Studying the time trend of Methicillinresistant Staphylococcus aureus (MRSA) in Norway by use of non-stationary γ-Poisson distributions

Abstract

Objectives Study the time development of methicillin-resistant Staphylococcus aureus (MRSA) and forecast future behaviour. The major question: Is the number of MRSA isolates in Norway increasing and will it continue to increase?

Design Time trend analysis using non-stationary γ-Poisson distributions.

Setting Two data sets were analysed. The first data set (data set I) consists of all MRSA isolates collected in Oslo County from 1997 to 2010; the study area includes the Norwegian capital of Oslo and nearby surrounding areas, covering approximately 11% of the Norwegian population. The second data set (data set II) consists of all MRSA isolates collected in Health Region East from 2002 to 2011. Health Region East consists of Oslo County and four neighbouring counties, and is the most populated area of Norway.

Participants Both data sets I and II consist of all persons in the area and time period described in the Settings, from whom MRSA have been isolated.

Primary and secondary outcome measures MRSA infections have been mandatory notifiable in Norway since 1995, and MRSA colonisation since 2004. In the time period studied, all bacterial samples in Norway have been sent to a medical microbiological laboratory at the regional hospital for testing. In collaboration with the regional hospitals in five counties, we have collected all MRSA findings in the South-Eastern part of Norway over long time periods.

Results On an average, a linear or exponential increase in MRSA numbers was observed in the data sets. A Poisson process with increasing intensity did not capture the dispersion of the time series, but a γ-Poisson process showed good agreement and captured the overdispersion. The numerical model showed numerical internal consistency.

Conclusions In the present study, we find that the number of MRSA isolates is increasing in the most populated area of Norway during the time period studied. We also forecast a continuous increase until the year 2017.