Valence and spatial explanations for voting in the 2013 Australian election

This paper examines the 2013 Australian federal election to test two competing models of vote choice: spatial politics and valence issues. Using data from the 2013 Australian Election Study, the analysis finds that spatial politics (measured by party identification and self-placement on the left-right spectrum) and valence issues both have significant effects on vote choice. However, spatial measures are more important than valence issues in explaining vote choice, in contrast with recent studies from Britain, Canada and the United States. Explanations for these differences are speculative, but may relate to Australia’s stable party and electoral system, including compulsory voting and the frequency of elections. The consequently high information burden faced by Australian voters may lead to a greater reliance on spatial heuristics than is found elsewhere.

Documenting the inevitable: Voting behaviour at the 2013 Australian election

This chapter uses data from the 2013 Australian Election Study (AES), conducted by Clive Bean, Ian McAllister, Juliet Pietsch and Rachel Gibson (Bean et al. 2014) to investigate political attitudes and voting behaviour in the election. The study was funded by the Australian Research Council and involved a national survey of political attitudes and behaviour using a self-completion questionnaire mailed to respondents on the day before the 7 September election. The sample was a systematic random sample of enrolled voters throughout Australia, drawn by the Australian Electoral Commission. Respondents were given the option of returning the completed questionnaire by reply-paid mail or completing the survey online. Non-respondents were sent several follow-up mailings and the final sample size was 3955, representing a response rate of 34 per cent. The data were weighted to reflect population parameters for gender, age, state and vote.

Ambient air pollution exposure estimation for the global burden of disease 2013

Exposure to ambient air pollution is a major risk factor for global disease. Assessment of the impacts of air pollution on population health and the evaluation of trends relative to other major risk factors requires regularly updated, accurate, spatially resolved exposure estimates. We combined satellite-based estimates, chemical transport model (CTM) simulations and ground measurements from 79 different countries to produce new global estimates of annual average fine particle (PM2.5) and ozone concentrations at 0.1° × 0.1° spatial resolution for five-year intervals from 1990-2010 and the year 2013. These estimates were then applied to assess population-weighted mean concentrations for 1990 – 2013 for each of 188 countries. In 2013, 87% of the world’s population lived in areas exceeding the World Health Organization (WHO) Air Quality Guideline of 10 μg/m3 PM2.5 (annual average). Between 1990 and 2013, decreases in population-weighted mean concentrations of PM2.5 were evident in most high income countries, in contrast to increases estimated in South Asia, throughout much of Southeast Asia, and in China. Population-weighted mean concentrations of ozone increased in most countries from 1990 - 2013, with modest decreases in North America, parts of Europe, and several countries in Southeast Asia.

The global burden of injury: Incidence, mortality, disability-adjusted life years and time trends from the Global Burden of Disease study 2013

Background The Global Burden of Diseases (GBD), Injuries, and Risk Factors study used the disability-adjusted life year (DALY) to quantify the burden of diseases, injuries, and risk factors. This paper provides an overview of injury estimates from the 2013 update of GBD, with detailed information on incidence, mortality, DALYs and rates of change from 1990 to 2013 for 26 causes of injury, globally, by region and by country. Methods Injury mortality was estimated using the extensive GBD mortality database, corrections for ill-defined cause of death and the cause of death ensemble modelling tool. Morbidity estimation was based on inpatient and outpatient data sets, 26 cause-of-injury and 47 nature-of-injury categories, and seven follow-up studies with patient-reported long-term outcome measures. Results In 2013, 973 million (uncertainty interval (UI) 942 to 993) people sustained injuries that warranted some type of healthcare and 4.8 million (UI 4.5 to 5.1) people died from injuries. Between 1990 and 2013 the global age-standardised injury DALY rate decreased by 31% (UI 26% to 35%). The rate of decline in DALY rates was significant for 22 cause-of-injury categories, including all the major injuries. Conclusions Injuries continue to be an important cause of morbidity and mortality in the developed and developing world. The decline in rates for almost all injuries is so prominent that it warrants a general statement that the world is becoming a safer place to live in. However, the patterns vary widely by cause, age, sex, region and time and there are still large improvements that need to be made.

The ambient aerosol characterization during the prescribed bushfire season in Brisbane 2013

Prescribed burnings are conducted in Queensland each year from August until November aiming to decrease the impact of bushfire hazards and maintain the health of vegetation. This study reports chemical characteristics of the ambient aerosol, with a focus on source apportionment of the organic aerosol (OA)fraction, during the prescribed biomass burning (BB) season in Brisbane 2013. All measurements were conducted within the International Laboratory for Air Quality and Health (ILAQH) located in Brisbane’s Central Business District. Chemical composition, degree of ageing and the influence of BB emission on the air quality of central Brisbane were characterized using a compact Time of Flight Aerosol Mass Spectrometer (cToF-AMS). AMS loadings were dominated by OA (64 %), followed by, sulfate (17 %), ammonium (14 %) and nitrates (5 %). Source apportionment was applied on the AMS OA mass spectra via the multilinear engine solver (ME-2) implementation within the recently developed Source Finder (SoFi) interface. Six factors were extracted including hydrocarbon-like OA (HOA), cooking-related OA (COA), biomass burning OA (BBOA), low-volatility oxygenated OA (LV-OOA), semivolatile oxygenated OA (SV-OOA), and nitrogen-enriched OA (NOA). The aerosol fraction that was attributed to BB factor was 9 %, on average over the sampling period. The high proportion of oxygenated OA (72 %), typically representing aged emissions, could possess a fraction of oxygenated species transformed from BB components on their way to the sampling site.