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Enregistrement W2762503645 · doi:10.1016/s2542-5196(17)30123-7

Human health: is it who you are or where you live?

2017· article· en· W2762503645 sur OpenAlexaboutno aff
Inês Paciência, André Moreira

Notice bibliographique

RevueThe Lancet Planetary Health · 2017
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueHealth disparities and outcomes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésUrbanizationEnvironmental healthPublic healthEconomic growthGeographySocial determinants of healthUrban studiesHealth carePolitical scienceGerontologyMedicineEconomics

Résumé

récupéré en direct d'OpenAlex

Urbanisation is one of the leading global trends of the 21st century and the UN predicts that seven in ten people will live in urban areas by 2050.1UN Department of Economic and Social AffairsThe 2014 revision. United Nations, New York2014Google Scholar With hasty global urbanisation, the importance of understanding relationships between environment, human health, and wellbeing is being increasingly recognised. Urban advance offers many opportunities, including access to better health care; however, this growth is also associated with many emerging environment and health hazards. Over the past decades, urbanisation and subsequent changes in our living standards, lifestyles, and dietary patterns have been suggested to be associated with different exposures and the risk of disease.2WHOBulletin of the World Health Organization, Urbanization and health.http://www.who.int/bulletin/volumes/88/4/10-010410/en/Date: 2010Google Scholar An increasing number of studies are investigating the effect of urban density and land-use mix on health gains, including reduced levels of obesity, and aim to identify types of neighbourhoods or characteristics of neighbourhoods that will promote health benefits.3Croucher KL Wallace A Duffy S The influence of land use mix, density and urban design on health: Centre for Housing Policy. University of York, York2012Google Scholar However, uncertainty regarding the effect of urbanisation on obesity remains due to discrepancies in conceptual and methodological approaches in urban definitions. In this issue of The Lancet Planetary Health, Chinmoy Sarkar and colleagues4Sarkar C Webster C Gallacher J Association between adiposity outcomes and residential density: a full-data, cross-sectional analysis of 419 562 UK Biobank adult participants.Lancet Planet Health. 2017; 1: e277-e288Summary Full Text Full Text PDF Scopus (51) Google Scholar report their findings on the association between adiposity and residential density using objective measures of the built environment. In a large and diverse population in the UK, they provide evidence for a curvilinear dose–response relationship indicating inflexion points at 1800 and 3200 residential units per km2. Findings were consistent across measures of adiposity, with stronger associations being found for people who were female, younger, and accumulating higher levels of physical activity.4Sarkar C Webster C Gallacher J Association between adiposity outcomes and residential density: a full-data, cross-sectional analysis of 419 562 UK Biobank adult participants.Lancet Planet Health. 2017; 1: e277-e288Summary Full Text Full Text PDF Scopus (51) Google Scholar This beneficial urban environment is particularly important because, irrespective of geographical location, residential density seems to be associated with decreased levels of obesity.5Rundle A Diez Roux AV Free LM Miller D Neckerman KM Weiss CC The urban built environment and obesity in New York City: a multilevel analysis.Am J Health Promot. 2007; 21: 326-334Crossref PubMed Scopus (248) Google Scholar This association suggests that high-density areas can provide and support increased levels of physical activity because they have nearby destinations that support walking.6Witten K Geographies of obesity: environmental understandings of the obesity epidemic. Ashgate Publishing, Farnham2010Google Scholar Taken together these findings inform policies and practices aiming to create healthier cities through optimisation of urban planning and built environment design and to reduce health expenses by decreasing urban detrimental exposures. Unfortunately, the urban neighbourhood effect might not be always beneficial for the obesity risk. First, the increased urbanisation associated with high residential density, street intersections, and mixed land-use, together with low physical activity, increases reliance on foods that are often highly processed and containing high levels of salt, sugar, and fat.7Kim J Shon C Yi S The relationship between obesity and urban environment in Seoul.Int J Environ Res Public Health. 2017; 14: 898Crossref Scopus (6) Google Scholar, 8Pouliou T Elliott SJ Individual and socio-environmental determinants of overweight and obesity in urban Canada.Health Place. 2010; 16: 389-398Crossref PubMed Scopus (88) Google Scholar, 9Sobal J Commentary: globalization and the epidemiology of obesity.Int J Epidemiol. 2001; 30: 1136-1137Crossref PubMed Scopus (42) Google Scholar Second, the complex interaction between human beings and urbanisation is dependent, not only on individual determinants and behaviours such as gender, age, social or economic resources, and lifestyle, but also on urbanisation landscapes, including air pollution, handiness of green areas and recreational facilities, neighbourhood safety, and opportunities for mobility and physical activity.10Papas MA Alberg AJ Ewing R Helzlsouer KJ Gary TL Klassen AC The built environment and obesity.Epidemiol Rev. 2007; 29: 129-143Crossref PubMed Scopus (783) Google Scholar, 11Brody J The global epidemic of childhood obesity: poverty, urbanization, and the nutrition transition. Nutrition Bytes.http://www.escholarship.org/uc/item/1xb9x54zDate: 2002Google Scholar Therefore, to be effective in promoting health and healthy behaviour, public health interventions have to address not only individual characteristics but also the physical and social environment.1UN Department of Economic and Social AffairsThe 2014 revision. United Nations, New York2014Google Scholar The complexity of the linkages between urbanisation, environmental change, and human health and wellbeing requires a systems approach towards these factors. The study by Sarkar and colleagues4Sarkar C Webster C Gallacher J Association between adiposity outcomes and residential density: a full-data, cross-sectional analysis of 419 562 UK Biobank adult participants.Lancet Planet Health. 2017; 1: e277-e288Summary Full Text Full Text PDF Scopus (51) Google Scholar provides us with an improved understating of how the urban environment and land use can affect a complex chronic disease such as obesity. In fact, the rewiring of public health and urban planning is an opportunity to understand the effect of urbanisation and many other urban exposures on health, providing information to build up successful community-based disease prevention efforts. Ultimately, it is not only who you are, but also, and mostly, where and how you live that affects your health. We declare no competing interests. Association between adiposity outcomes and residential density: a full-data, cross-sectional analysis of 419 562 UK Biobank adult participantsHousing-level policy related to the optimisation of healthy density in cities might be a potential upstream-level public health intervention towards the minimisation and offsetting of obesity; however, further research based on accumulated prospective data is necessary for evidencing specific pathways. The findings might mean that governments, such as the UK Government, who are attempting to prevent suburban densification by, for example, prohibiting the subdivision of single lot housing and the conversion of domestic gardens to housing lots, will potentially have the effect of inhibiting the conversion of suburbs into more healthy places to live. Full-Text PDF Open Access

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)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,472
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0080,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,164
Tête enseignante GPT0,424
Écart entre enseignants0,260 · 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.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations11
Publié2017
Routes d'admission1
Résumé présentoui

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