Commentary on Martin-Storey et al. (2011): Perception of Neighborhood Disorder and Health Service Usage in a Canadian Sample
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.048 | 0.041 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".