PT - JOURNAL ARTICLE
AU - Allen, Bradford D
AU - Perla, Rocco J
TI - A Long-term Forecast of MRSA Daily Burden Using Logistic Modeling
AID - 10.29074/ascls.22.1.26
DP - 2009 Jan 01
TA - American Society for Clinical Laboratory Science
PG - 26--29
VI - 22
IP - 1
4099 - http://hwmaint.clsjournal.ascls.org/content/22/1/26.short
4100 - http://hwmaint.clsjournal.ascls.org/content/22/1/26.full
SO - Clin Lab Sci2009 Jan 01; 22
AB - OBJECTIVE: This article presents a logistic model that describes the mean number of unique methicillin-resistant Staphylococcus aureus (MRSA) isolates collected daily at a 150-bed community hospital in central Massachusetts. The model is used to derive a long-term forecast of the mean MRSA isolate frequency.METHODS: The mean number of MRSA isolates collected daily was found for each quarter from the first quarter of 1996 to the first quarter of 2008. A logistic model was fit to the data and then extrapolated to obtain a long-term forecast.SETTING: Data was collected at a one-hundred-fifty bed community hospital in central Massachusetts.RESULTS: The coefficient of determination indicates that 87% of the variation in transformed data is explained by the model. The extrapolated logistic model prediction is that the mean number of MRSA isolates collected daily approaches 1.42 MRSA isolates per day.CONCLUSION: Logistic modeling of empirical data using modest mathematical assumptions is an effective way to understand, visualize, and forecast MRSA daily frequencies over time. The advantage for laboratorians and epidemiologists is that logistic models provide reliable trending and long-term prediction ability of multi-drug resistant organism frequencies. Moreover, as additional data is obtained, the logistic model assumptions can be checked, the model updated, and forecasts improved.ABBREVIATIONS: MRSA = methicillin-resistant Staphylococcus aureus.