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We conducted a frequency-matched cohort study. The cohort consisted of all patients with MRSA diagnosed between 1 January 2001 and 31 December 2004, and a population-based sample of patients without MRSA. We defined MRSA to be the diagnosis 'Methicillin-resistant Staphylococcus aureus positive' (READ code 4JP..00) entered into the patient's medical record. The date of the MRSA diagnosis was the MRSA patient's cohort entry date. For each patient with MRSA, we randomly selected up to 10 patients free of MRSA that were registered in the GPRD at their corresponding MRSA patient's cohort entry date and that were matching on practice, cohort entry date and age ( 2 years). We assigned to these MRSA-free patients their corresponding MRSA patient's cohort entry date. Patients of the cohort, both with and without MRSA, had to be at least 18 years of age at their cohort entry date, had been registered in the GPRD for at least 2 years prior to their cohort entry date and had no hospitalizations recorded in the GPRD in the 2 years before cohort entry date. Frequency matching on cohort entry date is particularly important to control for calendar time effects, because the rate of MRSA diagnosis in the GPRD was not constant over the time period of our study [13]. This time variation in risk could have been a potential source of bias in our estimates of effect [15]. By frequency matching on practice we also ensured that patients with and without an MRSA diagnosis came from the same geographic region of the UK, thus indirectly controlling for factors such as socio-economic status and geographical variations in disease frequency.
Follow-up was from day 1 to day 365 after cohort entry date. Patients were censored if they transferred out of practice for a reason other than death or were free of the outcome 365 days after cohort entry. Patients who died on day 0 were excluded. It was possible for a patient without MRSA to be diagnosed with MRSA during follow-up.
We conducted several sensitivity analyses. First, to determine the maximum possible effect of censoring on the results, we used both worst-case and best-case imputation, appropriate techniques because of the small number of censored observations [17]. For worst-case imputation, we assumed that all MRSA patients who were censored die immediately and that all patients without MRSA lived until the end of follow-up. For best-case imputation, we assumed that all censored MRSA patients lived until the end of follow-up and all censored MRSA-free patients died immediately. Second, as an additional control for overall health status, we considered past antibiotic use as a marker of susceptibility to infection. Third, we determined the effect of MRSA diagnosis among patients with none of the co-morbid conditions in our study to estimate the effect of MRSA among healthy patients. We used SAS version 9.1.3 in all analyses.
The cohort included 1439 patients diagnosed with MRSA between 2001 and 2004, and 14,090 matching disease-free patients from the GPRD. Over the one-year period after cohort entry, 21.8% of patients diagnosed with MRSA and 5.0% of those without the diagnosis died. The Kaplan-Meier plot is shown in Figure 1. The loss to follow up owing to transfer out of practice was comparable in the patients with and without MRSA (4.2% and 3.0%, respectively). Patients with MRSA diagnosis were more likely to be male and to have co-morbid conditions predictive of mortality as compared with patients without a diagnosis of MRSA (Table 1).
In this study we considered a large cohort of patients diagnosed with MRSA infections in the community and a large group of matching disease-free patients all selected from a database that is representative of the UK population [10]. The death rate among the disease-free patients is broadly consistent for this age and sex distribution with that seen in UK vital statistics [20]. Using the GPRD allows us to study patients with all levels of disease severity encountered by GPs, including those not treated in hospitals and thus not part of any hospital-based investigation. The study design (frequency matched cohort study) ensured that the distribution of key covariates (general practice, age and calendar time) was balanced between patients with and without MRSA at baseline. By matching on general practice we indirectly matched on unmeasured factors such as socio-economic status, area of residence and nursing home care, which are usually common to the patient population of a given general practice. These demographic factors have been shown to be important in the epidemiology of MRSA [21]. By matching for practice we control for the potential confounding nature of these variables.
PCRs were performed in a gradient thermal cycler (Eppendorf, Hamburg, Germany). The S. aureus-specific nuc gene (279 bp), methicillin resistance mecA gene (147 bp), and Staphylococcus genus-specific 16S rRNA gene (756 bp) were detected. Previously reported primers were used, along with Staphylococcus genus-specific 16S rRNA (756 bp) as an internal control [28]. Each 25-μl reaction mixture contained 5μlof genomic DNA, 12.5 μl of PCR master mix (Promega Corporation, Madison, WI, USA), 1 μl of 100 pmol of the forward and reverse primers, and the final volume was adjusted to 25 μl with 5.5 μl of nuclease-free water. DNA amplification involved denaturation at 94 C for 1 min, followed by 30 cycles at 94 C for 30 s, 55 C for 30 s, and 72 C for 1 min, with a final elongation step at 72 C for 5 min. The PCR products were analyzed by 1% agarose gel electrophoresis (Alpha Imager, Wiesbaden Germany), with ethidium bromide staining, and a gel documentation system (Alpha Imager) was used for photography. 153554b96e
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