Validation of ethnicity in administrative hospital data in women giving birth in England: cohort study
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
OBJECTIVE: To describe the accuracy of coding of ethnicity in National Health Service (NHS) administrative hospital records compared with self-declared records in maternity booking systems, and to assess the potential impact of misclassification bias. DESIGN: Secondary analysis of data from records of women giving birth in England (2015-2017). SETTING: NHS Trusts in England participating in a national audit programme. PARTICIPANTS: 1 237 213 women who gave birth between 1 April 2015 and 31 March 2017. PRIMARY AND SECONDARY OUTCOME MEASURES: (1) Proportion of women with complete ethnicity; (2) agreement on coded ethnicity between maternity (maternity information systems (MIS)) and administrative hospital (Hospital Episode Statistics (HES)) records; (3) rates of caesarean section and obstetric anal sphincter injury by ethnic group in MIS and HES. RESULTS: 91.3% of women had complete information regarding ethnicity in HES. Overall agreement between data sets was 90.4% (κ=0.83); 94.4% when collapsed into aggregate groups of white/South Asian/black/mixed/other (κ=0.86). Most disagreement was seen in women coded as mixed in either data set. Rates of obstetrical events and complications by ethnicity were similar regardless of data set used, with the most differences seen in women coded as mixed. CONCLUSIONS: Levels of accuracy in ethnicity coding in administrative hospital records support the use of ethnicity collapsed into groups (white/South Asian/black/mixed/other), but findings for mixed and other groups, and more granular classifications, should be treated with caution. Robustness of results of analyses for associations with ethnicity can be improved by using additional primary data sources.
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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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it