Preventing Childhood Obesity - Evidence Policy and Practice.pdf

Chapter 20
Box 20.2 Key f eatures of the
a ssessing c ost – e ffectiveness
( ACE ) a pproach
• Clear rationale and process for selection of interventions
is to be evaluated.
• Standardized evaluation methods are used to avoid
methodological confounding.
• The setting, context and comparator is common for
all interventions.
• Evaluations are conducted as an integral part of the
priority setting exercise.
• Country - specific data is used, wherever possible,
for demography, health systems costs and disease
incidence/prevalence patterns.
• Evidence - based approach is employed, with extensive
use of uncertainty and sensitivity testing
• Information isvassembled by an independent
research team.
• Involvement of stakeholders is required to achieve
“ due process ” .
• There is a two - stage approach to measurement of
benefit.
mechanisms for combining them into effective prevention
strategies for achieving healthy weight.
It is with this broader priority setting task in mind
that the ACE - Obesity project set out “ to assess from a
societal perspective the most cost - effective options for
preventing unhealthy weight gain in Australia, particularly
amongst children and adolescents ” in order
to inform state and national policy. 25 The key features
of the ACE approach to priority setting are shown in
Box 2 . Every effort was made to support the assumptions
underpinning the economic evaluations with
standardized methods and the best available evidence.
25 One distinguishing feature was the two - stage
approach adopted to the measurement of benefit, to
capture the broad - based nature of policy objectives.
The technical cost – effectiveness results (ICERs) were
placed within a broader decision - making framework,
which included considerations that did not lend themselves
readily to quantification. Such second stage
filters, as they were termed, included “ strength of evidence
” , “equity ” , “feasibility of implementation ” ,
“acceptability of stakeholders ” , “sustainability ” and
“side effects ” .
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The stakeholder Working Group opted
for the separate reporting of the technical results and
second stage filter results, arguing that the latter information
in a qualitative format would be more transparent
to policy - makers. The alternative would have
been to weight the ICER results and the second filters
and then combine them into a single index score.
In addition to confronting issues of how to combine
the technical analyses of interventions with broader
considerations of decision making, economists also
need to tackle issues of how to consider, in a meaningful
way, obesity outcomes with any intervention benefits
of a non - health nature. For example, in the
ACE - Obesity study, no attempt was made to quantify
benefits of the Walking School Bus program (such as
increased safety around schools, decreased traffic congestion
and pollution, increased social cohesiveness,
etc.) other than changes in obesity outcomes (increased
walking leading to increased energy expenditure
and reduced BMI). These non - obesity benefits were
acknowledged and threshold analysis was undertaken
to ascertain what proportion of costs would need to
be attributed to them for the intervention to become
cost - effective. An alternative approach would be to
include a wider range of outcome measures and
attempt more composite measurement approaches —
such as attaching monetary values to all outcomes via
a cost – benefit analysis (see Box 20.1 ). Such approaches
are possibly more important with respect to obesity
interventions than other public health areas, as many
lie outside the health sector and are what Shiell 36 terms
“ social interventions ” offering potential health and
non-health benefits beyond just the reductions in BMI
being measured.
Finally, to add to the complexity of the priority
setting process, any budgetary constraints imposed on
decision makers need also to be taken into account.
Decision makers may not be able to implement the
most cost - effective option(s) if the cost of implementation
exceeds budgetary limits. Several approaches
exist to facilitate this task of matching combinations
of interventions to particular budget levels; they range
from a simplistic deterministic analysis using a
cost – effectiveness league table to more sophisticated
methods using Generalized Cost - Effectiveness analysis,
37 stochastic league tables 38 and linear programming.
39 The more sophisticated methods are quite
complex and time consuming, yet may not necessarily
result in a more optimal ranking of the selected
interventions than a simple deterministic cost –
effectiveness league table approach. Furthermore, as
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