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Enregistrement W4386163009 · doi:10.1016/j.jdin.2023.08.011

Neighborhood characteristics and the risk of psoriasis: A systematic review

2023· review· en· W4386163009 sur OpenAlexaff
Owen Dan Luo, Zainab Ridha, Abdulhadi Jfri, Mohsen Rezaeian, Anastasiya Muntyanu, Julien Ringuet, Elena Netchiporouk

Notice bibliographique

RevueJAAD International · 2023
Typereview
Langueen
DomaineImmunology and Microbiology
ThématiquePsoriasis: Treatment and Pathogenesis
Établissements canadiensCentre de Recherche Dermatologique du Québec MétropolitainMcGill University Health CentreMcGill University
Organismes subventionnairesnon disponible
Mots-clésPsoriasisMedicineDermatology

Résumé

récupéré en direct d'OpenAlex

To the Editor: Psoriasis affects 1% to 5% of the North American population.1Boehncke W.-H. Schön M.P. Psoriasis.Lancet. 2015; 386: 983-994Abstract Full Text Full Text PDF PubMed Scopus (1627) Google Scholar The association between lifestyle (eg, physical activity, diet, alcohol, and smoking) and psoriasis/its comorbidities is well established.2Debbaneh M. Millsop J.W. Bhatia B.K. Koo J. Liao W. Diet and psoriasis, part I: impact of weight loss interventions.J Am Acad Dermatol. 2014; 71: 133-140Abstract Full Text Full Text PDF PubMed Scopus (85) Google Scholar,3Zhao S.S. Bellou E. Verstappen S.M.M. et al.Association between psoriatic disease and lifestyle factors and comorbidities: cross-sectional analysis and Mendelian randomisation.Rheumatology. 2022; 62: 1272-1285https://doi.org/10.1093/rheumatology/keac403Crossref Scopus (8) Google Scholar However, it is increasingly recognized that behavioral risk factors arise in a larger context of socioeconomic, cultural, and environmental determinants of health.4Biskup M. Macek P. Gozdz S. et al.Two-year follow-up cohort study focused on gender-specific associations between socioeconomic status and body weight changes in overweight and obese middle-aged and older adults.BMJ Open. 2021; 11e050127Crossref PubMed Scopus (1) Google Scholar Research in chronic diseases (eg, diabetes mellitus, metabolic syndrome) highlighted the importance of the living environment (LE) (ie, physical and socioeconomic conditions in which people live, work, and play) as a critical element to address population-level health differences. LE contributes to health inequity (ie, unjust and potentially avoidable differences in health outcomes among different populations). We aimed to conduct a systematic review to understand the impact of LE on psoriasis. MEDLINE, EMBASE, Web of Science, and CINAHL databases were searched on September 20, 2022, by ODL and ZR for studies exploring the association between LE and the prevalence, incidence, or severity of psoriasis. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Search strategy is detailed in Supplementary Tables I-III, available via Mendeley at https://doi.org/10.17632/vk94dhftw5.1. Studies’ quality was appraised using the Quality Assessment Tool for Quantitative Studies.5den Braver N.R. Lakerveld J. Rutters F. et al.Built environmental characteristics and diabetes: a systematic review and meta-analysis.BMC Med. 2018; 16: 12Crossref PubMed Scopus (135) Google Scholar Of 8 studies included (Fig 1 and Supplementary Table IV, available via Mendeley at https://doi.org/10.17632/vk94dhftw5.1 summarize the PRISMA flow diagram and studies’ details, respectively), 2 ascertained the association between urban versus rural residence and psoriasis risk with conflicting results. Two articles investigated the association between neighborhood socioeconomic conditions and psoriasis. People residing in high- and medium-deprivation neighborhoods (ie, deprivation of essential resources and/or goods) were more likely to have psoriasis whereas patients from the lowest income quartiles had a more severe disease. Four studies researched the association between air quality and psoriasis exacerbations. Particulate matter (PM2.5 and PM10) and NO2 were associated with a modest increase in outpatient visits and hospital admissions in South Korea and China. Italian studies demonstrated higher concentrations of all air pollutants (eg, PM2.5, PM10, CO, NO2, other nitrogen oxides, benzene) prior to psoriasis flares versus regular outpatient visits as well as daily increases of 10 μg/m3 in air pollutants were associated with therapeutic decisions such as dose increments or treatment changes. The available evidence suggests that neighborhoods with socioeconomic deprivation may be associated with a higher psoriasis risk and severity, whereas communities with worse air quality may increase the risk of psoriasis flare. However, this data should be interpreted with caution due to limited number of studies on the topic and at least moderate risk of bias identified across studies included (Supplementary Table V, available via Mendeley at https://doi.org/10.17632/vk94dhftw5.1) owing to data source, study design, patients’ number, and/or statistical analyses. Despite psoriasis disproportionately affecting North American, Western European, and Australasian populations, we identified no studies from these regions. Studying LE characteristics such as environmental (eg, air/noise/light pollution, greenness), built environment-related (eg, man-made buildings and spaces), and socioeconomic neighborhood characteristics (eg, material, social instability, and deprivation) as determinants of psoriasis incidence/severity is important to advance our understanding of population-level determinants of psoriatic disease spectrum. This is essential to reduce health disparities in chronic skin disease such as psoriasis and reduce the individual, societal and economic burden of this common and morbid disease. None disclosed.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,383
Score d'incertitude au seuil0,872

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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 Opus0,031
Tête enseignante GPT0,295
Écart entre enseignants0,264 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations2
Publié2023
Routes d'admission1
Résumé présentoui

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