Guidance and methods for indicator selection and specification

Guidance and methods for indicator selection and specification

First draft 5 April 2007

Mikko Pohjola, Eva Kunseler, Jouni Tuomisto KTL, Finland

Leendert van Bree MNP, The Netherlands

Workpackage 1.4 Risk Characterization

The INTARESE context: Full chain development

Integrated risk assessment, as applied in the INTARESE project, can be defined as the assessment of risks to human health from environmental stressors based on a ‘whole system’ approach. It thus endeavours to take account of all the main factors, links, effects and impacts relating to a defined issue or problem, and is deliberately more inclusive (less reductionist) than most traditional risk assessment procedures. (D. Briggs, 16.5.06)

Key characteristics of integrated assessment are:

1. It is designed to assess complex policy-related issues and problems, in a more comprehensive and inclusive manner than that usually adopted by traditional risk assessment methods.
2. It takes a ‘full-chain’ approach – i.e. it explicitly attempts to define and assess all the important links between source and impact, in order to allow the determinants and consequences of risk to be tracked in either direction through the system (from source to impact, or from impact back to source).
3. It takes account of the additive, interactive and synergistic effects within this chain and uses assessment methods that allow these to be represented in a consistent and coherent way (i.e. without double-counting or exclusion of significant effects).
4. It presents results of the assessment as a linked set of policy-relevant ‘outcome indicators’.
5. It makes the best possible use of the available data and knowledge, whilst recognising the gaps and uncertainties that exist; it presents information on these uncertainties at all points in the chain. (D. Briggs, 16.5.06)

The INTARESE approach emphasizes on creation of causal linkages between the determinants and consequences in the integrated assessment process. For this purpose, a full chain diagram should be constructed in which variables are the leading components. This full chain development represents the source-impact chain, which is based on different frameworks developed from the pressure-state-response (PSR) concept originally proposed by the US-EPA (e.g. DPSIR, DPSEEA) and the source-receptor models widely used to represent the fate of pollutants in the environment. (D. Briggs, 16.5.06)

Based on the INTARESE assessment framework, two approaches for full chain development can be distinguished, based on the point in development where causal linkages between variables are formulated.

File:Causal links defined with variables.PNG

Figure 1: Causal linkages are defined in line with variable development

File:Causal links defined after variables.PNG

Figure 2: Variables are defined and subsequently linked

The first approach in figure 1 starts from the assessment framework and translates this into a set of variables (circles) and functions (connecting arrows), representing the main elements of the system that will be assessed.

To supplement this, and to ensure that all the terms used in the assessment are consistent and explicit, the variables used in the assessment process should also be defined and described. Descriptions should cover the methods/models used to compute or derive the variable, and the data (and associated data sources) on which these are based. ‘Variables’ in this context may take different forms and serve different roles (often simultaneously); they represent inputs to models (derived variables), interim steps in the calculation process (derived variables) and outputs for reporting (indicators).

A tool for describing variables is available in the INTARESE Wiki site, though currently the descriptions that this provides are deliberately relatively simple. As such, it provides only limited descriptions of the computation procedures. (Tuomisto and Pohjola, April 2007)

The second approach in Figure 2 shows the issue framework. The main emphasis is on identification and definition of a set of variables, whilst the causal linkages between them are established and defined at a later stage. Issue framing definitely considers the relationship between the variables, since they are in some way positioned towards each other in the assessment framework. Variables are individual components in the assessment framework. Since computation methods and models as well as data are separately defined for each variable, they may be inconsistent, hampering the creation of causal linkages.

The terminology: Indicator

Indicators are variables of specific interest.

Indicator selection provides the bridge between the issue framework and the assessment process. It involves specifying the outcome measures to be used as a basis for risk characterisation. Indicators should ideally represent all the main nodes and links that make up the source-impact chain, and should be internally coherent – i.e. they should have clear and definable relationships within the context of this chain. (D. Briggs, 16.5.06)

Types of indicators

Indicators can take very different forms. Detailed categorisation of different types of indicator is fraught with difficulty, and is unlikely to be helpful. In terms of environmental health, a distinction has sometimes been made, however, between exposure-side indicators and health-side indicators. This distinction is useful in relation to INTARESE, because it discriminates between the forward looking indicators of exposure (i.e. those that presage, and need to be linked to, a potential health effect) and the backward looking indicators of outcome or effect (i.e. those that imply, and need to be attributed to, an exposure or source).

Exposure-side indicators are clearly relevant for policy, since they often provide the first indications of the potential for health risk, and the first evidence of the effects of intervention (since many policies are focused on the upper links in the source-impact chain). To be meaningful in the context of health risks, however, they must relate to factors with definable (or at least strongly plausible) links to health outcome.

Health-side (or outcome) indicators represent the consequences of exposures in terms of health effect (e.g. mortality, morbidity, DALYs) or its further societal impacts (e.g. economic costs, quality of life). Again, to be meaningful in the context of the full-chain approach, they need to have an explicit link back to causal environmental exposures and risk factors.

In each case, the indicators may be expressed in different ways, depending on:

• Whether they are static (state, condition) or dynamic (process, flux) indicators;
• Whether they are expressed in quantitative (‘objective’) or qualitative (perception) measures;
• Whether or not they relate to a formal (and internal) reference level or target (performance indicators). (D. Briggs, 16.5.06)

Indicator selection from different perspectives

File:Indicator development as a process.PNG

Figure 3: Indicator development as a process e.g. Pyrkilo

File:Indicator development as individual components.PNG

Figure 4: Indicator development as individual components e.g. WHO

Suggested Intarese attributes

1. Name
2. Scope
3. Description
   • Scale
   • Averaging period
   • References
4. Unit
5. Definition
   • Causality
   • Data
   • Formula
     • Variations and alternatives
6. Result
7. Discussion

Variable definition tools

Table. A comparison of attributes used in Intarese (suggestions), ENHIS indicators, pyrkilo method, and David's earlier version.

Suggested Intarese attributes	WHO indicator attributes	Pyrkilo variable attributes	David's variable attributes
Name	Name	Name	Name
Scope	Issue	Scope	Detailed definition
Description	Definition and description	Description (part of)	-
Description (part of)	Interpretation	Description (part of)	-
Description / Scale	Scale	Scope or Description	Geographical scale
Description / Averaging period	-	Scope or description	Averaging period
Description / Variations and alternatives	-	Description	Variations and alternatives
Description (part of)	Linkage to other indicators	Description (part of)	- R↻
Unit	Units	Unit	Units of measurement
Definition / Causality	Not relevant	Definition / Causality	Links to other variables
Definition / Data R↻	Data sources or Related data	Definition / Data	Data sources, availability and quality
Definition / Formula	Computation	Definition / Formula	Computation algorithm/model
Result (a very first draft of it) R↻	Not a specific attribute	Result (a very first draft of it)	Worked example
Discussion	-	-	-
Done by using categories	-	Done by using categories	Type
Done by links to glossary	-	Done by links to glossary	Terms and concepts
Done by argumentation on the Discussion area	Specification of data needed	Done by argumentation on the Discussion page	Data needs
The postition in a causal diagram justifies the existense	Justification	The postition in a causal diagram justifies the existense	-
Not relevant	Policy context	Not relevant	Not relevant
Not relevant	Reporting obligations	Not relevant	Not relevant