Dynamic transmission models are increasingly being used to improve clinicians' understanding of the epidemiology of healthcare-associated infections (HAI). However, there has been no recent comprehensive review of this emerging field. van Kleef et al. (2013) summarize how mathematical models have informed the field of HAI and how methods have developed over time.
MEDLINE, EMBASE, Scopus, CINAHL plus and Global Health databases were systematically searched for dynamic mathematical models of HCAI transmission and/or the dynamics of antimicrobial resistance in healthcare settings.
In total, 96 papers met the eligibility criteria. The main research themes considered were evaluation of infection control effectiveness (64 percent), variability in transmission routes (7 percent), the impact of movement patterns between healthcare institutes (5 percent), the development of antimicrobial resistance (3 percent), and strain competitiveness or co-colonization with different strains (3 percent). Methicillin-resistant Staphylococcus aureus was the most commonly modeled HAI (34 percent), followed by vancomycin resistant enterococci (16 percent). Other common HAIs, e.g. Clostridum difficile, were rarely investigated (3 percent). Very few models have been published on HAI from low or middle-income countries.
The first HAI model has looked at antimicrobial resistance in hospital settings using compartmental deterministic approaches. Stochastic models (which include the role of chance in the transmission process) are becoming increasingly common. Model calibration (inference of unknown parameters by fitting models to data) and sensitivity analysis are comparatively uncommon, occurring in 35 percent and 36 percent of studies respectively, but their application is increasing. Only 5 percent of models compared their predictions to external data.
The researchers conclude that transmission models have been used to understand complex systems and to predict the impact of control policies. Methods have generally improved, with an increased use of stochastic models, and more advanced methods for formal model fitting and sensitivity analyses. Insights gained from these models could be broadened to a wider range of pathogens and settings. Improvements in the availability of data and statistical methods could enhance the predictive ability of models. Their research was published in BMC Infectious Diseases.
Reference: van Kleef E, Robotham JV, Jit M, Deeny SR and Edmunds WJ. Modeling the transmission of healthcare-associated infections: a systematic review. BMC Infectious Diseases 2013, 13:294 doi:10.1186/1471-2334-13-294