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Record 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 on OpenAlexaff
Julie Vallée, M. Shareck

Bibliographic record

VenueAmerican Journal of Epidemiology · 2014
Typeletter
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsTobacco useEnvironmental healthPsychologyMedicinePopulation

Abstract

fetched live from 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.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.179
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.102
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.179
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.002
Science and technology studies0.0110.009
Scholarly communication0.0070.006
Open science0.0080.005
Research integrity0.0980.092
Insufficient payload (model declined to judge)0.0060.007

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.468
GPT teacher head0.469
Teacher spread0.001 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations18
Published2014
Admission routes1
Has abstractno

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