Transcript Lecture 4 Introduction to Environmentally Transmitted Pathogens

Lecture 5
Pathogen and Host Properties and Microbial
Quantification
ENVR 133
Mark D. Sobsey
Spring, 2006
Part I:
Pathogen and Host Properties and Their
Interactions
Pathogen Characteristics or Properties
Favoring Environmental Transmission
• Multiple sources and high endemicity in humans,
animals and environment
• High concentrations released into or present in
environmental media (water, food, air)
• High carriage rate in human and animal hosts
• Asymptomatic carriage in non-human hosts
• Proliferate in water and other media
• Adapt to and persist in different media or hosts
– mutation and gene expression
• Seasonality and climatic effects
• Natural and anthropogenic sources
Pathogen Characteristics or Properties
Favoring Environmental Transmission
• Ability to Persist or Proliferate in Environment and Survive
or Penetrate Treatment Processes
• Stable environmental forms; mechanisms to survive/multiply
– spores, cysts, oocysts, stable outer viral layer (protein coat),
capsule, etc.
– Colonization, biofilm formation, resting stages, protective stages,
parasitism
– Spatial distribution
– Aggregation, particle association, etc.
• Resistance to environmental stressors and antagonists:
– Biodegradation, heat, cold (freezing), drying, dessication, UV light,
ionizing radiation, pH extremes, etc.
– Resist proteases, amylases, lipases and nucleases
• Posses DNA repair mechanisms and other injury repair
processes
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
Pathogen Characteristics or Properties
Favoring Environmental Transmission
Ability to cause colonization, infection
and illness
• Low infectious dose
• Infects by multiple routes
– ingestion (GI)
– inhalation (respiratory)
– cutaneous (skin)
– eye
– etc.
• Does not kill off its hosts
– “agreeable” host-parasite relationship
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:
endotoxin (fever producer)
– Exotoxins
– Pili: for attachment and effacement to cells and tissues
– Invasins: to facilitate cell invasion
– Effacement factors
– Cell binding epitopes and receptors
• Spores:
– High resistance to physical and chemical agents
– very persistent in the environment
• Others:
– plasmids, lysogenic bacteriophages, VBNC state, etc.
Role of Selection of New Microbial Strains in
Susceptibility to Infection and Illness
• Antigenic changes in microbes overcome immunity, increasing
risks of re-infection or illness
– Antigenically different strains of microbes appear and are selected
for over time and space
– Constant selection of new strains (by antigenic shift and drift)
– Partly driven by “herd” immunity and genetic recombination,
reassortment , bacterial conjugation, bacteriophage infection and
point mutations
• Antigenic Shift:
– Major change in virus genetic composition by gene substitution or
replacement (e.g., reassortment)
• Antigenic Drift:
– Minor changes in virus genetic composition, often by mutation
involving specific codons in existing genes (point mutations)
• A single point mutation can greatly alter microbial
virulence
Microbe Levels in Environmental Media Vary Over Time Occurrence of Giardia Cysts in Water: Cumulative Frequency Distribution
Other Factors Influencing Pathogen Occurrence
and Proliferation
• Identification of water, food or other
media/vehicles of exposure
• Units of exposure (individual cells, virions, etc.)
• 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, processing, and recontamination
Host Factors in Pathogen Transmission
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•
•
•
Age
Immune status
Concurrent illness or infirmity
Genetic background
Pregnancy
Nutritional status
Demographics of the exposed population
(density, etc.)
• Social and behavioral traits
Infection and Illness Factors in Pathogen
Occurrence and Transmission
•
•
•
•
•
•
Duration of illness
Severity of illness
Infectivity
Morbidity, mortality, sequelae of illness
Extent or amount of secondary spread
Quality of life
• Chronicity or recurrence
Characteristics or Properties of Pathogen
Interactions with Hosts
• Disease characteristics and spectrum
• Persistence in hosts:
– Chronicity
– Persistence
– Recrudescence
– Sequelae and other post-infection health
effects
• cancer, heart disease, arthritis,
neurological effects
• Secondary spread
Other Infectious Disease
Considerations
• Health Outcomes of Microbial Infection
• Identification and diagnosis of disease caused by
the microbe
–
–
–
–
Disease (symptom complex and signs; case definition)
Acute and chronic disease outcomes
Mortality
Diagnostic tests and other detection tools
• Sensitive populations and effects on them
• Surveillance (syndromic, hospital-based, etc.),
disease databases, and epidemiological data
Health Outcomes of Microbial Infection
• Acute Outcomes
– Diarrhea, vomiting, rash, fever, etc.
• Chronic Outcomes
– Paralysis, hemorrhagic uremia, reactive
arthritis, encephalitis, heart disease, etc.
• Hospitalizations
• Deaths
Methods to Diagnose Infectious Disease
• Symptoms (subjective: headache, pain) and
Signs (objective: fever, rash, diarrhea)
• Case definition and syndromic surveillance
• 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
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
Campylobacter
E. coli O157:H7
Helicobacter
Sal., Shig., Yer.
Coxsackie B3
Giardia
Toxoplasma
Acute Outcomes
Diarrhea
Diarrhea
Gastritis
Diarrhea
Encephalitis, etc.
Diarrhea
Newborn
Syndrome
Chronic Outcomes
Guillain-Barre Syndrome
Hemolytic Uremic Syn.
Ulcers & Stomach Cancer
Reactive arthritis
Myocarditis & diabetes
Failure to thrive; joint pain
Mental 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
Sensitive Populations – Increased
Infectious Disease Risks
• Infants and young children
• Elderly
• Immunocompromized
– 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
Attack Rate Mortal. Comments
Ratio
(%)
Oxford/
Swindon,
UK, 1989
36
Milwaukee, 45
WI, 1993
Not
reporTed
3 of 28 renal transplants
pts. Shedding oocysts
asymptomatically
68
17% biliary disease; CD4
counts <50 associated
with high risks
Las Vegas, Not known; 52.6
NV, 1994 incr.
Crypto-+
stools
CD4 counts <100 at high
risk; bottled water casecontrols protective
Mortality Ratios Among Specific Immunocompromized
Patient Groups with Adenovirus Infection
Patient Group
% Mortality
(Case-Fatality Ratio)
Overall Mean Age of
Patient Group (Yrs.)
Bone marow
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
Part II - Microbial Quantification
• Determining microbial concentrations and loads
in various specimens and samples
• Critical information for:
– measuring exposures from environmental media
– for quantify human health risks of exposures;
human infectivity and dose-response
– risk assessment and risk management
• Based on fundamental statistical principles of
measuring concentrations of discrete objects
• Requires consideration of sources of nonhomogeniety, variability and uncertainty
Quantifying Microbes
• Essential for environmental monitoring and
surveillance
• Quality standards and guidelines
• Performance standards;
– Prevention and control processes; reductions
• Risk analysis (assessment and management)
– Dose-response and setting acceptable risk levels
• Ecology and natural history
• Analysis of vehicles, vectors, reservoirs, etc.
Concentration
Distributions of Microorganisms in Hosts
and Environmental Media
Mean or Median
Range
No. Organisms
Cumulative Frequency Distribution of Number
of Organisms in Unit Volume of Sample
Probability Density Function
Estimating Microbial Concentrations from Quantal
Data: Relationship Between Dilution and
Percentage of Positive Sample Volumes
Dose-response relationships:
100
Often based on few data points
Often sigmoidal
%
Pos.
50
Difficult to estimate mid-point
Difficult to extrapolate to low
dose or dilution
0
Dose or Dilution (log scale)
Probits and Their Application to Estimating 50% Infectious
Dose
Assume population response is "normally distributed", i.e., a
Gaussian distribution
Useful for “straightening” plots and facilitate extrapolation.
Cumulative dose-responses as % are often sigmoidal and not
linear.
The most accurate data are near the mid-point, which is the
average or mean or 50% point.
How does one extrapolate to the extremes where there are no
experimental data?
• To avoid positive and negative values (and be consistent with
the expression of cumulative frequency distributions and doseresponse data) the mean value of a normal probability function
is assigned a probit value of 5
– consistent with the 50% response point in a cumulative
frequency distribution or a dose-response relationship
Relationship Between Dilution and Percentage
of Positive Sample Volumes
Log-Normal Distribution
9
% Responses expressed as probits:
Can linearize the dose-response
Facilitates extrapolation
5
Probits
1
Dose or Dilution (log scale)
Probits and Their Use for Log-Normally Distributed Data
%
Response
Normal
Equivalent
Deviate
(Std. Dev.)
+3
+2
+1
0
-1
-2
-3
Probit
• Express ordinate (Y-axis)
in multiples of the
standard deviation, i.e.,
"normal equivalent
99.9
8
deviates.”
97.7
7
• Hence, probits are unit
84.0
6
values assigned to
50
5
standard deviations
16
4
• 1 standard deviation =
2.3
3
1 probit unit
0.1
2
• 2 standard deviations =
* 1 SD (16-84%) incl. 67% of pop.
2 probits units
* 2 SD (2.3-97.7%) incl. 95% of pop.
• …etc.
* 3 SD (0.1-99.9%) incl. 99.7% of pop.
Dose-Response Relationship Based on % Response
and Probits as a Function of Dose on a Log Scale
Estimating Microbial Concentrations from Quantal
Data: the Poisson Distribution and the Most
Probable Number (MPN)
• Poisson distribution describes “low probability” or rare
events
– such as, the probability that an inoculated culture of
broth will or will not contain 1 or more bacteria that
will grow.
• If large numbers of replicate tubes are inoculated with
large numbers of closely-spaced dilutions of a sample
containing microbes, a sigmoidal dose-response curve
for % positive tubes per dilution is likely to be
generated.
Estimating Microbial Concentrations from Quantal Data:
Relationship Between Dilution, % of Positive Sample Volumes and
The MPN Unit of Concentration • With increasing sample dilution,
100
63
50
0
1 organism
Dose or Dilution
fewer and fewer culture tubes are
positive
• For the sample dilution that
contains on average 1 microbe per
volume inoculated into a culture
tube, what %age of culture tubes
would be positive?
• According to the Poisson
distribution, 63% would be positive
• So, the “unit” of the Poisson
distribution estimated by the Most
Probable Number is the inoculum
volume that contains on average 1
organism and gives 63% positive
cultures
Random (Poisson) Distributions of Organisms
• Organisms are randomly distributed
• P(x = N) = [(V)N/N!]e(- V)
Where:
N = number of organisms
u = mean density of organisms
“true concentration”
V = volume of sample
P = probability
Poisson Distribution Example
• If the “true” mean number of microbes in a
sample is 1 per ml, what is the probability that a
given 1 ml sample will contain 0, 1, 2,…..n
microbes per ml?
• P(x = N) = [(V)N/N!]e(- V)
• So,
• P(0) = [(1x1)0/0!]e-1x1 = (1/1)e(-1) = 1e-1 = 0.37
• P(1) = [(1x1)1/1!]e-1x1 = (1/1)e-1 = 1e(-1) = 0.37
• P(2) = [(1x1)2/2!]e-1x1 = (1/2)e-1 = 0.5e(-1) = 0.185
• For the same sample, what is the probability that
a given 1 ml sample will be positive (contain 1 or
more microbes per ml?)
• P(1) = 1 - P(0) = 1 - 0.37 = 0.63
– (see previous figure showing dose-response curve)
Random (Poisson) Distributions of Organisms
• The mean ( ) is equal to the variance (s2) :
 = s2
• As an approximation:
 = s2
• and as an approximation the standard error, s,
equals the square root of (/n):
s = (/n)1/2
where n = number of samples
• If n = 1, this becomes the standard deviation
S.D. = x1/2
as an approximation, the 95% confidence interval is
2(S.D.)
Standard Error (of the Mean) of Poisson Counts
According to Poisson distribution, the standard error of
the mean (SE) for n number of assays is given by:
(x/n)1/2
The 95% confidence limit of the mean count for n
number of assays in then approximated by:
95% CL = x 2 (x/n)1/2
If the number of replicate assays, n, is small, then the
95% confidence limits should be adjusted by
substituting the appropriate t-value for n-1 degress of
freedom in place of the number “2” in the equation
above
Standard Deviation of Poisson Counts
Standard deviation (s):
s = x 1/2
Example: if x = 64
(e.g., colony counts on a plate)
s = 64 1/2 = 8
95% confidence limit = 2s, therefore:
2s = 2(8) = 16
and the upper and lower 95% confidence limits
are 64 16 or 80 and 48, respectively
Application of Poisson Distribution to
Enumerative (Count) Data
• When total counts of microbes are small, <30, it is useful
to estimate central tendencies and dispersions based on
properties of the Poisson distribution
• For the simplest example, a single count or count total,
as an approximation, the standard deviation equals the
square root of the microbe count:
• count = 4
SD = 2
• count = 9
SD = 3
• count = 16
SD = 4
• count = 25
SD = 5
Estimating Microbial Concentration and Its
Variability (Dispersion) from Enumerative Data:
Data Analyses Based on Normal Distribution
• Compute mean, standard deviation, 95%
confidence intervals and other confidence
intervals using statistical methods based
on normal distribution.
• Mean:
For N counts of numbers of organisms per
unit volume (say, per ml) of x1, x2, x3….xn,
the mean concentration, , is:
 =  (xn)/N
Estimating Microbial Concentration and Its
Variability (Dispersion) from Enumerative Data
Normal Probability Curve:
• Counts range according to a symmetrical bell-shaped curve
• Height of the curve is a measure of frequency
• Middle of the curve is the mean,  or 
• Width of the curve is a measure of variability or dispersion
• Standard deviation,s or  , is a measure of variability or
dispersion
1s = 68% of area
under the curve
 2s = 95% of area
under the curve
Estimating Microbial Concentration and Its
Variability (Dispersion) from Enumerative Data
• The estimation of viarability or dispersion of
microbe counts based on the suumprion of a
normal distribution works best when the counts are
high, that is >20 or 30.
• A measure of variation among N observations of
concentration, x1, x2, x3….xN, can be based on the
deviations from the sample mean, , or the values
of xN - 
• The set of N deviations can be combined into a
single index called the variance, s2, the square root
of which, s, is the standard deviation.
Estimating Microbial Concentration and Its Variability (Dispersion)
from Enumerative Data Based on the Normal Distribution
• The sum of the squares of the individual
deviations from the sample mean is
designated  (xN - )2
and
• The sample variance is defined as:
s2 =  (xN - )2
N-1
• The sample standard deviation is:
s =  (xN - )2
N-1
Enumerative Measures of Microbial
Concentration
Examples:
• Bacteria: colony counts or colony
forming units (CFU)
– on agar medium plates
– on membrane filters on media
• Viruses: plaque forming units (PFU),
pock-forming units or focus-forming units
(FFU)
• Parasites: numbers of ova, cysts, oocysts
or spores