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

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 3.04 Statin 53 157 2.96 Aspirin 93 176 1.91

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

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 (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 Exposed Cases Cases due to exposure

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)

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)