Application of Markov models to predict changes in nasal carriage of Staphylococcus aureus among industrial hog operations workers.

Industrial hog operation (IHO) workers can be occupationally exposed to Staphylococcus aureus and may carry the bacteria in their nares. Workers may persistently carry S. aureus or transition between different states of nasal carriage over time: no nasal carriage, nasal carriage of a human-associated strain, and nasal carriage of a livestock-associated strain. We developed a mathematical model to predict the proportion of IHO workers in each nasal carriage state over time, accounting for IHO worker mask use. We also examined data sufficiency requirements to inform development of models that produce reliable predictions. We used nasal carriage data from a cohort of 101 IHO workers in North Carolina, sampled every 2 weeks for 4 months, to develop a three-state Markov model that describes the transition dynamics of IHO worker nasal carriage status over the study period and at steady state. We also stratified models by mask use to examine their impact on worker transition dynamics. If conditions remain the same, our models predicted that 49.1% of workers will have no nasal carriage of S. aureus , 28.2% will carry livestock-associated S. aureus , and 22.7% will carry human-associated S. aureus at steady state. In stratified models, at steady state, workers who reported only occasional mask (<80% of the time) use had a higher predicted proportion of individuals with livestock-associated S. aureus nasal carriage (39.2%) compared to workers who consistently (≥80% of the time) wore a mask (15.5%). We evaluated the amount of longitudinal data that is sufficient to create a Markov model that accurately predicts future nasal carriage states by creating multiple models that withheld portions of the collected data and compared the model predictions to observed data. Our data sufficiency analysis indicated that models created with a small subset of the dataset (approximately 1/3 of observed data) perform similarly to models created using all observed data points. Markov models may have utility in predicting worker health status over time, even when limited longitudinal data are available.