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SummaryUsing average number of patients expected in a year, average length of stay and a target occupancy level to calculate the number of critical care beds needed is mathematically incorrect because of nonlinearity and variability in the factors that control length of stay. For a target occupancy in excess of 80%, this simple calculation will typically underestimate the number of beds required. More seriously, it provides no quantitative guidance information about other aspects of critical care demand such as the numbers of emergency patients transferred, deferral rates for elective patients and overall utilisation. The combination of appropriately analysing raw data and detailed mathematical modelling provides a much better method for estimating numbers of beds required. We describe this modelling approach together with evidence of its performance.

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SummaryUsing real data from a number of hospitals, we predicted the patient flows following a capacity or organisational change. Clinically recognisable patient groups obtained through classification and regression tree analysis were used to tune a simulation model for the flow of patients in critical care units. A tuned model which accurately reflected the base case of the flow of patients was used to predict alterations in service provision in a number of scenarios which included increases in bed numbers, alterations in patients' lengths of stay, fewer delayed discharges, caring for long stay patients outside the formal intensive care unit and amalgamating small units. Where available the predictions' accuracy was checked by comparison with real hospital data collected after an actual capacity change. The model takes variability and uncertainty properly into account and it provides the necessary information for making better decisions about critical care capacity and organisation. Critical care involves uncertainty, variability, nonlinearity, complexity, numerous constraints and expensive resources. It is very difficult, if at all possible, to use randomised controlled trials to obtain the necessary evidence for making good decisions about critical care capacity and organisation.Critical care is expensive by comparison with general ward care and so over-provision is not desirable. Underprovision, on the other hand, can have serious consequences for patients with life-threatening illnesses or awaiting major surgery. Historically, additional critical care capacity in a hospital has usually been provided in response to repeated crises. This reactive approach is often based on simple calculations involving average number of patients expected in a year, average length of stay, and a target bed occupancy level. Such simple calculations do not take the complexity, variability, and the nonlinearity involved in the flow of critical care patients into account. They provide incorrect information about required capacity and may give false assurances about the levels of the service that will be provided. It is a well-known mathematical fact that required capacities in the face of nonlinearity and considerable variability depend not only on the average values but also the amount of variability [1].In a review of critical care [2], the Department of Health recommended the use of good capacity planning to avoid crises that result from inadequate capacity. In this paper a combined intensive care unit (ICU) and high dependency unit (HDU) is called a critical care unit (CCU). Good decisions about the capacity and the organisation of a CCU require an understanding of the existing demands and the likely future demands for critical care beds together with predictions about likely effects of changing the capacity and the organisation of a CCU. These requirements can be met through an appropriate statistical analysis of data on individual critical care patients and detailed mathematical models based on individual patients. Models for...

BackgroundGeographically weighted Poisson regression (GWPR) was applied to the relation between cervical cancer disease incidence rates in England and socio-economic deprivation, social status and family structure covariates. Local parameters were estimated which describe the spatial variation in the relations between incidence and socio-economic covariates.ResultsA global (stationary) regression model revealed a significant correlation between cervical cancer incidence rates and social status. However, a local (non-stationary) GWPR model provided a better fit with less spatial correlation (positive autocorrelation) in the residuals. Moreover, the GWPR model was able to represent local variation in the relations between cervical cancer incidence and socio-economic covariates across space, whereas the global model represented only the overall (or average) relation for the whole of England. The global model could lead to misinterpretation of the relations between cervical cancer incidence and socio-economic covariates locally.ConclusionsCervical cancer incidence was shown to have a non-stationary relationship with spatially varying covariates that are available through national datasets. As a result, it was shown that if low social status sectors of the population are to be targeted preferentially, this targeting should be done on a region-by-region basis such as to optimize health outcomes. While such a strategy may be difficult to implement in practice, the research does highlight the inequalities inherent in a uniform intervention approach.