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

Abstract

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.

Keywords:

• Mathematical modeling
• stochastic modeling
• transition probability matrix
• zoonotic disease

Acknowledgments

The authors would like to thank the workers and community members who participated in the study from which these data were obtained. The authors also thank the community-based organization members who made essential contributions to this research and without whom, this study would not be possible.

Data availability

The data underlying this article will be shared on reasonable request consistent with protections for the privacy of study participants and existing multi-party agreements.

Additional information

Funding

Funding for this study was provided by National Institute for Occupational Safety and Health (NIOSH) grant K01OH010193; Johns Hopkins NIOSH Education and Research Center grant T42OH008428; a directed research award from the Johns Hopkins Center for a Livable Future; award 018HEA2013 from the Sherrilyn and Ken Fisher Center for Environmental Infectious Diseases Discovery Program at the Johns Hopkins University, School of Medicine, Department of Medicine, Division of Infectious Diseases; and National Science Foundation (NSF) grant 1316318 as part of the joint NSF–National Institutes of Health (NIH)–U.S. Department of Agriculture Ecology and Evolution of Infectious Diseases program. C.D.H. and G.R. were supported by NIOSH grant K01OH010193, E.W. “Al” Thrasher Award 10287, NIEHS grant R01ES026973, and NSF grant 1316318. M.F.D. was supported by the NIH Office of the Director (K01OD019918). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention.