Timeframe in assessment design

From Opasnet
Jump to: navigation, search
The text on this page is taken from an equivalent page of the IEHIAS-project.

If its results are to be meaningful, any assessment must relate to a clearly specified timeframe. The way in which this is defined will vary depending on the type of assessment. Diagnostic assessments often relate to current conditions; summative assessments to the period from when the policy (or other development) of concern was first introduced up to the present time; prognostic assessments to future impacts.

In the case of prognostic (and to a somewhat lesser extent, summative) assessments, defining a relevant timeframe for the assessment can pose substantial challenges. Problems arise not only because the effects of policies and technologies vary over their lifetime (as they are developed, established, operated and finally withdrawn), but also because many leave a much longer lasting legacy. Chemical works and old military sites, for example, remain contaminated scores of years after they have been decommissioned; landfill sites may continue to release pollutants for hundreds of years after they have been closed and infilled; modern cities and transport networks (and their attendant health implications) have, to a large extent, been shaped by historic planning decisions. In addition, many health effects persist long after the first symptoms emerge, and some (e.g. reproductive effects) have inter-generational effects.

In all these cases, ignoring these longer term consequences by confining attention to a single snapshot in time (e.g. one year) or to immediate impacts can seriously bias the assessment. Instead, if we wish to consider the total impacts of a policy or technology, we should try to sum the impacts over the full timespan of its influence.

Doing so has numerous implications for the way the assessment is designed. It means, for example, that we need data on human populations, hazards and exposures that cover many years, and in some situations extend far into the future. As a consequence, we have to rely on models not only for the analysis itself, but also to help us work out what the real timeframe for analysis should be (i.e. how long impacts might persist). These models may have to be extended far beyond the period over which they can be fully validated, which inevitably increases the uncertainties involved - though because the total health burden also tends to increase, the relative error (as a proportion of the total impact) may not change greatly.

It also means that we have to be able to accumulate impacts over time. This in turn requires that we often need to consider (and combine) very different types of effect, including acute and chronic outcomes, and thus to make allowance for the duration, as well as severity, of effect. By the same token, we may have to make judgements about the relative importance of more immediate compared with more remote effects (including, in some instances, those on generations as yet unborn). Standard measures of health outcome (e.g. life expectancy or total mortality) give a very limited perspective in these situations, so usually we are driven to use more synthetic indicators, such as disease- or quality-adjusted life years (DALYs and QALYs) or monetary evaluation.

See also

Integrated Environmental Health Impact Assessment System
IEHIAS is a website developed by two large EU-funded projects Intarese and Heimtsa. The content from the original website was moved to Opasnet.
Topic Pages
Toolkit
Data

Boundaries · Population: age+sex 100m LAU2 Totals Age and gender · ExpoPlatform · Agriculture emissions · Climate · Soil: Degredation · Atlases: Geochemical Urban · SoDa · PVGIS · CORINE 2000 · Biomarkers: AP As BPA BFRs Cd Dioxins DBPs Fluorinated surfactants Pb Organochlorine insecticides OPs Parabens Phthalates PAHs PCBs · Health: Effects Statistics · CARE · IRTAD · Functions: Impact Exposure-response · Monetary values · Morbidity · Mortality: Database

Examples and case studies Defining question: Agriculture Waste Water · Defining stakeholders: Agriculture Waste Water · Engaging stakeholders: Water · Scenarios: Agriculture Crop CAP Crop allocation Energy crop · Scenario examples: Transport Waste SRES-population UVR and Cancer
Models and methods Ind. select · Mindmap · Diagr. tools · Scen. constr. · Focal sum · Land use · Visual. toolbox · SIENA: Simulator Data Description · Mass balance · Matrix · Princ. comp. · ADMS · CAR · CHIMERE · EcoSenseWeb · H2O Quality · EMF loss · Geomorf · UVR models · INDEX · RISK IAQ · CalTOX · PANGEA · dynamiCROP · IndusChemFate · Transport · PBPK Cd · PBTK dioxin · Exp. Response · Impact calc. · Aguila · Protocol elic. · Info value · DST metadata · E & H: Monitoring Frameworks · Integrated monitoring: Concepts Framework Methods Needs
Listings Health impacts of agricultural land use change · Health impacts of regulative policies on use of DBP in consumer products
Guidance System
The concept
Issue framing Formulating scenarios · Scenarios: Prescriptive Descriptive Predictive Probabilistic · Scoping · Building a conceptual model · Causal chain · Other frameworks · Selecting indicators
Design Learning · Accuracy · Complex exposures · Matching exposure and health · Info needs · Vulnerable groups · Values · Variation · Location · Resolution · Zone design · Timeframes · Justice · Screening · Estimation · Elicitation · Delphi · Extrapolation · Transferring results · Temporal extrapolation · Spatial extrapolation · Triangulation · Rapid modelling · Intake fraction · iF reading · Piloting · Example · Piloting data · Protocol development
Execution Causal chain · Contaminant sources · Disaggregation · Contaminant release · Transport and fate · Source attribution · Multimedia models · Exposure · Exposure modelling · Intake fraction · Exposure-to-intake · Internal dose · Exposure-response · Impact analysis · Monetisation · Monetary values · Uncertainty
Appraisal