Primary outcome analysis
RSV, pneumonia, viral respiratory infections, generalised, bloodstream infections
BLOODSTREAM INFECTION, PNEUMONIA, URINARY TRACT INFECTIONS, SURGICAL SITE INFECTION, VIRAL RESPIRATORY INFECTION, INFLUENZA
The primary outcome is defined as the total fortnightly rates of HAI identified. The primary outcome will be analysed by a generalised linear mixed model (GLMM) with a logit link function, where the dependent variable is defined as will be used, to estimate changes in fortnightly cases of HAI. To standardise rates, the fortnightly numbers of HAIs identified (combined, all types of HAIs) will be divided by the number of at-risk patients. Models will have a random intercept for each ward to control for baseline differences. Fixed effects will include the intervention and study time in weeks and fixed effects for the intervention and study time. Study time will be modelled as a categorical fixed effect; inclusion as a linear fixed effect will also be tested. Models will have a random intercept for each ward to control for baseline differences between wards, and a linear fixed effect to control for unrelated changes over time. Intervention effectiveness will be represented in the model as a binary independent variable that will for the intervention switched from ‘no’ (0) to ‘yes’ (1) 1 week after the start of the intervention period. This will account for a delay in the intervention effect, as wards become familiarised with the enhanced cleaning procedures. Model estimates will be reported as odds ratios with 95% confidence.We will undertake sensitivity analyses to determine the possibility of a delayed intervention effect of longer than 1 week, the influence of each ward on model estimates and the effect of the intervention on the most common and serious HAIs—pneumonia, urinary tract infections, bloodstream infections and surgical site infection. The delayed intervention effect will be modelled at 2 and 4 weeks after each ward’s intervention start date. The influence of each ward will be examined using a leave-one-ward-out analysis examining changes to the intervention effect and Cook’s distances.Additional analyses will consider alternative model specifications if the proposed GLMM does not converge or provides an inadequate fit to the data. Planned alternatives will be testing of different link functions (identity, log), changing the distribution for the dependent variable from binomial to beta-binomial to account for over-dispersion, and the use of generalised estimating equations in place of a GLMM.To reduce the effect of confounders due to seasonality, viral respiratory infections (RSV, COVID-19, influenza) will be removed for the purpose of primary analysis. In addition, any HAI data related to an outbreak will be excluded from primary analysis. The study period is aligned to span over three full seasons (autumn, winter, spring), with the study midpoint corresponding to the usual epidemiological peak for respiratory infection in the region. Additionally, the use of historical coding data can identify underlying trends over recent years.