Preconception Health Indicators and Deprivation: A Cross-Sectional Study Using National Maternity Healthcare Data.

Cassinelli, Emma H; Kent, Lisa; Eastwood, Kelly-Ann; Schoenaker, Danielle A J M; McKinley, Michelle C; McGowan, Laura · BJOG · 2025

cross_sectional · Level IV

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Abstract

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. Retrospective population-based study. Northern Ireland (NI). 255 177 pregnancies recorded in the Northern Ireland MATernity System (NIMATS). 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. Preconception indicators, including behavioural factors (e.g., planned pregnancy), pre-existing health conditions (e.g., severe mental health) and area-based deprivation. 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). 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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