Development of Neighborhoods to Measure Spatial Indicators of Health
Notice bibliographique
Résumé
INTRODUCTION In place-based research, geographic information systems (GIS) can be used to derive the context of place and further our understanding of whether place influences health. The external context may include the quality of the physical environment, resources (material and social), and infrastructures that can affect individual health (Pearce et al. 2006). These contextual factors act directly in some instances and indirectly in others (Evans and Stoddart 1994). A strong relationship exists between individual social economic status (SES) and the quality of the neighborhood environment; this may amplify the disparities in health between the richer and the more deprived (Yen and Syme 1999, Fiscella and Williams 2004, Braveman 2006). Researchers only recently have begun to study the impact of various neighborhood-level factors on individual health and health inequalities. In this research, natural neighborhoods within Ottawa, Canada, were delineated, using data from DMTI Spatial Inc., Statistics Canada, the City of Ottawa, the National Capital Commission, the Ottawa Real Estate Board, DigitalGlobe satellite imagery, field-based observations, and expert and community knowledge. These neighborhood units were used within a GIS to derive contextual health indicators in the natural environment, social environment, goods, services and amenities, and the built environment. These indicators were organized into a set of health-relevant domains inspired by Maslow's hierarchy of needs (Maslow 1968, 1970), which was the basis of the conceptual framework for this research. The ultimate goal was to determine which, if any, contextual indicators act as predictors of health outcomes. In the subsequent sections, research goals are described and the methodology used to delineate the neighborhoods and the conceptual framework and methods used to derive the indicators are provided. In conclusion, the initial results compare a measure of socioeconomic status within the neighborhoods and neighborhood health indicators. DESCRIPTION OF RESEARCH This study was initiated by a multidisciplinary team from the University of Ottawa who engage in collaborative community-based research aimed at reducing regional health inequalities. The practical objective was to work with city policy makers, planners, and program implementers to develop strategies and procedures to reduce health inequalities in Ottawa (Kristjansson et al. 2007). This project was focused on spatial inequalities in neighborhood resources for health, which can lead to inequities from a social justice perspective. More specifically, this project had four objectives: * To develop a methodology for defining natural* neighborhoods; * To gather data on a number of neighborhood social and physical resources/amenities; to essentially create a community inventory and subsequent measures of accessibility using GIS capabilities (c.f. Pearce et al. 2006); * To map the relationships between neighborhood socioeconomic status (SES), the distribution of resources necessary for health, and health outcomes; and * To share the evidence with decision-makers and relevant community organizations and to assess the usefulness of the GIS tools in a participatory process of neighborhood delineation. Because the project is still under way, an analysis of all community resource indicators with socioeconomic status (SES) is not yet completed. However, the preliminary results suggest clear intra-urban variations in neighborhood SES and relations with health indicators. As such, others should benefit from this experience and methods thus far. The health-outcome-indicator analysis is for a future publication. Study Area Ottawa, Ontario, is the national capital of Canada (see Figure 1), with a population of 846,802 and a population density per square kilometer of 258. …
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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