Artificial outdoor light at night and depression in older adults in the USA, England, Northern Ireland, and Ireland
Bibliographic record
Abstract
• Artificial light at night (LAN) is linked to increased depression in older adults. • Data from four national aging studies in the US, UK, and Ireland were analyzed. • Associations by urbanicity varies across countries. • Associations remains adjusting for NO 2 and greenspace. Artificial Light at night (ALAN) is a potential environmental stressor for depression, but epidemiological evidence is limited. Cross-national surveys of aging were leveraged to examine LAN and depression. We used longitudinal aging surveys from the US (HRS; n = 20,868), England (ELSA; n = 9,848), Ireland (TILDA; n = 6,407), and Northern Ireland (NICOLA; n = 2,725). Depression was ascertained using Center for Epidemiologic Studies Depression Scale and dichotomized based on study-specific cutoffs. Annual mean outdoor ALAN exposure was estimated using satellite-derived nighttime light data (∼500 m resolution), then categorized using harmonized quartiles, based on cross-country population values (≤2.79, 11.69, and >23.93 nW/cm 2 /s). Poisson regression models estimated the prevalence ratios (PRs) of depression, adjusting for individual- and area-level factors. The prevalence of depression was highest in ELSA and HRS (24%), followed by NICOLA (14%) and lowest in TILDA (8%). The mean (SD) ALAN levels were 18.9 (9.0) nW/cm 2 /s in HRS, 13.4 (12.7) in ELSA, 10.4 (13.4) in TILDA, and 11.6 (10.3) in NICOLA. In fully-adjusted models, the highest ALAN quartile was associated with higher PRs of depression (reference: lowest quartile), in all surveys, with PRs (95% Confidence interval) of 1.40 (1.20–1.63) in HRS, 1.16 (0.98–1.38) in ELSA, 1.51 (1.08–2.10) in TILDA, and 1.79 (1.13–2.84) in NICOLA. The directions of the association were robust to adjustment for NO 2 , though attenuated. Findings from multiple countries suggest that outdoor ALAN exposure is associated with depression in older adults and highlight the value of international longitudinal aging cohorts for investigating the impact of environmental exposures on health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".