given chronic condition. The comparison group consisted of individuals who were invited to participate but who did not participate in DM for a given chronic condition.

Effect of DM by Chronic Condition on Expenditures and Absenteeism Previous analyses have noted the methodological challenges associated with evaluating this DM program.17–20 To summarize, there are two key concerns: (1) regression to the mean and (2) selection bias. Regression to the mean could bias results if those in the participation group were sicker than the comparison group during the preintervention years compared with the postintervention years. If this were the case, results could give the impression that DM is reducing expenditures when in fact it is due to sicker patients whose health improves naturally. We believe that the effect of regression to the mean is attenuated because the same invitation criteria were used for both participant and control groups. To overcome selection bias, we used a difference-in-difference regression to control for unobserved time-invariant confounders such as race. Equation 1 details the basic difference-in-difference regression equation. E it = γ0 + γ k1 X kit + γ t2 Tt + γ 3 Participatori + γ 4 Postit + γ5 (Participatori ∗ Postit ) + uit (Equation1) E represents the outcome variable (expenditures and absenteeism). Expenditures and absenteeism were used as dependent variables in separate regressions for each chronic condition. We evaluated 11 different models for each outcome, and each model was run conditional on being invited to participate for a chronic condition (eg, depression, asthma, cardiovascular). In the model, Xkit is a series of variables representing an individual demographic characteristics (age, sex, health plan, and if they ever participated in any other wellness program). To control for any potential spillover effect, we included indicators for whether or not an individual ever participated in DM for any other chronic condition (eg, participation for multiple conditions). Nevertheless, if an individual previously participated in DM but then declined to participate in DM for a different condition, they were excluded from the analysis of the condition for which they did not participate. For models evaluating absenteeism, an additional covariate that controlled for the number of individuals in the family was included to capture any tendency to miss work to care for children who may be ill. Tt is an indicator variable for the year. Participator represents whether the individual ever participated in the DM program for the invited chronic condition in any one or any combination of years 2006 through 2009. Post represents the year in which the individual first participated and any years subsequent to that one for a given chronic condition, regardless of whether they actively participated in the subsequent years or not (or the first year of eligibility for the DM program, for those in the control group). The

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assumption of a durable effect associated with the DM program is based on pervious analyses.18 Finally, Participator*DM represents a participator in the DM program for a given chronic condition during a participation year (and any subsequent year) and is the variable of interest; and uit is the error.

Effect of DM by Chronic Condition on Hospitalizations and Avoidable Hospitalizations The same type of analysis also was used to evaluate the effect of DM participation on hospitalizations and avoidable hospitalizations by chronic condition. Separate models were estimated with hospitalizations and avoidable hospitalizations as the dependent variables. The Ambulatory Care Sensitive Conditions developed by the Agency for Healthcare Research and Quality was used to define avoidable hospitalizations.18,21,22

Statistical Analysis Statistical analyses were performed using Stata 12.1 (College Station, Texas). Because of the skew in expenditures and absenteeism data, we used Manning and Mullahy’s algorithm for choosing between ordinary least squares (OLS) on a log-transformed dependent variable and a generalized linear model (GLM).23 On the basis of the algorithm and because of heteroskedasticity, we chose to use the GLM strategy with log-link and gamma distribution.* For models evaluating hospitalizations and avoidable hospitalizations, a GLM regression model with log-link and negative binomial distribution was used. Marginal contrasts were estimated from the average marginal effect, and standard errors were estimated using the delta method.

RESULTS Descriptive Statistics In total, 18,721 individuals were eligible to participate in the DM program (13,168 individuals declined to participate and 5553 individuals participated). After applying the inclusion criteria, the analytic sample consisted of 11,331 individuals (7585 individuals declined to participate and 3746 individuals participated). The absenteeism analysis consisted of 4321 individuals (2620 individuals declined to participate and 1,701 individuals participated). In the full sample, the mean number of observations in the preyears was 2.91 years for the comparison group and 2.83 years for the participation group. The mean number of observations in the postyears was 3.09 years for the comparison group and 3.16 years for the participation group. Descriptive statistics are summarized across all years of data (2004 to 2009) in Table 1. Because the data span multiple years for individuals, values are presented in terms of person-years. There are 21,888 person years of DM participation data and 43,942 person years of data for the comparison group. On average, DM participants were older and more likely to be female.

Effect of DM by Chronic Condition on Expenditures and Absenteeism Table 2 shows the savings from participating in DM by chronic condition. The results indicate that DM participants with asthma, cardiovascular problems, congestive heart failure, depression, musculoskeletal problems, low back pain, and migraines experienced a significant reduction in health care expenditures, relative to the comparison group. For example, average monthly health care expenditures for individuals with asthma who participated in DM were $260 (CI: $391 to $129) lower than those for nonparticipants, or $3120 (CI: $4692 to $1548) less per year over each of the 5 years of possible participation. In the arthritis analysis, 246 individuals were excluded *We also ran the absenteeism analysis using a log-link and Poisson distribution. This analysis generated similar results.

Health Promotion, Disease Management, Hospitalizations

from the control group because they previously participated in DM (this was the largest exclusion due to previous DM participation). Because of a small number of observations, the model evaluating the effect of DM for individuals with gastrointestinal problems could not converge and so no results were reported. DM participation did not have an effect on absenteeism for any of the chronic conditions.

Effect of DM by Chronic Condition on Hospitalizations and Avoidable Hospitalizations Tables 3 and 4 show the effect of DM participation on hospitalizations and avoidable hospitalizations by chronic condition. Results indicate that DM participants with asthma, cardiovascular disease, depression, musculoskeletal problems, low back pain, and migraines also experienced a significant reduction in hospitalizations. For example, individuals who participated in DM due to depression had 0.045 (−0.068 to −0.022) fewer hospitalizations in a year on average over 5 years compared to the control group. The DM participation also reduced avoidable hospitalizations for the same categories except for musculoskeletal problems, low back pain, and migraines.

DISCUSSION As the population ages and chronic disease becomes more prevalent, interventions that better manage such conditions are vital for improving health and controlling health care costs.24 The DM programs represent an intervention that can help achieve these goals. Previous studies have found that a DM program offered at the University of Minnesota significantly reduced health care expenditures, hospitalizations, and avoidable hospitalizations.17–20 This study identified the chronic conditions through which the program seems to be effective. Results from this study indicate that DM reduces health care expenditures for only certain chronic conditions (asthma, cardiovascular disease, congestive heart failure, depression, musculoskeletal problems, low back pain, and migraines). It does not seem to reduce health care expenditures for diabetes, arthritis, and osteoporosis. Furthermore, DM did not have an effect on absenteeism for any of the chronic diseases investigated. This is one of a few studies to attempt to identify the effect of DM aimed at specific chronic conditions delivered by a single vendor in a single setting. Nevertheless, the evidence on the effect of DM programs is inconclusive and depends on the intensity of the program and target condition.4 As such, results from this analysis differ from several previously published studies evaluating the effect of DM. In contrast to prior studies, this study found that DM reduced costs for depression, arthritis, and cardiovascular disease.4 In addition, this study did not find DM to reduce costs for individuals with diabetes. Yet, several studies have shown that DM programs aimed at diabetes are effective and save money.14 Specifically, Sidorov and colleagues25 found that DM participants with diabetes had lower health care expenditures. The difference between these results may be explained in part by a difference in setting and intensity of program delivery. Sidorov and colleagues evaluated a DM program within an integrated delivery system. In contrast, the DM program at the University of Minnesota was offered through an employer. In addition, statewide and system-level interventions aimed at diabetes are already in existence, so the program offered at the University of Minnesota may not have added value. The difference between these results may also be explained in part by different methods. Sidorov and colleagues did not attempt to control for other observed factors or for selection bias and simply compared means between groups. They also did not include pharmacy claims in their expenditure data. Results from this analysis corroborate the findings of other analyses.4 For example, this analysis found that participation in DM for congestive heart failure resulted in a savings of $1183 per member per month ($14,196 per year). This finding is similar to several other studies,26 including Gambetta and colleagues,27 who found that

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TABLE 1. Descriptive Statistics of the University of Minnesota Disease Management Program Variables Expenditures in the preyears, mean (SD) Absenteeism in the preyears, mean (SD) Number of hospitalizations, mean (SD) Number of avoidable hospitalizations, mean (SD) Age, mean (SD), yrs Male N (%) Ever insured by: N (%) Ever vendor 1 Ever vendor 2 Ever vendor 3 Ever vendor 4 Ever vendor 5 Invited to participate for: N (%) Diabetes Asthma Cardiovascular Congestive heart failure Arthritis Depression Osteoporosis Musculoskeletal Low back pain Migraines Gastrointestinal Participation in other programs, N (%) Life style management† Fitness rewards Miavita Ten thousand steps Weight management

DM Nonparticipation* (N = 43,942)

Participated in DM (N = 21,888)

P

$506 ($1,857) 66.52 (58.22) 0.056 (0.29) 0.008 (0.10) 44.72 (13.74) 20,990 (47.77)

$756 ($2,464) 69.99 (56.81) 0.089 (0.37) 0.015 (0.15) 47.82 (10.96) 8,757 (40.01)

For what illnesses is a disease management program most effective?

We examined the impact of a disease management (DM) program offered at the University of Minnesota for those with various chronic diseases...
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