A pragmatic method to compare hospital bed provision between countries and regions: Beds in the States of Australia

Abstract

Summary A simple method is presented to evaluate bed numbers between countries using a logarithmic relationship between beds per 1000 deaths and deaths per 1000 population, both of which are readily available. The method relies on the importance of the nearness to death effect. This method was tested using data from Australian States. Beds per 1000 deaths varied considerably between States. This variation reduced after adjusting for the ratio of deaths per 1000 population which is a measure of population age structure. After this adjustment, most Australian States roughly approximate to the international average for developed countries while Tasmania was shown to have a chronic bed shortage, as has been recognized for many years. The Northern Territory and the Australian Capital Territory, both of which have the youngest populations, have more beds relative to the other States. The nearness to death effect must be incorporated into capacity planning models in order to give robust estimates of future bed demand and to evaluate differences between countries and health care systems.

Would the United States Have Had Too Few Beds for Universal Emergency Care in the Event of a More Widespread Covid-19 Epidemic?

“…Research into better methods for comparing hospital bed numbers between countries and regions has culminated in a new method which relies on the operation of the NTD effect [ 24 , 25 ]. Based on this observation, bed numbers in different countries can be plotted on a graph showing beds per 1000 deaths (Y-axis) versus the log of deaths per 1000 population (X-axis), also called the crude death rate.…”

“…The average for developed countries has the equation [ 25 ]: Beds per 1000 deaths = 1057 − 230.3 × ln(deaths per 1000 population), …”

How many medical beds does a country need? An international perspective

“…The line of best fit for the medical-only bed data, Y = 259.8 -24.8 x ln(X), is presented as a blue dotted line. Meanwhile, the grey dashed line shows the line of best fit for the data covering all bed types, Y = 1056.9 -230.3 x ln(X); this formula was developed in a previous study (Jones, 2019c). Note that the intercept on the formula for all bed types (dashed line) has been modified to allow the line to fit in the scale used in Figure 4, as it is the slope that is of interest.…”

“…Based on this understanding of the nearness to death effect, it has been proposed that the ratio of beds per 1000 deaths and the number of deaths per 1000 people in a population may be more important parameters in forecasting the number of beds needed. This is because a model incorporating these two factors does not simply assume that age dictates bed use (Jones, 2018;2019c). Consequently, this article proposes a model that compares international bed numbers by plotting the number of available beds per 1000 deaths against the number of deaths per 1000 people in a population (Jones, 2018).…”

“…This method has been used previously to quantify the deficit of beds in Tasmania, Australia (Jones, 2019c) and to demonstrate which states in the USA are most susceptible to a capacity shock, such as the COVID-19 pandemic (Jones, 2020). However, these studies used data for surgical, mental health and maternity beds, as well as medical group beds (Jones, 2019c(Jones, , 2020. The model therefore needs to be validated for the medical group of specialties.…”