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.

METHODS

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;374. Wallach-Kildemoes H, et al. Eur J Clin

Pharmacol 2013;69

Scenario III: Population-wide policy interventions

Assumes • Obesity rise slowed by sugar sweetened beverages tax1-3

• Systolic blood pressure decreased by 0.8 mmHg by

mandatory salt reformulation4 • Fruit & veg consumption up by 0.5 portion /day by subsidies5,6

• Smoking prevalence falls by 13% (relative) by full compliance with Framework Convention on Tobacco Control7

1. Sharma A, et al. Health Econ 2014;23 2. Briggs ADM, et al. BMJ 2013;3473. Cabrera EMA, et al. BMC PH 2013;134. 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;387. 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

Model validation

RESULTS

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 atBMJ 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 cardiovascular diseasePublic Health England. Explore NHS Health Check DataForster 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 associations are extracted from a series of nationally representative cross-sectional surveys (Health Survey for England).

Cases & deaths prevented or postponed 2016-2030

ScenariosCASES 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

http://www.travelsinparadise.com/australia/sydney/pictures/sydney-opera-house-04.html

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 statusBehavioural 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 (incidence/prevalence)

Relevant cancers risk (incidence/prevalence)

Modelled diseases assuming multiplicative effects for risk factors


Body mass index


Total cholesterol

Diabetes mellitus

Passive smoking


Age, sex, socioeconomic status Modelled interventions


Body mass index


Total cholesterol

Diabetes mellitus

Passive smoking

Coronary heart disease mortality Stroke mortality Relevant cancers

mortalityAll other causes

mortality

Translate incidence/prevalence to mortality

Coronary heart disease risk

(incidence/prevalence)

Stroke risk (incidence/prevalence)

Relevant cancers risk (incidence/prevalence)

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

Close to reality???

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 is estimated using the Horvitz–Thompson estimator

• denotes the index set of households in stratum k of the survey data with household size l, and 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 is the rank vector constructed from a random observation vector

• Then

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.

INDIVIDUALISED DISEASE RISK

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)

where is the prevalence of the risk factor at level in the population and is the relative risk associated with 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

Where is the disease incidence and are the PAF of each risk factor from the previous step

Step 2: Individualised probability of disease

Where 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 be the respective index set of households in the population

data such that • To prevent unrealistic structures in the population households,

basic information from the survey households is resampled.• Let and denote the value of person from household in variable

for the sample and population data, respectively, and let the first variables contain the basic information on the household structure.

• For each population household , a survey household is selected with probability and the household structure is set to


Let • denote the variables in the sample, the number

of individuals• denote the variables in the population, the

number of individuals• additional categorical variables • the personal sample weights

Simulation of categorical variablesFor each stratum and each variable let and be the index sets of individuals in stratum k for the survey and population data, respectively

The survey data given by the indices in is used to fit the model with response and predictors thereby considering the sample weights ,

let be the set of possible outcome categories of the response variable

For every individual , the conditional probabilities are estimated by…


where are the estimated coefficients

Simulation of continuous variables

Let • and , denote the continuous variables• First is discretised to variable ’• Multinomial logistic regression models with response

and predictors are then fitted for every stratum separately

• to simulate the values of the categorized population variable ’

Simulation of continuous variablesFinally, the values of are generated by random draws from uniform distributions within the corresponding categories of .

For continuous variables, the values of individual are generated as

if

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 • The parameters in the regression model are

estimated by sampling-weighted least squares

,

where is the sum of squared residuals and is the sampling probability for unit

Lumley T. Complex Surveys: A Guide to Analysis Using R. John Wiley & Sons; 2011. 292 p.

AN INDIVIDUALS’ JOURNEY INTO IMPACTNCD

Entry

Age, sex, QIMD

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)

Repeat every year until death or end of simulation

Step 1

Step 2

Step 3

Step 5

Step 4

Step 6

Step 7

Entry

Age, sex, QIMD

CHD(1st episode)



Stroke(1st episode)





Step 1

Step 2

Step 3

Step 5

Step 4

Step 6

Step 7

From the synthetic population or the

birth engine

Entry

Age, sex, QIMD

CHD(1st episode)



Stroke(1st episode)





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

Age, sex, QIMD

CHD(1st episode)



Stroke(1st episode)





Step 1

Step 2

Step 3

Step 5

Step 4

Step 6

Step 7

Sample from the conditional distributions

derived from GLM

Entry

Age, sex, QIMD

CHD(1st episode)



Stroke(1st episode)





Step 1

Step 2

Step 3

Step 5

Step 4

Step 6

Step 7

Ageing and scenario driven

Entry

Age, sex, QIMD

CHD(1st episode)



Stroke(1st episode)





Step 1

Step 2

Step 3

Step 5

Step 4

Step 6

Step 7

Informed by ONS fertility projections

Entry

Age, sex, QIMD

CHD(1st episode)



Stroke(1st episode)





Step 1

Step 2

Step 3

Step 5

Step 4

Step 6

Step 7

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Entry

Age, sex, QIMD

CHD(1st episode)



Stroke(1st episode)





Step 1

Step 2

Step 3

Step 5

Step 4

Step 6

Step 7

𝐵𝑒𝑟𝑛𝑢𝑙𝑙𝑖𝑡𝑟𝑖𝑎𝑙

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

Healthcare