derive health output. The level of needed details on the exposure data depends on
the output chosen, its occurrence and the strength of the causal
relationship. However in general, the following input are needed to compute
impacts:
– Air pollution concentrations
– Population data
– Dose-response functions.
Functionality
The input-functionality-output chain can be implemented at different levels of
complexity. It depends on the strength and the robustness of the causal relationship
between the exposure indicator (STATE or PRESSURES) and the health indicator
chosen to support the decisions (RESPONSES) to be taken. The chosen approach to
compute health impact (retrospective, prospective, counterfactual) does not restrict
the level of complexity to be applied; it only demands more or less detailed data in
the input-output chain.
– LEVEL 1: A coarse description of exposure provided either by measurement or
modelling of AQ (e.g. average mean annual exposure for a city), a
dose-response function or concentration-response function and a simple population description would give a rather coarse output. For examples: the number
of hospital emergency visits related to increased ozone levels for a city or
region.
– LEVEL 2: Similar to level 1, but with spatial details in the STATE description.
– LEVEL 3: A detailed temporal and spatial resolution for exposure and population data allows an accurate health analysis integrating, for instance, distance
to roads, spatial distribution and vulnerable groups. For examples: The number
of hospital emergency visits of those who live in greener or more trafficked areas
of a city, related to local changes in ozone.
Output
The choice of health indicators to support decisions has to be made to show the
potential policy action or inaction impact. Outputs have different strength in supporting policies. The burden of disease related to air quality can be expressed as
such or translated into YOLL, DALY (Disability-Adjusted Life Year), life expectancy related to changes in exposure. Other indicators such as morbidity or mortality rate, number of hospital visits related to exposure and exposure changes can
be used with a known dose-response or concentration-response function. The
output representativeness strongly depends on the level of detail of population data.
The temporal resolution is also of importance, decisions on short-term exposure
or on long-term exposure should be addressed separately using related health data.
Synergies among scales
Concerning the IMPACT and specially those on human health, the scale is strictly
related to the level of uncertainties. The challenges of synergies encountered in
30
N. Blond et al.
the output chosen, its occurrence and the strength of the causal
relationship. However in general, the following input are needed to compute
impacts:
– Air pollution concentrations
– Population data
– Dose-response functions.
Functionality
The input-functionality-output chain can be implemented at different levels of
complexity. It depends on the strength and the robustness of the causal relationship
between the exposure indicator (STATE or PRESSURES) and the health indicator
chosen to support the decisions (RESPONSES) to be taken. The chosen approach to
compute health impact (retrospective, prospective, counterfactual) does not restrict
the level of complexity to be applied; it only demands more or less detailed data in
the input-output chain.
– LEVEL 1: A coarse description of exposure provided either by measurement or
modelling of AQ (e.g. average mean annual exposure for a city), a
dose-response function or concentration-response function and a simple population description would give a rather coarse output. For examples: the number
of hospital emergency visits related to increased ozone levels for a city or
region.
– LEVEL 2: Similar to level 1, but with spatial details in the STATE description.
– LEVEL 3: A detailed temporal and spatial resolution for exposure and population data allows an accurate health analysis integrating, for instance, distance
to roads, spatial distribution and vulnerable groups. For examples: The number
of hospital emergency visits of those who live in greener or more trafficked areas
of a city, related to local changes in ozone.
Output
The choice of health indicators to support decisions has to be made to show the
potential policy action or inaction impact. Outputs have different strength in supporting policies. The burden of disease related to air quality can be expressed as
such or translated into YOLL, DALY (Disability-Adjusted Life Year), life expectancy related to changes in exposure. Other indicators such as morbidity or mortality rate, number of hospital visits related to exposure and exposure changes can
be used with a known dose-response or concentration-response function. The
output representativeness strongly depends on the level of detail of population data.
The temporal resolution is also of importance, decisions on short-term exposure
or on long-term exposure should be addressed separately using related health data.
Synergies among scales
Concerning the IMPACT and specially those on human health, the scale is strictly
related to the level of uncertainties. The challenges of synergies encountered in
30
N. Blond et al.