BACKGROUND: A healthy start to life is a major priority in efforts to reduce health inequalities across Europe, with important implications for the health of future generations. There is limited combined evidence on inequalities in health among newborns across a range of European countries. METHODS: Prospective cohort data of 75 296 newborns from 12 European countries were used. Maternal education, preterm and small for gestational age births were determined at baseline along with covariate data. Regression models were estimated within each cohort and meta-analyses were conducted to compare and measure heterogeneity between cohorts. RESULTS: Mother's education was linked to an appreciable risk of preterm and small for gestational age (SGA) births across 12 European countries. The excess risk of preterm births associated with low maternal education was 1.48 (1.29 to 1.69) and 1.84 (0.99 to 2.69) in relative and absolute terms (Relative/Slope Index of Inequality, RII/SII) for all cohorts combined. Similar effects were found for SGA births, but absolute inequalities were greater, with an SII score of 3.64 (1.74 to 5.54). Inequalities at birth were strong in the Netherlands, the UK, Sweden and Spain and marginal in other countries studied. CONCLUSIONS: This study highlights the value of comparative cohort analysis to better understand the relationship between maternal education and markers of fetal growth in different settings across Europe.

• Keywords
• CHILD HEALTH, EPIDEMIOLOGY, INEQUALITIES,
• MeSH
• Infant, Small for Gestational Age * MeSH
• Humans MeSH
• Linear Models MeSH
• Mothers statistics & numerical data   MeSH
• Infant, Newborn MeSH
• Premature Birth epidemiology   MeSH
• Prospective Studies MeSH
• Regression Analysis MeSH
• Risk Factors MeSH
• Cross-Cultural Comparison MeSH
• Educational Status MeSH
• Pregnancy MeSH
• Pregnancy Outcome epidemiology   MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Infant, Newborn MeSH
• Pregnancy MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Meta-Analysis MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Europe epidemiology   MeSH

INTRODUCTION: The purpose of this research was to assess factors associated with quit attempts and successful smoking cessation among a sample of socioeconomically disadvantaged pregnant women living in Eastern Hungary. MATERIALS AND METHODS: In-person interviews were conducted among 201 women residing in Eastern Hungary who self-identified as occasional or regular smokers at the time they learned they were pregnant. RESULTS: 54% of the women were smokers at the time they learned they were pregnant. Just over half tried to quit, but only 20% were successful. Factors associated with reduced likelihood of quit attempts included being a regular (vs. occasional) smoker (OR = 0.36, 95% CI 0.13-1.00) and being Roma (vs. non-Roma) (OR = 0.32, 95% CI 0.14-0.72). Women who completed high school were 7% times more likely to quit (OR = 7.5, 95% CI 1.68-33.2) and those who were employed were 7% times more likely to quit (OR = 7.6, 95% CI 1.88-30.35). Regular smokers were 88% less likely to quit than occasional smokers. DISCUSSION: Smoking cessation interventions targeting pregnant women are needed in Eastern Hungary. Efforts to integrate smoking cessation into the current excellent pre-natal care and health visitor program will reach most women who are pregnant or who have given birth within the preceding 3 years.

• MeSH
• Smoking adverse effects   ethnology   MeSH
• Humans MeSH
• Smoking Cessation economics   ethnology   psychology   MeSH
• Pilot Projects MeSH
• Prenatal Care methods   standards   MeSH
• Smoking Prevention * MeSH
• Risk Factors MeSH
• Roma psychology   statistics & numerical data   MeSH
• Socioeconomic Factors MeSH
• Pregnancy MeSH
• Pregnant People ethnology   psychology   MeSH
• Pregnancy Outcome ethnology   MeSH
• Health Behavior MeSH
• Check Tag
• Humans MeSH
• Pregnancy MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, N.I.H., Extramural MeSH
• Geographicals
• Hungary epidemiology   MeSH