Commentary on Martin-Storey et al. (2011): Perception of Neighborhood Disorder and Health Service Usage in a Canadian Sample
Notice bibliographique
Résumé
Is the neighborhood we live in harming our health? One of the most exciting new areas of research addresses this question. Access to data from the census, electronic health records, and national surveys allows researchers to identify aspects of the local environment that promote (or impair) health and to begin to understand the biopsychosocial factors that may explain the outcomes. Data on the effects of the local environment on health have tremendous implications for public health policy. Over the long run, this research can help researchers identify where to intervene, identifying the locations of greatest risk. And these data can help policy makers determine how their efforts and our tax dollars can leverage the greatest benefit—through programs to change individual behavior, support family functioning, or enhance neighborhood resources, or through initiatives to modify county, state, or national regulations that affect neighborhood conditions. But because the research is still in its early phases, the studies raise as many questions as they answer. Examining the questions raised by the work of Alexa Martin-Storey and colleagues can illuminate both the benefits of this research and the need for additional knowledge [1]. This paper presents a high-quality investigation of the association of neighborhood disorder and neighborhood poverty on health care utilization in a large sample of Canadian adults. Neighborhood disorder was assessed with a self-report instrument inquiring both about physical disorder (e.g., litter) and social disorder and safety (e.g., public drunkenness, gang presence). The investigators also accessed national survey data to obtain ratings of the participant's levels of aggression when they were children. Even when controlling for census-derived measures of poverty and childhood aggression (as well as a host of other variables), higher levels of neighborhood disorder were associated with higher levels of health care utilization, including significant effects for total utilization, visits for lifestyle-related illness (i.e., diabetes), visits to a specialist, and visits to the emergency room. These data suggest that efforts to rehabilitate neighborhoods (e.g., improving sanitation and safety) might yield returns in improved health and reduced health care costs (as well as better property values). But another important finding from the paper illustrates that factors at many different levels (i.e., the individual, the family, as well as the neighborhood) are also at play. The authors report a significant main effect of childhood aggression on service utilization and an interaction of childhood aggression and neighborhood disorder on health care utilization. The effects of high levels of neighborhood disorder on utilization were significant only for those who also had high levels of childhood aggression. There is fairly consistent evidence that childhood behavior problems are associated with neighborhood disadvantage [2], and a portion of these effects are a mediated through family functioning, in particular through the relationship between neighborhood disadvantage and harsh parenting [3]. Some investigators have suggested that high levels of disorder and poverty impair the development of the types of social cohesion that would inhibit harsh parenting [4]. So the results may reflect the extended effects of early exposure to disordered neighborhoods, initially manifesting as aggressive behavior and later leading to health problems. On the other hand, childhood aggression is also likely to reflect biologically driven impairments in impulsivity and/or mood regulation. These impairments in mood and behavioral regulation are themselves associated with health problems over the lifespan [5]. And environmental stress (from parents or the neighborhood) may promote changes in gene expression in brain areas (e.g., PFC) responsible for behavioral self-regulation in vulnerable individuals [6]. The results may reflect the effects of a gene-by-environment interaction. Health care utilization costs money. As we come to understand the ways in which environmental factors influence both development and health, we can determine the levels on which we need to intervene to reduce health care costs over the lifespan.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,048 | 0,041 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,005 |
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 source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».