Smoking During Pregnancy Among Immigrant Women With Same-Origin and Swedish-Born Partners
Bibliographic record
Abstract
INTRODUCTION: Although ethnically mixed couples are on the rise in industrialized countries, their health behaviors are poorly understood. We examined the associations between partner's birthplace, age at immigration, and smoking during pregnancy among foreign-born women. METHODS: Population-based register study including all pregnancies resulting in a livebirth or stillbirth in Sweden (1991-2012) with complete information on smoking and parental country of birth. We compared the prevalence of smoking during pregnancy between women in dual same-origin foreign-born unions (n = 213 111) and in mixed couples (immigrant women with a Swedish-born partner) (n = 111 866) using logistic regression. Swedish-born couples were used as a benchmark. RESULTS: The crude smoking rate among Swedish women whose partners were Swedish was 11%. Smoking rates of women in dual same-origin foreign-born unions varied substantially by birthplace, from 1.3% among women from Asian countries to 23.2% among those from other Nordic countries. Among immigrant groups with prevalences of pregnancy smoking higher than that of women in dual Swedish-born unions, having a Swedish-born partner was associated with lower odds of smoking (adjusted odds ratios: 0.72-0.87) but with higher odds among immigrant groups with lower prevalence (adjusted odds ratios: 1.17-5.88). These associations were stronger among women immigrating in adulthood, whose smoking rates were the lowest. CONCLUSIONS: Swedish-born partners "pull" smoking rates of immigrant women toward the level of smoking of Swedish-born women, particularly among women arrived during adulthood. Consideration of a woman's and her partner's ethnic background and life stage at migration may help understand smoking patterns of immigrant women. IMPLICATIONS: We found that having a Swedish-born partner is associated with higher rates of smoking during pregnancy among immigrants from regions where women smoke less than Swedish women, but with lower smoking rates among immigrants from regions where women smoke more. This implies that prevention efforts should concentrate on newly arrived single women from low prevalence regions, such as Africa and Asia, whereas cessation efforts may target women from high prevalence regions, such as other European countries. These findings suggest that pregnancy smoking prevention or cessation interventions may benefit from including partners and approaches culturally tailored to mixed unions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".