Population impact measure

To describe the impact of preventive and treatment interventions, the number of events prevented in a population (NEPP) is defined as "the number of events prevented by the intervention in your population over a defined time period". NEPP extends the well-known measure number needed to treat (NNT) beyond the individual patient to the population. To describe the impact of a risk factor on causing ill health and disease the Population Impact Number of Eliminating a Risk factor (PIN − ER − t) is defined as "the potential number of disease events prevented in a population over the next t years by eliminating a risk factor". The PIN − ER − t extends the well-known population attributable risk (PAR) to a particular population and relates it to disease incidence, converting the PAR from a measure of relative to absolute risk.

The components for the calculations are as follows: population denominator (size of the population); proportion of the population with the disease; proportion of the population exposed to the risk factor or the incremental proportion of the diseased population eligible for the proposed intervention (the latter requires the actual or estimated proportion who are currently receiving the interventions ‘subtracted’ from best practice goal from guidelines or targets, adjusted for likely compliance with the intervention); baseline risk – the probability of the outcome of interest in this or similar populations; and relative risk of outcome given exposure to a risk factor or relative risk reduction associated with the intervention.

The formula is

${\displaystyle {\text{NEPP}}=N\times P_{d}\times P_{e}\times r_{u}\times {\text{RRR}}}$

where

• N = population size,
• P_d = prevalence of the disease,
• P_e = proportion eligible for treatment,
• r_u = risk of the event of interest in the untreated group or baseline risk over appropriate time period (this can be multiplied by life expectancy to produce life-years),
• RRR = relative risk reduction associated with treatment.

In order to reflect the incremental effect of changing from current to ‘best’ practice, and to adjust for levels of compliance, the proportion eligible for treatment, P_e, is ${\textstyle (P_{b}-P_{t})P_{c}}$, where P_t is the proportion currently treated, P_b is the proportion that would be treated if best practice were adopted, and P_c is the proportion of the population who are compliant with the intervention.

You can calculate the NEPP and its confidence intervals at this page on the population health decision support & simulation site.

[Note: number needed to treat (NNT): 1/(baseline risk x relative risk reduction)]

The formula is

${\displaystyle {\text{PIN}}-{\text{ER}}-t=NI_{p}\cdot {\text{PAR}}}$

where

• N is the number of people in the population;
• I_p is the baseline risk of the outcome of interest in the population as a whole;
• t is the amount of time over which the outcome is measured.

The PAR/F, population attributable risk (or fraction), is calculated for two or multiple strata. The basic formula to compute the PAR for dichotomous variables is

${\displaystyle {\text{PAR}}=P_{e}{\frac {{\text{RR}}-1}{1+P_{e}({\text{RR}}-1)}}}$

where

• P_e is the prevalence of the population within each income stratum as the exposure, and
• RR is the prevalence of risk factors in each stratum relative to the highest income fifth.

This is modified where there are multiple strata to:

${\displaystyle {\text{PAR}}={\frac {P_{e1}({\text{RR}}_{1}-1)+P_{e2}({\text{RR}}_{2}-1)+P_{e3}({\text{RR}}_{3}-1)+\cdots }{1+P_{e1}({\text{RR}}_{1}-1)+P_{e2}({\text{RR}}_{2}-1)+P_{e3}({\text{RR}}_{3}-1)+\cdots }}}$

You can calculate the PIN − ER − t and its confidence intervals at this page on the population health decision support & simulation site.