| Autor: |
Tremblay, Marlène, Crim, Stacy M., Cole, Dana J., Hoekstra, Robert M., Henao, Olga L., Döpfer, Dörte |
| Zdroj: |
Foodborne Pathogens & Disease; October 2017, Vol. 14 Issue: 10 p587-592, 6p |
| Abstrakt: |
AbstractThe Foodborne Diseases Active Surveillance Network (FoodNet) is currently using a negative binomial (NB) regression model to estimate temporal changes in the incidence of Campylobacterinfection. FoodNet active surveillance in 483 counties collected data on 40,212 Campylobactercases between years 2004 and 2011. We explored models that disaggregated these data to allow us to account for demographic, geographic, and seasonal factors when examining changes in incidence of Campylobacterinfection. We hypothesized that modeling structural zeros and including demographic variables would increase the fit of FoodNet's Campylobacterincidence regression models. Five different models were compared: NB without demographic covariates, NB with demographic covariates, hurdle NB with covariates in the count component only, hurdle NB with covariates in both zero and count components, and zero-inflated NB with covariates in the count component only. Of the models evaluated, the nonzero-augmented NB model with demographic variables provided the best fit. Results suggest that even though zero inflation was not present at this level, individualizing the level of aggregation and using different model structures and predictors per site might be required to correctly distinguish between structural and observational zeros and account for risk factors that vary geographically. |
| Databáze: |
Supplemental Index |
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