How do perceived and objective measures of neighbourhood disadvantage vary over time? Results from a prospective-longitudinal study in the UK with implications for longitudinal research on neighbourhood effects on health
Bibliographic record
Abstract
BACKGROUND: Theories of health outcomes often hypothesize that living in more socially and economically disadvantaged neighbourhoods will lead to worse health. Multiple measures of neighbourhood disadvantage are available to researchers, which may serve as better or worse proxies for each other across time. To inform longitudinal study design and interpretation we investigated how perceived and objective measures of neighbourhood disadvantage vary over time and the factors underlying this variation. METHODS: Data were from 8,918 mothers with at least three time-points of neighbourhood data in the Avon Longitudinal Study of Parents and Children in the UK. We analyzed measures of objective (Indices of Multiple Deprivation) and perceived (neighbourhood quality, social cohesion, and stress) exposure to neighbourhood disadvantage at 10 time-points over 18 years. We used group-based trajectory modelling to determine the overlap in participants' trajectories on the different measures and evaluated the baseline factors associated with different perceived trajectories over time. RESULTS: There was evidence of heterogeneity in both perceived and objective measures of neighbourhood disadvantage over time (e.g., on the objective measure, 5% of participants moved to more deprived neighbourhoods, 11% moved to less deprived neighbourhoods, 20% consistently lived in deprived neighbourhoods, and 64% consistently lived in non-deprived neighbourhoods). Perceived social cohesion showed the weakest relationship with exposure to objective neighbourhood deprivation: most participants in each trajectory group of objective neighbourhood deprivation followed non-corresponding trajectories of perceived social cohesion (61-80%). Accounting for objective deprivation exposure, poorer socioeconomic and psychosocial indicators at baseline were associated with following more negative perceived neighbourhood trajectories (e.g., high neighbourhood stress) over time. CONCLUSION: Trajectories of perceived and objective measures of neighbourhood disadvantage varied over time, with the extent of variation depending on the time point of measurement and individual-level social factors. Researchers should be mindful of this variation when choosing and determining the timing of measures of neighbourhood disadvantage in longitudinal studies and when inferring effect mechanisms.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".