ORIGINAL CONTRIBUTION
Relationship Between Modifiable Health
Risks and Short-term Health Care Charges
Nicolaas P. Pronk, PhD
Michael J. Goodman, PhD
Patrick J. O’Connor, MD, MPH
Brian C. Martinson, PhD
S
EDENTARY LIFESTYLE, OBESITY,
and tobacco use are strongly related to a variety of long-term adverse health outcomes, and over
a lifetime, have significant costs to society.1,2 In formulating clinical policy and
making resource allocation decisions,
health care delivery organizations need
to know the short-term relationship of
modifiable health risks to health care
charges. Managed care organizations
(MCOs) attempt to maximize the value
obtained from each health care dollar
spent. Because adverse, modifiable
health risks are detrimental to health and
contribute to higher costs of health care,
many MCOs may be interested in devoting resources to initiatives that favorably affect the health-related behaviors of their members. This is particularly
true in light of recent reports documenting the efficacy of behavior change intervention strategies,3 lower health care
charges with risk reduction,4 and a good
return on investments in healthrelated behaviors.5 However, little financial data are available to guide resource allocation decisions, or to
estimate the potential financial impact
of programs that may modify health
risks. Moreover, to our knowledge, no
report has adequately controlled in the
analysis for chronic conditions, which
may confound the relationship of health
risks to charges.
In this study we analyze the shortterm cost to health plans of modifiable health risks. These data provide an
Context If physical inactivity, obesity, and smoking status prove to contribute significantly to increased health care charges within a short period of time, health plans
and payers may wish to invest in strategies to modify these risk factors. However, few
data are available to guide such resource allocation decisions.
Objective To examine the relationship of modifiable health risks to subsequent health
care charges after controlling for age, race, sex, and chronic conditions.
Design, Setting, and Participants Cohort study of a stratified random sample of
5689 adults (75.5% of total sample of 7535) aged 40 years or older who were enrolled in a Minnesota health plan and completed a 60-item questionnaire.
Main Outcome Measure Resource use as measured by billed health care charges
from July 1, 1995, to December 31, 1996, compared by health risk (physical activity,
body mass index [BMI], and smoking status).
Results The mean annual per patient charge in the total study population was $3570
(median, $600), and 15% of patients had no charges during the study period. After
adjustment for age, race, sex, and chronic disease status, physical activity (4.7% lower
health care charges per active day per week), BMI (1.9% higher charges per BMI unit),
current smoking status (18% higher charges), and history of tobacco use (25.8% higher
charges) were prospectively related to health care charges over 18 months. Neversmokers with a BMI of 25 kg/m2 and who participated in physical activity 3 days per
week had mean annual health care charges that were approximately 49% lower than
physically inactive smokers with a BMI of 27.5 kg/m2.
Conclusions Our data suggest that adverse health risks translate into significantly
higher health care charges within 18 months. Health plans or payers seeking to minimize health care charges may wish to consider strategic investments in interventions
that effectively modify adverse health risks.
JAMA. 1999;282:2235-2239
estimate of excess costs incurred by
health plans that do nothing to influence modifiable health risks, and may
assist health plans in deciding whether
strategic investments to modify certain health risks in members is a wise
use of scarce resources.
METHODS
Study Subjects
The study was approved by the HealthPartners Institutional Review Board and
conducted at HealthPartners, a Minnesota health plan with both owned and
contracted clinics. All patients who
were aged 40 years or older and enrolled on December 15, 1994, were po-
©1999 American Medical Association. All rights reserved.
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www.jama.com
tential subjects for the study. These individuals were classified using the
International Classification of Diseases,
Ninth Revision, Clinical Modification
(ICD-9-CM) and pharmacotherapy databases as having 1 of 4 chronic conditions. A diagnosis of diabetes mellitus was assigned if the patients had 2
or more ICD-9 codes 250.xx, or a filled
prescription for a diabetes-specific drug
such as insulin, a sulfonylurea, or a biAuthor Affiliation: HealthPartners Center for Health
Promotion and HealthPartners Research Foundation,
Minneapolis, Minn.
Corresponding Author and Reprints: Patrick J.
O’Connor, MD, MPH, HealthPartners Research Foundation, 8100 34th Ave S, Minneapolis, MN 554401309 (e-mail: [email protected]).
JAMA, December 15, 1999—Vol 282, No. 23
2235
COST OF MODIFIABLE HEALTH RISKS
Table 1. Comparison of Respondents and
Nonrespondents*
ResponNonredents
spondents
P
(n = 5977) (n = 1558) Value
47.2
55.1
.001
Male
,.001
Age, mean, y
59
54
Staff clinic
members
44.3
37.9
.001
36.7
31.5
40.4
31.6
.005
31.8
27.9
Chronic
conditions†
0
1
$2
*Values are percentages unless otherwise indicated. Values are not weighted.
†Presence of each of 4 chronic disease conditions determined by administrative records review of International
Classification of Diseases, Ninth Revision, Clinical Modification codes and/or additional information related to
pharmacotherapy. Conditions included diabetes mellitus, dyslipidemia, hypertension, and coronary heart
disease.
guanide in 1994. Heart disease was assigned if the patients had 1 or more
ICD-9 codes 412, 413.9, 429.2, or 428.0
in 1994. Hypertension was assigned if
the patients had 1 or more ICD-9 codes
401, 401.1, or 401.9 in 1994. Dyslipidemia was assigned if the patients had
an ICD-9 code of 272.4 in 1994. A more
detailed description of the identification of patients with specific conditions and the sensitivity, specificity, and
positive predictive value of this method
has been published.6
From 158 415 patients with none of
the 4 conditions, a random sample of
3000 members (1.89%) was selected.
From 34 159 patients who had 1 chronic
condition, a random sample of 2500 patients (7.3%) was selected. From 7571
patients who had 2 or more of the
chronic conditions, a random sample of
2500 (33%) was selected. Hence, the total study population included a stratified random sample of 8000 individuals aged 40 years or older. In August
1995, a mailed survey was administered and 533 subjects who were unable to complete it due to death, disenrollment, or language problems were
excluded. In addition, 159 proxy respondents were excluded from all analyses.
Following postcard reminders at 7 days,
a second mailing to nonrespondents at
day 21, and telephone follow-up, data of
a total of 5977 respondents (represent2236
ing 79% of the total sample [5977/
7535]) were available for analysis.
Data Definitions
The 60-question survey instrument included items on demographics, health
status, use of preventive services, modifiable health risks, and readiness to
change modifiable health risks. The core
of the survey items was adapted from the
Centers for Disease Control and Prevention’sBehavioralRiskFactorSurveillance
System, which has reliability coefficients
for behavioral risk factors above 0.70.7
Health care charges billed from July
1, 1995, to December 31, 1996, were
used to measure relative resource use,
and were gathered from the HealthPartners claims system. Each encounter
in either MCO-owned or MCO-contracted clinics generated such a claim.
Important independent variables included age, race, sex, and chronic disease status. Prior research has shown
that health care charges are associated
with these variables before and after adjustment for functional health status
and other factors.8,9 Age and sex were
obtained from MCO administrative databases. Age was calculated in years
from date of birth to the date of the initial survey. Chronic disease status was
determined based on 1994 data.
Body mass index (BMI) was calculated as self-reported body weight in kilograms divided by self-reported height
in meters squared, and was centered on
its mean value. Physical activity was assessed via self-report and quantified in
relation to recommended guidelines.10
In addition, physical fitness was predicted using self-reported variables according to procedures outlined by Ainsworth et al.11 Respondents who reported
ever having smoked at least 100 cigarettes and who indicated they currently smoked were defined as current
smokers. Those who reported ever having smoked at least 100 cigarettes but
denied currently smoking were defined as former smokers.
Analytic Model
We used a conditional probability (2-part
model) approach to study charges.12 This
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approach recognizes that charges are the
product of the probability of utilization
multiplied by the expected level of utilization conditional on positive utilization.
The statistical properties of each model
were systematically assessed in nested
fashion. First, the significance of each
individual partial regression coefficient
was assessed using a t test. This tested
the hypothesis of whether an individual
variable was related to variation in health
care charges. Collinearity was assessed
using variance inflation factors. In the
semilogarithmic models, none of the
variance inflation factors were greater
than 1.20, suggesting that collinearity
was not a significant problem in these
models. Second, we analyzed residuals
to be sure the distributional assumptions
of the semilogarithmic linear regression
were met. Third, overall goodness of fit
was measured with the coefficient of determination, the adjusted r2. Since the
sample size was fixed for each model, the
denominator used to calculate r2 was
fixed, allowing direct comparison of
semilogarithmic models. Overall logistic model fit was assessed using the
Hosmer-Lemeshow x2 test.13 In the logistic regression, the performance of an
individual coefficient was tested using
a x2 test.
The sampling structure of the survey
required that we weight each observation based on its sampling probability to
obtain population estimates. This was
done in all multivariate analyses using
standard methods.13 We further tested a
generalized linear model assuming a
gamma distribution and log link, which
produced essentially identical results.
Analyses were restricted to individuals
with complete responses on all analysis
variables. Results from the semilog model
were retransformed using standard methods to present estimated effects in terms
of mean charges in dollar units.14
RESULTS
Of the 5977 subjects (79%) who responded to the 1995 survey, 5689 had
complete data on all study variables, and
provide the basis of this report. Characteristics of survey respondents and nonrespondents are shown in TABLE 1.
©1999 American Medical Association. All rights reserved.
COST OF MODIFIABLE HEALTH RISKS
Health care charges were highly skewed,
with a small proportion of the population accounting for a large proportion of
expense. The upper quintile of subjects
accounted for 86% of total charges and
the upper decile accounted for 71% of
total charges. The median annual charge
in our total study population was $600
(interquartile range, $151-$2080) compared with a mean (SD) of $3570
($12 823). Also, a significant proportion (15%) of study subjects had no
medical encounters or charges during the
18-month study period.
The independent variable with the
most missing data was BMI. Missing data
precluded the calculation of BMI for 229
study subjects (198 users). Among those
with health care charges, those with
missing BMI had higher charges when
measured on the log scale (P,.001),
were older (P = .006), more likely to be
men (P,.001), and more likely to be
hospitalized (P = .005). The likelihood
of health care charges did not differ between those with and without missing
data for BMI.
TABLE 2 shows characteristics of
study subjects with and without health
care charges during the 18-month study
period. Those with no health care
charges were significantly younger,
more likely to be men, more likely to
be current smokers, and had significantly less chronic disease.
In models predicting probability of
medical charges, each 10-year increase
inageresultedina32%(P,.001)increase
in the odds of nonzero medical charges.
Men were 44% (P,.001) less likely to use
medical services than women. Other predictors examined in this model, including race, diagnoses of diabetes or heart
disease, BMI, physical activity, and smokingstatus,werenotpredictiveoftheprobability of having health care charges.
In all models, we introduced blocks
of variables in the following order: demographic characteristics, chronic diseases, modifiable health risks (physical activity, obesity, and smoking), and
interactions. Acceptable measures for
alcohol intake and nutrition were not
available for inclusion in this analysis.
We assessed a limited set of 2-way in-
teraction terms, and none were found
to be significant. Measurements of hypertension and lipid disorders were
omitted since models including these
measurements consistently indicated
that they did not predict medical
charges and did not improve model fit.
TABLE 3 shows the combined results of a 2-part model predicting the
presence of charges in the total sample,
and the log of medical charges for those
with positive, nonzero log charges. For
the modifiable health risks, medical care
charges for study subjects with no days
of physical activity were approximately 4.7% higher than charges for
those who were physically active 1 day
a week. Each 1-unit increase in BMI was
associated with a 1.9% increase in medical charges. Current smokers had medical care charges 18% higher than neversmokers and former smokers had
medical care charges 25.8% higher than
never-smokers did. The final column
in Table 3 demonstrates the relationship of these and other risk factors with
annualized median health care charges.
Table 2. Characteristics of Study Subjects
With and Without Health Care Charges*
Age, y†
Body mass index,
kg/m2†
Physical activity†‡
Fitness†§
Dyslipidemia
Diabetes mellitus\
Hypertension\
Heart disease\
Male
White
Current smoker
Former smoker
Without
With
P
Charges Charges Value
55.59
61.00
,.001
27.16
27.46
.09
2.68
29.87
16.28
13.58
29.16
13.23
57.38
94.03
19.79
41.33
2.65
27.11
20.87
19.57
41.59
21.61
46.47
94.56
13.42
43.85
.73
,.001
.002
.001
.001
.001
.001
.53
.001
.17
*Values are based on data from 5689 subjects except for
fitness in which data from only 4030 subjects was used.
Values are percentages unless otherwise indicated. Results are not weighted.
†Values are means.
‡Data based on subjects’ activity in the days during the
prior week.
§Predicted oxygen consumption per unit time expressed
as milliliter per kilogram per minute. Data based on selfreported physical activity behavior and demographic
characteristics and calculated using the prediction equation by Ainsworth et al.11
\Presence of each chronic disease condition determined
by administrative records review of International Classification of Diseases, Ninth Revision, Clinical Modification codes and/or additional information related to pharmacotherapy.
Table 3. Combined Multivariate Results From Models Predicting Presence and Level
of Health Care Charges*
Covariates
Age
Combined
Effect on
Charges, %†
3.7
Clinical Interpretation
An additional year of age yields a 3.7% increase
in median charges
Annual
Effect on
Median
Charges, $‡
22.35
Male
−39.0
Males have median charges 39% lower than
females
−234.00
White
−27.2
−163.23
Diabetes mellitus
137.1
Heart disease
150.3
Whites have 27% lower median charges than
nonwhites
Members with diabetes have 137% higher
charges than nondiabetic members
Median charges are 150% higher for members
with heart disease
A 1-unit increase in body mass index yields a
1.9% increase in median charges
An additional day of physical activity (above
zero) yields a 4.7% reduction in median
charges
Current smokers have 18% higher median
charges than nonsmokers
Former smokers have 26% higher median
charges than never-smokers
Body mass index
1.9
Physical activity§
−4.7
Current smoker
18.0
Former smoker
25.8
822.81
901.60
11.26
−27.99
107.81
154.86
*Results derived from combining the results from a multivariate logistic regression predicting presence of charges and
a multivariate least-squares regression predicting log of charges, among those having charges. For the logistic model,
effects were significant for age and male sex at P,.01. All other effects were not statistically significant. All effects in
the least-squares regression were significant at P,.01. Direct results of these 2 models are available from the corresponding author.
†Effects assessed by combining the odds of charges and effects on charges.
‡Assessed among members with and without charges. Median annual charges for the total group were $600.00.
§Data based on subjects’ activity in the days during the prior week.
©1999 American Medical Association. All rights reserved.
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JAMA, December 15, 1999—Vol 282, No. 23
2237
COST OF MODIFIABLE HEALTH RISKS
Table 4. Bias-Corrected Estimates of Mean
Annual Health Care Charges for Hypothetical
Members With Nonzero Charges and
Selected Profiles of Modifiable Health Risks*
Subject
Profile†
White woman
White man
LowHighRisk
Risk
Profile‡ Profile§ Difference
3993.95 5939.51
3122.65 4643.78
1945.56
1521.13
Nonwhite woman 5266.32 7831.69
2565.37
Nonwhite man
2005.72
4117.45 6123.17
*All values are in US dollars. To correct estimates for bias
resulting from retransformation from log dollars to dollars, the Duan14 smearing method was used. Estimates may reflect selection bias since they are based
on a regression predicting log charges only among those
having charges. Overall mean charges for those with
charges was $4201.10.
†Age 60 years without congenital heart disease or diabetes mellitus.
‡Body mass index of 25 kg/m2, never-smoker, participate in physical activity 3 days per week.
§Body mass index of 27.5 kg/m2, current smoker, does
not participate in physical activity.
The changes in mean dollars implied by our models vary depending on
the specific characteristics of individuals.
TABLE 4 presents descriptive estimates
of mean annual charges for hypothetical patients with high-risk vs low-risk
profiles of modifiable health risks. These
estimates are based on least-squares
regression results, estimated only for
subjects with nonzero charges. The highrisk profile was associated with a fixedpercentage increase of 49% mean annual
charges higher than the low-risk profile.
However, across the range of individuals with characteristics as presented in
column 1, the absolute increase in mean
charges ranged from approximately
$1500 to more than $2500.
COMMENT
In this study, we found that modifiable
health risks were significantly related to
higher health care charges, even after
controlling for age, sex, race, diabetes,
and heart disease. Excess medical care
charges related to physical inactivity,
obesity, and smoking, were substantial. The findings suggest that health
plans that do not systematically support members’ efforts to improve healthrelated behaviors may be incurring significant short-term health care charges
that may be at least partly preventable.
Goetzel et al15 described the relationship between modifiable health risks and
2238
health care costs and found that 7 of 10
health risks were significantly related to
higher health care expenditures. In that
report, the estimated per-capita annual
impact of poor exercise habits and current smoking amounted to $172 and
$227, respectively, in an employed
population that completed a voluntary
health-risk appraisal. However, previous studies have failed to control for diagnosed chronic conditions such as heart
disease or diabetes.15,19 In a similar analysis, we have presented evidence that employed health plan members who have
increased behavioral risk factors, including obesity and low physical fitness incur significantly higher annual excess
health care expenses.16 The magnitude
was $135 per member per year for obesity and $176 per member per year for
low fitness. The present study establishes the relationship of modifiable
health risks in a population-based, random sample, while also controlling for
the presence of major chronic conditions. Despite differences in the study
sample demographics and characteristics, the results of these studies all support the hypothesis that behavioral risk
factors significantly influence shortterm health care expenditures.
However, the present study does not
prove that changing modifiable health
risks can reduce health care charges. The
fraction of such charges that can be reversed is not yet known. To reduce
charges, health plans would have to invest resources in effective intervention
strategies to reduce the burden of modifiable health risks in members. Such behavioral interventions are sometimes
viewed with skepticism for several reasons. First, the cost of the interventions
may offset potential savings in health care
charges.Second,interventionsthattarget
modifiable health risks may have limited
effectiveness. Third, for some modifiable
health risks, there may be substantial lag
time between change in the risk factor
and subsequent changes in health care
costs. Finally, if health plan disenrollment rates are high, savings associated
with health-risk reduction may not be
fully realized by the health plan that invested in behavior change.
JAMA, December 15, 1999—Vol 282, No. 23
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While health-risk reduction is not a
panacea for controlling health care costs,
we believe that payers often undervalue its merits. Other reports document better clinical outcomes,17,18 and
suggest lower subsequent costs,3,5,19,20
when modifiable health risks are
improved. While some behavior change
interventions are expensive, others are
less costly, yet effective, and the return
on investment may be favorable.5 Moreover, we have shown in previous work
that readiness to change and willingness to participate in health improvement activities tend to be greatest in those
at highest risk of poor outcomes.16,21,22
Further, this report documents a short
lag time between the measurement of
modifiable health risks and their impact
onhealthcarecharges.Thisshortlagtime,
only 18 months in this study, is plausible
fromthebiologicalpointofviewforphysical activity and weight management.23,24
Moreover, while disenrollment rates may
vary widely from one health plan to another,25,26 there is evidence in recent reports that older, sicker health plan members have lower rates of disenrollment
than do younger, healthier members.27
Because the percentage change in health
care charges is relatively constant across
age groups, potential short-term savings
may be highest in older, sicker members.
However, an additional problem
relates to the notion that when behavior
change interventions target only highrisk individuals, the observed shift from
high to average risk is offset by individuals who move from average risk to highrisk status. To achieve lasting health benefit in defined populations, health plans
and their community partners would be
well-advised to address the needs of both
average-risk and high-risk members,
through intervention at both the individual and community levels.28
The relationship of smoking status
to health care charges is complex. The
higher charges among former smokers than current smokers are likely to
reflect the occurrence of health problems that caused smokers to stop, for
example, after a myocardial infarction.29 Our data corroborate previously published reports30 suggesting
©1999 American Medical Association. All rights reserved.
COST OF MODIFIABLE HEALTH RISKS
that primary prevention of smoking is
an important strategy to improve population health and reduce charges.
This study has several limitations. Our
data were limited to a large group of insured patients enrolled in a single health
plan. The magnitude of these patients’
health care charges may differ from those
with indemnity insurance, although the
expected impact would be to make our
estimates of charges conservative. Further, the relatively high educational level
and demographic homogeneity of the
MCO membership studied here limit the
generalizability of our results to other
groups, especially minority populations.
While we did not include data on alcohol intake and nutrition in our analyses,
both appear to be related in a complex
fashiontohealthcarecharges.15 Ourmeasurements of BMI and physical activity
are based on self-report, but reporting
bias would likely make our estimates of
the effect of modifiable risks on charges
conservative.
These limitations should be balanced
against the strengths of this study. The
population we studied was large enough
to ensure sufficient power in the analysis, and is comparable with populations
enrolled in many other health plans. The
data on health care charges, while not
perfect, are derived from a single source
with uniform databases and charges, and
are virtually complete.6 The ability to
control for important comorbidities and
the use of appropriate analytic models
are also strengths of this study. Also, few
other studies have analyzed the relationship of modifiable health risks to direct
health care charges using empiric, individual level data and appropriate multivariate statistical models.31
Our results suggest that adverse,
modifiable health risks contribute substantially to health care charges. While
the reversibility of these charges with
changes in health risks remains uncertain, a recent report4 suggests that reduction in risks may lead to reduced
charges, and other work5 indicates a high
return on investment to payers with interventions that reduce health risks.
Other data32,33 indicate that risk reduction leads to reduced mortality, which
provides a plausible biological pathway between risks and charges. Health
plans and self-insured employers seeking to maximize health return on each
dollar spent for medical care may wish
to consider strategic investments in interventions that effectively improve
modifiable health risks. From a behavioral perspective, primary prevention of
smoking and increased physical activity appear to have substantial potential
to reduce health care charges.
12. Robinson J, Gardner L. Adverse selection among
multiple competing health maintenance organizations. Med Care. 1995;33:1161-1175.
13. Hosmer D, Lemeshow S. Applied logistic regression. In: Wiley Series in Probability and Mathematical Statistics: Applied Probability and Mathematical
Statistics. New York, NY: Wiley; 1989:xiii, 307.
14. Duan N. Smearing estimate: a non-parametric retransformation method. J Am Stat Assoc. 1983;78:
605-610.
15. Goetzel R, Anderson D, Whitmer R, Ozminkowski
R, Dunn R, Wasserman J. The relationship between
modifiable health risks and health care expenditures:
an analysis of the multi-employer HERO health risk
and cost database. J Occup Environ Med. 1998;40:
843-854.
16. Pronk N, Tan A, O’Connor P. Obesity, fitness, willingness to communicate and health care costs. Med
Sci Sports Exerc. In press.
17. Goetzel R, Kahr T, Aldana S, Kenny G. An evaluation of Duke University’s Live for Life health promotion program and its impact on employee health. Am
J Health Promotion. 1996;10:340-342.
18. Prochaska J, Velicer W, Rossi J, et al. Stages of
change and decisional balance for 12 problem behaviors. Health Psychol. 1994;13:39-46.
19. Baum W, Bernacki E, Tsai S. A preliminary investigation: effect of a corporate fitness program on absenteeism and health care cost. J Occup Med. 1986;
28:18-22.
20. Kaman R. Worksite Health Promotion Economics: Consensus and Analysis. Champaign, Ill: Human
Kinetics Publishers; 1995.
21. Boyle R, O’Connor P, Pronk N, Tan A. Stages of
change for physical activity, diet, and smoking among
HMO members with chronic conditions. Am J Health
Promotion. 1998;12:170-175.
22. O’Connor P, Rush W, Rardin K, Isham G. Are HMO
members willing to engage in two-way communication to improve health? HMO Pract. 1996;10:17-19.
23. Department of Health and Human Services. Physical Activity and Health: A Report of the Surgeon General. Atlanta, Ga: US Dept of Health and Human Services, Centers for Disease Control and Prevention,
National Center for Chronic Disease Prevention and
Health Promotion; 1996.
24. NHLBI Obesity Education Initiative Expert Panel.
Clinical guidelines on the identification, evaluation and
treatment of overweight and obesity in adults: the evidence report. Obes Res. 1998;6(suppl 2):51-210.
25. Gilmer T, O’Connor P, Rush W. Disenrollment in
adults with diabetes is not related to comorbidity or
glycemic control. Presented at: Minnesota Health Services Research Conference Annual Meeting; February 25, 1998; Minneapolis, Minn.
26. O’Connor P, Crabtree B, Yanoshik K. Differences between diabetic patients who do and do not
respond to a diabetes care intervention: a qualitative
analysis. Fam Med. 1997;29:424-428.
27. Gilmer T, O’Connor P, Rush W. Does baseline
HbA1c predict subsequent HMO disenrollment? Diabetes. 1998;47(suppl 1):A190.
28. Pronk N, O’Connor P. Systems approach to population health improvement. J Ambulatory Care Manage. 1997;20:24-31.
29. Prochaska J, Goldstein M. Process of smoking cessation: implications for clinicians. Clin Chest Med. 1991;
12:727-735.
30. Velicer W, DiClemente C. Understanding and intervening with the total population of smokers. Tob
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31. Kortt M, Langley P, Cox E. A review of cost-of-illness
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32. Blair S, Barlow C, Paffenbarger R, Gibbons L, Macera C. Changes in physical fitness and all-cause mortality. JAMA. 1995;273:1093-1098.
33. Paffenbarger R, Hyde R, Lee I-M, Jung D, Kampert J. The association of changes in physical activity
level and other life style characteristics with mortality
among men. N Engl J Med. 1993;328:538-545.
Funding/Support: This study was supported by a contract from HealthPartners to HealthPartners Research Foundation.
Acknowledgement: We wish to acknowledge the
support of George Isham, MD, George Halvorsen,
and Ted Wise in the conceptualization of this study,
and the advice of Agnes Tan, PhD, Michael Maciosek, PhD, and Raymond Boyle, PhD, in the analysis of these data.
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