Air Pollution and Mental Disorders in Youth: An Epidemiological Assessment Integrating Nursing, Public Health, and Evidence-Based Nutritional Strategies
Notice bibliographique
Résumé
Background: The intersection of environmental epidemiology and pediatric psychiatry represents a critical frontier in modern public health. As urbanization accelerates and climate change intensifies, the global youth population is increasingly exposed to a complex "exposome" of atmospheric pollutants. Concurrently, the prevalence of mental health disorders among adolescents—specifically internalizing pathologies such as depression and anxiety—has reached crisis levels, with suicide remaining a leading cause of mortality in this demographic. While the respiratory and cardiovascular impacts of air pollution are well-documented, emerging evidence points to a "silent crisis" of neurotoxicity affecting the developing brains of adolescents. Objectives: This comprehensive systematic review aims to: (1) synthesize epidemiological data linking ambient and indoor air pollution (specifically PM2.5, NO2, and NOx) to internalizing (depression, anxiety, suicide) and externalizing (ADHD, conduct disorder) psychopathology in youth aged 10–24; (2) elucidate the biological mechanisms of neuroinflammation, oxidative stress, and HPA-axis dysregulation driving these outcomes; (3) evaluate the efficacy of school-based environmental interventions, such as HEPA filtration and green infrastructure; and (4) define the evolving role of school nurses in mitigation, surveillance, and advocacy within this environmental health framework. Methods: A systematic review of the literature was conducted, utilizing data from epidemiological cohorts, toxicological studies, and public health intervention trials. The search encompassed major databases (PubMed, Scopus, CINAHL) and grey literature from 2010–2023. Quality assessment was rigorous, utilizing the Newcastle-Ottawa Scale (NOS) for observational studies and the Cochrane Risk of Bias 2.0 (RoB 2.0) tool for randomized interventions. The review synthesizes findings from diverse geographical contexts, including high-exposure regions in Asia and varied exposure gradients in North America and Europe. Results: The synthesis reveals a robust, statistically significant association between exposure to particulate matter and nitrogen oxides and adverse mental health outcomes. Meta-analytic data suggests that long-term exposure to PM2.5 increases the odds of depression by approximately 10% per 10µg/m3 increase, with stronger effects observed in cumulative lag models. Short-term exposures are linked to immediate spikes in psychiatric emergency department visits (lags 0–3 days). Biologically, systemic inflammation (elevated IL-8, TNF-alpha) serves as a key mediating pathway. In the school setting, engineering interventions like HEPA filtration demonstrate a capacity to reduce indoor PM2.5 by 30-50%, correlating with improved cognitive function, reduced absenteeism, and potential behavioral benefits. Conclusion: Air pollution acts as a modifiable, pervasive environmental risk factor for youth mental health disorders. The evidence supports a paradigm shift in school nursing practice, moving beyond traditional somatic care to encompass "environmental mental health" surveillance. Integrated public health strategies—combining urban planning (Low Emission Zones), building engineering (filtration), nutritional resilience, and clinical vigilance—are essential to protect the neurodevelopmental trajectory of the next generation. The school nurse serves as the linchpin in this strategy, positioned to bridge the gap between environmental data and student well-being.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,006 |
| Bibliométrie | 0,023 | 0,019 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».