Learning from a Rapid Health Impact Assessment of a proposed maternity service reconfiguration in the English NHS
Bibliographic record
Abstract
BACKGROUND: Within many parts of the country, the NHS is undertaking reconfiguration of services. Such proposals can prove a tipping point and provoke public protest, often with significant involvement of local and national politicians. We undertook a rapid Health Impact Assessment (HIA) of a proposed reconfiguration of maternity services in Huddersfield and Halifax in England. The aim of the HIA was to help the PCT Boards to assess the reconfiguration's possible consequences on access to maternity services, and maternal and infant health outcomes across different socio-economic groups in Kirklees. We report on the findings of the HIA and the usefulness of the process to decision making. METHODS: This HIA used routine maternity data for 2004-2005 in Huddersfield, in addition to published evidence. Standard HIA techniques were used. RESULTS: We re-highlighted the socio economic differences in smoking status at booking and quitting during pregnancy. We focused on the key concerns of the public, that of adverse obstetric events on a Midwife Led Unit (MLU) with distant obstetric cover. We estimate that twenty percent of women giving birth in a MLU may require urgent transfer to obstetric care during labour. There were no significant socio economic differences. Much of the risk can be mitigated though robust risk management policies. Additional travelling distances and costs could affect lower socio-economic groups the greatest because of lower car ownership and geographical location in relation to the units. There is potential that with improved community antenatal and post natal care, population outcomes could improve significantly, the available evidence supports this view. CONCLUSION: Available evidence suggests that maternity reconfiguration towards enhanced community care could have many potential benefits but carries risk. Investment is needed to realise the former and mitigate the latter. The usefulness of this Health Impact Assessment may have been impeded by its timing, and the politically charged environment of the proposals. Nonetheless, the methods used are readily applicable to assess the impact of other service reconfigurations. The analysis was simple, not time intensive and used routinely available data. Careful consideration should be given to both the timing and the political context in which an analysis is undertaken.
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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 | 0.063 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".