1. Population Impact Measures (PIM)
Richard F Heller, Emeritus Professor, Universities of Manchester UK, and Newcastle, Australia

2. Population Impact Measures
Extensions of two frequently used measures, providing a population perspective:
Number Needed to Treat
Population Attributable Risk

3. 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

4. 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)

5. Interventions, patients and the population

6. 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

7. 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

8. 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
3.04
Statin 53
157
2.96
Aspirin 93
176
1.91

9. The cost ACE-I 2.12 14,700 6,944 Beta Blocker 3.04 6,615 2,174 Statin
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

10. 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

11. 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

12. 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
(7.8) 13
(1.8)

13. 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

14. 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

15. A population perspective to risks
Exposed
Cases
Cases due to exposure

16. 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

17. 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)

18. 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”

19. 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.

20. 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

21. 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

22. 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)

23. 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

24. 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

25. 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)