Environmental Engineering Reference
In-Depth Information
Table 21.2
Selected factors leading to inequality of health risk
Factor
Effects
Age
The very young and old are more likely to acquire infections due to naive or waning immunity and, once infected,
are more likely to develop more severe outcomes
Pre-existing disease
A person with AIDS, severe combined immunode
ciency syndrome, diabetes, heart disease, cancer, etc. is more
likely to be vulnerable to infection and more likely to suffer severe symptoms
Genetic
People with certain genotypes are more likely to experience complications, such as joint problems, following
gastrointestinal illness
Gender/pregnancy
Certain infections are more severe in pregnancy, either increasing the risk of fatality for the mother or resulting
in damage to the fetus
Behaviour
Behaviour [such as a refusal to evacuate or geophagy (soil eating)] may result in higher exposure to infectious agents
Methods
people) with the population characteristics shown
in Table 21.3 (based on an urban population in the
north of England, using data fromthe 2001 census).
Based on the age distribution and the mid-point
of the age groups, the average age of the population
is 35.6. A total of 20.25% of the population have
a limiting long-term illness.
In this example, it was assumed that this pop-
ulation was flooded with a mixture of river water
(80%), raw sewage (10%) and urban runoff (10%).
The scenario investigated was the risk of illness
associated with the flood clean-up process.
Where possible, parameters for inclusion in the
QMRA were represented as probability distribu-
tions (see following sections) rather than point
estimates, in order to examine the effects of un-
certainty. Monte Carlo sampling (5000 iterations)
was used for each simulation run using @Risk
version 4.5 Professional edition software (Palisade
Corporation 2002). The resulting estimates of in-
fection were quantified in terms of disability-ad-
justed life years (DALYs) in order to give an overall
health impact and allow comparisons between
different scenarios. DALYs are summary mea-
sures of health that allowthe comparison of effects
across a wide range of health outcomes, including
mortality and morbidity. The measure combines
years of life lost by premature mortality (YLL),
with years (or days, weeks or months) lived with
a disability (YLD), standardized using severity or
disability weights. The weights range from 0 (per-
fect health) to 1 (dead). The calculation of YLL due
to premature mortality requires an estimate of life
expectancy. This varies according to age group and
gender, but in the UK overall average life expec-
tancy at birth is 79 (GAD 2007).
Pathogen choice
There are far too many pathogens, and a lack of
data about most, to consider all possible causes
of gastrointestinal infection that could be present
in flood water. A common approach is, therefore,
to consider a number of reference pathogens
(WHO2004), usually consisting of a bacterial, viral
and protozoan pathogen. Suitable
reference
Table 21.3
Population characteristics for an urban flood
affecting 400 households
People
Flood scenario
Age (years)
%
Number
The demographic profile of the flooded population
will obviously be case-specific and may be ac-
counted for when such variables are known. For
the purposes of illustration, a hypothetical scenar-
io was based on the assumption that 400 houses
were flooded (mean household composition of 2.57
0
-
4
7.68
80
5
-
9
7.93
80
10
-
14
8.03
84
15
-
65
63.85
656
66
-
74
6.35
65
75
รพ
6.16
63
Total
100
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