Predicting risk of cardiovascular disease and the cost-effectiveness of interventions in Thailand

Predicting risk of cardiovascular disease and the cost-effectiveness of interventions

in Thailand

Stephen LimOn Behalf of the Setting Priorities using Information on Cost-Effectiveness

(SPICE) Project

Top Ten Causes of Disability Adjusted Life Year (DALYs) by Sex, Thailand 1999

Rank

Disease category

MALE

DALYs

%

Disease category

FEMALE

DALYs

%

1 HIV/AIDS 960,087 17% HIV/AIDS 372,947 9%

2 Traffic accidents 510,907 9% Stroke 280,673 7%

3 Stroke 267,567 5% Diabetes 267,158 7%

4 Liver cancer 248,083 4% Depression 145,336 4%

5 Diabetes 168,372 3% Liver cancer 118,384 3%

6 Ischaemic heart disease 164,094 3% Osteoarthritis 117,994 3%

7 COPD (emphysema) 156,861 3% Traffic accidents 114,963 3%

8 Homicide and violence 156,371 3% Anaemia 112,990 3%

9 Suicides 147,988 3% Ischaemic heart disease 109,592 3%

10 Drug dependence/harmful

use

137,703 2% Cataracts 96,091 2%

0% 2% 4% 6% 8% 10% 12% 14% 16%

Malnutrition - Thai standard

Not wearing seatbelt

Water & sanitation

Malnutrition - int standard

Physical inactivity

Air pollution

Fruit & vegies

Cholesterol

Illicit drugs

Obesity

Not wearing helmet

Tobacco

Blood pressure

Alcohol

Unsafe sex

% of total burden

Thai Burden of risk factors, 1999

Prevention of CVD 2 different but complementary

approaches to prevention:1. Population-wide approach – aims to reduce

levels of risk factor(s) across the whole population

2. High risk approach – targets prevention towards those who are at higher risk, e.g. high blood pressure, high cholesterol

Targeting high-risk

How do we target those at high risk? Traditionally, by thresholds of individual

risk factors, e.g. systolic blood pressure ≥ 140mmHg (hypertension)

More recent approach uses absolute risk of CVD in, e.g. next 10 years E.g. using risk prediction equations from the

Framingham study

Absolute risk

Absolute risk of CVD takes into account 1. Multiple risk factors determine CVD risk

age, sex, blood pressure, cholesterol, smoking, diabetes, etc

Absolute risk Absolute risk of CVD takes into account

1. Multiple risk factors determine CVD risk age, sex, blood pressure, cholesterol,

smoking, diabetes, etc

2. Continuous measurements of risk factors e.g. relationship between blood pressure

and CVD is not dichotomous (i.e. having hypertension or not having hypertension) but is continuous

Source: World Health Report 2002

Absolute risk Absolute risk of CVD takes into account

1. Multiple risk factors determine CVD risk age, sex, blood pressure, cholesterol, smoking,

diabetes, etc

2. Continuous measurements of risk factors e.g. relationship between blood pressure and CVD is

not dichotomous (i.e. having hypertension or not having hypertension) but is continuous

An individual with moderately elevated levels of multiple risk factors may be at higher risk than an individual with high levels of a single risk factor

Risk prediction equations Determination of absolute risk is based on

cohort studies examining the relationship between risk factors and CVD outcomes

Uses survival analysis (Cox regression or Weibull models) to determine predictive risk equation

Many of the risk equations in use are based on the Framingham study

These have been validated and “adjusted” for use in other cohorts and settings, e.g. China, Australia, Europe, New Zealand

Risk prediction equations The Electricity Generating Authority of

Thailand (EGAT) cohort study provides important information on the relationship between risk factors and CVD outcomes in a Thai population

3,499 employees of EGAT (2,702 males, 797 females) aged 35-54 years

Physical examinations (including blood) 1985, 1997, 2002

Information on a range of fatal and non-fatal CVD events

2nd cohort of individuals followed from 1997

Risk prediction equations EGAT:

Developed a range of risk prediction equations Coronary Heart Disease (CHD), Diabetes

Equations used to develop a point score system for predicting absolute risk

Validation of other risk prediction equations from the Framingham study and China cohorts

Show, like other studies, that Framingham equations predict relative risk well, but overestimate absolute risk

EGAT-SPICE collaboration Use EGAT equations to determine

predicted CHD risk for individuals in the National Health Examination Survey 3

Cox proportional hazards model from EGAT 2418 subjects, 74 CHD events

Variable β-coefficient p-value Hazard ratio Lower UpperAge 0.077 0.001 1.080 1.032 1.130SBP 0.019 0.004 1.019 1.006 1.033Total cholesterol 0.005 0.045 1.005 1.000 1.010HDL cholesterol -0.038 0.002 0.963 0.940 0.987Diabetes 0.812 0.006 2.252 1.257 4.036Current smoking 0.552 0.024 1.736 1.074 2.806Waist circumference ? 90cm 0.618 0.014 1.855 1.136 3.030Current alcohol use -0.808 0.001 0.446 0.276 0.718

95% CI for HR

Developed by Dr Sukit

Apply EGAT score to NHES 3 Using raw data from NHES

No sample weightsNot yet cleaned

Apply to males aged 35-59 onlyExcluding HDL as this is not measuredSome inconsistencies between EGAT and

NHES risk factors definitions NHES: Alcohol in last 12 months EGAT: Current alcohol use

Apply EGAT score to NHES 3 Cox-proportional harzards model was

used to determine individual risk

Risk estimate = 1 – S0(t)exp(∑βX- ∑βX)

where, S0(t) is the average survival time at time tβ’s are the Cox-regression coefficientsX are the individual RF valuesX are the mean RF values

Apply EGAT score to NHES 3 For male aged 55, SBP 160, Chol 250mg/dl, diabetic,

smoker, waist 102cm, no alcohol ∑βX = age*0.07185 + sbp*0.01958 + Tch*0.00491 +

diabetes*0.81009 + smoke*0.60459 -alcohol*0.92253 + waist90*0.75886

∑βX = 55*0.07185 + 160*0.01958 + 250*0.00491 + 1*0.81009 + 1*0.60459 - 0*0.92253 + 1*0.75886 = 10.485

S0(10) from Kaplan-Meier estimate from EGAT is 0.9891

∑βX is 7.78547

Risk estimate = 1 – 0.9891exp(10.485-7.78547) =0.1504

15% risk of CHD event over the next 10 years

05

01

00

15

00

50

10

01

50

0 .05 .1 .15 .2 0 .05 .1 .15 .2 0 .05 .1 .15 .2

35-39 yrs 40-44 yrs 45-49 yrs

50-54 yrs 55-59 yrs Total

Den

sity

Probability of CHD event in next 10-yearsGraphs by 5-year age groups

Distribution of 10-year CHD risk by age

Preliminary analysis – Please do not quote

Overall predicted 10-year CHD risk Predicted risk of CHD lower in NHES 3

(0.62% compared with 1.09% from EGAT)

35-39 yrs 40-44 yrs 45-49 yrs 50-54 yrs 55-59 yrs 35-59 yrs<2.5% 99.9% 99.3% 99.2% 93.7% 87.2% 96.2%

2.5 to 4.9% 0.1% 0.7% 0.6% 5.4% 8.9% 2.9%5 to 9.9% 0.0% 0.0% 0.2% 0.8% 2.7% 0.7%

10 to 14.9% 0.0% 0.0% 0.0% 0.1% 1.0% 0.2%15 to 19.9% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0%20 to 24.9% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0%

Mean 10-year risk 0.20% 0.31% 0.47% 0.83% 1.46% 0.62%

Preliminary analysis – Please do not quote

Comparison of mean RF values

Variable Mean Std. Dev. Mean Std. Dev.Age 42.900 5.003 46.562 7.062SBP 122.094 15.727 122.768 17.381Total cholesterol 223.773 42.465 199.992 46.568Diabetes 0.073 0.261 0.098 0.298Current smoking 0.548 0.498 0.688 0.463Waist circumference ? 90cm0.180 0.385 0.225 0.418Current alcohol use 0.745 0.436 0.827 0.378

EGAT (n=2422) NHES 3 (n=4023)

Preliminary analysis – Please do not quote

Ongoing work Repeated measures analysis

Currently using only 1985 examination with 17 year follow-up

Repeated measures allows us to use 1997, 2002 examination also

Causal web estimation using hierarchical models

Causal web

Risk prediction equations Limitations

Males aged 35-59 - not sufficient numbers to generate risk equation for women

Time period is 1985-2002 Risk of CVD in this period may be quite different from

risk of CVD today

Alternative approach is to calibrate Framingham risk prediction equations for use in Thailand

Calibration of absolute risk

Population estimates of disease incidence, e.g. from Thai BOD

Framingham Risk prediction+

Absolute risk specific to the populationAdjusted for local risk factor prevalence

and underlying risk

+Risk factor

prevalence datae.g. from NHES3

Example

Framingham 1-year CHD risk for this individual is 0.034

Framingham 1-year CHD risk for NHES females aged 55 is 0.007

Female, 55 years, total cholesterol 6.7, no diabetes, current smoker, SBP 140mmHg

0.034

0.007= 4.70

Risk for this individual relative to all Thai females aged 55:

Population-level incidence of CHD for females aged 55 is 0.0051Individual calibrated CHD risk is 4.7 * 0.0051 = 0.024In other words, this individual has a 2.4% chance of having a CHD event in the next year

RR =

Risk prediction Approach adjusts for:

Risk factor prevalence (NHES) Underlying risk of CVD (population-level

incidence of CVD from Thai BOD) Underlying assumption is that relative

risk of risk factors is the same across the two populations

Supported by EGAT data for males

Issues for CVD prevention

Many different strategies exist for reducing the risk of CVD

How can we target high-risk individuals? Traditional approach using thresholds of individual

risk factors, e.g. systolic blood pressure ≥ 140mmHg

Absolute risk approach takes into account multiple risk factors e.g. age, sex, blood pressure, cholesterol, smoking, diabetes

Should a cholesterol test be included to identify high-risk individuals?

Issues for CVD prevention

Due to these difficulties, it is likely that the large amount of resources that are devoted to preventive strategies for CVD are not being used in an optimal manner.

Cost-effectiveness analysis can tell us which interventions are optimal given currently available resources Which mix of strategies is most efficient in reducing

the burden of CVD?

Modelling cost-effectiveness Rely on state transition (“Markov”)

models Portions of a cohort move through different

mutually exclusive states over timeMovement between states is determine by

transition probabilities Model the current Thai population in

terms of CVD outcomes over time

Modelling cost-effectiveness

Year 1

Year 2

Year 0

ALIVE CHD DEAD

Modelling cost-effectiveness Transition probabilities for CVD can be

determined in a similar way to calibration of CVD absolute risk equations

Allows us to simulate individuals with different risk factor profiles / absolute risk through the state transition model

1. Population risk of CHD

2. Individual’s risk relative to Thais of the

same age and sex

3. Individual’s risk of CHD (Transition probability

between ALIVE and CHD)

Year 0

0.0051

(Aged 55)

4.70

0.024

Year 1

0.0054

(Aged 56)

4.70

0.025

Year 2

0.0058

(Aged 57)

4.70

0.026

Modelling cost-effectiveness Repeat process under “no intervention” and

“intervention” scenarios e.g. statins may reduce the transition between ALIVE

and CHD by 30%

Can then determine health years gained by the intervention cost of interventions potential cost savings due to reduced cases of CVD Cost-effectiveness

Natural history of CVD Model structure depends on natural history

of the disease being modelled 2 major types of CVD events

Acute coronary syndromes (ACS), including myocardial infarction and unstable angina pectoris

Major sequelae are angina and heart failure

Stroke including both hemorrhagic and ischemic sub-types

Multiple risk factors for both ACS and stroke Age, sex, blood pressure, cholesterol, diabetes, etc

Natural history of CVD Prognosis of both ACS and Stroke are

similar(very) high case-fatality in first 28-daysrisk of mortality in 28-days survivors remains

elevated thereafter Need to differentiate between initial

mortality (first 28-days or first year) and risk of mortality thereafter

T7

T5

T3

T1

T4

CVD model structure

Data sources EGAT

Incidence:mortality ratios Some information on case-fatality rates Risk prediction equation

Vital registration with cause of death corrections Mortality from CVD

National Health Examination Survey Self-reported prevalence of CHD and stroke Risk factor prevalence

ACS and Stroke registries In-hospital case fatality

Major limitation is lack of information on out-of-hospital case-fatality

Results from analysis in Australia

Current practice

-5,000

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

- 100 200 300 400 500 600 700

Lifetime DALYs averted ('000)

Lif

etim

e C

ost

s (m

illi

on

AU

S$)

CHHP

Diuretic & Aspirin

β-blocker

Dietician

Phytosterol

Statin

Ezetimibe

Reasons for inefficiency of current practice in Australia Absolute risk vs Risk factor thresholds Not enough attention to lifestyle and public health

interventionsCommunity programsDietary counsellingPhytosterol supplementation

Current resources directed at less efficient classes of BP lowering drugse.g. ACE inhibitors

Summary There is potential to increase the

efficiency of CVD prevention efforts with :The development of robust tools to predict

absolute risk of CVD in clinical practiceEstimates of the cost-effectiveness of different

prevention strategies