Transcript Infectious Disease Epidemiology

Infectious Disease Epidemiology
Sharyn Orton, Ph.D.
American Red Cross, Rockville, MD
Suggested reading:
Modern Infectious Disease Epidemiology
(1994) by Johan Giesecke
Modern Epidemiology (1998) by Kenneth
Rothman and Sander Greenland
My interest in infectious disease
epidemiology stems from my 20+ years
as a Medical Technologist. An advanced
degree in Epidemiology and Biostatistics
has enabled me to better understand the
dynamics and power of infectious
disease epidemics, as well as the
important differences from diseases
caused by “non” infectious agents.
Learning/Performance Objectives
1. Understand the unique differences
between infectious and “non”infectious disease epidemiology.
2. Understand the terminology.
3. Be able to calculate sensitivity,
specificity, predictive values and
transmission probabilities.
Features unique to infectious diseases:
1. A case may also be a source.
2. People may be immune.
3. A case may be a source without being
recognized.
4. There is often a need for urgency.
5. Preventive measures often have good
scientific basis.
Outcomes of exposure
1. No infection
2. Clinical infection resulting in death,
immunity, carrier or non-immunity
3. Sub-clinical infection resulting in
immunity, carrier or non-immunity
4. Carrier
Definitions:
1. Incidence
2. Prevalence
3. Attack rate
4. Primary/secondary cases
5. Case fatality rate or ratio
6. Virulence
Definitions continued:
7. Mortality
8. Reproductive rate
9. Vector
10. Transmission routes
11. Reservoir vs source
12. Zoonosis
Definitions continued:
13. Incubation period
14. Serial interval
15. Infectious period
16. Latent period
17. Epidemic
Mathematical Models for Epidemics
Person to person spread relies on the
reproduction rate, which is the average
number of people infected by one case.
This is influenced by the attack rate of
disease, the frequency of contact, the
duration of infectivity and the immune
status of the population.
Outbreak Analysis
Early analysis:
Person: who is the case?
Place: where was the case infected?
Time: when was the case infected?
Outbreak Analysis continued
Epidemic Curve
1. Plot the date on the horizontal
axis.
2. Plot the number of cases on the
vertical axis.
3. Determine if the outbreak is point
source, continuous or person to
person.
Outbreak Analysis continued
Check the geography.
Check the age and sex.
Factors Affecting Surveillance
Outbreak discovery
Outbreak analysis
Validity of notification data
Notification delays
Information feedback
Sources of data
Factors Affecting Infectivity
Dose and route
Immunity
Co-factors
Subclinical infection
Seroepidemiology
Used for:
1. Description of seroprevalence in
populations
2. Follow incidence by estimation from
changes using multiple samples
from a population
Seroepidemiology continued
Importance of case and control
classification:
Use of a gold standard reference.
Use of clinical diagnosis.
Seroepidemiology continued
Sensitivity
Specificity
Positive predictive value
Negative predictive value
Pre-test probability of disease
Contact Patterns
Use graphs or matrices to describe the
network of contacts.
Study the networks by interviewing the
cases about their contacts.
Study the contact structure.
Transmission Probability Ratio
TPR is a measure of risk of transmission
from infected to susceptible individuals
during a contact.
For any given type of contact or agent, an
estimate of the effect of a covariate on
susceptibility, infectiousness or both can
be made.
TPR continued
TPR of differing types of contacts,
infectious agents, infection routes or
strains can be calculated.
There are 4 types of transmission
probabilities (tp).
Binomial Transmission Probabilities
Used when susceptibles make more than
one potentially infectious contact.
The maximum likelihood estimate of the
tp under the binomial model=
# of susceptibles who become infected 
total number of contacts with infectives
Study Designs
Cross-sectional: risk or prevalence ratio
Case control: odds ratio
Cohort: relative risk
Survival analysis
Study Issues
Confounding
Bias
Misclassification
Interaction
Epidemiology of vaccination
Direct: immunity by infection or
vaccination
Indirect: herd immunity
Vaccine efficacy (%) =Iu-Iv/Iu x 100
Conclusion
Infectious and “non”-infectious disease
epidemiology have important
differences due to the inherently
different nature of the risk factors
(biological agent i.e. virus, bacteria vs
chemical, environmental or genetic).
It is important to understand and
consider these differences when
conducting infectious disease research.