Sex, age and socioeconomic inequalities in older people’s unscheduled care
Bibliographic record
Abstract
Abstract Background In Scotland, unscheduled care is usually received at hospital accident and emergency (A&E) departments or referral by GPs to medical assessment units (MAU). Almost a quarter attendances are for those aged 65 years+. Demand for unscheduled care will increase as the population ages. This study measures inequalities in unscheduled care presentations among those aged 65 years+. Methods A&E and MAU attendance data between April 2017 and March 2018 for Glasgow residents were analysed. Data were modelled using poisson modelling for outcome measures attendances, rate of attendance and length of stay, adjusting for agegroup, sex and deprivation. A second set of models also adjusted for time of day, month and referral source, including interaction terms. Results While there was a higher number of attendances among females (RR and 95% CI = 0.30 (0.28, 0.32)), and among those aged 65-69 years compared with older ages (RR = 0.03 (0.01, 0.06)), modelling rates showed that males were significantly more likely to attend (RR = 0.14 (0.13, 0.16)) and that likelihood rose with age, eg RR for 85+ years significantly greater than 80-84 years, significantly greater than for 75-79 years etc. There was no gender difference in length of stay but this increased with increasing age. Attendance was significantly more likely for those living in the most deprived quintile of deprivation (RR = 0.30 (0.27, 0.34) compared with the most affluent quintile). SES inequalities in attendance and length of stay became less pronounced with increasing age. Referrals via 999 emergency services increased with age while referrals by GP and NHS24 reduced with age. Attendance was more likely in December (RR = 0.15 (0.11, 0.18)) and likelihood of a morning attendance reduced significantly with increasing age and deprivation. Conclusions Inequalities in attendance, length of stay and methods of referral are observed which should be considered when planning to meet the demand for unscheduled care. Key messages Age, sex and socioeconomic inequalities in unscheduled care exist even at the oldest ages. When planning to meet the demand of future unscheduled care, patterns of current use and population projections should be considered in tandem.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".