Population Impact Measures (PIM)

Richard F Heller, Emeritus Professor, Universities of Manchester UK, and Newcastle, Australia

rfheller@peoples-uni.org

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)