Population Impact Measures (PIM )

1. Population Impact Measures (PIM) Richard F Heller, Emeritus Professor, Universities of Manchester UK, and Newcastle, Australia rfheller@peoples-uni.org

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

8. Relate to a GP population of 10,000 people

9. The cost

10. Drugs post-MI in Oldham 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

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)

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

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 *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 *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).

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)