Screening for Future Cardiovascular Disease Using Age Alone Compared with Multiple Risk Factors and Age
Morris JK (2011) Screening for Future Cardiovascular Disease Using Age Alone Compared with Multiple Risk Factors and
Age. PLoS ONE 6(5): e18742. doi:10.1371/journal.pone.0018742
Screening for Future Cardiovascular Disease Using Age Alone Compared with Multiple Risk Factors and Age
Nicholas J. Wald 0
Mark Simmonds 0
Joan K. Morris 0
Giuseppe Biondi-Zoccai, University of Modena and Reggio Emilia, Italy
0 Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London , London , United Kingdom
Background: Risk factors such as blood pressure and serum cholesterol are used, with age, in screening for future cardiovascular disease (CVD) events. The value of using these risk factors with age compared with using age alone is not known. We compared screening for future CVD events using age alone with screening using age and multiple risk factors based on regular Framingham risk assessments. Methods: Ten-year CVD risk was estimated using Framingham risk equations in a hypothetical sample population of 500,000 people aged 0-89 years. Risk estimates were used to identify individuals who did and did not have a CVD event over a ten-year period. For screening using age alone (age screening) and screening using multiple risk factors and age (Framingham screening) we estimated the (i) detection rate (sensitivity); (ii) false-positive rate; (iii) proportion of CVD-free years of life lost in affected individuals with positive results (person-years detection rate); and (iv) cost per CVD-free life year gained from preventive treatment. Results: Age screening using a cut-off of 55 years detected 86% of all first CVD events arising in the population every year and 72% of CVD-free years of life lost for a 24% false-positive rate; for five yearly Framingham screening the false-positive rate was 21% for the same 86% detection rate. The estimated cost per CVD-free year of life gained was 2,000 for age screening and 2,200 for Framingham screening if a Framingham screen costs 150 and the annual cost of preventive treatment is 200. Conclusion: Age screening for future CVD events is simpler than Framingham screening with a similar screening performance and cost-effectiveness. It avoids blood tests and medical examinations. The advantages of age screening in the prevention of heart attack and stroke warrant considering its use in preference to multiple risk factor screening.
Competing Interests: Nicholas J. Wald holds patents (EU1272220 and GB2361186) for a combination pill for the prevention of cardiovascular disease. This does
not alter the authors adherence to all the PLoS ONE policies on sharing data and materials.
Cardiovascular disease (CVD: coronary death, non-fatal
myocardial infarction, and stroke) is the commonest cause of
death and a major cause of morbidity worldwide . Preventive
treatments should therefore be more widely used, given their
efficacy, low cost, and safety .
Guidelines recommend that primary preventive treatment be
based on assessment of absolute risk of cardiovascular events using
multiple risk factor algorithms such as the Framingham risk
equations, which include age, sex, smoking status, diabetic status,
serum cholesterol, and blood pressure . Age is by far the strongest
determinant of CVD risk in multiple risk factor algorithms. Offering
preventive treatment to everyone over a specified age without
measuring other risk factors would be a simpler screening strategy
than offering preventive treatment to everyone exceeding a specified
CVD risk cut-off based on multiple risk factor measurement. It
would avoid the multiple risk factor measurement costs. The loss in
screening performance may be small enough to warrant
consideration of using age alone as the screening method of choice.
To investigate this we compared the efficacy of offering
preventive treatment based on age alone (age screening) with
Framingham risk estimation (Framingham screening). Using
illustrative costs, we also compared the cost effectiveness of these
Screening performance was assessed by estimating the detection
rate (proportion of affected individuals (those who have a first
CVD event within a specified time period) with positive screening
results) for a given false-positive rate (the proportion of unaffected
individuals (those who do not have a first CVD event within the
same specified time period) with positive results). For example, in
age screening, a 60% detection rate for a 20% false-positive rate at
a given age cut-off means that 60% of all individuals in a
population with a first CVD event occurring over a specified time
period, and 20% of individuals without a CVD event over the
same time period, are at or above the age cut-off. Regardless of the
specified time period, this is equivalent to detecting 60% of all
individuals in a population who have a first CVD event in every
Estimates of detection rates and false-positive rates were used to
compare the performance of age screening with multiple risk
factor screening. Obtaining reliable estimates for such
comparisons requires a very large population with known CVD risk factor
values, and the identification over 10 years of individuals who do
or do not have a first CVD event (ie. distinguishing affected from
unaffected). A simulation study is the appropriate method of
analysis because it generates a large complete dataset that reflects
the distribution of age and risk factors in a whole population.
1. Generating a population with known values of cardiovascular risk factors
A sample of 500,000 individuals aged from 0 to 89 was
generated, having the same age and sex distributions as England
and Wales (2007)  using Monte Carlo simulation. This sample
size was sufficient to give precise estimates of screening
performance (to within one decimal place). The means and
standard deviations of risk factors in 10-year age and sex groups,
taken from the Health Survey for England  (summarized in
tables S1 and S2 in appendix S1), were used to classify each of the
500,000 individuals as smokers or non-smokers and diabetic or
non-diabetic, and to assign values for systolic blood pressure and
total and HDL cholesterol, taking their distributions to be
Gaussian. In this way the distributions of cardiovascular risk
factors perfectly reflected the age- and sex-specific distributions of
the England and Wales population. Correlations between these
risk factors, given age and sex, are low (see table S3 in appendix
S1) and were taken to be zero. Left ventricular hypertrophy was
excluded from the Framingham risk calculation because the
Health Survey for England does not provide data on its prevalence
and it is not usually included in risk factor assessments.
2. Determining affected and unaffected individuals
Framingham estimates of the annual risk of a first CVD event
(fatal or non fatal) were calculated for each of the 500,000
individuals in the simulated population  for each of the next ten
years of their lives, based on age, sex, and risk factor values. The
risk of a first CVD event was taken to be the sum of the risks of
CHD death, non-fatal myocardial infarction and stroke, calculated
from the results of the report of the Framingham Heart study, in
which these three outcomes were individually specified . The
Framingham risk equations were based on people aged 3074; it
was assumed in our analysis that the same regression coefficients
applied to people aged under 30 and 75 and over, and the validity
of these assumptions was tested (see 5 below). For each year of the
simulated 10-year follow-up period, those individuals who would
have a first CVD event (affected) in the absence of preventive
treatment were identified using Monte Carlo simulation; the
probability of having a CVD event in a given year being the
Framingham risk estimate. Conceptually this is equivalent to
spinning a roulette wheel for each individual for each year such
that the proportion of reds on the wheel is exactly the same as his
or her Framingham risk estimate. If the wheel turns up red, the
individual is classified as having had a CVD event in that year,
otherwise the individual is classified as unaffected. Individuals who
died of non-CVD causes were identified using England and Wales
age-specific non-CVD death rates in the same way. The expected
years of CVD-free life lost were calculated by estimating the
average time to the first CVD event or to death from any cause in
the simulated population according to sex and age. For example, a
man aged 60 has an expectation of life of 14 years without a CVD
event, so if he had a CVD event at age 60, he was deemed to have
lost 14 years of CVD-free life.
3. Estimating screening performance
In our evaluation of Framingham screening, risk assessments are
performed either annually or five-yearly from age 40 until an
individuals 10-year CVD risk reaches a specified level (eg. a 20%,
or 1 in 5 risk over 10 years), after which time the individual
remains screen-positive, with no further screening assessments. In
age screening, an individual becomes screen-positive when they
reach a specified age.
Applying these two approaches to the sample population we
determined, for specified 10-year CVD risk cut-off levels and age
cut-offs, the: (i) detection rate; (ii) false-positive rate; (iii)
personyears detection rate, which is the proportion of all CVD-free years
of life lost in affected individuals which is lost by those who are
screen-positive, ie. CVD-free years of life lost in affected
individuals with a positive result divided by the CVD-free years
of life lost in all affected individuals. The person-years detection
rate is lower than the detection rate for the same cut-off because
CVD events in younger people lead to more years of life lost
without a CVD event than events in older people. For example,
three individuals aged 50, 60 and 70 have life expectancies without
a CVD event of 21, 16 and 13 years respectively. If they have a
CVD event at 50, 60 and 70, in age screening with a cut-off of age
55, two of these three events (at age 60 and 70) would be detected:
a 67% (2/3) detection rate, but the person-years detection rate
would be 58% because 29 (16+13) of the 50 years (21+16+13) of
CVD-free life lost would be detected.
4. Comparing the costs of the screening methods
The years of CVD-free life gained were calculated on the basis
that a standard dose statin and three half-standard dose blood
pressure-lowering drugs administered together, regardless of an
individuals cholesterol concentration or blood pressure, would
prevent 80% of coronary heart disease deaths and non-fatal
myocardial infarctions, and 70% of strokes . The
proportional reduction in cardiovascular risk was based on the
observation that this is independent of the levels of the risk factors
. The estimates are based on the age group
5569, with some attenuation of effect with increasing age that will
affect all screening strategies similarly. The full impact of
preventive treatment would be achieved about 23 years after
the start of therapy because the benefits of serum cholesterol
reduction take some time to be realized .
Cost-effectiveness was estimated by calculating the costs per
CVD-free year of life gained using three illustrative costs for
annual treatment (100, 200, 400 including prescribing costs)
and three illustrative costs of the screening assessments (100,
150 or 200 per assessment). The cost of inviting people to be
seen is not included because it is small and applies equally to the
different methods. The cost per CVD-free year of life gained was
calculated by multiplying the annual cost of treatment by the
person-years of treatment plus, for Framingham screening, the
cost of a Framingham risk assessment multiplied by the number of
assessments, all divided by CVD free life-years gained.
5. Validation of methods
We tested the validity of the assumptions that the Framingham
risk equations ranked risk correctly in people of all ages, including
those under 35 and those 75 and over by comparing the expected
performance of age screening based on the expected age-specific
incidence of CVD events using the Framingham risk equation with
those observed from CVD registry data in England and Wales 
(see Appendix S1). If the screening performance of the two
methods yields identical or near identical results, the methodology
Figure 1 shows the detection rate plotted against the false
positive rate (the receiver-operator characteristic (ROC) curve) for
age screening and annual and five-yearly Framingham screening
with selected age and Framingham risk cut-offs. The curves show
that for a given false-positive rate the detection rate for age
screening is less than for Framingham screening, but the proximity
of the three curves indicates that the methods have similar
screening performances. At a detection rate of 65%, for example,
(achievable using five-yearly Framingham screening with a 20%
10-year CVD risk cut-off) the false positive rates are 7% for annual
Framingham screening, 9% for five-yearly Framingham screening
and 12% for age screening. A summary measure of screening
performance is the area under the curve, 0.89 for age screening,
0.90 for five-yearly Framingham screening and 0.91 for annual
Framingham screening. There are disadvantages with this
measure; it conceals the different degrees of discrimination at
different points along the curve, (ie. at different false-positive rates),
and a useless test has a value of 0.5, not zero. Precise estimates of
the screening performance shown in figure 1 are given in
Appendix table S4 in Appendix S1.
Figure 2a shows, in the same way as figure 1, the screening
performance among people aged 4089 (the age range that would
be offered screening according to the NICE guidelines ). The
pattern of results is similar but for the same detection rates the
false-positive rates are about double because about half the
unaffected population (those under 40) are excluded, almost all of
whom are screen-negative. The detection rate is unchanged
because there are few CVD events in people under 40. (The areas
under the curves are 0.79, 0.83, and 0.86 respectively.) Figure 2b
shows the screening performance of Framingham screening aged
4074 and age screening thereafter (as proposed by NICE in the
UK ) compared with age screening in people aged 4089. The
results are similar to those in figure 2a.
Figure 3 shows the person-years detection rate plotted against
the false positive rate. The person-years detection rate is lower
than the detection rate for the same false positive rate, but the
pattern of results remains the same. For example, the person-years
detection rate using five-yearly Framingham screening with a 20%
10-year CVD risk cut-off is 48%, so this screening strategy would
identify less than half of the years of life that could be gained using
preventive treatment. Precise estimates of screening performance
are given in Table S5 in Appendix S1.
Figure 4 shows the cost per CVD-free life year gained for age
screening and Framingham screening every 5 years according to
person-years detection rate using the specified illustrative unit costs
of treatment and screening. The cost-effectiveness of age screening
and 5-year Framingham screening is similar in all the examples for
a given annual treatment cost (for example, about 2,000 per
CVD-free life year gained for age screening and 2,200 for
Framingham screening if a Framingham screen costs 150 and
the annual cost of preventive treatment is 200). The
costeffectiveness estimates, as expected, are more favourable for age
screening when the treatment costs are lower and the screening
costs are higher and more favourable for Framingham screening
when the opposite applies, but the differences are small typically
100 or 200 per CVD-free life year gained. Even if the cost per
risk assessment were much lower, for example 50, the difference
Framingham screening with annual assessments is, as expected,
less cost-effective than five-yearly screening as it involves more
screening assessments. For example, with an annual treatment cost
of 200 and a Framingham screening cost of 150, the cost per
CVD-free life year gained at a 60% person-years detection rate is
3,200 for annual screening compared with 2,100 for screening
every five years. The number of screening assessments per
CVDfree year of life gained would be, respectively, 23 and 6.
Table 1 summarizes the main results for Framingham screening
with assessments every 5 years using the widely adopted 20% 10
year CVD risk cut-off, and the corresponding age screening results
applied to achieve the same detection rate. It also shows the results
of offering treatment to everyone aged 55 and over, and to
everyone 50 and over, together with the corresponding
Framingham screening results. Age screening with an age cut-off of 55 is
equivalent to 5-yearly Framingham screening using a 10 year risk
cut-off of 8%. Both methods yield person-years detection rates of
72% (86% detection rate), with age screening having a 24%
falsepositive rate compared with 21% with Framingham screening and
a cost of 2000 per CVD-free year of life gained, instead of
About 90% of individuals have concordant results from
Framingham screening and age screening. For example, using
the above cut-offs (age 55 and 8% 10-year risk using 5-yearly
Framingham screening) among affected individuals, 3% would be
missed using Framingham but detected using age, and 6% would
be missed using age but detected using 5-yearly Framingham.
Among unaffected individuals 6% would be classified positive
using age but negative using Framingham, and 4% would be
positive using Framingham but negative using age. Framingham
screening does not identify people at younger ages that have CVD
events. Five-yearly Framingham screening with a 20% 10 year risk
cut-off would miss only 5% of people over 70 who have a CVD
event, but would miss 52% of people with such events in their 50s.
The age-specific risk of cardiovascular disease is higher in men
than in women, so we explored the use of sex-specific age cut-offs.
Figure 2. Detection rate against false-positive rate for Framingham screening and age screening: (a) in people aged 4089 (b) as
proposed by NICE (Framingham to age 74, then age screening to 89).
At a fixed detection rate the age cut-offs would be about 12 years
younger in men and about 34 years older in women than the age
cut-off for both sexes combined.
Sensitivity analyses were performed in respect of the
costeffectiveness estimates in Figure 3 and Table 1. We here present a
summary of the results. The absolute cost estimates depend on the
size of the effect of preventive treatment but the relative differences
are similar. If, for example, the effect of treatment is halved the
costs are doubled but the percentage difference is similar. With
Framingham screening every five years at a 20% CVD risk cut-off,
the cost per CVD life year gained is 3700 instead of 2200 and
for age screening at the same detection rate the cost is 3300
instead of 1800. Discounting costs and benefits has little effect on
the estimates because both are spread similarly over time.
Adherence to screening or treatment also has little effect on the
estimates because for those who do not adhere there is no cost or
benefit and for those who do adhere the same costs and benefits
apply. No adjustment was made for quality of life because every
life-year gained without a first CVD event was taken to be of equal
The simulated population took account of advancing age, but
ignored changes in risk factors and habits that affect risk. Allowing
other risk factors to vary over the ten year follow-up period had a
negligible effect on our results. The simulated population was
based on Gaussian distributions of the risk factors but results are
robust to changes in these distributions, for example, the results
were not altered if all the risk factors were regarded as
logGaussian. Framingham screening was taken to have started from
age 40 as widely practised. Varying the age of starting
Framingham screening has only a small effect on screening
performance. For example starting at age 30 the screening
performance is almost identical but the costs are greater. Starting
at age 50 the detection rate and the false-positive rate are both
about 2 percentage points less and the costs are less, but not
reduced sufficiently to make such screening materially cheaper
than an equivalent age screening policy.
Figure 5 shows the detection rate plotted against the
falsepositive rate for age screening based on the expected CVD
incidence from the Framingham risk assessment and that based on
the observed incidence of CVD in England and Wales . The
two curves are essentially identical, validating our methodology.
They also show that estimates of screening performance are robust
to the recognized relative overestimation of risk using Framingham
equations in people under about 65 . This arises because
the ranking of people according to their risk of the same disorders
is little influenced by the overall over- or under-estimation of the
magnitude of their risk.
Our results show that age screening loses little in screening
performance compared with multiple risk factor measurement
methods and, with appropriately priced preventive treatment, is
less expensive. Offering preventive treatment to everyone above a
specified age has the advantage of simplicity. It avoids needless
worry that would be caused through selecting individuals on
account of the results of a personal medical assessment.
Age screening avoids the costs and time spent in connection
with the measurement and explanation of risk factor levels and
avoids having to issue regular invitations for blood tests and
medical examinations. With age screening people are not singled
out as being at risk other than on account of their age, so those
taking preventive treatment are less likely to feel abnormal or
that they have become patients and possibly given a medical
diagnosis. Age screening moves the emphasis from the assessment of
risk to the reduction of risk.
In multiple risk factor screening, a 10-year CVD risk cut-off of 1
in 5 (20%) has been adopted by the UK government, and
recommended by the National Institute for Health and Clinical
Excellence . The cut-off is high for such devastating medical
events as heart attacks and strokes, and does not offer preventive
treatment to many people who would benefit. Over half the
Framingham screening every 5 years
using a 20% 10-year CVD risk cut-off
Age screening every year to achieve
the same detection rate
Age screening using a cut-off of 55 years
Framingham screening every 5 years
to achieve the same detection rate
Age screening using a cut-off of 50 years
Framingham screening every 5 years
to achieve the same detection rate
*Risk and age cut-offs differ marginally between detection rate and person-years detection rate, but the difference is less than 1% or 1 year.
**Based on 200 annual cost of preventive treatment and 150 cost of a Framingham risk assessment.
preventable CVD-free years of life lost would occur in people with
a lower risk (see Figure 3). If this same risk were expressed as an
individual having a 1 in 50 chance of a heart attack or stroke
within the next 12 months, the seriousness of the situation would
be more apparent and the individuals concerned would be better
motivated to take steps to reduce the risk.
Preventive treatment has adverse effects but these are largely
minor and reversible. With age screening a higher proportion of
people are treated for the same number of cardiovascular events
prevented but, given the extremely low incidence of serious
adverse effects, the difference is not large enough to influence
which screening method to adopt.
All methods of screening for CVD involve some people
receiving preventive treatment without benefit because they die
of another cause without having a CVD event, while others who
would benefit do not receive treatment. For a given detection rate
the proportions in these two categories are similar with
Framingham screening and with screening using age alone. The
prediction of CVD events using a Framingham risk assessment is
relatively poor even though the Framingham equations were used
to determine CVD events. This is because a Framingham risk only
determines the probability of having a CVD event, not who
actually has an event (eg. who, among 100 individuals with a 20%
risk, are the 20 who have a CVD event).
The curves in figure 4 show that it is similarly cost-effective to
deliver a screening policy designed to achieve a person-years
detection rate of about 75% (a detection rate of about 85%) as it is
to deliver one designed to achieve a 45% person-years detection
rate, in that the number of CVD-free years of life gained for a
given expenditure is similar. There is little justification for
screening using a Framingham-based 20% CVD risk cut-off that
has a person-years detection rate of 45% and, provided the cost of
treatment is not high, is less cost-effective than the alternative of
age screening using a 50 or 55 year age cut-off (see Table 1) which
would achieve a detection rate of about 85%. Our results indicate
that there is no practical justification for using different age cut-offs
for men and women. The age cut-off, however, could be lower in
people with diabetes; they have a high CVD risk and will already
be aware of this.
The monetary costs we have used are illustrative and designed
to provide an indication of the financial implications arising from
the three methods of screening in relation to their efficacy. The
costs will vary according to healthcare setting. Some costs
associated with both methods have not been considered here,
including the initial treatment consultation with a health
professional, which does not affect the comparative costs. With
Framingham screening, physicians may vary treatment according
to the assessment results, and such treatment tailoring is likely to
increase costs relative to age screening. Our analysis provides a
reasonable indication of the relative cost-effectiveness of the two
screening methods using illustrative unit costs.
We used the Framingham risk algorithm published in 1991
because it provides risk equations for the three cardiovascular
outcomes we specified in this analysis (myocardial infarction,
fatal coronary heart disease and stroke). Framingham risk
equations published in 2008  combine various cardiovascular
outcomes, including, for example, angina and intermittent
claudication . These added outcomes are less well predicted
both by age screening and by multiple risk factor measurement
and consequently the detection rate is about 10 percentage
points less for a 20% false-positive rate (see figure S1 in appendix
S1), but our conclusions regarding the similar screening
performances of age, 5-yearly, and Framingham screening still
apply. They are also likely to apply to other similar algorithms,
for example the Reynolds risk score  or QRISK2 
because screening performance with respect to the same clinical
outcomes depends on the ranking of risk rather than the
magnitude of risk. While the algorithms differ in estimating the
magnitude of risk, there is little difference in the ranking of risk
between individuals, mainly because in all the algorithms risk is
dominated by age.
Screening performance was based on a population aged 089, but
screening programmes would invite people aged about 40 or over for
a risk assessment or simply for preventive treatment if about 5055
or over. Using the whole population in estimating screening
performance has several advantages. First, it standardizes the
estimates of screening performance and avoids variation arising
from the age range selected. Starting at age 40, as in Figure 2 gives
similar detection rates at the same age or risk cut-off as Figure 1
but higher false-positive rates. Second, it means that all CVD
events are included in the analysis to derive the estimates of
screening performance, particularly CVD events in older people,
in whom the disease is common and who stand to benefit
considerably from preventive treatment.
A perceived limitation of this study is that it is based on
statistical modelling and not on observed measurements from a
cohort of individuals. The modelling is, however, based on
observed data used to define the distributions of the risk factors in
the population at large. The method is therefore no different from
the modelling used in estimating the screening performance of, for
example, Downs syndrome in pregnancy . Such data-derived
modelling is the preferred method of estimating and comparing
screening performance because it can be based on a large enough
sample to provide the necessary precision, the sample genuinely
represents the population at large, and there are no missing values,
with complete ascertainment of clinical events. Nonetheless it
would be desirable for the estimates to be independently validated
against data from a cohort study.
Causal CVD risk factors, even in combination, are poor CVD
screening tests . To achieve even a 50% detection rate for
a 5% false-positive rate, a risk factor must have a relative risk
across the top and bottom quintile groups of about 100 .
Combining the measurement of risk factors that individually have
a poor screening performance has only a small effect in improving
screening performance . Inappropriate emphasis on causal risk
factors in CVD screening may have arisen from analyses of studies
to identify causes of the disease, where the effect of age is
deliberately minimized (eg. by age stratification) so that the effect
of a causal risk factor is revealed. However, as we have shown, age
may be, and in cardiovascuolar screening is, the dominant factor
in determining risk so in assessing the value of a risk factor in
screening the effect of age must be retained, and the impact of
adding the risk factor to age in improving screening performance
European guidelines on the prevention of cardiovascular disease
 recommend that global risk of CVD should be used to
determine who should receive preventive treatment. Age alone
does this. Any age can be converted into a risk; for example in
Britain, at age 50 the 10-year CVD risk is 2.8%, at age 55 it is
4.5% and at age 60 it is 7.1% ; the risk doubles every 7.6
years, so a 90 year old person has a risk 240 times greater than a
30 year old.
In summary, CVD is common and serious. To have a major
impact on its incidence a proactive cost-effective public health
policy is needed. This should be designed to prevent most CVD
events and should simplify access to preventive treatment without
making people become patients. Age screening meets these
objectives and warrants serious consideration, given its advantages
over current methods of cardiovascular disease screening and
We thank Jeffrey Aronson, Jacob Canick, Alan Hackshaw, James Haddow,
Aroon Hingorani, Malcolm Law, David Wald and Jon Bestwick for their
helpful comments on drafts of this paper.
Conceived and designed the experiments: NW MS JM. Performed the
experiments: NW MS JM. Analyzed the data: NW MS JM. Contributed
reagents/materials/analysis tools: NW MS JM. Wrote the paper: NW MS
JM. Statistical calculations: MS JM.
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