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Record W4415305787 · doi:10.1016/j.lanwpc.2025.101711

Psychotropic medication consumption before and after onset of COVID-19 pandemic in 91 countries and regions: a time-series analysis

2025· article· en· W4415305787 on OpenAlexaff
Caige Huang, Yang Yu, Yue Wei, Vincent Ka Chun Yan, Kyung Jin Lee, Shek Ming Leung, Francisco Tsz Tsun Lai, Yi Chai, Ruth Brauer, David Castle, Li Wei, Joseph Hayes, Hao Luo, Dan Siskind, Eric Yan, Esther W. Chan

Bibliographic record

VenueThe Lancet Regional Health - Western Pacific · 2025
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPandemicConsumption (sociology)Psychotropic medicationCoronavirus disease 2019 (COVID-19)Trend analysisPsychotropic drugMEDLINE

Abstract

fetched live from OpenAlex

Background: The availability of psychotropic medications serves as a key indicator of global mental health status, underscoring the critical importance of continued monitoring. However, comprehensive studies assessing the global effect of the COVID-19 pandemic on such trends remain lacking. This study aimed to describe psychotropic consumption trends before and after the onset of the COVID-19 pandemic and investigate the pandemic's short- and long-term effects on psychotropic consumption across 91 countries and regions. Methods: This study used country-level sales data of psychotropic medications between Q1, 2012, and Q2, 2023 of 91 countries and regions from the IQVIA-Multinational Integrated Data Analysis System. Average annual sales trends were estimated and expressed as defined daily dose per 1000 inhabitants per day (DDD/TID) at the overall level and stratified by medication class and country income level. Relative average annual changes were assessed for the periods 2017-2019 and 2020-2022 both overall and within specific medication classes and income groups. The pandemic's short- and long-term effects on psychotropic medication sales were examined through interrupted time series analyses using quarterly data, conducted for each country and at overall level. Findings: Globally, the total consumption of psychotropic medications increased from 34.12 DDD/TID in 2020 to 36.15 DDD/TID in 2022, corresponding to a relative average increase of 2.94% [95% CI 0.97, 4.94] annually. The estimated relative change during 2020-2022 were 1.82% [1.02, 2.64] in Lower-Middle-Income-Countries (LMICs), 6.77% [-0.39, 14.45] in Upper-Middle-Income-Countries (UMICs), and 2.48% [0.72, 4.27] in High-Income-Countries (HICs). Overall psychotropic consumption showed an initial surge in Q1 2020 (level change: 1.94 DDD/TID [1.67, 2.21]), followed by a rapid decline during Q2 2020 (level change: -1.03 [-1.52, -0.54]). Most HICs exhibited a similar pattern. Following the pandemic onset, there was an increasing trend in overall psychotropic consumption (trend change: 0.13 [0.07, 0.19]). 70 of 91 countries showed an increasing slope change. Interpretation: Psychotropic medication consumption increased globally after the onset of the COVID-19 pandemic. During the pandemic, consumption rates in LMICs and UMICs appeared to slow down, however, patterns of change in psychotropic medication consumption following onset of the pandemic vary on a country level. To address these disparities, strategies for equitable psychotropic medication distribution and enhanced mental health care access in LMICs and UMICs are needed to improve global mental health. Funding: None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.433
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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