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Predicting risk of cardiovascular disease and the cost-effectiveness of interventions in Thailand Stephen Lim On Behalf of the Setting Priorities using Information on Cost-Effectiveness (SPICE) Project. MALE. FEMALE. Rank. Disease category. DALYs. %. Disease category. DALYs. %. 1. - PowerPoint PPT Presentation
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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