Preconception Health Indicators and Deprivation: A Cross‐Sectional Study Using National Maternity Healthcare Data
Bibliographic record
Abstract
OBJECTIVE: To use routinely-collected maternity healthcare data to (1) describe the prevalence of key preconception indicators (e.g., smoking, folic acid supplement use) and (2) explore differences in prevalence by deprivation. DESIGN: Retrospective population-based study. SETTING: Northern Ireland (NI). POPULATION: 255 177 pregnancies recorded in the Northern Ireland MATernity System (NIMATS). METHODS: Anonymised NIMATS data recorded during antenatal booking appointments (2011-2021) were accessed through the Honest Broker Service and analysed using R. Prevalences were calculated for each indicator, and logistic regression models explored the relationships between each preconception indicator and area-level deprivation quintiles. The indicators included were selected based on the current evidence base, availability in NIMATS, indicator modifiability and Patient and Public Involvement and Engagement. MAIN OUTCOME MEASURES: Preconception indicators, including behavioural factors (e.g., planned pregnancy), pre-existing health conditions (e.g., severe mental health) and area-based deprivation. RESULTS: A high proportion of women had sub-optimal preconception indicators (e.g., 21.3% living with obesity). Women living in the most deprived quintile generally had a higher prevalence of risk factors than women in the least deprived quintile (e.g., smoking prevalence was 25.7% in the most deprived quintile and 5.6% in the least deprived quintile). CONCLUSIONS: Population-based maternity data in NI highlight many areas of women's preconception health that require improvement and support, especially for women living in the areas of greatest deprivation. Although these findings are a reference point to inform interventions, policy and ongoing monitoring of preconception health in NI, they should be interpreted in light of the methodological limitations of the data.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 0.001 |
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".