Transcript Quantitative microbial risk assessment

Microbial Risk Assessment
Global Water Sanitation and Health
Mark D. Sobsey and Lisa Casanova
Spring, 2007
WHO Health-Risk Based Framework:
Application to WHS
These principles apply to all
types of WSH activities
WHO Health-Risk Based Framework:
Application to WHS
• A risk-based framework
• Source-to-consumer management approach to protection from
exposure to environmental agents
• Establishes health based-targets for control (specific microbes
and chemicals)
• Sets acceptable level of risk appropriate to setting and
population
• Helps establish and carry out Management Plans (Safety Plans)
to achieve control
• Includes independent surveillance
• Is an integrated, proactive approach
• Consistent across, compatible with and applicable to all WSH
measures
Quantitative Microbial Risk Assessment:
The Definition
Applications of the principles of risk
assessment to the estimation of the
consequences from anticipated or actual
exposure to infectious microorganisms
Exposure, Level of Protection and Microbial
Risk: The Relationship
= Confidence Region or Interval
Risk 
Exposure 
 Level of Protection (e.g., technologic control)
Important Differences Between Microbial &
Chemical Risks: The Microbial
• A single microbe (one unit) is infectious and can cause dramatic
effects
• Microbes multiply in a host (increases adverse effects)
• Microbes multiply in environmental media (some microbes)
• Microbes are capable of secondary spread
– Can infect a host from an environmental route of exposure
(water, food, etc.)
– Can then spread to other hosts by person-to-person
transmission
• Some microbes cause a wide range (spectrum) of adverse effects
• Microbes can change: mutate, evolve, adapt, change gene
expression, etc.
Important Differences Between Microbial &
Chemical Risks: the Chemical
• Unique and specific structures that define (predict) activities
• Many molecules may be required for an effect; gradation of
effects
• Do not multiply/reproduce
• No secondary spread
• Accumulation and compartmentalization
• Metabolism and chemical reactivity
• Detoxification
• Threshold (no adverse effect level)
• Cumulative effects
• Magnitude of exposure influences magnitude of adverse effects
and their appearance/manifestation
• Distinctive health effects based on chemical reactions with
specific molecules, tissues and organs
Quantitative Risk Assessment for Agents from
Environmental Sources: a Conceptual Framework
Adapted from: National Academy of Sciences - National Research Council framework by US EPA and the International
Life Sciences Institute (ILSI)
RISK ASSESSMENT FOR ENVIRONMENTALLY
TRANSMITTED PATHOGENS: ILSI/EPA PARADIGM
PROBLEM FORMULATION: HAZARD IDENTIFICATION
CHARACTERIZATION
OF EXPOSURE EFFECTS
CHARACTERIZATION OF
HUMAN HEALTH EFFECTS
RISK CHARACTERIZATION
Risk Management and Communication
ILSI/EPA Risk Assessment Framework and
Steps: Analysis Phase
QRA for Agents from Environmental Sources: Steps in
the Conceptual Framework
Conducting Hazard Identification for Microbes
• Identify microbe(s) that is (are) the causative
agent(s) of disease
• Develop/identify diagnostic tools to:
– identify symptoms
– identify infection
– isolate causative microbe in host specimens
– identify causative microbe in host specimens
• Understand the disease process from exposure to
infection, illness (pathophysiology) and death
• Identify transmission routes
• Identify transmission scenarios
Conducting Hazard Identification for Microbes
• Assess virulence factors and other properties of the microbe
responsible for disease, including life cycle
• Identify and apply diagnostic tools to determine incidence
and prevalence in populations and investigate disease
outbreaks
• Develop models (usually animals) to study disease process
and approaches to treatment
• Evaluate role of immunity in overcoming/preventing
infection and disease and possible vaccine development
• Study epidemiology of microbe associated with exposure
scenarios
QRA for Agents from Environmental Sources: Steps in
the Conceptual Framework
Exposure Assessment
Purpose: determine the quantity or dose
Dose = number, quantity or amount of
microorganisms corresponding to a single
exposure (e.g., by ingestion)
•Average or typical dose
– A measure of central tendency (mean or
median)
•Distribution of doses
– microbe quantity varies in time and space
– described as a probability or frequency
distribution
CHARACTERIZATION OF EXPOSURE - ELEMENTS INCLUDED
IN PATHOGEN CHARACTERIZATION: OCCURRENCE
• Temporal distribution, duration and frequency
• Concentration in food or environmental media
• Spatial distribution
– clumping, aggregation, association with particles,
clustering
• Niche
– ecology and non-human reservoirs: Where are they in
the environment and what other host harbors them?
– potential to multiply/survive in specific media
CHARACTERIZATION OF EXPOSURE - ELEMENTS INCLUDED
IN PATHOGEN CHARACTERIZATION: OCCURRENCE
• Survival, persistence, and amplification
• Seasonality
• Meteorological and climatic events
• Presence of control or treatment processes
– reliability and variability of processes
• Indicators/surrogates for indirect evaluation
– predictive of pathogen
ELEMENTS CONSIDERED IN PATHOGEN
CHARACTERIZATION
• Virulence and pathogenicity of the microorganism
• Pathologic characteristics and diseases caused
• Survival and multiplication of the microorganism
• Resistance to control or treatment processes
• Host specificity
• Infection mechanism and route; portal of entry
• Potential for secondary spread
• Taxonomy and strain variation
• Ecology and natural history
Pathogen Characteristics or Properties
Favoring Environmental Transmission
KEY: Multiple sources and high endemicity (continued
presence) in humans, animals and environment
• High concentrations released into or present in
environmental media (water, food, air, etc.)
• High carriage rate in human and animal hosts
• Asymptomatic carriage in non-human hosts
• Ability to proliferate in water and other media
• Ability to adapt to and persist in different media or hosts
• Seasonality and climatic effects
• Natural and anthropogenic sources
Pathogen Characteristics or Properties
Favoring Environmental Transmission
• Ability to persist or proliferate in environment
• Ability to survive or penetrate treatment processes
• Stable environmental forms
– spores, cysts, oocysts, stable outer viral layer (protein coat), bacterial
capsule (outer polysaccharide layer), etc.
• Resistance to biodegradation, heat, cold (freezing), drying,
dessication, UV light, ionizing radiation, pH extremes, etc.
• Resists proteases, amylases, lipases and nucleases
– Possesses DNA repair mechanisms and other injury repair processes
• Colonization, biofilm formation, resting stages, protective stages,
parasitism
– Spatial distribution
– Aggregation, particle association, intercellular accumulation, etc.
Virulence Properties of Pathogenic Bacteria Favoring
Environmental Transmission
Virulence properties: structures or chemical constituents
that contribute to pathophysiology
• Outer cell membrane of Gram negative bacteria: an
endotoxin (fever producer)
• Exotoxins: release toxic chemicals
• Pili: for attachment and effacement to cells and tissues
• Invasins: to facilitate cell invasion
• Effacement factors
• Spores
• highly resistant to physical and chemical agents
• very persistent in the environment
• plasmids, lysogenic bacteriophages, etc.
Pathogen Characteristics or Properties
Favoring Environmental Transmission
Genetic properties favoring survival and pathogenicity
• Double-stranded DNA or RNA
• DNA repair
• Ability for genetic exchange, mutation and selection
–recombination
–plasmid exchange, transposition, conjugation, etc.
–point mutation
–reassortment
–gene expression control
• Virulence properties: expression, acquisition, exchange
• Antibiotic resistance
Role Emergence and Selection of New Microbial
Strains on Exposure Risks
• Antigenic changes in microbes can create changes that
overcome immunity, increasing risks of re-infection or
illness
– Antigenically different strains of microbes appear in hosts or
are created in the environment; are selected for over time and
space
– Constant selection of new strains by antigenic shift and drift
– Genetic recombination, reassortment , bacterial conjugation,
bacteriophage infection or bacteria and point mutations
• Antigenic Shift in viruses:
– Major change in virus genetic composition by gene substitution
or replacement (e.g., reassortment); Influena A viruses (e.g.,
H?N?)
Role Emergence and Selection of New Microbial
Strains on Exposure Risks
• Antigenic Drift:
– Minor changes in genetic composition, often by
mutation involving specific codons in existing genes
(point mutations)
– A single point mutation can greatly alter microbial
virulence
• Microbial mimicking of host antigens; e.g.
malaria
– Antigens expressed by pathogen resemble host
antigens; they can change
Other Pathogen Characteristics or Properties
Favoring Environmental Transmission
• Ability to Cause Infection and Illness
– Low infectious dose
– High probability of infection and illness from exposure
to one or a few microbes
• Infects by multiple routes
– Ingestion: gastrointestinal (GI)
– Inhalation: respiratory
– Cutaneous: skin
– eye
– Other routes
Microbe Levels in Environmental Media Vary Over Time
Occurrence of Giardia Cysts in a Water: Cumulative Frequency
Distribution
CHARACTERIZATION OF EXPOSURE:
ELEMENTS CONSIDERED IN EXPOSURE ANALYSIS
• Identification of water, food or other media/vehicles of exposure
• Units of exposure (e.g number of cells)
• Routes of exposure and transmission potential
• Size of exposed population
• Demographics of exposed population
• Spatial and temporal nature of exposure
(single or multiple; intervals)
• Behavior of exposed population
• Treatment (e.g. of water), processing (e.g., of foods), and
recontamination
QRA for Agents from Environmental Sources: Steps in
the Conceptual Framework
CHARACTERIZATION OF HUMAN HEALTH EFFECTS:
ELEMENTS OF HOST CHARACTERIZATION
• Age
• Immune status
• Concurrent illness or infirmity
• Genetic background or status
• Pregnancy
• Nutritional status
• Demographics of the exposed population (density,
movement or migration, etc.)
• Social and behavioral traits and conditions
CHARACTERIZATION OF HUMAN HEALTH EFFECTS:
ELEMENTS OF HOST CHARACTERIZATION
•
•
•
•
•
•
Infectivity
Illness
Duration of illness
Severity of illness
Morbidity, mortality, sequelae of illness
Extent or amount of secondary spread
– Initial host from an environmental exposure
spreads infection and illness to others
• Quality of life
• Chronicity or recurrence
Characteristics or Properties of
Pathogens -Interactions with Hosts
• Disease characteristics and spectrum
– Signs, symptoms, pathophysiology
• Persistence in hosts:
– Chronicity
– Persistence
– Recrudescence
– Sequelae and other post-infection health effects
– cancer, heart disease, arthritis, neurological effects
– Yes, some microbes can cause these conditions!
• Secondary spread
Elements That May be Included in DoseResponse Analysis
• Statistical model(s) to analyze or quantify doseresponse relationships
– probability of infection/illness as a function of microbe
dose
• Human dose-response data
• Animal dose-response data
• Utilization of outbreak or intervention data
• Route of exposure or administration
Elements That May be Included in DoseResponse Analysis
• Source and preparation of exposure material or
inoculum
• Organism type and strain
– including virulence factors or other measures of
pathogenicity
• Characteristics of the exposed population
– age, immune status, etc.
• Duration and multiplicity of exposure
Dose-Response Data and Probability of
Infection for Human Rotavirus
Dose # Dosed # Infected
90,000
3
3
9,000
7
5
900
8
7
90
7
6
9
7
1
0.9
7
0
0.09
5
0
Dose-Response Models and
Extrapolation to Low Dose Range
• Most dose-response data for microbes are for:
– high doses of the microbes
– few hosts
• Practicalities and cost limits
• Dosing hundreds or thousands of volunteers
is not possible
• But, many people become ill during
epidemics
– if we can be there, we can study them as “natural”
experiments
Dose-Response Models and
Extrapolation to Low Dose Range
• Real world exposures to microbes from water,
food and air are often much lower microbial
doses than used in human volunteer studies
• It becomes necessary to extrapolate the doseresponse relationship of human volunteer
studies
– Extrapolation to the low dose range
– This is the range where there are no experimental
data points having discrete values above zero from
the low exposure doses
• a best-fit modelling approach is employed
Models Typically Applied in Microbial
Dose-Response Analyses
Exponential model
Pinfection = 1 - e-rx
•r = probability of infection
•x = mean concentration/dose
•Assumes
– organisms are distributed randomly (Poisson)
– approaches a linear model at low doses
Models Typically Applied in Microbial
Dose-Response Analyses
• Exponential (linear) model; two populations:
– one-hit kinetics, but
– two classes of human susceptibility to microbe
– or perhaps two form of microbes with different
infectivity or illness risks
• Beta-Poisson: a distributed threshold model
– assumes Poisson distribution of microbes and a Betadistributed probability of infection
– r is not a constant but a probability distribution (Betadistribution)
– two variables in the model
Probabilities of Exposure and Infection
• Pexp (j Dose) = Probability of having j
pathogenic microbes in an ingested
dose
• Pinf (j Inf) = Conditional probability of
infection from j pathogens ingested
Probability of Exposure
Exponential Dose-Response Model
Beta-Poisson Dose-Response Model
Rotavirus Dose-Response Relationships:
Experimental Data, Exponential Model
and Beta-Poisson Model
Daily and Annual Risks of Various Outcomes from
Exposure to Water Containing Rotaviruses
4 Rotaviruses per 1000 Liters
Volunteer Dose-Response Data
for Norwalk Virus*
Dose (mL)
4
1
0.01
0.0001
# dosed # ill
16
11
21
14
4
2
4
0
% ill
69
67
50
0
*"1st passage NV": Dolin et al. 1972; Wyatt et al., 1974.
Norwalk Virus Dose-Response
Analysis Using Alternative Models
1
0.9
Measured
P(D) Fraction withEffect
0.8
Linear (exp)
0.7
Lin(2pop)
0.6
b-Poisson
0.5
0.4
0.3
0.2
0.1
0
0
0.0001
0.01
Dose (ml)
1
4
Dose-Response Relationships for Various
Waterborne Pathogens:
Downward Extrapolation to Low-Dose Range
Comparing Risks of Disease Agents
• Comparing chemical to microbial risks as well as
among agents of each type
• Effects vary widely in severity, mortality rates and
time scale of exposure
• Need to protect both quality and quantity of life
• WSH policy needs to be linked to overall public
health policy
• Decision making process needs to take social and
economic factors into account
Desirable attributes of an integrated
measure of risk
• Address probability, nature and
magnitude of adverse health
consequences
• Incorporate age and health status of those
affected
DALYs as unit measures for health
• Conceptually simple:
– health loss = N x D x S
– N = number of affected persons
– D = duration of adverse health effect
– S = measure for severity of the effect
• Disability Adjusted Life Years (DALYs)
– mortality: years of life lost (YLL)
– morbidity: years lived with disability (YLD)
– DALY = YLL + YLD
Hypothetical example
Disability weight
1
0.8
0.6
Acute
(infectious)
disease
0.4
Prematur
e death
0.2
0
0
20
40
60
Age Residua
l
disabilit
y
80
Key Question: How do we define health?
• ‘a state of complete physical, mental and
social well-being, and not merely the absence
of disease or infirmity’ (WHO charter, 1946)
• ‘the ability to cope with the demands of daily
life’ (the Dunning Committee on Medical
Cure and Care, 1991)
• the absence of disease and other physical or
psychological complaints (NSCGP, 1999)
Deriving severity weights
Global Burden of Disease Project
• Define 22 indicator conditions
• Use Person Trade Off method to elicit severity
weights
• Panel of physicians and public health scientists
• Use scale of indicator conditions to attribute
severity weights to other conditions
• Methodology also applied in other studies
Using Epidemiology for Microbial Risk
Analysis
Problem Formulation
• what’s the problem?
• determine what infectious disease is posing a risk
• its clinical features
• causative agent
• routes of exposure/infection
• health effects
Using Epidemiology for Microbial Risk
Analysis
Exposure Assessment
• how
• how much
• when
• where and why exposure occurs
• vehicles
• vectors
• doses
• loads
Using Epidemiology for Microbial Risk
Analysis
Health Effects Assessment
• Human clinical trials for dose-response
• field studies of endemic and epidemic disease in
populations
Using Epidemiology for Microbial Risk
Analysis
Risk characterization
• Epidemiologic measurements and analyses of risk:
–
–
–
–
–
relative risk
risk ratios
odds ratios
regression models of disease risk
dynamic models of population disease risk
• Other disease burden characterizations:
– relative contribution to overall disease burdens
– effects of prevention and control measures and
interventions
– economic considerations (monetary cost of the disease,
cost effectiveness of prevention and control measures)
Elements That May Be Considered in Risk
Characterization
• Evaluate health consequences of exposure scenario
– Risk description (event)
– Risk estimation (magnitude, probability)
• Characterize uncertainty/variability/confidence in estimates
• Conduct sensitivity analysis
– evaluate most important variables and information needs
• Address items in problem formulation (reality check)
• Evaluate various control measures and their effects on risk
magnitude and profile
• Conduct decision analysis
– evaluate alternative risk management strategies
Types of Epidemiological Studies that Have Been
Used in Risk Assessment for Waterborne Disease
Some More Epidemiological Terms and
Concepts
• Outbreaks: two or more cases of disease
associated with a specific agent, source, exposure
and time period
• Epidemic Curve (Epi-curve): Number of cases or
other measure of the amount of illness in a
population over time during an epidemic
– Describes nature and time course of outbreak
– Can estimate incubation time if exposure time is known
– Can give clues to modes of transmission: point source,
common source, and secondary transmission
Some More Epidemiological Terms and
Concepts: Epidemic Curves
Time
Point Source
Time
Common Source
Databases for Quantification and Statistical
Assessment of Disease - USA
•
•
•
•
National Notifiable Disease Surveillance System
National Ambulatory Medical Care Survey
International Classification of Disease (ICD) Codes
Other Databases
– Special surveys
– Sentinel surveillance efforts
• Resources for disease surveillance vary greatly by country.
– WHO and other international health entities assist countries
lacking capacity for disease surveillance to obtain such data in
various ways
– Tracking is poor for some diseases, such as gastroenteritis and its
specific causative agents (etiologies)
Additional Analyses of Health Effects:
Health Effects Assessments
• Health Outcomes of Microbial Infection
• Identification and diagnosis of disease caused
by the microbe
– disease (symptom complex and signs)
– Acute and chronic disease outcomes
– mortality
– diagnostic tests
• Sensitive populations and effects on them
• Disease Databases and Epidemiological Data
Methods to Diagnose Infectious Disease
• Symptoms (subjective: headache, pain) and Signs
(objective: fever, rash, diarrhea)
• Clinical diagnosis: lab tests
– Detect causative organism in clinical specimens
– Detect other specific factors associated with
infection
• Immune response
– Detect and assay antibodies
– Detect and assay other specific immune responses
Health Outcomes of Microbial Infection
• Acute Outcomes
– Diarrhea, vomiting, rash, fever, etc.
• Chronic Outcomes
– Paralysis, hemorrhagic uremia, reactive
arthritis, etc.
• Hospitalizations
• Deaths
Morbidity Ratios for Salmonella (Non-typhi)
Study
1
2
3
4
5
6
7
8
9
10
11
12
Avg.
Population/Situation
Children/food handlers
Restaurant outbreak
College residence outbreak
Nursing home employees
Hospital dietary personnel
"
Nosocomial outbreak
Summer camp outbreak
Nursing home outbreak
Nosocomial outbreak
Foodborne outbreak
Foodborne outbreak
Morb. (%)
50
55
69
7
8
6
27
80
23
43
54
66
41
Acute and Chronic Outcomes Associated
with Microbial Infections
Microbe
Campylobact
E. er
coli
Helicobacter
O157:H7
Sal., Shig.,
Coxsackie
Yer. B3
Giardia
Toxoplasma
Acute
Diarrhea
Outcomes
Diarrhea
Gastritis
Diarrhea
Encephalitis,
Diarrhea
etc.
Newborn
Syndrome
Chronic Outcomes
Guillain-Barre
Hemolytic
Uremic
Syndrome
Ulcers Syn.
& Stomach
Reactive
arthritis
Cancer
Myocarditis &
Failurediabetes
to thrive; joint
Mental pain
retardation,
dementia, seizures
Outcomes of Infection Process
to be Quantified
Exposure
Advanced
Illness,
Chronic
Infections
and
Sequelae
Infection
Disease
Asymptomatic
Infection
Acute Symptomatic Illness:
Severity and Debilitation
Sensitive Populations
Mortality
Hospitalization
Health Effects Outcomes: E. coli O157:H7
Health Effects Outcomes: Campylobacter
Sensitive Populations
• Infants and young children
• Elderly
• Immunocompromised
– Persons with AIDs
– Cancer patients
– Transplant patients
• Pregnant
• Malnourished
Mortality Ratios for Enteric Pathogens in
Nursing Homes Versus General Population
Microbe
Mortality Ratio (%) in:
General Pop.
Nursing Home Pop.
Campylobacter
jejuni
E. coli O157:H7
0.1
1.1
0.2
11.8
Salmonella
0.01
3.8
Rotavirus
0.01
1.0
Snow Mtn. Agent
0.01
1.3
Impact of Waterborne Outbreaks of
Cryptosporidiosis on AIDS Patients
Outbreak
Oxford/
Swindon,
UK, 1989
Attack Rate
36
Mortal.
Ratio (%)
Comments
Not
reported
3 of 28 renal transplants pts.
Shedding oocysts
asymptomatically
Milwaukee, 45
WI, 1993
68
17% biliary disease; CD4
counts <50 associated with high
risks
Las Vegas,
NV, 1994
52.6
CD4 counts <100 at high risk;
bottled water case-controls
protective
Not known;
increase in
Crypto-+
stools
Mortality Ratios Among Specific
Immunocompromised Patient Groups with
Adenovirus Infection
Patient Group
% Mortality
(Case-Fatality
Ratio)
Overall Mean Age
of Patient Group
(Yrs.)
Bone marrow
transplants
Liver transplant
recipients
Renal
transplant
recipients
Cancer patients
60
15.6
53
2.0
18
35.6
53
25
AIDS patients
45
31.1
Databases for Quantification and
Statistical Assessment of Disease
• National Notifiable Disease
Surveillance System
• National Ambulatory Medical Care
Survey
• International Classification of Disease
(ICD) Codes
• Other Databases
– Special surveys
– Sentinel surveillance efforts
Waterborne Outbreak Attack Rates- USA
Waterborne Outbreak Hospitalizations USA
Predicted Waterborne Cryptosporidiosis in NYC in
AIDS Patients Compared to the General Population
Total NYC population
Reported cases
(1995)
Predicted tapwater-related
reported cases (% of total
actually reported)
Predicted annual risk from
tapwater unreported (% of
those predicted to be reported)
Adults
Children
Pediatric AIDS
1,360,000
30
Adults
with
AIDS
30,000
390
6,080,000
40
2 (5%)
3 (10%)
33 (8.5%)
1(10%)
5,400
(0.03%)
940
(0.3%)
56 (59%)
1 (100%)
Perz et al., 1998, Am. J. Epid., 147(3):289-301
1,200
10
Waterborne Adenovirus: A Risk
Assessment
• Adapted from Crabtree et al. (Wat. Sci.
Tech., Vol 35, No. 11-12, 1997)
• Steps of the risk assessment framework
using human adenoviruses in water
Step 1: Hazard Identification
Infection and clinical disease
• About 1/3 of known adenoviruses cause human
illness
• A wide range of illnesses, involving several different
organ systems
–
–
–
–
–
–
pharyngitis
pneumonia
conjunctivitis
gastroenteritis and intussusception
hemorrhagic cystitis
meningoencephalitis
• Diagnosed by culture and immunologic techniques
Step 1: Hazard Identification
The transmission routes
• Fecal oral route
– Makes waterborne transmission possible
– Contamination of water supplies with fecal waste, spread when
others come in contact with water
• Inhalation of aerosols (sneezing, coughing)
• Proximity of individuals encourages transmission
– groups of military recruits, hospitals, day care centers, schools
• Virus is shed for extended periods in feces and
respiratory secretions
– Encourages transmission: large window of opportunity for
spread to others
Step 2: Exposure Assessment
Occurrence in the environment and in human populations
• Infected people shed virus for long periods
• Adenoviruses have been found in water
– Appear to be stable (survives and remains infectious) in seawater
and tap water
– Spread via swimming pools (outbreak study)
– Exposure via recreational waters is possible
• Occur worldwide in sewage
– Appears to be stable in sewage
– Fecal oral transmission if drinking or recreational water becomes
contaminated by sewage
• Adenoviruses seem to be particularly resistant to
disinfection by ultraviolet light
– May be difficult to remove by water treatment processes
Step 3: Health Effects Assessment
Characteristics of illness
• Illnesses of varying severity
– from eye infection to brain infections and pneumonia
• Can have secondary spread, especially in crowded
environments
• Secondary spread also seen in waterborne outbreaks
• Many serotypes and many illnesses, making prevention
difficult
• Vaccination currently not available
Step 3: Health Effects Assessment
Susceptible populations
• The elderly
– Outbreaks in nursing homes: people in close proximity
encourages spread
– Elderly may consume more water than other populations,
increasing their risk of exposure to waterborne adenovirus
• Children
– Spread in schools and day care environments
– Children may be frequent users of recreational waters,
increasing their risk of exposure
– Hygiene habits of small children in schools and daycares may
encourage spread
Step 3: Health Effects Assessment:
Dose Response Assessment
• Relationship between dose of virus received and
probability of illness
• Use data from a human volunteer study
• Inhalation of aerosols of adenovirus 4
• Probability of illness calculated using the exponential
model
Pi = 1-exp(-rN)
• Pi = probability of illness
• N = number of viruses ingested or inhaled
• R = parameter calculated from experimental doseresponse data
Step 3: Health Effects Assessment:
Dose Response Assessment
Calculating r from dose-response data
Dose of virus
(infectious units)
11
No. of volunteers
exposed
3
No. of volunteers
infected
3
5
1
3
3
3
1
• When N=11, Pi = 1
• When N=5, Pi = 1
• When N=1, Pi = (3/1), or Pi = 0.333
• We can use this value to calculate the value of r
Step 3: Health Effects Assessment:
Dose Response Assessment
• Calculating R from dose-response data
• When N=1, Pi = (3/1), or Pi = 0.333
Pi = 1-exp(-rN)
ln(0.667) = ln(exp(-r))
0.333 = 1-exp(-r(1))
-0.4049 = -r
0.333 = 1-exp(-r)
r = 0.4049
-0.667 = -exp(-r)
0.667 = exp(-r)
Step 3: Health Effects Assessment:
Dose Response Assessment
• We have solved for a value of r specific to
the dose-response relationship of this
organism
• Using this value of r, we can determine the
probability of infection, Pi, from any dose
N
• Question: What is the dose N??
Step 3: Health Effects Assessment:
Dose Response Assessment
• For waterborne adenovirus, we must evaluate the
exposure (remember exposure assessment)
• Two main routes of exposure
– Drinking water
– Recreational water
• In order to evaluate exposure, we need to know:
How much water do people drink?
How much recreational water are they exposed to?
How many viruses are in these waters?
Step 3: Health Effects Assessment:
Dose Response Assessment
• We can gather these data from various
sources
– Studies of adenovirus occurrence in
environmental and drinking water
– Studies of people’s water consumption habits
(it’s been done!)
• These data can then be used to calculate
doses to feed into the model
Step 3: Health Effects Assessment:
Dose Response Assessment
• On average, people drink about 2L of water per
day
• Data on the occurrence of adenoviruses in
drinking water is limited
– There is data on the occurrence of other enteric viruses
in drinking water
– We can use this data as a surrogate measure of
adenovirus occurrence in water
• Estimate: enteric viruses can occur in drinking
water at levels of 1 per 100L to 1 per 1000L
Step 3: Health Effects Assessment:
Dose Response Assessment
• On average, people will be exposed to about 30
mL of recreational freshwater per day (try not to
swallow the swimming pool water!)
• There is some data on the occurrence of
adenoviruses in environmental waters
– From a monitoring study of a by
– We can use this data as a surrogate measure of
adenovirus occurrence in recreational water
• Estimate: adenovirus can be present in
recreational water at levels of 0.118 per 100L to
12.8 per 100L
Step 3: Health Effects Assessment:
Dose Response Assessment
• Risks from exposure to drinking water
• Assume: 2L per day
• 1 virus in every 1000L of drinking water
• Dose: 0.002 viruses/day
• r = 0.4049
Pi = 1-exp(-0.4049*0.002)
Pi = 8.09×10-4
Step 3: Health Effects Assessment:
Dose Response Assessment
• Therefore, the daily probability of infection from
exposure to drinking water with 1 virus per 1000L
= 8.09×10-4
• This is the probability of infection, not illness
• To determine the probability of illness, we need a
value for the fraction of infected individuals who
will actually become clinically ill
• This will be unique for each organism
Step 3: Health Effects Assessment:
Dose Response Assessment
• The morbidity rate (fraction of infected
individuals who actually become clinically ill) for
adenovirus is 0.5
• We can multiply the probability of infection from
the model by this value to determine the
probability of illness
• We can also calculate the probability of death if we
know the fraction of ill people who will die from
their illness (for adenovirus, value=0.01%)
Example: Daily risks from
adenovirus in drinking water
•
•
•
•
Assume: 2L per day
1 virus in every 1000L of drinking water
Probability of illness in the infected = 0.5
Probability of death in the ill = 0.01%
Risk of infection
8.09×10-4
Risk of illness
4.05×10-4
Risk of death
4.05×10-8
Example: Yearly risks from
adenovirus in drinking water
• Knowing the daily risks, we can also calculate
these risks over a period of a year using the
equation:
Pyear = 1- (1- Pi)365
Risk of infection
Risk of illness
2.56×10-1
1.37×10-1
Risk of death
1.48×10-5
Step 3: Health Effects Assessment:
Dose Response Assessment
• We now know the daily and yearly risks
from waterborne adenovirus in drinking
water at a level of 1 virus per 1000L
• The same model can be used to assess risk
for other levels of adenovirus in water
• The model can also be applied in exactly
the same way to recreational water risks
Step 4: Risk Characterization
• Using the dose-response relationship and
exponential model, we now have information
about the risks from waterborne adenovirus
– Risk from drinking water and recreational water
– Risk from different amounts of virus in these water
sources
– Daily risks and yearly risks
– Risk of infection, illness, and death
• What can we do with this information?
Step 4: Risk Characterization
• Compare the values to predetermined
benchmarks of acceptable risk
• Example: EPA recommends that the risk of
infection from drinking water should not exceed
1 in 10,000 per year
• Risk levels from our models exceed this risk
• Suggests that waterborne adenovirus in water
poses an unacceptable risk to consumers
Step 4: Risk Characterization
What can be done about this?
• First: determine the dose of adenovirus that does
not exceed the 1:10,000/year benchmark
– This can be done using the model (Pi = 10-4)
• How do we ensure that people’s exposure does
not exceed this dose?
– Evaluating water treatment efficacy
– Does water treatment reduce adenovirus levels below
the level of acceptable risk?
– How do we improve treatment to achieve acceptable
levels of risk?
– Changes in water treatment practices