Ethnic disparities in estimated cardiovascular disease risk in Amsterdam, the Netherlands
Ethnic disparities in estimated cardiovascular disease risk in Amsterdam, the Netherlands
The HELIUS study 0 1
W. Perini 0 1
M. B. Snijder 0 1
R. J. G. Peters 0 1
A. E. Kunst 0 1
0 Department of Clinical Epidemiology , Biostatistics and Bioinformatics , Academic Medical Center Amsterdam , Amsterdam , The Netherlands
1 Department of Cardiology, Academic Medical Center Amsterdam , Amsterdam , The Netherlands
Background Ethnic differences have been reported in cardiovascular disease (CVD) risk factors. It is still unclear which ethnic groups are most at risk for CVD when all traditional CVD risk factors are considered together as overall risk. Objectives To examine ethnic differences in overall estimated CVD risk and the risk factors that contribute to these differences. Design Using data of the multi-ethnic HELIUS study (HEalthy LIfe in an Urban Setting) from Amsterdam, we examined whether estimated CVD risk and risk factors among those eligible for CVD risk estimation differed between participants of Dutch, South Asian Surinamese, African Surinamese, Ghanaian, Turkish and Moroccan origin. Using the Systematic COronary Risk Evaluation (SCORE) algorithm, we estimated risk of fatal CVD and risk of fatal plus non-fatal CVD. These risks were compared between ethnic groups via age-adjusted linear regression analyses. Results The SCORE algorithm was applicable to 9,128 participants. Relative to the fatal CVD risk of participants of Dutch origin, South Asian Surinamese participants showed a higher fatal CVD risk, Ghanaian males a lower fatal CVD risk, and participants of other ethnic origins a similar fatal CVD risk. For fatal plus non-fatal CVD risk, African Surinamese and Turkish men also showed a higher risk. When diabetes was incorporated in the CVD risk algorithm, all but Ghanaian men showed a higher CVD risk relative to the participants of Dutch origin (betas ranging from 0.98-3.10%). The CVD risk factors that contribute the most to these ethnic differences varied between ethnic groups. Conclusion Ethnic minority groups are at a greater estimated risk of fatal plus non-fatal CVD relative to the group of native Dutch. Further research is necessary to determine whether this will translate to ethnic differences in CVD incidence and, if so, whether ethnic-specific CVD prevention strategies are warranted.
Department of Public Health, Academic Medical Center
Amsterdam, Amsterdam, The Netherlands
Cardiovascular disease (CVD) is one of the leading causes
of mortality and morbidity and is distributed unequally
among ethnic groups [
]. Among several ethnic minority
groups, studies have reported a high burden of CVD [
Therefore, there has been a call for multi-ethnic research
on CVD prevention [
Targeted CVD prevention strategies are based on
identifying high-risk individuals [
]. CVD risk is often
defined as the ten-year risk of mortality due to coronary heart
disease or stroke, which can be estimated by CVD risk
algorithms based on the occurrence of CVD risk factors [
]. Based on the estimated CVD risk, the initiation of
preventive intervention can be tailored to the individual [
Ethnic groups may differ in the prevalence of individual
CVD risk factors and, consequently, in estimated CVD risk
1, 6, 7
]. For example, South Asians are predisposed to
developing dyslipidaemia and diabetes, whereas Ghanaians
have a lower prevalence of smoking, but a higher prevalence
of hypertension compared with most other ethnic groups
]. In addition, the prevalence of individual CVD risk
factors is generally expected to be relatively high among
ethnic minority groups due to a high exposure to conditions
associated with CVD risk (e. g. low socioeconomic status
and ethnic discrimination) [
Although previous studies have examined ethnic
disparities in isolated CVD risk factors, European studies
regarding ethnic disparities in overall estimated CVD risk are
lacking. Therefore, it is unknown which ethnic groups may
be most at risk for CVD when all CVD risk factors are
considered together, which is why we do not know among
which ethnic groups preventive intervention for CVD may
be the most warranted. In this study, we aim to estimate
CVD risk based on the presence of CVD risk factors via
the Systematic COronary Risk Evaluation [SCORE]
algorithm and determine whether ethnic disparities in estimated
CVD risk occur. To better understand these ethnic
disparities, we will examine ethnic differences in the individual
CVD risk factors used to estimate CVD risk.
During the HELIUS (Healthy Life in an Urban Setting)
study, a large-scale, multi-ethnic cohort study on health and
health care utilisation among different ethnic groups
living in Amsterdam, the Netherlands, data were obtained via
questionnaires, physical examinations and biological
samples. The aims and design of the HELIUS study have been
]. In brief, participants between 18–70 years
of age living in Amsterdam were randomly sampled via the
municipality register, after stratification by ethnicity. Both
questionnaire and physical examination data were obtained
among 22,165 participants. The study protocols were
approved by the AMC Ethical Review Board, and all
participants provided written informed consent.
Ethnicity was defined according to the country of birth of
the participant as well as that of his/her parents [
participant was considered as of non-Dutch ethnic origin if
he/she was born abroad and had at least one parent born
abroad (first generation); or he/she was born in the
Netherlands but both his/her parents were born abroad (second
]. Surinamese subgroups were classified
according to self-reported ethnic origin. Approximately 80%
are either of Creole (African) origin or of Hindustani (South
CVD risk was estimated via the SCORE algorithm based
on total cholesterol/high-density lipoprotein cholesterol
ratio (TC/HDL) for low-risk countries [
]. This algorithm
estimates the ten-year risk of mortality due to coronary
heart disease or stroke based on gender, age, blood
pressure, TC/HDL and smoking status (yes/no) among those
who are 40–65 years of age without diabetes or prior CVD
]. In addition, we estimated the total cardiovascular risk
(mortality plus morbidity) using the Dutch SCORE
algorithm (dSCORE), which is derived from the same
algorithm, but includes an age-dependent conversion factor
between 3 and 5, with a higher conversion factor among
younger people, to estimate the ten-year risk of fatal plus
non-fatal CVD [
]. The dSCORE algorithm also adds
fifteen years to the calendar age among those with diabetes
and includes a specific algorithm to estimate CVD risk for
participants above the age of 65. In addition, we also
estimated total CVD risk using the age-specific conversion
factors described by Jorstad et al. [
], which are based
on the European Prospective Investigation into Cancer and
Nutrition cohort (eSCORE).
Smoking status and prior CVD were assessed by
questionnaire. Blood pressure was measured in duplicate
using a validated automated digital blood pressure device
(WatchBP Home; Microlife AG) on the left arm in a seated
position after the person had been seated for at least
5 min. Fasting blood samples were drawn, and glucose and
TC/HDL were determined. Participants were considered
to have diabetes if they reported a diabetes diagnosis, use
of glucose-lowering medication, or in case of a fasting
glucose equal or above 7.0 mmol/l [
We excluded participants with a Javanese Surinamese
(n = 233), ‘other Surinamese’ (n = 267) or unknown/another
ethnicity (n = 48). Furthermore, we excluded participants
based on missing data regarding cardiovascular risk factors
(i. e. age, sex, smoking status, blood pressure, lipid profile
or diabetes status) (n = 372) and due to not being eligible for
CVD risk estimation using the SCORE algorithm based on
age below 40 (n = 7581), age above 65 (n = 868), the
presence of diabetes (n = 1,911) or prior myocardial infarction
and/or stroke (n = 1,756). We also excluded one
participant with unlikely high cholesterol values. This resulted in
a study population of 9,128 participants.
Analyses were stratified by gender. We used multiple linear
regression analyses to determine ethnic differences in
estimated CVD risk, adjusted for age. Because of non-linear
associations between age and estimated CVD risk, age was
stratified into five-year age categories. Next, we determined
ethnic differences in CVD risk factors using logistic
regression for dichotomous, and linear regression for continuous
CVD risk factors. Similar analyses were performed using
the dSCORE and the eSCORE as outcome variable in the
same study sample.
Data are presented as mean (standard deviation) or percentages
SBP systolic blood pressure, HDL high-density lipoprotein, TC total cholesterol
For sensitivity analyses, we excluded participants
using antihypertensive and/or lipid-lowering medication
(n = 1,639) and repeated all analyses. Furthermore, we
repeated the dSCORE analyses including participants who
are not eligible for CVD risk estimation using the SCORE
algorithm, but who are eligible for CVD risk estimation
using the dSCORE (in accordance to the current clinical
practice in Dutch primary care). We therefore included
participants aged 25–55 with diabetes (n = 863) and
participants without diabetes above the age of 65 (n = 499).
Ethnic groups differed in age, occurrence of CVD risk
factors, and use of preventive medication (Tab. 1). Ghanaian
participants and Moroccan women showed a relatively low
prevalence of smoking. African Surinamese and Ghanaian
participants showed a high mean systolic blood pressure,
but favourable lipid profiles.
Fatal CVD risk as estimated by SCORE was similar
among most ethnic groups (Tab. 2). However, relative to
the native Dutch, South Asian Surinamese participants
showed higher estimated fatal CVD risk, while
Ghanaian men showed lower estimated fatal CVD risk. When
using the dSCORE algorithm, estimated fatal plus
nonfatal CVD risk was also higher among African Surinamese
participants and Turkish men. When using the eSCORE
algorithm, estimated CVD risk was higher relative to the
native Dutch group among all ethnic groups except for
Ghanaian men and Moroccan participants who showed
similar estimated CVD risk compared with the participants
of Dutch origin.
Clusters of risk factors differed between the ethnic
groups (Supplementary Table 1). Relative to the native
Dutch, Turkish and Moroccan men showed a
dyslipidaemic CVD risk profile (i. e. higher prevalence of
dyslipidaemia but lower prevalence of hypertension), Ghanaian
and African Surinamese participants showed a hypertensive
CVD risk profile (i. e. higher prevalence of hypertension
but lower prevalence of dyslipidaemia) and South Asian
Surinamese participants and Moroccan and Turkish women
showed a combined risk profile of both hypertension and
dyslipidaemia. Among men, all ethnic minority groups
showed a higher prevalence of smoking except for
Ghanaian men whereas ethnic minority women showed a similar
or lower prevalence of smoking relative to the native Dutch
group. Results were similar when we excluded participants
taking antihypertensive medication and/or lipid-lowering
medication (Supplementary Tables 2 and 3).
When the dSCORE was used among all participants
eligible for CVD risk estimation according to Dutch
clinical guidelines (i. e. including participants with diabetes
aged 25–55 and participants without diabetes above the
age of 65), estimated fatal plus non-fatal CVD risk was
higher among all ethnic groups relative to the native Dutch
group except among Ghanaian men, who showed a similar
estimated CVD risk (Tab. 3). Ethnic differences in CVD
risk factors were similar to those found among participants
only eligible for CVD risk estimation using SCORE
(Supplementary Table 1).
Fatal CVD risk as estimated by SCORE is higher among
South Asian Surinamese men and women, lower among
Ghanaian men and similar among all other ethnic groups
relative to the native Dutch group. When fatal plus non-fatal
CVD risk is estimated using traditional SCORE CVD risk
factors plus diabetes, all ethnic minority groups show higher
estimated CVD risk relative to the native Dutch group,
except for Ghanaian men who show a similar CVD risk.
Ethnic groups differ in whether they show a dyslipidaemic,
hypertensive or combined CVD risk profile relative to the
participants of Dutch origin.
SBP (mm Hg)
SBP (mm Hg)
Italic indicates statistical significant difference from the Dutch
CI confidence interval, CVD cardiovascular disease, dSCORE Dutch systematic coronary risk evaluation, SBP systolic blood pressure,
TC/HDL total cholesterol/high-density cholesterol
aTen-year risk (%) of fatal plus nonfatal CVD as estimated by dSCORE
Strengths and limitations
Due to the large multi-ethnic sample size, we were able to
determine ethnic differences in estimated CVD risk,
stratified for gender. Furthermore, this study compared ethnic
differences in both fatal CVD risk and fatal plus non-fatal
A potential weakness is the application of the SCORE
algorithm to different ethnic groups, as SCORE is
validated mainly among ethnic majority populations [
The SCORE algorithm may underestimate or overestimate
CVD risk among ethnic minority groups . Moreover,
recent studies have identified CVD risk factors which may
substantially increase CVD risk independently of traditional
CVD risk factors (e. g. psoriasis, human immunodeficiency
virus infection or lipoprotein (a)) [
1, 20, 21
]. These factors
may not be distributed equally among ethnic groups [
]. Thus, ethnic differences in true CVD risk may differ
from the risk differences as estimated in our study.
As this study was conducted in Amsterdam, results may
not be generalisable to ethnic minority populations
residing in other cities or countries. Studies in other settings are
needed to identify possible common patterns. For example,
a study from Norway described a similar higher estimated
CVD mortality risk among foreign-born participants
relative to participants born in Norway [
Discussion of key findings
In contrast to our results, an earlier nation-wide study
showed lower CVD mortality among the Moroccan
population relative to the population of Dutch origin [
discrepancy may be attributable to risk factors not included
in SCORE, but may also reflect that, in this nation-wide
study, the ethnic differences in CVD mortality were
observed within older participants and were mainly driven by
the lifetime exposures to risk factors of these participants.
Among Ghanaians, estimated CVD risk was relatively
low compared with other ethnic groups despite a higher
prevalence of systolic hypertension. This may be due to
a low prevalence of smoking. Earlier nationwide analysis
also showed a lower incidence of acute myocardial
infarction among Ghanaians compared with the native Dutch [
However, estimated CVD risk among Ghanaians may
underestimate true lifetime CVD risk, for example due to an
early onset of hypertension and thus a longer risk
exposure, which the SCORE algorithm does not take into
]. If so, current guidelines for antihypertensive
treatment may not be optimal for the Ghanaian population.
Our final analyses included diabetes as a CVD risk factor
by adding 15 years to the calendar age among those with
diabetes, which may result in a great increase in estimated
CVD risk [
]. Considering the relatively high prevalence
of diabetes among ethnic minority groups, it is not
surprising that considering diabetes resulted in greater ethnic
disparities in estimated CVD risk. These disparities may be
even greater when considering the relatively young onset
of type 2 diabetes among ethnic minority groups and thus
the relatively long exposure to hyperglycaemia among these
This study found greater ethnic differences in CVD risk
when CVD risk is estimated based on fatal plus non-fatal
CVD instead of fatal CVD alone. This may suggest that
ethnic disparities in CVD risk already occur at a young age,
as conversion factors to estimate non-fatal CVD risk based
on fatal CVD risk are higher at a young age. Accordingly,
previous studies reported a younger onset of CVD risk and
a higher prevalence or mean of CVD risk factors at a young
age among ethnic minority groups relative to the host group
10, 26, 27
Earlier studies from several countries have reported
relatively low access to health care facilities among ethnic
minority groups [
]. Therefore, these groups may benefit
less from efforts to reduce CVD risk. However, as a result
from the universally accessible health care system, ethnic
differences in health care access may not be as pervasive
in the Netherlands [
]. Thus, in the Netherlands, we
do not expect that access to health care facilities will
contribute to ethnic disparities in the potential effect of CVD
prevention strategies on CVD risk.
Although estimated fatal CVD risk that is based on
traditional SCORE risk factors is generally similar between
ethnic groups, estimated fatal plus non-fatal CVD risk that
is based on traditional SCORE risk factors plus diabetes is
generally higher among ethnic minority groups relative to
the native Dutch. The CVD risk factors contributing most
to the increased estimated CVD risk differ between ethnic
groups. Further research is necessary to determine whether
these ethnic differences in estimated CVD risk will translate
to ethnic differences in CVD incidence and, if so, whether
ethnic-specific CVD prevention strategies are warranted.
Funding The HELIUS study is conducted by the Academic Medical
Center Amsterdam and the Public Health Service (GGD) of
Amsterdam. Both organisations provided core support for HELIUS. The
HELIUS study is also funded by the Dutch Heart Foundation, the
Netherlands Organisation for Health Research and Development (ZonMw),
the European Union (FP-7), and the European Fund for the Integration
of non-EU immigrants (EIF).
Conflict of interest W. Perini, M.B. Snijder, R.J.G. Peters and
A.E. Kunst declare that they have no competing interests.
Open Access This article is distributed under the terms of the
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