An update on cardiovascular disease epidemiology in South East Asia. Rationale and design of the LIFE course study in CARdiovascular disease Epidemiology (LIFECARE)

Abstract

The burden of cardiovascular disease (CVD) is likely to increase dramatically in Asia over the next several decades. In this paper, we review the existing data on CVD epidemiology in Asia, with a focus on the INTERHEART study and the Asia Pacific Cohort Studies Collaboration. Existing data suggests that much of CVD may be preventable through reduction in the levels of well-established CVD risk factors and that these findings are likely to be relevant to Asian populations. However, these studies have several important limitations. These include a lack of longitudinal studies with collection of repeated measures of CVD risk factors and the environmental factors that may result in the age-related increase in the levels of these risk factors. As such, the natural history of the development of CVD risk factors such as obesity, diabetes, hypertension and dyslipidemia in Asia, and their relationship in terms of duration and timing of exposure to various environmental influences is currently unknown. In addition, there is a paucity of data related to psychosocial factors that may be involved in the pathogenesis of CVD, either directly or through effects on other CVD risk factors. Finally, little data is available with regards to the impact of CVD and its attendant risk factors on health related quality of life and health care utilization. This information is crucial for the design and evaluation of evidence based programs for primary prevention. We have designed a LIFE Course Study in CARdiovascular disease Epidemiology (LIFECARE) involving 12,000 individuals in four South East Asian countries to address these data needs.

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An update on cardiovascular disease epidemiology
in South East Asia. Rationale and design of the LIFE
course study in CARdiovascular disease
Epidemiology (LIFECARE)
E Shyong Tai
a,b,
*, Richie Poulton
c
, Julian Thumboo
d
, Rody Sy
e
,
Nina Castillo-Carandang
f
, Piyamitr Sritara
g
, John M.F. Adam
h
,
Kui Hian Sim
i,j
, Alan Fong
k
, Hwee Lin Wee
l,d
, Mark Woodward
m
a
Center for Molecular Epidemiology, National University of Singapore, C/O Department of Community,
Occupational and Family Medicine, 16 Medical Drive, Singapore 117597, Singapore
b
Office of Research, Singapore Health Services, Singapore
c
Dunedin Multidisciplinary Health and Development Research Unit, Department of Preventive & Social
Medicine, Dunedin School of Medicine, National Centre for Lifecourse Research, University of Otago,
New Zealand
d
Singapore General Hospital, Department of Rheumatology & Immunology, Singapore
e
College of Medicine, University of the Philippines, Philippines
f
Department of Clinical Epidemiology, College of Medicine, University of the Philippines, Manila,
Philippines
g
Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
h
Division of Endocrinology and Metabolism, Faculty of Medicine, Hsanuddin University, Makassar,
Indonesia
i
Clinical Research Centre (CRC), Department of Cardiology, Sarawak General Hospital, Malaysia
j
Faculty of Medicine & Health Sciences, University Malaysia Sarawak (UNIMAS), Malaysia
k
Department of Cardiology, and Clinical Research Centre, Sarawak General Hospital, Malaysia
l
Department of Pharmacy, National University of Singapore, Singapore
m
Mount Sinai School of Medicine, New York University, NY, USA
Available online 14 March 2009
1875-4570/$ - see front matter Ó2009 World Heart Federation. Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.cvdpc.2009.02.003
*Corresponding author. Address: Center for Molecular Epidemiology, National University of Singapore, C/O Department of
Community, Occupational and Family Medicine, 16 Medical Drive, Singapore 117597, Singapore. Tel.: +65 6478 9547; fax: +65 6478
9058.
E-mail address: eshyong@pacific.net.sg (E.S. Tai).
CVD Prevention and Control (2009) 4, 93–102
www.elsevier.com/locate/precon

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KEYWORDS
Cardiovascular disease;
Asia;
Psychosocial factors;
Life course epidemiol-
ogy;
Quality of life;
Health care utilization
Summary The burden of cardiovascular disease (CVD) is likely to increase dra-
matically in Asia over the next several decades. In this paper, we review the exist-
ing data on CVD epidemiology in Asia, with a focus on the INTERHEART study and
the Asia Pacific Cohort Studies Collaboration. Existing data suggests that much of
CVD may be preventable through reduction in the levels of well-established CVD
risk factors and that these findings are likely to be relevant to Asian populations.
However, these studies have several important limitations. These include a lack of
longitudinal studies with collection of repeated measures of CVD risk factors and
the environmental factors that may result in the age-related increase in the levels
of these risk factors. As such, the natural history of the development of CVD risk
factors such as obesity, diabetes, hypertension and dyslipidemia in Asia, and their
relationship in terms of duration and timing of exposure to various environmental
influences is currently unknown. In addition, there is a paucity of data related to
psychosocial factors that may be involved in the pathogenesis of CVD, either
directly or through effects on other CVD risk factors. Finally, little data is available
with regards to the impact of CVD and its attendant risk factors on health related
quality of life and health care utilization. This information is crucial for the design
and evaluation of evidence based programs for primary prevention. We have
designed a LIFE Course Study in CARdiovascular disease Epidemiology (LIFECARE)
involving 12,000 individuals in four South East Asian countries to address these
data needs.
Ó2009 World Heart Federation. Published by Elsevier Ltd. All rights reserved.
Introduction
Socio-economic development, accompanied by ra-
pid urbanization, has resulted in an epidemiologic
transition in the burden of diseases, from those
associated with infection and malnutrition to those
associated with non-communicable chronic dis-
eases. Cardiovascular diseases (CVD) represent
some of the major causes of morbidity and mortal-
ity in developed countries today [1]. In developing
countries, this transition is still in progress and
many populations in Asia can be expected to expe-
rience a doubling of the burden of CVD over the
next several decades [2].
The INTERHEART study
The INTERHEART study [3] was a standardized case-
control study of acute myocardial infarction (MI) in
52 countries, representing every inhabited conti-
nent, involving 15,152 cases and 14,820 controls.
It was found that nine risk factors for CVD ex-
plained over 90% of the population attributable risk
for myocardial infarction in both men and women,
in most of the geographical regions studied. These
risk factors were cigarette smoking, hypertension,
diabetes, obesity, blood lipids, exercise, alcohol
ingestion, consumption of fruits and vegetables
and psychosocial factors. Although some regional
and ethnic variations in the risk associated with
each factor were observed, most of the risk factors
with larger effect sizes showed consistent associa-
tions across regions.
Despite the impressive breadth of its coverage,
and its influential results, INTERHEART used a study
design, which is susceptible to bias error [4]. One
particular issue is that of establishing whether
the proposed cause, such as smoking, really did
precede the disease, MI. To this extent, the INTER-
HEART results might be regarded as hypothesis gen-
erating rather than definitively quantifying
associations. Furthermore, although INTERHEART
included significant numbers of individuals from
China and Hong Kong, the rest of South East Asia
and Japan were represented by only 969 and 1199
cases and controls, respectively. Given the diver-
sity of the populations in South East Asia, this num-
ber is too small to establish reliable estimates of
relative risks in this most populous region of the
world.
The Asia Pacific Cohort Studies
Collaboration (APCSC)
In an effort to address some of the limitations of
earlier studies in Asia, the Asia Pacific Cohort Stud-
ies Collaboration was set up in 1999 to investigate
the associations of major cardiovascular risk fac-
tors with stroke (fatal and non-fatal, ischaemic
and haemorrhagic), coronary heart disease (CHD)
94 E.S. Tai et al.

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(fatal and non-fatal) and total cardiovascular dis-
ease (CVD) [5]. Secondary outcomes were all-cause
mortality and non-cardiovascular causes of death.
Studies from Australia and New Zealand were in-
cluded to provide a ‘‘Western”comparison group,
but are not mentioned further here. Studies were
eligible for inclusion if based in the region, used a
prospective cohort study design and had at least
5000 person-years of follow-up. The prospective
design is important for establishing that the out-
come came after the risk factor was measured.
Individuals within studies were included when their
date of birth (or age), sex and blood pressure were
recorded at baseline and when their vital status
was known at the end of follow-up and, for those
that died, their age (or date) at death was known.
Cohorts selected on the basis of a positive disease
history, or diagnosis, were ineligible. Studies were
identified by searches of electronic databases,
from abstracts and proceedings of meetings and
from personal knowledge.
For each identified study, the principal investi-
gators were invited to supply individual partici-
pant data to the study secretariat. This was to
include a raft of commonly-accepted CVD risk fac-
tors, both at baseline and (if available) repeated
values from subsequent evaluations at intervals
during the follow-up period. These repeat obser-
vations were used to correct continuous associa-
tions for regression dilution error. Data on the
timing and cause of death were also requested,
as well as (where available) information on non-
fatal CVD events.
In all, APCSC has data from 513,000 Asians. It in-
cludes 35 studies from Asia: 16 from mainland Chi-
na, 12 from Japan, two from Taiwan and from
Singapore and one each from Hong Kong, South
Korea and Thailand. The extent of the data pro-
vided varied greatly from study-to-study, such as
in the number of risk factors measured, how often
(if at all) these were remeasured and the length of
follow-up.
Fig. 1 gives an example of some key results pro-
duced from APCSC data [6]. This shows that
increasing systolic blood pressure is an important
risk factor for mortality from both haemorrhagic
and ischaemic stroke and CHD in Asia [7], with
some attenuation of relative risk, taking these
three outcomes in this order. Diabetes [8] and
smoking [9] are also important risk factors for all
three of these outcomes. Increasing total choles-
terol (TC) is a significant (i.e. the 95% confidence
interval crosses the line of unity; p< 0.05) risk fac-
tor for CHD death, and almost significant for
ischaemic stroke, but as TC increases the risk of
haemorrhagic stroke death decreases significantly
[10]. Increasing body mass index increases the risk
of coronary death significantly, but there is no
definitive evidence for it having an effect on death
due to stroke [11]. For increasing triglycerides,
there is a suggestion of an increased risk of all
three types of cardiovascular death, but without
achieving significance [12]. The uncertainty in esti-
mates of relative risk (hazard ratio), measured by
the widths of the confidence limits, is affected by
differences in the amount of data that were avail-
able in APCSC – greatest for SBP and least for
triglycerides.
Many of the conclusions that may be drawn from
Fig. 1 are just what would be expected from the
classical studies done in the West, such as Framing-
ham, but showing that Western associations are
also important in Asia is crucial to motivating local
health promotion and treatment initiatives. Fur-
thermore, precise quantification of associations al-
lows for an Asian risk prediction score to be
constructed, so as to quantify individual risk [13].
But APCSC has also shown evidence for associations
that might otherwise have been missed – the in-
verse association between TC and haemorrhagic
stroke in Fig. 1 being an example. This is possible
to see only in a large database from a region where
haemorrhagic stroke is relatively common and TC
levels vary from very low to high.
Life course studies
In simple terms, life course studies follow a group
of people over time to discover what happens to
them. The longitudinal nature of these studies
has a number of advantages. Because information
is collected prospectively before the outcome of
interest occurs, life course studies avoid recall bias
and other errors that can arise from trying to
remember events that occurred many years ear-
lier. Often life course studies are the only way to
obtain accurate information about events and cir-
cumstances earlier in the life of study participants.
Life course studies can also reliably establish the
temporal sequence between exposure and disease,
which is not possible in cross-sectional or case-con-
trol studies. This allows us to assess whether the
association between exposure and disease is cau-
sal, since exposure must antedate the onset of dis-
ease for a causal association, as was mentioned in
the preceding section on the APCSC.
In addition, a life course understanding of
chronic disease explicitly recognizes the impor-
tance of ‘‘time and timing”for understanding the
development of multifactorial disorders [14–16].
The ‘time’ aspect of life course theory relates to
An update on cardiovascular disease epidemiology in South East Asia. Rationale and design of the LIFE 95

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the emphasis on the accumulation of lifetime expo-
sures in determining disease outcomes. In other
words, it is the total burden of risk exposure accru-
ing over time that is the key determinant of a per-
son’s health, not simply proximal events. The
‘timing’ aspect posits that there are certain critical
and sensitive developmental periods in life (e.g.
adolescence, young adult hood, early childhood)
during which people are especially sensitive to risk
exposures, with risk effects associated with these
exposures magnified during this period. As such,
it is important to determine not only the magni-
tude or duration of exposure to a particular risk
factor, but also the period in a person’s life at
which he or she is exposed to the risk factor. This
requires repeated measurement of disease and
exposures.
Importantly, analytic strategies, based on pro-
pensity scores [17], are now available to quantify
‘‘treatment”effects in observational life course
designs when randomized controlled trials are not
a viable option for hypothesis testing (e.g. ethical
constraints prevent random allocation to toxic psy-
chosocial or physical exposures). Such methods are
designed to recreate the desirable features of
experimental designs by creating balance between
exposed (‘‘treatment”) vs. non-exposed (‘‘con-
trol”) groups that have formed naturally over the
life course and have been applied, for example,
to examine the importance of exposure of adoles-
cents to drugs and alcohol [18].
Limitations of the Asia Pacific Cohort
Studies Collaboration
Despite its success, APCSC has some important
drawbacks. Most obvious is the lack of a common
core protocol for the studies involved, since their
recruitment into APCSC was retrospective. Thus
definitions, assay methods and means of ascertain-
Systolic blood pressure (+10 mmHg)
Coronary heart disease
Ischaemic stroke
Haemorrhagic stroke
Total cholesterol (+1 mmol/l)
Coronary heart disease
Ischaemic stroke
Haemorrhagic stroke
Triglycerides (+0.5 mmol/l)
Coronary heart disease
Ischaemic stroke
Haemorrhagic stroke
Body mass index (+5 kg/m
2
)
Coronary heart disease
Ischaemic stroke
Haemorrhagic stroke
Diabetes (yes v no)
Coronary heart disease
Ischaemic stroke
Haemorrhagic stroke
Smoking (yes v no)
Coronary heart disease
Ischaemic stroke
Haemorrhagic stroke
1.31 (1.23, 1.38)
1.44 (1.35, 1.54)
1.73 (1.64, 1.83)
1.25 (1.11, 1.41)
1.09 (0.92, 1.29)
0.85 (0.73, 0.98)
1.18 (0.96, 1.45)
1.09 (0.77, 1.54)
1.15 (0.88, 1.51)
1.36 (1.17, 1.58)
1.07 (0.87, 1.31)
0.96 (0.80, 1.14)
1.84 (1.35, 2.50)
2.27 (1.43, 3.61)
1.53 (1.03, 2.27)
1.83 (1.50, 2.24)
1.61 (1.24, 2.08)
1.16 (0.94, 1.43)
Hazard
1.31 (1.23, 1.38)
1.44 (1.35, 1.54)
1.73 (1.64, 1.83)
1.25 (1.11, 1.41)
1.09 (0.92, 1.29)
0.85 (0.73, 0.98)
1.18 (0.96, 1.45)
1.09 (0.77, 1.54)
1.15 (0.88, 1.51)
1.36 (1.17, 1.58)
1.07 (0.87, 1.31)
0.96 (0.80, 1.14)
1.84 (1.35, 2.50)
2.27 (1.43, 3.61)
1.53 (1.03, 2.27)
1.83 (1.50, 2.24)
1.61 (1.24, 2.08)
1.16 (0.94, 1.43)
(95% CI)
Hazard ratio
1.5 1 2 3
Systolic blood pressure (+10 mmHg)
Coronary heart disease
Ischaemic stroke
Haemorrhagic stroke
Total cholesterol (+1 mmol/l)
Coronary heart disease
Ischaemic stroke
Haemorrhagic stroke
Triglycerides (+0.5 mmol/l)
Coronary heart disease
Ischaemic stroke
Haemorrhagic stroke
Body mass index (+5 kg/m
2
)
Coronary heart disease
Ischaemic stroke
Haemorrhagic stroke
Diabetes (yes v no)
Coronary heart disease
Ischaemic stroke
Haemorrhagic stroke
Smoking (yes v no)
Coronary heart disease
Ischaemic stroke
Haemorrhagic stroke
1.31 (1.23, 1.38)
1.44 (1.35, 1.54)
1.73 (1.64, 1.83)
1.25 (1.11, 1.41)
1.09 (0.92, 1.29)
0.85 (0.73, 0.98)
1.18 (0.96, 1.45)
1.09 (0.77, 1.54)
1.15 (0.88, 1.51)
1.36 (1.17, 1.58)
1.07 (0.87, 1.31)
0.96 (0.80, 1.14)
1.84 (1.35, 2.50)
2.27 (1.43, 3.61)
1.53 (1.03, 2.27)
1.83 (1.50, 2.24)
1.61 (1.24, 2.08)
1.16 (0.94, 1.43)
Hazard
1.31 (1.23, 1.38)
1.44 (1.35, 1.54)
1.73 (1.64, 1.83)
1.25 (1.11, 1.41)
1.09 (0.92, 1.29)
0.85 (0.73, 0.98)
1.18 (0.96, 1.45)
1.09 (0.77, 1.54)
1.15 (0.88, 1.51)
1.36 (1.17, 1.58)
1.07 (0.87, 1.31)
0.96 (0.80, 1.14)
1.84 (1.35, 2.50)
2.27 (1.43, 3.61)
1.53 (1.03, 2.27)
1.83 (1.50, 2.24)
1.61 (1.24, 2.08)
1.16 (0.94, 1.43)
(95% CI)
Hazard ratio
1.5 1 2 3
Figure 1 Hazard ratios with 95% confidence intervals (CIs) for death from coronary heart disease, ischaemic stroke
and haemorrhagic stroke in Asian cohorts from the Asia Pacific Cohort Studies Collaboration. Results are from Cox
regression models, stratified by study and sex. Hazard ratios for systolic blood pressure (SBP), total cholesterol (TC),
body mass index (BMI) and smoking are adjusted for each other and for age; hazard ratios for diabetes and triglycerides
are adjusted for SBP, TC, BMI, smoking and age. Continuous relationships are adjusted for regression dilution error.
96 E.S. Tai et al.

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ing outcomes all vary between studies and reduces
our ability to directly compare data between pop-
ulations. In this manuscript, we highlight several
other limitations that we believe to be important
which will need to be addressed in order to facili-
tate evidence based disease prevention.
Longitudinal repeated measurements of risk
factors
Although some studies in the APCSC included longi-
tudinal repeated measurements of risk factors, this
was unusual and rarely was there more than one re-
peat measure per person. Hence the life course of
risk factor levels cannot be estimated with any rea-
sonable validity. This has several implications.
Firstly, we do not know the time of life when the in-
creases in these risk factors occur, or whether there
are critical periods in a person’s life when the risk
factor levels increase. As such, we cannot assess
the duration of exposure to the risk factor and the
effect that this might have on cardiovascular dis-
ease risk. If it is true, as hypothesized in life course
theory (described in the preceding section), that
cardiovascular disease is a consequence of the
accumulated exposure to risk factors over time,
then a single measurement, which does not con-
sider the duration of exposure, would under-esti-
mate the risk of CVD associated with that risk
factor. Secondly, the pathogenesis of most of these
risk factors has a significant environmental compo-
nent. Diet, physical activity, psychosocial factors,
cigarette smoking, alcohol intake, all have impor-
tant effects on the risk of hypertension, dyslipide-
mia, obesity and diabetes mellitus. Amongst the
CVD risk factors identified in the INTERHEART
study, these environmental risk factors are the
most imprecisely measured, in part due to difficul-
ties in recall. In fact, the risk of myocardial infarc-
tion associated with these environmental factors
showed the greatest regional and ethnic variation
in the INTERHEART study [3]. Repeated measure-
ments prior to the onset of disease, with attention
to the ‘time’ and ‘timing’ of exposures could allow
us better assessment of the risk associated with
these environmental factors. Finally, the levels
for most of the risk factors that the APCSC has
shown to be important in Asia increase with increas-
ing age. Single measurements of risk factors tell us
nothing about the factors that drive the age-related
increase. Thus, while the findings from the APCSC
suggest that public health policies to prevent the
uptake of smoking and promote quitting, restrict
salt consumption, increase exercise and restrict
the intake of fatty foods, by reducing obesity, dysl-
ipidemia, hypertension and diabetes mellitus, are
likely to have great consequence for CVD in Asia,
the APCSC provides no information as to the specific
measures that might be relevant to the various pop-
ulations in Asia (which may be culturally and ethni-
cally distinct), nor the actual impact that these
changes might have on risk factor levels.
Prospective measurement of psychosocial
factors
We believe that, of the environmental factors
that have been implicated, psychosocial factors,
which accounted for 32.5% of the population
attributable risk in the INTERHEART study [3],
deserve special mention. Only dyslipidemia and
smoking had a higher population attributable risk.
There is increasing interest in the role that
psychosocial risk factors might play in the devel-
opment of chronic physical disease from a life
course perspective [19]. A key question that
remains is whether psychosocial factors directly
influence poor physical health outcomes, or
whether they exert their influence by virtue of
their relation to established risk factors (e.g.
smoking, high cholesterol, obesity) [15]? Research
is beginning to provide some insights into the
nature, strength and direction of associations be-
tween various psychosocial factors early in life
and cardiovascular risk in adulthood, while apply-
ing appropriate controls for (i) selection effects
(ii) co-occurring (to the exposure) risk factors;
(iii) mediating factors; and (iv) risk variables
measured at the same time as the outcome
[20]. These types of studies illustrate the value
and promise of the life course approach for
understanding how psychosocial pathways lead
to both good and poor health, and ultimately
may shed light on just how the psychosocial
world ‘‘gets under the skin”to cause physical
disease [21,22].
To date, empirical work has been limited by sev-
eral factors. First, the majority of research on the
psychosocial correlates of physical health has been
cross-sectional. In contrast, life course methods
(i.e. prospective-longitudinal studies) and within-
subject comparisons offer a stronger strategy for
inferring developmental influences [23]. Second,
and more generally, epidemiological studies whose
primary focus is on physical health tend to have
comparatively weak psychosocial data, and vice
versa. Gold standard measurement of both psycho-
social and physical health variables, collected pro-
spectively and therefore uncontaminated by recall
bias, is paramount.
An update on cardiovascular disease epidemiology in South East Asia. Rationale and design of the LIFE 97

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Health related quality of life (HRQoL) and
health care utilization
Data on health care utilization and HRQoL are not
included in APCSC, yet these are now recognized
as crucial indicators of the burden of CVD. The
treatment of CVD, and its attendant risk factors,
is expensive and consumes considerable health
care resources. Diabetes and its associated co-mor-
bidities such as hypertension increase both hospi-
talization [24–26] and the utilization of primary
care services [27,28]. Furthermore the presence
of multiple co-morbidities had an additive effect
on health care utilization [29]. In particular, the
concomitant presence of CVD increased the cost
of treating a diabetic patient by 2–3-fold [30,31].
The patterns of health care utilization are ethnic
and culturally specific. Even in North America,
the patterns of health care utilization for patients
with diabetes differ between Canada and the Uni-
ted States [32]. The impact that these disorders
have on health care utilization is unknown in most
countries in Asia, and forms an important aspect of
the economic analysis required to adequately as-
sess the cost vs. benefit of any interventional strat-
egies to prevent CVD.
HRQoL is defined as ‘‘the physical, psychologi-
cal and social domains of health, seen as distinct
areas that are influenced by a person’s experi-
ences, beliefs, expectations and perceptions”
[33]. The health perspective from a patient’s
point of view is increasingly being recognized as
an important clinical outcome [33,34]. This per-
spective is especially important for patients
undergoing therapy or treatment, who may expe-
rience significant reductions in HRQoL despite
being adequately treated for a particular illness
or disease [35]. For instance, it has been seen that
patients with systemic lupus erythromatosus (SLE)
may experience fatigue and poor physical and
emotional functioning even if SLE is inactive
[36]. This emphasis on patient’s perceptions of
health (which is measured by HRQoL) is thus
important given that patient’s functional status
and perceptions of health often differ from physi-
cians’ assessment of the patient’s health status. In
the United States between the years 1993 and
2001, studies show a worsening trend for the
HRQoL in most demographic group studies [37].
In recent years, HRQoL has become a focus for
clinical research in which it is used as an outcome
measure among people with chronic diseases [38].
In many chronic illnesses, it is difficult to find any
linear association between a patient’s improve-
ment in disease symptoms and improvements in
functional status. In such instances, HRQoL plays
a significant role in assessing a patient’s satisfac-
tion with and functional response to a given treat-
ment or therapy. At present little is known about
the patient perspective on function as measured
by HRQoL in Asia. The published literature on
HRQoL in CVD in Asia is sparse, and has largely
emphasized the cross-cultural validation of exist-
ing scales [39], development of new scales [40]
or assessment of existing CVD in hospital based
studies [41]. To the best of our knowledge, no
population based studies assessing HRQoL in CVD
exist.
The life course study in cardiovascular
disease epidemiology (LIFECARE)
To address these limitations, we have designed a
multi-center life course study to examine the link
between environmental (psychosocial factors,
exercise, smoking, alcohol intake), and CVD risk
factors (obesity, diabetes mellitus, hypertension
and dyslipidemia) and ultimately, their impact on
health related quality of life and health care utili-
zation in four countries in Asia.
The LIFECARE study will recruit four cohorts of
3000 subjects each (total 12,000 subjects) in four
countries in South East Asia: Indonesia, Malaysia,
the Philippines and Thailand. Each of these coun-
tries has experienced an increased burden of CVD
and its risk factors. This has been most clearly doc-
umented in Thailand [42] and the Philippines [43].
Although each country has the independence to
incorporate distinct aspects into their individual
studies, each study site also follows a standardized
core protocol that is described in this manuscript.
This core protocol was jointly developed by the
study advisory committee, which includes repre-
sentative from each country-specific cohort. The
study was initiated in 2008, and partially funded
by Pfizer Inc. through an Investigator Initiated Re-
search Grant.
The primary aims of the study are to:
(1) Identify factors that underlie changes in CVD
risk factors over time in Asia.
a. The CVD risk factors of interest include:
i. Blood glucose.
ii. Blood lipids.
iii. Blood pressure.
iv. Obesity.
b. The exposures of interest include:
i. Psychosocial factors (socio-economic
status, psychological distress).
ii. Lifestyle factors (exercise, alcohol
intake, cigarette smoking, obesity).
98 E.S. Tai et al.

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(2) To determine the impact of CVD and its risk
factors on:
a. Health related quality of life.
b. Health care utilization.
Secondary aims are to:
(1) To determine baseline epidemiologic data on
prevalence of risk factors for CVD on a selected
cohort including dyslipidaemia, diabetes,
hypertension, obesity and smoking that are
present within the cohort and compare them
with those in other countries in the region.
(2) To create and maintain a bio-bank of speci-
mens that could be used for future clinical
epidemiology studies related to CVD, includ-
ing studies of genetic and other biomarkers.
Methods for the LIFECARE study
Study population
The study populations will comprise individuals
aged 18–50, with the youngest members giving us
an opportunity to observe them prior to the onset
of disease so that we can observe the changes over
time. The populations will be mostly recruited from
urban or semi-urban areas, which we anticipate will
experience the greatest increase in CVD risk fac-
tors. In Indonesia, Malaysia and the Philippines,
the studies will be population based and selected
randomly from a defined geographical region. In
Thailand, the study will comprise an occupational
workforce with a history of long term employment
of individuals. This approach has been extensively
used in Thailand with great success for the study
of CVD risk factors [44–49]. Although we recognize
that some of the findings of this study will not nec-
essarily be generalizable to the entire population in
the countries involved (particularly in rural areas),
we believe that this approach will provide valid
data that will be useful to regions with the greatest
population density and at the greatest risk for CVD.
Measurements
The initial plan is to study all subjects at baseline,
followed by two additional cycles at intervals of 3–
5 years. At each cycle, a core-set of measurements
will be made.
Questionnaire
An interviewer-administered questionnaire will be
used to collect data on demographics, socio-eco-
nomic status, smoking habits, alcohol ingestion,
physical activity, medical history (including diabe-
tes, hypertension, dyslipidemia and CVD) and any
medication use.
Assessment of psychosocial factors
Socio-economic status will be measured via (i)
country-specific (i.e. culturally appropriate) met-
rics, augmented by (ii) standard questions about in-
come, education, occupation.
The 10-item Kessler Psychological Distress Scale
(K10) questionnaire will be used to assess anxiety
and depression. It is a brief 10-item questionnaire
designed to measure the level of distress and its
severity associated with psychological symptoms
in population surveys [50]. Using data from a large
(n= 10,641) nationally representative household
survey undertaken in Australia, Furukawa and col-
leagues found that the K10 outperformed the Gen-
eral Health Questionnaire (GHQ-12) in screening
for the CIDI/DSM-IV mood and anxiety disorders
[51]. The K10 questionnaire is currently being uti-
lized in multiple countries (such as annual govern-
ment health surveys in the US and Canada as well
as in the World Health Organization World Mental
Health Surveys) with an estimated combined sam-
ple of over 200,000 respondents. The careful con-
struction and brevity of the K10 scale means that
it is likely to become one of the more widely used
mental health screening instruments in contempo-
rary psychiatry. The same features of brevity,
validity in multiple populations and widespread
utilization internationally, were the same reasons
for the selection of this instrument for use in this
study. In addition, levels of stress experienced by
participants will be measured using questionnaires
from the INTERHEART study [52].
Assessment of health related quality of life
Health related quality of life with be assessed using
a profile based measure (the Short Form 36 Health
Survey (SF-36) and a utility-based measure (EQ-
5D). Profile based measures provide information
of a subject’s HRQoL in a variety of domains (e.g.
physical functioning), while preference based mea-
sures provide information on a subject’s prefer-
ence for a given health state, which is necessary
for pharmacoeconomic studies including cost util-
ity analysis.
Assessment of health care utilization
Utilization of health care will be assessed using a
questionnaire developed by the study investigators
which will collect data on any hospitalization or
visits to outpatient clinics as well as the health
care expenditure in the six months prior to partic-
ipation in the study.
Translation and local validation of the
questionnaires
An update on cardiovascular disease epidemiology in South East Asia. Rationale and design of the LIFE 99

Author's personal copy
Each site will carry out the translations locally,
with appropriate forward and back-translations.
The assessment of health related quality of life will
be carried out using the official versions of the SF-
36 and EQ-5D. Where appropriate, local validation
of the instruments was carried out in a standard-
ized manner, including both informal and cognitive
debriefing, an assessment of test–retest reliability
as well as tests of construct/criterion validity.
Assessment of CVD risk factors
All participants will have a clinic examination
where we will measure anthropometry (weight,
height, waist circumference, hip circumference)
and blood pressure. Fasting blood specimens will
be collected for measurement of blood glucose
and lipids (total cholesterol, triglyceride and high
density lipoprotein cholesterol). Low density lipo-
protein cholesterol will be calculated using the
Friedewald formula. Each country will use a local
laboratory for the biochemical measurements.
However, for each examination, 100 randomly se-
lected samples will be analyzed at an accredited
laboratory which participates in the Centers for
Disease Control and Prevention-National Heart,
Lung, and Blood Institute Lipid standardization pro-
gram. This will allow us to calibrate the measure-
ments across countries.
Study and data management
The principal investigator of the study (EST) chairs
an advisory committee comprising the co-principal
investigator (MW) and the principal investigators
for each of the four countries: Indonesia (JA),
Malaysia (KHS), the Philippines (RS) and Thailand
(PS). Additional members provide advice, both in
general and in specific areas such as HRQoL and
health care utilization (JT, WHL) and psychosocial
factors and life course epidemiology (RP). This
committee oversees the development of the stan-
dard protocol which all countries will follow, the
analytical plan and eventual communication of find-
ings pertaining to more than one country. In each
country, the principal investigator chairs a working
committee which will conduct the study, collate
and analyze local data in accordance with the stan-
dard protocol and analytical plan. The country-spe-
cific working committee will also be responsible for
the communication of country-specific findings.
Conclusion
South East Asia is likely to see a large increase in
the morbidity and mortality associated with CVD
over the next several decades. Efforts to reduce
the levels of common CVD risk factors have tre-
mendous potential to reduce the burden that CVD
poses for the individual and for society. However,
several knowledge gaps exist that limit our ability
to design and assess the potential efficacy of inter-
ventional programs. Specifically, the natural his-
tory for the development of diabetes mellitus,
hypertension, obesity and dyslipidemia are largely
unknown in relation to the timing of their develop-
ment and also the factors that underlie their devel-
opment. Several of these factors include
modifiable environmental factors such as physical
activity, smoking, alcohol ingestion and psychoso-
cial factors. In addition, there is little or no infor-
mation regarding the impact of CVD and its
attendant risk factors on health related quality of
life or health care utilization. We have designed a
life course study in four South East Asian countries
that will address these issues, and link environmen-
tal factors to the development of CVD risk factors
and, ultimately, CVD, health related quality of life
and health care utilization. This information is crit-
ical for the design and evaluation of evidence
based programs for primary prevention.
Acknowledgement
Funding: The LIFECARE study is supported by an
Investigator Initiated Research Grant from Pfizer Inc.
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