Monitoring New Zealand's Alcohol Consumption using Wastewater-Based Epidemiology
Bibliographic record
Abstract
Wastewater-based epidemiology (WBE) involves the chemical analysis of raw pooled wastewater for specific excreted biomarkers of interest to gain information on the consumption of or exposure of a population to certain substances or microorganisms. In this study, WBE was applied to estimate alcohol consumption in seven cities/towns around New Zealand (NZ) to investigate whether certain factors affect alcohol consumption. Prior to this study, there was no information on the alcohol consumption habits of New Zealanders besides surveys and sales information which can be inaccurate and subject to bias. This information is important to uncover as it can be used as an indicator of NZ’s public health to inform patient-based health policies. Daily 24-hour composite wastewater samples for a period of a week were collected from the inlet of wastewater treatment plants (WWTPs) across NZ each month for six months (April–September 2021). A Triple Quad LC-MS (Liquid Chromatography-Mass Spectrometry) instrument was used to determine the concentration of the biomarker, ethyl sulfate (EtS), using a developed and optimised analytical method. Back calculations then allowed for representation of alcohol consumption in mL/person/day for the seven NZ locations. The average alcohol consumption from this research was 12.2 mL/person/day, equal to 0.96 standard drinks/person/day or 4.5 litres/person/year. The lowest value recorded in one day was 1.5 mL, and the highest value; 44 mL. Factors including NZ locations, public holidays, weekends, population size, NZ Island and COVID-19 Alert Levels were found to significantly affect alcohol consumption in NZ. Comparing to international literature, NZ’s average is lower than most WBE studies including the U.K, U.S., Canada, and Australia. Additionally, from a stability study of the alcohol biomarker (EtS) it was determined plastic storage and sunlight exposure were unsuitable for alcohol WBE storage. From this research, unfair and racist stereotypes of high drinking in certain locations are proven wrong. It is hoped this research can teach New Zealanders to overcome their stereotypical ideology of Māori and Pasifika as ‘big drinkers’. This research aims to inform the public and relevant NZ district health boards about alcohol consumption and trends that affect this. This investigation and the data and discoveries in it have the potential to support patient-based health policies, which could eventually lead to reducing alcohol-related harm in NZ and saving lives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".