Heat-related morbidity in brisbane, australia: spatial variation and area-level predictors

Abstract

Background: Extreme heat is a leading weather-related cause of illness and death in many locations across the globe, including subtropical Australia. The possibility of increasingly frequent and severe heat waves warrants continued efforts to reduce this health burden, which could be accomplished by targeting intervention measures toward the most vulnerable communities.

Objectives: We sought to quantify spatial variability in heat-related morbidity in Brisbane, Australia, to highlight regions of the city with the greatest risk. We also aimed to find area-level social and environmental determinants of high risk within Brisbane.

Methods: We used a series of hierarchical Bayesian models to examine city-wide and intracity associations between temperature and morbidity using a 2007-2011 time series of geographically referenced hospital admissions data. The models accounted for long-term time trends, seasonality, and day of week and holiday effects.

Results: On average, a 10°C increase in daily maximum temperature during the summer was associated with a 7.2% increase in hospital admissions (95% CI: 4.7, 9.8%) on the following day. Positive statistically significant relationships between admissions and temperature were found for 16 of the city's 158 areas; negative relationships were found for 5 areas. High-risk areas were associated with a lack of high income earners and higher population density.

Conclusions: Geographically targeted public health strategies for extreme heat may be effective in Brisbane, because morbidity risk was found to be spatially variable. Emergency responders, health officials, and city planners could focus on short- and long-term intervention measures that reach communities in the city with lower incomes and higher population densities, including reduction of urban heat island effects.

Figure 1

A map of the boundaries of the 158 Statistical Local Areas (SLAs) in the city of Brisbane. The inset shows the central business district (CBD) and the locations of Brisbane (BRIS) and Archerfield (ARCH) weather stations.

Figure 2

The time series of daily hospital admissions in Brisbane (2007–2011). The dashed line is the mean admissions of 193 per day; the solid black line is a 50-day LOESS moving average used to highlight periodicity that is not evident from the scatter of points.

Figure 3

The estimated associations between temperature and hospital admissions for each SLA in Brisbane in terms of the change in daily admissions rate per 10°C increase in temperature on the previous day. White crosses identify areas where the slope is statistically significantly positive.

Figure 4

(A) The relationship between area-level heat sensitivity, measured as the percent increase in hospital admissions per 10°C increase in temperature on the previous day, and the population density of each area. (B) The relationship between temperature sensitivity and the percent of high income-earning residents in each area. The line indicates the best fit using linear regression.