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There is concern in Australia that droughts substantially increase the incidence of suicide in rural populations, particularly among male farmers and their families. We investigated this possibility for the state of New South Wales (NSW), Australia between 1970 and 2007, analyzing data on suicides with a previously established climatic drought index. Using a generalized additive model that controlled for season, region, and long-term suicide trends, we found an increased relative risk of suicide of 15% (95% confidence interval, 8%–22%) for rural males aged 30–49 y when the drought index rose from the first quartile to the third quartile. In contrast, the risk of suicide for rural females aged >30 y declined with increased values of the drought index. We also observed an increased risk of suicide in spring and early summer. In addition there was a smaller association during unusually warm months at any time of year. The spring suicide increase is well documented in nontropical locations, although its cause is unknown. The possible increased risk of suicide during drought in rural Australia warrants public health focus and concern, as does the annual, predictable increase seen each spring and early summer. Suicide is a complex phenomenon with many interacting social, environmental, and biological causal factors. The relationship between drought and suicide is best understood using a holistic framework. Climate change projections suggest increased frequency and severity of droughts in NSW, accompanied and exacerbated by rising temperatures. Elucidating the relationships between drought and mental health will help facilitate adaptation to climate change.

There are numerous studies that show an increased incidence of cardiovascular disease with increasing levels of socio-economic disadvantage. Exposures that might influence the relationship include elements of the built environment and social systems that shape lifestyle risk behaviors. In Canberra (the Australian capital city) there has been a particular housing policy to create 'mixed-tenure' neighborhoods so that small pockets of disadvantage are surrounded by more affluent residences (known as a 'salt-and-pepper' pattern). This may contribute to a scatter of higher incidence rates in very small areas in this population that may be obscured if aggregated data are used. This study explored the effect of changing the scale of the spatial units used in small area disease modelling, aiming to understand the impact of this issue and the implications for local public health surveillance. The residence location of hospitalized individuals were aggregated to two differently scaled area units. First, the Australian Bureau of Statistics Statistical Area 2 (SA2) which is normally used as the basis for deidentification and release of health data. Second, these data were aggregated to a smaller level: the Statistical Area 1 (SA1). Generalized Additive Models with penalized regression splines were used to assess the association of age-sex-standardized rates for cardiovascular disease hospital admissions with disadvantage. The relationships observed were different between the two types of spatial units. The SA1 level exposure-response curve for rates against the disadvantage index extended in a linear fashion above the midrange level, while that found at SA2-level suggested a curvilinear form with no evidence that rates increased with higher disadvantage beyond the midrange. Our result supports findings of other work that has found disadvantage increases risk of cardiovascular disease. The shape of the curves suggest a difference in associations of cardiovascular disease rates with disadvantage scores between SA1 versus SA2. From these results it can be concluded that scale of analysis does influence the understanding of geographical patterns of socio-economic disadvantage and cardiovascular disease morbidity. Health surveillance and interventions in Canberra should take into account the impact of the scale of aggregation on the association between disadvantage and cardiovascular disease observed.

Smoke haze events have increasingly affected Australia’s environmental quality, having demonstrable effects on air quality, climate, and public health. This study employs a hybrid methodology, merging satellite-based aerosol optical depth (AOD) data with Chemical Transport Model (CTM) simulations to comprehensively characterize these events. The AOD data are sourced from the Japan Aerospace Exploration Agency (JAXA), Copernicus Atmosphere Monitoring Service (CAMS), and the Commonwealth Scientific and Industrial Research Organization (CSIRO), and they are statistically evaluated using mean, standard deviation, and root mean square error (RMSE) metrics. Our analysis indicates that the combined dataset provides a more robust representation of smoke haze events than individual datasets. Additionally, the study investigates aerosol distribution patterns and data correlation across the blended dataset and discusses possible improvements such as data imputation and aerosol plume scaling. The outcomes of this investigation contribute to enhancing our understanding of the impacts of smoke haze on various environmental factors and can assist in developing targeted mitigation and management strategies.

Heatwaves are associated with increased mortality and are exacerbated by the urban heat island (UHI) effect. Thus, to inform climate change mitigation and adaptation, we quantified the mortality burden of historical heatwave days in Sydney, Australia, assessed the contribution of the UHI effect and used climate change projection data to estimate future health impacts. We also assessed the potential for tree cover to mitigate against the UHI effect. Mortality (2006–2018) records were linked with census population data, weather observations (1997–2016) and climate change projections to 2100. Heatwave-attributable excess deaths were calculated based on risk estimates from a published heatwave study of Sydney. High resolution satellite observations of UHI air temperature excesses and green cover were used to determine associated effects on heat-related mortality. These data show that >90% of heatwave days would not breach heatwave thresholds in Sydney if there were no UHI effect and that numbers of heatwave days could increase fourfold under the most extreme climate change scenario. We found that tree canopy reduces urban heat, and that widespread tree planting could offset the increases in heat-attributable deaths as climate warming progresses.

To determine if global warming has changed the balance of summer and winter deaths in Australia. Counts of summer and winter cause‐specific deaths of subjects aged 55 and over for the years 1968–2018 were entered into a Poisson time‐series regression. Analysis was stratified by states and territories of Australia, by sex, age and cause of death (respiratory, cardiovascular, and renal diseases). The warmest and coldest subsets of seasons were compared. Warming over 51 years was associated with a long‐term increase in the ratio of summer to winter mortality from 0.73 in the summer of 1969 to 0.83 in the summer of 2018. The increase occurred faster in years that were warmer than average. Mortality in the warmest and coldest times of the year is converging as annual average temperatures rise. If climate change continues, deaths in the hottest months will come to dominate the burden of mortality in Australia.

Robust high spatiotemporal resolution daily PM2.5 exposure estimates are limited in Australia. Estimates of daily PM2.5 and the PM2.5 component from extreme pollution events (e.g., bushfires and dust storms) are needed for epidemiological studies and health burden assessments attributable to these events. We sought to: (1) estimate daily PM2.5 at a 5 km × 5 km spatial resolution across the Australian continent between 1 January 2001 and 30 June 2020 using a Random Forest (RF) algorithm, and (2) implement a seasonal-trend decomposition using loess (STL) methodology combined with selected statistical flags to identify extreme events and estimate the extreme pollution PM2.5 component. We developed an RF model that achieved an out-of-bag R-squared of 71.5% and a root-mean-square error (RMSE) of 4.5 µg/m3. We predicted daily PM2.5 across Australia, adequately capturing spatial and temporal variations. We showed how the STL method in combination with statistical flags can identify and quantify PM2.5 attributable to extreme pollution events in different locations across the country.

Air pollution is a global issue affecting health and the environment. This study investigated associations between PM10, NO2, and admissions from cardiovascular and respiratory diseases in Makkah (2019–2022), comparing Hajj cultural events and the COVID-19 lockdown with non-event periods, using time-series Poisson regression models adjusted for time and seasonality. Event interactions, particularly the impact of the Hajj and COVID-19 periods, were examined to assess potential effects on morbidity. The study findings showed that PM10 was significantly associated with increased respiratory admissions during the Hajj period (lag 0: RR = 1.066; 95% CI: 1.030–1.104), and with decreased risk during the non-Hajj period (lag 0: RR = 0.966; 95% CI: 0.942–0.991) and non-COVID periods (lag 0: RR = 0.946; 95% CI: 0.920–0.973). NO2 demonstrated a strong positive association with respiratory admissions during the Hajj period across all lags, peaking at lag 0 with a 16.2% increased risk (RR = 1.162; 95% CI: 1.118–1.207). Exposure to PM10 during Hajj was associated with a 3.1% increased risk of cardiovascular admissions (lag 0: RR = 1.031; 95% CI: 1.012–1.050) and decreased risk during non-Hajj (lag 0: RR = 0.981; 95% CI: 0.963–0.999) and non-COVID periods (lag 0: RR = 0.962; 95% CI: 0.942–0.983). NO2 exposure was positively associated with cardiovascular admissions during Hajj (lag 0: RR = 1.039; 95% CI: 1.019–1.056) and non-COVID periods (lag 0: RR = 1.037; 95% CI: 1.007–1.068). These findings provide event-specific evidence to guide targeted air quality management during mass gatherings, helping policymakers protect the health of Makkah’s residents and visitors.

BackgroundAlthough environmental determinants play an important role in suicide mortality, the quantitative influence of climate change-induced heat anomalies on suicide deaths remains relatively underexamined.ObjectiveThe objective is to quantify the impact of climate change-induced heat anomalies on suicide deaths in Australia from 2000 to 2019.MethodsA time series regression analysis using a generalised additive model was employed to explore the potentially non-linear relationship between temperature anomalies and suicide, incorporating structural variables such as sex, age, season and geographic region. Suicide deaths data were obtained from the Australian National Mortality Database, and gridded climate data of gridded surface temperatures were sourced from the Australian Gridded Climate Dataset.FindingsHeat anomalies in the study period were between 0.02°C and 2.2°C hotter than the historical period due to climate change. Our analysis revealed that approximately 0.5% (264 suicides, 95% CI 257 to 271) of the total 50 733 suicides within the study period were attributable to climate change-induced heat anomalies. Death counts associated with heat anomalies were statistically significant (p value 0.03) among men aged 55+ years old. Seasonality was a significant factor, with increased deaths during spring and summer. The relationship between high heat anomalies and suicide deaths varied across different demographic segments.Conclusions and implicationsThis study highlights the measurable impact of climate change-induced heat anomalies on suicide deaths in Australia, emphasising the need for increased climate change mitigation and adaptation strategies in public health planning and suicide prevention efforts focusing on older adult men. The findings underscore the importance of considering environmental factors in addition to individual-level factors in understanding and reducing suicide mortality.

Droughts may increase the risk of mental health problems, but evidence suggests a complex story with some groups being vulnerable while others are not. Previous studies from Australia have found associations with suicide, depression and distress that vary by age, gender and remoteness. Understanding the effects of drought on mental health is important because drought is predicted to be more intense in some areas in the future. We investigated the associations between drought and distress in a survey of rural Australians by age, gender and farming status. We collected distress data using a survey of 5312 people from across the state of Victoria, Australia, in 2015. Respondents completed the Kessler 10 (K10) Psychological Distress Index, and demographic and general health data were collected. We linked a climatic drought index to the locality of residence of respondents. Associations between distress and drought were analyzed using multivariable regression models with interactions by age, gender and farming occupation. Parts of Victoria were in drought in 2015. Drought duration was associated with higher distress in younger rural women (aged 40–54: odds ratio 1.18 per inter-quartile range increase in drought duration) but not older rural women or men. This pattern did not vary between farmers and non-farmers. Drought was associated with increased distress, but this differed between subgroups. Our results suggest that supporting younger women may be particularly important, and understanding ways older Australian rural women cope may enable us to build adaptive capacity and resilience.

Introduction Road traffic noise increases the risk of mortality from ischemic heart disease (IHD). Because noise is highly localized, high resolution maps of exposures and health outcomes are key to urban planning interventions that are informed by health risks. In Australia, publicly accessible IHD deaths data are only available at the coarse spatial aggregation level of local government area (LGA), in which about 130,000 people reside. Herein, we addressed this limitation of health data using statistical downscaling and generated environmental health risk maps for noise at the meshblock level (MB; ~ 90 people). Methods We estimated noise exposures at the MB level using a model of road traffic noise in Melbourne, Australia, from 2011. As recommended by the World Health Organization, a non-linear exposure–response function for traffic noise and IHD was used to calculate odds ratios for noise related IHD in all MBs. Noise attributable risks of IHD death were then estimated by statistically downscaling LGA-level IHD rates to the MB level. Results Noise levels of 80 dB were recorded in some MBs. From the given noise maps, approximately 5% of the population was exposed to traffic noise above the risk threshold of 55 dB. Maps of excess risk at the MB level identified areas in which noise levels and exposed populations are large. Attributable rates of IHD deaths due to noise were generally very low, but some were as high as 5–10 per 100,000, and in extremely noisy and populated MBs represented more than 8% excess risk of IHD death. We presented results as interactive maps of excess risk due to noise at the small neighbourhood scale. Conclusion Our method accommodates low-resolution health data and could be used to inform urban planning and public health decision making for various environmental health concerns. Estimated noise related IHD deaths were relatively few in Melbourne in 2011, likely because road traffic is one of many noise sources and the current noise model underestimates exposures. Nonetheless, this novel computational framework could be used globally to generate maps of noise related health risks using scant health outcomes data.