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Participants with missing exposure, covariate, or outcome data were excluded from the analyses (Fig. 2).
In wave 3, 247 participants (1.7 %) had missing exposure or covariate data, while in wave 4, 251 participants had missing covariate or exposure data.
These reported outbreaks and their reported incidence of illnesses are likely to be an underestimation of actual numbers because of nonreported cases and missing exposure information.
To explore whether missing data might have resulted in selection bias we completed two sets of analyses: one including the 4797 participants (2456 women) with complete data on exposure and all covariables, and one including all eligible participants (6476; 3425 women) with missing exposure or covariable data imputed.
Missing exposure data.
Missing exposure data were imputated using 'last observation carried forward'.
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Because of missing exposure-time information for two subjects, 100 subjects were included in the CGA and GWA models.
In stratified analyses, strata-specific modeling of missing exposures was performed using values of Zs specific to each gender.
We also removed the contribution of intersections from the exposure model and imputed missing exposures according to census place (i.e., city, town, or village) (Beyea et al. 2013).
Although we attempted to capture BC exposure from all residential addresses, we may be missing exposures at work and/or other locations where the participants spend a portion of their time.
where l c is the log-likelihood with complete data, l c 1 is the log-likelihood with observed exposures, and l c 2 is the log-likelihood with the missing exposures.
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