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Record W2889955532 · doi:10.1080/14767058.2018.1520835

Exposure to tobacco use in pregnancy and its determinants among sub-Saharan Africa women: analysis of pooled cross-sectional surveys

2018· article· en· W2889955532 on OpenAlexaff
Sanni Yaya, Olalekan A. Uthman, Vissého Adjiwanou, Ghose Bishwajit

Bibliographic record

VenueThe Journal of Maternal-Fetal & Neonatal Medicine · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineSierra leoneLogistic regressionCross-sectional studyTobacco usePregnancyDemographyEnvironmental healthDeveloping countryPublic healthTobacco controlPopulationSocioeconomics

Abstract

fetched live from OpenAlex

Background: Tobacco use in any form and exposure to second-hand smoking are major threat to human health globally. Worse still, it is an important threat to the health of pregnant women and their children. However, the prevalence of tobacco use among pregnant women in sub-Saharan Africa countries remains uncertain. This study assessed the prevalence and factors of tobacco use among pregnant women in sub-Saharan Africa countries.Methods: This study utilized data from Demographic and Health Surveys (DHS) conducted in 31 sub-Saharan Africa countries between 2008 and 2017, comprising 44,715 pregnant women (aged 15–49 years). We calculated sampling weights to account for differentials in probabilities of selection and estimated proportions and 95% CIs for tobacco use in pregnant women across various countries. The factors associated with tobacco use were examined using multivariable binary logistic regression models at a significant level of 5%.Results: Prevalence of tobacco use among pregnant women was ∼2%. In Madagascar, the prevalence of tobacco use was 11.0%, while Lesotho (5.4%), Sierra Leone (4.8%), Namibia (4.4%) and Burundi (4.2%) were among the leading countries with high tobacco use pregnancy. The results of multivariable binary logistic regression model showed that pregnant women aged 25–34 years and ≥35 years were 2.26 times (OR = 2.26; 95%CI: 1.23, 4.15) and 2.45 times (OR = 2.45; 95%CI: 1.10, 5.45) as likely to use tobacco products, compared to women aged ≤24 years. The religious beliefs of pregnant women, who belong to other religion besides Islam, were 2.26 times as likely to use tobacco products compared to Christian women (OR = 2.26; 95%CI: 1.19, 4.31). In addition, pregnant women from households with middle-class wealth index had 64% reduction in tobacco products use among pregnant women, compared to those from poor households (OR = 0.36; 95%CI: 0.15–0.87).Conclusion: Overall, tobacco use in pregnant women in sub-Saharan Africa was low; however high prevalence estimates were noted in some countries. Prevention and management of tobacco use and exposure to second-hand smoke during pregnancy is crucial to protect maternal and child health in Africa continent. Pregnant women should be examined about their tobacco use preferably with a biochemical test and those who use tobacco products be encouraged to use cessation supports such as nicotine replacement therapy (NRT) where available. Health professionals should identify tobacco products users and advise to quit, most importantly by offer cessation support. When tobacco products users become pregnant, the health benefits of cessation of tobacco use should be well discussed with them especially during antenatal care. The tobacco use of other members of the household is also crucial, as having a user partner could widely predict the exposure of a pregnant woman.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.313
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations12
Published2018
Admission routes1
Has abstractyes

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