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Record W2172019174 · doi:10.1111/add.13084

Commentary on Leung <i>et al</i>. (2015): Inequalities in mental health begin in utero – the case of prenatal tobacco exposure

2015· letter· en· W2172019174 on OpenAlexaboutno aff
Andrew J. Lewis

Bibliographic record

VenueAddiction · 2015
Typeletter
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsnot available
Fundersnot available
KeywordsPregnancyMedicineTobacco smokeEnvironmental healthPublic healthMental healthCohortConfoundingPsychiatryPediatrics

Abstract

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Tobacco smoking in pregnancy is arguably the most important preventable cause of adverse pregnancy outcomes and a number of childhood developmental problems. Recent findings suggest a very early effect of socio-economic disadvantage and that public health research ought to refocus on a ‘womb to tomb’ perspective In western populations the prevalence of maternal cigarette smoking in pregnancy has been estimated at approximately 12% 1. Fetal exposure to tobacco smoke—both maternal and passive—has been associated consistently with fetal growth restriction, preterm delivery, childhood respiratory problems and both cognitive and behavioural problems 2. Leung et al. add important findings to this literature that raise significant public health issues 3. Leung et al. report associations between prenatal exposure to second-hand smoke and adolescent behaviour problems in a large cohort growing up in Hong Kong. In this setting, only approximately 3% of pregnant women smoke while approximately 20% of men smoke. Uniquely, this allows estimates of the effect of environmental exposure to cigarette smoke with minimal confounding from maternal smoking. The neurodevelopmental effects of prenatal exposures are often subtle—so-called ‘sleeper’ effects—so follow-up into adolescence is an impressive feature of the study 4, 5. The study found no association with lower self-esteem or depressive symptoms, but importantly confirmed many previous reports that fetal exposure to tobacco smoke from both paternal and maternal smoking is associated with lower socio-economic status. Tobacco smoke is but one of a wide range of prenatal exposures that have been investigated as predictors of child mental health problems. In our recent review of such studies we classified exposures into three major categories: maternal mental health; life-style factors (e.g. diet); and environmental neurotoxins (e.g. lead, alcohol, cigarettes) 2, 6. One of the key issues not well addressed in Leung et al. is that these pregnancy exposures often co-occur, and therefore complicate investigations of child outcomes. Women who smoke in pregnancy have been found to be more likely to have depressive symptoms and to use both psychotrophic and other prescribed medications 7. Poor maternal nutrition is often part of this picture, and may lead to iron deficiency, low vitamin D and chronic inflammation 8. Unfortunately, in the Leung et al. paper, maternal depression is used as a covariate only when the children are 13 years old, and the analyses do not account for the co-occurrence of exposures in pregnancy. The data also show that the effect of ‘any maternal smoking’ is substantially greater than ‘occasional’ or ‘daily’ paternal smoking. This differs from other studies, which found that the effect size for paternal smoking on child attention deficit hyperactive disorder (ADHD) was of similar magnitude to that of mothers, which led to claims that adverse mental health outcomes in children may be due to confounding by genetic or household factors 9. The current findings are markedly different, and suggest an aetiological process more consistent with fetal exposure effects. While the authors note that cigarette smoke may have an effect on the fetus’ neurological development, there is a lively debate concerning whether the observed associations between smoking during pregnancy and childhood mental health are causal 10, 11. However, a Taiwanese genetic study showed that the association between cord blood cotinine and childhood behavioural difficulties is modified by a genetic polymorphism involved in the metabolic pathway linked to the toxic substances in tobacco smoke 12. There is also consistent evidence of epigenetic modifications in the AHRR gene in neonatal blood following maternal smoking in pregnancy 13. While the debate about mechanisms continues, one thing is clear: fetal exposure to smoking is associated consistently with lower socio-economic status. Recent large survey studies from Australia and Canada show smoking in pregnancy to be predicted by being younger, having a smoker in their household, irregular medical care, reporting a mental disorder and lower socio-economic status 14, 15. Leung et al. attribute the higher prevalence of exposure to smoking in pregnancy in low SES settings to overcrowding and greater exposure to smoking environments, but the issue is far deeper. Fetal exposure to smoking reflects health inequities operating at the very earliest stage of the life-course and can be viewed as a key factor in the intergenerational transmission of social disadvantage. Of particular note is that such inequalities appear to impact upon long-term development from conception onwards. A concerning picture emerges from the wider body of research on fetal exposures of economically marginalized new parents, often struggling with poor maternal mental health, often receiving insufficient pregnancy health care and ineffective health information. Such circumstances increase the risks of one or several hazardous fetal exposures occurring during the most vulnerable developmental period, sometimes with life-long consequences. Exposure to tobacco smoke in pregnancy is arguably one of the most important preventable causes of a wide range of adverse maternal and child outcomes 15. These findings on passive smoking have a number of policy and prevention implications: a broader family-based intervention is required; preconception planning is essential; legislation around tobacco control needs to be pushed forward and education and advice for health professionals should be proactive. While developmental researchers have championed life-course models from ‘cradle to grave’, the findings of this and many other studies now suggest such research ought to refocus on a ‘womb to tomb’ perspective. None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.016
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.318
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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