Population impact measure explained

Population impact measures (PIMs) are biostatistical measures of risk and benefit used in epidemiological and public health research. They are used to describe the impact of health risks and benefits in a population, to inform health policy.[1] [2] [3]

Frequently used measures of risk and benefit identified by Jerkel, Katz and Elmore,[4] describe measures of risk difference (attributable risk), rate difference (often expressed as the odds ratio or relative risk), population attributable risk (PAR), and the relative risk reduction, which can be recalculated into a measure of absolute benefit, called the number needed to treat. Population impact measures are an extension of these statistics, as they are measures of absolute risk at the population level, which are calculations of number of people in the population who are at risk to be harmed, or who will benefit from public health interventions.

They are measures of absolute risk and benefit, producing numbers of people who will benefit from an intervention or be at risk from a risk factor within a particular local or national population.[5] [6] [7] [8] [9] [10] They provide local context to previous measures, allowing policy-makers to identify and prioritise the potential benefits of interventions on their own population.[11] [12] They are simple to compute, and contain the elements to which policy-makers would have to pay attention in the commissioning or improvement of services. They may have special relevance for local policy-making. They depend on the ability to obtain and use local data, and by being explicit about the data required may have the added benefit of encouraging the collection of such data.

Measures

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 a 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.

NEPP

The formula for calculating the NEPP is

NEPP=N x Pd x Pe x ru x RRR

where

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, Pe, is (P_b - P_t)P_c , where Pt is the proportion currently treated, Pb is the proportion that would be treated if best practice were adopted, and Pc is the proportion of the population who are compliant with the intervention.

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

PIN − ER − t

The formula for calculating the PIN − ER − t is

PIN-ER-t=NIpPAR

where

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

PAR=Pe

RR-1
1+Pe(RR-1)

where

This is modified where there are multiple strata to:

PAR=

Pe1(RR1-1)+Pe2(RR2-1)+Pe3(RR3-1)+ …
1+Pe1(RR1-1)+Pe2(RR2-1)+Pe3(RR3-1)+ …

Notes and References

  1. Heller . R. F . Dobson . AJ . Disease impact number and population impact number: population perspectives to measures of risk and benefit . BMJ . 321 . 7266 . 950–3 . 2000 . 11030691 . 1118742 . 10.1136/bmj.321.7266.950.
  2. 10.1186/1471-2458-3-7 . Heller . RF . Edwards . R . McElduff . P . Implementing guidelines in primary care: can population impact measures help? . BMC Public Health . 3 . 7 . 2003 . 12542840 . 149228 . free .
  3. Heller . R. F . Buchan . I . Edwards . R . Lyratzopoulos . G . McElduff . P . St Leger . S . Communicating risks at the population level: application of population impact numbers . BMJ . 327 . 7424 . 1162–5 . 2003 . 14615346 . 261823 . 10.1136/bmj.327.7424.1162.
  4. Jekel JF, Katz DL, Elmore JG Epidemiology, biostatistics, and preventive medicine: Chapter 6 Assessment of risk and benefit in epidemiologic studies Elsevier Health Sciences, 2001
  5. Torun . P. . Heller . R. F. . Verma . A. . Potential population impact of changes in heroin treatment and smoking prevalence rates: using Population Impact Measures . The European Journal of Public Health . 19 . 1 . 28–31 . 2008 . 10.1093/eurpub/ckn103. 19001458 . free .
  6. Heller . Richard F . Gemmell . Islay . Edwards . Richard . Buchan . Iain . Awasthi . Shally . Volmink . James A . Prioritising between direct observation of therapy and case-finding interventions for tuberculosis: use of population impact measures . BMC Medicine . 4 . 35 . 2006 . 17181867 . 1764027 . 10.1186/1741-7015-4-35 . free .
  7. Heller . RF . Gemmell . I . Patterson . L . Helping to prioritise interventions for depression and schizophrenia: use of Population Impact Measures . Clinical Practice and Epidemiology in Mental Health . 2 . 3 . 2006 . 16553956 . 1475571 . 10.1186/1745-0179-2-3 . free .
  8. Gemmell . I . Heller . RF . McElduff . P . Payne . K . Butler . G . Edwards . R . Roland . M . Durrington . P . Population impact of stricter adherence to recommendations for pharmacological and lifestyle interventions over one year in patients with coronary heart disease . Journal of Epidemiology and Community Health . 59 . 12 . 1041–6 . 2005 . 16286491 . 1732977 . 10.1136/jech.2005.035717.
  9. Gemmell . I . Heller . RF . Payne . K . Edwards . R . Roland . M . Durrington . P . Potential population impact of the UK government strategy for reducing the burden of coronary heart disease in England: comparing primary and secondary prevention strategies . Quality & Safety in Health Care . 15 . 5 . 339–43 . 2006 . 17074870 . 2565818 . 10.1136/qshc.2005.017061.
  10. Syed AM, Talbot-Smith A, Gemmell I. The use of epidemiological measures to estimate the impact of primary prevention interventions on CHD, stroke and cancer outcomes: experiences from Herefordshire, UK. J Epidemiol Glob Health. 2012 Sep;2(3):111-24.
  11. Chamnan . P . Simmons . RK . Khaw . KT . Wareham . NJ . Griffin . SJ . Estimating the population impact of screening strategies for identifying and treating people at high risk of cardiovascular disease: modelling study . BMJ . 340 . c1693 . 2010 . 20418545 . 2859321 . 10.1136/bmj.c1693.
  12. Heller . RF . Gemmell . I . Wilson . EC . Fordham . R . Smith . RD . Using economic analyses for local priority setting : the population cost-impact approach . Applied Health Economics and Health Policy . 5 . 1 . 45–54 . 2006 . 10.2165/00148365-200605010-00006 . 16774292.