Download Microbial Risk Assessment, Part 2

Microbial Risk Assessment
Part 2: Dynamic Epidemiology
Models of Microbial Risk
Envr 133
Mark D. Sobsey
Spring, 2006
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 and health effects
• Exposure Assessment: How, how much, when, where and why
exposure occurs; vehicles, vectors, doses, loads, etc.
• Health Effects Assessment:
– Human clinical trials for dose-response
– field studies of endemic and epidemic disease in populations
• Risk characterization: Epidemiologic measurements and
analyses of risk: relative risk, risk ratios, odds ratios; regression
models of disease risk; dynamic model of disease risk
– other disease burden characterizations: relative contribution to
overall disease burdens; effects of prevention and control
measures; economic considerations (monetary cost of the disease
and cost effectiveness of prevention and control measures
Types of Epidemiological Studies that Have Been Used in
Risk Assessment for Waterborne Disease
Epidemiology Intervention Study
POPULATION
randomly select from population
CASE GROUP
(intervene to change level of exposure)
CONTROL GROUP
Types of Epidemiological Studies that Have Been Used in
Risk Assessment for Waterborne Disease
Epidemiology Cohort Study
POPULATION 1
POPULATION 2
(exposure 1)
(exposure 2)
randomly select from population
randomly select from population
COHORT 1
COHORT 2
Types of Epidemiological Studies that Have Been Used in
Risk Assessment for Waterborne Disease
Epidemiology Case-Control
Study
POPULATION 1
POPULATION 2
(exposure 1)
(NO exposure)
randomly select from population
randomly select from population
CASE GROUP
CONTROL GROUP
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
Point
Source
Time
Common Source
Time
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
DEFINED: “Dynamic Compartment
Epidemiology Model” of Microbial Risk
• DYNAMIC:
a force that stimulates change or progress
within a system
• COMPARTMENT:
a small space or subdivision for
storage
• EPIDEMIOLOGY:
the statistical study of the distribution
and determinants of disease in populations
• MODEL:
process
a hypothetical description of a complex entity or
Infectious Disease Transmission (SIR) Model:
Host States in Relation to Pathogen Transmission
Pathogen
Exposure
Susceptible

Infected

Resistant

 = the rate or probability of movement from one state to another
“Dynamic State” Epidemiological Model of
Microbial Risk - Modeling Infectious Disease
Dynamics and Transmission in Populations
• Members of population move between states
– States describe status with respect to a pathogen
• Movement from state-to-state is modeled with ordinary
differential equations;
– define rates of movement between states: rate terms
• Each transmission process is assumed to be independent
• Change in fraction of population in any state from one time
period to another can be described and quantified
• Different sources of pathogen exposure can be identified
and included in the model
“Dynamic State” Epidemiological Model of
Microbial Risk - State Variables
“SIR” Model of Infectious Disease
State Variables: track no. people in each state at a point in time
• S = susceptible = not infectious; not symptomatic
• I = Infected
– C = carrier = infectious; not symptomatic
– D = disease = infectious; symptomatic
• R = Resistant; same as P = post infection (or) not infectious;
not symptomatic; short-term or partial immunity
• In epidemiology these states are called SIR
Simple SIR Model
•
•
dynamic in that the numbers in each compartment fluctuate over time
also dynamic in the sense that individuals are born susceptible, then
may acquire the infection (move into the infectious compartment) and
finally recover (move into the recovered compartment)
– each member of the population typically progresses from susceptible to
infectious to recovered
•
•
•
diseases tend to occur in cycles of outbreaks due to the variation in
number of susceptibles (S(t)) over time
number of susceptibles falls rapidly as more of them are infected and
thus enter the infectious and recovered compartments
disease cannot break out again until the number of susceptibles has
built back up as a result of babies being born into the compartment
SEIR Model
Similar to the simple SIR model with the following
exception:
• For many infections, there is a period of time during
which the individual has been infected but is not yet
infectious himself. During this latent period the
individual is in compartment E (for exposed).
MSIR Model
Similar to the simple SIR model with the following
exception:
• For many infections, babies are not born into the
susceptible compartment but are immune to the
disease for the first few months of life due to
protection from maternal antibodies.
Simple SIR Model
Similar to the simple SIR model with the following exception:
• With certain infectious diseases, some people who have been
infected never completely recover and continue to carry the
infection, while not suffering the disease themselves. They may
then move back into the infectious compartment and suffer
symptoms (as in tuberculosis) or they may continue to infect
others in their carrier state, while not suffering symptoms. (Ex.
Typhoid Fever)
Simple SIR Model
• Similar to the simple SIR model with the following
exception:
• Some infections, such as influenza, do not confer
long lasting immunity. Such infections do not have a
recovered state and individuals become susceptible
again after infection.
Infectious Disease Transmission Model at
the Population Level: Dynamic Model
• Risk estimation depends on transmission dynamics
and exposure pathways. Example: Water
Model Development: Household-level Model
of Pathogen Transmission from Water
“Dynamic State” Epidemiological Model
of Microbial Transmission and Disease Risk
Susceptible
Carrier I
Diseased I
Post-infection
“Dynamic State” Epidemiological Model
of Microbial Transmission and Disease Risk
Susceptible
Carrier I
Diseased I
Post-infection
Additional Analyses of Health Effects:
Health Effects Assessments
(previous lecture)
• 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
(previous lecture)
• 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
(previous lecture)
• Acute Outcomes
– Diarrhea, vomiting, rash, fever, etc.
• Chronic Outcomes
– Paralysis, hemorrhagic uremia, reactive
arthritis, etc.
• Hospitalizations
• Deaths
Impacts of Household Water Quality on Gastrointestinal
Illness - Payment Study #1 (An Intervention Study)
Percent of Study Subjects Reporting HCGI Symptoms and Mean Number of
Episodes per Unit of Observation in Both Periods Combined
Group
Filtered Water (n=272)
Tap Water (n=262)
Unit of
% with
Mean Number % with
Mean Number
Observation Episodesa
of Episodesb Episodes
of Episodes
Family
62.0
3.82
67.7
4.81
Informant
20.0
1.70
23.1
2.10
Youngest
42.3
1.83
46.3
2.37
child
aDerived by logistic regression with covariables age, sex, geographic subregion.
bMean number of episodes among those subjects who reported at least one
episode.
Morbidity Ratios for Salmonella (Non-typhi)
(previous lecture)
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
(previous lecture)
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
(previous lecture)
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
(previous lecture)
• 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
(previous lecture)
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,
WI, 1993
45
Las Vegas, Not known;
NV, 1994
incr.
Crypto-+
stools
Not 3 of 28 renal transplants
repor- pts. Shedding oocysts
ted asymptomatically
68
17% biliary disease; CD4
counts <50 associated
with high risks
52.6
CD4 counts <100 at high
risk; bottled water casecontrols protective
Mortality Ratios Among Specific Immunocompromised
Patient Groups with Adenovirus Infection
(previous lecture)
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
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
Waterborne Outbreak Hospitalizations
Predictied Waterborne Cryptosporidiosis in NYC in AIDS Patients Compared to the General Population
Adults
Adults with
AIDS
6,080,000 1,360,000 30,000
40
30
390
Total NYC population
Reported cases
(1995)
Predicted tapwater-related reported 2 (5%)
cases (% of total actually reported)
Predicted annual risk from tapwater 5,400
unreported (% of those predicted to (0.03%)
be reported)
Children
Pediatric AIDS
1,200
10
3 (10%)
33 (8.5%)
1(10%)
940
(0.3%)
56 (59%)
1 (100%)
Perz et al., 1998, Am. J. Epid., 147(3):289-301
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