Temporal trends of key preconception indicators among women in Northern Ireland, UK: an analysis of maternity healthcare data 2011–2021
Bibliographic record
Abstract
Optimizing preconception health offers an opportunity to reverse unfavourable trends in modifiable risk factors and improve reproductive outcomes. This study aims to report the yearly prevalence of key biopsychosocial preconception indicators for over a decade, as reported at antenatal booking appointments in Northern Ireland (UK). The indicators include area-level deprivation, planned pregnancies, and body mass index (BMI) between 2011 and 2021, as well as pre- and early-pregnancy folic acid supplement use between 2015 and 2020. This population-based study was conducted using annual routinely collected maternity data from the Northern Ireland Maternity System (NIMATS). R, accessed via the UK Secure eResearch Platform, was used to calculate yearly proportions. Multinomial regression models explored the relationship between each preconception indicator and year of booking appointment. Patient and Public Involvement and Engagement were integrated throughout the study. Of the 255 117 pregnancies included between 2011 and 2021, 21.4% were from women living in the most deprived quintile and 70.6% from women who reported a planned pregnancy. Obesity rates increased over the study period (e.g. obesity class I: 12.0%-16.1%), and preconception folic acid supplement use remained inadequate, though the use of supplements containing 5 mg of folic acid increased between 2015 and 2020 (400 µg: 34.4%-30.03%; 5 mg: 3.6%-5.0%). Efforts are needed to reverse negative public health consequences of sub-optimal preconception health indicators. Notably, folic acid supplement use was predominantly initiated after conception, suggesting that a renewed focus is needed, particularly supporting women with the greatest need, such as those in the most deprived areas.
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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 | 0.006 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".