population impact measures (pim )
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Population Impact Measures (PIM ). Richard F Heller, Emeritus Professor, Universities of Manchester UK, and Newcastle, Australia [email protected]. Population Impact Measures. Extensions of two frequently used measures, providing a population perspective: Number Needed to Treat - PowerPoint PPT PresentationTRANSCRIPT
Population Impact Measures (PIM)
Richard F Heller, Emeritus Professor, Universities of Manchester UK, and Newcastle, Australia
Population Impact Measures
• Extensions of two frequently used measures, providing a population perspective:– Number Needed to Treat– Population Attributable Risk
Calculate NNT
• Beta-blockers in heart failure
• Baseline risk of outcome of interest– 8% death in next year
• Relative Risk Reduction from beta-blockers– 34%
• NNT
)(
11
RRRskBaselineRiARRNNT
Beta-blockers in heart failure
• Older woman, risk of death in next year 24% instead of 8%
• Same 34% relative risk reduction
• NNT 12 (Compared with 37 for younger woman)
Interventions, patients and the population
Number of events prevented in the population (NEPP)
• NEPP = n pd pe ru RRR
• n = no. of people in population of interest
• pd = prevalence of the disease in the population
• pe = incremental increase in the use of the treatment
• ru = baseline risk of a cardiac event in 5 years
• RRR = relative risk reduction associated with the treatment
Secondary prevention after myocardial infarction (MI): Number Needed to Treat (NNT)
– to prevent one death in next year post-MI
Drug NNT
ACE-I 69
Beta Blocker 48
Statin 53
Aspirin 93
Relate to a GP population of 10,000 people
Drug NNT N to be Treated in Population
N Events Prevented in Population
ACE-I 69 147 2.12
Beta Blocker
48 147 3.04
Statin 53 157 2.96
Aspirin 93 176 1.91
The cost
Drug N Events Prevented in Population
Drug cost (£) Drug cost per event prevented (£)
ACE-I 2.12 14,700 6,944
Beta Blocker
3.04 6,615 2,174
Statin 2.96 60,525 20,423
Aspirin 1.91 1,940 1,019
Drugs post-MI in Oldham
% on drug NEPP Extra NEPP if
NSF target met
Aspirin 83 20 2
Beta Blocker
46 22 21
Statin 73 31 8
NEPP = Number of Events Prevented in your Population in next year
Secondary prevention for CHD
• Full implementation of NSF in E&W (from current to ‘best practice’)
• Number of lives saved in next year
Post AMI
Heart failure
Drugs 1027 37899
Lifestyle 848 7249
Secondary prevention for CHD
• Full implementation of NSF in E&W (from current to ‘best practice’)
• Total cost in £ millions (per life saved in £ thousands)
Post AMI
Heart failure
Drugs 6.6(6.4)
537(1.4)
Lifestyle 6.6(7.8)
13(1.8)
Primary or Secondary prevention for CHD
• Full implementation of NSF in E&W (from current to ‘best practice’)
• Number of CHD events prevented in next year
Prevention group
Drugs Lifestyle
Primary 73,522
High Risk 2,008 4,410
Secondary 3,067 1,103
PIMS for risk
• Providing local context to measures of risk– Similar concepts and requires – baseline risk,
population size and characteristics, the relative risk of exposure and the proportion of the population exposed
A population perspective to risks
Populationat risk
Total Population
Cases due to exposure
Exposed
Cases
PAR, or PAF, or PARP
• Population Attributable Risk, PAR, is the proportion of the risk that would be removed if the risk factor was removed
• Calculated from estimates of relative risk (RR) published in epidemiological literature, and the estimated proportion (Pe) of the population exposed to the risk factor
• Does not use baseline risk
Population Attributable Risk
• For a dichotomous relative risk:
• PAR: population attributable risk (Levin definition)• RR: relative risk• Pe: proportion of population exposed to the risk factor
(level)
)1(1
)1(
RRPe
RRPePAR
Population Impact Measure for Risk
• PIN-ER-t, “the potential number of disease events prevented in your population over the next t years by eliminating a risk factor”
PIN-ER-t“the potential number of disease events
prevented in your population over the next t years by eliminating a risk factor”
Requires:Relative Risk of an outcome event if the risk factor
is present,Proportion of the population with the risk factor,
Population size, Incidence of the outcome in the whole population
over t years.
Smoking and health inequalities:Men aged 25+ from UK GP population of 10,000
% Smokers Potential number of deaths prevented in your population over the next 3 years by eliminating smoking*
Non-manual
(0.458: n=1529) 22 5.1
Manual (0.542: n=1810)
33 12.9
*PIN-ER-t derived from PAR (prevalence of risk factor and RR of outcome from the risk factor), number at risk, incidence of outcome in whole population in next t years
Risk of death in next 3 years
Blood cholesterol level (mmol/l)
Relative Risk Numbers of deaths due
to cholesterol level* [PIN-ER-t]
7.8 or more 3.5 1.6
6.5 – 7.8 2.6 3.1
5.2 – 6.5 1.7 2.9
*in men aged less than 75 in a GP population of 10,000 people
TB in a population of 100 000 in India
• The directly observed component of the Directly Observed Treatment, Short-course (DOTS) programme or increase TB case finding (by 20%).
• Number of deaths prevented in next year
• Costs in international dollars (and costs per life saved).
Direct observation
Increase case finding
0.188 1.79
5960(31702)
4839(2703)
PIMs and health economics
• QALYs are not often actually used in local decision-making
• They do not have a population perspective, or apply to a local population
• NICE recommendations may need an additional step before they can be used for local prioritisation
PIMs and health economics: Population cost-impact analysis
• Step 1. Calculation of benefit of the intervention in your population– PIMs
• Step 2. Add cost data– Over time course of policy cycle; costs to whole local
health economy
• Step 3. Add utilities/preferences of local decision-makers – Prioritisation exercise
Components of Population Impact Assessment
• Ask the question – make the options explicit
• Collect data – local data on population denominator/prevalence and current practice (or published data from similar populations)/estimated data on baseline risk of identified outcomes (from Observatory etc)/library of evidence for risks (Relative Risk and Relative Risk Reduction).
• Calculate impact – Population Impact Measures or alternatives
• Understand – apply values, offer training, consultation
• Use – implement results in prioritising services using change management and knowledge management principles (generate, store, distribute and apply)