Cardiometabolic prevention consultation in the Netherlands: screening uptake and detection of cardiometabolic risk factors and diseases – a pilot study
Cardiometabolic prevention consultation in the Netherlands: screening uptake and detection of cardiometabolic risk factors and diseases - a pilot study
Victor Van der Meer 0
Markus MJ Nielen
Anton JM Drenthen
Mieke Van Vliet
Willem JJ Assendelft 0
Francois G Schellevis
0 Department of Public Health and Primary Care, Leiden University Medical Centre , Postzone V-0-P, PO Box 9600, 2300, RC, Leiden , The Netherlands
Background: Until now, cardiometabolic risk assessment in Dutch primary health care was directed at case-finding, and structured, programmatic prevention is lacking. Therefore, the Prevention Consultation cardiometabolic risk (PC CMR), a stepwise approach to identify and manage patients with cardiometabolic risk factors, was developed. The aim of this study was 1) to evaluate uptake rates of the two steps of the PC CMR, 2) to assess the rates of newly diagnosed hypertension, hypercholesterolemia, diabetes mellitus and chronic kidney disease and 3) to explore reasons for non-participation. Methods: Sixteen general practices throughout the Netherlands were recruited to implement the PC CMR during 6 months. In eight practices eligible patients aged between 45 and 70 years without a cardiometabolic disease were actively invited by a personal letter ('active approach') and in eight other practices eligible patients were informed about the PC CMR only by posters and leaflets in the practice ('passive approach'). Participating patients completed an online risk estimation (first step). Patients estimated as having a high risk according to the online risk estimation were advised to visit their general practice to complete the risk profile with blood pressure measurements and blood tests for cholesterol and glucose and to receive recommendations about risk lowering interventions (second step). Results: The online risk estimation was completed by 521 (33%) and 96 (1%) of patients in the practices with an active and passive approach, respectively. Of these patients 392 (64%) were estimated to have a high risk and were referred to the practice; 142 of 392 (36%) consulted the GP. A total of 31 (22%) newly diagnosed patients were identified. Hypertension, hypercholesterolemia, diabetes and chronic kidney disease were diagnosed in 13%, 11%, 1% and 0%, respectively. Privacy risks were the most frequently mentioned reason not to participate. Conclusions: One third of the patients responded to an active invitation to complete an online risk estimation. A passive invitation resulted in only a small number of participating patients. Two third of the participants of the online risk estimation had a high risk, but only one third of them attended the GP office. One in five visiting patients had a diagnosed cardiometabolic risk factor or disease.
This article reports data that has already been published
in Dutch . This has been reproduced in English, with
permission from the copyright holder.
In the Netherlands (total population almost 17 million)
more than 1 million people have cardiovascular disease,
about 740.000 have diabetes and about 40.000 have
chronic kidney disease ((pre)dialysis or transplantation)
. Mortality rates due to ischaemic heart disease and
stroke are low compared to the rest of Europe (top-3 and
top-4, respectively) . Cardiovascular disease, diabetes
mellitus and chronic kidney disease (further referred to as
cardiometabolic disease) and cardiovascular mortality
are highly associated with modifiable lifestyle factors such
as smoking, physical inactivity and poor diet [4,5]. In the
Netherlands, more than a quarter of the population
currently smokes and about half of all people are overweight
or obese [6,7]. These risk factors, together with biomedical
indices such as glucose and cholesterol levels, blood
pressure level and the family history of cardiometabolic
disease  generate a personal risk profile which predicts the
future development of cardiovascular disease and diabetes
mellitus. In addition, the metabolic syndrome, with the
hypertriglyceridaemic waist as its most prominent clinical
criterion, is a contributing factor to global cardiometabolic
Self-tests for glucose and cholesterol assessments and
home devices for measuring blood pressure have become
commercially available , but in the Netherlands no
evidence-based cardiometabolic screening program exists
within current medical practice. So far, cardiometabolic
risk assessment in primary health care has been directed
at case-finding, and structured, programmatic prevention
General practitioners report to have a positive attitude
towards preventing cardiometabolic disease, but they
emphasize that screening should be directed at the group
of patients with the highest cardiometabolic risk .
Therefore the Dutch College of General Practitioners, the
National Association of General Practitioners and the
Netherlands Society of Occupational Medicine together
with three health foundations (Netherlands Heart
Foundation, Dutch Diabetes Research Foundation and Dutch
Kidney foundation) developed the guideline Prevention
Consultation cardiometabolic risk (PC CMR) . The
PC CMR is based on current evidence regarding
cardiometabolic risk estimation and comprises of a
stepwise approach. Based on an online risk estimation
(first step), high risk patients are referred to the
general practice (second step), where the risk profile is
completed and appropriate interventions are initiated.
The prototype of the PC CMR was implemented in 16
general practices throughout the Netherlands for a
period of 6 months. Aims of the study were 1) to
evaluate uptake rates of the two steps of the PC CMR; 2)
to assess the rates of newly diagnosed patients with
hypertension, hypercholesterolemia, diabetes and chronic
kidney disease at risk for cardiometabolic disease; and 3) to
explore reasons for non participation.
Sixteen general practices (49 general practitioners (GPs)
and 27 practice nurses) were recruited who were willing
to implement the prototype of the PC CMR. Eligible
patients were aged between 45 and 70 years and had no
cardiovascular disease, diabetes mellitus and/or chronic
kidney disease according to their electronic patient
record (Table 1). In the Netherlands, GPs have a fixed
practice list, and all non-institutionalized inhabitants are
obligatory listed in a general practice.
Eight practices identified all eligible patients born in
1939, 1946, 1952, 1958 or 1964, sent them a personal
letter and invited them to complete the online risk
estimation (active approach). This selection procedure was
based on practical reasons regarding implementation. By
choosing 5 birth cohorts spread out over the actual age
of 45 70 years, we were able to reach a wide age range
and participating GPs had a practical and uniform tool
to invite their patients. It was not feasible for
participating GPs to invite all persons between 4570 years, since
Table 1 Exclusion criteria for prevention consultation
cardiometabolic risk (ICPC-codes [International
classification of primary care]) (not reported previously)
Ischaemic heart disease with angina pectoris
Acute myocardial infarction
Ischaemic heart diseases without angina pectoris
Pulmonary heart disease
Heart valve disease
Other disease of heart
Hypertension with involvement target organs
Transient cerebral ischemia
Peripheral vascular diseases
Lipid metabolism disorder
Other disease urinary system
all GPs had their regular GP practice duties and
activities. The first invitation letter was sent between
October 2009 and January 2010. A reminder letter
was sent in March 2010.
The eight other practices passively invited all eligible
patients by a poster in the waiting room of the practice
and by leaflets in the waiting room and consulting room
(passive approach). The poster and leaflets contained
information on the purpose of the PC CMR and invited
patients to complete the online risk estimation. Poster
and leaflets were present between October 2009 and
A medical ethics committee approval was not required
according to Dutch legislation.
The first step of the PC CMR is an online risk
estimation. The online risk estimation was offered in a
nonsecure, open web environment.
The online risk estimation consists of a web-based
questionnaire on risk factors for cardiovascular disease,
diabetes mellitus and chronic kidney disease . The
questionnaire contains three items of the Systematic
Coronary Risk Evaluation (SCORE) risk function: age,
gender and smoking status  and all items of the
Finnish Diabetes Risk Score (FINDRISK): height, weight,
waist circumference, history of high blood glucose and
family history of diabetes mellitus . Body mass index
(weight (kg)/height2 (m2)) was derived from the
webbased questionnaire. Since a positive family history
doubles the future risk, a question on family history of
cardiovascular disease was added . A positive family
history of cardiovascular disease was defined as a first
degree relative with cardiovascular disease below the age
of 65 years. Based on the algorithm in Figure 1
participants were categorized as having a low, intermediate or
high risk for cardiometabolic disease.
In the second step of the PC CMR, patients estimated
as having high risk according to the online risk
estimation are advised to visit their general practice in order to
complete their risk profile and discuss follow-up
treatment. The risk profile includes assessments of serum
cholesterol ratio (total cholesterol: HDL), serum glucose
level and blood pressure measurements.
Uptake and participation
A representative of each general practice (either a
general practitioner or practice nurse) identified the eligible
population (patients between 45 and 70 years without
cardiometabolic disease) by using the electronic medical
records. An anonymised list of the eligible population
was sent to the researchers.
Results of the online risk estimations, completed by
the participants, were saved in a web-based log file.
From the log file, which contained all answers to the
questions of the online risk estimation, we were able to
calculate the uptake of the first step of the PC CMR and
Age 60 yrs
Age 45 yrs
50 yrs (men)
55 yrs (women)
Findrisk 7, 8, or 9
Positive family history CVD and age < 45 yrs
OR Smoking and age < 50 yrs (men) Smoking and age < 55 yrs (women) yes
Figure 1 Algorithm for estimating cardiometabolic risk (not published previously).
to categorize the participants as having low, intermediate
or high risk. Response rates to the second step of the PC
CMR were calculated on the basis of the GPs insurance
claims of practice visits.
After the study period of 6 months, GPs provided data
from the electronic medical records of patients who
consulted the practice on the basis of the estimated high
risk at the online risk estimation. GPs reported the
presence or absence of hypertension, hypercholesterolemia,
diabetes mellitus and chronic kidney disease. Additionally,
they reported results of diagnostic assessments and
Table 2 Results from the online risk estimation; means
and percentages (not reported previously)
Age, years, mean
BMI, kg/m2, mean
Gender, n (%)
woman, n (%)
> 88 cm (%)
Ever high blood
diabetes, n (%)
Family history of
diabetes, n (%)
Family history of
disease, n (%)
Smoking, n (%)
laboratory tests on blood pressure, serum cholesterol,
LDL and HDL levels, serum glucose, glycated hemoglobin,
serum creatinine, creatinine clearance according to the
Modification of Diet in Renal Disease (MDRD) formula
 and urine albumin-to-creatinine ratio. Finally, new
prescriptions for antihypertensive medication, statins and
oral antidiabetics were reported.
Reasons for non-response
We conducted a survey among the eligible population in
order to evaluate differences between responders and
non-responders. A questionnaire was sent to all eligible
patients in the practices that had used the active
approach, and to a random sample of 200 persons of the
eligible population in practices that had invited patients
passively. The questionnaire contained items on
demography, health risk behaviour and attitudes towards the
PC CMR. Alcohol use of >6 drinks/day was used to
describe the proportion of participants with excessive
alcohol abuse .
We evaluated uptake rates and the incidence of
cardiometabolic disease as a percentage of the eligible
population. Additionally, we reported the number needed
Table 3 Diagnostic test results, diagnoses and prescribed
medication of 142 GP office visitors; percentages, and
calculated number to screen among high risk patients
(not reported previously)
Identified N (%)
pressure 180 mmHg
Cholesterol 8.0 mmol/l
or cholesterol/HDL-ratio 8.0
Impaired Fasting Glucose
(6.1 and 6.9 mmol/l)
Chronic kidney disease
ratio >3.5 mg/mmol
Blood glucose lowering drugs
* NNS: Number needed to screen.
to screen (NNS) as the inverse of the proportion of
patients diagnosed with a cardiometabolic disease.
We calculated statistical differences between participants
and non-participants by two sample Student t-tests for
continuous outcomes and Chi-square tests for dichotomous
outcomes. We used the statistical software package STATA
10.0 (StataCorp; College Station TX, US).
All results have been previously reported in Dutch ,
except for Tables 2 and 3 and Figure 1.
Uptake and cardiometabolic disease
In the 8 practices using the active approach, 1,583
patients received an invitation letter to participate in the
PC CMR. Of these, 521 (32.9%) completed the online
risk estimation (Figure 2). Their mean age was 54 years
and 59% were women. In 283 (54.3%) cases the online
questionnaire was completed after the date a reminder
letter was sent.
The eligible population in the 8 practices using the
passive approach consisted of 8,313 patients. Ninety-six
(1.2%) patients completed the online risk estimation
(Figure 2). Their mean age was 56 years and 57% were
women. Their age and gender did not statistically significantly
differ from participants in the active approach (p = 0.17 and
p = 0.82, respectively).
Table 2 shows the results of the online risk estimation.
Participants from practices with the passive approach
had a higher body mass index than patients from
practices in the active approach (25.4 versus 26.8, p < 0.01).
Of all participants, 129 (21%) smoked, 253 (41%) were
overweight (BMI 25 and <30), 74 (12%) were obese,
and 36 (6%) had a history of high blood glucose or
diabetes mellitus. A positive family history of diabetes
mellitus and cardiovascular disease was reported by 173
(28%) and 166 (27%), respectively.
A total of 392 (63.5%) participants had a high risk for
cardiometabolic disease, based on the online risk
estimation; 83 (13.5%) and 142 (23.0%) participants had an
intermediate or low risk, respectively. There was no
statistically significant difference of the distribution of
estimated risk profiles between the participants in the two
types of practices.
Only patients with a high risk score were advised to
visit the practice. A total of 142 participants visited the
practice. Nine percent of the visitors had a low or
intermediate score at the online risk estimation, but
nevertheless visited the GP office despite the negative advice.
Results of diagnostic tests, and prescribed medication
8 General practices
8 General practices 8313 Patients Completed on-line risk estimation
High risk score at screening
N = 327 (20.7%)
High risk score at screening
N = 65 (0.8%)
Low risk score: N = 126
Medium risk score: N = 68
Low risk score: N = 16
Medium risk score: N = 15
Attendance at general practice
N = 131 (8.3%)
N = 25 (1.6%)
Attendance at general practice
N = 11 (0.1%)
N = 6 (0.07%)
Figure 2 Flow chart of the results of the prevention consultation cardiometabolic risk.
Table 4 Demographic characteristics and health risk
behaviour of responders and non-responders to online
Table 4 Demographic characteristics and health risk
behaviour of responders and non-responders to online
risk estimation (Continued)
are shown in Table 3. Eighteen participants (13%) had a
newly diagnosed hypertension, 15 (11%)
hypercholesterolemia, two (1%) had diabetes mellitus and two (4%) had
albuminuria. Four participants (3%) had both
hypertension and hypercholesterolemia. Based on a physicians
diagnosis of hypertension, hypercholesterolemia,
diabetes mellitus or chronic kidney disease, a total of 31
newly diagnosed patients (22%) were identified.
The questionnaire to assess the reasons for non-response
was sent to 3,183 patients of whom 932 (29.3%) returned
the questionnaire (427 from practices with the active
approach, and 505 from practices with the passive
approach). A large proportion of patients listed in
practices of the passive approach answered that they were
not familiar with the PC CMR (n = 433 (85.7%)). Of
these 433 patients, 203 (46.9%) had not visited the
practice in the past six months and have therefore not
been able to take notice of a poster of leaflet in the
practice about the PC CMR.
For the analysis of reasons for non-response data were
available of 274 patients (29% of the questionnaire
responders) who reported that they only had completed
the first step of the PC CMR (the online risk estimation)
and 177 patients who were familiar with the PC CMR
(had seen or heard about it), but had not completed the
online risk estimation.
Age, gender, marital status, education level and ethnic
background did not differ between responders and
nonresponders (Table 4). There were only small, non-significant
differences between responders and non-responders with
regard to smoking status, physical activity and body mass
index. Non-responders more often excessively used alcohol
than responders (p < 0.001) and less often had a history of
high blood sugar (p = 0.03).
The most frequently mentioned reason for not
participating was the fear that online assessment is a privacy
risk (23.9%) (Figure 3). Other frequently mentioned
reasons were lack of time (21.4%) and fear of medical
consequences related to high-risk assessment (19.6%). It
must be noted that patients who did not participate
more often reported difficulties accessing the internet
than participants (24.1% vs 10.9%, p < 0.001).
No influence on risk profile
Don't want to know risk profile
Afraid of problems with mortgage / insurance
Afraid of high risk outcome
Did not understand invitation / poster
Already yearly check-up
Afraid of medical consequences in case of high risk
Lack of time
Online assessment harms privacy
Figure 3 Attitude towards participating in online risk estimation of PC CMR.
We evaluated uptake rates, newly detected cardiometabolic
disease and reasons for non-response of the newly developed
Prevention Consultation cardiometabolic risk (PC CMR) in
a 6-month, multi-center implementation study. The uptake
rates of both steps of the PC CMR were substantially higher
in practices that actively invited patients to participate
compared to practices that only used leaflets and posters to invite
patients. In one out of five patients who attended the GP
office a cardiometabolic disease, defined as hypertension,
hypercholesterolemia, diabetes or chronic kidney disease
was diagnosed. Limited access to the internet and the fear
that participation in the PC CMR is a privacy risk were the
major reasons for non-participation.
Although patients who were actively invited to
participate in the health check more often participated than
patients who were passively invited, only one third of
the eligible group completed the online risk estimation.
In the Netherlands 87% of all inhabitants have home
access to internet , ranging from 79% in low educated
to 95% in high educated persons. Apparently, this high
internet coverage does not guarantee a high
participation in an online risk estimation. Non-participants
mention concerns regarding privacy with online assessments
as the most important reason not to participate. Other
studies show that paper-and-pencil questionnaires, sent
to an eligible population, results in participation rates up
to 75% [18,19]. In our study the PC CMR was offered in
a non-secure, open web environment. Participants were
able to complete the online questionnaire without the
use of a log-in account or SMS authentication. It
remains questionable whether the use of a more secured
web environment or a better explanation about the
privacy within the project would increase participation.
Previous studies suggest that participants of preventive
health checks are better educated, better motivated to
look after their health and perform more health-approved
practices than non-participants [18,20,21]. However, our
evaluation shows that participation was not confined to
the worried-well: the prevalence of smoking, physical
inactivity and overweight did not differ between responders
and non-responders. It must be noted that the number of
non-Western immigrants in the analyses was low, which
is remarkable since four participating practices were
located in the multi-cultural city of Rotterdam. We
recommend that with further implementation of the Prevention
Consultation in the Netherlands paper questionnaires
are used beside online risk assessments and that both
paper and online questionnaires are available in
Detection of cardiometabolic risk factors and disease
This study identified large numbers of smokers, patients
with overweight and/or physical inactivity. Recent Dutch
guidelines (on smoking and on obesity) for primary care
emphasize the need to guide and treat patients with these
modifiable cardiovascular risk factors [7,22]. The easily
accessible and integral setting of primary care is the ideal
place to guide these patients to a healthier lifestyle.
Moreover, our study identified one quarter of participants with a
positive family history of cardiovascular disease or diabetes.
Although a validated treatment algorithm is lacking,
intensified follow-up or risk management is justifiable for this
group at relatively high risk of cardiovascular disease .
In 22% of patients with a high risk who attended the
practice a cardiometabolic disease (hypertension,
hypercholesterolemia, diabetes) was diagnosed. Obviously,
these patients may benefit from lifestyle advice and
cardiovascular follow-up assessments. Whether this group
also needs drug treatment depends on the integrated
Three issues regarding the used outcome measures need
attention. First, the diagnoses hypertension and
hypercholesterolemia were based on physicians records and
not on absolute cut-off points for blood pressure or
cholesterol. In fact, absolute cut-off points do not exist
(anymore), since cardiovascular risk management depends
on the integrated profile and not on a single blood
pressure (SBP) or cholesterol . For example, according to
the Dutch guidelines, a 55-year old man who smokes, has
an SBP of 160 and cholesterol ratio of 8 needs drug
treatment, whereas a 55-year old non-smoking man with a
similar SBP of 160, but cholesterol ratio of 4 may not need
drug treatment. Although a physicians diagnosis of
hypertension or hypercholesterolemia probably differs between
professionals, our approach reflects current daily medical
practice. Second, the outcome measures of this
implementation study are limited by the fact that measurements
were only taken once (blood pressure, blood and urine
tests). Follow-up measurements are warranted to establish
a more valid diagnosis. Third, obviously the presented
outcomes are intermediate measures. The design and time
frame of the study did not allow analysis of endpoints
such as cardiovascular morbidity and morbidity.
Nationwide implementation of the Prevention
Consultation in general practice is likely to be successful when
patients are approached actively (with a reminder) and
are able to complete the cardiometabolic risk estimation
not only online, but also using a written questionnaire in
multiple languages. The privacy of all assessments needs
to be guaranteed and this should be made clear to
The Prevention Consultation seems to adequately
detect cardiometabolic risk factors and diseases in those
patients who attend the practice. Future efforts for both
professionals and researchers should be directed towards
longitudinal follow-up, lifestyle coaching and drug
treatment in order to assess the effects of the Prevention
Consultation cardiometabolic risk on long-term
morbidity and mortality.
GP: General Practitioner; MDRD: Modification of Diet in Renal Disease;
NNS: Number Needed to Screen; PC CMR: Prevention Consultation
cardiometabolic risk; SBP: Systolic blood pressure.
The authors declare that they have no competing interests.
VvdM, MN, WA and FS contributed to conception and design. VvdM and MN
acquired and analysed the data. All authors interpreted the data and were
involved in drafting and revising the manuscript. All authors read and
approved the final manuscript.
This is an elaborated and translated version of a recently published article in
Huisarts en Wetenschap (in Dutch).
This study was supported by a grant from the governmental
Netherlands Organization for Health Research and Development
(ZonMw grantnr 50-50115-96-700).
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