BACKGROUND: An important concept in epidemiology is attributable risk, defined as the difference in risk between an exposed and an unexposed group. For example, in an intervention trial, the attributable risk is the difference in risk between a group that receives an intervention and another that does not. A fundamental assumption in estimating the attributable risk associated with the intervention is that disease outcomes are independent. When estimating population risks associated with treatment regimens designed to affect exposure to infectious pathogens, however, there may be bias due to the fact that infectious pathogens can be transmitted from host to host causing a potential statistical dependency in disease status among participants. METHODS: To estimate this bias, we used a mathematical model of community- and household-level disease transmission to explicitly incorporate the dependency among participants. We illustrate the method using a plausible model of infectious diarrheal disease. RESULTS: Analysis of the model suggests that this bias in attributable risk estimates is a function of transmission from person to person, either directly or indirectly via the environment. CONCLUSIONS: By incorporating these dependencies among individuals in a transmission model, we show how the bias of attributable risk estimates could be quantified to adjust effect estimates reported from intervention trials.
BACKGROUND: An important concept in epidemiology is attributable risk, defined as the difference in risk between an exposed and an unexposed group. For example, in an intervention trial, the attributable risk is the difference in risk between a group that receives an intervention and another that does not. A fundamental assumption in estimating the attributable risk associated with the intervention is that disease outcomes are independent. When estimating population risks associated with treatment regimens designed to affect exposure to infectious pathogens, however, there may be bias due to the fact that infectious pathogens can be transmitted from host to host causing a potential statistical dependency in disease status among participants. METHODS: To estimate this bias, we used a mathematical model of community- and household-level disease transmission to explicitly incorporate the dependency among participants. We illustrate the method using a plausible model of infectious diarrheal disease. RESULTS: Analysis of the model suggests that this bias in attributable risk estimates is a function of transmission from person to person, either directly or indirectly via the environment. CONCLUSIONS: By incorporating these dependencies among individuals in a transmission model, we show how the bias of attributable risk estimates could be quantified to adjust effect estimates reported from intervention trials.
Authors: Alicia N M Kraay; Andrew F Brouwer; Nan Lin; Philip A Collender; Justin V Remais; Joseph N S Eisenberg Journal: Proc Natl Acad Sci U S A Date: 2018-03-01 Impact factor: 11.205
Authors: Joseph N S Eisenberg; Alan Hubbard; Timothy J Wade; Matthew D Sylvester; Mark W LeChevallier; Deborah A Levy; John M Colford Journal: Environ Health Perspect Date: 2006-08 Impact factor: 9.031
Authors: Joseph N S Eisenberg; Manish A Desai; Karen Levy; Sarah J Bates; Song Liang; Kyra Naumoff; James C Scott Journal: Environ Health Perspect Date: 2007-08 Impact factor: 9.031
Authors: Travis C Porco; Karen A Holbrook; Susan E Fernyak; Diane L Portnoy; Randy Reiter; Tomás J Aragón Journal: BMC Public Health Date: 2004-08-06 Impact factor: 3.295