The low uptake of breast screening in cities is a major public health issue and may be due to organisational factors: A Census-based record linkage study
Bibliographic record
Abstract
BACKGROUND: Cancer screening uptake is generally lower in UK cities but quantifying city-level effects from causes due to population composition that comprise cities is hampered by data limitations. METHODS: A unique data linkage project combining a 2001 Census-based longitudinal study in Northern Ireland with the NHS Breast Screening Program. Validated uptake in the three years following the Census for Belfast Metropolitan Urban Area was compared against the rest of the country with adjustment for cohort attributes defined at Census. RESULTS: Belfast Metropolitan Urban Area contained 34.8% of invited women but a greater proportion who rented their accommodation (40.3%) or who did not have a car (47.1%). After full adjustment for demographic and socio-economic factors, Belfast Metropolitan Urban Area uptake was lower for first and subsequent screen (Odds ratio (OR) 0.72; 95% CIs 0.66, 0.78 and OR 0.58; 95% CIs 0.55, 0.62 respectively). There were no significant interactions between patient characteristics and area of residence indicating that all residents in Belfast Metropolitan Urban Area are equally affected. CONCLUSION: The reduced uptake of screening in cities is a major public health issue; the effects are large and a large proportion of the population are affected, organisational factors appear to be the primary cause. Strategies to correct this imbalance might help reduce inequalities in health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".