No abstract

Wastewater analysis, or wastewater-based epidemiology, has become a common tool to monitor trends of illicit drug consumption around the world. In this study, we examined trends in cocaine, 3,4-methylenedioxymethamphetamine (MDMA) and methamphetamine consumption by measuring their residues in wastewater from two wastewater treatment plants in Australia (specifically, an urban and a rural catchment, both in South East Queensland) between 2009 and 2015. With direct injection of the samples, target analytes were identified and quantified using liquid chromatography-mass spectrometry. Cocaine and MDMA residues and metabolites were mainly quantifiable in the urban catchment while methamphetamine residues were consistently detected in both urban and rural catchments. There was no consistent trend in the population normalised mass loads observed for cocaine and MDMA at the urban site between 2009 and 2015. In contrast, there was a five-fold increase in methamphetamine consumption over this period in this catchment. For methamphetamine consumption, the rural area showed a very similar trend as the urban catchment starting at a lower baseline. The observed increase in per capita loads of methamphetamine via wastewater analysis over the past six years in South East Queensland provides objective evidence for increased methamphetamine consumption in the Australian population while the use of other illicit stimulants remained relatively stable.

Purpose Researchers who study mortality among survey participants have multiple options for obtaining information about which participants died (and when and how they died). Some use public record and commercial databases; others use the National Death Index; some use the Social Security Death Master File; and still others triangulate sources and use Internet searches and genealogic methods. We ask how inferences about mortality rates and disparities depend on the choice of source of mortality information. Methods Using data on a large, nationally representative cohort of people who were first interviewed as high school sophomores in 1980 and for whom we have extensive identifying information, we describe mortality rates and disparities through about age 50 using four separate sources of mortality data. We rely on cross-tabular and multivariate logistic regression models. Results These sources of mortality information often disagree about which of our panelists died by about age 50, and also about overall mortality rates. However, differences in mortality rates (i.e., by sex, race/ethnicity, education) are similar across of sources of mortality data. Conclusion Researchers’ source of mortality information affects estimates of overall mortality rates but not estimates of differential mortality by sex, race/ethnicity, or education.

Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R 2 = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCSH model (R 2 = 0.966) was ±4.05% with 95% confidence intervals. The CCSH model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCSNa, R 2 = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

Methamphetamine, MDMA, cocaine, cannabis, and alcohol in samples from 20 wastewater treatment plants servicing the eight state or territory capitals of Australia were analyzed, with equivalent coverage of >45% of the national population. Trends in drug consumption were calculated and assessed from samples collected from 2016 to 2020, with a focus on pre-COVID-19 (August 2016 to December 2019), versus February to June 2020, when Australia observed a nationwide lockdown. Results showed delayed but significant decreases in methamphetamine, >50% in Western Australia. In contrast, significant increases in cannabis in most jurisdictions were observed. This suggests changes in consumption may be somewhat linked to reduced supply of imported substances, with increased use of locally produced drugs. Initial decreases in cocaine and MDMA consumption were evident in many parts of the country, but pre-COVID trends were re-established after April 2020. Interestingly, weekend–weekday differences were narrowed for cocaine, MDMA, and alcohol during lockdown, which might be expected due to bars being closed and social gathering not allowed. With this study providing insight into the first four months of COVID-19 restrictions in Australia, it remains to be seen what the longer-term effect of the pandemic will be.