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Risks 2013, 1(1), 34-42; doi:10.3390/risks1010034

Article
Understanding the “Black Box” of Employer Decisions about Health Insurance Benefits: The Case of Depression Products
Kathryn Rost 1,*, Airia Papadopoulos 1, Su Wang 1 and Donna Marshall 2
1
Department of Mental Health Law and Policy, College of Behavioral and Community Sciences, 13301 Bruce B. Downs Blvd., University of South Florida, Tampa FL 33612, USA; E-Mails: asp@usf.edu (A.P.); wangs@usf.edu (S.W.)
2
Colorado Business Group on Health, 12640 West Cedar Drive, Lakewood, CO 80228, USA; E-Mail: donna.marshall@cbghealth.org
*
Author to whom correspondence should be addressed; E-Mail: kmrost@usf.edu; Tel: 1-720-231-3679; Fax: 1-813-974-4602.
Received: 7 March 2013; in revised form: 20 May 2013 / Accepted: 21 May 2013 /
Published: 29 May 2013

Abstract

: In a randomized trial of two interventions on employer health benefit decision-making, 156 employers in the evidence-based (EB) condition attended a two hour presentation reviewing scientific evidence demonstrating depression products that increase high quality treatment of depression in the workforce provide the employer a return on investment. One-hundred sixty-nine employers participating in the usual care (UC) condition attended a similar length presentation reviewing scientific evidence supporting healthcare effectiveness data and information set (HEDIS) monitoring. This study described the decision-making process in 264 (81.2%) employers completing 12 month follow-up. The EB intervention did not increase the proportion of employers who discussed depression products with others in the company (29.2% versus 32.1%, p > 0.10), but it did significantly influence the content of the discussions that occurred. Discussion in EB companies promoted the capacity of a depression product to realize a return on investment (18.4% versus 4.7%, p = 0.05) and to improve productivity (47.4% versus 25.6%, p = 0.06) more often than discussions in UC companies. Almost half of EB and UC employers reported that return on investment has a large impact on health benefit decision-making. These results demonstrate the difficulty of influencing employer decisions about health benefits using group presentations.
Keywords:
: health benefits; insurance; depression; employers; return on investment; productivity; absenteeism; collaborative care

1. Introduction

Health insurance purchasers need to actively support efforts to enhance primary care depression treatment to translate science to practice [1], because intervention models that improve outcomes [2,3] also increase the direct costs of care [4], at least initially [5]. Employers represent a substantial segment of purchasers, offering health insurance coverage to 88.8% of individuals in the workforce [6]. Employers suffer when 7.6% of the workforce suffers a major depressive episode [7], because the illness substantially reduces work functioning [8,9]. Employers interested in ensuring their workers receive these intervention models can purchase depression products from their health plans or disease management companies [10]. Randomized trials demonstrate that these models improve absenteeism and productivity at work [11] sufficiently for selected employers to realize a return on investment [12] with competitive pricing.

The objective of the study is to test a randomly assigned intervention to influence employers to purchase depression products for their employees. In the trial, 156 evidence-based (EB) condition employers attended a presentation describing the scientific evidence supporting depression products, and 169 usual care (UC) condition employers attended a presentation describing the scientific evidence supporting healthcare effectiveness data and information set (HEDIS) monitoring. The EB presentation provided employers specific arguments to support the purchase of a depression product, including company-specific estimates derived from the scientific literature for return on investment [13]. The objective of this study is to test EB intervention impact on decision-making about depression products in the year following the presentation. Key content areas included arguments made for and against the purchase of a depression product, triggers to the future purchase of a depression product and the importance of return on investment in the company’s health benefit decision-making.

2. Results and Discussion

2.1. Sample

The sample consisted of 264 employers who responded at 12 month follow-up (81.2% of the baseline sample). The organizational characteristics of responding employers are described in Table 1.

Table Table 1. Organizational characteristics (n = 264).

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Table 1. Organizational characteristics (n = 264).
Size
% small (100 to 500 employees)35.2
% medium (501 to 2,500 employees)33.0
% large (2,501 plus employees)31.8
Type
% for-profit54.2
% not-for-profit26.3
% public sector19.5
Company age (SD)73.6 (47.5)
Health plan carriers (SD)1.9 (2.1)
Insurance Risk
% fully insured24.3
% self-insured51.0
% mixture of fully and self-insured24.7

Although the EB and UC cohorts contained companies of statistically comparable size (p > 0.10), large employers were less likely to complete 12 month follow-up (p < 0.05). Thus, we controlled for company size in the comparisons we present in this manuscript. At 12 months, 29.2% (n = 38) of EB employers reported internal discussion of depression products by email exchange, telephone call, in-person meeting or group meeting with other employees of the organization only, compared to 33.1% (n = 43) of UC employers (p = 0.82).

2.2. Arguments Made in Support of and Against Depression Product Purchase

Employers reporting internal discussion were asked about arguments made in support of and against depression product purchase. As Table 2 demonstrates, EB and UC employers reported their discussions included generally similar arguments in support of purchasing a depression product. Given that the EB presentation emphasized the scientific evidence demonstrating that depression products increase productivity and provide a return on investment, it was not surprising that these arguments occurred more often in EB than UC discussions.

One EB employer indicated that “associates who are directly or indirectly impacted by depression need some type of assistance to help them through the depression” and then went on to say that “in addition to direct health care costs…there can be lower productivity for the associate and co-workers.” Another EB employer indicated that offering a depression product “benefits us as the employer because the employee can get the help they need to get well and ultimately return to work as a major contributor.” A third EB employer succinctly stated that having a product would “keep overall costs down and production up.” UC employers echoed these arguments when they noted a “healthier workforce and less absenteeism (equals) greater productivity,” and the importance of “(keeping) employees healthy” in order to “keep medical costs from going up due to untreated depression.”

Table Table 2. Arguments made in support of depression product purchase (n = 81).

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Table 2. Arguments made in support of depression product purchase (n = 81).
“What kinds of arguments were made in support of purchasing a depression product?”Percentage of EB Employer Responses by Theme (n = 38)Percentage of UC Employer Responses by Theme (n = 43)
Greater productivity**47.4%25.6%
Want to provide for employee’s needs29.0%20.9%
Consider depression to be valuable and a needed product26.3%16.3%
Healthy workforce21.1%20.9%
Company costs/return on investment (ROI)*18.4%4.7%
Concerns about health care costs15.8%27.9%
Having a depression product helps meet employer’s goals7.9%11.6%
A product is already available5.3%4.7%
No arguments made in support5.3%11.6%
Co-morbid physical and mental health issues0%4.7%

EB, evidence-based; UC, usual care; *p = 0.05, **p = 0.06.

As Table 3 demonstrates, the arguments made against a depression product purchase were generally comparable in EB and UC discussions.

Table Table 3. Arguments made against depression product purchase (n = 81).

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Table 3. Arguments made against depression product purchase (n = 81).
“What kinds of arguments were made against purchasing a depression product?”Percentage of EB Employer Responses by Theme (n = 38)Percentage of UC Employer Responses by Theme (n = 43)
No arguments made47.4%30.2%
Cost or benefit to employer31.6%46.5%
Confidentiality concerns18.4%11.6%
Value is not certain15.8%7.0%
It is (or should be) covered by health insurance7.9%7.0%
Not needed or used by employees7.9%20.9%
Government mandate0%4.7%
Too stigmatized an issue for employer to offer for employees0%4.7%
Not employer’s business0%2.3%
Less valuable than other health conditions0%2.3%
Not sure how to publicize0%2.3%

EB, evidence-based; UC, usual care

One EB employer reflected the information in the presentation by noting “this [purchasing a depression product] is a no brainer.” Contradicting information in the presentation, a second EB employer commented that “depression products are so person to person specific [that] it is impossible to gauge what will be helpful and what will not” and a third commented “does anything work?” Many UC employers, who had not been educated about the product’s return on investment indicated that “cost” was an argument made against depression product adoption without elaborating; however, one employer considered the benefits of a depression product to be a “poor performer [with regard to] costs.”

2.3. Triggers to Depression Product Purchase

All employers not currently in the purchasing process were asked what would trigger their organization’s purchase of a depression product. As Table 4 demonstrates, EB and UC employers reported similar triggers might stimulate the purchase of a depression product. The most commonly reported trigger in both groups was a visible episode of depression in the company, often referred to as a “poster child.” Other common triggers were company-specific data on lost productivity, (further) increases in healthcare costs, management change and economic/financial improvement.

Table Table 4. Triggers to depression product purchase (n = 246).

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Table 4. Triggers to depression product purchase (n = 246).
“Your organization has not purchased a depression product. Imagine that two years from now, your organization decided to purchase a depression product. Can you describe the changes that must have occurred for your employer to make that decision?”Percentage of EB Employer Responses by Theme (n = 122)Percentage of UC Employer Responses by Theme (n = 124)
Visible (public) depression episode or otherwise demonstrated need25.4%20.2%
Data showing lost productivity or poor performance21.3%17.7%
Increased health care costs17.2%14.5%
Change in/support from management10.7%13.7%
Economy/company finances have to change11.5%5.7%
Demonstrated return on investment (ROI) or cost-benefit analysis8.2%5.7%
None, does not apply or no response7.4%5.7%
Better products made available6.6%2.4%
Need additional information, understanding or training5.7%4.8%
Products should be part of health package4.9%7.3%
Company has or is taking steps to implement program3.3%4.8%
No need for product/need would have to be demonstrated3.3%6.5%
Not sure2.5%2.4%
Requests from employees1.6%2.4%
Change in provider network1.6%0%
Confidentiality1.6%0%
Want to offer support to employees1.6%0%
Other more urgent health issues must be resolved first0.8%0.8%
Government mandate0.8%1.6%
Better relationship with HMO/providers0.8%0%
A depression product should be simple to use0.8%0%
Do not want to appear to encourage treatment for depression0%0.8%

EB, evidence-based; UC, usual care

One EB employer noted: “huge increase in costs related to depression—or a tragedy like the suicide of one of our employees due to depression.” Another responded: “obvious loss of productivity clearly tied to incidents of prolonged depression.” Despite having received an estimate of the return on investment for his/her company for a depression product, a third EB employer said: “the demand for the product and impact on productivity must have been identified and the cost of now providing the product estimated to be higher than the cost.” One UC employer responded: “a trend where employees are absent more frequently using short-term disability pay with the diagnosis of depression.” Another said: “We are 100% self-insured. Until we recover financially, we will not be able to purchase any product. It is purely financial.” A third said “depression would have to be in our top five chronic conditions for us to purchase a depression depression management product.”

2.4. Influence of Return on Investment in Benefit Decision-Making

All employers were asked about how return on investment impacted benefit decision-making. Reflecting the organizational comparability of EB and UC companies, it is not surprising the two groups reported that return on investment had a similar impact on decision-making about additional health benefits. As shown in Table 5, close to half of employers in each group reported return on investment had a large impact, while 20% indicated it had no impact or failed to answer the question.

Table Table 5. Influence of return on investment in benefit decision-making (n = 257).

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Table 5. Influence of return on investment in benefit decision-making (n = 257).
“Some health benefits/initiatives have a positive return on investment to the organization, while others do not. During the past 12 months, to what degree did return on investment influence your decision-making about additional health benefits/initiatives?”Percentage of EB Employer Responses by Theme (n = 127)Percentage of UC Employer Responses by Theme (n = 130)
Large influence/this is an important issue44.1%45.4%
None/did not answer21.3%14.6%
Some influence19.7%17.7%
Not sure11.0%16.9%
Hard to calculate, but trying to determine6.3%6.2%
Does not apply3.9%6.9%
Hard to calculate and not trying to determine3.9%2.3%
Have other priorities now2.4%3.9%

EB, evidence-based; UC, usual care

2.5. Discussion

Approximately 30% of employers in both the EB and UC groups raised the idea that their companies should consider depression products in internal discussions in their organizations. From the glass half empty perspective, two out of every three EB employers failed to ‘bring home’ any ideas from a two hour presentation. While the presentation contained one role play between the presenter and the audience, it is possible that future interventions could be strengthened by utilizing more targeted role plays to increase employer skill/comfort to lead these discussions. However, we suspect that EB employers who chose to keep silent speak volumes about their company’s interest in improving depression treatment in the workforce. Earlier interest may have been “knocked off the radar screen” by the considerable retooling that healthcare reform required from already overstretched health benefit professionals. From the glass half full perspective, the intervention appears to have equipped committed health benefit professionals to be stronger advocates for depression products. Almost half of EB discussions noted that depression products can improve productivity; however, that message may be already circulating among health benefit professionals, as one quarter of UC discussions recognized that as an advantage. Also encouraging is that almost half of participating employers recognized that return on investment had a large influence on new benefits. Our anecdotal data indicate that future research is needed to deconstruct their understanding of return on investment and the level of evidence necessary to establish it.

Employers provided unanticipated perspectives on company changes that would trigger the purchase of a depression product. While disappointing, it probably should not be surprising that a visible episode of depression in the workforce (“poster child”) was the most often reported trigger. Visible episodes of depression are rarely evident in the workplace, because depression is more likely to erode performance “in the cubicle” than in dramatic venues. More surprising was the finding that company-specific data on poor performance could act as a trigger. Rather than accept findings from “somebody else’s company,” employers may believe that their company is not beset by productivity loss when employees become depressed. While this may be true in some circumstances, national studies [8,9] suggest that reduced productivity is the rule, not the exception.

These findings are limited by the purposive sample we studied. Employers represent a group of potentially early innovators with sufficient interest in the topic to volunteer to participate in a longitudinal study. Because it should be easier to persuade this group than a nationally representative sample, we do not think this limitation reduces our certainty about the challenges of influencing employer benefit decisions using group presentations. Thus, we encourage future researchers to develop more innovative interventions to motivate employers to translate science to benefit design.

3. Experimental Section

This manuscript utilizes a cross-sectional design in analyzing the second wave of a three wave study. The design of the three wave study has been previously published [14]. In brief, the research team collaborated with 21 National Business Coalition on Health regional coalitions [15] and 12 related professional associations. Regional coalitions/associations recruited health benefit professionals (referred to in this manuscript as employers) from their membership, who indicated an interest in depression in the workplace. The data analyzed for this paper are derived from 5 open-ended questions embedded in a 57-item web-based survey completed by 264 employers (81.2%) approximately 12 months after the presentations. The second author developed a coding scheme to capture the major themes for each item before coding each response. Respondents frequently provided answers that identified more than one theme, so percentages do not add up to 100%. A second analyst coded 20% of responses to the first three items independently, demonstrating an average inter-rater agreement of 76% or higher. Because EB and UC employers reported similar organizational characteristics at baseline and were comparably likely to complete 12 month follow-up, we attribute the differences we observed to the different presentations in which EB and UC employers participated.

4. Conclusions

In summary, the EB intervention was not successful in increasing the internal discussion of depression products, but it did influence the content of discussions that occurred. Discussion in EB companies promoted the capacity of depression products to improve productivity and to realize a return on investment more often than discussion in UC companies. Almost half of EB and UC employers reported that return on investment has a large impact on health benefit decision-making. These results demonstrate the difficulty of influencing employer decisions about health benefits using group presentations. Alternative intervention strategies need to be developed and tested.

Acknowledgments

The authors wish to acknowledge the organizations that assisted the research team, including the National Business Coalition on Health, Mental Health America, the Business Health Care Group, Dallas Fort Worth Business Group on Health, Employer Coalition for Healthcare Options, Employers’ Coalition on Health, Employers' Health Coalition, Employers Health Purchasing Corporation of Ohio, Indianapolis Business Group on Health, Louisiana Business Group on Health, Maine Health Management Coalition, Memphis Business Group on Health, Mental Health America of Georgia, Mental Health America of Greater Houston, Midwest Business Group on Health, Nevada Health Care Coalition, Northeast Business Group on Health Oregon Coalition of Health Care Purchasers, Pacific Business Group on Health, Savannah Business Group, South Carolina Business Coalition on Health, St. Louis Area Business Health Coalition, State and Local Government Benefits Association, Tri-State Health Care Coalition, Virginia Business Coalition on Health and Volunteers of America of Greater New Orleans. The authors wish to express their deep appreciation to Kristen Berg, JD, Susan Henke, Patty Shamrock, MSW, Ben Shearer, PhD, and Shannon Vaughn, JD, for their many contributions to the project. This project was funded by the National Institute of Mental Health MH76277.

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