Incident dementia across neighbourhood material deprivation: Implications for underdiagnosis
Bibliographic record
Abstract
BACKGROUND: Evidence consistently demonstrates that lower socioeconomic status (SES) confers greater dementia risk and that it is associated with poorer outcomes including reduced access to care. Furthermore, underdiagnosis is a widespread issue in dementia. However, few studies investigate whether one of the poorer outcomes associated with lower SES is a greater degree of dementia underdiagnosis. METHODS: We conducted a province-wide repeated yearly cross-sectional study (2000-17) of community-dwelling people with incident dementia in Quebec. Data were sourced from health administrative data held at the Quebec National Institute of Public Health and SES was assessed through a material deprivation index (a composite measure of the SES of census-based neighbourhoods). Given our near population-level sample, we used a descriptive approach: we described the proportion of incident dementia cases in each of 5 SES categories, from least to most material deprivation. RESULTS: Of the 193,834 community-dwelling people with a new diagnosis of dementia between 2000-17, around 20 % belonged to each material deprivation category on average. Incident cases in the two least deprived categories comprised 18 % each of total incident cases, and 22 % each in the two most deprived categories. CONCLUSION: Despite global findings of higher dementia incidence in lower SES, we found similar incidence across levels of material deprivation. Considering that recent work indicates that lower SES in Quebec is associated with poorer health outcomes consistent with literature, our discrepant finding of comparable incidence cases in the least and most deprived neighbourhoods indicates that there is likely severe underdiagnosis of dementia in people living in more materially deprived neighbourhoods in Quebec.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".