Drugs and the internet.

Key findings: The number of retailers on the Silk Road increased (from 282 at time 1 to 374 at the last time point), while the number of retailers on the surface web remained relatively stable (92 at time 1 and 101 at the last time point). The increase on the Silk Road is largely driven by international rather than domestic retailers. • On the Silk Road, cannabis and EPS were sold by the largest number of retailers consistently across all time points, followed by MDMA (3,4-methylenedioxy-Nmethylamphetamine) and pharmaceuticals (primarily benzodiazepines and sildenafil). • The type of EPS available from surface web retailers differed substantially from the EPS available from those selling on the Silk Road. EPS sold on the Silk Road more closely mirrored those most commonly used by EDRS participants (i.e. people who regularly use psychostimulants) including drugs from the 2C-x and NBOMe categories, followed by DMT (dimethyltryptamine), Mephedrone and Methylone. • Average prices of methamphetamine, cocaine and ecstasy being sold on the Silk Road remained stable across the time period. Average domestic prices for common quantities of these substances were comparable to prices paid for these same quantities by 2012 EDRS participants. Average international prices for these substances were substantially lower

Who sells what? Country specific differences in substance availability on the Agora cryptomarket

Background: To date monitoring of cryptomarkets operating on the dark net has largely focused on market size and substance availability. Less is known of country specific differences in these indicators and how they may corroborate population prevalence estimates for substance use in different countries.Methods: All substance listings from the cryptomarket Agora were recorded over seven time points throughout February and March 2015. Agora was chosen due to its size as the second largest cryptomarket operating and the level of detail of information provided in individual substance listings. Data were collated and the number of unique sellers selling each substance by country of origin was analysed.Results: An average of 14,456.7 substance listings were identified across sampled days from 868.7 unique sellers. The top five countries by number of listings were the USA, United Kingdom, Australia, China and the Netherlands, collectively accounting for 61.8% of all identified listings and 68% of all unique sellers. Australia was over represented in terms of sellers per capita, while China was over represented in new psychoactive substance (NPS) listings. When examined by number of listings per seller, the Netherlands and China stood out as particularly large, likely due to these countries’ role in the local production of various illicit and new psychoactive substances.Conclusions: Numbers of sellers by country of origin appear to be influenced by several factors. Australia’s overrepresentation in sellers per capita may indicate its relative geographical isolation and the potential for profit margins from selling online, while China’s overrepresentation in NPS listings may reflect domestic production of these substances. Continued monitoring will provide enhanced understanding of the increasingly complex and globalised nature of illicit drug markets

Drugs and the internet

This bulletin is the seventh in a series by Drug Trends that provides analysis of trends over time in the availability and type of substances sold via the internet on the darknet. The current bulletin reports for the time period January 2016 to June 2016

Cleaner air for vulnerable people – Finding better locations for essential building premises

Long-term exposure to ambient air pollution even at relatively low concentrations is associated with adverse health effects especially for children, the elderly and people with pre-existing chronic disease. Due to a variety of urban planning decisions, essential building premises attended by these vulnerable groups may be sited in locations exposed to higher levels of air pollution. Using high spatial resolution air pollution concentrations estimated by satellite-based Land Use Regression (LUR) models in Australia, we mapped and approximated the annual average concentrations of particulate matter (PM2.5) and nitrogen dioxide (NO2) at schools, child care centres, aged care facilities and hospitals across Sydney. We found that 137 (3.1 %) and 287 (6.4 %) of the total number of essential buildings assessed were exposed to annual average PM2.5 and NO2 concentrations, respectively, where: the air pollutant concentrations were greater than the median concentration of other locations in the surrounding Local Government Area (LGA), and; air pollutant levels were greater than the 90th percentile concentrations for Sydney, and; air pollution at the essential building location was at least 1 µg/m3 for PM2.5 or 2 ppb for NO2 greater than the 25th percentile concentration of other locations in the LGA. Based on these criteria, we found that many essential building premises in Sydney were in high air pollution locations and there were other meaningfully lower air pollution locations within the surrounding area. Air quality is becoming an increasingly important issue for local jurisdictions to consider as more essential amenities are required to serve denser populations in busier places exposed to more air pollution. Our study showed that high resolution maps can be used as a health-based planning tool to encourage the siting of buildings at locations better protective of health

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

Physical activity, diet quality and all-cause cardiovascular disease and cancer mortality: a prospective study of 346 627 UK Biobank participants

Objectives To examine independent and interactive associations of physical activity and diet with all-cause, cardiovascular disease (CVD) and physical activity, diet and adiposity-related (PDAR) cancer mortality. Methods This population-based prospective cohort study (n=346 627) is based on the UK Biobank data with linkage to the National Health Service death records to 30 April 2020. A left-truncated Cox proportional hazards model was fitted to examine the associations between exposures (self-reported total moderate-to-vigorous intensity physical activity (MVPA), vigorous-intensity physical activity (VPA) and a diet quality index (score ranged 0-3)) and outcomes (all-cause, CVD and PDAR cancer mortality). Results During a median follow-up of 11.2 years, 13 869 participants died from all causes, 2650 from CVD and 4522 from PDAR cancers. Compared with quartile 1 (Q1, 0-210 min/week), Q2-Q4 of MVPA were associated with lower risks of all-cause (HR ranged from 0.87 (95% CI: 0.83 to 0.91) to 0.91 (95% CI: 0.87 to 0.96)), CVD (HR ranged from 0.85 (95% CI: 0.76 to 0.95) to 0.90 (95% CI: 0.81 to 1.00)) and PDAR cancer mortality (HR ranged from 0.86 (95% CI: 0.79 to 0.93) to 0.94 (95% CI: 0.86 to 1.02)). Compared with no VPA, any VPA was associated with lower risk for all-cause and CVD mortality (HR ranged from 0.85 (95% CI: 0.80 to 0.89) to 0.88 (95% CI: 0.84 to 0.93) and from 0.75 (95% CI: 0.68 to 0.83) to 0.90 (95% CI: 0.80 to 1.02), respectively). Although not reaching statistical significance for all-cause and CVD mortality, being in the best dietary category (diet quality index=2-3) was associated with a reduction in PDAR cancer mortality (HR=0.86, 95% CI: 0.78 to 0.93). No additive or multiplicative interactions between physical activity categories and dietary quality was found. When comparing across physical activity and diet combinations, the lowest risk combinations consistently included the higher levels of physical activity and the highest diet quality score. Conclusions Adhering to both quality diet and sufficient physical activity is important for optimally reducing the risk of mortality from all causes, CVD and PDAR cancers

Aboriginal Population and Climate Change in Australia: Implications for Health and Adaptation Planning

The health impacts of climate are widely recognised, and extensive modelling is available on predicted changes to climate globally. The impact of these changes may affect populations differently depending on a range of factors, including geography, socioeconomics and culture. This study reviewed current evidence on the health risks of climate change for Australian Aboriginal populations and linked Aboriginal demographic data to historical and projected climate data to describe the distribution of climate-related exposures in Aboriginal compared to non-Aboriginal populations in New South Wales (NSW), Australia. The study showed Aboriginal populations were disproportionately exposed to a range of climate extremes in heat, rainfall and drought, and this disproportionate exposure was predicted to increase with climate change over the coming decades. Aboriginal people currently experience higher rates of climate-sensitive health conditions and soci-oeconomic disadvantages, which will impact their capacity to adapt to climate change. Climate change may also adversely affect cultural practices. These factors will likely impact the health and well-being of Aboriginal people in NSW and inhibit measures to close the gap in health between Aboriginal and non-Aboriginal populations. Climate change, health and equity need to be key considerations in all policies at all levels of government. Effective Aboriginal community engagement is urgently needed to develop and implement climate adaptation responses to improve health and social service preparedness and secure environmental health infrastructure such as drinking water supplies and suitably managed social housing. Further Aboriginal-led research is required to identify the cultural impacts of climate change on health, including adaptive responses based on Aboriginal knowledges

Correction:Aboriginal Population and Climate Change in Australia: Implications for Health and Adaptation Planning(Int. J. Environ. Res. Public Health, (2022), 19, (7502), 10.3390/ijerph19127502)

Error in Figures 2c and 3a In the original publication [1], there was an error in Figure 2c containing a map ofclimate exposures with bar charts indicating relative exposure by category across Aboriginal and non-Aboriginal populations. The exposures in Figure 2c were projected additional days exceeding 35˚C annually, 2020–2039. There was also an error in Figure 3a containing a map of annual days with Macarthur Forest Fire Danger Index exceeding 50 (i.e., “severe” fire danger), with bar charts indicating relative exposure by category across Aboriginal and non-Aboriginal populations for historical data between 1990 and 2009. During publication, formatting changes of the accepted manuscript occurred. The categories in the bar charts in Figures 2c and 3a were incorrect. The corrected Figures 2 and 3 appear below. There are no changes to the text in the manuscrip