ORIGINAL ARTICLE
The 3-Year Disease Management Effect Understanding the Positive Return on Investment John A. Nyman, PhD, Molly Moore Jeffery, MPP, Jean M. Abraham, PhD, Eric Jutkowitz, BA, and Bryan E. Dowd, PhD
Objective: Conventional wisdom suggests that health promotion programs yield a positive return on investment (ROI) in year 3. In the case of the University of Minnesota’s program, a positive ROI was achieved in the third year, but it was due entirely to the effectiveness of the disease management (DM) program. The objective of this study is to investigate why. Methods: Differences-in-differences regression equations were estimated to determine the effect of DM participation on spending (overall and service specific), hospitalizations, and avoidable hospitalizations. Results: Disease management participation reduced expenditures overall, and especially in the third year for employees, and reduced hospitalizations and avoidable hospitalizations. Conclusions: The positive ROI at Minnesota was due to increased effectiveness of DM in the third year (mostly due to fewer hospitalizations) but also to the simple durability of the average DM effect.
B
roadly held, but poorly understood, is the belief that workplace health promotion programs require at least 3 years to generate a positive return on investment (ROI).1 Theoretical explanations for a delay in effectiveness are straightforward for some programs. In the case of interventions designed to modify risky behaviors, participation would likely take some time to generate the changes in physiology that could then manifest themselves in lower health care utilization and expenditures. For example, it would take time for a weight loss program to result in a substantial loss of weight and more time for the weight loss to decrease doctor visits or hospitalizations. Similar expectations would apply to smoking cessation interventions, exercise programs, and other risk-modifying interventions. In the case of programs directed at those with chronic diseases, however, the effect would appear to be more immediate. Programs directed at employees with asthma, diabetes, congestive heart failure, angina, and similar diseases tend to work by managing the disease better. That is, they work by changing the behavior of the ill participant to take medications more compliantly, monitor biomarkers more regularly, or recognize adverse physiological changes more quickly. Better management would lead to health care savings early on, mainly through the elimination of management failures that lead 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. 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. None of the authors have any financial, consultant, institutional, or other relationships that might lead to bias or conflict of interest with respect to this study. Address correspondence to: John A. Nyman, PhD, Division of Health Policy and management, School of Public Health, University of Minnesota, Minneapolis, 15-219 Philips Wangensteen Building, 420 Delaware Street SE, MMC 729 Minneapolis, MN 55455 ([email protected]). C 2013 by American College of Occupational and Environmental Copyright Medicine DOI: 10.1097/JOM.0b013e3182a4fffe
1356
to hospitalizations, and the diagnostic tests and emergency department visits that often accompany them. Because inpatient stays are so expensive, avoidance of just a few could generate substantial savings. The puzzle is that, although these changes are expected to occur early (and actually do occur early, as is evidenced by the effectiveness of the disease management [DM] program in its first year), the large reductions in expenditures that generated a positive ROI for the University of Minnesota’s health promotion program did not occur until the third year and were produced entirely by the DM program.2 This timing is consistent with conventional wisdom, but it raises the question: Why the lag in generating a positive overall ROI if DM programs are designed to work right away? The University of Minnesota implemented its health promotion program in 2006, originally consisting of DM and six of other interventions. Disease management took the form of diseasespecific telephone coaching for those with any of 11 chronic diseases. The other interventions included (1) a health risk assessment; (2) a lifestyle management program for those with behavioral risks; (3) self-help guides for those with specific health risk issues, originally called Miavita; (4) a daily walking program, originally called 10,000 Steps; (5) a nurse call-in line; and (6) a Web-based library of informational materials on various health issues. Employees, spouses, and dependents older than 16 years received a $65 payment for completing the health assessment survey and another $65 for participating in and completing one of the programs for which participation records were kept (DM, lifestyle management, Miavita, and 10,000 Steps). In 2008, (7) an exercise incentive, originally called the Fitness Rewards Program, was added. In 2010, (8) weight management, R or Choose Your Own Weight R , (9) stress manWeight Watchers agement, Mindfulness-Based Stress Management, and (10) another incentivized exercise program, Group Strength Express, were made available. Previous work found that the University’s health promotion program exhibited the expected 3-year pattern, but only through the DM program. In the first year of the program, DM participation reduced health care expenditures significantly, but the savings did not cover the costs of the overall program.3 After 2 years, again DM was effective, but did not generate a positive ROI.4 After 3 years, however, DM participation had become so effective that it alone generated a positive ROI for the entire program, in keeping with conventional wisdom.2 Further analysis of the 3-year data tested whether the effect was due to “refinements” in the program (perhaps the health coaches were able to improve their coaching methods over 3 years), or whether it was due to the “maturing” of the management techniques in an individual (perhaps with practice, the individual learns to become more proficient at managing his own disease). Neither the refinement nor maturing explanations were supported by analyses.2 In these previous studies, the effectiveness of the other programs (for which participation was recorded) was also evaluated, but no consistent effect was found on either expenditures or absenteeism. As a result, we focus entirely on the DM program’s effect in reducing health care spending in this article. Other than our previous work JOEM r Volume 55, Number 11, November 2013
Copyright © 2013 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
JOEM r Volume 55, Number 11, November 2013
analyzing the University of Minnesota’s programs, we could find no other studies that investigated the source of a third-year effect. In this analysis, the years 2009 and 2010 are added to the data, permitting a confirmatory analysis of the 3-year ROI and 3 additional analyses. In the first of these, the relationship between effectiveness and the timing of DM program participation is investigated by isolating the effectiveness of the DM in the third year after first participating compared to its effectiveness in the other years after participating for all participating employees, regardless of when participation began. The “refinement” and “maturing” explanations were again tested, but using this new approach. In the second, expenditures are broken down by type of provider to determine whether it is primarily the reduction in hospital expenditures that generates the 3-year DM result. And in the third, whether the reduced hospitalizations were avoidable ones is investigated. In the next three sections, the methods are discussed, the results presented, and the policy implications considered, in that order.
METHODS Data Administrative records (claims data) from 2004 to 2010 were obtained for the health care expenditures of University of Minnesota employees, spouses, and dependents who were enrolled in one of the health plans offered by the University. To accommodate employees who were not employed for an entire year, the total annual expenditures for each employee were divided by the number of months employed to obtain average monthly expenditures for each year enrolled. All expenditure data were adjusted by the medical care portion of the Consumer Price Index to the year 2010. Information was also available on the employee’s age, gender, which of the five health plans the employee was enrolled in, and with which of a series of 11 chronic diseases (diabetes, asthma, cardiovascular disease, congestive heart failure, arthritis, depression, osteoporosis, a musculoskeletal disease, low back pain, migraine headaches, or a gastrointestinal disease) the employee had been diagnosed. To be enrolled in the DM program, it was necessary to have been invited to enroll by the vendor. Invitations were based on evidence that the employee suffered from at least 1 of these 11 diseases as discovered during a review of either (1) the employee’s health risk assessment survey responses or (2) the employee’s medical claims records. Spouses of employees and the dependents aged 16 years and older were eligible for the DM program and included in the analysis. Participation in other health promotion programs (for which participation records were kept) also was included in the data. In our initial investigations of the University’s program,2–4 employees were required to be enrolled continuously from at least June 2004 to the end of the most recent calendar year of data, which represented at least 18 months of pre-intervention period data because the program began in January 2006. Our original concern was that we would have sufficient data on the pre-intervention period to assure that the spending was representative for this period. With the additional 2 years of data in the present study, we were also concerned that such a requirement would reduce our sample excessively and perhaps systematically exclude the new employees. Therefore, we permitted any employee to be included as long as they had 18 months of expenditure data before their first year of participation in the DM program (or the year of qualifying to participate, for the control group) and a full year of participation data (or of eligibility data, for the control). Thus, the 2 years of pre-intervention expenditures data were based on the average monthly spending for an entire 12 months in the year immediately before participating, and on the average monthly spending for at least 6 months of the second year before participating. This “moving window” analysis could be used
Understanding the 3-Year Disease Management Effect
to evaluate the effectiveness of the third year of participation in the DM program, regardless of when participation began.∗
Overall Effect on Expenditures The basic differences-in-differences regression equation used is written as follows: E it = Y0 + Yk1 X kit + Yt2 Tt + Y3 DMi + Y4 Postit + Y5 (Postit ∗ DMi ) + u it
(equation 1)
where E is the average monthly expenditures; Xk is a vector of k variables representing the respondent’s age, gender, health plan, indicators for each of the 11 DM chronic conditions, and indicators for whether the enrollee ever participated in each of the other programs for which records were kept; Tt is the year; DM represents whether the individual was ever a participator in the DM program in any one or any combination of years 2006 through 2010; Post represents the year in which the employee first participated and any years subsequent to that one, regardless of whether he or she actively participated in the subsequent years or not (or the first year of eligibility for the DM program, for those in the control group); Post*DM represents a participator in the DM program during a participation year (and any subsequent year) and is the variable of interest; and uit is the error. The index i represents the employee and t represents the year (2005 through 2010, with 2004 being the reference category). This differences-in-differences approach is designed to capture and eliminate the effect of any time-invariant unobservable respondent characteristics related to the decision to participate that might bias the program effectiveness coefficient. To be clear, the Post variable captures both the effect of participation in the year of participation and any effect of participation in subsequent years, whether the employee actually participated in a subsequent year or not. Thus, no distinction is made in the Post variable between a third year of program participation (third year still participating after first year) and the third year after first year of program participation (third year not participating). This is important because all the return-on-investment calculations use a participantyear definition that is consistent with this specification of the Post variable. Propensity score analysis represents an alternative analytical approach that would allow us to roll up the effects of the observed explanatory variables into a single index and estimate an average treatment effect. We believe this would provide little added value because these differences are already accounted for by including the observed variables in the regression. The real threat to causal inference is not an observed variable bias, but an unobserved variable bias. Our difference-in-difference model controls for time-invariant omitted personal characteristics by comparing the change in the dependent variable from the pre-intervention period to the post-intervention period for the treatment versus control groups continuously. Nevertheless, to investigate the contribution of the control variables as a single propensity score, we estimated a probit participation equation to determine the range in propensity scores where the greatest overlap occurred between participants and nonparticipants for the predicted probability of participation. Then, we estimated equation 1 at five quintiles of the propensity score to determine whether there was support for the effectiveness of the DM program among those quintiles where the greatest exchangeability (overlap) ∗
It should be noted that the University switched DM vendors in 2010. Rather than introducing the ambiguity of a potentially different DM program, new participation in DM in 2010 was excluded from the analysis. Because the 2010 contribution to our effectiveness results is based on participation in the past, it is likely to result in a smaller and more conservative annual cost-savings estimate than would an analysis that also included the effect of 2010 participation in the year of participation.
C 2013 American College of Occupational and Environmental Medicine
Copyright © 2013 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
1357
JOEM r Volume 55, Number 11, November 2013
Nyman et al
was exhibited. Because this is a confirmatory analysis, these results are left to the discussion section later. A second source of possible selection bias in this study is regression to the mean. Regression to the mean would occur if the treatment group consisted of sicker individuals and their severity of illness contained a stochastic element or “shock” that would be less likely to exist in the post-intervention period. Should such a stochastic element exist, our estimates of the treatment effect would not be biased as long as the stochastic element was distributed equally in the treatment and control groups. Because the same invitation criteria were used for both participant and control groups, we do not expect regression to the mean to occur at that level. Because of the skew in the expenditure data, we could not estimate this equation using ordinary least squares on raw expenditures. Our choice of an alternative approach was informed by the protocol suggested by Manning and Mullahy.5 Because of the heteroscedasticity, we opted to use a generalized estimating equation approach with a log link and gamma family distribution to estimate this equation, relying on the relatively large number of observations rather than the precision of this estimator to identify significance where significance exists. A two-part analysis was deemed unnecessary for the aggregated expenditures equation because less than 5% of the person-year observations had no expenditures at all. Nevertheless, a two-part approach was used for the analysis of hospitalizations and other types of utilization because of the large percentage of observations with zero expenditures or counts in some categories of services. The intent of this initial regression analysis was to determine whether DM was still effective given all 5 years (2006 to 2010) that the health promotion program was in effect. This analysis was conducted separately for employees, spouses, and dependents combined and for employees only.
Third-Year Effect One explanation of the 3-year ROI effect is that after enrolling in a DM program, some individuals might experience a period of additional expenditures as their medical treatments are refined or adjusted. For example, an adjustment for asthma management might require additional diagnostic tests. Once these costly refinements or adjustments have occurred, the resulting management protocol is more effective and so subsequent expenditures are reduced. Thus, participation might result in both early increases and decreases in expenditures among some participants, but show up more cleanly later on, generating the 3-year effect we have observed in previous work. To investigate this possibility, the equation was re-specified to measure the differential effectiveness of the DM program in any third year after first participating, compared with other years. To do this, a series of five dummy variables was employed indicating the interval between the year in question and the year the employee first participated in the DM program (or for the control group, the year the employee first became eligible for the DM program by virtue of contracting 1 of the 11 chronic diseases specified earlier).6 Specifically in equation 2, the m index indicates whether or not the year in question was the year of first participating (m = 1) in the DM program, the next year after first participating (m = 2), the third year of participation (m = 3), or the fourth year or greater (m = 4), with the reference case being any year before the employee’s first year of participation. These participation interval dummy variables (PIm ), both singly and interacted, were entered into the base equation in place of the interaction variable (Postit *DMi ). Thus, the equation is written as follows: E it = Y0 +Yk1 X kit +Y2 Tt +Y3 DMi +Y4 Postit +Ym5 PIim + Ym6 (Postit ∗PIim )+u it (equation 2) 1358
A significant negative coefficient of the interaction term for the third year of participation would indicate that any third year of participation results in expenditure reductions. Comparing this coefficient with the coefficients for the other participation interval interaction term would determine the extent that timing matters in generating the expenditure savings for individual participants. In comparison, in equation 1, the interaction term indicates the average expenditure reductions for any year of participation. The equation 2 analysis was conducted separately on employees, spouses, and dependents combined and on employees only.
Expenditure Components The hypothesis that the effectiveness of DM is due largely to preventing lapses or breakdowns in the management of the disease that would result in an expensive hospitalization, emergency department, and other types of inpatient use is tested next. Expenditures were disaggregated into those for (1) physician care unrelated to hospitalizations or surgery, (2) laboratory and pathology testing, (3) pharmaceuticals, (4) radiology, (5) surgery, (6) emergency department use, (7) hospitalizations alone, and (8) hospitalizations plus all physician medical care associated with hospitalizations and surgeries combined. Note that these categories are not mutually exclusive. For example, expenditures for surgery are included in the expenditures for both hospitalizations alone and hospitalizations plus all physician medical care associated with hospitalizations. Each of these expenditure categories was regressed on DM participation in the post period as the variable of interest, as specified by equation 1. Reductions in hospital-related utilization including surgeries, emergency department use and standard inpatient stays, are hypothesized to be associated with participation in the DM program. As indicated earlier, a two-part model was used to account for the large mass of zero expenditures in this type-specific analysis. In the first part, a probit version of equation 1 was used to determine if participation in DM had an impact on whether or not the individual used that type of expenditures at all (ie, the probability of expenditures being greater than 0). In the second part, equation 1 was used to determine the relative effect of participation on all individuals who had expenditures greater than 0. Our interest was mainly in the part 1 probit analysis because it was hypothesized that participation in DM would mainly work by reducing the likelihood of a lapse or a breakdown in DM that would require a major, costly medical intervention, such as a hospital stay or surgery.
Hospitalizations and Avoidable Hospitalizations Finally, the effect of the DM program in eliminating avoidable hospitalizations is addressed by using the Ambulatory Care Sensitive Conditions developed by the Agency for Healthcare Research and Quality to define avoidable hospitalizations. The Agency for Healthcare Research and Quality algorithm defines 27 different avoidable hospitalizations by their respective codes of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9CM), and excludes some avoidable events if certain procedures or secondary diagnoses are made.∗ In general, these conditions would serve as a measure of the quality of ambulatory care in a community. In our analysis, however, they serve as an indicator of the effectiveness of the DM program in better managing chronic diseases and avoiding lapses or breakdowns that would require hospitalizations. For example, asthma is listed as one of the Ambulatory Care Sensitive Conditions, meaning that in general patients should not be admitted to the hospital with asthma as the primary diagnosis. Because DM theoretically works by improving the management of persons with ∗
For example, congestive heart failure is considered an avoidable hospitalization if patients are hospitalized with ICD-9 code 428, 402.01, 402.11, 402.91, or 518.4 and do not have a surgical procedure with the ICD-9 procedure code 36.01, 36.02, 36.05, 36.1 37.5, or 37.7. For a complete list of the AHRQ ambulatory care sensitive conditions see http://archive.ahrq.gov/data/safetynet/billappb.htm.
C 2013 American College of Occupational and Environmental Medicine
Copyright © 2013 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
JOEM r Volume 55, Number 11, November 2013
chronic illnesses in an ambulatory care setting, it is hypothesized that participation in DM will reduce avoidable hospitalizations. Two different methods were used to identify Ambulatory Care Sensitive Conditions. In the first method, ICD-9 diagnosis codes in the “inpatient file” were used to identify hospitalizations that were avoidable and the ICD-9 procedure codes from the “medical file” (converted from Current Procedural Terminology codes) were used to identify surgical exclusions.∗ In the second method, avoidable hospitalizations were defined on the basis of ICD-9 diagnosis codes in both the “inpatient file” and “medical ‘‘file.’’ †; To determine the effect of DM on hospitalizations and avoidable hospitalizations, both were used as dependent variables in separate differences-indifferences equations (equation 1). As with our other analyses, the variable of interest is the interaction term Post*DM. A zero-inflated negative binomial regression was used to determine the impact of DM on the number of hospitalizations and avoidable hospitalizations in a year. The benefit of a zero-inflated negative binomial model over a Poisson model is that it is better able to handle large masses of zeros.
RESULTS Descriptive Statistics Because of the many years of data, the descriptive statistics have been summarized in Table 1 for the years 2004 to 2010 for employees, spouses, and dependents combined and separately for employees only. There are 5413 employees, spouses, and dependents in the participation group (4226 employees alone) whereas the control group includes 12,289 employees, spouses, and dependents (8677 employees alone). Table 1 compares the age, insurance type, gender, and percentage of the sample with specific chronic diseases that would qualify them for invitation into the DM program for the participation and nonparticipation groups. Among both samples, employees who participated were older, more likely to be female and were likely to have multiple conditions, as indicated by the higher percentages of participants with each of the chronic conditions. They were also more likely to participate in the other programs offered by the University of Minnesota for which participation records were kept.
Overall Effect on Expenditures Table 2 shows the regression equation coefficients for variables associated with participation in the DM program, with the DM effectiveness coefficient translated into dollar amounts at the bottom of the table. The results show that participation in the DM program and subsequent years generated a significant reduction in expenditures that amounted to $132.29 per month or $1,587.48 annualized, on average over all 5 years of possible participation. For employees only, the savings were larger at $161.22 per month. These results, compared with the results from our previous studies, seem to indi-
Understanding the 3-Year Disease Management Effect
cate that the DM effect is relatively constant over time and may even be increasing.
Return on Investment Table 3 shows the ROI calculations comparing the cost of the entire health promotion program to the savings from the DM program alone. Table 3 shows program costs in each year disaggregated and adjusted to 2010 dollars by the same medical care portion of the Consumer Price Index as was used to adjust health care expenditures. The total program cost for the University was $16,626,209. Applying the $1587.48 average annual savings (also in 2010 dollars) over all 5 years of participation to the 17,071 participant years by employees, spouses, and dependents, the DM program generated a total savings of $27,099,871 and an average ROI of 1.63.
Third-Year Effect The results for equation 2 are presented in Table 4, broken down by all enrollees and by employees alone. The results suggest that the first year of participation makes a significant contribution to savings for all enrollees and the third year makes a significant additional contribution to savings for employees alone. The amount of annual savings was similar to the amount of annual savings found using equation 1.
Expenditure Components The results of the expenditure components appear in Table 5 and are broken down by the probability of any such expenditure and the level of expenditure, for those who had expenditures. Any participation in the DM program generated a reduction in the likelihood of inpatient expenditures of all types—inpatient, emergency, surgery, radiology, and laboratory—but not in the likelihood of physician or pharmacy expenditures. Disease management participation also reduced the level of physician expenditures—and laboratory, surgery, and emergency expenditures—for those who had them.
Hospitalizations and Avoidable Hospitalizations With regard to hospitalizations, our equation 1 analysis found that DM participation is associated with 0.025 fewer hospitalizations per participant in a year from the analysis using the zero-inflated negative binomial model (results not reported in a table but available from the corresponding author upon e-mail request). Before implementing the DM program, the annual rate of hospitalizations for University employees was 0.071 and 0.075 hospitalizations per individual in 2004 and 2005, respectively. Thus, the reduction of 0.025 hospital stays among those participating in DM represents about a 33% reduction of the number of hospitalizations.‡ With regard to avoidable hospitalizations, recall that we conducted analyses on the basis of two different methods for defining avoidable hospitalizations. A series of specification tests indicated that the zero-inflated negative binomial model should be used.§ A
∗
In our data, hospitalizations initially were identified from our “inpatient “file.” This file contained a unique record for each hospitalization for any member of the University’s health plan as well as the primary ICD-9 code under which the member was admitted to the hospital. Exclusions were based on ICD-9 diagnosis codes from the “inpatient “file.” Dehydration and iron deficiency were evaluated using all ICD-9-CM codes. † In this alternative analysis, we combined the “inpatient file” with the “medical “file.”The “medical file” contained a record for each physician-related event that occurred during a hospitalization and an IDC-9 diagnosis code related to the event. Physician-related events were defined by CPT code and included surgical procedures. To convert CPT codes from the “medical file” into ICD-9 procedure codes so that ambulatory care sensitive exclusions could be identified, we used the 2011 Procedural Cross Coder.6 Surgical exclusions were based on ICD-9 procedure codes in the “medical file,” and diagnosis exclusions were based on ICD-9 diagnosis codes in the “medical file” and “inpatient “file.” Ambulatorycare sensitive conditions were defined on the basis of ICD-9 diagnosis codes in both the “inpatient file” and “medical “file.” Surgical exclusions were based on ICD-9 procedure codes in the “medical file,” and diagnosis exclusions were based on ICD-9 diagnosis codes in the “medical file” and “inpatient “file.”
‡
In alternative specifications of the model using random effects (RE) and fixed effects (FE) terms, Stata “margins” command was not able to calculate the number of events. Stata is able, however, to calculate the predicted number of events in an RE and a FE model if it assumes a zero random or fixed effect. To test between the RE and FE model, a Hausman test was run. Results from the Hausman test indicate we reject the null (P < 0.001) and that we should use the FE model. Rather than using the marginal effect in the RE and FE model, we used the incident rate ratio (IRR) to determine the percent change in number of hospitalizations. In the RE model, the IRR is derived by taking the exponentiation of the coefficients, exp(−0.308) = 0.74. Therefore, DM participation reduces the number of hospitalizations by 26%. In the FE model, the IRR is derived by taking the exponentiation of the coefficients, exp(−0.288) = 0.75. Therefore, DM participation reduces the number of hospitalizations by 25%. Although lower, these estimates are consistent with the population average estimate. § The Vuong test was used to compare the zero-inflated negative binomial model to the negative binomial model. Results from the test (P value 0.001) indicate a zeroinflated negative binomial should be used rather than a normal negative binomial
C 2013 American College of Occupational and Environmental Medicine
Copyright © 2013 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
1359
JOEM r Volume 55, Number 11, November 2013
Nyman et al
TABLE 1. Descriptive Statistics* Employees, Spouses, and Dependents DM Nonparticipation (n = 12,289)
Variables
Age 41.34 (13.55) Insurance Medica 0.33 (0.47) Definity 0.02 (0.13) Health partners 0.33 (0.47) Other 0.08 (0.26) Male (%) 0.45 (0.50) DM chronic conditions (%) Diabetes 0.07 (0.26) Asthma 0.34 (0.47) Cardiovascular disease 0.22 (0.41) Congestive heart failure 0.01 (0.12) Arthritis 0.09 (0.29) Depression 0.25 (0.43) Osteoporosis 0.02 (0.14) Musculoskeletal disease 0.14 (0.34) Low back pain 0.57 (0.49) Migraine headaches 0.11 (0.31) Gastrointestinal disease 0.00 (0.02) Participation in other programs (%) Life style management§ 0.13 (0.34) Fitness rewards 0.24 (0.43) Miavita 0.08 (0.27) 10,000 steps 0.09 (0.29) Weight management 0.02 (0.14)
Employees Only
P†
DM Nonparticipation (n = 8,677)
Participated in DM (n = 4,226)
P†
45.04 (11.71)
The 3-Year Disease Management Effect Understanding the Positive Return on Investment John A. Nyman, PhD, Molly Moore Jeffery, MPP, Jean M. Abraham, PhD, Eric Jutkowitz, BA, and Bryan E. Dowd, PhD
Objective: Conventional wisdom suggests that health promotion programs yield a positive return on investment (ROI) in year 3. In the case of the University of Minnesota’s program, a positive ROI was achieved in the third year, but it was due entirely to the effectiveness of the disease management (DM) program. The objective of this study is to investigate why. Methods: Differences-in-differences regression equations were estimated to determine the effect of DM participation on spending (overall and service specific), hospitalizations, and avoidable hospitalizations. Results: Disease management participation reduced expenditures overall, and especially in the third year for employees, and reduced hospitalizations and avoidable hospitalizations. Conclusions: The positive ROI at Minnesota was due to increased effectiveness of DM in the third year (mostly due to fewer hospitalizations) but also to the simple durability of the average DM effect.
B
roadly held, but poorly understood, is the belief that workplace health promotion programs require at least 3 years to generate a positive return on investment (ROI).1 Theoretical explanations for a delay in effectiveness are straightforward for some programs. In the case of interventions designed to modify risky behaviors, participation would likely take some time to generate the changes in physiology that could then manifest themselves in lower health care utilization and expenditures. For example, it would take time for a weight loss program to result in a substantial loss of weight and more time for the weight loss to decrease doctor visits or hospitalizations. Similar expectations would apply to smoking cessation interventions, exercise programs, and other risk-modifying interventions. In the case of programs directed at those with chronic diseases, however, the effect would appear to be more immediate. Programs directed at employees with asthma, diabetes, congestive heart failure, angina, and similar diseases tend to work by managing the disease better. That is, they work by changing the behavior of the ill participant to take medications more compliantly, monitor biomarkers more regularly, or recognize adverse physiological changes more quickly. Better management would lead to health care savings early on, mainly through the elimination of management failures that lead 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. 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. None of the authors have any financial, consultant, institutional, or other relationships that might lead to bias or conflict of interest with respect to this study. Address correspondence to: John A. Nyman, PhD, Division of Health Policy and management, School of Public Health, University of Minnesota, Minneapolis, 15-219 Philips Wangensteen Building, 420 Delaware Street SE, MMC 729 Minneapolis, MN 55455 ([email protected]). C 2013 by American College of Occupational and Environmental Copyright Medicine DOI: 10.1097/JOM.0b013e3182a4fffe
1356
to hospitalizations, and the diagnostic tests and emergency department visits that often accompany them. Because inpatient stays are so expensive, avoidance of just a few could generate substantial savings. The puzzle is that, although these changes are expected to occur early (and actually do occur early, as is evidenced by the effectiveness of the disease management [DM] program in its first year), the large reductions in expenditures that generated a positive ROI for the University of Minnesota’s health promotion program did not occur until the third year and were produced entirely by the DM program.2 This timing is consistent with conventional wisdom, but it raises the question: Why the lag in generating a positive overall ROI if DM programs are designed to work right away? The University of Minnesota implemented its health promotion program in 2006, originally consisting of DM and six of other interventions. Disease management took the form of diseasespecific telephone coaching for those with any of 11 chronic diseases. The other interventions included (1) a health risk assessment; (2) a lifestyle management program for those with behavioral risks; (3) self-help guides for those with specific health risk issues, originally called Miavita; (4) a daily walking program, originally called 10,000 Steps; (5) a nurse call-in line; and (6) a Web-based library of informational materials on various health issues. Employees, spouses, and dependents older than 16 years received a $65 payment for completing the health assessment survey and another $65 for participating in and completing one of the programs for which participation records were kept (DM, lifestyle management, Miavita, and 10,000 Steps). In 2008, (7) an exercise incentive, originally called the Fitness Rewards Program, was added. In 2010, (8) weight management, R or Choose Your Own Weight R , (9) stress manWeight Watchers agement, Mindfulness-Based Stress Management, and (10) another incentivized exercise program, Group Strength Express, were made available. Previous work found that the University’s health promotion program exhibited the expected 3-year pattern, but only through the DM program. In the first year of the program, DM participation reduced health care expenditures significantly, but the savings did not cover the costs of the overall program.3 After 2 years, again DM was effective, but did not generate a positive ROI.4 After 3 years, however, DM participation had become so effective that it alone generated a positive ROI for the entire program, in keeping with conventional wisdom.2 Further analysis of the 3-year data tested whether the effect was due to “refinements” in the program (perhaps the health coaches were able to improve their coaching methods over 3 years), or whether it was due to the “maturing” of the management techniques in an individual (perhaps with practice, the individual learns to become more proficient at managing his own disease). Neither the refinement nor maturing explanations were supported by analyses.2 In these previous studies, the effectiveness of the other programs (for which participation was recorded) was also evaluated, but no consistent effect was found on either expenditures or absenteeism. As a result, we focus entirely on the DM program’s effect in reducing health care spending in this article. Other than our previous work JOEM r Volume 55, Number 11, November 2013
Copyright © 2013 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
JOEM r Volume 55, Number 11, November 2013
analyzing the University of Minnesota’s programs, we could find no other studies that investigated the source of a third-year effect. In this analysis, the years 2009 and 2010 are added to the data, permitting a confirmatory analysis of the 3-year ROI and 3 additional analyses. In the first of these, the relationship between effectiveness and the timing of DM program participation is investigated by isolating the effectiveness of the DM in the third year after first participating compared to its effectiveness in the other years after participating for all participating employees, regardless of when participation began. The “refinement” and “maturing” explanations were again tested, but using this new approach. In the second, expenditures are broken down by type of provider to determine whether it is primarily the reduction in hospital expenditures that generates the 3-year DM result. And in the third, whether the reduced hospitalizations were avoidable ones is investigated. In the next three sections, the methods are discussed, the results presented, and the policy implications considered, in that order.
METHODS Data Administrative records (claims data) from 2004 to 2010 were obtained for the health care expenditures of University of Minnesota employees, spouses, and dependents who were enrolled in one of the health plans offered by the University. To accommodate employees who were not employed for an entire year, the total annual expenditures for each employee were divided by the number of months employed to obtain average monthly expenditures for each year enrolled. All expenditure data were adjusted by the medical care portion of the Consumer Price Index to the year 2010. Information was also available on the employee’s age, gender, which of the five health plans the employee was enrolled in, and with which of a series of 11 chronic diseases (diabetes, asthma, cardiovascular disease, congestive heart failure, arthritis, depression, osteoporosis, a musculoskeletal disease, low back pain, migraine headaches, or a gastrointestinal disease) the employee had been diagnosed. To be enrolled in the DM program, it was necessary to have been invited to enroll by the vendor. Invitations were based on evidence that the employee suffered from at least 1 of these 11 diseases as discovered during a review of either (1) the employee’s health risk assessment survey responses or (2) the employee’s medical claims records. Spouses of employees and the dependents aged 16 years and older were eligible for the DM program and included in the analysis. Participation in other health promotion programs (for which participation records were kept) also was included in the data. In our initial investigations of the University’s program,2–4 employees were required to be enrolled continuously from at least June 2004 to the end of the most recent calendar year of data, which represented at least 18 months of pre-intervention period data because the program began in January 2006. Our original concern was that we would have sufficient data on the pre-intervention period to assure that the spending was representative for this period. With the additional 2 years of data in the present study, we were also concerned that such a requirement would reduce our sample excessively and perhaps systematically exclude the new employees. Therefore, we permitted any employee to be included as long as they had 18 months of expenditure data before their first year of participation in the DM program (or the year of qualifying to participate, for the control group) and a full year of participation data (or of eligibility data, for the control). Thus, the 2 years of pre-intervention expenditures data were based on the average monthly spending for an entire 12 months in the year immediately before participating, and on the average monthly spending for at least 6 months of the second year before participating. This “moving window” analysis could be used
Understanding the 3-Year Disease Management Effect
to evaluate the effectiveness of the third year of participation in the DM program, regardless of when participation began.∗
Overall Effect on Expenditures The basic differences-in-differences regression equation used is written as follows: E it = Y0 + Yk1 X kit + Yt2 Tt + Y3 DMi + Y4 Postit + Y5 (Postit ∗ DMi ) + u it
(equation 1)
where E is the average monthly expenditures; Xk is a vector of k variables representing the respondent’s age, gender, health plan, indicators for each of the 11 DM chronic conditions, and indicators for whether the enrollee ever participated in each of the other programs for which records were kept; Tt is the year; DM represents whether the individual was ever a participator in the DM program in any one or any combination of years 2006 through 2010; Post represents the year in which the employee first participated and any years subsequent to that one, regardless of whether he or she actively participated in the subsequent years or not (or the first year of eligibility for the DM program, for those in the control group); Post*DM represents a participator in the DM program during a participation year (and any subsequent year) and is the variable of interest; and uit is the error. The index i represents the employee and t represents the year (2005 through 2010, with 2004 being the reference category). This differences-in-differences approach is designed to capture and eliminate the effect of any time-invariant unobservable respondent characteristics related to the decision to participate that might bias the program effectiveness coefficient. To be clear, the Post variable captures both the effect of participation in the year of participation and any effect of participation in subsequent years, whether the employee actually participated in a subsequent year or not. Thus, no distinction is made in the Post variable between a third year of program participation (third year still participating after first year) and the third year after first year of program participation (third year not participating). This is important because all the return-on-investment calculations use a participantyear definition that is consistent with this specification of the Post variable. Propensity score analysis represents an alternative analytical approach that would allow us to roll up the effects of the observed explanatory variables into a single index and estimate an average treatment effect. We believe this would provide little added value because these differences are already accounted for by including the observed variables in the regression. The real threat to causal inference is not an observed variable bias, but an unobserved variable bias. Our difference-in-difference model controls for time-invariant omitted personal characteristics by comparing the change in the dependent variable from the pre-intervention period to the post-intervention period for the treatment versus control groups continuously. Nevertheless, to investigate the contribution of the control variables as a single propensity score, we estimated a probit participation equation to determine the range in propensity scores where the greatest overlap occurred between participants and nonparticipants for the predicted probability of participation. Then, we estimated equation 1 at five quintiles of the propensity score to determine whether there was support for the effectiveness of the DM program among those quintiles where the greatest exchangeability (overlap) ∗
It should be noted that the University switched DM vendors in 2010. Rather than introducing the ambiguity of a potentially different DM program, new participation in DM in 2010 was excluded from the analysis. Because the 2010 contribution to our effectiveness results is based on participation in the past, it is likely to result in a smaller and more conservative annual cost-savings estimate than would an analysis that also included the effect of 2010 participation in the year of participation.
C 2013 American College of Occupational and Environmental Medicine
Copyright © 2013 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
1357
JOEM r Volume 55, Number 11, November 2013
Nyman et al
was exhibited. Because this is a confirmatory analysis, these results are left to the discussion section later. A second source of possible selection bias in this study is regression to the mean. Regression to the mean would occur if the treatment group consisted of sicker individuals and their severity of illness contained a stochastic element or “shock” that would be less likely to exist in the post-intervention period. Should such a stochastic element exist, our estimates of the treatment effect would not be biased as long as the stochastic element was distributed equally in the treatment and control groups. Because the same invitation criteria were used for both participant and control groups, we do not expect regression to the mean to occur at that level. Because of the skew in the expenditure data, we could not estimate this equation using ordinary least squares on raw expenditures. Our choice of an alternative approach was informed by the protocol suggested by Manning and Mullahy.5 Because of the heteroscedasticity, we opted to use a generalized estimating equation approach with a log link and gamma family distribution to estimate this equation, relying on the relatively large number of observations rather than the precision of this estimator to identify significance where significance exists. A two-part analysis was deemed unnecessary for the aggregated expenditures equation because less than 5% of the person-year observations had no expenditures at all. Nevertheless, a two-part approach was used for the analysis of hospitalizations and other types of utilization because of the large percentage of observations with zero expenditures or counts in some categories of services. The intent of this initial regression analysis was to determine whether DM was still effective given all 5 years (2006 to 2010) that the health promotion program was in effect. This analysis was conducted separately for employees, spouses, and dependents combined and for employees only.
Third-Year Effect One explanation of the 3-year ROI effect is that after enrolling in a DM program, some individuals might experience a period of additional expenditures as their medical treatments are refined or adjusted. For example, an adjustment for asthma management might require additional diagnostic tests. Once these costly refinements or adjustments have occurred, the resulting management protocol is more effective and so subsequent expenditures are reduced. Thus, participation might result in both early increases and decreases in expenditures among some participants, but show up more cleanly later on, generating the 3-year effect we have observed in previous work. To investigate this possibility, the equation was re-specified to measure the differential effectiveness of the DM program in any third year after first participating, compared with other years. To do this, a series of five dummy variables was employed indicating the interval between the year in question and the year the employee first participated in the DM program (or for the control group, the year the employee first became eligible for the DM program by virtue of contracting 1 of the 11 chronic diseases specified earlier).6 Specifically in equation 2, the m index indicates whether or not the year in question was the year of first participating (m = 1) in the DM program, the next year after first participating (m = 2), the third year of participation (m = 3), or the fourth year or greater (m = 4), with the reference case being any year before the employee’s first year of participation. These participation interval dummy variables (PIm ), both singly and interacted, were entered into the base equation in place of the interaction variable (Postit *DMi ). Thus, the equation is written as follows: E it = Y0 +Yk1 X kit +Y2 Tt +Y3 DMi +Y4 Postit +Ym5 PIim + Ym6 (Postit ∗PIim )+u it (equation 2) 1358
A significant negative coefficient of the interaction term for the third year of participation would indicate that any third year of participation results in expenditure reductions. Comparing this coefficient with the coefficients for the other participation interval interaction term would determine the extent that timing matters in generating the expenditure savings for individual participants. In comparison, in equation 1, the interaction term indicates the average expenditure reductions for any year of participation. The equation 2 analysis was conducted separately on employees, spouses, and dependents combined and on employees only.
Expenditure Components The hypothesis that the effectiveness of DM is due largely to preventing lapses or breakdowns in the management of the disease that would result in an expensive hospitalization, emergency department, and other types of inpatient use is tested next. Expenditures were disaggregated into those for (1) physician care unrelated to hospitalizations or surgery, (2) laboratory and pathology testing, (3) pharmaceuticals, (4) radiology, (5) surgery, (6) emergency department use, (7) hospitalizations alone, and (8) hospitalizations plus all physician medical care associated with hospitalizations and surgeries combined. Note that these categories are not mutually exclusive. For example, expenditures for surgery are included in the expenditures for both hospitalizations alone and hospitalizations plus all physician medical care associated with hospitalizations. Each of these expenditure categories was regressed on DM participation in the post period as the variable of interest, as specified by equation 1. Reductions in hospital-related utilization including surgeries, emergency department use and standard inpatient stays, are hypothesized to be associated with participation in the DM program. As indicated earlier, a two-part model was used to account for the large mass of zero expenditures in this type-specific analysis. In the first part, a probit version of equation 1 was used to determine if participation in DM had an impact on whether or not the individual used that type of expenditures at all (ie, the probability of expenditures being greater than 0). In the second part, equation 1 was used to determine the relative effect of participation on all individuals who had expenditures greater than 0. Our interest was mainly in the part 1 probit analysis because it was hypothesized that participation in DM would mainly work by reducing the likelihood of a lapse or a breakdown in DM that would require a major, costly medical intervention, such as a hospital stay or surgery.
Hospitalizations and Avoidable Hospitalizations Finally, the effect of the DM program in eliminating avoidable hospitalizations is addressed by using the Ambulatory Care Sensitive Conditions developed by the Agency for Healthcare Research and Quality to define avoidable hospitalizations. The Agency for Healthcare Research and Quality algorithm defines 27 different avoidable hospitalizations by their respective codes of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9CM), and excludes some avoidable events if certain procedures or secondary diagnoses are made.∗ In general, these conditions would serve as a measure of the quality of ambulatory care in a community. In our analysis, however, they serve as an indicator of the effectiveness of the DM program in better managing chronic diseases and avoiding lapses or breakdowns that would require hospitalizations. For example, asthma is listed as one of the Ambulatory Care Sensitive Conditions, meaning that in general patients should not be admitted to the hospital with asthma as the primary diagnosis. Because DM theoretically works by improving the management of persons with ∗
For example, congestive heart failure is considered an avoidable hospitalization if patients are hospitalized with ICD-9 code 428, 402.01, 402.11, 402.91, or 518.4 and do not have a surgical procedure with the ICD-9 procedure code 36.01, 36.02, 36.05, 36.1 37.5, or 37.7. For a complete list of the AHRQ ambulatory care sensitive conditions see http://archive.ahrq.gov/data/safetynet/billappb.htm.
C 2013 American College of Occupational and Environmental Medicine
Copyright © 2013 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
JOEM r Volume 55, Number 11, November 2013
chronic illnesses in an ambulatory care setting, it is hypothesized that participation in DM will reduce avoidable hospitalizations. Two different methods were used to identify Ambulatory Care Sensitive Conditions. In the first method, ICD-9 diagnosis codes in the “inpatient file” were used to identify hospitalizations that were avoidable and the ICD-9 procedure codes from the “medical file” (converted from Current Procedural Terminology codes) were used to identify surgical exclusions.∗ In the second method, avoidable hospitalizations were defined on the basis of ICD-9 diagnosis codes in both the “inpatient file” and “medical ‘‘file.’’ †; To determine the effect of DM on hospitalizations and avoidable hospitalizations, both were used as dependent variables in separate differences-indifferences equations (equation 1). As with our other analyses, the variable of interest is the interaction term Post*DM. A zero-inflated negative binomial regression was used to determine the impact of DM on the number of hospitalizations and avoidable hospitalizations in a year. The benefit of a zero-inflated negative binomial model over a Poisson model is that it is better able to handle large masses of zeros.
RESULTS Descriptive Statistics Because of the many years of data, the descriptive statistics have been summarized in Table 1 for the years 2004 to 2010 for employees, spouses, and dependents combined and separately for employees only. There are 5413 employees, spouses, and dependents in the participation group (4226 employees alone) whereas the control group includes 12,289 employees, spouses, and dependents (8677 employees alone). Table 1 compares the age, insurance type, gender, and percentage of the sample with specific chronic diseases that would qualify them for invitation into the DM program for the participation and nonparticipation groups. Among both samples, employees who participated were older, more likely to be female and were likely to have multiple conditions, as indicated by the higher percentages of participants with each of the chronic conditions. They were also more likely to participate in the other programs offered by the University of Minnesota for which participation records were kept.
Overall Effect on Expenditures Table 2 shows the regression equation coefficients for variables associated with participation in the DM program, with the DM effectiveness coefficient translated into dollar amounts at the bottom of the table. The results show that participation in the DM program and subsequent years generated a significant reduction in expenditures that amounted to $132.29 per month or $1,587.48 annualized, on average over all 5 years of possible participation. For employees only, the savings were larger at $161.22 per month. These results, compared with the results from our previous studies, seem to indi-
Understanding the 3-Year Disease Management Effect
cate that the DM effect is relatively constant over time and may even be increasing.
Return on Investment Table 3 shows the ROI calculations comparing the cost of the entire health promotion program to the savings from the DM program alone. Table 3 shows program costs in each year disaggregated and adjusted to 2010 dollars by the same medical care portion of the Consumer Price Index as was used to adjust health care expenditures. The total program cost for the University was $16,626,209. Applying the $1587.48 average annual savings (also in 2010 dollars) over all 5 years of participation to the 17,071 participant years by employees, spouses, and dependents, the DM program generated a total savings of $27,099,871 and an average ROI of 1.63.
Third-Year Effect The results for equation 2 are presented in Table 4, broken down by all enrollees and by employees alone. The results suggest that the first year of participation makes a significant contribution to savings for all enrollees and the third year makes a significant additional contribution to savings for employees alone. The amount of annual savings was similar to the amount of annual savings found using equation 1.
Expenditure Components The results of the expenditure components appear in Table 5 and are broken down by the probability of any such expenditure and the level of expenditure, for those who had expenditures. Any participation in the DM program generated a reduction in the likelihood of inpatient expenditures of all types—inpatient, emergency, surgery, radiology, and laboratory—but not in the likelihood of physician or pharmacy expenditures. Disease management participation also reduced the level of physician expenditures—and laboratory, surgery, and emergency expenditures—for those who had them.
Hospitalizations and Avoidable Hospitalizations With regard to hospitalizations, our equation 1 analysis found that DM participation is associated with 0.025 fewer hospitalizations per participant in a year from the analysis using the zero-inflated negative binomial model (results not reported in a table but available from the corresponding author upon e-mail request). Before implementing the DM program, the annual rate of hospitalizations for University employees was 0.071 and 0.075 hospitalizations per individual in 2004 and 2005, respectively. Thus, the reduction of 0.025 hospital stays among those participating in DM represents about a 33% reduction of the number of hospitalizations.‡ With regard to avoidable hospitalizations, recall that we conducted analyses on the basis of two different methods for defining avoidable hospitalizations. A series of specification tests indicated that the zero-inflated negative binomial model should be used.§ A
∗
In our data, hospitalizations initially were identified from our “inpatient “file.” This file contained a unique record for each hospitalization for any member of the University’s health plan as well as the primary ICD-9 code under which the member was admitted to the hospital. Exclusions were based on ICD-9 diagnosis codes from the “inpatient “file.” Dehydration and iron deficiency were evaluated using all ICD-9-CM codes. † In this alternative analysis, we combined the “inpatient file” with the “medical “file.”The “medical file” contained a record for each physician-related event that occurred during a hospitalization and an IDC-9 diagnosis code related to the event. Physician-related events were defined by CPT code and included surgical procedures. To convert CPT codes from the “medical file” into ICD-9 procedure codes so that ambulatory care sensitive exclusions could be identified, we used the 2011 Procedural Cross Coder.6 Surgical exclusions were based on ICD-9 procedure codes in the “medical file,” and diagnosis exclusions were based on ICD-9 diagnosis codes in the “medical file” and “inpatient “file.” Ambulatorycare sensitive conditions were defined on the basis of ICD-9 diagnosis codes in both the “inpatient file” and “medical “file.” Surgical exclusions were based on ICD-9 procedure codes in the “medical file,” and diagnosis exclusions were based on ICD-9 diagnosis codes in the “medical file” and “inpatient “file.”
‡
In alternative specifications of the model using random effects (RE) and fixed effects (FE) terms, Stata “margins” command was not able to calculate the number of events. Stata is able, however, to calculate the predicted number of events in an RE and a FE model if it assumes a zero random or fixed effect. To test between the RE and FE model, a Hausman test was run. Results from the Hausman test indicate we reject the null (P < 0.001) and that we should use the FE model. Rather than using the marginal effect in the RE and FE model, we used the incident rate ratio (IRR) to determine the percent change in number of hospitalizations. In the RE model, the IRR is derived by taking the exponentiation of the coefficients, exp(−0.308) = 0.74. Therefore, DM participation reduces the number of hospitalizations by 26%. In the FE model, the IRR is derived by taking the exponentiation of the coefficients, exp(−0.288) = 0.75. Therefore, DM participation reduces the number of hospitalizations by 25%. Although lower, these estimates are consistent with the population average estimate. § The Vuong test was used to compare the zero-inflated negative binomial model to the negative binomial model. Results from the test (P value 0.001) indicate a zeroinflated negative binomial should be used rather than a normal negative binomial
C 2013 American College of Occupational and Environmental Medicine
Copyright © 2013 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
1359
JOEM r Volume 55, Number 11, November 2013
Nyman et al
TABLE 1. Descriptive Statistics* Employees, Spouses, and Dependents DM Nonparticipation (n = 12,289)
Variables
Age 41.34 (13.55) Insurance Medica 0.33 (0.47) Definity 0.02 (0.13) Health partners 0.33 (0.47) Other 0.08 (0.26) Male (%) 0.45 (0.50) DM chronic conditions (%) Diabetes 0.07 (0.26) Asthma 0.34 (0.47) Cardiovascular disease 0.22 (0.41) Congestive heart failure 0.01 (0.12) Arthritis 0.09 (0.29) Depression 0.25 (0.43) Osteoporosis 0.02 (0.14) Musculoskeletal disease 0.14 (0.34) Low back pain 0.57 (0.49) Migraine headaches 0.11 (0.31) Gastrointestinal disease 0.00 (0.02) Participation in other programs (%) Life style management§ 0.13 (0.34) Fitness rewards 0.24 (0.43) Miavita 0.08 (0.27) 10,000 steps 0.09 (0.29) Weight management 0.02 (0.14)
Employees Only
P†
DM Nonparticipation (n = 8,677)
Participated in DM (n = 4,226)
P†
45.04 (11.71)
