Place and health in diabetes: the neighbourhood environment and risk of depression in adults with Type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Depression is a common co-illness in people with diabetes. Evidence suggests that the neighbourhood environment impacts the risk of depression, but few studies have investigated this effect in those with diabetes. We examined the effect of a range of neighbourhood characteristics on depression in people with Type 2 diabetes. METHODS: This cohort study used five waves of data from 1298 participants with Type 2 diabetes from the Diabetes Health Study (2008-2013). We assessed depression using the Patient Health Questionnaire. We measured neighbourhood deprivation using census data; density of services using geospatial data; level of greenness using satellite imagery; and perceived neighbourhood characteristics using survey data. The effect of neighbourhood factors on risk of depression was estimated using survival analysis, adjusting for sociodemographic variables. We tested effect modification by age, sex and socio-economic characteristics using interaction terms. RESULTS: More physical activity facilities, cultural services and a greater level of greenness in the neighbourhood were associated with a lower risk of depression in our sample, even after adjusting for confounders. Material deprivation was associated with increased risk of depression, particularly in participants who were older or retired. CONCLUSIONS: Characteristics of neighbourhoods were associated with the risk of depression in people with Type 2 diabetes and there were vulnerable subgroups within this association. Clinicians are encouraged to consider the neighbourhood environment of their patients when assessing the risk of depression. Future intervention research is need for health policy recommendations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".