Million Migrants study of healthcare and mortality outcomes in non-EU migrants and refugees to England: Analysis protocol for a linked population-based cohort study of 1.5 million migrants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> In 2017, 15.6% of the people living in England were born abroad, yet we have a limited understanding of their use of health services and subsequent health conditions. This linked population-based cohort study aims to describe the hospital-based healthcare and mortality outcomes of 1.5 million non-European Union (EU) migrants and refugees in England. </ns4:p> <ns4:p> <ns4:bold>Methods and analysis:</ns4:bold> We will link four data sources: first, non-EU migrant tuberculosis pre-entry screening data; second, refugee pre-entry health assessment data; third, national hospital episode statistics; and fourth, Office of National Statistics death records. Using this linked dataset, we will then generate a population-based cohort to examine hospital-based events and mortality outcomes in England between Jan 1, 2006, and Dec 31, 2017. We will compare outcomes across three groups in our analyses: 1) non-EU international migrants, 2) refugees, and 3) general population of England. </ns4:p> <ns4:p> <ns4:bold>Ethics and dissemination:</ns4:bold> We will obtain approval to use unconsented patient identifiable data from the Secretary of State for Health through the Confidentiality Advisory Group and the National Health Service Research Ethics Committee. After data linkage, we will destroy identifying data and undertake all analyses using the pseudonymised dataset. The results will provide policy makers and civil society with detailed information about the health needs of non-EU international migrants and refugees in England. </ns4:p>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it