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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.

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Infectious Disease Epidemiology

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  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. Definitions: 1. Incidence 2. Prevalence 3. Attack rate 4. Primary/secondary cases 5. Case fatality rate or ratio 6. Virulence

  7. Definitions continued: 7. Mortality 8. Reproductive rate 9. Vector 10. Transmission routes 11. Reservoir vs source 12. Zoonosis

  8. Definitions continued: 13. Incubation period 14. Serial interval 15. Infectious period 16. Latent period 17. Epidemic

  9. 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.

  10. Outbreak Analysis Early analysis: Person: who is the case? Place: where was the case infected? Time: when was the case infected?

  11. 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.

  12. Outbreak Analysis continued Check the geography. Check the age and sex.

  13. Factors Affecting Surveillance Outbreak discovery Outbreak analysis Validity of notification data Notification delays Information feedback Sources of data

  14. Factors Affecting Infectivity Dose and route Immunity Co-factors Subclinical infection

  15. Seroepidemiology Used for: 1. Description of seroprevalence in populations 2. Follow incidence by estimation from changes using multiple samples from a population

  16. Seroepidemiology continued Importance of case and control classification: Use of a gold standard reference. Use of clinical diagnosis.

  17. Seroepidemiology continued Sensitivity Specificity Positive predictive value Negative predictive value Pre-test probability of disease

  18. 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.

  19. 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.

  20. 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).

  21. 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

  22. Study Designs Cross-sectional: risk or prevalence ratio Case control: odds ratio Cohort: relative risk Survival analysis

  23. Study Issues Confounding Bias Misclassification Interaction

  24. Epidemiology of vaccination Direct: immunity by infection or vaccination Indirect: herd immunity Vaccine efficacy (%) =Iu-Iv/Iu x 100

  25. 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.

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