| Summary: | Comparisons of the incidence of blood stream infection (BSI) between neonatal intensive care units (NICUs) can promote sharing of potentially better practices for infection control. Comparisons should take into account differences in babies’ vulnerability and the invasive procedures which can introduce infection. I carried out a systematic review of methods reported in the literature, or used by regional monitoring systems, for comparing the incidence of BSI among NICUs. I found substantial variation, especially in the risk factors used to adjust incidence estimates. The use of routinely recorded administrative data would minimize and accelerate staff workload for BSI monitoring. I investigated which risk factors recorded in routine data should be adjusted for when comparing BSI incidence between NICUs. I linked microbiology laboratory records with administrative records collected over four years for three London NICUs. I analysed rates of BSI using various methods, including Poisson regression and logistic regression assuming a matched case control design. With both approaches, National Health Service level of care was the strongest predictor for BSI incidence. Using Poisson regression models, the rate ratio for BSI, adjusted for birth weight, inborn/outborn status and postnatal age, was 3.15 (95% confidence interval (CI) 2.01, 4.94) for intensive care and 6.58 (95% CI 4.18, 10.36) for high dependency care, relative to special care. The case control study gave slightly larger estimates of effect than the Poisson regression models. Total parenteral nutrition was significantly associated with BSI incidence but explained less of the variance among babies than level of care. Using the results from the risk adjustment model, I demonstrated how routine data can be integrated into a method for prospective, risk adjusted monitoring. This method involved standardised infection ratios and a sequential probability ratio test. The method can evaluate changes in BSI rates over time and between NICUs. It could also be used to quantify improvements following infection control interventions.
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