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Mental Health Care Utilization and Prescription Rates Among Children, Adolescents, and Young Adults in France

2025· article· en· W4406144081 on OpenAlexaff
Guillaume Fond, Vanessa Pauly, Yann Brousse, Pierre-Michel Llorca, Samuele Cortese, Masoud Rahmati, Christoph U. Correll, Corentin J. Gosling, Michele Fornaro, Marco Solmi, Lee Smith, Nicola Veronese, Dong Keon Yon, Pascal Auquier, Antoine Duclos, Laurent Boyer

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Institutes of HealthResearch Executive AgencyNational Institute for Health and Care Research
KeywordsMedicineMedical prescriptionPoisson regressionMental healthPopulationDemographyHealth carePandemicYoung adultPsychiatryGerontologyEnvironmental healthCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Importance: Amid escalating mental health challenges among young individuals, intensified by the COVID-19 pandemic, analyzing postpandemic trends is critical. Objective: To examine mental health care utilization and prescription rates for children, adolescents, and young adults before and after the COVID-19 pandemic. Design, Setting, and Participants: This population-based time trend study used an interrupted time series analysis to examine mental health care and prescription patterns among the French population 25 years and younger. Aggregated data from the French national health insurance database from January 2016 to June 2023. Data were analyzed from September 2023 to February 2024. Main Outcomes and Measures: The number of individuals with at least 1 outpatient psychiatric consultation, those admitted for full-time psychiatric hospitalization, those with a suicide attempt, and those receiving psychotropic medication was computed. Data were stratified by age groups and sex. Quasi-Poisson regression modeled deseasonalized data, estimating the relative risk (RR) and 95% CI for differences in slopes before and after the pandemic. Results: This study included approximately 20 million individuals 25 years and younger (20 829 566 individuals in 2016 and 20 697 169 individuals in 2022). In 2016, the population consisted of 10 208 277 of 20 829 566 female participants (49.0%) and 6 091 959 (29.2%) aged 18 to 25 years. Proportions were similar in 2022. Significant increases in mental health care utilization were observed postpandemic compared with the prepandemic period, especially among females and young people aged 13 years and older. Outpatient psychiatric consultations increased among women (RR, 1.13; 95% CI, 1.07-1.20), individuals aged 13 to 17 years (RR, 1.15; 95% CI, 1.06-1.23), and individuals aged 18 to 25 years (RR, 1.08; 95% CI, 1.03-1.14). Hospitalizations for suicide attempt increased among women (RR, 1.14; 95% CI, 1.02-1.27) and individuals aged 18 to 25 years (RR, 1.07; 95% CI, 1.03-1.12). Regarding psychotropic medications, almost all classes, except hypnotics, increased in prescriptions between 2016 and 2022 for females, with a particularly marked rise in the postpandemic period. For men, only increases in the prescriptions of antidepressants (RR, 1.03; 95% CI, 1.01-1.06), methylphenidate (RR, 1.09; 95% CI, 1.06-1.12), and medications prescribed for alcohol use disorders (RR, 1.08; 95% CI, 1.04-1.13) were observed, and these increases were less pronounced than for women (antidepressant: RR, 1.13, 95% CI, 1.09-1.16; methylphenidate: RR, 1.15; 95% CI, 1.13-1.18; alcohol use dependence: RR, 1.12; 95% CI, 1.08-1.16). Medications reserved for severe mental health situations, such as lithium or clozapine, were prescribed more frequently starting at the age of 6 years. Conclusions and Relevance: In this study, an interrupted time-series analysis found a marked deterioration in the mental health of young women in France in the after the COVID-19 pandemic, accentuating a trend of deterioration that was already observed in the prepandemic period.

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)0.000
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 score0.124
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.020
GPT teacher head0.366
Teacher spread0.346 · 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

Citations19
Published2025
Admission routes1
Has abstractyes

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