Life Expectancy and Life Expectancy With Disability of Normal Weight, Overweight, and Obese Smokers and Nonsmokers in Europe

Article (PDF Available)inObesity 19(7):1451-9 · March 2011with25 Reads
DOI: 10.1038/oby.2011.46 · Source: PubMed
Abstract
The goal of this study was to estimate life expectancy (LE) and LE with disability (LwD) among normal weight, overweight, and obese smokers and nonsmokers in Western Europe. Data from four waves (1998-2001) of the European Community Household Panel (ECHP) were used; a standardized multipurpose annual longitudinal survey. Self-reported health and socioeconomic information was collected repeatedly using uniform questionnaires for 66,331 individuals in nine countries. Health status was measured in terms of disability in daily activities. Multistate Markov (MSM) models were applied to obtain hazard ratios (HRs) and age-specific transition rates according to BMI and smoking status. Multistate life tables were computed using the predicted transition probabilities to estimate LE and LwD. Significant associations were observed between disability incidence and BMI (HR = 1.15 for overweight, HR = 1.64 for obese, compared to normal weight). The risk of mortality was negatively associated with overweight status among disabled (HR = 0.77). Overweight people had higher LE than people with normal-weight and obesity. Among women, overweight and obese nonsmokers expect 3.6 and 6.1 more years of LwD than normal weight persons, respectively. In contrast, daily smokers expect lower LE but a similar LwD. The same patterns were observed among people with high education and those with low education. To conclude, daily smoking is associated with mortality more than with disability, whereas obesity is associated with disability more than with mortality. The findings suggest that further tobacco control would contribute to increasing LE, while tackling the obesity epidemic is necessary to prevent an expansion of disability.

Figures

OBESITY | VOLUME 19 NUMBER 7 | JULY 2011 1451
nature publishing group
articles
epidemiology
INTRODUCTION
Life expectancy (LE) has been increasing for decades (1), but
whether and to what extent disability-free LE will increase is
not entirely clear (2,3). In the past, advances in medical tech-
nologies and their increased availability have contributed to
the independence of older people (4). However, any optimism
may be diminished by the impact of obesity (5). e worldwide
epidemic has already resulted in a doubling of the prevalence
of obesity in Western and Westernizing countries (6).
Early studies of US populations found large eects of over-
weight and obesity (referred to the summary term overweight
statusbelow) on both premature death risk and on disability
prevalence (7,8). However, these eects reect life histories
of older cohorts and there is evidence that the excess risk of
higher BMI on mortality has diminished over time (9,10).
Studies using more recent data from the United States showed
smaller impacts on LE, but still large eects on disability-free
LE (11,12).
Results for Europe might be dierent from those of the
United States though, due to dierent epidemiological proles
between these regions. In Southern Europe for example, lower
cardiovascular mortality rates have been recorded than in the
United States, despite the relatively high prevalence of classi-
cal risk factors (13). Consequently, one might hypothesize that
the diminishing eect of high BMI on mortality over time may
vary across the Atlantic.
Unfortunately, the evidence on the impact of overweight
status on LE and the burden of disability in Europe is largely
incomplete. Previous studies were based on small sample sizes
and were restricted to single countries (14,15). Consequently,
a comprehensive picture on the population health associated
with overweight status in Europe is still missing.
e aim of this study was to assess how much the overweight
status is associated with longevity and the burden of disability
in Western-Europe. To measure this relationship, LE and LE
with disability (LwD) were estimated by overweight status and
Life Expectancy and Life Expectancy With
Disability of Normal Weight, Overweight, and
Obese Smokers and Nonsmokers in Europe
Istvan M. Majer
1
, Wilma J. Nusselder
1
, Johan P. Mackenbach
1
and Anton E. Kunst
2
The goal of this study was to estimate life expectancy (LE) and LE with disability (LwD) among normal weight,
overweight, and obese smokers and nonsmokers in Western Europe. Data from four waves (1998–2001) of the
European Community Household Panel (ECHP) were used; a standardized multipurpose annual longitudinal survey.
Self-reported health and socioeconomic information was collected repeatedly using uniform questionnaires for
66,331 individuals in nine countries. Health status was measured in terms of disability in daily activities. Multistate
Markov (MSM) models were applied to obtain hazard ratios (HRs) and age-specific transition rates according
to BMI and smoking status. Multistate life tables were computed using the predicted transition probabilities to
estimate LE and LwD. Significant associations were observed between disability incidence and BMI (HR = 1.15 for
overweight, HR = 1.64 for obese, compared to normal weight). The risk of mortality was negatively associated with
overweight status among disabled (HR = 0.77). Overweight people had higher LE than people with normal-weight
and obesity. Among women, overweight and obese nonsmokers expect 3.6 and 6.1 more years of LwD than normal
weight persons, respectively. In contrast, daily smokers expect lower LE but a similar LwD. The same patterns were
observed among people with high education and those with low education. To conclude, daily smoking is associated
with mortality more than with disability, whereas obesity is associated with disability more than with mortality. The
findings suggest that further tobacco control would contribute to increasing LE, while tackling the obesity epidemic is
necessary to prevent an expansion of disability.
Obesity (2011) 19, 1451–1459. doi:10.1038/oby.2011.46
1
Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands;
2
Department of Public Health, Academic Medical Centre
(AMC), University of Amsterdam, Amsterdam, The Netherlands. Correspondence: Istvan M. Majer (i.majer@erasmusmc.nl)
Received 13 July 2010; accepted 25 January 2011; published online 17 March 2011. doi:10.1038/oby.2011.46
1452 VOLUME 19 NUMBER 7 | JULY 2011 | www.obesityjournal.org
articles
epidemiology
smoking status. Furthermore, it was investigated in subgroups
of men and women as well as of low and high educated.
LE and LwD are both aggregate measures of population
health referring to a certain period of time. LwD has simi-
lar interpretation to (total) LE but it refers to the number of
years that people expect to live with disability. LE and LwD
(and their dierence, disability-free LE”) are routinely used
aggregate indicators of population health for a certain calendar
period (16).
e European Community Household Panel (ECHP) was
used as data source. e main advantages of ECHP were that it
provided comparable information on health and mortality for
several European countries, and that it gave sucient statisti-
cal power to obtain precise estimates for disability incidence
and recovery rates, and for life table calculations for specic
risk factor groups.
METHODS AND PROCEDURES
Data
e ECHP is a standardized multipurpose annual longitudinal social
survey carried out at the level of the European Union between 1994 and
2001. It is centrally designed and coordinated by the Statistical Oce
of the European Communities (Eurostat), and includes demographics,
labor force behavior, income, health, education, training, housing, and
migration. e data were collected by the National Statistical Institutes
or research centers of the participating countries using uniform ran-
dom sampling design and common blueprint questionnaires. Although
Eurostat le the National Institutes of Statistics free to organize the data
collection, and national reporting on the survey organization is lacking
(making assessment of data quality dicult), data checks, imputation,
and weighting were done centrally to maximize data comparability. e
ECHP is intended to be both cross-sectionally and longitudinally repre-
sentative with respect to the national household populations. For more
information about the design and the data procedures of the ECHP we
refer to an extensive review of Peracchi (17).
Data from the h wave of 1998 onward until 2001 were used for the
current study, as 1998 was the rst year that height, weight, and smoking
status were asked from the participants. Countries for which mortal-
ity data (the Netherlands), disability data (Luxembourg), or BMI data
(France) was not available were omitted from the study. Similarly, data
from Germany and the UK had to be le out because the original ECHP
data were replaced by data from national surveys (German Social Eco-
nomic Panel and British Household Panel Survey) which did not contain
information on smoking and BMI.
Information on nonresponse rates at baseline and cumulative retention
percentages, i.e., the cumulative percentages of individuals retained until
the fourth wave of the panel are presented in Supplementary Table S1
online. ere were dierences between countries, with high retention
percentages in Italy (83%) and Portugal (87%), whereas samples in Ire-
land, Denmark, and Spain suered somewhat higher attrition. Respond-
ents were followed over a maximum of 4 years. Since respondents could
die, while new individuals could also join the survey in calendar years
later than 1998, the average follow-up time was less than the maximum
achievable. However, most people (85%) participated in all four waves.
Detailed information on the distribution of age at entry, number of deaths
as well as average follow-up time is presented in Tabl e 1 .
Indicators
All individuals were asked if they were hampered in their daily activities
by any physical or mental health problem, illness, or disability in each
wave. Positive response were categorized in two classes of severity: “Yes,
to some extentor “Yes, severely” (18). For this study, we considered
them as a single disabled category. Persons who were lost to the ECHP
because of moving to an institution were also considered as “disabled
for the remainder of the study period.
e BMI variable was recoded into four categories: (i) underweight:
BMI ≤18.5, (ii) normal weight: 18.5 < BMI ≤ 25, (iii) overweight: 25 <
BMI ≤ 30, and (iv) obese: BMI >30. Aer preliminary analysis, under-
weight persons were excluded from further analysis because of the small
number of respondents and the high prevalence of disability in this cat-
egory. Moreover, this group is less relevant for the purpose of our study as
their prevalence of disability is commonly explained as a result of weight
loss caused by ill-health, rather than a causal eect of underweight on
disability.
Smoking status was classied into three categories: (i) current daily
smokers, (ii) former (daily) smokers, and (iii) never (daily) smokers. e
latter included those who smoke occasionally or who used to smoke occa-
sionally. With regard to former smokers, health problems may increase
the chances of smoking cessation; therefore the association of health
with former smoking may in part reect a selection eect. Additionally,
the group of former smokers is a heterogeneous group in terms of time
since quitting. As a result, the interpretation of results for this group is
less straightforward. erefore, former smokers were excluded from the
presentation of the detailed results.
e distribution of overweight and smoking status in the study popula-
tion is presented in Tab le 1 .
e level of completed education at the h wave was used to measure
education status. Individuals were divided into three groups according to
their level of educational attainment based on the International Stand-
ard Classication of Education (19): (i) lower secondary education or
lower; (ii) upper secondary education; and (iii) tertiary education, which
includes higher vocational and university education. For this study, we
considered people with upper secondary or tertiary education as those
with high education.
Data analysis
We employed a multistate Markov (MSM) model, which is oen used
to describe the process in which individuals move through health states
over time (20). By tting MSM models to longitudinal data one is able
to estimate hazard or transition rates. e association between variables
and a particular transition rate was modeled assuming proportional
hazards.
We dened three health states, nondisabled, disabled, and dead, and
four possible transitions between the health states: incidence (from non-
disabled to disabled), recovery (from disabled to nondisabled), and state-
specic mortality (from both nondisabled and disabled). We merged
data from the countries aer preliminary analyses showed only minor
cross-national dierences in associations with overweight and smoking.
Transition rates were estimated on the pooled dataset adjusted for age,
sex, overweight status, and smoking status. e advantage of preparing
estimates using the data of all nine countries together was the very large
number of observations that ensured sucient power in estimating the
age proles of disability incidence and recovery.
To detect signicant and meaningful interactions and to obtain esti-
mates of transition rates that accurately describe the data, various mod-
els were considered and evaluated. Models including two- or three-way
interaction terms between sex, overweight, and smoking status were
tested. No signicant interactions were found between smoking and
overweight status. Akaike Information Criteria values indicated that
the best model t was achieved by including two-way sex-interactions.
In additional analyses according to educational subgroup, we included
interaction terms between education and overweight and smoking
status. Age–sex interaction terms were included in both models.
Because mortality cases were under-registered in the ECHP in most
of the countries, the mortality rates were adjusted to the pooled level
(pooled across country and time) in four steps. First, using national
statistics, mortality rates were calculated over the period 1998–2001
for each age and sex. Second, they were transformed into mortality
rates of nondisabled and disabled assuming that (i) the age- and sex-
specic mortality rates in the overall (i.e., mixed nondisabled–disabled)
OBESITY | VOLUME 19 NUMBER 7 | JULY 2011 1453
articles
epidemiology
population are the weighted average of mortality rates of nondisabled
and disabled populations, with the proportion of nondisabled and
disabled, respectively as weights estimated from the ECHP, and (ii)
that the ratio between the mortality rate of disabled and nondisabled
populations is equal to the hazard ratio (HR) as estimated from the
ECHP data (21). ird, rescaling factors were calculated to specify how
much age- and sex-specic mortality rates estimated from the ECHP
had to be scaled to make them consistent with the estimates from step
two. Fourth, all predicted mortality rates were multiplied by the cor-
responding rescaling factors, assuming that under-representation of
mortality was the same for each overweight and smoking status com-
bination. A more formal explanation of the decomposition is shown
in Supplementary Data online. Furthermore, we provide a gure in
Supplementary Figure S1 online with typical age proles to help visu-
alize transition rates.
Multistate life tables were used to estimate LE and LwD at the age of
16. e empirical input for the multistate life tables were the transition
rates described above, aer converting them into probabilities (assum-
ing that hazard rates were constant over a life year). Once a life table was
set-up, probabilistic sensitivity analysis (22) was performed to estimate
condence intervals (CIs) around the LEs and LwD. For this, random
log-HRs were drawn from a multivariate normal distribution, which
was dened by the natural logarithm of the regression coecients and
their variance–covariance structure. e corresponding transition rates
and life expectancies were calculated for each of the 1,000 draws. e
25th and 975th of the latter ordered values indicated the 2.5% and 97.5%
boundaries of the CIs.
All MSM analyses were performed in R (23), whereas life table
calculations were carried out in Microso Excel. Due to technical
limitations of R taking into account ECHP sampling weights was not
possible.
RESULTS
e HRs of transitions by overweight status and smoking status
of the main eect model are given in Table 2. e risk of dis-
ability onset was moderately higher for overweight (HR = 1.15,
CI: 1.10–1.20) and considerably higher for obese (HR = 1.64,
CI: 1.54–1.74) than for normal weight persons. Conversely,
overweight showed to be protective for dying among those
who were disabled (HR = 0.77, CI: 0.66–0.90). Daily smoking
was strongly associated with early death among nondisabled
(HR = 1.68, CI: 1.29–2.19), weakly associated with disability
onset, and not associated with recovery.
e combined eect of two risk factors can be obtained as
well by multiplying the corresponding HRs. For example, the
HR of overweight and smoking on disability incidence could
be calculated as follows: 1.09 × 1.15 = 1.25 (compared to a per-
son who has normal weight and is nonsmoker).
e HRs of incidence and recovery of the models assessing
interaction with sex and educational level are given in Table 3.
For men, disability incidence was not associated with over-
weight (HR = 1.00, CI: 0.94–1.07), whereas it was associated
with obesity (HR = 1.35, CI: 1.23–1.48). Among women both
Table 1 Characteristics of the study population
Number of individuals
a
Mean age (range) Mean follow-up time Overweight (%) Obese (%) Smoker
b
(%)
Men
Finland 3,179 44.7 (16.8–89.8) 2.4 42.2 10.9 24.8
Denmark 1,694 46.5 (16.8–89.6) 2.8 38.6 9.6 35.8
Ireland 2,412 45.2 (16.7–90.2) 2.5 41.7 7.8 25.9
Austria 2,721 45.4 (15.3–89.6) 2.8 41.9 11.3 29.5
Belgium 2,072 46.2 (16.3–88.7) 2.7 39.2 10.1 29.9
Greece 3,985 49.1 (17.3–90.3) 2.0 51.4 9.9 44.2
Italy 6,735 45.1 (16.8–89.7) 2.6 38.9 8.2 31.1
Spain 5,212 44.9 (15.9–89.7) 2.8 43.0 13.2 37.9
Portugal 4,628 46.8 (16.9–89.8) 2.8 43.5 9.5 28.6
Sum 32,638 45.9 (15.3–90.3) 2.6 42.3 10 32.5
Women
Finland 3,112 45.8 (16.8–89.5) 2.4 28.7 13.2 14.9
Denmark 1,706 47.1 (16.7–89.4) 2.8 26.5 9.4 33.8
Ireland 2,375 46.2 (16.7–89.6) 2.5 28.1 8.5 23.9
Austria 2,814 47.9 (15.3–89.8) 2.9 30.2 10.8 15.9
Belgium 2,278 47.5 (16.4–89.4) 2.7 24.6 11.1 20.7
Greece 4,389 50.6 (17.4–90.4) 2.0 35.7 10.0 14.7
Italy 6,742 47.1 (16.8–89.8) 2.6 24.8 7.4 13.2
Spain 5,262 47.7 (15.9–89.7) 2.8 28.9 13.6 20.2
Portugal 5,015 49.6 (16.9–89.9) 2.8 32.5 11.1 4.8
Sum 33,693 47.9 (15.3–90.4) 2.6 28.9 10.4 16.0
ECHP, European Community Household Panel.
a
The number of individuals refers to those who participated in at least two waves of ECHP survey between 1998 and 2001.
b
Those who are not daily smoker could be
never smokers and former smoker, treating these two groups separately.
1454 VOLUME 19 NUMBER 7 | JULY 2011 | www.obesityjournal.org
articles
epidemiology
overweight and obesity were related to an increased risk of
disability onset (overweight: HR = 1.28, CI: 1.20–1.36; obese:
HR = 1.87, CI: 1.72–2.03). With regard to recovery from dis-
ability no substantial dierences between the risk factors were
found among men or women; however, the negative associa-
tion between obese women and recovery, while marginal, is
noteworthy (HR = 0.92, CI: 0.85–0.99).
Educational level was strongly associated with the transition
rates. Low educated people faced higher risk of disability inci-
dence, high state-specic mortality, and lower chance of recov-
ery from disability. However, the relationship between the risk
factors and disability incidence and recovery were similar for
high and low educational level people (results not shown).
Table 4 shows estimates of LE, LwD, and the ratio of LwD
to LE by overweight status and smoking status. A positive
association between overweight and LE was found for both
nonsmokers and smokers, as well as for men and women. For
example, among nonsmokers, the dierence in LE relative to
normal weight was 2.0 years for men and 2.9 years for women.
Conversely, obesity was negatively associated with LE, although
much more clearly for women (−1.6 years) than for men (−0.2
years). Daily smoking was inversely related to LE. Smokers had
3.5 and 2.3 years lower LE than nonsmokers, among normal
weight men and women, respectively.
e relationship between overweight status and LwD was dif-
ferent: the higher the BMI category the more years are expected
to be spent with disability. Normal weight, overweight, and
obese nonsmoker men can expect to live 9.5, 10.1, and 11.8
years with disability, respectively. Smokers expect lower LE
but the same number of years with disability. In general, over-
weight status was the main driver of the proportion of LE spent
with disability. For example, normal weight, overweight, and
obese nonsmoker women expect to live 11.9 (18.1% of LE),
15.5 (22.6%), and 18.0 (28.2%) years with disability. ese gen-
eral patterns were observed for both sexes despite some varia-
tion between men and women.
Figure 1 presents LE and LwD estimates by overweight and
smoking status among low and high educated. e positive
association between overweight and LE was observed among
both high educated and among low educated. For both high
and low educated people, the previously observed patterns were
found: in higher BMI categories people expect to live more
years in disability, whereas smoking is not related to the pro-
portion of life spent with disability. e magnitude of LwD and
its proportions of LE were considerably higher for low educated
than for high educated persons due to the association between
educational level and both mortality and disability burden.
DISCUSSION
is study quantied the relationship of overweight status
with longevity and the burden of disability in Western-Europe
in terms of LE and LwD. Overweight people can expect to
live slightly longer than those with normal weight, which—
suggested by other studies—might be a consequence of the
protective eect of overweight on mortality in disabled popu-
lations. In contrast, overweight and obese people can expect
to live 3.6 and 6.1 more years with disability though, respec-
tively. Smoking had a dierent relationship with LE and LwD,
as it was associated with lower LE but with an unchanged
LwD. Similar patterns were observed both among men and
women, and among low and high educated populations.
Sensitivity analyses
A number of sensitivity analyses were performed to further
explore the relationship between overweight status, smoking
status, and the burden of disability.
Table 2 Hazard ratios associated with disability incidence, recovery from disability, and state-specific mortality by overweight
status and smoking status
Hazard ratios
a
(CI) (number of transitions)
Normal weight +
never-smoker
Normal weight +
daily smoker
Overweight +
never-smoker
Obese +
never-smoker
Disability incidence 1.00 1.09* 1.15* 1.64*
(1.04, 1.15) (1.10, 1.20) (1.54, 1.74)
(2,955) (1,043) (2,742) (1,071)
Recovery from disability 1.00 1.01 1.03 0.96
(0.95, 1.07) (0.98, 1.08) (0.90, 1.02)
(2,619) (921) (2,539) (973)
Mortality of nondisabled 1.00 1.68* 0.81 1.06
(1.29, 2.19) (0.63, 1.03) (0.73, 1.52)
(162) (55) (121) (43)
Mortality of disabled 1.00 1.04 0.77* 0.82
(0.81, 1.34) (0.66, 0.90) (0.65, 1.03)
(257) (49) (190) (73)
Hazard ratios are interpreted at average age.
a
Derived from a multistate Markov (MSM) model that included age, sex, age × sex, overweight, obese, past daily smoking, and daily smoking.
*P < 0.05.
OBESITY | VOLUME 19 NUMBER 7 | JULY 2011 1455
articles
epidemiology
Table 3 Hazard ratios associated with disability incidence and recovery from disability by overweight status and smoking status
according to sex and educational level
Number of
transitions
Normal weight +
never-smoker
Normal weight +
daily smoker
Overweight +
never-smoker
Obese +
never-smoker
Hazard ratio of incidence
Baseline model
a
10,244 1.00 1.09* 1.15* 1.64*
(1.04, 1.15) (1.10, 1.20) (1.54, 1.74)
Male
b
4,602 1.00 1.15* 1 1.35*
(1.07, 1.24) (0.94, 1.07) (1.23, 1.48)
Female
b
5,642 1.00 1.05 1.28* 1.87*
(0.97, 1.16) (1.20, 1.36) (1.72, 2.03)
Low educated
c
7,307 1.00 1.11* 1.13* 1.54*
(1.04, 1.18) (1.07, 1.19) (1.43, 1.66)
High educated
c
2,937 1.00 1.13* 1.12* 1.72*
(1.03, 1.24) (1.03, 1.22) (1.53, 1.94)
Hazard ratio of recovery
Baseline model
a
9,171 1.00 1.01 1.03 0.96
(0.95, 1.07) (0.98, 1.08) (0.90, 1.02)
Male
b
4,057 1.00 1.08 1.04 1.01
(0.99, 1.17) (0.97, 1.11) (0.92, 1.12)
Female
b
5,114 1.00 0.93 1.03 0.92*
(0.85, 1.02) (0.97, 1.10) (0.85, 1.00)
Low educated
c
6,524 1.00 1.02 1.07* 1.00
(0.95, 1.10) (1.01, 1.13) (0.92, 1.07)
High educated
c
2,647 1.00 1.01 0.96 0.88*
(0.92, 1.11) (0.88, 1.05) (0.78, 0.99)
Hazard ratios are interpreted at average age.
a
Derived from a multistate Markov (MSM) model that included age, sex, age × sex, overweight, obese, past daily smoking, and daily smoking.
b
Derived from a multistate
Markov (MSM) regression model that included: age, sex, age × sex, overweight, obese, past smoking, daily smoking, overweight × sex, obese × sex, past smoker × sex,
and daily smoker × sex.
c
Derived from a multistate Markov (MSM) regression model that included: age, sex, age × sex, education, overweight, obese, past smoking, daily
smoking, overweight × education, obese × education, past smoker × education, and daily smoker × education.
*P < 0.05.
Table 4 Total life expectancy and life expectancy with disability by smoking and overweight status at the age of 16
Never-smoker Daily smoker
Normal weight Overweight Obese Normal weight Overweight Obese
Male
LE 60.4 62.4 60.2 56.9 58.8 56.7
(60.2, 60.5) (62.2, 62.5) (60.0, 60.4) (56.7, 57.0) (58.7, 59.0) (56.6, 56.8)
LwD 9.5 10.1 11.8 8.9 9.5 11.1
(9.1, 9.9) (9.7, 10.5) (11.1, 12.6) (8.4,9.4) (9.0, 10.0) (10.3, 11.9)
LwD/LE 15.70% 16.20% 19.60% 15.70% 16.10% 19.60%
Female
LE 65.5 68.4 63.9 63.2 66.4 61.9
(65.4, 65.7) (68.2, 68.6) (63.7, 64.1) (62.9, 63.4) (66.2, 66.7) (61.6, 62.2)
LwD 11.9 15.5 18.0 12.0 15.7 18.3
(11.5, 12.3) (15.0, 15.9) (17.3, 18.8) (11.3, 12.7) (14.8, 16.7) (17.2, 19.5)
LwD/LE 18.10% 22.60% 28.20% 19.00% 23.70% 29.60%
Derived from an multistate Markov regression model that included: age, sex, age × sex, overweight, obese, past daily smoking, daily smoking, overweight × sex, obese × sex,
past daily smoker × sex, and daily smoker × sex.
LE, total life expectancy; LwD, life expectancy with disability.
1456 VOLUME 19 NUMBER 7 | JULY 2011 | www.obesityjournal.org
articles
epidemiology
First, an additional analysis was carried out to assess the sen-
sitivity of the results to dierences in the ways in which surveys
in the participating countries were carried out. To this end,
dummy variables for each country were added to the main
eect” MSM model. Such model can be seen as a xed eects
model. Dierences between the countries were found in terms
of the general level of transition rates. However, these inter-
country dierences did not substantially alter our key ndings.
For example the HR of disability incidence among obese were
1.49 (CI: 1.40–1.59) vs. 1.64 (CI: 1.54–1.74) with and without
controlling for counties, respectively. Similar (but generally
smaller) eects were found for all risk factors.
Second, the original obese category (BMI >30) was split into
two subcategories: BMI between 30 and 35 (mild obese), and
>35 (severe obese). Our results indicated heterogeneity within
the group of obese people, with the severe obese being worse
o. e incidence of disability was associated with mild obes-
ity (HR = 1.60, CI: 1.50–1.70) and slightly stronger with severe
obesity (HR = 1.91, CI: 1.69–2.16). Besides, the relative risk
of recovering from disability was lower in the severely obese
group (HR = 0.79, CI: 0.69–0.90) than in the mild obese group
(HR = 1.00, CI: 0.94–1.07). No statistically signicant dier-
ences were found in mortality risk. HRs of the other risk fac-
tors remained stable aer recoding the obese category.
70.0
65.0
60.0
55.0
50.0
45.0
Years
40.0
35.0
30.0
75.0
70.0
65.0
60.0
55.0
50.0
45.0
Years
40.0
35.0
30.0
NW OW OB NW OW OB NW OW OB NW OW OB
NW = Normal weight
OW = Overweight
OB = Obese
High educated
Low educated
Nonsmoker
High educated
Low educated
Smoker
NW OW OB NW OW OB NW OW OB NW OW OB
High educated
Low educated
Nonsmoker
High educated
Low educated
Smoker
Men
LWD
DFLE
NW = Normal weight
OW = Overweight
OB = Obese
LWD
DFLE
62.0
65.6
61.3
58.1
60.3
58.1
57.9
61.4
57.7
55.9
58.3
56.1
7.4
9.8
12.1
11.0
12.3
14.7
7.0
9.4
11.7
11.0
12.3
14.7
68.0
71.7
66.8
64.6
66.7
64.9
65.2
68.9
64.9
63.6
65.8
64.1
10.6
13.9
16.7
15.1
16.7
19.9
10.6
13.8
16.8
15.6
17.2
20.5
Women
a
b
Figure 1 Life expectancy estimates according to smoking and overweight status at age 16 by educational level. DFLE, disability-free life expectancy;
LwD, life expectancy with disability.
OBESITY | VOLUME 19 NUMBER 7 | JULY 2011 1457
articles
epidemiology
ird, the inuence of age on the association was explored.
It is oen argued that the size of the association between a risk
factor and outcome decreases with age. erefore two-way
interaction variables between age and the risk factors were
added to the “main eects” MSM model. We found signicant
interaction terms between age and obesity (for disability inci-
dence), and between age and daily smoking (for both disability
incidence and recovery). e age-interaction term was fairly
small indicating an approximately a 0.4% decrease with every
year of increasing age.
Data strength and limitations
e ECHP data has a number of strengths of note. e most
evident of which is the availability of a large number of observa-
tions on disability incidence or recovery, and moreover the use
of an identical survey design and survey questionnaire for all
participating countries. Although the data collection was car-
ried out by the National Institutes of Statistics separately, and
hence national versions of the ECHP are not perfectly compa-
rable, these common questions ensured a much higher degree
of comparability between countries than would have been
possible using national data sources. e key variables used in
this article, on health status and risk factors, are comparable
across all ECHP countries. Our study was the rst to assess the
impact of overweight for large number of Western-European
countries simultaneously. In contrast, previous European stud-
ies focused exclusively on single countries, and used relatively
much smaller data sets.
is study also has a number of limitations, however. First,
disability data were self-reported, which can result in either
under- or over-reporting of disability. If the reporting of dis-
ability diers by risk factor group then our estimates of impact
of LE and LwD will also be biased. Studies have shown that
measurements of functional limitations by self-report or objec-
tive measures are consistently associated, and that they reect
similar assessment of function (24,25). It is therefore likely that
objective measures of disability would show similar associa-
tions with smoking, overweight status, and education.
A second potential limitation is related to the use of self-
reports on smoking and on weight and height. It has been found
that smoking prevalence rates are underestimated if estimates
are based on self-report (26). Similarly, people tended to under-
report weight and over-report height (27). ese ndings were
conrmed by recent studies (28,29). If such under-reporting of
daily smoking and BMI levels is nondierential, it may lead to
an underestimation of the association with disability, and thus
an underestimation of the dierences in LwD.
A third drawback relates to nonresponse and attrition, which
might be a problem in our study if they are related to disability
or risk factor groups. Some studies have explored the attrition
in the ECHP. For example, analyses on attrition in the ECHP
showed a positive relationship between attrition and worsen-
ing health in all countries (30) and only a weak relationship
between attrition and educational level (31). Additional to
these studies, we assessed the likelihood of attrition in the cur-
rent study population in relation to characteristics at the last
wave in which the respondent participated (32). We found that
the risk of loss to follow-up for reasons other than death or
institutionalization was hardly related to disability status, sex,
overweight status, or educational level (relative dierences
of ~10% or less). We found slightly higher risk of attrition in
former and daily smokers only (relative dierence 15%). ese
ndings imply that the relative dierences are unlikely to have
a major eect on dierential retention, and therefore attrition
could not have strongly biased our estimates of LE and LwD by
overweight status.
When we adjusted the estimates for underestimation of
mortality in the ECHP data, we had to assume that the degree
of underestimation was identical in each risk factor group. If,
for a specic risk factor group the under-registration of mor-
tality would in fact be larger than what we assumed, the LE for
this group would be overestimated. It is dicult to assess to
what extent this is a problem. Even though the causes of under-
registration of deaths in the ECHP data are not known, we see
no reason to expect a strong relationship with overweight sta-
tus or other risk factors. Furthermore, we would like to point
out that even though this problem might aect LE estimates, it
could not explain the large dierences between BMI groups in
estimates of LwD.
Previous studies
Data from previous studies are not directly comparable with
our data for several reasons: dierent ages for which life expect-
ancies were calculated; many of the previous studies reect life
histories of older cohorts; and dierent measures and clas-
sications of disability were used. Nonetheless, comparisons
could be made with regard to the general patterns observed.
Peeters et al. (7,33) found large eects of overweight and
obesity on premature death. According to their results non-
smoking men and women lost 5.8 and 7.1 years of LE at age 40,
respectively, due to obesity. Because of both higher disability
prevalence and higher mortality in the obese population, the
authors found no signicant dierence in LwD (measured by
activities of daily living scores) between the obese and those of
normal weight. However, these eects reect life histories of
older cohorts. ere is evidence that the eects of high BMI on
mortality have diminished over time (10,34).
Studies using more recent data from the United States have
already justied smaller impacts on LE but large eects on LwD.
For example, Reuser et al. (11) estimated the burden of mortal-
ity of obesity among middle- and old-age adults in the Health
and Retirement Survey. LE and LE with activities of daily liv-
ing disability were calculated in relation to self-reported BMI,
smoking, and education at age 55. Obesity was found to have
only a limited eect on mortality, and overweight was esti-
mated to be protective for dying. Both overweight and obesity
increased LwD for both sexes. ese results are closely consist-
ent with our ndings.
Walter et al. estimated the inuence of overweight and obes-
ity on mortality and disability by quantifying its eect in terms
of disability-free LE and years lost to disability for a suburban
elderly population in the Netherlands (35). Similar to our
1458 VOLUME 19 NUMBER 7 | JULY 2011 | www.obesityjournal.org
articles
epidemiology
conclusions, they did not nd that increased BMI reduces LE
because of a protective eect of overweight on death (HR = 0.81
compared to normal weight). As in our study, both overweight
and obesity were found to be associated with a larger number
of years lived with disability.
Evaluation
e relation between overweight and mortality has been a con-
troversial topic. We showed that overweight is associated with
protection for dying among the disabled, whereas the relation-
ship between obesity and mortality is modest, especially among
men. Our results rearm recent doubts about overstated con-
cerns of overweight and obesity in terms of excess mortality
(36). Possible explanations for the small and even protective
mortality eect of overweight include improved survival of
overweight persons from major diseases of developed socie-
ties, e.g., heart failure (37) or cardiovascular disease (38), and
a better nutritional status providing necessary reserves during
chronic disease (39). Recent studies rearmed the protective
eect of overweight as well documenting that increased BMI
protects against mortality aer hospitalization (40).
e steadily increasing LE in the developed countries is a
great achievement but at the same time a major concern.
It raises the question of whether living longer lives will be
accompanied by a decrease or an increase of disability during
old age. e main concern is that the obesity epidemic may,
in the long run, increase the prevalence of disability in ageing
populations.
Our results show that tobacco control is still highly relevant
to the prevention of premature death. Continued success in
this area may contribute to a further increase in LE, but with-
out substantially aecting the burden of disability over the life
course (i.e., in terms of LwD). is stresses the importance of
further public health aimed to address the obesity epidemic.
Given the large impact of overweight and obesity on the bur-
den of disability over the life course, halting the obesity epi-
demic is essential if an increase of LwD is to be stopped as LE
continues to grow.
SUPPLEMENTARY MATERIAL
Supplementary material is linked to the online version of the paper at http://
www.nature.com/oby
ACKNOWLEDGMENTS
This work was supported by MicMac, an international research project
funded by the European Commission in the context of the Sixth
Framework Programme (grant number: SP23-CT-2005-006637). The
funding organization did not participate in the design and conduct of the
study, collection, management, analysis, and interpretation of the data;
and preparation, review, or approval of the manuscript. MicMac has been
developed by a team of researchers from the Netherlands Interdisciplinary
Demographic Institute (NIDI), The Hague, the Vienna Institute of Demography
(VID), the Institut National d’Etudes Demographiques (INED), Paris, the
Bocconi University, Milan, the Erasmus Medical Centre, Rotterdam, the
Max Planck Institute for Demographic Research, Rostock, the International
Institute for Applied System Analysis (IIASA), Laxenburg, and the University
of Rostock. This study was also part of the project “Living longer in good
health,” which was financially supported by Netspar (grant number:
2007.3900.027).
DISCLOSURE
The authors declared no conflict of interest.
© 2011 The Obesity Society
REFERENCES
1. Vaupel JW, Carey JR, Christensen K et al. Biodemographic trajectories of
longevity. Science 1998;280:855–860.
2. Hubert HB, Bloch DA, Oehlert JW, Fries JF. Lifestyle habits and compression
of morbidity. J Gerontol A Biol Sci Med Sci 2002;57:M347–M351.
3. Khaw KT. Healthy aging. BMJ 1997;315:1090–1096.
4. Mor V. The compression of morbidity hypothesis: a review of research and
prospects for the future. J Am Geriatr Soc 2005;53:S308–S309.
5. Olshansky SJ, Passaro DJ, Hershow RC et al. A potential decline in
life expectancy in the United States in the 21
st
century. N Engl J Med
2005;352:1138–1145.
6. Waxman A, Norum KR. Why a global strategy on diet, physical activity and
health? The growing burden of non-communicable diseases. Public Health
Nutr 2004;7:381–383.
7. Peeters A, Bonneux L, Nusselder WJ, De Laet C, Barendregt JJ.
Adult obesity and the burden of disability throughout life. Obes Res
2004;12:1145–1151.
8. Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath CW Jr. Body-mass
index and mortality in a prospective cohort of U.S. adults. N Engl J Med
1999;341:1097–1105.
9. Adams KF, Schatzkin A, Harris TB et al. Overweight, obesity, and mortality
in a large prospective cohort of persons 50 to 71 years old. N Engl J Med
2006;355:763–778.
10. McGee DL; Diverse Populations Collaboration. Body mass index and
mortality: a meta-analysis based on person-level data from twenty-six
observational studies. Ann Epidemiol 2005;15:87–97.
11. Reuser M, Bonneux LG, Willekens FJ. Smoking kills, obesity disables: a
multistate approach of the US Health and Retirement Survey. Obesity (Silver
Spring) 2009;17:783–789.
12. Reynolds SL, Saito Y, Crimmins EM. The impact of obesity on active
life expectancy in older American men and women. Gerontologist
2005;45:438–444.
13. Lahoz-Rallo B, Blanco-Gonzalez M, Casas-Ciria I et al. Cardiovascular
disease risk in subjects with type 2 diabetes mellitus in a population in
southern Spain. Diabetes Res Clin Pract 2007;76:436–444.
14. Brønnum-Hansen H, Juel K, Davidsen M, Sørensen J. Impact of selected
risk factors on expected lifetime without long-standing, limiting illness in
Denmark. Prev Med 2007;45:49–53.
15. Visscher TL, Rissanen A, Seidell JC et al. Obesity and unhealthy life-years in
adult Finns: an empirical approach. Arch Intern Med 2004;164:1413–1420.
16. Robine JM, Jagger C, Mathers CD, Crimmins EM, Suzman RM. Determining
Health Expectancies. Wiley: Chichester, England, 2003.
17. Peracchi F. The European Community Household Panel: A review. Empir
Econ 2002;27:63–90.
18. Eurostat. ECHP UDB - Description of Variables. Data Dictionnary, Codebook
and Differences Between Countries and Waves. Luxemburg, 2003.
19. UNESCO. International Standard Classification of Education 1997. 1997.
20. Jackson CE, Snyder PJ. Electroencephalography and event-related
potentials as biomarkers of mild cognitive impairment and mild Alzheimer’s
disease. Alzheimers Dement 2008;4:S137–S143.
21. Nusselder WJ, Looman CW, Marang-van de Mheen PJ, van de Mheen H,
Mackenbach JP. Smoking and the compression of morbidity. J Epidemiol
Community Health 2000;54:566–574.
22. Briggs A, Schulpter M, Claxton K. Decision Modelling for Health Economic
Evaluation. Oxford University Press: Oxford, 2006.
23. Team RDC. R: A Language and Environment for Statistical Computing.
R Foundation for Statistical Computing: Vienna, Austria, 2008.
24. Coman L, Richardson J. Relationship between Self-Report and Performance
Measures of Function: A Systematic Review. Can J Aging 2006;25:253–270.
25. van den Brink CL, Tijhuis M, Kalmijn S et al. Self-reported disability and its
association with performance-based limitation in elderly men: a comparison
of three European countries. J Am Geriatr Soc 2003;51:782–788.
26. Gorber SC, Schofield-Hurwitz S, Hardt J, Levasseur G, Tremblay M. The
accuracy of self-reported smoking: a systematic review of the relationship
between self-reported and cotinine-assessed smoking status. Nicotine Tob
Res 2009;11:12–24.
OBESITY | VOLUME 19 NUMBER 7 | JULY 2011 1459
articles
epidemiology
27. Gorber SC, Tremblay M, Moher D, Gorber B. A comparison of direct vs.
self-report measures for assessing height, weight and body mass index:
a systematic review. Obes Rev 2007;8:307–326.
28. Keith SW, Fontaine KR, Pajewski NM, Mehta T, Allison DB. Use of
self-reported height and weight biases the body mass index-mortality
association. Int J Obes (Lond) 2010.
29. Stommel M, Schoenborn CA. Accuracy and usefulness of BMI measures
based on self-reported weight and height: findings from the NHANES &
NHIS 2001-2006. BMC Public Health 2009;9:421.
30. Eurostat. Attrition in the ECHP. Eurostat: Luxemburg, 2002.
31. Eurostat. Sample Attrition Between Wave 1 and 4 in the European
Community Household Panel. Eurostat: Luxemburg, 2002.
32. Majer IM, Nusselder W, Kunst AE. Age Profiles of Mortality and Disability-
Related Transitions in European Countries. Estimates According to Smoking
Status, and Overweight Status, Educational Level and Marital Status. In:
MicMac reports; Erasmus MC: Rotterdam: 58, 2008.
33. Peeters A, Barendregt JJ, Willekens F et al.; NEDCOM, the Netherlands
Epidemiology and Demography Compression of Morbidity Research Group.
Obesity in adulthood and its consequences for life expectancy: a life-table
analysis. Ann Intern Med 2003;138:24–32.
34. Flegal KM, Graubard BI, Williamson DF, Gail MH. Excess deaths
associated with underweight, overweight, and obesity. JAMA
2005;293:1861–1867.
35. Walter S, Kunst A, Mackenbach J, Hofman A, Tiemeier H. Mortality
and disability: the effect of overweight and obesity. Int J Obes (Lond)
2009;33:1410–1418.
36. Basham P, Luik J. Is the obesity epidemic exaggerated? Yes. BMJ
2008;336:244.
37. Oreopoulos A, Padwal R, Kalantar-Zadeh K et al. Body mass index
and mortality in heart failure: a meta-analysis. Am Heart J 2008;156:
13–22.
38. Nusselder WJ, Franco OH, Peeters A, Mackenbach JP. Living healthier
for longer: comparative effects of three heart-healthy behaviors on life
expectancy with and without cardiovascular disease. BMC Public Health
2009;9:487.
39. Baik I, Ascherio A, Rimm EB et al. Adiposity and mortality in men. Am J
Epidemiol 2000;152:264–271.
40. Peake SL, Moran JL, Ghelani DR, Lloyd AJ, Walker MJ. The effect of obesity
on 12-month survival following admission to intensive care: a prospective
study. Crit Care Med 2006;34:2929–2939.
    • "Several recently released studies found that health status in the overweight group was the same as or better than in the normal weight group. They suggest that standards for obesity and management of the overweight group may need to be changed31323334.We draw attention to the fact that depressive symptom were highest in the underweight group. With the rise of obesity in the population, social interest and healthcare policies are focused on the obesity problem; however, we should also pay attention to the health problems of the underweight group. "
    Full-text · Dataset · Apr 2015 · Demographic Research
    • "Several recently released studies found that health status in the overweight group was the same as or better than in the normal weight group. They suggest that standards for obesity and management of the overweight group may need to be changed31323334.We draw attention to the fact that depressive symptom were highest in the underweight group. With the rise of obesity in the population, social interest and healthcare policies are focused on the obesity problem; however, we should also pay attention to the health problems of the underweight group. "
    [Show abstract] [Hide abstract] ABSTRACT: The relationship between weight problems and depression has been the focus of many studies; however, results from these studies vary. The purpose of this study is to describe the association between depression and BMI using data from a national sample of middle aged and older Koreans and to examine whether gender moderates the relationship between depression and weight. We used data from the Korean Longitudinal Study of Aging (KLoSA). Of the 7,920 respondents that participated in KLoSA in 2010, 7,672 adults aged between 50 and 102 years were included in the final analysis. The relationship between depression and obesity status was examined in both the full sample and in sub-samples stratified by gender. The observed U-shaped association between obesity status and CES-D score was tested by regressing CES-D score on linear and quadratic terms of BMI scores. The distribution of CES-D scores by respondents’ obesity status (i.e., underweight, normal weight, overweight, obese and severely obese) showed a U-shaped association. Specifically, the highest CES-D scores were found in underweight individuals; this was followed by the severely obese and obese groups in the full sample and in gender-specific subsamples. The lowest CES-D scores were found in the overweight group when considering the entire population and males alone and in the normal weight group for females. This U-shaped association between CES-D and obesity status was confirmed by a model in which CES-D scores were regressed on BMI scores and other covariates. This study found a U-shaped association between BMI and levels of depressive symptoms among adults in Korea overall and also within each gender. Specifically, the highest level of depressive symptoms was found among the underweight, followed by the severely obese and then the obese. Slightly different patterns between male and female adults were found regarding the weight status associated with the fewest depressive symptoms.
    Full-text · Article · Mar 2015
    • "For mortality, on the other hand, rates among non-obese persons are higher for disabled than for non-disabled persons, while among obese persons the reverse is true (seeFigure 8). The higher incidence risk for obese people together with mitigated mortality rates for the obese once disabled, are consistent with the literature: " smoking kills, obesity disables " (Reuser, Bonneux, and Willekens 2008 Willekens , 2009 Majer et al. 2011).Figure 9 shows the effects of the three obesity scenarios on disability prevalence. Note that the assumptions on obesity will affect disability among the elderly aged 65 and over only from 2018 onwards. "
    [Show abstract] [Hide abstract] ABSTRACT: BACKGROUND Prevalence of disability depends on when a person becomes disabled (disability incidence) and when he or she dies (mortality). Multistate projection models can take into account both underlying processes of disability prevalence. The application of these models, however, is often hampered by high data requirements. OBJECTIVE This paper describes a generic estimation procedure for calculating disability incidence rates and mortality rates by disability status from data on disability prevalence and overall mortality. The procedure allows for the addition of risk factors. METHODS We estimate disability incidence rates from disability prevalence and mortality rates by disability status using prevalence data on disability from SHARE and mortality data from Eurostat and the Rotterdam Study of Health (ERGO). We use these rates to project future trends of ADL-disability prevalence among the elderly in the Netherlands for the period 2008-2040 using the multistate projection model LIPRO. RESULTS This paper shows that even in the case of limited data, multistate projection models can be applied to project trends in disability prevalence. In a scenario that assumes constant disability incidence rates, disability prevalence among the elderly will increase even though the mortality rates of disabled persons exceed those of non-disabled people. In a scenario that assumes declining incidence rates at the same pace as declining mortality rates, disability prevalence will be significantly lower. This latter scenario results in an almost similar decline in disability prevalence as the scenario assuming a strong reduction of age-specific obesity among the elderly. One conclusion, therefore, could be that the prevalence of obesity should be seriously reduced to reach a strong reduction of disability incidence. CONCLUSIONS The strength of this method to calculate disability incidence-rates based on disability prevalence-rates is that the relationship between changes in mortality and changes in disability is taken into account, and that the effects of risk factors can be estimated. The improved transparency of the projections, the generic nature of the model and the applicability to all (European) countries with disability prevalence data make it a useful instrument for making plausible projections of future patterns of disability prevalence based on disability incidence.
    Full-text · Article · Jan 2015
Show more