Background: The estimate of diarrhea burden attributed to a specific enteric pathogen—the population attributable fraction (PAF)—depends on the specific calculation method. Two conventional methods are commonly used to estimate the PAF for enteric infections: the “detection-as-etiology” (DE) method, which defines the PAF as the pathogen prevalence in diarrheal cases; and the “odds-ratio” (OR) method, which expresses the PAF as a function of the OR between pathogen detection and diarrhea. A third, less frequently used method uses the risk ratio (RR) to quantify the strength of infection. Methods: We compared each conventional PAF (DE, OR, or RR PAF) to a model-based (MB) PAF, derived from a transmission model of enteric infection, and defined bias as the crude difference from this “true” MB PAF. We fitted the transmission model to site-specific qPCR data for norovirus and rotavirus detection from MAL-ED (an eight-country birth cohort studying enteric infections) and used the equilibrium states to calculate the MB PAF. Results: For both pathogens, the OR and RR biases were small at all sites (ranging from -5% to +3%), whereas the DE method consistently overestimated the PAF and its bias was the largest of the conventional methods. Conclusions: Our mechanistic model provides an independent alternative to conventional methods, quantifying pathogens-specific enteric burden and the biases in those methods. Our model suggests the DE PAF estimations are consistently biased, and validates the OR and RR methods as feasible, low-bias measures for quantifying enteric burden.

: Increasing attention has focused on health outcomes of Campylobacter infections among children younger than 5 years in low-resource settings. Recent evidence suggests that colonization by Campylobacter species contributes to environmental enteric dysfunction, malnutrition, and growth faltering in young children. Campylobacter species are zoonotic, and factors from humans, animals, and the environment are involved in transmission. Few studies have assessed geospatial effects of environmental factors along with human and animal factors on Campylobacter infections. Here, we leveraged Campylobacter Genomics and Environmental Enteric Dysfunction project data to model multiple socioenvironmental factors on Campylobacter burden among infants in eastern Ethiopia. Stool samples from 106 infants were collected monthly from birth through the first year of life (December 2020–June 2022). Genus-specific TaqMan real-time polymerase chain reaction was performed to detect and quantify Campylobacter spp. and calculate cumulative Campylobacter burden for each child as the outcome variable. Thirteen regional environmental covariates describing topography, climate, vegetation, soil, and human population density were combined with household demographics, livelihoods/wealth, livestock ownership, and child–animal interactions as explanatory variables. We dichotomized continuous outcome and explanatory variables and built logistic regression models for the first and second halves of the infant’s first year of life. Infants being female, living in households with cattle, reported to have physical contact with animals, or reported to have mouthed soil or animal feces had increased odds of higher cumulative Campylobacter burden. Future interventions should focus on infant-specific transmission pathways and create adequate separation of domestic animals from humans to prevent potential fecal exposures.