Is cardiovascular screening the best option for reducing future cardiovascular disease burden?

Is cardiovascular screening the best
option for reducing future cardiovascular
disease burden?
Chris Kypridemos, Kirk Allen, Piotr Bandosz, Maria Guzman-
Castillo, Iain Buchan, Simon Capewell, Martin O’Flaherty
A microsimulation study to
quantify the policy options

In my talk today...
I will
• briefly review the evidence regarding universal
screening for primary prevention of
cardiovascular disease (CVD)
• use a modelling approach to estimate
potential screening effectiveness & equity in
England.

Is screening for CVD effective??
• “NHS Health Check: an approach to engage and activate the
public about their health, focus on prevention & risk reduction,
& strengthen place-based leadership for health improvement” 1
• “General health checks did not reduce morbidity or mortality,
neither overall nor for cardiovascular or cancer causes…” 2
• “…screening for risk of ischaemic heart disease and repeated
lifestyle intervention over five years had no effect on
ischaemic heart disease, stroke, or mortality at the population
level after 10 years.”3
1. Waterall et al. NHS Health Check: an innovative component of local adult health improvement
and well-being programmes in England. Journal of Public Health 2015; 37 177–184
2. Krogsboll LT et al. General health checks in adults for reducing morbidity and mortality from
disease: Cochrane systematic review and meta-analysis. BMJ 2012;345
3. Jørgensen T et al. Effect of screening and lifestyle counselling on incidence of ischaemic heart
disease in general population: Inter99 randomised trial. BMJ 2014;348

Study aims
• To estimate the potential impact of universal
screening for primary CVD prevention, on disease
burden and socioeconomic health inequalities in
England.
• To compare universal CVD screening with:
1) feasible population-wide policy interventions, &
2) population-wide policy combined with screening
targeted on the most deprived areas.

Baseline scenario (current trends)
• Assumes recent trends in risk factor trajectories
and case fatality will continue in near future
• Stratified by
– Age & sex
– Deprivation quintiles using Index of Multiple
Deprivation (IMD)

Scenario II: Universal screening
• All healthy individuals aged 40 to 74 are invited every
5 years
• Assume 50% uptake of health checks
• CVD risk distributed across participants, as observed1,2
• People with 10 year QRISK2 score >10% are offered
lifestyle advice & medical treatment
• Prescription uptake(~25%), persistence (~80%), and
adherence(~70%) , all reflect observed data3,4
1. Public Health England. Explore NHS Health
Check Data.
2. Chang KC-M, et al. Prev Med 2015;78
3. Forster AS, et al. J Public Health 2015;37
4. Wallach-Kildemoes H, et al. Eur J Clin
Pharmacol 2013;69

Scenario III:
Population-wide policy interventions
Assumes
• Obesity rise slowed by sugar sweetenedbeveragestax1-3
• Systolic blood pressure decreased by 0.8 mmHg by
mandatory salt reformulation4
• Fruit & vegconsumptionup by 0.5 portion /day by subsidies5,6
• Smoking prevalence fallsby 13% (relative)by full compliance
with FrameworkConvention on TobaccoControl7
1. Sharma A, et al. Health Econ 2014;23
2. Briggs ADM, et al. BMJ 2013;347
3. Cabrera EMA, et al. BMC PH 2013;13
4. Gillespie DOS.PLoS ONE 2015;10
5. Bartlett S, et al. Evaluation of the Healthy
Incentives Pilot (HIP) Final Report. 2014
6. Nnoaham KE, et al. Int J Epidemiol 2009;38
7. Levy DT, et al. Health Policy Plan 2014;29

Scenario IV:
”Proportionate Universalism” combination
• Population-wide policy interventions
PLUS
• Targeted screening only in most deprived areas (IMD
4 and 5), where CVD risk is more concentrated

Conclusions
• Universal screening appears less effective and less
equitable than strategies including population-
wide policy approaches
• Modelling can help policy-makers identify the
best mix of population-wide and risk-targeted
CVD strategies to maximise cost effectiveness and
minimise inequalities

Thank you!
The full study is available at
BMJ 2016;353:i2793

NHS Health Checks
• Screening
• All adults aged 40 to 74 years without a diagnosis of
vascular disease are invited for CVD risk stratification
every 5 years
• Risk assessment includes collection of demographic
data, family history, smoking status, diabetes,
cholesterol & blood pressure measurement
• An individualised management plan is then developed
according to the risk assessment which might include
lifestyle interventions and/or medication
http://www.healthcheck.nhs.uk/about_nhs_health_check/

Can NHS Health Checks
reduce health inequalities?
• Interventions, which require mobilisation of
an individual’s material or psychological
resources, generally favour those with more
resources1
• Evidence regarding differential uptake and/or
adherence of NHS Health Checks is
contradictory
1. White M et al. How and why do interventions that increase health overall widen inequalities within
populations? In: Babones SJ, ed. Social inequality and public health. Policy Press 2009

IMPACTNCD
Stochastic dynamic microsimulation model
• Each unit is a person and is represented by a
record containing a unique identifier and a set of
associated attributes
• Associated attributes evolve over time as
simulation progresses
• Parameters & estimated uncertainties included

IMPACTNCD information flow
Inputs
• Health Survey for England (exposures & their correlations)
• Population vital statistics from Office for National Statistics
• Effect sizes from meta-analyses
• Scenario assumptions (user defined)
Process
• Create a close to reality synthetic population (synthetic individuals)
• Evolve the synthetic population over time, under a set of stochastic
rules grounded on epidemiological principles
Outputs
• Burden of the modelled diseases (incidence, prevalence, mortality,
healthy life expectancy)
• Distributional nature of the burden (can explore impact on
socioeconomic inequality)

Is screening for CVD effective?
• “General health checks did not reduce morbidity
or mortality, neither overall nor for
cardiovascular or cancer causes…”1
• “…screening for risk of ischaemic heart disease
and repeated lifestyle intervention over five
years had no effect on ischaemic heart disease,
stroke, or mortality at the population level after
10 years.”2
1. Krogsboll LT et al. General health checks in adults for reducing morbidity and mortality from
disease: Cochrane systematic review and meta-analysis. BMJ 2012;345
2. Jørgensen T et al. Effect of screening and lifestyle counselling on incidence of ischaemic heart
disease in general population: Inter99 randomised trial. BMJ 2014;348

NHS Health Checks
• Estimated annual cost: ≈ £300 million (Cost
effective???) (Department of Health)
• Uptake: ≈ 50% (Public Health England)
• Medical prescription within a year: (Forster 2014)
Department of Health. Cardiovascular disease outcomes strategy: improving outcomes for people with or at risk of cardiovasculardisease
Public Health England. Explore NHS Health Check Data
Forster AS, et al. Estimating the yield of NHS Health Checks in England: a population-based cohort study. J Public Health 2014
Risk ≥ 20% ≈ 15%
Risk 10 – 20 % ≈ 5%
Risk < 10% ≈ 1%

For all scenarios…
• All interventions start in 2011
• Diffusion period for interventions, 5 years
• Time lag between exposure and effect, 5 years
• Cardiovascular disease case fatality improves
by 3% annually (relative)
• Social gradient of case fatality 10% per QIMD
group

CHD mortality for men. Years 2002-13

Population module Immigration is not considered.
Social mobility is not considered.
Quintile groups of index of multiple deprivation (QIMD) is a relative marker of (area)
deprivation with several versions since 2003. We considered all version of QIMD identical.
We assume that the surveys used, are truly representative of the population. For example,
the adjustments for selection bias in the Health Surveys for England are perfect.
Disease module We assume multiplicative risk effects.
We assume log-linear dose-response for the continuous risk factors.
We assume that the effects of the risk factors on incidence and mortality are equal and risk
factors are not modifying survival.
We assume all stroke types have common risk factors.
We assume 5-year lag time for CVD.
We assume 100% risk reversibility.
We assume that trends in disease incidence are attributable only to trends of the relevant
modelled risk factors.
Only well accepted associations between upstream and downstream risk factors that have
been observed in longitudinal studies are considered. However, the magnitudes of the

Cases & deaths prevented or
postponed 2016-2030
Scenarios
CASES prevented
or postponed
(Interquartile range)
DEATHS prevented
or postponed
(Interquartile range)
Health checks 19,000
(11,000 to 28,000)
3,000
(-1000 to 6,000)
Population wide
policy interventions
67,000
(57,000 to 77,000)
8,000
(4,000 to 11,000)
Population wide
polices + targeted
screening
82,000
(73,000 to 93,000)
9,000
(6,000 to 13,000)

What is modelling?
Courtesy of Simon Capewell
a simplification of reality
“a LOGICAL MATHEMATICAL
FRAMEWORK that permits the
integration of facts and values
to produce outcomes of
interest to decision makers
and clinicians”
M Weinstein 2003

Why modelling?
• Modelling can synthesise all the available
evidence and critically estimate what cannot
be directly observed
• Simulation driven decision support tools allow
for “in silico” experimentation
• Help stakeholders to understand better a
phenomenon and its dynamics

IMPACTNCD hierarchical engine
Age, sex, socioeconomic status
Behavioural risk factors
Biological risk factors
Modelled interventions
Salt Fruit & Veg Smoking Physical activity
Body mass
index
Systolic blood
pressure
Total
cholesterol
Diabetes
mellitus
Passive smoking

Age, sex, socioeconomic status Modelled interventions
Coronary heart disease
risk
(incidence/prevalence)
Stroke risk
Relevant cancers risk
Modelled diseases assuming
multiplicative effects for risk
factors
Body mass
index
Systolic blood
pressure
Total
cholesterol
Diabetes
mellitus
Passive smoking

Age, sex, socioeconomic status Modelled interventions
Body mass
index
Systolic blood
pressure
Total
cholesterol
Diabetes
mellitus
Passive smoking
Coronary heart
disease mortality
Stroke mortality
Relevant cancers
mortality
All other causes
mortality
Translate incidence/prevalence to mortality
Coronary heart disease
risk
Stroke risk
Relevant cancers risk

Determinants of health
Adapted from: Dahlgren G. and Whitehead M. (1993) Tackling inequalities in health: what can we learn from what has been tried?
Building the synthetic population

Adapted from: Dahlgren, G. and Whitehead, M. (1993) Tackling inequalities in health: what can we learn from what has been tried?
Setup the
household and
individual’s
structure…

Simulate the
socioeconomic
circumstances
(QIMD, household
income, employment
status of the head of
the household )

Simulate the
behavioural risk
factors (diet,
smoking, physical
activity)

Simulate the
biological risk factors
(BMI, systolic blood
pressure, cholesterol,
etc.)

Close to reality synthetic population
• Statistical framework was originally developed
by Alfons and colleagues
• it ‘expands’ a population survey into a close to
reality synthetic population
• captures the clustering of risk factors
• creates individuals with combinations of traits
not present in the original survey

Social production of disease
Social
context
Policy
context
Social position
Causes (exposure)
Disease / injury
Socioeconomic
consequences
SOCIETY INDIVIDUAL
Feedback
Differential
consequences
Differential
exposure
Differential
vulnerability
Diderichsen, Evans and Whitehead

BUILDING THE SYNTHETIC
POPULATION
“Maybe the only significant difference between a really smart simulation
and a human being was the noise they made when you punched them”
Terry Pratchett, The Long Earth

‘Close to reality’ synthetic population
• The statistical framework was originally
developed by Alfons and colleagues
• ‘expands’ a population survey into a ‘close to
reality’ synthetic population
• captures the clustering of risk factors
• creates individuals with combinations of traits
not present in the original survey
Alfons A, Kraft S, Templ M, Filzmoser P. Simulation of close-to-reality population data for household surveys
with application to EU-SILC. Stat Methods Appl. 2011 Aug 1;20(3):383–407.

Setup of the household structure
• For each combination of stratum k and household size
l, the number of households 𝑀 𝑘𝑙 is estimated using the
Horvitz–Thompson estimator
𝑀 𝑘𝑙 ≔
ℎ∈𝐻 𝑘𝑙
𝑆
𝑤ℎ
• 𝐻 𝑘𝑙
𝑆
denotes the index set of households in stratum k of
the survey data with household size l, and 𝑤ℎ, ℎ ∈ 𝐻 𝑘𝑙
𝑆
are the corresponding household weights
Alfons A, Kraft S, Templ M, Filzmoser P. Simulation of close-to-reality population data for household surveys
with application to EU-SILC. Stat Methods Appl. 2011 Aug 1;20(3):383–407.

Simulation of categorical variables
• Estimate conditional distributions with
multinomial logistic regression models per
stratum
• Sample from these distributions (allow to
generate combinations of traits that do not
occur in the sample)

Simulation of continuous variables
• First the continuous variable is discretised
• Then multinomial logistic regression models are
fitted for every stratum separately
• Finally, the values of continuous variable for the
population are generated by random draws from
uniform distributions within the corresponding
categories of the discretised variable.
Tail modelling was used to simulate extreme values.
In that case, values from the highest categories
were drawn from a generalized Pareto distribution.

EVOLVE THE SYNTHETIC
POPULATION
“You realize that there is no free will in what we create with AI.
Everything functions within rules and parameters”
Clyde DeSouza, Maya

Risk factors trajectories
• Assumes that the recent trends in risk factor
trajectories by age, sex and QIMD will continue in
the future
• Generalised linear models were fitted in
individual level data from the HSE 2001-2012
with each risk factor as the dependant variable
and the year, age, sex, QIMD and any other
relevant risk factor as the independent variables.
• The models then are projected to the future

Histogram and density plot of systolic
blood pressure in men, by age group

Percentile rank
• Let 𝑅 = (𝑅1, … , 𝑅 𝑛) is the rank vector
constructed from a random observation
vector X = (𝑋1, … , 𝑋 𝑛)
• Then
𝑅 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒 =
𝑅 − 1
𝑛 + 1

Ageing (the solution)
• Instead of tracking the actual risk factor, we
track its percentile ranks by age, sex and
QIMD, of the individual for each projected
year.

Plot of the percentile rank against the systolic blood pressure of
males living in QIMD 3 area for age groups 20-24 and 70-74.

Algorithm
1. The portion of the disease incidence
attributable to all the modelled risk factors is
estimated and subtracted from the observed
incident.
2. For each individual in the synthetic
population, the probability to develop the
disease is estimated.

The population attributable fraction (PAF)
• The PAF is calculated for all the relevant modelled
risk factors (by 5-year age group and sex)
𝑃𝐴𝐹 =
𝑖=1
𝑛
𝑃𝑖 ∗ (𝑅𝑅𝑖 − 1)
𝑖=1
𝑛
𝑃𝑖 ∗ (𝑅𝑅𝑖 − 1) + 1
where 𝑃𝑖 is the prevalence of the risk factor at level
𝑖 in the population and 𝑅𝑅𝑖 is the relative risk
associated with 𝑖 level of exposure
Levin ML. The occurrence of lung cancer in man. Acta - Unio Int Contra Cancrum 1953;9:531–41.

Step 1: The theoretical minimum risk
• The annual probability of an individual with all
modelled risk factors at optimal levels,
conditional on age and sex
𝐼 𝑇ℎ.𝑚𝑖𝑛 = 𝐼 𝑂𝑏𝑠 ∗ 1 − 𝑃𝐴𝐹1 ∗ 1 − 𝑃𝐴𝐹2 ∗ ⋯ ∗ 1 − 𝑃𝐴𝐹𝑚
Where 𝐼 𝑂𝑏𝑠 is the disease incidence and 𝑃𝐴𝐹1…𝑚
are the PAF of each risk factor from the previous
step

Step 2: Individualised probability of disease
𝑃𝑟 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 = 𝐼 𝑇ℎ.𝑚𝑖𝑛 ∗ 𝑅𝑅1 ∗ 𝑅𝑅2 ∗ ⋯ ∗ 𝑅𝑅 𝑚
Where 𝑅𝑅1…𝑚 are the relative risks related to
the specific risk exposures of the modelled
individual

Strengths
• The framework can, relatively easy, be
extended to any other noncommunicable
disease (e.g. cancers, COPD) or other risk
factors etc.
• It is reusable and can be easily updated to
accumulate new knowledge
• Minimal data requirements (survey of the
population)

Limitations
• Ignores effects of risk factors on disease
mortality (cannot be directly used for
secondary prevention)
• Information about behavioural risk factors
exposure is self reported
• Massive computational requirements

Summary
• IMPACTNCD allows for the effect of complex
primary prevention interventions to be
assessed
• Impact on disease burden, healthy life
expectancy and inequality is estimated
• The framework is easily expandable to other
diseases and populations

Setup of the household structure
• Let 𝐻 𝑘𝑙
𝑈
be the respective index set of households in the
population data such that 𝐻 𝑘𝑙
𝑈
= 𝑀 𝑘𝑙
• To prevent unrealistic structures in the population
households, basic information from the survey households
is resampled.
• Let 𝑥ℎ𝑖𝑗
𝑆
and 𝑥ℎ𝑖𝑗
𝑈
denote the value of person 𝑖 from
household ℎ in variable 𝑗 for the sample and population
data, respectively, and let the first 𝑝1 variables contain the
basic information on the household structure.
• For each population household ℎ ∈ 𝐻 𝑘𝑙
𝑈
, a survey
household ℎ′ ∈ 𝐻 𝑘𝑙
𝑆
is selected with probability 𝑤ℎ′/ 𝑀 𝑘𝑙and
the household structure is set to
𝑥ℎ𝑖𝑗
𝑈
≔ 𝑥ℎ′ 𝑖𝑗
𝑆
, 𝑖 = 1, … , 𝑙, 𝑗 = 1, … , 𝑝1

Let
• 𝒙𝑗
𝑆
= (𝑥1𝑗
𝑆
, … , 𝑥 𝑛𝑗
𝑆
)′ denote the variables in the
sample, 𝑛 the number of individuals
• 𝒙𝑗
𝑈
= (𝑥1𝑗
𝑈
, … , 𝑥 𝑁𝑗
𝑈
)′ denote the variables in the
population, 𝑁 the number of individuals
• 𝑝1 < 𝑗 ≤ 𝑝2 additional categorical variables
• 𝒘 = (𝑤1, … , 𝑤 𝑛)′ the personal sample weights

For each stratum 𝑘 and each variable 𝑗 let
𝐼 𝑘
𝑆
and 𝐼 𝑘
𝑈
be the index sets of individuals in stratum k for the survey
and population data, respectively
The survey data given by the indices in 𝐼 𝑘
𝑆
is used to fit the model with
response 𝒙𝑗
𝑆
and predictors 𝒙1
𝑆
, … , 𝒙𝑗−1
𝑆
thereby considering the
sample weights 𝑤𝑖, 𝑖 ∈ 𝐼 𝑘
𝑆
let {1, . . . , 𝑅} be the set of possible outcome categories of the
response variable
For every individual 𝑖 ∈ 𝐼 𝑘
𝑈
, the conditional probabilities 𝑝𝑖𝑟
𝑈
≔
𝑃(𝑥𝑖𝑗
𝑈
= 𝑟|𝑥𝑖1
𝑈
, … , 𝑥𝑖𝑗−1
𝑈
) are estimated by…

𝑝𝑖1
𝑈
≔
1
1 + 𝑟=2
𝑅
exp 𝛽0 𝑟
+ 𝛽1 𝑟
𝑥𝑖1
𝑈
+ ⋯ + 𝛽𝑗−1,𝑟 𝑥𝑖,𝑗−1
𝑈
,
𝑝𝑖𝑟
𝑈
≔
exp( 𝛽0 𝑟
+ 𝛽1 𝑟
𝑥𝑖1
𝑈
+ ⋯ + 𝛽𝑗−1,𝑟 𝑥𝑖,𝑗−1
𝑈
)
1 + 𝑟=2
𝑅
exp 𝛽0 𝑟
+ 𝛽1 𝑟
𝑥𝑖1
𝑈
+ ⋯ + 𝛽𝑗−1,𝑟 𝑥𝑖,𝑗−1
𝑈
,
𝑟 = 2, … , 𝑅
where 𝛽0 𝑟
, … , 𝛽𝑗−1,𝑟, 𝑟 = 2, … 𝑅, are the estimated coefficients

Let
• 𝒙𝑗
𝑆
and 𝒙𝑗
𝑈
, 𝑝2 < 𝑗 ≤ 𝑝3 denote the continuous
variables
• First 𝒙𝑗
𝑆
is discretised to variable 𝒚 𝑗
𝑆
= (𝑦1
𝑆
, … , 𝑦𝑛
𝑆
)’
• Multinomial logistic regression models with response
𝒚 𝑆
and predictors 𝒙𝑗−1
𝑆
are then fitted for every
stratum 𝑘 separately
• to simulate the values of the categorized population
variable 𝒚 𝑈
= (𝑦1
𝑆
, … , 𝑦 𝑁
𝑆
)’

Finally, the values of 𝒙𝑗
𝑈
are generated by random draws from uniform
distributions within the corresponding categories of 𝒚 𝑈
.
For continuous variables, the values of individual 𝑖 = 1, … , 𝑁 are
generated as
𝑥𝑗
𝑈
~𝑈(𝑏𝑟, 𝑏𝑟 + 1)
if 𝑦𝑖
𝑈
= 𝑟
When simulating variables that contain extreme values, such as BMI,
tail modelling was used. In that case, values from the largest categories
were drawn from a generalized Pareto distribution (GPD).

Ageing (the problem)
• As the simulation projects into the future, the
initial biological characteristics of individuals
need to get updated in order to reflect ageing
and its physiological mechanisms.
• Otherwise, as the generally healthy young
individuals would mature and replace the
older population, the BMI, SBP and TC of the
population would be improving artificially
over time, causing bias.

Risk factors trajectories
• Systematic part 𝐸 𝑌 = 𝑎 + 𝑋𝛽
• The parameters in the regression model are
estimated by sampling-weighted least squares
RSS = 𝑖=1
𝑛 1
𝜋 𝑖
𝑌𝑖 − 𝑎 − 𝑋𝑖 𝛽 2,
where RSS is the sum of squared residuals and 𝜋𝑖 is
the sampling probability for unit 𝑖
Lumley T. Complex Surveys: A Guide to Analysis Using R. John Wiley & Sons; 2011. 292 p.

AN INDIVIDUALS’ JOURNEY INTO
IMPACTNCD

Entry
CHD
(1st episode)
Death from CHD (in
the first 30 days)
Death from CHD
(post 30 days)
Stroke
(1st episode)
Death from stroke (in
the first 30 days)
Death from stroke
(post 30 days)
Death (other causes)
Age, sex, QIMD
Behavioural risk
factors
Give birth
Repeat every year until death or end of simulation
Step 1
Step 2
Step 3
Step 5
Step 4
Step 6
Step 7

Entry
CHD
(1st episode)
Death from CHD (in
the first 30 days)
Death from CHD
(post 30 days)
Stroke
(1st episode)
the first 30 days)
Death from stroke
(post 30 days)
Age, sex, QIMD
Behavioural risk
factors
Give birth
Step 1
Step 2
Step 3
Step 5
Step 4
Step 6
Step 7
From the synthetic
population or the
birth engine

Entry
CHD
(1st episode)
Death from CHD (in
the first 30 days)
Death from CHD
(post 30 days)
Stroke
(1st episode)
the first 30 days)
Death from stroke
(post 30 days)
Age, sex, QIMD
Behavioural risk
factors
Give birth
Step 1
Step 2
Step 3
Step 5
Step 4
Step 6
Step 7
Sample from the joint
distribution of age, sex
and QIMD of the Census
2011

Entry
CHD
(1st episode)
Death from CHD (in
the first 30 days)
Death from CHD
(post 30 days)
Stroke
(1st episode)
the first 30 days)
Death from stroke
(post 30 days)
Age, sex, QIMD
Behavioural risk
factors
Give birth
Step 1
Step 2
Step 3
Step 5
Step 4
Step 6
Step 7
Sample from the
conditional distributions
derived from GLM

Entry
CHD
(1st episode)
Death from CHD (in
the first 30 days)
Death from CHD
(post 30 days)
Stroke
(1st episode)
the first 30 days)
Death from stroke
(post 30 days)
Age, sex, QIMD
Behavioural risk
factors
Give birth
Step 1
Step 2
Step 3
Step 5
Step 4
Step 6
Step 7
Ageing and scenario
driven

Entry
CHD
(1st episode)
Death from CHD (in
the first 30 days)
Death from CHD
(post 30 days)
Stroke
(1st episode)
the first 30 days)
Death from stroke
(post 30 days)
Age, sex, QIMD
Behavioural risk
factors
Give birth
Step 1
Step 2
Step 3
Step 5
Step 4
Step 6
Step 7
Informed by ONS fertility
projections

Entry
CHD
(1st episode)
Death from CHD (in
the first 30 days)
Death from CHD
(post 30 days)
Stroke
(1st episode)
the first 30 days)
Death from stroke
(post 30 days)
Age, sex, QIMD
Behavioural risk
factors
Give birth
Step 1
Step 2
Step 3
Step 5
Step 4
Step 6
Step 7
𝑃𝑟 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 =
= 𝐼 𝑇ℎ.𝑚𝑖𝑛 ∗ 𝑅𝑅1 ∗ 𝑅𝑅2 ∗ ⋯ ∗ 𝑅𝑅 𝑚

Entry
CHD
(1st episode)
Death from CHD (in
the first 30 days)
Death from CHD
(post 30 days)
Stroke
(1st episode)
the first 30 days)
Death from stroke
(post 30 days)
Age, sex, QIMD
Behavioural risk
factors
Give birth
Step 1
Step 2
Step 3
Step 5
Step 4
Step 6
Step 7
𝐵𝑒𝑟𝑛𝑢𝑙𝑙𝑖 𝑡𝑟𝑖𝑎𝑙

Is cardiovascular screening the best option for reducing future cardiovascular disease burden?

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (15)

Similaire à Is cardiovascular screening the best option for reducing future cardiovascular disease burden?

Similaire à Is cardiovascular screening the best option for reducing future cardiovascular disease burden? (20)

Plus de UKFacultyPublicHealth

Plus de UKFacultyPublicHealth (20)

Dernier

Dernier (20)

Is cardiovascular screening the best option for reducing future cardiovascular disease burden?

Notes de l'éditeur