Hangover-Related Internet Searches Before and During the COVID-19 Pandemic in England: Observational Study
Bibliographic record
Abstract
BACKGROUND: It is unclear whether heavy alcohol use and associated hangover symptoms changed as a result of the COVID-19 pandemic. Due to a lack of available accurate and nonretrospective self-reported data, it is difficult to directly assess hangover symptoms during the COVID-19 pandemic. OBJECTIVE: This study aimed to examine whether alcohol-induced hangover-related internet searches (eg, "how to cure a hangover?") increased, decreased, or remained the same in England before versus during the COVID-19 pandemic (2020-2021) and during periods of national lockdown. Secondary aims were to examine if hangover-related internet searches in England differed compared to a country that did not impose similar COVID-19 lockdown restrictions. METHODS: Using historical data from Google Trends for England, we compared the relative search volume (RSV) of hangover-related searches in the years before (2016-2019) versus during the COVID-19 pandemic (2020-2021), as well as in periods of national lockdown versus the same periods in 2016-2019. We also compared the RSV of hangover-related searches during the same time frames in a European country that did not introduce national COVID-19 lockdowns at the beginning of the pandemic (Sweden). Hangover-related search terms were identified through consultation with a panel of alcohol researchers and a sample from the general public. Statistical analyses were preregistered prior to data collection. RESULTS: There was no overall significant difference in the RSV of hangover-related terms in England during 2016-2019 versus 2020-2021 (P=.10; robust d=0.02, 95% CI 0.00-0.03). However, during national lockdowns, searches for hangover-related terms were lower, particularly during the first national lockdown in England (P<.001; d=.19, 95% CI 0.16-0.24; a 44% relative decrease). In a comparison country that did not introduce a national lockdown in the early stages of the pandemic (Sweden), there was no significant decrease in hangover-related searches during the same time period (P=.06). However, across both England and Sweden, during later periods of COVID-19 restrictions in 2020 and 2021, the RSV of hangover-related terms was lower than that in the same periods during 2016-2019. Exploratory analyses revealed that national monthly variation in alcohol sales both before and during the COVID-19 pandemic were positively correlated with the frequency of hangover-related searches, suggesting that changes in hangover-related searches may act as a proxy for changes in alcohol consumption. CONCLUSIONS: Hangover-related internet searches did not differ before versus during the COVID-19 pandemic in England but did reduce during periods of national lockdown. Further research is required to confirm how changes in hangover-related search volume relate to heavy episodic alcohol use. TRIAL REGISTRATION: Open Science Framework 2Y86E; https://osf.io/2Y86E.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".