ARTICLE
RESEARCH
Krueger
et al.ON
/ BODY
AGING
MASS, SMOKING, AND MORTALITY
10.1177/0164027503258518
Body Mass, Smoking, and
Overall and Cause-Specific
Mortality Among Older U.S. Adults
PATRICK M. KRUEGER
University of Colorado–Boulder
RICHARD G. ROGERS
University of Colorado–Boulder
ROBERT A. HUMMER
University of Texas, Austin
JASON D. BOARDMAN
University of Colorado–Boulder
The authors examine the relationships between body mass, smoking, and overall and
cause-specific mortality among U.S. adults aged 60 and older, using data from the
National Health Interview Survey linked to the Multiple Cause of Death file and Cox
proportional hazard models. The authors find that, compared to those who are normal
weight, obese individuals have higher risks of overall, circulatory disease, and diabetes mortality. Furthermore, smoking status suppresses the relationships between obesity and overall, circulatory disease, and cancer mortality, and interacts with low body
weight to increase mortality risks. Finally, underweight individuals initially face
increased risks of death over the follow-up period, although over time their mortality
risks diminish to those of normal-weight individuals, likely due to the presence of
unobserved illness. Researchers and health practitioners must account for smoking
status, body mass, and specific causes of death to understand and improve the health
of our increasingly obese elderly population.
Keywords: body mass; mortality; cause-specific mortality; smoking; aging
The relationship between body mass and mortality among older
individuals is a prominent public health concern due to consistent
increases in obesity in recent decades. Although prior research suggests that older overweight and obese individuals may experience
attenuated mortality risks compared to younger individuals (Elia
2001; Grabowski and Ellis 2001), few have examined specific causes
RESEARCH ON AGING, Vol. 26 No. 1, January 2004 82-107
DOI: 10.1177/0164027503258518
© 2004 Sage Publications
82
Krueger et al. / BODY MASS, SMOKING, AND MORTALITY
83
of death. Furthermore, smoking status is an important confounding
factor: although smokers have higher mortality risks than nonsmokers, smoking also associates with lower body masses (Flegal et al.
1995; Garrison et al. 1983; Wannamethee, Shaper, and Walker 2001).
To date, none have examined how smoking status shapes the association between body mass and overall and cause-specific mortality
among the elderly. We aim to elucidate these relationships with a
national sample of U.S. adults aged 60 and older.
AGE AND BODY MASS
The proportion of overweight and obese individuals in the United
States has risen consistently since the 1960s—even among those aged
60 and older—with profound implications for morbidity and mortality (Flegal et al. 1998; Rogers, Hummer, and Krueger 2003). Figure 1
compares the distributions of the body mass index (BMI) by age for
the years 1987 and 2000 in the United States. BMI, calculated as
weight in kilograms over height in meters squared, is a standard measure of body mass in demographic and public health research. The
graph presents individuals aged 18 to 59 with solid lines and those
aged 60 and older with dotted lines and depicts 1987 data without
asterisks and 2000 data with asterisks. The table further shows percentage distributions of BMI by age and year, categorized according
to international standards as underweight (BMI < 18.5), normal
weight (18.5 ≤ BMI < 25.0), overweight (25.0 ≤ BMI < 30.0), obese
class I (30.0 ≤ BMI < 35.0), obese class II (35.0 ≤ BMI < 40.0), and
obese class III (BMI ≥ 40.0) (World Health Organization 1995,
1997). Two important patterns emerge within the cohorts examined
here. First, the average body mass is higher among older individuals
than at the younger ages. Among adults aged 60 and older in 1987,
36% were overweight and 10% were obese class I, but among younger
adults, only 29% were overweight and 8% were obese class I—a pattern that persisted into the year 2000. Second, the U.S. elderly population has been getting heavier over this time period. Compared to those
AUTHORS’ NOTE: Please address all correspondence to Patrick M. Krueger, University of
Colorado–Boulder, Population Program, Campus Box 484, Boulder, CO 80309-0484; e-mail:
[email protected]. The authors would like to thank the National Science Foundation for
support (Grants SES-0221093, SES-0243249, and SES-0243189). An earlier draft of this article
benefited greatly from insightful comments from Christine Himes and two anonymous reviewers
as well as presentation at the 2003 meetings of the Population Association of America.
84
RESEARCH ON AGING
12.0
Adults 60+, 1987
Adults 18-59, 1987
Adults 60+, 2000
Adults 18-59, 2000
10.0
Percent
8.0
6.0
4.0
2.0
0.0
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
Body Mass Index
Adults 60+, 1987
Adults 18-59, 1987
Adults 60+, 2000
Adults 18-59, 2000
Under
Weight
Normal
Weight
Over
Weight
Obese
Class I
Obese
Class II
Obese
Class III
3.9
3.9
2.2
2.3
47.5
56.0
38.6
41.8
35.8
28.8
38.6
34.4
9.7
8.2
14.5
14.1
2.3
2.1
4.1
4.9
0.8
1.0
1.9
2.5
Figure 1: Percent Distribution of BMI by Age and Year, U.S. Adults, 1987 and 2000
SOURCE: Derived from National Center for Health Statistics (various years).
NOTE: The graph presents a three-unit moving average of BMI to smooth height and weight irregularities. BMI = body mass index.
aged 60 and older in 1987, a smaller percentage of older individuals in
2000 were underweight or normal weight, but a larger percentage
were overweight or obese.
If obesity continues to increase and smoking rates persistently
decline in the coming years, then obesity may begin to rival smoking
as the single most important cause of premature adult mortality in the
United States (Allison, Fontain, et al. 1999; Schoenborn, Adams, and
Barnes 2002; Sturm 2002). But the relationship between body mass,
age, and health is complex. At least in the current cohorts, older individuals generally display higher body masses than those who are
younger, perhaps resulting from more sedentary lifestyles or declining height due to bone loss and spinal compression. Conversely,
around age 60, many individuals experience declining body mass due
to loss of muscle mass, bone density, and other lean tissues (Elia
2001). Although, on average, body mass tends to increase with age,
weight loss may be problematic for the elderly and could indicate
health problems.
Perhaps because of these changes in body composition, the relationship between body mass and mortality changes with age. Among
Krueger et al. / BODY MASS, SMOKING, AND MORTALITY
85
younger individuals, body mass and mortality exhibit a U or J shaped
relationship, so that underweight or obese individuals have higher
mortality risks than those who are normal weight or slightly overweight (Cornoni-Huntly et al. 1991; Rogers et al. 2003). But this relationship softens at the older ages. With increasing age, the effect of
overweight and obesity on overall mortality declines (Elia 2001;
Grabowski and Ellis 2001). However, these associations may operate
differently for specific causes of death, and might be modified by
smoking.
BODY MASS, SMOKING, AND MORTALITY
This article has three objectives. First, we examine the relationship
between body mass and overall, circulatory disease, cancer, respiratory disease, and diabetes-specific mortality. Body mass differentially affects specific causes of death. Among adults, obesity contributes to heart disease, high blood pressure, gallbladder disease,
respiratory problems, complications from diabetes, osteoarthritis,
and functional disability (Farrell et al. 2002; Himes 2000; Must et al.
1999; Schoenborn et al. 2002), factors that often result in higher risks
of diabetes and circulatory disease mortality. But only increased
obesity-related circulatory disease mortality has been documented
among older individuals (Heiat, Voccarino, and Krumholz 2001;
Stevens et al. 1998), and no research has examined body-mass-related
diabetes mortality at the older ages. Alternately, although many have
found increased obesity-related cancer mortality risks at younger
ages, some have found a modest protective effect of low levels of obesity for cancer mortality among older adults (Calle et al. 2003; Nilsson
et al. 2002).
Smoking status and body mass associate with some of the same
causes of death, although much prior research either excludes smokers from their sample or fails to control for smoking status (Grabowski
and Ellis 2001; Nilsson et al. 2002; Rogers et al. 2003). Because the
relationship between body mass and mortality is potentially confounded by the impact of smoking, we investigate whether controlling
for smoking status shapes the relationship between body mass and
overall and cause-specific mortality.
Second, we examine whether the lower weights associated with
smoking lead to health costs or benefits. Both smoking and obesity
86
RESEARCH ON AGING
often associate with higher mortality (Rogers et al. 2003; Rogers,
Hummer, and Nam 2000; Schoenborn 2002; Wannamethee et al.
2001). But current smokers often have lower body masses than former
or never smokers (Flegal et al. 1995). If lower weights among smokers
come from higher metabolisms and lower levels of adiposity, then current smokers may face higher mortality due to their smoking but lower
mortality due to their decreased body mass (Fujimoto et al. 1990).
Alternately, lower weights among smokers may indicate poor nutrition, bone loss, or other health problems (Hollenbach et al. 1993).
Thus, current smokers may face multiple insults to physical health and
have increased mortality due to both smoking and smoking-related
weight loss. Former smokers often face weight gain after quitting
smoking, so that their mortality advantages from smoking cessation
may be partially counteracted by disadvantages associated with weight
gain (Garrison et al. 1983). Although smoking status is important to
consider when examining the relationship between body mass and
mortality, little work examines whether lower weights among current
smokers affect mortality.
Third, we examine whether the unobserved illness hypothesis or
the frailty hypothesis best explains the increased mortality often
found among underweight individuals. Underweight associates with
increased risks of overall and cause-specific mortality at the older
ages (Cornoni-Huntly et al. 1991; Tayback, Kumanyika, and Chee
1990) for three possible reasons. First, smoking may confound this
relationship because it associates with lower body mass and increased
mortality (Flegal et al. 1995; Wannamethee et al. 2001). Thus, we control for smoking status in our analyses. Second, unobserved illness
theories posit that underweight individuals likely have some occult
illness at the point of interview that simultaneously causes both
weight loss and increased short-run mortality (Allison, Faith, et al.
1999; Losonczy et al. 1995). Although unobserved illnesses may lead
some individuals to lose weight and experience higher short-term
mortality, underweight but healthy individuals may have similar mortality risks as normal-weight individuals well into the future. Finally,
frailty theories suggest that, at the older ages, a true relationship
between underweight and mortality may derive from frailty or a
“multi-system reduction in reserve capacity to the extent that a number of physiological systems are close to, or past, the threshold of
symptomatic clinical failure” (Campbell and Buchner 1997:315).
Krueger et al. / BODY MASS, SMOKING, AND MORTALITY
87
Although underweight individuals may not have any specific illness,
they could have numerous physiological problems including compromised immune systems or decreased organ functioning, which might
increase mortality risks into the indefinite future (Alter and Riley
1989; Schulz and Williamson 1993).
Data and Methods
DATA
We employ the National Health Interview Survey (NHIS) to examine the effects of body mass on overall and cause-specific mortality
among the elderly. The NHIS annually surveys noninstitutionalized
U.S. residents aged 18 and older and includes a core set of questions
that remains virtually unchanged from 1986 to 1994. Furthermore, the
NHIS uses the same sampling frame during this time period, which
allows us to pool the surveys across years. We pool six supplements to
the NHIS because we require data on body mass and smoking, which
are included in the 1987 and 1992 Cancer Control, 1988 Occupational
Health, 1990 and 1991 Health Promotion and Disease Prevention, and
1993 Year 2000 Objectives supplements (National Center for Health
Statistics [NCHS], various years). Pooling these six supplements
yields a sample of 45,162 persons aged 60 and older, thus permitting
analyses of numerically small groups, such as those who die of
specific causes.
We then link this pooled cross-sectional data to the Multiple Cause
of Death files (MCD) via the National Death Index (NDI) for the years
1987 through 1997. The linked data are prospective and allow us to
examine whether individuals survived or died from the point of interview to the end of 1997. The NCHS devised a probabilistic matching
scheme that assigns weights to each of 12 items: social security number; first and last name; middle initial; race; sex; marital status; day,
month, and year of birth; and state of birth and residence (NCHS,
2000). This matching methodology is highly accurate (Patterson and
Bilgrade 1986). About 1.8% of the NHIS records contains insufficient
information to be matched to any death records and is dropped from
subsequent analyses. An additional 1.5% of the sample was excluded
due to missing values on height, weight, education, income, marital
88
RESEARCH ON AGING
status, or smoking status. Matches to the MCD indicate that 11,964
individuals aged 60 and older died over the follow-up period,
including 5,705 from heart diseases, 3,013 from cancers, 1,207
from respiratory diseases, and 288 from diabetes. These data are
ideal for our analyses because they are nationally representative of
elderly adults, permit prospective analyses, and contain an extensive
array of covariates and links to overall and cause-specific mortality.
VARIABLES AND MEASUREMENT
The BMI measures body mass in our analysis and is widely used to
assess body composition. Compared to other possible measures, BMI
is advantageous because it can be self-reported, is easy to obtain at low
cost via interviews, and is strongly related to health and mortality.1 We
follow convention and calculate BMI as weight in kilograms divided
by height in meters squared. The World Health Organization (1995,
1997) has recommended categorization of BMI into six groups:
underweight (BMI less than 18.5), normal weight (the reference category: BMI 18.5 to 24.9), overweight (BMI 25.0 to 29.9), obese class I
(BMI 30.0 to 34.9), obese class II (BMI 35.0 to 39.9), and obese class
III (BMI 40.0 or greater). Much scholarship examines the relationship
between mortality and less detailed BMI categories; we include additional detail because the relationship between BMI and mortality at
the older ages is incompletely understood.
The prospective design of the data set minimizes recall bias in
height and weight reports. Compared with actual measurements, selfreported heights and weights are relatively accurate, even among
overweight individuals (Stewart 1982). Nonetheless, respondents
may misreport their height and/or weight (Andriola et al. 1988), and
efforts to correct for these biases have been unsuccessful (Plankey
et al. 1997).
We examined the height and weight distributions by sex to assess
the degree of possible heaping or exaggeration. Although males younger than the age of 60 who are anywhere from one to three inches shy
of six feet are more likely to respond that they are six feet tall, perhaps
because of the greater status attached to taller males (Rogers et al.
2003), the height distribution for older males fails to show consistent
heaping at the six-foot mark. Furthermore, we find relatively little
heaping at six- or 12-inch intervals for older individuals. Because the
Krueger et al. / BODY MASS, SMOKING, AND MORTALITY
89
status associated with height may decline with age and because individuals might be aware of their recent losses in height, the distributions for males and females at the older ages appear more normally
distributed than at the younger ages. Both sexes demonstrate marked
heaping for weight, although the distributions as a whole approximate
normality, indicating that most people simply round to the nearest
five-pound increment. Plankey et al. (1997) found that overweight
individuals of both sexes likely underestimate their weight, whereas
underweight individuals likely overestimate their weight. Tendencies
to report weights closer to ideal BMIs may provide conservatively
biased mortality estimates in this and other studies.
Although our data do not allow us to examine changes in body mass
over time, both weight loss and gain may signify health problems that
could associate with increased mortality over the follow-up period. To
assess the impact of health conditions that may lead to changes in
body mass as well as premature mortality, we ran separate analyses
that controlled for self-rated health and functional ability. Including
those variables produced substantive findings that were identical to those
presented here, although the magnitude of the relationship between
BMI and mortality declined substantially (results not shown). But we
exclude those variables from our final analyses for two reasons. First,
obesity likely causes functional disability (Himes 2000). Because our
interest is in assessing the total impact of body mass on mortality,
rather than the mechanisms that lead from body mass to mortality, we
exclude that variable. Second, although self-reported health aptly predicts future mortality, it may simply measure people’s assessments of
their body masses and smoking statuses. Self-reported health is a relatively imprecise control for other health factors that lead to premature
mortality. Nevertheless, the fact that models that include controls for
baseline health provide results that are substantively identical to those
presented here suggests that our findings likely mirror true relationships
in the population, although future work with data that can assess the
effect of changes in body mass on prospective mortality is warranted.
Both body mass and the risk of death vary by sociodemographic,
socioeconomic, and smoking status variables (Rogers et al. 2000,
forthcoming). Sociodemographic variables include age, sex, race/
ethnicity, and marital status. We limit our sample to those aged 60 and
older because at roughly that age, individuals may begin to lose muscle mass, bone density, and other lean body tissues (Elia 2000) and
90
RESEARCH ON AGING
also to ensure adequate numbers of individuals for cause of death
analyses. We further include age as a continuous variable ranging
from 60 to 99 years and older.2 Sex is coded dichotomously as male
and female (the referent). We code race/ethnicity categorically as nonHispanic White (the referent), non-Hispanic Black, Hispanic, Asian,
and all others. Marital status is coded as currently (the referent),
previously, and never married.
Socioeconomic variables include income equivalence and educational status. We measure family income on a continuous income
equivalence scale, which takes into account family income and family
size. Income equivalence (W) is equal to family income (I) divided by
family size (S), raised to an equivalence elasticity (e): W = I / Se. We
follow Van der Gaag and Smolensky (1982) and use an equivalence
elasticity of 0.38 to adjust for economies of scale across differentsized families. Because detailed income values are missing for about
17% of the records, we imputed income.3 Furthermore, NHIS income
categories do not use equal intervals. For values less than $50,000, we
take the midpoint of the interval and divide it by 10,000 to approximate a continuous income value. Because the top category is openended and lacks a clear midpoint, we estimate a median value for this
category using the Pareto Curve (Parker and Fenwick 1983).4 We
standardize all dollar amounts to 1995 values with the Consumer Price
Index. Finally, we code education as less than a high school degree,
high school graduate, and any college or more (the referent). We code
smoking status categorically as current, former, or never smoker (the
referent).
Our analyses include several dependent variables. Overall mortality assesses whether individuals died of any cause or survived
the follow-up period. Also, we analyze specific causes of death,
which are coded based on the 9th revision of the International Classification of Diseases (ICD-9) (NHIS 2000). We examine circulatory disease (ICD-9 390 to 459), which includes heart disease, hypertension,
artherosclerosis, and cerebrovascular disease; cancer at all locations
(ICD-9 140 to 239); respiratory disease (ICD-9 460 to 519), which
includes bronchitis, pneumonia and influenza, and chronic obstructive pulmonary diseases; and diabetes (ICD-9 250) (World Health
Organization 1977). These are major causes of death: Among those
aged 65 and older in 1999, circulatory disease was the leading cause of
death, cancer was the second leading cause of death, respiratory
Krueger et al. / BODY MASS, SMOKING, AND MORTALITY
91
disease was the third major cause of death, and diabetes was the fourth
major cause of death (Anderson 2001). 5
STATISTICAL MODELING
Because respondents were interviewed throughout the calendar
year and because individuals surveyed in 1987 were at risk of death
for up to 132 follow-up months, Cox proportional hazard models estimate the risk of death over time.6 We measure the duration at risk of
death in months because NCHS (2000) masks the week and day of
death to ensure confidentiality. When examining overall mortality,
those who survived past 1997 are right-censored. When we examine
cause-specific mortality, death from a specific cause is contrasted
with survivors as well as those who die from other causes during the
follow-up interval, whereupon they are right-censored at the time of
death (Allison 1995). Stata 8 (StataCorp 2003) correctly estimates the
hazard ratios and standard errors, as NCHS (various years) uses a
complex weighted, clustered, and stratified sampling design.
Results
Table 1 presents percentage distributions of the covariates across
the BMI categories. A number of important patterns emerge. First, the
distributions of many of the sociodemographic and socioeconomic
risk factors vary across body mass. For example, within the population of adults aged 60 and older, older adults often have lower BMIs
than younger individuals. Among those aged 90 and older, 12% are
underweight and 67% are normal weight, although less than 0.01%
are obese class II or III. But, of those aged 60 to 69, only 2% are underweight and 42% are normal weight, although more than 4% are obese
class II or III. Second, body mass varies by smoking status, a key variable in our analyses. For example, 53% of current smokers are normal
weight, compared to only 42% of former smokers and 46% of never
smokers, thus suggesting that smoking associates with lower average
body masses. Conversely, 41% of former smokers are overweight,
compared to only 35% of never smokers and 30% of current smokers,
indicating that individuals may gain weight after they quit smoking.
92
Sociodemographic status
Age
90 and older
80 to 89
70 to 79
60 to 69
Sex
Female
Male
Race/ethnicity
Non-Hispanic Whites
Non-Hispanic Blacks
Hispanic
Asian
Other
Marital status
Currently married
Previously married
Never married
Socioeconomic status
Income equivalence
Lower half
Upper half
66.8
55.9
47.0
41.9
49.1
41.4
46.8
34.3
40.3
68.0
44.0
44.6
47.4
49.5
44.1
47.4
4.7
1.9
3.5
2.7
2.6
7.8
2.8
2.5
4.9
5.5
4.2
2.8
Normal Weight
11.9
7.6
3.7
2.1
Underweight
35.4
37.7
4.0
32.1
31.0
36.5
40.0
38.4
19.9
33.8
30.4
44.7
18.3
29.2
36.0
39.3
Overweight
TABLE 1
12.2
9.6
10.6
11.5
10.0
10.3
16.2
14.3
3.3
13.9
11.5
10.1
3.0
6.1
10.4
12.6
Obese Class I
3.1
2.0
2.2
3.1
2.9
2.3
4.9
3.2
0.4
4.0
3.2
1.6
0.0
1.0
2.3
3.1
Obese Class II
1.1
0.5
0.6
1.2
1.1
0.7
2.0
1.2
0.6
1.5
1.2
0.3
0.0
0.3
0.7
1.1
Obese Class III
Percentage Distributions of the Covariates Across BMI—U.S. Adults Aged 60 and Older, 1987-1997
93
41.5
46.5
51.0
53.3
42.1
46.0
44.9
48.5
3.8
3.3
3.2
6.5
2.1
3.4
2.5
6.5
SOURCE: Derived from National Center for Health Statistics (various years).
NOTE: BMI = body mass index.
Education
Less than high school
High school degree
Any college
Smoking status
Current smoker
Former smoker
Never smoker
Vital status
Survived the follow-up period
Died during the follow-up period
37.9
32.6
30.0
41.4
35.2
37.4
36.6
35.5
11.4
9.3
7.9
11.1
11.7
12.9
10.6
8.3
2.6
2.3
1.8
2.6
2.7
3.4
2.4
1.4
0.8
0.9
0.5
0.7
1.0
1.1
0.7
0.5
94
RESEARCH ON AGING
Finally, vital status at the end of the follow-up period varies by
body mass. For example, only 3% of those who survive the follow-up
period are underweight, although 7% of those who die are underweight. Conversely, 38% of those who survived are overweight, compared to only 33% of individuals who died over the follow-up period.
These patterns suggest that overweight or moderately obese individuals may contribute relatively fewer deaths than those who are underweight, normal weight, or obese class III. We turn to the multivariate
results to account for the complex associations among BMI, smoking
status, and mortality.
OVERALL AND CAUSE-SPECIFIC MORTALITY
Table 2 examines how BMI relates to overall and cause-specific
mortality and whether smoking status shapes those relationships.
Model 1 regresses overall mortality on BMI and the sociodemographic and socioeconomic factors. Compared to those who are normal weight, obese class III individuals have 48% higher but not statistically different risks of death, obese class II individuals have 15%
higher risks of death, obese class I individuals have similar risks of
death, overweight individuals have 14% lower risks of death, and
underweight individuals have 80% higher risks of death over the
follow-up period. Model 2 further controls for smoking and suggests
that smoking status suppresses the relationship between obesity and
mortality. Model 1 does not find a statistically significant association
between obese class III and overall mortality, but Model 2 shows a
nearly 60% increased risk of death among obese class III individuals.
This suggests that failing to control for smoking status may mask the
relatively large mortality risks associated with very high body masses
among aging adults. Because overall morality may conceal variation
in cause-specific mortality, we now turn to specific causes of death.
The remainder of Table 2 examines the relationships between body
mass, smoking, and cause-specific mortality. Each pair of models
examines the risk of death from a specific cause, with surviving individuals and individuals who die of other causes right-censored at the
end of the follow-up period or point of death, respectively. Models 3
and 4 indicate that smoking status suppresses the effect of BMI on circulatory disease mortality. Model 3 finds a strong graded relationship
between obesity and circulatory disease mortality after accounting for
95
Body mass index
Obese class III
Obese class II
Obese class I
Overweight
Normal weight
Underweight
Sociodemographic status
Age
Sex (male = 1)
Race/ethnicity
Non-Hispanic Whites
Non-Hispanic Blacks
Hispanic
Asiana
Other
Marital status
Currently married
Previously married
Never married
TABLE 2
1.59*
1.21***
1.02
0.89***
ref
1.68***
1.09***
1.71***
ref
1.04
0.86
0.52*
0.97
ref
1.08*
1.11
1.08***
1.97***
ref
1.03
0.81**
0.48**
0.99
ref
1.13**
1.09
Model 2
1.48
1.15**
0.96
0.86**
ref
1.80***
Model 1
Overall
ref
1.16
1.13
ref
1.00
0.72**
0.52**
0.88
1.10***
1.93***
1.48***
1.47***
1.18**
0.97*
ref
1.58***
Model 3
ref
1.13
1.14*
ref
1.01
0.75**
0.54**
0.87
1.10***
1.74***
1.55***
1.52***
1.23**
1.00
ref
1.51***
Model 4
Circulatory Disease
ref
1.06**
1.10
ref
1.08**
0.68*
0.56
0.92
1.04***
2.00***
1.17
0.82**
0.77***
0.79**
ref
1.42**
Model 5
ref
0.99
1.13
ref
1.09***
0.74
0.63
0.89
1.05***
1.66***
1.32
0.89
0.84**
0.84*
ref
1.25
Model 6
Cancer
ref
1.14*
1.05
ref
0.70***
0.88
0.47
0.49**
1.07***
2.52***
1.27
0.59
0.48***
0.62***
ref
3.58***
Model 7
ref
1.08
1.13
ref
0.72***
0.98
0.59
0.47**
1.09***
1.79***
1.44
0.63
0.53***
0.66***
ref
3.20***
Model 8
Respiratory Disease
Hazard Ratios of BMI, Smoking Status, and Other Covariates for Overall and
Cause-Specific Mortality—U.S. Adults Aged 60 and Older, 1987-1997
ref
0.93
0.75
ref
1.95***
2.22
.—
1.42
1.06***
1.38**
6.51***
5.78***
2.41***
1.62***
ref
1.40
(continued)
ref
0.93
0.76
ref
1.97***
2.23
.—
1.43
1.06***
1.36*
6.50***
5.78***
2.41***
1.62***
ref
1.40
Model 9 Model 10
Diabetes
96
–65,611
1.15***
1.08***
ref
1.18***
1.09***
ref
1.97***
1.38***
ref
–65,328
0.92***
0.92***
–30,013
1.20***
1.12***
ref
0.92***
1.68***
1.26***
ref
–29,942
1.18***
1.12***
ref
0.93***
Model 4
Circulatory Disease
Model 3
–17,826
1.21**
1.19*
ref
0.94***
2.61***
1.57***
ref
–17,665
1.16**
1.17
ref
0.95***
Model 6
Cancer
Model 5
–6,622
1.38***
1.09
ref
0.85***
Model 7
3.39***
2.46***
ref
–6,509
1.34**
1.07
ref
0.86***
Model 8
Respiratory Disease
SOURCE: Derived from National Center for Health Statistics (various years).
NOTE: ref = referent; BMI = body mass index.
a. Asians were grouped with other race/ethnic groups when examining diabetes mortality due to small sample size.
*p < .05. **p < .01. ***p < .001, two-tailed tests.
Socioeconomic status
Income equivalence
Education
Less than high school
High school degree
Any college
Smoking status
Current smoker
Former smoker
Never smoker
Log Likelihood
Model 2
Overall
Model 1
TABLE 2 (continued)
Diabetes
–1,656
1.10
1.08
ref
1.00
1.03
1.06
ref
–1,656
1.10
1.08
ref
1.00
Model 9 Model 10
Krueger et al. / BODY MASS, SMOKING, AND MORTALITY
97
social and economic factors, although, compared to those who are
normal weight, overweight individuals have 3% lower risks of death
over the follow-up period. But controlling for smoking status in
Model 4 slightly augments the association between obesity and mortality and fully accounts for the decreased risk of circulatory disease
mortality among overweight adults.
Models 5 and 6 suggest that smoking status shapes the association
between BMI and cancer mortality. Model 5 indicates that, compared
to normal-weight individuals, those who are overweight, obese class
I, and obese class II have lower risks of death, a finding consistent with
prior research (Nilsson et al. 2002), and underweight adults have 42%
higher risks of death over the follow-up period. But the exclusion of
smoking status biases these relationships. Model 6 shows that, after
controlling for smoking, obese class II individuals no longer experience significantly lower mortality risks than normal-weight individuals and only overweight or obese class I individuals experience the
lowest mortality, a significant shift from Model 5. Indeed, prior
research finds that obesity may be a protective factor with respect to
cancer mortality, although we find that this relationship may partially
result from lower levels of smoking among obese adults. Furthermore,
higher levels of smoking among underweight adults account for their
increased risks of death compared to normal-weight individuals.
Models 7 and 8 indicate that the relationship between BMI and
respiratory disease mortality is largely independent of smoking status.
As with cancer, compared to normal-weight individuals, those who
are overweight or obese class I have lower risks of respiratory disease
mortality, and obese class II or III confers no increased mortality risk.
Also, underweight individuals have substantially higher mortality
risks than normal-weight individuals, a relationship that persists after
controlling for smoking status. Models 9 and 10 find that, although
BMI is strongly associated with diabetes mortality, smoking does not
impact that risk. Model 9 shows that, compared to normal-weight
adults, obese class III individuals have 6.5 times the risk of death,
obese class II individuals have 5.8 times the risk of death, obese class I
individuals have 2.4 times the risk of death, and obese individuals
have 1.6 times the risk of death over the follow-up period—a relationship that remains unchanged in model 10. 7, 8
98
RESEARCH ON AGING
3
Current smoker
Former smoker
Never smoker
2.5
Risk of Death
2
1.5
1
0.5
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Body Mass Index
Figure 2: Interaction of BMI and Smoking Status on Overall Mortality
SOURCE: Derived from National Center for Health Statistics (various years).
NOTE: This model also controls for age, sex, race/ethnicity, marital status, family income equivalence, education, and smoking status. Never smokers, with a BMI of 25, are set to a hazard ratio
of 1.00. BMI = body mass index.
SMOKING, BODY MASS, AND MORTALITY
To examine whether the lower weights associated with smoking
affects mortality, we tested for interactions between BMI and smoking
when predicting overall and cause-specific mortality. We found significant interactions between smoking and BMI when predicting
overall, circulatory disease, and respiratory disease mortality.9 Because
the patterns are similar for all three models, for parsimony, Figure 2
presents only the results for overall mortality. We present our results in
graphical rather than tabular form for clarity of presentation and
model this relationship with continuous BMI and BMI-squared terms
to simplify the results. We show the patterns for the BMI range
between 17 and 37, which captures more than 99% of all the cases,
because estimates outside of that range become less reliable. Never
smokers with a BMI of 25, the upper limit of normal weight, are the
referent group and are set to a hazard ratio of 1.00.
Figure 2 indicates that never smokers have the lowest risks of death
over the follow-up period, followed by former and current smokers,
respectively. Furthermore, compared to never smokers, underweight
current and former smokers have disproportionately higher risks of
death. Although we cannot directly examine changes in body mass
Krueger et al. / BODY MASS, SMOKING, AND MORTALITY
99
that result from initiating or maintaining smoking, these findings suggest that the lower weights among current smokers—which may indicate poorer nutrition or bone loss—demonstrate multiple insults to
physical well-being. We also tested interactions for age and BMI on
overall and cause-specific mortality: These were only significant
when predicting circulatory disease mortality—a finding consistent
with prior research (Lee, Blair, and Jackson 1999; Lee, Jackson, and
Blair 1998; Stevens et al. 1998)—so we do not show them here.10
Importantly, the nonsignificant interactions between BMI and age for
overall and diabetes mortality suggest that the increased mortality due
to obesity persists as long as there are elderly individuals in those
categories.
Underweight and Mortality
Finally, to determine whether the increased mortality associated
with underweight persists over time across the follow-up period, we
specify Cox proportional hazard models that allow for the effect of the
BMI categories on overall, circulatory disease, and respiratory disease mortality risk to vary over time. Tests indicate that the effect of
BMI on mortality is not proportional over time, and the models fit best
when BMI is allowed to vary across a quadratic transformation of
time. Although the effects of overweight and obesity on mortality risk
either remain constant or increase with time, the effect of underweight
diminishes with time. Figure 3 presents the effect of underweight on
overall mortality across the months of follow-up, although the results
were similar for all causes of death.11 Compared to normal-weight
individuals, those who are underweight are nearly twice as likely to
die in the first month of the follow-up period, but this effect diminishes over time. Indeed, 87 months after the point of interview, the
risks of death among those who are underweight converge with those
of normal-weight individuals.12
Conclusion
These findings provide three specific insights into the associations
between body mass and mortality in the U.S. elderly population. First,
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RESEARCH ON AGING
2.5
Hazard Ratio
95% Confidence Interval
2
Mortality Risk
1.5
1
0.5
0
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96 101 106 111 116 121 126 131
Duration of Follow-up in Months
Figure 3: The Effect of Underweight on Mortality Risk Over the Follow-Up Period,
Compared to Normal-Weight Individuals
SOURCE: Derived from National Center for Health Statistics (various years).
NOTE: This model also controls for age, sex, race/ethnicity, marital status, family income equivalence, education, smoking status, and interactions between overweight, obesity, and time.
the relationship between body mass and overall mortality conceals
marked differences in cause-specific mortality. For example, compared to those who are normal weight, overweight and obese class I
individuals have equivalent or lower risks of overall, cancer, and
respiratory disease mortality. But those who are obese class I have
increased risks of circulatory disease mortality, and overweight and
obese class I individuals have increased risks of diabetes mortality,
relative to normal-weight individuals. Furthermore, compared to
those who are normal weight, obese class II and III individuals have
equivalent risks of cancer and respiratory disease mortality but face
increased risks of overall, circulatory disease, and diabetes mortality.
These relationships are especially important given the persistent
increases in obesity within the elderly population and the fact that circulatory disease, cancer, and diabetes are the first, second, and fourth
leading causes of death, respectively, among those aged 65 and older
(Anderson 2001; Rogers et al. 2003). The profound variation in bodymass-related, cause-specific mortality suggests that general claims
about the protective or harmful effects of obesity at the oldest ages
may be misguided (Heiat et al. 2001).
Krueger et al. / BODY MASS, SMOKING, AND MORTALITY
101
Second, analyses that fail to control for smoking status when
examining the relationship between body mass and overall and causespecific mortality will likely provide biased results. Indeed, obesity is
strongly related to overall mortality only after controlling for smoking
status, and smoking behavior fully accounts for the protective effect of
overweight on circulatory disease mortality. Furthermore, differences
in smoking status partially account for the protective effects of obesity
on cancer-related mortality (Nilsson et al. 2002) and fully account for
the increased risk of cancer-related mortality among underweight
individuals. Smoking status and body mass also interact to shape mortality outcomes. Never smokers experience relatively low mortality
risks, although underweight current and former smokers face markedly higher risks of death from overall, circulatory disease, and respiratory disease mortality (Farrell et al. 2002; Garrison et al. 1983; Rogers et al. 2000, 2003). Future research on body mass and mortality at
the older ages must control for smoking status to avoid understating
the importance of obesity on the risk of death among aging adults.
Third, although underweight was associated with increased risks of
death from overall, circulatory disease, and respiratory disease mortality, this effect appears to derive from unobserved illness at the point
of interview rather than from being underweight itself. The unobserved illness theory posits that underweight individuals may experience weight loss due to occult illnesses and suggests that because they
are already ill, these individuals should have higher short-run mortality (Allison, Faith, et al. 1999; Losonczy et al. 1995). In contrast,
frailty theory posits that underweight individuals may not have specific illnesses, but being underweight may decrease organ functioning, compromise immune systems, or cause other ailments that may
increase mortality risks well into the future (Alter and Riley 1989;
Campbell and Buchner 1997; Schulz and Williamson 1993). Our findings support the unobserved illness theory and suggest that underweight individuals experience higher mortality because they are
already ill. Significantly, however, these illnesses may increase mortality risks for up to 7 years in the future, a much longer duration than
other research has examined (Allison, Faith, et al. 1999), perhaps
because advances in medical technologies allow ailing individuals to
survive for several years.
Unfortunately, we cannot control for the effect of weight loss at the
middle ages on older age mortality (Nilsson et al. 2002). Some
102
RESEARCH ON AGING
work finds that decreased mortality among elderly overweight and
obese individuals may result from concentrations of less healthy individuals in the normal-weight categories who lost weight in prior years
(Losonczy et al. 1995). Thus, the normal-weight category may include healthy and robust individuals with previously obese individuals who lost weight because of health problems and previously underweight individuals who gained weight due to sedentary lifestyles.
Individuals may also have gained or lost weight over the follow-up
period due to changes in health status. Future research with panel data
could disentangle the effect of changes in BMI over the life course on
health and mortality outcomes. Furthermore, individuals who were
underweight or obese at younger ages may have been less likely to
survive to age 60. Although we find persistent relationships between
body mass and mortality in this somewhat more robust sample, our
results are likely conservatively biased. Future work could further
examine the effect of selective mortality at earlier ages on the relationships between body mass and mortality among older adults.
Even though the benefits of weight loss at the older ages are
unclear, many find that controlled physical activity, especially among
those who are physically able but living sedentary lifestyles, might
help to increase muscle mass, improve balance and endurance, reduce
risks of arthritis and circulatory disease, and generally enhance physical and social-psychological well-being in aging populations (Bath
and Morgan 1998; Elia 2001). Regular exercise may reduce body fat
and the risks of death from some of the causes noted above and regulate overall BMI through additions to bone density and muscle mass.
Indeed, obese individuals experience lower mortality risks if they are
fit and even normal-weight individuals may fail to obtain lower mortality risks unless they are at least moderately fit (Lee et al. 1998,
1999).
Although individuals at most all ages might benefit by avoiding
obesity, the aging population is becoming heavier. Within the population aged 60 and older, roughly 13% were obese in 1987, but more
than 20% were obese in 2000 (see Figure 1). This increasing obesity
presages rising levels of disease, medical costs, and preventable mortality (Allison, Fontain, et al. 1999; Must et al. 1999; Sturm 2002).
Our findings suggest that medical health practitioners must consider
an individual’s smoking status, body mass, and risks from specific
diseases, including diabetes, cancer, circulatory disease, and respira-
Krueger et al. / BODY MASS, SMOKING, AND MORTALITY
103
tory disease, when attempting to determine the benefits of weight loss
or gain. By revealing the complex associations among body mass,
smoking, and overall and cause-specific mortality among older individuals, we have contributed to knowledge about body-mass-related
mortality risks within an increasingly obese and aging population.
NOTES
1. There are various ways to assess body composition including hydrostatic weighing, skinfold thickness, waist-to-hip ratios, and bioelectrical impedance (American College of Sports
Medicine 1995; Pollock and Jackson 1984; Wang et al. 2000). But unlike self-reported body
mass index (BMI), most of these measures cannot be conducted in an interview setting, require
trained clinicians, and might be difficult for older individuals to undertake.
2. Preliminary analyses included age-squared terms to test for nonlinear relationships
between age and mortality. Because the squared term was often nonsignificant and did not alter
the relationships between other variables and mortality, we dropped it from our models.
3. We use ordinary least squares regression to estimate income separately for those with
family incomes less than $20,000 and for those with incomes equal to or above that amount—a
question in the survey with a much higher response rate. For both groups, we regress family
income on age, age-squared, marital status, employment status, education, and race, as well as
veteran status and whether individuals have a telephone in the household, to add a stochastic
component to our estimates. We then use the predicted values to impute the missing family
income data.
4. Parker and Fenwick (1983) noted that the double log form of the Pareto Curve is linear at
the upper tail of the income distribution, so that as the level of income in a category increases, the
number of people in that category decreases. This allows them to estimate this slope, v, as:
ν=
log( n t + n n − 1 ) − log( n t )
log( x t ) − log( x t − 1 )
(1)
where nt is the number of people in the open-ended category, nt-1 is the number of people in the
income category immediately preceding the open-ended category, xt is the lower limit of the
open-ended category, and xt-1 is the lower limit of the penultimate category. They then use this
value to estimate a median value (MD) for the category, as specified in Wright (1976:163):
MD = 10 (.30 / ν ) ( x t )
(2)
Thus, for our analyses, we use the estimated median value of $69,633 for those individuals with
family incomes of $50,000 or more, the open-ended category.
5.We take several steps to ensure that these specific causes of death are specified appropriately. First, because some individuals may have congenital anomalies of the heart and circulatory
system that may foster inactivity, promote obesity, and ultimately lead to death, we exclude those
who die of congenital causes (ICD-9 745 to 747) from our analyses of circulatory disease mortality. Second, diabetes may often contribute to circulatory disease mortality. Separate analyses (not
shown) compare mortality where circulatory disease is the underlying cause, regardless of any
104
RESEARCH ON AGING
contributing causes, and mortality where circulatory disease is the underlying cause and diabetes
is listed as a contributing cause. The models yield similar results, although we find slightly stronger associations between obesity and circulatory disease mortality if diabetes is listed as a contributing cause. Future work could focus more closely on the relationships between underlying
and contributing causes of death.
6. We tested various parametric and nonparametric hazard models, and in all cases the direction, magnitude, and significance of the estimates were identical. We report results from the Cox
proportional models because they make the fewest assumptions about the underlying hazard.
7. Figure 1 and prior work (Flegal et al. 1998; Rogers et al. 2003) find that more contemporary cohorts average higher BMIs than earlier cohorts. To determine whether the effect of BMI on
mortality changes with time, we ran separate analyses with dummy variables for each year of
interview. These variables were nonsignificant: Although BMI is increasing in our population
over time, the effect of BMI on mortality is constant across recent cohorts.
8. Other work finds that racial, ethnic, and sex groups exhibit important differences in specific causes of death and the prevalence of smoking behaviors and obesity. Although disaggregating our analyses by race/ethnicity and sex is beyond the scope of this article, future work
could more closely examine whether our findings change among various subpopulations.
9. We use the G statistic to test whether models with interactions fit significantly better than
similarly specified models without interaction terms (Hosmer and Lemeshow 1989). We calculate G = –2 (Log-Likelihood for Model 1 – Log-Likelihood for Model 2), where the distribution
is chi-square and the degrees of freedom equal the variables added between models.
10. Prior work suggests that the effect of overweight and obesity on mortality diminishes
with age. For example, Rogers et al. (2003) found that, compared to those who are normal
weight, the risk of death for obese class III individuals is 77% greater among those aged 25 to 44,
61% greater among those aged 45 to 64, and just 21% greater among adults aged 65 and older
over the follow-up period. But our findings suggest that the diminishing effect of obesity on mortality as age increases is only important for circulatory disease mortality among adults aged 60
and older.
11. The models in Table 2 assume that the relationship between body mass and mortality is
constant over time. Thus, those equations provide estimates of the average effect of being underweight on mortality throughout the 132 follow-up months (Allison 1995).
12. We also tested for three-way interactions between smoking status, BMI, and duration of
follow-up. Although these were significant, underweight individuals displayed the same pattern
of decreasing mortality for each smoking status. Therefore, for clarity of presentation, we present
the two-way interactions between BMI and mortality.
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Patrick M. Krueger is a doctoral candidate in the Department of Sociology and Population Program, University of Colorado–Boulder. His dissertation has recently
received support from the National Science Foundation and the Agency for Healthcare Research and Quality and examines whether and how disparities in social, cultural, and economic capital account for race/ethnic and sex differentials in exercise.
Richard G. Rogers is professor of sociology and director of the Population Program,
University of Colorado–Boulder. His work investigates the social, economic, and behavioral causes of physical health and length of life. He recently published Living and
Dying in the USA with Robert Hummer and Charles Nam (Academic Press, 2000).
Robert A. Hummer is director of the Population Research Center at the University of
Texas at Austin. His work focuses on race/ethnic, socioeconomic, and behavioral factors related to health and mortality patterns, including a new project that examines
the linkage between religious involvement and adult mortality in the United States.
Jason D. Boardman is assistant professor of sociology and research associate in the
Population Program, University of Colorado–Boulder. His work focuses on the social
determinants of health with a particular emphasis on race/ethnic differentials in
physical and mental well-being at various stages in the life course.
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