Neighbourhood walkability, road density and socio-economic status in Sydney, Australia

Background Planning and transport agencies play a vital role in influencing the design of townscapes, travel modes and travel behaviors, which in turn impact on the walkability of neighbourhoods and residents\u27 physical activity opportunities. Optimising neighbourhood walkability is desirable in built environments, however, the population health benefits of walkability may be offset by increased exposure to traffic related air pollution. This paper describes the spatial distribution of neighbourhood walkability and weighted road density, a marker for traffic related air pollution, in Sydney, Australia. As exposure to air pollution is related to socio-economic status in some cities, this paper also examines the spatial distribution of weighted road density and walkability by socio-economic status (SES). Methods We calculated walkability, weighted road density (as a measure of traffic related air pollution) and SES, using predefined and validated measures, for 5858 Sydney neighbourhoods, representing 3.6 million population. We overlaid tertiles of walkability and weighted road density to define sweet-spots (high walkability-low weighted road density), and sour- spots (low walkability-high weighted road density) neighbourhoods. We also examined the distribution of walkability and weighted road density by SES quintiles. Results Walkability and weighted road density showed a clear east-west gradient across the region. Our study found that only 4 % of Sydney\u27s population lived in sweet-spot neighbourhoods with high walkability and low weighted road density (desirable), and these tended to be located closer to the city centre. A greater proportion of neighbourhoods had health limiting attributes of high weighted road density or low walkability (about 20 % each), and over 5 % of the population lived in sour-spot neighbourhoods with low walkability and high weighted road density (least desirable). These neighbourhoods were more distant from the city centre and scattered more widely. There were no linear trends between walkability/weighted road density and neighbourhood SES. Conclusions Our walkability and weighted road density maps and associated analyses by SES can help identify neighbourhoods with inequalities in health-promoting or health-limiting environments. Planning agencies should seek out opportunities for increased neighbourhood walkability through improved urban development and transport planning, which simultaneously minimizes exposure to traffic related air pollution

Long-term exposure to low concentrations of air pollutants and hospitalisation for respiratory diseases:A prospective cohort study in Australia

Background: Short- and long-term spatiotemporal variation in exposure to air pollution is associated with respiratory morbidity in areas with moderate-to-high level of air pollution, but very few studies have examined whether these associations also exist in areas with low level exposure. Objectives: We assessed the association between spatial variation in long-term exposure to PM and NO and hospitalisation for all respiratory diseases, asthma, chronic obstructive pulmonary disease (COPD), and pneumonia, in older adults residing in Sydney, Australia, a city with low-level concentrations. Methods: We recorded data on hospitalisations for 100,084 participants, who were aged >45 years at entry in 2006–2009 until June 2014. Annual NO and PM concentrations were estimated for the participants’ residential addresses and Cox proportional hazards regression was used to model the association between exposure to air pollutants and first episode of hospitalisation, controlling for personal and area level covariates. We further investigated the shape of the exposure-response association and potential effect modification by age, sex, education level, smoking status, and BMI. Results: NO and PM annual mean exposure estimates were 17.5 μg·m and 4.5 μg·m respectively. NO and PM was positively, although not significantly, associated with asthma. The adjusted hazard ratio for a 1 μg·m increase in PM was 1.08, 95% confidence interval 0.89–1.30. The adjusted hazard ratio for a 5 μg·m increase in NO was 1.03, 95% confidence interval 0.88–1.19. We found no positive statistically significant associations with hospitalisation for all respiratory diseases, and pneumonia while negative associations were observed with COPD. Conclusions: We found weak positive associations of exposure to air pollution with hospitalisation for asthma while there was no evidence of an association for all respiratory diseases

All-cause mortality and long-term exposure to low level air pollution in the ‘45 and up study’ cohort, Sydney, Australia, 2006–2015

Epidemiological studies show that long-term exposure to ambient air pollution reduces life expectancy. Most studies have been in environments with relatively high concentrations such as North America, Europe and Asia. Associations at the lower end of the concentration-response function are not well defined.We assessed associations between all-cause mortality and exposure to annual average particulate matte

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BACKGROUND: Methods for estimating air pollutant exposures for epidemiological studies are becoming more complex in an effort to minimise exposure error and its associated bias. While land use regression (LUR) modelling is now an established method, there has been little comparison between LUR and other recent, more complex estimation methods. Our aim was to develop a LUR model to estimate intra-city exposures to nitrogen dioxide (NO2) for a Sydney cohort, and to compare those with estimates from a national satellite-based LUR model (Sat-LUR) and a regional Bayesian Maximum Entropy (BME) model. METHODS: Satellite-based LUR and BME estimates were obtained using existing models. We used methods consistent with the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to develop LUR models for NO2 and NOx. We deployed 46 Ogawa passive samplers across western Sydney during 2013/2014 and acquired data on land use, population density, and traffic volumes for the study area. Annual average NO2 concentrations for 2013 were estimated for 947 addresses in the study area using the three models: standard LUR, Sat-LUR and a BME model. Agreement between the estimates from the three models was assessed using interclass correlation coefficient (ICC), Bland-Altman methods and correlation analysis (CC). RESULTS: The NO2 LUR model predicted 84% of spatial variability in annual mean NO2 (RMSE: 1.2 ppb; cross-validated R2: 0.82) with predictors of major roads, population and dwelling density, heavy traffic and commercial land use. A separate model was developed that captured 92% of variability in NOx (RMSE 2.3 ppb; cross-validated R2: 0.90). The annual average NO2 concentrations were 7.31 ppb (SD: 1.91), 7.01 ppb (SD: 1.92) and 7.90 ppb (SD: 1.85), for the LUR, Sat-LUR and BME models respectively. Comparing the standard LUR with Sat-LUR NO2 cohort estimates, the mean estimates from the LUR were 4% higher than the Sat-LUR estimates, and the ICC was 0.73. The Pearson's correlation coefficients (CC) for the LUR vs Sat-LUR values were r = 0.73 (log-transformed data) and r = 0.69 (untransformed data). Comparison of the NO2 cohort estimates from the LUR model with the BME blended model indicated that the LUR mean estimates were 8% lower than the BME estimates. The ICC for the LUR vs BME estimates was 0.73. The CC for the logged LUR vs BME estimates was r = 0.73 and for the unlogged estimates was r = 0.69. CONCLUSIONS: Our LUR models explained a high degree of spatial variability in annual mean NO2 and NOx in western Sydney. The results indicate very good agreement between the intra-city LUR, national-scale sat-LUR, and regional BME models for estimating NO2 for a cohort of children residing in Sydney, despite the different data inputs and differences in spatial scales of the models, providing confidence in their use in epidemiological studies

Seismic structure across the rift valley of the Mid-Atlantic Ridge at 23°20′ (MARK area): Implications for crustal accretion processes at slow spreading ridges

International audienc

The Star Formation and Nuclear Accretion Histories of Normal Galaxies in the AGES Survey

We combine IR, optical and X-ray data from the overlapping, 9.3 square degree NOAO Deep Wide-Field Survey (NDWFS), AGN and Galaxy Evolution Survey (AGES), and XBootes Survey to measure the X-ray evolution of 6146 normal galaxies as a function of absolute optical luminosity, redshift, and spectral type over the largely unexplored redshift range 0.1 < z < 0.5. Because only the closest or brightest of the galaxies are individually detected in X-rays, we use a stacking analysis to determine the mean properties of the sample. Our results suggest that X-ray emission from spectroscopically late-type galaxies is dominated by star formation, while that from early-type galaxies is dominated by a combination of hot gas and AGN emission. We find that the mean star formation and supermassive black hole accretion rate densities evolve like (1+z)^3, in agreement with the trends found for samples of bright, individually detectable starburst galaxies and AGN. Our work also corroborates the results of many previous stacking analyses of faint source populations, with improved statistics.Comment: 19 pages, 15 figures, 3 tables, accepted for publication in Ap

Host galaxies, clustering, Eddington ratios, and evolution of radio, X-ray, and infrared-selected AGNs

We explore the connection between different classes of active galactic nuclei (AGNs) and the evolution of their host galaxies, by deriving host galaxy properties, clustering, and Eddington ratios of AGNs selected in the radio, X-ray, and infrared. We study a sample of 585 AGNs at 0.25 < z < 0.8 using redshifts from the AGN and Galaxy Evolution Survey (AGES) and data in the radio (WSRT 1.4 GHz), X-rays (Chandra XBootes), and mid-IR (IRAC Shallow Survey). The radio, X-ray, and IR AGN samples show modest overlap, indicating that to the flux limits of the survey, they represent largely distinct classes of AGNs. We derive host galaxy colors and luminosities, as well as Eddington ratios (lambda), for obscured or optically faint AGNs. We also measure the two-point cross-correlation between AGNs and galaxies on scales of 0.3-10 h^-1 Mpc, and derive typical dark matter halo masses. We find that: (1) radio AGNs are mainly found in luminous red galaxies, are strongly clustered (with M_halo ~ 3x10^13 h^-1 M_sun), and have very low lambda <~ 10^-3; (2) X-ray-selected AGNs are preferentially found in galaxies in the "green valley" of color-magnitude space and are clustered similarly to typical AGES galaxies (M_halo ~ 10^13 h^-1 M_sun), with 10^-3 <~ lambda <~ 1; (3) IR AGNs reside in slightly bluer, less luminous galaxies than X-ray AGNs, are weakly clustered (M_halo <~ 10^12 h^-1 M_sun), and have lambda > 10^-2. We interpret these results in terms of a simple model of AGN and galaxy evolution, whereby a "quasar" phase and the growth of the stellar bulge occurs when a galaxy's dark matter halo reaches a critical mass between ~10^12 and 10^13 M_sun. Subsequently, star formation ceases and AGN accretion shifts from radiatively efficient (optical- and IR- bright) to radiatively inefficient (optically-faint, radio-bright) modes.Comment: 30 emulateapj pages, 21 figures, 3 tables, v2: minor changes match version to appear in Ap

Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

A Comparative Risk Assessment Of Burden Of Disease And Injury Attributable To 67 Risk Factors And Risk Factor Clusters In 21 Regions, 1990–2010: A Systematic Analysis For The Global Burden Of Disease Study 2010

Background Quantification of the disease burden caused by different risks informs prevention by providing an account of health loss different to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time