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Enregistrement W2140533018 · doi:10.1093/aje/kwn298

Psychiatric Epidemiology: Reducing the Global Burden of Mental Illness

2008· article· en· W2140533018 sur OpenAlexaboutno aff
Stephen L. Buka

Notice bibliographique

RevueAmerican Journal of Epidemiology · 2008
Typearticle
Langueen
DomainePsychology
ThématiqueMental Health Treatment and Access
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute of Mental HealthNational Institutes of Health
Mots-clésEpidemiologyMental illnessMedicinePsychiatryNational Comorbidity SurveyPsychiatric epidemiologyPopulationComorbidityMental healthPublic healthGerontologyEnvironmental healthPathology

Résumé

récupéré en direct d'OpenAlex

Few areas of medicine have posed a greater challenge to the adoption of a population-level or epidemiologic approach than psychiatry. From the privacy required for many therapeutic methods to the complexity and seeming individuality of each patient, the history and nature of mental illness has often appeared antithetical to the epidemiologic method. At the same time, the prevalence, severity, and overall burden of morbidity and mortality represented by mental disorders reflects an urgent global public health concern ripe for the application of epidemiology. The gains realized through advances in psychiatric epidemiology over the past decade, the resulting potential of an empirically informed, population-oriented approach to reducing the global burden of mental illness, and major methodological challenges for the field are highlighted in the current issue of Epidemiologic Reviews, which accompanies this issue of the Journal. It has been over a decade since the last issue of Epidemiologic Reviews dedicated to psychiatric epidemiology appeared in 1995. Despite impressive advances in genetics, neuroscience, brain imaging, and other technologies during these past 13 years, the scope and magnitude of the problem are no less demanding. High-quality epidemiologic investigations in the United States and worldwide have yielded quite consistent estimates of the prevalence of major mental disorders. Data from the recent replication of the National Comorbidity Survey, a representative sample of US adults, suggest that an estimated 13 million American adults (approximately 1 in 17) have a seriously debilitating mental illness (1, 2). The strikingly prominent place of mental disorders in the total pattern of morbidity and mortality worldwide was highlighted in the World Health Organization's initial Global Burden of Disease (3) report, a finding that has been sustained in recent updates (4). According to World Health Organization estimates for 2002, mental health disorders are the leading cause of disability in the United States and Canada, accounting for 25% of all years of life lost to disability and premature mortality (4). Worldwide, it is estimated that mental disorders account for 12% of disability-adjusted life years. In terms of mortality, suicide alone is the 11th leading cause of death in the United States, with approximately 30,000 deaths per year (5), and this is an issue of concern worldwide as well. Estimating the costs associated with mental illness is challenging, but estimates suggest annual treatment costs in the United States of $100 billion, with significantly more for indirect costs; $193 billion per year is estimated for lost earnings alone (2). While these findings reveal concrete developments in the foundations of psychiatric epidemiology (standardized and replicated nosology; high-quality prevalence estimates), advances in the identification of risk factors, etiologic mechanisms, and preventive interventions have been more modest. For instance, a 2004 literature review commissioned by the National Institute of Mental Health revealed that while there has been considerable growth in the number of high-quality longitudinal studies with well-characterized psychiatric outcomes, there is strikingly little consistency in the coverage, measurement, and (therefore) replication of potential risk factors across investigations (6). Clearly, considerable ground remains to be covered in psychiatric epidemiology to move from statements of prevalence to insights regarding disease etiology and prevention. The forthcoming issue of Epidemiologic Reviews provides an informative summary of many of these strengths and challenges in psychiatric epidemiology. The 10 articles in the issue provide a succinct presentation of the many contributions the field brings to broadening the knowledge base and investigating strategies for disease prevention in the realm of mental illness. These include: 1) detailed narrative reviews of the epidemiology of individual disorders (schizophrenia (7), suicide (8), and Alzheimer's disease (9)); 2) systematic reviews and meta-analyses of exposure-disease associations of long-standing interest (place of residence/neighborhood attributes and risks of both depression (10) and psychosis (11), working conditions and depression (12), premorbid intelligence quotient and psychosis (13)); 3) major findings and methodological advances regarding the analysis of co-occurring or “comorbid” forms of psychopathology (14); 4) challenges and opportunities in moving from observational studies to preventive clinical trials (15); and 5) an overarching summary of the prevalence, impairment, and cost associated with 11 major mental disorders of adults (16). One clear sign of encouragement from this body of work is that the number of well-conducted prospective and cross-sectional population-based studies is considerable and growing. On the basis of these studies, the authors in this special issue have been able to not only summarize more accurately the prevalence and incidence of major adult disorders but, more interestingly, also discern meaningful variation in disease distributions that set the stage for new etiologic investigations. For example, Nock et al.'s review of global suicide and suicidal behavior (8) concludes that “the significant cross-national variability… appears to reflect the true nature of this behavior and is not due to variation in research methods” and proceeds to elaborate new methodological and theoretical advances that are likely to yield new insight into the origins of these behaviors. Similarly, McGrath et al. (7) challenge the common perception that the incidence and prevalence of schizophrenia are relatively even across regions, and they highlight a number of potential risk factors related to urbanicity, economic status, migrant status, and latitude that might play an etiologic role and account for this variation. Despite these positive advances, Eaton et al.'s summary of the “global burden” associated with adult mental disorders (16) provides succinct guidance by highlighting a number of disorders for which basic incidence and prevalence data are sparse and how extremely limited are even the most rudimentary data on disability, mortality, and costs. With a focus on a range of potential etiologic factors ranging from social conditions to specific polymorphisms, psychiatric epidemiology has been and should continue to be recognized as related to but distinct from other branches of our discipline, most notably social epidemiology (17). At the same time, the methodological and conceptual advancements of social epidemiology have enhanced a long-standing tradition of inquiry into the impact of the broader social environment on the genesis of mental illness. These cross-fertilizations are elegantly displayed with the work of March et al. (11) and Kim (12). Kim's systematic review of 28 studies (12) supports a positive association between neighborhood social disorder and depression and to a lesser extent suggests protective effects for neighborhood socioeconomic status. March et al. (11) observe positive associations between urbanicity, ethnic density, social isolation, and nonaffective (but not affective) psychoses. Both papers provide a compelling impetus for new lines of inquiry and the possibility of community-level (rather than more traditional individual-level) interventions for the prevention or minimization of these high-burden disorders. Finally, the contribution by Cerda et al. (14) reminds us that while most investigators consider single disease endpoints, co-occurrence or comorbidity both is common and greatly increases the burden on patient and society. Their careful summary of findings from 58 articles documents the extent of comorbidity, summarizes conceptual approaches that will be needed to account for it, and makes valuable recommendations for the future research that will be required to obtain new insight into the pathways leading to such co-occurrence. This special issue of Epidemiologic Reviews provides an excellent summary of the ground covered and the road ahead regarding the epidemiologic investigation and public health strategies needed to reduce the prevalence, severity, and impairment resulting from mental illness. It provides high-quality, well-written summaries of large bodies of literature and sheds new light on both the limitations of standard epidemiologic approaches in this arena and valuable future directions for the field. Old-timers in the field will read it with a sense of appreciation for the advances we have made, while newcomers will be energized and stimulated by an awareness of the immediacy of the problem and the clear avenues available for meaningful new discovery. The author thanks Dr. Angela Paradis for her contributions to this editorial. Conflict of interest: none declared.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,015
score de la tête « metaresearch » (Gemma)0,047
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,081

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0150,047
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0040,003
Études des sciences et des technologies0,0010,003
Communication savante0,0060,009
Science ouverte0,0020,005
Intégrité de la recherche0,0050,008
Charge utile insuffisante (le modèle a refusé de juger)0,0100,003

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.

Tête enseignante Opus0,070
Tête enseignante GPT0,426
Écart entre enseignants0,357 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations23
Publié2008
Routes d'admission1
Résumé présentoui

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