Medicines prescribing for homeless persons: analysis of prescription data from specialist homelessness general practices
Bibliographic record
Abstract
BACKGROUND: Specialist homelessness practices remain the main primary care access point for many persons experiencing homelessness. Prescribing practices are poorly understood in this population. OBJECTIVE: This study aims to investigate prescribing of medicines to homeless persons who present to specialist homelessness primary care practices and compares the data with the general population. SETTING: Analyses of publicly available prescribing and demographics data pertaining to primary care in England. METHODS: Prescribing data from 15 specialist homelessness practices in England were extracted for the period 04/2019-03/2020 and compared with data from (a) general populations, (b) the most deprived populations, and (c) the least deprived populations in England. MAIN OUTCOME MEASURE: Prescribing rates, measured as the number of items/1000 population in key disease areas. RESULTS: Data corresponding to 20,572 homeless persons was included. Marked disparity were observed in regards to prescribing rates of drugs for Central Nervous System disorders. For example, prescribing rates were 83-fold (mean (SD) 1296.7(1447.6) vs. 15.7(9.2) p = 0.033) items), and 12-fold (p = 0.018) higher amongst homeless populations for opioid dependence and psychosis disorders respectively compared to the general populations. Differences with populations in the least deprived populations were even higher. Prescribing medicines for other long-term conditions other than mental health and substance misuse was lower in the homeless than in the general population. CONCLUSIONS: Most of the prescribing activities in the homeless population relate to mental health conditions and substance misuse. It is possible that other long-term conditions that overlap with homelessness are under-diagnosed and under-managed. Wide variations in data across practices needs investigation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".