HEALTH PROMOTION INTERNATIONAL
© Oxford University Press 1999
Vol. 14, No. 1
Printed in Great Britain
Do employees participating in voluntary health
promotion programs incur lower health care costs?
GEORGE HAYNES, TIM DUNNAGAN1 and VINCE SMITH2
Assistant Professor, Consumer Economics, Department of Health and Human Development,
Montana State University, 106 Herrick Hall, Bozeman, MT 59717, 1Assistant Professor, Health
Promotion, Health Promotion Programming, Planning, and Evaluation, Department of Health
and Human Development, Montana State University, 122 Hosaeus Complex, Bozeman, MT 59717
and 2Professor, Agricultural Economics, Agricultural Economics and Economics,
Montana State University, Linfield Hall 307C, Bozeman, MT 59715, USA
SUMMARY
During the past two decades there has been a rapid increase
in the number of wellness activities within public and
private companies. A rationale for implementing worksite
wellness programs has been the assumption that wellness
programming can contain health-related costs. This investigation examined the relationship between health insurance
costs and employee wellness program participation using a
sample of 1757 university employees over a 3-year period.
Based upon empirical models and analytic techniques that
are appropriate for these investigations, the authors suggest
that voluntary wellness programs may face a serious adverse
selection problem in that relatively unhealthy individuals
may self-select into wellness programming. Specifically, the
authors show that employees who participate in worksite
wellness programming incur higher rather than lower health
claims costs.
Key words: cost/benefit; cost containment; health promotion; wellness
INTRODUCTION
During the last two decades, numerous public
(state and federal) and private organizations
have implemented worksite wellness programs.
This growth has been fueled in part by the rapid
increase in health-related costs and the relationship between wellness programming (health
screenings, exercise, etc.) and the containment of
health-related costs. Health researchers demonstrated the foundation for this relationship by
showing the association of health status and
mortality with lifestyle behaviors (Belloc and
Breslow, 1972; Belloc, 1973; Lalonde, 1974; Fries
et al., 1989). Because of this association, many
organizations initiated wellness programming as
a tool to contain health-related costs (Aldana,
1998). The logic was that health promotion
programming would allow participants to enjoy
better health, require health care services less
frequently and use less costly health care
services. Consequently, an outcome of the
employee wellness program would be lower
health-related costs for the organization. This
study examines the impact of employee wellness
programming on health insurance costs in an
environment where participation in wellness
programming is voluntary.
BACKGROUND
Because of the cost containment logic associated
with wellness programming, researchers have
43
44
G. Haynes et al.
investigated the effects of wellness programs
on health care costs by examining health care
expenditures on wellness participants and nonparticipants (Belloc and Breslow, 1972; Erfurt
and Foote, 1984; Pelletier and Lutz, 1988;
Vickery et al., 1988). Other researchers examined
specific interventions, including medical self-care
(Vickery et al., 1988), hypertension screenings
(Erfurt and Foote, 1984), stress management
(Pelletier and Lutz, 1988), smoking cessation
(Bertera et al., 1990), cholesterol screenings
(Wilson et al., 1992), and physical fitness
(Browne et al., 1984). Additional research was
conducted to evaluate the cost-effectiveness of
comprehensive health promotion programs
(Gibbs et al., 1985; Bly et al., 1986; Sciacca, 1986;
Bertera, 1990; Golaszewski et al., 1992).
Typically, comprehensive programs offer employees multiple services such as medical
screenings, physical fitness activities and nutritional interventions. The vast majority of these
investigations related to specific interventions
and comprehensive programming showed that
health promotion programming was associated
with lower health-related costs (Pelletier, 1996;
Aldana, 1998).
The research that has taken place within
worksite settings varies based upon whether
participation was voluntary (Evans et al., 1989),
mandatory (Cady et al., 1985; Steinhardt et al.,
1991) or facilitated through large incentives
(Browne et al., 1984; Gibbs et al., 1985; Sciacca,
1986; Bertera et al., 1990). These policies are
significant because the employee’s reasons for
participating will differ based upon the
organization’s participation policy.
Similarly, researchers have incorporated diverse
techniques in the development of participant
classification systems. For example, many investigators compared employees who participate in
worksite-based wellness programs with employees who do not participate. The criteria used
to classify an employee as a participant varied
across studies. In some assessments, participation
was based upon the completion of health screenings (Gibbs et al., 1985). In others, the selection
criteria was based on the individual’s worksite
assignment (Bly et al., 1986); length of employment and a willingness to follow exercise
prescriptions (Golaszewski et al., 1992); or,
participation in selected health enhancement
activities (Baun et al., 1986). Still other investigators conducted cost analyses based upon
employee health status (Steinhardt et al., 1991;
Kingery et al., 1994). These studies exhibit little
consistency with respect to the methods used
to group employees’ participation in wellness
programming.
Finally, there is little consensus about the
statistical procedures that should be used in
cost-effectiveness studies of worksite health
promotion programs. Numerous authors and
investigators have discussed the difficulties
associated with these studies (Murphy et al.,
1987; Tze-ching et al., 1991; Lynch et al., 1991,
1992; Kingery et al., 1994). It has been recognized
that health care cost data are highly skewed and
violate normality assumptions with respect to the
error term. Consequently, the use of standard
statistical procedures that rely on the normality
assumption is problematic. This study uses a nonlinear tobit statistical algorithm to address this
problem.
EMPIRICAL MODEL
Subjects
The sample used in this investigation was
comprised of 1757 Montana State University
(MSU) employees which represent more than
5000 employee life-years of health insurance
costs data. Approximately 44% (785) of the
subjects were participants in the Montana State
University Employee Program (MSUEWP). The
MSUEWP participants were well educated with
60% of the subjects having at least a bachelor’s
degree. The group was primarily female (60%)
with a group mean age of 45.9 and just over 50%
were classified as non-professional clerical/
service workers. The MSUEWP non-participants
were also well educated with 65% of the subjects
having at least a bachelor’s degree. The group
was primarily male (61%), with a group mean
age of 45.5 and was predominately comprised of
faculty or university administrators (57%). The
population characteristics of the subjects are
detailed in Table 1.
Data
The amount of health care costs incurred by the
individual is affected by many socio-economic
and demographic characteristics, as well as
participation in wellness programs. This study
integrated data from three sources: health
insurance claim files administered by Mutual of
Voluntary health promotion programs
45
Table 1: Population characteristics by employee wellness classification
Item
Proportion submitting a health claim in 1989
Proportion of the employees who are faculty members
Education:
No college
Bachelors degree
Masters degree
Terminal degree
Earnings (maximum earnings, 1989–91), dollars
Age (years)
Male
Dependants (W-4 dependants claimed)
Number of observations
Non-EWP participants
Mean
EWP participants
Meana
0.44
0.57
0.66 *
0.49 *
0.40
0.18
0.17
0.24
26 931
45.5
0.61
0.09
972
0.35 *
0.17
0.20
0.28 *
29 265.00 *
45.9
0.40 *
0.07
785
a
5% level of significance is indicated by the asterisk.
Omaha, human resource files administered by
the Vice President for Administration at MSU,
and employee wellness participation files administered by the Director of the MSUEWP.
Health claims information supplied by Mutual
of Omaha contained data on the amount of
health insurance claims using morbidity and
mortality information (i.e. ICD-9 codes) for each
employee for 3 years from July 1, 1988 through
June 30, 1991. The first year of data (for the
period July 1, 1988 through June 30, 1989) was
utilized as an indicator of previous health care
utilization of each individual in the sample. The
remaining health insurance claim information
(for the period July 1, 1989 through June 30,
1991) was used as the measure of total health
insurance claim amounts.
The MSU human resources database was used
to determine the gender, age, job classification,
level of education, number of dependants, and
earnings for employees. This data set included
information on all individuals who were MSU
employees during the period July 1, 1988 through
June 30, 1991. Any employee who was not
employed at MSU during the 3-year evaluation
period was excluded from the analysis.
Employee Wellness office files were used to
determine whether an employee participated in
health promotion programs. Each individual who
participated in at least one health promotion
activity during the period from 1988 to 1991 was
categorized as an active participant. Wellness
program health promotion activities included
blood, mammogram and colon screening, the
completion of a Health Risk Appraisal and the
completion of a liability form for participating in
exercise classes. However, the data did not
provide a measure of intensity of participation, e.g.
how much an employee exercised. In addition,
employees who participated in health enhancement activities independently, using a health club
or other providers of wellness activities outside
the university, were considered non-participants
even though they were actively participating in
health enhancement programming.
Model
An empirical model was developed to examine
the relationship between health insurance claims
and participation in the MSUEWP. The dependent variable (CLM) was the log of the dollar
value of health care claims for the 2-year period
July 1, 1989–June 30, 1991. The log transformation was conducted to decrease the variance and
more normally distribute the health care claims
scores.
The set of explanatory variables includes the
individual’s participation in any employee wellness activity (EWP), previous health status
(PREVIOUS),
employment
classification
(CLASS), education (ED), earnings (LEARN),
age (AGE and AGE2), gender (GENDER) and
number of dependants (DEPEND). The model is
specified as follows:
CLM = a0 + a1EWP + a2PREVIOUS + a3CLASS
+ a4ED + a5LEARN + a6AGE + a7AGE2 +
a8GENDER + a9DEPEND + e
where:
CLM = log amount of all health claims (7/1/1989–
6/30/1991);
46
G. Haynes et al.
EWP = participation in at least one employee
wellness activity;
PREVIOUS = previous health insurance utilization (7/1/1988–6/30/89);
CLASS = faculty classification (non-professional
service and clerical and faculty/administrator);
ED = level of education (dummy variables are
used for no college, bachelor’s, master’s and
terminal degree);
LEARN = log of maximum earnings during any
1 year from 7/1/1988 through 6/30/1991;
AGE = age from the individuals date of birth to
June 30, 1991;
AGE2 = age squared;
GENDER = male gender;
DEPEND = dummy variable for having any dependants; and,
e = error term.
Each explanatory variable identified an important characteristic of the population which
influences the demand for health care services,
and consequently, claims submitted to the health
insurance program. Classification as an MSUEWP
participant was set as a zero-one dichotomous
variable, which was set equal to 1 if the individual
participated in health promotion activities.
Previous research would suggest that the expected
sign on the coefficient associated with wellness
programming to be negative. That is, employee
wellness participants have lower health insurance
claims than non-wellness participants.
Past health insurance utilization is often an
important predictor of future utilization.
Therefore, the investigators utilized information
through the Mutual of Omaha health insurance
data to assess the employee’s previous health
insurance utilization. The previous utilization
variable PREVIOUS, a 0–1 dummy variable,
identifies individuals who submitted health
insurance claims in fiscal year 1989. Individuals
submitting health claims in fiscal year 1989 were
assumed to be more prominent users of health
insurance benefits than individuals who did not
submit health care claims.
The job classification, educational status,
income, and dependent variables were all needed
because these variables are important in determining health care utilization. Employees were
divided into two employment groups: faculty/
administrators and non-professional clerical/
service employees. Educational status was coded
using dummy variables for no college, bachelor’s,
master’s, and terminal degrees. Earnings were
measured by the highest salary paid over the
2-year period from July 1, 1989 through June 30,
1991. The log of the earnings was used to help
decrease the variance and more normally
distribute the scores.
Finally, the age, age squared, and gender
variables were included in the model. Age is a
significant predictor of health care utilization
because as an individual grows older the use of
curative health services increase. An age squared
term was added to the model to accommodate
the fact that older individuals realize a higher
rate of increase in health care costs than younger
individuals. Gender was also included because
females, particularly females in their childbearing years tend to have more health-related
costs than males. Based upon this rationale, the
investigators included these dependent and
exploratory variables to develop the empirical
model used in this investigation.
ANALYSIS
The researchers utilized a non-linear regression
technique (tobit) that accounts for the fact that a
high percentage of the employees submit no
health insurance claims each year. In evaluations
of health insurance costs, a large number of the
dependent variable observations take on a zero
value and hence, the error term in the estimated
model is not normally distributed due to the
truncation of the health care expenditures at
zero. Tobit statistical estimation procedures
(Tobin, 1958) explicitly recognize that the
dependent variable is not normally distributed at
the limit value, but still use all observations,
including those clustered at the limit value, to
obtain efficient (minimum variance) estimates of
the parameters of the relationship between the
dependent variable and the explanatory variables. Thus, tobit estimation procedures were
used to explore the relationship between health
care claims and the explanatory variables
described in the empirical modeling section of
this manuscript. The details of the tobit statistical
Voluntary health promotion programs
estimation are discussed elsewhere (McDonald
and Moffitt, 1980; Roncek, 1992).
RESULTS
The results of this investigation differ from those
reported in most studies but were extremely
robust for this sample. For example, the results
of the detailed data on health care expenditures
by ICD-9 classification show that wellness
program participants had higher costs than nonparticipants (Table 2). Specifically, wellness
participants had higher average claims for mental
disorders, genitourinary illness, and skin disease.
In addition, wellness participants had a higher
probability of submitting any claim for infections
and parasitic diseases, endocrine (nutritional
and metabolic) illness, mental disorders, nervous system problems, circulatory, respiratory,
genitourinary, complications of pregnancy/child
birth, skin diseases, musculoskeletal/connective
tissue and three general classifications (i.e. illdefined conditions, injury or poisoning and
supplemental factors). Furthermore, wellness
participants had a higher probability of submitting a high-cost claim (claim over $200) for
mental disorders, genitourinary problems, complications of pregnancy/childbirth, connective
tissue and ill-defined conditions.
The non-linear regression results based upon
1757 subjects and over 5000 life-years were
obtained by using the tobit procedure for estimating empirical model are reported in Table 3
and are congruent with the results described in
Table 2. The coefficients associated with the level
of education (as indicated by bachelor’s, master’s
and terminal degrees) and the presence of
dependants in the household were not statistically significant. However, all other variables,
including previous utilization, employment
classification, earnings, age, gender, and employee wellness participation were all statistically
significant. [When reading Table 3, it is important
to note that the parameter estimations only
provide information related to the direction of
the change (positive score relates to higher costs
and a negative score relates to lower costs) and
does not directly provide information related to
the magnitude of the change.]
The results summarized in Table 3 show that
those individuals submitting health care claims
during the fiscal year prior to this study had
significantly higher health care claims. While
47
some of these claims may be carryovers from a
previous time period for the same illness, it is
important to note that submitting a health care
claim in the previous year is an important
determinant of the amount of future health care
expenditures.
In addition, individuals classified as faculty/
administrator incur lower health care claims than
non-professional clerical/service employees.
Moreover, individuals earning higher salaries
have significantly lower health care claims than
those earning lower salaries. This relationship
may exist because individuals with greater
resources (flexible scheduling, money, etc.) are
better able to take appropriate actions to reduce
the probability of experiencing poor health. As a
result, these individuals are likely to have lower
health care costs. However, the reason for this
relationship cannot be determined through this
investigation.
The coefficients associated with the variables
AGE and AGE2 indicate that health care claims
decline until the individual turns approximately
40 years of age. Thereafter, costs begin to rise.
This result provides support to the notion that
adolescents incur higher health care costs than
young and maturing adults and that elderly
adults incur higher health care costs than young
and maturing adults. Also, males in this study
incur lower health care claims than females, in
part because females in their 20s and 30s incur
costs associated with pre- and postnatal care.
Most importantly, the results indicate that
participants in the MSUEWP have higher
average claims than non-participants. Specifically,
the regression results imply that, over the
estimation period, health care claims for participants were approximately $40 higher than those
for non-participants. This estimate is lower than
the simple difference between average costs of
health care claims submitted by EWP participants and non-participants because other factors,
such as personal and demographic characteristics
explain some of this difference.
DISCUSSION
This study has examined the association of
participation in the MSUEWP on health insurance claims costs. Participation in employee
wellness activities offered at MSU is strictly
voluntary. Hence, this study has considered a
group of individuals who actively decided
48
G. Haynes et al.
Table 2: Summary of health insurance claims filed by EWP and non-EWP employees
Item
Claim likelihood
Charges
High claim likelihood
Mean claim totals, dollars
Infections and parasitic diseases
Neoplasm
Endocrine, nutritional, metabolic
Mental disorders
Nervous system
Circulatory
Respiratory
Digestive
Genitourinary
Complications of pregnancy/childbirth
Skin diseases
Musculoskeletal/connective tissue
Congenital anomalies
Conditions originating in perinatal period
Ill-defined conditions
Injury, poisoning
Supplementary factors
External causes of injury/poisoning
Probability of submitting a claim
Infections and parasitic diseases
Neoplasm
Endocrine, nutritional, metabolic
Mental disorders
Nervous system
Circulatory
Respiratory
Digestive
Genitourinary
Complications of pregnancy/childbirth
Skin diseases
Musculoskeletal/connective tissue
Congenital anomalies
Conditions originating in perinatal period
Ill-defined conditions
Injury, poisoning
Supplementary factors
External causes of injury/poisoning
Probability of submitting a high claim in one category
Infections and parasitic diseases
Neoplasm
Endocrine, nutritional, metabolic
Mental disorders
Nervous system
Circulatory
Respiratory
Digestive
Genitourinary
Complications of pregnancy/childbirth
Skin diseases
Musculoskeletal/connective tissue
Congenital anomalies
Conditions originating in perinatal period
Ill-defined conditions
Injury, poisoning
Supplementary factors
External causes of injury/poisoning
Number of observations
a
5% level of significance is indicated by the asterisk.
Non-EWP participants
Mean
EWP participants
Meana
0.28
1131
0.17
0.49 *
1377
0.29 *
$81.50
119.13
61.56
59.85
55.43
102.49
94.97
104.92
79.97
20.51
11.41
177.73
1.82
11.57
65.96
65.04
16.79
0.00
$19.30
74.36
68.77
151.83 *
39.30
116.69
118.66
109.14
165.31 *
44.69
28.54 *
204.58
1.33
0.00
108.29
78.16
48.11
0.09
0.04
0.03
0.05
0.05
0.07
0.07
0.12
0.06
0.09
0.01
0.08
0.13
0.01
0.00
0.10
0.08
0.02
0.00
0.10 *
0.05
0.11 *
0.13 *
0.12 *
0.10 *
0.24 *
0.08
0.25 *
0.02 *
0.17 *
0.22 *
0.01
0.00
0.18 *
0.12 *
0.05 *
0.00
0.00
0.01
0.02
0.03
0.02
0.03
0.04
0.03
0.02
0.00
0.00
0.04
0.00
0.00
0.02
0.02
0.01
0.00
972
0.00
0.02
0.02
0.07 *
0.02
0.03
0.04
0.03
0.06 *
0.01 *
0.01
0.07 *
0.00
0.00
0.05 *
0.02
0.02
0.00
785
Voluntary health promotion programs
Table 3: Determinants of the amount of health
insurance claims submitted in 1989 and 1990.
Dependent variable = log of amount of total
health insurance claims (tobit regression)
Item
Parameter standard
estimate error
Intercept
EWP participant
Health insurance claim, 1989
Faculty classification
Bachelors
Masters
Terminal degree
Log of earnings
Age
Age squared
Male gender
Dependants
Scale
–2 log likelihood
–40.6215
6.7516
1.6082
0.4143
6.9867
0.4586
–2.1403
0.6706
–0.8699
0.6140
0.3752
0.7721
0.3743
0.8379
–4.0644
0.6297
–0.3087
0.1654
0.0043
0.0017
–1.7509
0.4693
0.2886
0.7096
6.6614
0.2143
p-value
0.0001
0.0001
0.0001
0.0014
0.1565
0.6270
0.6551
0.0001
0.0620
49
health-related problems are more likely to
participate in employee-sponsored wellness
programs than other individuals. This relationship would be enhanced in established programs
because problematic employees are more likely
to be referred into worksite programs.
Second, the investigators used a theoretical
framework and modeling procedures that were
appropriate for this type of investigation.
Specifically, the use of a tobit analysis and
exploratory variables such as previous utilization
and select demographic variables were based
upon a conceptually sound framework and
represent relevant empirical and analytical
contributions to this area of study. The model
used by the researchers demonstrated that the
overwhelming impact of higher health care
claims can be determined by previous utilization
followed by income, a non-professional service/
clerical job classification, female gender, participation in the MSUEWP, and age (older
employees).
0.0126
0.0002
0.6843
2714
whether or not to participate in MSUEWP
activities. This section discusses the study’s
important results, limitations, and identifies
potential avenues for future research.
Important results
The contribution this investigation has made to
the study of wellness participation and health
care costs containment is two-fold. First, based
upon 1757 subjects and over 5000 life-years the
investigators found a robust association that is
contrary to the majority of studies that have been
conducted in this area. That is, wellness participants are significantly associated with higher
health insurance costs than non-wellness participants. The investigators did not find these results
surprising because in an environment where the
individual voluntarily chooses to participate in
worksite wellness programming, this result is
expected. That is to say, individuals having more
Limitations
The primary limitation in this study was that the
investigators did not have a satisfactory proxy
which controls for possible adverse-selection bias
and an accurate measure for wellness participation. Specifically, the fact that participation in the
MSUEWP is voluntary may introduce an
adverse-selection bias problem, in that individuals with adverse health conditions (and
higher health care costs) ‘self-select’ into the
MSUEWP. The control of possible adverseselection within voluntary wellness programs
needs to be addressed in future studies.
In addition, better measures are needed to
classify the health behaviors of wellness and nonwellness participants. Specifically, the classification procedure used in this study did not allow
the investigators to measure intensity of participation in health-promoting behaviors. In fact, if
employees were not using the MSUEWP, implicitly they were assumed not to be engaged in
any other health enhancement activity. Furthermore, no assessment was made about the intensity of participation for the wellness participants
and non-participants. It is quite possible that the
intensity of participation is a critical factor in the
evaluation of health insurance costs and wellness
participation. In future studies, information about
the individual’s health promotion activities, both
within and outside the work environment needs
50
G. Haynes et al.
to be considered as an important exploratory
variable.
Further research is needed to more thoroughly
assess whether wellness programs generate
economic gains or losses. This research should
incorporate conceptually sound modeling and
analytical techniques. Furthermore, investigators
should use realistic assessments of the health
status and health care costs of program participants had they not joined the wellness program
because, in this context the right question is not
whether participants use more health care
services than non-participants. Rather, it is
whether or not wellness programs reduce the
health care costs of participants below those that
they would have incurred in the absence of the
program.
Address for correspondence:
George Haynes
Department of Health and Human Development
Montana State University
106 Herrick Hall
Bozeman, MT 59717
USA
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