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Enregistrement W2127176510 · doi:10.1093/aje/kwt436

Re: "Examination of How Neighborhood Definition Influences Measurements of Youths' Access to Tobacco Retailers: A Methodological Note on Spatial Misclassification"

2014· letter· en· W2127176510 sur OpenAlexaff
Julie Vallée, M. Shareck

Notice bibliographique

RevueAmerican Journal of Epidemiology · 2014
Typeletter
Langueen
DomaineSocial Sciences
ThématiqueHealth disparities and outcomes
Établissements canadiensUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Organismes subventionnairesnon disponible
Mots-clésTobacco useEnvironmental healthPsychologyMedicinePopulation

Résumé

récupéré en direct d'OpenAlex

In a recent article, Duncan et al. (1) compared tobacco retailer density and proximity measures computed within 2 administrative and 4 egocentric neighborhood definitions and precisely identified which measures significantly differed across neighborhood definitions. This comparative analysis led the authors to conclude, rightly so, that how one defines “neighborhood” may considerably influence neighborhood-level exposure measures. These findings echo the importance of both zoning and spatial scale, which has often been underlined in the health literature (2–4) since Openshaw first defined the Modifiable Areal Unit Problem in 1984 (5). The authors went on to conclude that “whenever possible, egocentric neighborhood definitions should be used” and that “the use of larger administrative neighborhood definitions can bias exposure estimates for proximity” (1). However, the authors should not have extended their findings, which concerned the difference between neighborhood resource accessibility measures, to a judgment on the most relevant spatial units to use in neighborhood and health research. We believe their conclusion stems from the following 2 assumptions that are pervasive in the literature and deserve to be discussed: that “egocentric is better” and that “smaller is better.” First, when concluding that egocentric neighborhoods should be preferred, the authors assume that isotropic areas (i.e., spreading out uniformly in all directions around individuals' homes) are necessarily the optimal way to delineate neighborhoods (6). However, historical, social, and political processes may prevent people from experiencing certain places and reaching specific resources despite being their located close to their homes. Administrative areas, which are, by definition, not centered on individuals' homes, may in some cases provide more adequate estimates of neighborhood resource accessibility than egocentric areas would, notably when they have been delineated by taking historical, social, and political divisions into account. There is a real need to discuss the unjustified use of administrative areas to define neighborhoods. However, one should not fall into the opposite extreme by claiming that egocentric neighborhoods are necessarily better. Second, the authors assume that using administrative areas larger than census tracts would inevitably increase the likelihood of spatial misclassification. Actually, depending on the profile and location of individuals in a city, it could be more relevant to derive neighborhood exposure measures from units larger than census tracts. In a study in the Paris, France, metropolitan area, people's health-seeking behaviors were better modeled by neighborhood resource densities computed from groups of adjacent census tracts than from the residential census tract only (4). Areas larger than census tracts have also been found to better fit with inner-Paris inhabitants' perceived neighborhoods (7). We therefore urge neighborhood and health researchers to justify their choice of a given neighborhood definition by comparing it with validity criteria involving people. Although there is no “gold standard,” the following 3 people-based criteria may be distinguished:The above are mere suggestions, but whichever validity criterion is chosen, we strongly recommend that researchers refer to people, and not only to places, before concluding that there is spatial misclassification in neighborhood exposures. The most common approach is to undertake sensitivity analyses by correlating neighborhood exposure measures with people's health indicators to identify the neighborhood definition that maximizes measures of association (4, 8, 9). Investigating the correlation between people's subjective assessments of neighborhood resources and objective measures of these same resources in various spatial units (10) is also a promising avenue for selecting spatial units that closely approximate people's assessments (6). It may be relevant to choose a neighborhood delineation that fits people's neighborhood experiences. Cognitive mapping, which has highlighted that perceived neighborhoods may vary greatly in spatial extent according to people's profile and location, may, for instance, provide invaluable insights for comparing neighborhood definitions (7, 11). The authors are supported by the Département de Médecine Sociale et Preventive of the Université de Montréal, the Institut de Recherche en Santé Publique of the Université de Montréal, and the Spatial Health Research Lab in CHUM Research Centre. J.V. was also supported by the Centre National de la Recherche Scientifique in France. 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)0,035
score de la tête « metaresearch » (Gemma)0,179
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: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,102
Score d'incertitude au seuil0,203

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0350,179
Méta-épidémiologie (sens strict)0,0020,002
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0020,002
Études des sciences et des technologies0,0110,009
Communication savante0,0070,006
Science ouverte0,0080,005
Intégrité de la recherche0,0980,092
Charge utile insuffisante (le modèle a refusé de juger)0,0060,007

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 Opus0,468
Tête enseignante GPT0,469
Écart entre enseignants0,001 · 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
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

Citations18
Publié2014
Routes d'admission1
Résumé présentnon

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