Transcript presented at Evaluation Considerations: Measures & Methods Shrikant I. Bangdiwala, PhD Professor of Research in Biostatistics Injury Prevention Research Center University of North Carolina at Chapel.

presented at
Evaluation Considerations:
Measures & Methods
Shrikant I. Bangdiwala, PhD
Professor of Research in Biostatistics
Injury Prevention Research Center
University of North Carolina at Chapel Hill, USA
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WHO VIP Webinar 2011
Content
Purpose of evaluation
 Cycle of program planning & evaluation
 Indicators
 Study designs
 Statistical modeling
 Challenges

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What are we ‘evaluating’?
Actions, programs, activities
 Conducted in a community setting, over a
period of time
 Aimed at reducing deaths, injuries, and/or
events and behaviors that cause injuries

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Example:
Suwon,
South
Korea
 area of
‘safety
promotion’
http://www.phs.ki.se/csp/safecom/suwon2.htm
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Why do we ‘evaluate’?

To know ourselves what works and if we are doing
some good
In performing some activity
 In the community
 In the country

To convince funders and supporters that their
investment is worthwhile
 To convince the community about the benefits of the
multiple activities and actions carried out

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Main purposes of evaluation

Evaluation helps determine:
How well a program/policy works relative to its
goals & objectives
 Why a program/policy did or didn’t work, relative
to planned process
 How to restructure a program/policy to make it
work, or work better
 Whether to change funding for a program

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Methodological complications

Multiplicities
Multiple components of a program
 Multiple populations at risk
 Multiple study designs
 Multiple types of effects/impacts/outcomes &
severities
 Multiple audiences/objectives of ‘evaluation’
 Multiple methods for conducting evaluation

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When should evaluation be
considered?
Evaluation needs to begin in, and be part of,
the planning process…
 Otherwise, “if you do not know where you are
going, it does not matter which way you go,
and you will never know if you got there or
not!”

Lewis Carroll (1872)
Alice in Wonderland
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Adapted from M. Garrettson
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Types of evaluation depending
on program phase
Program
Planning
Phase
Program
Implementation
Phase
Post
Program
Phase
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Formative Evaluation
How can the program activities be improved before
implementation?
Process Evaluation
How is/was the program (being) implemented?
Impact / Outcome
Did the program succeed in achieving the intended
impact or outcome?
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Cycle of program planning and
evaluation
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Adapted from C Runyan
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Identify population & problem
Surveillance data
 Other needs assessment strategies

key informant interviews
 focus groups
 surveys
 evaluations of past programs
 literature
 consultation with peers
 other info…

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Disseminate
Identify
problem
& population
Evaluation:
•Process
•Impact
•Outcome
Identify
resources
Implement
Test, Refine,
Implement
Test & refine
implementation
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Define target
audience
Evaluation:
•Formative
Choose
strategies
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Set goals/
objectives
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Define target audience

To whom is the program directed?
Whose injuries need to be reduced?
 Who is the target of the program?

•
•
•
•
•
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at risk persons
care givers (e.g. parents)
general public
media
decision makers
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Understand target audience

What are their characteristics?
Special needs (e.g. literacy)
 Interests, concerns, priorities
 Attitudes & beliefs re: problem & solutions to
problem
 Cultural issues

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Disseminate
Identify
problem
& population
Evaluation:
•Process
•Impact
•Outcome
Identify
resources
Implement
Test, Refine,
Implement
Test & refine
implementation
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Define target
audience
Evaluation:
•Formative
Choose
strategies
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Set goals/
objectives
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Identify resources

Community partners
interest in topic
 working on similar projects

On-going activities
 Sources of financial support
 Interests in community

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Disseminate
Identify
problem
& population
Evaluation:
•Process
•Impact
•Outcome
Identify
resources
Implement
Test, Refine,
Implement
Test & refine
implementation
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Define target
audience
Evaluation:
•Formative
Choose
strategies
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Set goals/
objectives
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Set goals & objectives

Goal


broad statement of what program is trying to
accomplish
Objectives
Specific
 Measurable
 Time-framed

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Disseminate
Identify
problem
& population
Evaluation:
•Process
•Impact
•Outcome
Identify
resources
Implement
Test, Refine,
Implement
Test & refine
implementation
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Define target
audience
Evaluation:
•Formative
Choose
strategies
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Set goals/
objectives
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Choose Strategies

Identify existing strategies/programs
Literature: evidence based? promising practice?
 WHO manuals
 Successes from other communities-regionscountries


Develop new strategies:
Logic model (how would it work)
 Haddon matrix

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Haddon Matrix
Person
Vehicle/
vector
Physical
Environ.
Social
Environ.
Preevent
Event
Postevent
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Haddon 1970 Am J Public Health
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3-dimensional Haddon Matrix
Other??
Feasibility
Preferences
Stigmatization
Equity
Freedom
Cost
Event
Post-event
Effectiveness
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Runyan 1998 Injury Prevention
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Disseminate
Identify
problem
& population
Evaluation:
•Process
•Impact
•Outcome
Identify
resources
Implement
Test, Refine,
Implement
Test & refine
implementation
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Define target
audience
Evaluation:
•Formative
Choose
strategies
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Set goals/
objectives
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Formative Evaluation
Why it’s useful
Questions it answers



What is the best way to
influence the target
population?
Will the activities reach the
people intended, be
understood and accepted
by target population?
How can activities be
improved?
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


Improves (pilot-tests)
program activities before
full-scale implementation
May increase likelihood
program or policy will
succeed
May help stretch resources
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* Modified from Thompson & McClintock, 2000
Disseminate
Identify
problem
& population
Evaluation:
•Process
•Impact
•Outcome
Identify
resources
Implement
Test, Refine,
Implement
Test & refine
implementation
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Define target
audience
Evaluation:
•Formative
Choose
strategies
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Set goals/
objectives
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Implementation

As planned, with attention to detail

Documented clearly so others can replicate if
appropriate
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Disseminate
Identify
problem
& population
Evaluation:
•Process
•Impact
•Outcome
Identify
resources
Implement
Test, Refine,
Implement
Test & refine
implementation
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Define target
audience
Evaluation:
•Formative
Choose
strategies
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Set goals/
objectives
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Process evaluation

Purpose is to address:
What was done?
 How was it implemented?
 How well was it implemented?
 Was it implemented as planned?

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Process evaluation –
examples of questions
•
•
•
•
Who carried out intervention?
Was this the appropriate person/group?
Who supported and opposed intervention?
What methods/activities were used?
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Process evaluation
- why is it useful?
•
•
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Allows replication of programs that work.
Helps understand why programs fail.
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* Modified from Thompson & McClintock, 2000
The intervention cannot be a
black box…
It must be clearly understood
Idea
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?
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Outcome
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Disseminate
Identify
problem
& population
Evaluation:
•Process
•Impact
•Outcome
Identify
resources
Implement
Test, Refine,
Implement
Test & refine
implementation
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Define target
audience
Evaluation:
•Formative
Choose
strategies
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Set goals/
objectives
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Impact evaluation

Purpose is to address changes in:





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knowledge
attitudes
beliefs/ values
skills
behaviors / practices
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Using impact measures for

Establishing effectiveness
Suppose we have a public safety campaign as our
strategy
 Need to show

Campaign

 Outcome
If we already have demonstrated that
 Behavior

 Behavior
 Outcome
We simply need to show
Campaign
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 Behavior
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Outcome evaluation

Purpose is to address changes in:
injury events (e.g. frequency, type, pattern)
 morbidity (e.g. frequency, severity, type)
 mortality (e.g. frequency, time to death)
 cost (e.g. direct and indirect)

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Example: Bike helmets
Intervention
Physician
counseling
parents
Enforcement of
helmet law
Impacts
Outcomes
Parental
attitudes
toward child
helmet use
Head injury in
bike crashes
Purchase of
helmets
Deaths from head
injury in crashes
Media campaign
Use of helmets
by children
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Evaluation examples of questions
for local policy of smoke alarms
Did the local policy of smoke alarms in apartments…






Get passed
Where people aware of it?
Did people have access to smoke alarms?
Did people get them installed properly?
Do people keep them maintained?
Lead to a reduction in the number or rates of:
events (e.g. apartment fires)
 injuries
 deaths
 37
costs (e.g. burn centerWHOcosts,
family
burden, property loss)
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Evaluation – selection of
measures
‘Quantitative Indicators’
Process
 Impact
 Outcome

Health related
 Financial

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Choice of measure or indicator
We need to choose appropriate impact and outcome measures



‘Soft’ (more difficult to measure) outcomes –

Perceptions constructs: fear, insecurity, wellbeing, quality of life

Knowledge, attitudes and behaviors constructs
Hard outcomes –

Deaths, hospitalizations, disabilities due to injuries and violence

Societal impacts – local development indicators
Economics outcomes –

Direct $/€/£/¥, indirect DALYs, QALYs, opportunities lost, burdens
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Evidence of effectiveness

Obtain qualitative ‘evidence’ to complement
the quantitative ‘evidence’

Ex. Are “multisectorial collaborations and
partnerships” friendly and functioning well?

Ex. Is “community participation” optimal?
Incorporate process indicators
 Incorporate narratives & testimonials

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Disseminate
Identify
problem
& population
Evaluation:
•Process
•Impact
•Outcome
Identify
resources
Implement
Test, Refine,
Implement
Test & refine
implementation
41
Define target
audience
Evaluation:
•Formative
Choose
strategies
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Set goals/
objectives
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Dissemination

Dissemination not done well
Not attempted
 Not based on research about how to disseminate
information to intended audience


Dissemination done well
Defining audience
 How to access audience
 How best to communicate change message to them
 Presentation of clear, straightforward messages

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Evaluation measures
Lead to evidence of effectiveness
 But only if the research and study
methodologies, and the statistical analyses
methodologies, are appropriate to convince
the funders and supporters, the skeptics,
the stakeholders, the community


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and understandable
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Research methodology approach:
Evidence of effectiveness


Obtain quantitative ‘evidence’ that favors the hypothesis that
the intervention is effective as opposed to the (null) hypothesis
that the intervention is not effective.
How?
 Experimental study designs - randomized clinical trials,
grouped randomized experiments, community-randomized
studies
 Quasi-experimental study designs - non-randomized
comparative studies, before-after studies
 Observational studies - cohort studies, case-control studies
and comparative cross-sectional studies
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Randomized controlled trial
(RCT) / Experiment
‘strongest’ evidence
Intervention
Group
O
X
O
O
X’
O
Randomize
Control
Group
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Quasi-experimental designs
‘qualified’ evidence
Intervention Group
Comparison Group
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O
O
X
O
O
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One group pre/post
‘weak’ evidence
Intervention
Group
47
O
X
O
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One group –
multiple pre / multiple post
better ‘weak’ evidence
Intervention
Group
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O
O
O
X
O
O
O
O
O
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One group, post only
‘basically ignorable’ evidence
Intervention
Group
49
X
O
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Observational designs
- cohort study
evidence?
Self-chosen Intervention Group
Self-chosen Non-intervention Group
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X
X
X
O
O
O
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Observational designs
- case-control study
evidence?
X
Cases
O
X
Controls
O
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Observational designs
- cross-sectional study
evidence?
X
X
O Injured X
O
O
X
Non-injured O
O
O
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Statistical analysis
methodologies

Choice - often guided by what has been done
previously, or what is feasible to do, or easy to
explain

Choice should be tailored to the audience &
their ability to understand results; but also on
the ability of the presenter to explain the
methodologies
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Statistical analysis


Determined by research question(s)
Guided by study design – experimental or observational





Guided by whether outcome is studied at a single time point or
multiple time points


Group randomized controlled experiment
Non-randomized comparison study
Single site pre/post; surveillance study
Retrospective or cross-sectional
Time series analyses
Guided by audience

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Visual
and descriptiveWHO
appreciation
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Visual and descriptive analysis
– longitudinal time series
Example:
Espitia et al (2008)
Salud Pública Mexico
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Visual and descriptive analysis
– comparisons over time
Example:
www.gapminder.org
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Statistical analysis - challenge
But what we as a field have not done as well as other
fields, is to draw strength from numbers  develop
collective evidence
 Combine results from multiple studies

Systematic reviews (of observational studies)
 Meta analysis (of experimental & observational studies)
 Meta regression (of heterogeneous studies)
 Mixed treatment meta regression (for indirect
comparisons)

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Systematic reviews

A protocol driven comprehensive review and
synthesis of data focusing on a topic or on related key
questions







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formulate specific key questions
developing a protocol
refining the questions of interest
conducting a literature search for evidence
selecting studies that meet the inclusion criteria
appraising the studies critically
synthesizing and interpreting the results
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Example –
Systematic review
Shults et al (2001) Amer J Prev Med
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Systematic reviews

Of particular value in bringing together a number
of separately conducted studies, sometimes with
conflicting findings, and synthesizing their results.
Zaza et al (2001) Amer. J Preventive Medicine – motor vehicle

To this end, systematic reviews may or may not
include a statistical synthesis called meta-analysis,
depending on whether the studies are similar
enough so that combining their results is
Green (2005) Singapore Medical Journal
meaningful
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Meta analysis

A method of combining the results of studies
quantitatively to obtain a summary estimate of the
effect of an intervention


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Often restricted to randomized controlled trials
Recently, the Cochrane Collaboration is
‘branching out’ to include both experimental and
observational studies in meta analyses
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Meta analysis

e.g. Liu et al
(2008)
Cochrane
Collaboration
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Meta analysis

The combining of results should take into account:

the ‘quality’ of the studies
• Assessed by the reciprocal of the variance

the ‘heterogeneity’ among the studies
• Assessed by the variance between studies
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Meta analysis
– estimation of effect


The estimate is a weighted average, where the weight of a study
is the reciprocal of its variance
In order to calculate the variance of a study, one can use either a
‘fixed’ effects model or a ‘mixed’/’random’ effects model


Fixed effects model:
utilizes no information from other studies  var(Yi )  var(ei )  VYi
Random effects model:
considers variance among and within studies  Yi     i  ei
var( )   2
var(Yi )   2  VY*i
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Borenstein et al (2009) Introduction to Meta Analysis
Meta analysis & meta regression

Dealing with ‘heterogeneity’ among the studies - 2


Decompose the total variance into among and within
components  using mixed effects models for
getting a more precise estimate of the intervention
effect
If there is still residual heterogeneity

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Expand the mixed effects model to include study-level
covariates that may explain some of the residual
variability among studies  meta regression
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Meta regression

e.g.
Yi    1 X1i  2 X 2i   i  ei
random error
study random effect
Overall mean
X1 study variable – EU/USA
X2 study variable – population type
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Meta analysis




Standard meta-analytical methods are typically restricted
to comparisons of 2 interventions using direct, head-tohead evidence alone.
So, for example, if we are interested in the Intervention A
vs Intervention B comparison, we would include only
studies that compare Intervention A versus Intervention B
directly.
Many times we have multiple types of interventions for
the same type of problem, and we hardly have head-tohead comparisons
We may also have multiple component interventions
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Mixed treatment meta analysis

Let the outcome variable be a binary response




1 = positive response
0 = negative response
We can calculate the binomial counts rj:k out of a total
number at risk n j:k on the kth intervention in the jth study
rj:k
We can then calculate
p 
j:k
n j:k
the estimated probability of the outcome (risk of response)
for the kth intervention in the jth study
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Mixed treatment meta analysis


Let each study have a reference ‘‘standard’’ intervention arm,
sj, with study-specific ‘‘standard’’ log odds of outcome, j .
The log odds ratio, j:k, of outcome for intervention k, relative
to standard sj, is assumed to come from a random effects
model with


mean log odds ratio (dk  ds j ) , and
between-study standard deviation 
where dk is the mean log odds ratio of outcome for
intervention k relative to control (so that d1 = 0).
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Mixed treatment meta analysis

This leads to the following logistic regression model:
ln
where
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Welton et al 2009 Amer J Epid
p j:k
1  p j:k
 j

 j   j:k
 j:k ~ N[(dk  ds ), ]
j
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int  s j
int  k
Mixed treatment meta analysis
- multiple-methods interventions

If we have multiple methods in the ith intervention
M1i , M 2i , M 3i ,...

Plus we have multiple times when the outcome is
assessed
Yit     i  1t  2 M1it  3M 2it  4 X i  eit
Study effect
Components 1 & 2 effects
Time effect
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Study covariable
Error term
Statistical analysis


Methodology does exist for developing stronger
collective evidence, evaluating the effectiveness of
community based interventions, using different types
of study designs and interventions
Developing “practice-based evidence”
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Dissemination



We should not stop at developing the evidence
We must work alongside economists in developing
ways to effectively communicate ‘what works’ 
methodology and cost models do exist for estimating
the “return on investment”
Money talks !!
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Challenges – Evaluation requires







Integration of evaluation from the beginning
Appropriate measures, possible to be collected objectively,
unbiasedly, easily and with completeness
Appropriate qualitative and process information, to complement
the quantitative information
Concrete and convincing evidence of what aspects work in
individual communities
Formal methodological statistical evaluation of specific elements
of programs
Collective evidence of what common elements of programs work
Effective dissemination strategies – “return on investment”
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