Hospital selection

Initially all 264 facilities listed in the financial year 2007-08 Information Services Division (ISD) cost book reports (R020 and R020LS, http://www.isdscotlandarchive.scot.nhs.uk/isd/6058.html) were considered. For the purposes of analysis, a hospital was defined as a secondary healthcare facility with at least one inpatient per year and with at least one hospital specialty (previously known as departments). Hospitals from island Health Boards (NHS Shetland, NHS Orkney and NHS Western Isles) were excluded since hospitals there were considered atypical and not comparable to other ‘mainland’ hospitals included in the study as connectivity is difficult to assess. This resulted in 198 National Health Service (NHS) hospitals being included in the study for the financial year 2007-08 (Fig. 1) and 66 facilities being excluded.

Fig. 1 Map of Mainland Scotland with NHS Health Boards. Circles show the 198 hospitals included in this study for the financial year 2007–2008. Each circle represents one hospital and hospitals are colour coded by type (pink, Teaching; green, General (no teaching); grey (Other). a. Number of cases per hospital is continuous (ranging from 0–72) and the size of circle represents the number of cases with increasing number of cases illustrated by increasingly larger circles. The legend highlights the size of a circle that represents 0, 20, 40 and 60 cases. b. Hospital size (Occupied Bed Days (OBD)) is continuous (ranging from 990–322494) and the size of circle represents the number of OBD with increasing OBD illustrated by increasingly larger circles. The legend highlights the size of a circle that represents 100,000, 200,000 and 300,000 OBD. c. Connectivity (Indegree) is continuous (ranging from 0–70) and the size of circle represents the connectivity with increasing connectivity illustrated by increasingly larger circles. The legend highlights the size of a circle that represents Connectivity of 0, 20, 40 and 60 Full size image

Data collection

(1) Case data. Methicillin-resistant S. aureus (MRSA) bacteraemia case data by hospital for the financial year 2007-08 (6 April 2007 – 5 April 2008) were received from Health Protection Scotland (HPS). This financial year was chosen because at the time it was the only year for which data were available for all risk factors (hospital size, type and connectivity). (2) Risk factors. Data of potential risk factors were obtained from Information Services Division (ISD) for each hospital for the financial year 2007-08 (a full list of variables in Additional file 1). As the aim was to test specific hypotheses regarding the effect of hospital size, type and connectivity; the majority of the data that were obtained represent different measures of those characteristics. Although various other measures including average occupancy rate, total number of whole time equivalent (medical and dentistry, nursing and midwifery, domestic, and support services) staff, patient-staff ratios (number of patients to medical and dentistry, nursing and midwifery, domestic, and support services staff), and cleaning by hospital size were considered (Additional file 1). (2a) Hospital size. Measures of size included occupied bed days (OBD), surface area (m2), average staffed beds, total inpatients discharged, total staff members and others (Additional file 1). (2b) Hospital type. ISD group Scottish into the following categories: Category A (General (mainly acute) including Teaching (A1), large General (A2), General (A3), and Sick children’s (A4)); Category B (Long stay), Category C (Mental); Category D (psychiatry of learning difficulties); Category E (Maternity); and Category J (Community). Information on the presence and absence of 46 specialties was also acquired. For a complete list of these specialties see Additional file 2. (2c) Connectivity. To quantify movements of patients between hospitals, patient admission data were obtained from ISD. The patient admission data covered all admissions to healthcare facilities in Scotland for the calendar year 2007 (1 January – 31 December 2007). From this dataset the movements of all patients between hospitals were extracted either as direct transfers, i.e. from one hospital directly to another hospital, or as indirect transfers, i.e. when a patient was discharged from one hospital into the community and subsequently (within the period covered by the data) admitted to another hospital. From these data, a movement matrix was derived for all connected hospitals in Scotland [11] and then various summary measures of hospital connectivity were generated (Table 1).

Table 1 Summary of connectivity measures Full size table

Statistical analysis

Descriptive analyses were carried out to describe hospital type and the number of cases per hospital. Several variables were log 10 (x + 1) transformed to correct for non-normal distribution of the data while others were categorised. A full list of all 39 explanatory variables is shown in Additional file 1. The hospital connectivity variables were examined as both continuous and categorical variables. Cut-off levels of connectivity variables were determined using Receiver Operating Characteristic (ROC) curve analysis [14], above which hospitals were considered positive for MRSA bacteraemia. The cut-off chosen was one that maximised the sum of the sensitivity and specificity. Confidence intervals (25th and 75th percentiles) for the cut-off values were generated from 10,000 bootstrap simulations. Data regarding hospital specialties (n = 46) were summarised using nonmetric multi-dimensional scaling (NMS) in order to reduce the number of variables. The NMS was performed in PC-ORD version 6.08 (MJM software Design, Gleneden Beach, OR) using the 36 specialties that were sufficiently represented across all hospitals (i.e. present in at least 5 % of the hospitals, therefore 10 specialties were excluded from this analysis). Multi-response Permutation Procedures (MRPP) analysis [15] was used a posteriori to test the hypothesis of no difference between hospital status (presence/absence of MRSA bacteraemia) and hospital type (Category A, B, C, D, E, J).

For both the single variable and multivariable analyses, two models were considered. Model 1 was a logistic regression model with binomial distribution fitted to presence-absence data for all hospitals (n = 198) to identify risk factors for having cases versus not. Model 2 was a generalised linear model with a Poisson distribution fitted to count data, offset by the logarithm of OBD, to identify risk factors associated with higher rates of MRSA bacteraemia in General (Category A) hospitals only (n = 38). A multiplicative over dispersion parameter was added to address over dispersion in the Poisson model.

A hypothesis driven approach to model selection was performed. Before undertaking the multivariable analysis, groups of variables representing hospital size, connectivity and hospital type were examined individually in a single variable analysis. As size was viewed a priori as important, a size-related variable was entered into the model first. With size in the model, variables associated with connectedness and hospital type were tested, including interactions. Likelihood-ratio tests and the Akaike Information Criterion (AIC) were used to select the most parsimonious model. Statistical significance was set at p < 0.05. Unless otherwise stated all analyses were carried out using Proc Glimmix in SAS version 9.3.1 (SAS Institute Inc., Cary, NC).