Exploring the influence of short-term temperature patterns on temperature-related mortality: a case-study of Melbourne, Australia.

BACKGROUND: Several studies have identified the association between ambient temperature and mortality; however, several features of temperature behavior and their impacts on health remain unresolved. We obtain daily counts of nonaccidental all-cause mortality data in the elderly (65 + years) and corresponding meteorological data for Melbourne, Australia during 1999 to 2006. We then characterize the temporal behavior of ambient temperature development by quantifying the rates of temperature change during periods designated by pre-specified windows ranging from 1 to 30 days. Finally, we evaluate if the association between same day temperature and mortality in the framework of a Poisson regression and include our temperature trajectory variables in order to assess if associations were modified by the nature of how the given daily temperature had evolved. RESULTS: We found a positive significant association between short-term mortality risk and daily average temperature as mortality risk increased 6 % on days when temperatures were above the 90th percentile as compared to days in the referent 25-75th. In addition, we found that mortality risk associated with daily temperature varied by the nature of the temperature trajectory over the preceding twelve days and that peaks in mortality occurred during periods of high temperatures and stable trajectories and during periods of increasing higher temperatures and increasing trajectories. CONCLUSION: Our method presents a promising tool for improving understanding of complex temperature health associations. These findings suggest that the nature of sub-monthly temperature variability plays a role in the acute impacts of temperature on mortality; however, further studies are suggested.

The effects of summer temperature, age and socioeconomic circumstance on acute myocardial infarction admissions in Melbourne, Australia.

BACKGROUND: Published literature detailing the effects of heatwaves on human health is readily available. However literature describing the effects of heat on morbidity is less plentiful, as is research describing events in the southern hemisphere and Australia in particular. To identify susceptible populations and direct public health responses research must move beyond description of the temperature morbidity relationship to include social and spatial risk factors. This paper presents a spatial and socio-demographic picture of the effects of hot weather on persons admitted to hospital with acute myocardial infarction (AMI) in Melbourne. RESULTS: In this study, the use of a spatial and socio-economic perspective has identified two groups within the population that have an increased 'risk' of AMI admissions to hospital during hot weather. AMI increases during hot weather were only identified in the most disadvantaged and the least disadvantaged areas. Districts with higher AMI admissions rates during hot weather also had larger proportions of older residents. Age provided some explanation for the spatial distribution of AMI admissions on single hot days whereas socio-economic circumstance did not. During short periods (3-days) of hot weather, age explained the spatial distribution of AMI admissions slightly better than socioeconomic circumstance. CONCLUSIONS: This study has demonstrated that both age and socioeconomic inequality contribute to AMI admissions to hospital in Melbourne during hot weather. By using socioeconomic circumstance to define quintiles, differences in AMI admissions were quantified and demographic differences in AMI admissions were described. Including disease specificity into climate-health research methods is necessary to identify climate-sensitive diseases and highlight the burden of climate-sensitive disease in the community. Cardiac disease is a major cause of death and disability and identifying cardiac-specific climate thresholds and the spatio-demographic characteristics of vulnerable groups within populations is an important step towards preventative health care by informing public health officials and providing a guide for an early heat-health warning system. This information is especially important under current climatic conditions and for assessing the future impact of climate change.

Socio-environmental predictors of Barmah forest virus transmission in coastal areas, Queensland, Australia.

OBJECTIVE: To assess the socio-environmental predictors of Barmah forest virus (BFV) transmission in coastal areas, Queensland, Australia. METHODS: Data on BFV notified cases, climate, tidal levels and socioeconomic index for area (SEIFA) in six coastal cities, Queensland, for the period 1992-2001 were obtained from the relevant government agencies. Negative binomial regression models were used to assess the socio-environmental predictors of BFV transmission. RESULTS: The results show that maximum and minimum temperature, rainfall, relative humidity, high and low tide were statistically significantly associated with BFV incidence at lags 0-2 months. The fitted negative binomial regression models indicate a significant independent association of each of maximum temperature (beta = 0.139, P = 0.000), high tide (beta = 0.005, P = 0.000) and SEIFA index (beta = -0.010, P = 0.000) with BFV transmission after adjustment for confounding variables. CONCLUSIONS: The transmission of BFV disease in Queensland coastal areas seemed to be determined by a combination of local social and environmental factors. The model developed in this study may have applications in the control and prevention of BFV disease in these areas.

Geographic, Demographic, and Temporal Variations in the Association between Heat Exposure and Hospitalization in Brazil: A Nationwide Study between 2000 and 2015.

BACKGROUND: Limited evidence is available regarding the association between heat exposure and morbidity in Brazil and how the effect of heat exposure on health outcomes may change over time. OBJECTIVES: This study sought to quantify the geographic, demographic and temporal variations in the heat­hospitalization association in Brazil from 2000­2015. METHODS: Data on hospitalization and meteorological conditions were collected from 1,814 cities during the 2000­2015 hot seasons. Quasi-Poisson regression with constrained lag model was applied to examine city-specific estimates, which were then pooled at the regional and national levels using random-effect meta-analyses. Stratified analyses were performed by sex, 10 age groups, and 11 cause categories. Meta-regression was used to examine the temporal change in estimates of heat effect from 2000 to 2015. RESULTS: For every 5°C increase in daily mean temperature during the 2000­2015 hot seasons, the estimated risk of hospitalization over lag 0-7 d rose by 4.0% [95% confidence interval (CI): 3.7%, 4.3%] nationwide. Estimated 6.2% [95% empirical CI (eCI): 3.3%, 9.1%] of hospitalizations were attributable to heat exposure, equating to 132 cases (95% eCI: 69%, 192%) per 100,000 residents. The attributable rate was greatest in children [Formula: see text] and was highest for hospitalizations due to infectious and parasitic diseases. Women of reproductive age and those [Formula: see text] had higher heat burden than men. The attributable burden was greatest for cities in the central west and the inland of the northeast; lowest in the north and eastern coast. Over the 16-y period, the estimated heat effects declined insignificantly at the national level. CONCLUSIONS: In Brazil's hot seasons, 6% of hospitalizations were estimated to be attributed to heat exposure. As there was no evidence indicating that thermal adaptation had occurred at the national level, the burden of hospitalization associated with heat exposure in Brazil is likely to increase in the context of global warming. https://doi.org/10.1289/EHP3889.

Climate variability, social and environmental factors, and ross river virus transmission: research development and future research needs.

BACKGROUND: Arbovirus diseases have emerged as a global public health concern. However, the impact of climatic, social, and environmental variability on the transmission of arbovirus diseases remains to be determined. OBJECTIVE: Our goal for this study was to provide an overview of research development and future research directions about the interrelationship between climate variability, social and environmental factors, and the transmission of Ross River virus (RRV), the most common and widespread arbovirus disease in Australia. METHODS: We conducted a systematic literature search on climatic, social, and environmental factors and RRV disease. Potentially relevant studies were identified from a series of electronic searches. RESULTS: The body of evidence revealed that the transmission cycles of RRV disease appear to be sensitive to climate and tidal variability. Rainfall, temperature, and high tides were among major determinants of the transmission of RRV disease at the macro level. However, the nature and magnitude of the interrelationship between climate variability, mosquito density, and the transmission of RRV disease varied with geographic area and socioenvironmental condition. Projected anthropogenic global climatic change may result in an increase in RRV infections, and the key determinants of RRV transmission we have identified here may be useful in the development of an early warning system. CONCLUSIONS: The analysis indicates that there is a complex relationship between climate variability, social and environmental factors, and RRV transmission. Different strategies may be needed for the control and prevention of RRV disease at different levels. These research findings could be used as an additional tool to support decision making in disease control/surveillance and risk management.

Demographic, seasonal, and spatial differences in acute myocardial infarction admissions to hospital in Melbourne Australia.

BACKGROUND: Seasonal patterns in cardiac disease in the northern hemisphere are well described in the literature. More recently age and gender differences in cardiac mortality and to a lesser extent morbidity have been presented. To date spatial differences between the seasonal patterns of cardiac disease has not been presented. Literature relating to seasonal patterns in cardiac disease in the southern hemisphere and in Australia in particular is scarce. The aim of this paper is to describe the seasonal, age, gender, and spatial patterns of cardiac disease in Melbourne Australia by using acute myocardial infarction admissions to hospital as a marker of cardiac disease. RESULTS: There were 33,165 Acute Myocardial Infarction (AMI) admissions over 2186 consecutive days. There is a seasonal pattern in AMI admissions with increased rates during the colder months. The peak month is July. The admissions rate is greater for males than for females, although this difference decreases with advancing age. The maximal AMI season for males extends from April to November. The difference between months of peak and minimum admissions was 33.7%. Increased female AMI admissions occur from May to November, with a variation between peak and minimum of 23.1%. Maps of seasonal AMI admissions demonstrate spatial differences. Analysis using Global and Local Moran's I showed increased spatial clustering during the warmer months. The Bivariate Moran's I statistic indicated a weaker relationship between AMI and age during the warmer months. CONCLUSION: There are two distinct seasons with increased admissions during the colder part of the year. Males present a stronger seasonal pattern than females. There are spatial differences in AMI admissions throughout the year that cannot be explained by the age structure of the population. The seasonal difference in AMI admissions warrants further investigation. This includes detailing the prevalence of cardiac disease in the community and examining issues of social and environmental justice.

Weather variability, tides, and Barmah Forest virus disease in the Gladstone region, Australia.

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (b=0.15, p-value<0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (b=-1.03, p-value=0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention.

Development of a predictive model for ross river virus disease in Brisbane, Australia.

This paper describes the development of an empirical model to forecast epidemics of Ross River virus (RRV) disease using the multivariate seasonal auto-regressive integrated moving average (SARIMA) technique in Brisbane, Australia. We obtained computerized data on notified RRV disease cases, climate, high tide, and population sizes in Brisbane for the period 1985-2001 from the Queensland Department of Health, the Australian Bureau of Meteorology, the Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model was developed and validated by dividing the data file into two data sets: the data between January 1985 and December 2000 were used to construct a model, and those between January and December 2001 to validate it. The SARIMA models show that monthly precipitation (beta = 0.004, P = 0.031) was significantly associated with RRV transmission. However, there was no significant association between other climate variables (e.g., temperature, relative humidity, and high tides) and RRV transmission. The predictive values in the model were generally consistent with actual values (root mean square percentage error = 0.94%). Therefore, this model may have applications as a decision supportive tool in disease control and risk-management planning programs.

Mortality-temperature thresholds for ten major population centres in rural Victoria, Australia.

Mortality-temperature relationships in small regional towns in Victoria, Australia, were used to ascertain whether the effects of high ambient temperatures documented in the literature for major population centres in Europe and America are also noted in small rural communities in Australia. The establishment of threshold temperatures in all major rural regions of Victoria indicate that hot weather results in an increase in mortality in persons aged 65 years and older. This adds considerable strength to the argument that human populations are vulnerable to heat events regardless of location. Heat alerts can be issued through local health and welfare agencies, to increase awareness of 'hot' weather as a health hazard for elderly people by providing education campaigns involving local authorities based on these simple thresholds.

A simple heat alert system for Melbourne, Australia.

A simple heat alert system, based solely on predicted maximum and minimum daily temperatures, has been developed for the city of Melbourne in southeast Australia. The system is based upon a demonstration that, when mean daily temperature exceeds a threshold of 30 degrees C (mean of today's maximum temperature and tonight's minimum temperature), the average daily mortality of people aged 65 years or more is about 15-17% greater than usual. Similar numbers of excess deaths also occur when daily minimum temperatures exceed 24 degrees C (increases of 19-21% over expected death rate), so a heat alert system based solely on this widely available weather forecast variable is also feasible. No strong signal of excess heat-related deaths appears when the data are stratified using daily maximum temperatures. This may be because in Melbourne some days with very high maximum temperatures will be affected by the passage of cool changes and cold fronts in the afternoon, leading to a rapid drop in temperature (i.e., some days with high maximum temperatures will not continue to be hot throughout the day and into the evening). A single day with temperatures exceeding the thresholds noted above is sufficient to cause this increase in mortality, rather than requiring an extended heat wave. The increased daily mortality does not appear to represent a short-term advancement of mortality.

Inter-annual rainfall variations and suicide in New South Wales, Australia, 1964-2001.

The suicide rate in New South Wales is shown to be related to annual precipitation, supporting a widespread and long-held assumption that drought in Australia increases the likelihood of suicide. The relationship, although statistically significant, is not especially strong and is confounded by strong, long-term variations in the suicide rate not related to precipitation variations. A decrease in precipitation of about 300 mm would lead to an increase in the suicide rate of approximately 8% of the long-term mean suicide rate.