Neurological outcome following out of hospital cardiac arrest: Evaluation of performance of existing risk prediction models in a UK cohort.

Introduction: Out of hospital cardiac arrest (OHCA) is a common problem. Rates of survival are low and a proportion of survivors are left with an unfavourable neurological outcome. Four models have been developed to predict risk of unfavourable outcome at the time of critical care admission - the Cardiac Arrest Hospital Prognosis (CAHP), MIRACLE2, Out of Hospital Cardiac Arrest (OHCA), and Targeted Temperature Management (TTM) models. This evaluation evaluates the performance of these four models in a United Kingdom population and provides comparison to performance of the Acute Physiology and Chronic Health Evaluation II (APACHE-II) score. Methods: A retrospective evaluation of the performance of the models was conducted over a 43-month period in 414 adult, non-pregnant patients presenting consecutively following non-traumatic OHCA to the five units in our regional critical care network. Scores were generated for each model for where patients had complete data (CAHP = 347, MIRACLE2 = 375, OHCA = 356, TTM = 385). Cerebral Performance Category (CPC) outcome was calculated for each patient at last documented follow up and an unfavourable outcome defined as CPC ⩾ 3. Performance for discrimination of unfavourable outcome was tested by generating receiver operating characteristic (ROC) curves for each model and comparing the area under the curve (AUC). Results: Best performance for discrimination of unfavourable outcome was demonstrated by the high risk group of the CAHP score with an AUC of 0.87 [95% CI 0.83-0.91], specificity of 97.1% [95% CI 93.8-100] and positive predictive value (PPV) of 96.3% [95% CI 92.2-100]. The high risk group of the MIRACLE2 model, which is significantly easier to calculate, had an AUC of 0.81 [95% CI 0.76-0.86], specificity of 92.3% [95% CI 87.2-97.4] and PPV of 95.2% [95% CI 91.9-98.4]. Conclusion: The CAHP, MIRACLE2, OHCA and TTM scores all perform comparably in a UK population to the original development and validation cohorts. All four scores outperform APACHE-II in a population of patients resuscitated from OHCA. CAHP and TTM perform best but are more complex to calculate than MIRACLE2, which displays inferior performance.

Climate change, environmental extremes, and human health in Australia: challenges, adaptation strategies, and policy gaps.

Climate change presents a major public health concern in Australia, marked by unprecedented wildfires, heatwaves, floods, droughts, and the spread of climate-sensitive infectious diseases. Despite these challenges, Australia's response to the climate crisis has been inadequate and subject to change by politics, public sentiment, and global developments. This study illustrates the spatiotemporal patterns of selected climate-related environmental extremes (heatwaves, wildfires, floods, and droughts) across Australia during the past two decades, and summarizes climate adaptation measures and actions that have been taken by the national, state/territory, and local governments. Our findings reveal significant impacts of climate-related environmental extremes on the health and well-being of Australians. While governments have implemented various adaptation strategies, these plans must be further developed to yield concrete actions. Moreover, Indigenous Australians should not be left out in these adaptation efforts. A collaborative, comprehensive approach involving all levels of government is urgently needed to prevent, mitigate, and adapt to the health impacts of climate change.

Acute health effects of bushfire smoke on mortality in Sydney, Australia.

BACKGROUND: Bushfire smoke is a major ongoing environmental hazard in Australia. In the summer of 2019-2020 smoke from an extreme bushfire event exposed large populations to high concentrations of particulate matter (PM) pollution. In this study we aimed to estimate the effect of bushfire-related PM of less than 2.5 µm in diameter (PM2.5) on the risk of mortality in Sydney, Australia from 2010 to 2020. METHODS: We estimated concentrations of PM2.5 for three subregions of Sydney from measurements at monitoring stations using inverse-distance weighting and cross-referenced extreme days (95th percentile or above) with satellite imagery to determine if bushfire smoke was present. We then used a seasonal and trend decomposition method to estimate the Non-bushfire PM2.5 concentrations on those days. Daily PM2.5 concentrations above the Non-bushfire concentrations on bushfire smoke days were deemed to be Bushfire PM2.5. We used distributed-lag non-linear models to estimate the effect of Bushfire and Non-bushfire PM2.5 on daily counts of mortality with sub-analyses by age. These models controlled for seasonal trends in mortality as well as daily temperature, day of week and public holidays. RESULTS: Within the three subregions, between 110 and 134 days were identified as extreme bushfire smoke days within the subregions of Sydney. Bushfire-related PM2.5 ranged from 6.3 to 115.4 µg/m3. A 0 to 10 µg/m3 increase in Bushfire PM2.5 was associated with a 3.2% (95% CI 0.3, 6.2%) increase in risk of all-cause death, cumulatively, in the 3 days following exposure. These effects were present in those aged 65 years and over, while no effect was observed in people under 65 years. CONCLUSION: Bushfire PM2.5 exposure is associated with an increased risk of mortality, particularly in those over 65 years of age. This increase in risk was clearest at Bushfire PM2.5 concentrations up to 30 µg/m3 above background (Non-bushfire), with possible plateauing at higher concentrations of Bushfire PM2.5.

The mortality effect of PM2.5 sources in the Greater Metropolitan Region of Sydney, Australia.

We describe an assessment of the impact on mortality of eight major sources of PM2.5 in the Greater Metropolitan Region of Sydney, Australia (GMR). We modeled exposure to PM2.5 for the year July 2010 to June 2011 and estimated the burden of current mortality attributable to these sources. We also estimated the number of life-years that would be produced if emissions from wood heaters and power stations, the two largest emissions sources, were reduced. Wood heaters (assuming a real-world emissions factor of 11.4 g of PM2.5 per kg of wood burned) were the most important source of PM2.5 exposure, responsible for around 24.0% of the total anthropogenic PM2.5 concentration. On-road sources and power stations were also important, responsible for 16.9% and 10.5% of anthropogenic PM2.5 exposure respectively. Around 1.2% of mortality (5,900 YLL) was attributable to long-term exposure to all anthropogenic PM2.5, including 0.3% (1,400 YLL) attributable to wood heater-related PM2.5, 0.2% (990 YLL) to on-road sources and 0.1% (620 YLL) to power stations. Compared to ongoing emissions at 2010/11 levels, we estimated that a sustained reduction in emissions from wood heaters due to the introduction of an emissions standard of 1.5 g of PM2.5 per kilogram of wood burned (real world emissions factor of 3.9 g of PM2.5 per kg of wood burned) and the associated reduction in PM2.5 population exposure would produce 90,000 life-years among the cohort of people alive in 2010/11. Complete removal of sulphur oxide emissions from power stations would produce 14,000 life-years and complete removal of nitrogen oxide emissions would produce 38,000 life-years. A range of sensitivity analyses indicate the true impact of PM2.5 from these sources is likely to be at least as large as these estimates. This assessment shows that eight sources are responsible for more than 60% of exposure to anthropogenic PM2.5 in the Sydney GMR. Although the burden of mortality attributable to each source is relatively small, interventions that achieve sustained reductions in emissions could provide substantial health benefits, which are likely to far outweigh the costs.

Avoidable Mortality Attributable to Anthropogenic Fine Particulate Matter (PM2.5) in Australia.

Ambient fine particulate matter <2.5 µm (PM2.5) air pollution increases premature mortality globally. Some PM2.5 is natural, but anthropogenic PM2.5 is comparatively avoidable. We determined the impact of long-term exposures to the anthropogenic PM component on mortality in Australia. PM2.5-attributable deaths were calculated for all Australian Statistical Area 2 (SA2; n = 2310) regions. All-cause death rates from Australian mortality and population databases were combined with annual anthropogenic PM2.5 exposures for the years 2006-2016. Relative risk estimates were derived from the literature. Population-weighted average PM2.5 concentrations were estimated in each SA2 using a satellite and land use regression model for Australia. PM2.5-attributable mortality was calculated using a health-impact assessment methodology with life tables and all-cause death rates. The changes in life expectancy (LE) from birth, years of life lost (YLL), and economic cost of lost life years were calculated using the 2019 value of a statistical life. Nationally, long-term population-weighted average total and anthropogenic PM2.5 concentrations were 6.5 µg/m3 (min 1.2-max 14.2) and 3.2 µg/m3 (min 0-max 9.5), respectively. Annually, anthropogenic PM2.5-pollution is associated with 2616 (95% confidence intervals 1712, 3455) deaths, corresponding to a 0.2-year (95% CI 0.14, 0.28) reduction in LE for children aged 0-4 years, 38,962 (95%CI 25,391, 51,669) YLL and an average annual economic burden of $6.2 billion (95%CI $4.0 billion, $8.1 billion). We conclude that the anthropogenic PM2.5-related costs of mortality in Australia are higher than community standards should allow, and reductions in emissions are recommended to achieve avoidable mortality.

Lessons Learnt From Exercise Celestial Navigation: The Application of a Geographic Information System to Inform Legionnaires' Disease Control Activity.

Geographic information systems (GIS) have emerged in the past few decades as a technology capable of assisting in the control of infectious disease outbreaks. A Legionnaires' disease cluster investigation in May 2016 in Sydney, New South Wales (NSW), Australia, demonstrated the importance of using GIS to identify at-risk water sources in real-time for field investigation to help control any immediate environmental health risk, as well as the need for more staff trained in the use of this technology. Sydney Local Health District Public Health Unit (PHU) subsequently ran an exercise (based on this investigation) with 11 staff members from 4 PHUs across Sydney to further test staff capability to use GIS across NSW. At least 80% of exercise participants reported that the scenario progression was realistic, assigned tasks were clear, and sufficient data were provided to complete tasks. The exercise highlighted the multitude of geocoding applications and need for inter-operability of systems, as well as the need for trained staff with specific expertise in spatial analysis to help assist in outbreak control activity across NSW. Evaluation data demonstrated the need for a common GIS, regular education and training, and guidelines to support the collaborative use of GIS for infectious disease epidemiology in NSW. (Disaster Med Public Health Preparedness. 2019;13:372-374).

Impact of emergency department occupancy on waiting times, rates of admission and representation, and length of stay when hospitalised: A data linkage study.

OBJECTIVES: To assess the association between ED occupancy and relevant outcomes including ED waiting times, rates of admission and representation and length of stay when hospitalised. METHODS: Retrospective study of all ED presentations by New South Wales (NSW), Australia, residents to 15 NSW public, principal referral or paediatric specialist hospitals between 1 January to 31 December 2015 (N = 935 282). ED data were linked longitudinally (to ED data) and cross-sectionally to hospital admissions data. An ED-system measure of occupancy was assigned to each ED record. The study outcomes were ED waiting time, admission to hospital, 28 day representation, and length of stay (LOS) when admitted. Outcomes were analysed using univariate analyses and multivariable general linear and binary logistic regression models. RESULTS: Increased ED occupancy was associated with increased ED waiting times, particularly at low-baseline occupancy (e.g. rate ratio = 2.22, 95% confidence interval [CI] [2.10-2.35], for non-urgent triaged patients). However, results were conditional on triage category, such that estimated effects were smaller or not significant in emergency and resuscitation triaged patients (e.g. rate ratio = 1.59, 95% CI [1.52-1.65], for emergency patients). ED occupancy only showed small or no associations with admission to hospital, 28 day representation and LOS when admitted. CONCLUSIONS: Higher ED occupancy was associated with increased waiting times conditional on triage category and baseline occupancy. Collectively, the results show that NSW principal referral EDs are robust, and are currently capable of handling variation in occupancy by prioritising treatment for the most urgent patients.

Age and cause-of-death contributions to area socioeconomic, sex and remoteness differences in life expectancy in New South Wales, 2010-2012.

OBJECTIVES: To determine age group- and cause-of-death-specific contributions to area socioeconomic status (SES), sex and remoteness life expectancy inequalities. METHODS: Mortality and estimated residential population data from New South Wales, Australia, over 2010-2012 was used to calculate life expectancy. Inequalities by sociodemographic groups were partitioned into age group- and cause-of-death-specific contributions. RESULTS: The largest contributions to SES differentials in life expectancy were observed at 60-84 years of age; for cancer, cardiovascular, endocrine and respiratory causes of death; and additionally external causes of death for males. Sex inequalities ranged from 3.6 to 5.2 years, with common causes of death such as cardiovascular disease and cancer in late adulthood (60+ years) accounting for the bulk of the differences. Smaller differences in life expectancy were observed by remoteness, with the largest contributions observed in ages 85 years and above, and for cardiovascular, mental, cancer and external causes of death. CONCLUSIONS: Common causes of death in late adulthood accounted for the bulk of life expectancy inequalities. Implications for public health: Development of policy and interventions aimed at addressing social determinants, such as proposed by the WHO's Global Plan of Action, are needed to help reduce sociodemographic inequalities in lifespan.

The health impacts and economic value of wildland fire episodes in the U.S.: 2008-2012.

INTRODUCTION: Wildland fires degrade air quality and adversely affect human health. A growing body of epidemiology literature reports increased rates of emergency departments, hospital admissions and premature deaths from wildfire smoke exposure. OBJECTIVE: Our research aimed to characterize excess mortality and morbidity events, and the economic value of these impacts, from wildland fire smoke exposure in the U.S. over a multi-year period; to date no other burden assessment has done this. METHODS: We first completed a systematic review of the epidemiologic literature and then performed photochemical air quality modeling for the years 2008 to 2012 in the continental U.S. Finally, we estimated the morbidity, mortality, and economic burden of wildland fires. RESULTS: Our models suggest that areas including northern California, Oregon and Idaho in the West, and Florida, Louisiana and Georgia in the East were most affected by wildland fire events in the form of additional premature deaths and respiratory hospital admissions. We estimated the economic value of these cases due to short term exposures as being between $11 and $20B (2010$) per year, with a net present value of $63B (95% confidence intervals $6-$170); we estimate the value of long-term exposures as being between $76 and $130B (2010$) per year, with a net present value of $450B (95% confidence intervals $42-$1200). CONCLUSION: The public health burden of wildland fires-in terms of the number and economic value of deaths and illnesses-is considerable.

Patterns of low acuity patient presentations to emergency departments in New South Wales, Australia.

OBJECTIVE: To explore the patterns of low acuity patient (LAP) presentations to EDs in New South Wales (NSW), Australia. METHODS: Retrospective study of NSW public hospital ED presentations between January 2013 and December 2014 that were registered in the NSW Emergency Department Data Collection (n = 409 035). LAPs were defined according to the Australian Institute of Health and Welfare (AIHW), Sprivulis and multiple ACEM methods. Multivariable logistic regression was used to assess the adjusted odds of LAP ED presentation by a suite of sociodemographic factors. RESULTS: The percentage of LAPs varied considerably by definition, being as high as 54.7% (inner regional areas) and as low as 3.2% (major cities) using revised ACEM methods modified to contain unlimited consultation times or consultation times of 15 min or less, respectively. For each method, higher proportions of LAPs were observed in inner regional and remote/very remote areas relative to major cities. LAP ED presentations, based on ACEM definition with 1 h or 15 min consultation times, were greater in younger patients, increased during out of business hours and weekends, and decreased with increasing general practitioner (GP) density. CONCLUSION: The percentage of LAPs varied substantially by definition, and further work is required to validate the methods, particularly around the appropriateness of length of consultation time with ACEM, between different hospitals and remoteness areas. Age was strongly associated with low acuity, with substantial effects also observed for GP density, and attendances during out of hours and weekends.

Geographic and socioeconomic factors in patients with malignant pleural mesothelioma in New South Wales and their impact upon clinical outcomes.

BACKGROUND AND OBJECTIVE: Whilst the impact of clinicopathological factors on the prognosis of malignant pleural mesothelioma (MPM) is well understood, socioeconomic and geographic factors have received less attention. We analysed the relationship between geographic and socioeconomic factors upon survival and treatment provision in a large series of patients with MPM. METHODS: We assessed MPM patients awarded compensation between 2002 and 2009 with additional MPM incidence data from the New South Wales (NSW) Cancer Registry. The impact of geographic remoteness, distance from oncological multidisciplinary team (MDT) and Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD) upon survival, clinical features and treatment received was analysed. RESULTS: We identified 910 patients (67% residing in major cities; 92% <50 km from MDT). Median overall survival was 10.0 months. On multivariate analysis, age >70 (hazard ratio (HR) = 1.39), male gender (HR =1.36), non-epithelioid histological subtype (HR = 2.18) and IRSAD status by decreasing quintile (HR = 1.06) were independent prognostic factors. There was no significant advantage for patients residing in major cities (10.6 months vs 8.8 months; P = 0.162) or within 50 km of MDT (10.3 months vs 7.8 months; P = 0.539). Patient's geographic location and distance to MDT did not impact chemotherapy, adjuvant radiotherapy or extrapleural pneumonectomy provision. Socioeconomically disadvantaged patients were significantly less likely to receive chemotherapy (37.4% vs 54.8%; P = 0.001). CONCLUSION: This study provides evidence for differences in the treatment and survival according to socioeconomic status for compensated MPM patients in NSW. Further research is warranted to seek additional explanations for the differences noted by comparing the treatments and outcomes of compensated and non-compensated MPM patients in NSW.

Socioeconomic, remoteness and sex differences in life expectancy in New South Wales, Australia, 2001-2012: a population-based study.

OBJECTIVES: Despite being one of the healthiest countries in the world, Australia displays substantial mortality differentials by socioeconomic disadvantage, remoteness and sex. In this study, we examined how these mortality differentials translated to differences in life expectancy between 2001 and 2012. DESIGN AND SETTING: Population-based study using mortality and estimated residential population data from Australia's largest state, New South Wales (NSW), between 2001 and 2012. Age-group-specific death rates by socioeconomic disadvantage quintile, remoteness (major cities vs regional and remote areas), sex and year were estimated via Poisson regression, and inputted into life table calculations to estimate life expectancy. RESULTS: Life expectancy decreased with increasing socioeconomic disadvantage in males and females. The disparity between the most and least socioeconomically deprived quintiles was 3.77 years in males and 2.39 years in females in 2012. Differences in life expectancy by socioeconomic disadvantage were mostly stable over time. Gender gaps in life expectancy ranged from 3.50 to 4.93 years (in 2012), increased with increasing socioeconomic disadvantage and decreased by ∼1 year for all quintiles between 2001 and 2012. Overall, life expectancy varied little by remoteness, but was 1.8 years higher in major cities compared to regional/remote areas in the most socioeconomically deprived regions in 2012. CONCLUSIONS: Socioeconomic disadvantage and sex were strongly associated with life expectancy. The disparity in life expectancy across the socioeconomic spectrum was larger in males and was stable over time. In contrast, gender gaps reduced for all quintiles between 2001 and 2012, and a remoteness effect was evident in 2012, but only for those living in the most deprived areas.

The mortality effect of ship-related fine particulate matter in the Sydney greater metropolitan region of NSW, Australia.

This study investigates the mortality effect of primary and secondary PM2.5 related to ship exhaust in the Sydney greater metropolitan region of Australia. A detailed inventory of ship exhaust emissions was used to model a) the 2010/11 concentration of ship-related PM2.5 across the region, and b) the reduction in PM2.5 concentration that would occur if ships used distillate fuel with a 0.1% sulfur content at berth or within 300 km of Sydney. The annual loss of life attributable to 2010/11 levels of ship-related PM2.5 and the improvement in survival associated with use of low-sulfur fuel were estimated from the modelled concentrations. In 2010/11, approximately 1.9% of the region-wide annual average population weighted-mean concentration of all natural and human-made PM2.5 was attributable to ship exhaust, and up to 9.4% at suburbs close to ports. An estimated 220 years of life were lost by people who died in 2010/11 as a result of ship exhaust-related exposure (95% CIß: 140-290, where CIß is the uncertainty in the concentration-response coefficient only). Use of 0.1% sulfur fuel at berth would reduce the population weighted-mean concentration of PM2.5 related to ship exhaust by 25% and result in a gain of 390 life-years over a twenty year period (95% CIß: 260-520). Use of 0.1% sulfur fuel within 300 km of Sydney would reduce the concentration by 56% and result in a gain of 920 life-years over twenty years (95% CIß: 600-1200). Ship exhaust is an important source of human exposure to PM2.5 in the Sydney greater metropolitan region. This assessment supports intervention to reduce ship emissions in the GMR. Local strategies to limit the sulfur content of fuel would reduce exposure and will become increasingly beneficial as the shipping industry expands. A requirement for use of 0.1% sulfur fuel by ships within 300 km of Sydney would provide more than twice the mortality benefit of a requirement for ships to use 0.1% sulfur fuel at berth.

The health benefits of reducing air pollution in Sydney, Australia.

Among industrialised countries, fine particle (PM2.5) and ozone levels in the Sydney metropolitan area of Australia are relatively low. Annual mean PM2.5 levels have historically remained below 8 µg/m(3) while warm season (November-March) ozone levels occasionally exceed the Australian guideline value of 0.10 ppm (daily 1 h max). Yet, these levels are still below those seen in the United States and Europe. This analysis focuses on two related questions: (1) what is the public health burden associated with air pollution in Sydney; and (2) to what extent would reducing air pollution reduce the number of hospital admissions, premature deaths and number of years of life lost (YLL)? We addressed these questions by applying a damage function approach to Sydney population, health, PM2.5 and ozone data for 2007 within the BenMAP-CE software tool to estimate health impacts and economic benefits. We found that 430 premature deaths (90% CI: 310-540) and 5800 YLL (95% CI: 3900-7600) are attributable to 2007 levels of PM2.5 (about 2% of total deaths and 1.8% of YLL in 2007). We also estimate about 630 (95% CI: 410-840) respiratory and cardiovascular hospital admissions attributable to 2007 PM2.5 and ozone exposures. Reducing air pollution levels by even a small amount will yield a range of health benefits. Reducing 2007 PM2.5 exposure in Sydney by 10% would, over 10 years, result in about 650 (95% CI: 430-850) fewer premature deaths, a gain of 3500 (95% CI: 2300-4600) life-years and about 700 (95% CI: 450-930) fewer respiratory and cardiovascular hospital visits. These results suggest that substantial health benefits are attainable in Sydney with even modest reductions in air pollution.

The impact of heat on mortality and morbidity in the Greater Metropolitan Sydney Region: a case crossover analysis.

BACKGROUND: This study examined the association between unusually high temperature and daily mortality (1997-2007) and hospital admissions (1997-2010) in the Sydney Greater Metropolitan Region (GMR) to assist in the development of targeted health programs designed to minimise the public health impact of extreme heat. METHODS: Sydney GMR was categorized into five climate zones. Heat-events were defined as severe or extreme. Using a time-stratified case-crossover design with a conditional logistic regression model we adjusted for influenza epidemics, public holidays, and climate zone. Odds ratios (OR) and 95% confidence intervals were estimated for associations between daily mortality and hospital admissions with heat-event days compared to non-heat event days for single and three day heat-events. RESULTS: All-cause mortality overall had similar magnitude associations with single day and three day extreme and severe events as did all cardiovascular mortality. Respiratory mortality was associated with single day and three day severe events (95th percentile, lag0: OR = 1.14; 95%CI: 1.04 to 1.24). Diabetes mortality had similar magnitude associations with single day and three day severe events (95th percentile, lag0: OR = 1.22; 95%CI: 1.03 to 1.46) but was not associated with extreme events. Hospital admissions for heat related injuries, dehydration, and other fluid disorders were associated with single day and three day extreme and severe events. Contrary to our findings for mortality, we found inconsistent and sometimes inverse associations for extreme and severe events with cardiovascular disease and respiratory disease hospital admissions. Controlling for air pollutants did not influence the mortality associations but reduced the magnitude of the associations with hospital admissions particularly for ozone and respiratory disease. CONCLUSIONS: Single and three day events of unusually high temperatures in Sydney are associated with similar magnitude increases in mortality and hospital admissions. The trend towards an inverse association between cardio-vascular admissions and heat-events and the strong positive association between cardio-vascular mortality and heat-events suggests these events may lead to a rapid deterioration in persons with existing cardio-vascular disease resulting in death. To reduce the adverse effects of high temperatures over multiple days, and less extreme but more frequent temperatures over single days, targeted public health messages are critical.

Emergency department visits, ambulance calls, and mortality associated with an exceptional heat wave in Sydney, Australia, 2011: a time-series analysis.

BACKGROUND: From January 30-February 6, 2011, New South Wales was affected by an exceptional heat wave, which broke numerous records. Near real-time Emergency Department (ED) and ambulance surveillance allowed rapid detection of an increase in the number of heat-related ED visits and ambulance calls during this period. The purpose of this study was to quantify the excess heat-related and all-cause ED visits and ambulance calls, and excess all-cause mortality, associated with the heat wave. METHODS: ED and ambulance data were obtained from surveillance and administrative databases, while mortality data were obtained from the state death registry. The observed counts were compared with the average counts from the same period from 2006/07 through 2009/10, and a Poisson regression model was constructed to calculate the number of excess ED visits, ambulance and deaths after adjusting for calendar and lag effects. RESULTS: During the heat wave there were 104 and 236 ED visits for heat effects and dehydration respectively, and 116 ambulance calls for heat exposure. From the regression model, all-cause ED visits increased by 2% (95% CI 1.01-1.03), all-cause ambulance calls increased by 14% (95% CI 1.11-1.16), and all-cause mortality increased by 13% (95% CI 1.06-1.22). Those aged 75 years and older had the highest excess rates of all outcomes. CONCLUSIONS: The 2011 heat wave resulted in an increase in the number of ED visits and ambulance calls, especially in older persons, as well as an increase in all-cause mortality. Rapid surveillance systems provide markers of heat wave impacts that have fatal outcomes.