Incorporating transmission into causal models of infectious diseases for improved understanding of the effect and impact of risk factors

Paynter, Stuart (2016) Incorporating transmission into causal models of infectious diseases for improved understanding of the effect and impact of risk factors. American Journal of Epidemiology, 183 6: 574-582. doi:10.1093/aje/kwv234


Author Paynter, Stuart
Title Incorporating transmission into causal models of infectious diseases for improved understanding of the effect and impact of risk factors
Journal name American Journal of Epidemiology   Check publisher's open access policy
ISSN 1476-6256
0002-9262
Publication date 2016-03-15
Year available 2016
Sub-type Article (original research)
DOI 10.1093/aje/kwv234
Open Access Status Not Open Access
Volume 183
Issue 6
Start page 574
End page 582
Total pages 9
Place of publication Cary, NC, United States
Publisher Oxford University Press
Collection year 2017
Language eng
Abstract Conventional measures of causality (which compare risks between exposed and unexposed individuals) do not factor in the population-scale dynamics of infectious disease transmission. We used mathematical models of 2 childhood infections (respiratory syncytial virus and rotavirus) to illustrate this problem. These models incorporated 3 causal pathways whereby malnutrition could act to increase the incidence of severe infection: increasing the proportion of infected children who develop severe infection, increasing the children's susceptibility to infection, and increasing infectiousness. For risk factors that increased the proportion of infected children who developed severe infection, the population attributable fraction (PAF) calculated conventionally was the same as the PAF calculated directly from the models. However, for risk factors that increased transmission (by either increasing susceptibility to infection or increasing infectiousness), the PAF calculated directly from the models was much larger than that predicted by the conventional PAF calculation. The models also showed that even when conventional studies find no association between a risk factor and an outcome, risk factors that increase transmission can still have a large impact on disease burden. For a complete picture of infectious disease causality, transmission effects must be incorporated into causal models.
Keyword Communicable diseases
Epidemiologic measurements
Epidemiologic methods
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Public Health Publications
 
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