Dataset Information

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.

SUBMITTER: Hondula DM

PROVIDER: S-EPMC4123028 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Publications

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

Hondula David M DM Barnett Adrian G AG

Environmental health perspectives 20140430 8

<h4>Background</h4>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.<h4>Objectives</h4>We sought to quantify spatial variability in heat-related morbidity in Brisbane, Australia, to highlig ...[more]

PMID: 24787277

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Project description:BackgroundClimate change is expected to cause increases in heat-related mortality, especially among the elderly and very young. However, additional studies are needed to clarify the effects of heat on morbidity across all age groups and across a wider range of temperatures.ObjectivesWe aimed to estimate the impact of current and projected future temperatures on morbidity and mortality in Rhode Island.MethodsWe used Poisson regression models to estimate the association between daily maximum temperature and rates of all-cause and heat-related emergency department (ED) admissions and all-cause mortality. We then used downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5; a standardized set of climate change model simulations) projections to estimate the excess morbidity and mortality that would be observed if this population were exposed to the temperatures projected for 2046-2053 and 2092-2099 under two representative concentration pathways (RCP): RCP 8.5 and 4.5.ResultsBetween 2005 and 2012, an increase in maximum daily temperature from 75 to 85°F was associated with 1.3% and 23.9% higher rates of all-cause and heat-related ED visits, respectively. The corresponding effect estimate for all-cause mortality from 1999 through 2011 was 4.0%. The association with all-cause ED admissions was strongest for those < 18 or ≥ 65 years of age, whereas the association with heat-related ED admissions was most pronounced among 18- to 64-year-olds. If this Rhode Island population were exposed to temperatures projected under RCP 8.5 for 2092-2099, we estimate that there would be 1.2% (range, 0.6-1.6%) and 24.4% (range, 6.9-41.8%) more all-cause and heat-related ED admissions, respectively, and 1.6% (range, 0.8-2.1%) more deaths annually between April and October.ConclusionsWith all other factors held constant, our findings suggest that the current population of Rhode Island would experience substantially higher morbidity and mortality if maximum daily temperatures increase further as projected.CitationKingsley SL, Eliot MN, Gold J, Vanderslice RR, Wellenius GA. 2016. Current and projected heat-related morbidity and mortality in Rhode Island. Environ Health Perspect 124:460-467; http://dx.doi.org/10.1289/ehp.1408826.

Project description:The risk posed by wildlife to air transportation is of great concern worldwide. In Australia alone, 17,336 bird-strike incidents and 401 animal-strike incidents were reported to the Air Transport Safety Board (ATSB) in the period 2010-2019. Moreover, when collisions do occur, the impact can be catastrophic (loss of life, loss of aircraft) and involve significant cost to the affected airline and airport operator (estimated at globally US$1.2 billion per year). On the other side of the coin, civil aviation, and airport operations have significantly affected bird populations. There has been an increasing number of bird strikes, generally fatal to individual birds involved, reported worldwide (annual average of 12,219 reported strikes between 2008-2015 being nearly double the annual average of 6,702 strikes reported 2001-2007) (ICAO, 2018). Airport operations including construction of airport infrastructure, frequent take-offs and landings, airport noise and lights, and wildlife hazard management practices aimed at reducing risk of birdstrike, e.g., spraying to remove weeds and invertebrates, drainage, and even direct killing of individual hazard species, may result in habitat fragmentation, population decline, and rare bird extinction adjacent to airports (Kelly T, 2006; Zhao B, 2019; Steele WK, 2021). Nevertheless, there remains an imperative to continually improve wildlife hazard management methods and strategies so as to reduce the risk to aircraft and to bird populations. Current approved wildlife risk assessment techniques in Australia are limited to ranking of identified hazard species, i.e., are 'static' and, as such, do not provide a day-to-day risk/collision likelihood. The purpose of this study is to move towards a dynamic, evidence-based risk assessment model of wildlife hazards at airports. Ideally, such a model should be sufficiently sensitive and responsive to changing environmental conditions to be able to inform both short and longer term risk mitigation decisions. Challenges include the identification and quantification of contributory risk factors, and the selection and configuration of modelling technique(s) that meet the aforementioned requirements. In this article we focus on likelihood of bird strike and introduce three distinct, but complementary, assessment techniques, i.e., Algebraic, Bayesian, and Clustering (ABC) for measuring the likelihood of bird strike in the face of constantly changing environmental conditions. The ABC techniques are evaluated using environment and wildlife observations routinely collected by the Brisbane Airport Corporation (BAC) wildlife hazard management team. Results indicate that each of the techniques meet the requirements of providing dynamic, realistic collision risks in the face of changing environmental conditions.

Project description:ObjectiveHospitalisation rates for many chronic conditions are higher in socioeconomically disadvantaged and less accessible areas. We aimed to map diabetes hospitalisation rates by local government area (LGA) across Western Victoria, Australia, and investigate their association with socioeconomic status (SES) and accessibility/remoteness.DesignCross-sectional study METHODS: Data were acquired from the Victorian Admitted Episodes Dataset for all hospitalisations (public and private) with a diagnosis of type 1 or type 2 diabetes mellitus during 2011-2014. Crude and age-standardised hospitalisation rates (per 1000 population per year) were calculated by LGA for men, women and combined data. Associations between accessibility (Accessibility/Remoteness Index of Australia, ARIA), SES (Index of Relative Socioeconomic Advantage and Disadvantage, IRSAD) and diabetes hospitalisation were investigated using Poisson regression analyses.ResultsHigher LGA-level accessibility and SES were associated with higher rates of type 1 and type 2 diabetes hospitalisation, overall and for each sex. For type 1 diabetes, higher accessibility (ARIA category) was associated with higher hospitalisation rates (men incidence rate ratio [IRR]=2.14, 95% CI 1.64 to 2.80; women IRR=2.45, 95% CI 1.87 to 3.19; combined IRR=2.30, 95% CI 1.69 to 3.13; all p<0.05). Higher socioeconomic advantage (IRSAD decile) was also associated with higher hospitalisation rates (men IRR=1.25, 95% CI 1.09 to 1.43; women IRR=1.32, 95% CI 1.16 to 1.51; combined IRR=1.23, 95% CI 1.07 to 1.42; all p<0.05). Similarly, for type 2 diabetes, higher accessibility (ARIA category) was associated with higher hospitalisation rates (men IRR=2.49, 95% CI 1.81 to 3.43; women IRR=2.34, 95% CI 1.69 to 3.25; combined IRR=2.32, 95% CI 1.66 to 3.25; all p<0.05) and higher socioeconomic advantage (IRSAD decile) was also associated with higher hospitalisation rates (men IRR=1.15, 95% CI 1.02 to 1.30; women IRR=1.14, 95% CI 1.01 to 1.28; combined IRR=1.13, 95% CI 1.00 to 1.27; all p<0.05).ConclusionOur observations could indicate self-motivated treatment seeking, and better specialist and hospital services availability in the advantaged and accessible areas in the study region. The determinants for such variations in hospitalisation rates, however, are multifaceted and warrant further research.

Dataset Information

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

Background

Objectives

Methods

Results

Conclusions

Publications

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

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