Population Impact Measures

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Transcript Population Impact Measures

Population Impact Measures
(PIM)
Richard F Heller, Emeritus Professor, Universities
of Manchester UK, and Newcastle, Australia
[email protected]
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 betablockers
– 34%
• NNT
1
1
NNT
 
ARR
Baselin
sk

(
RRR
)
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
147
3.04
Statin
53
157
2.96
Aspirin
93
176
1.91
The cost
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 Heart
AMI failure
Drugs
1027 37899
Lifestyle 848
7249
Secondary prevention for CHD
• Full implementation of
NSF in E&W (from
current to ‘best
practice’)
Drugs
• Total cost in £ millions
Post Heart
AMI failure
6.6
537
(6.4)
(1.4)
(per life saved in £ thousands)
Lifestyle 6.6
(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
Primary
Drugs
Lifestyle
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
Total Population
Population
at risk
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:
Pe
(
RR

1
)
PAR

1

Pe
(
RR

1
)
• 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)