Tobacco control policies and perinatal and child health: a systematic review and meta-analysis protocol

Been, Jasper V; Mackenbach, Johan P.; Millett, Christopher; Basu, Sanjay; Sheikh, Aziz

doi:10.1136/bmjopen-2015-008398

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…This systematic review and meta-analysis was done according to a peer-reviewed protocol that is published 20 and registered with PROSPERO (CRD42015023448). We followed the PRISMA checklist when reporting our findings.…”

Section: Methodsmentioning

confidence: 99%

Effect of tobacco control policies on perinatal and child health: a systematic review and meta-analysis

Faber

Kumar

Mackenbach

et al. 2017

The Lancet Public Health

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164

130

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SummaryBackgroundTobacco smoking and smoke exposure during pregnancy and childhood cause considerable childhood morbidity and mortality. We did a systematic review and meta-analysis to investigate whether implementation of WHO's recommended tobacco control policies (MPOWER) was of benefit to perinatal and child health.MethodsWe searched 19 electronic databases, hand-searched references and citations, and consulted experts to identify studies assessing the association between implementation of MPOWER policies and child health. We did not apply any language restrictions, and searched the full time period available for each database, up to June 22, 2017. Our primary outcomes of interest were perinatal mortality, preterm birth, hospital attendance for asthma exacerbations, and hospital attendance for respiratory tract infections. Where possible and appropriate, we combined data from different studies in random-effects meta-analyses. This study is registered with PROSPERO, number CRD42015023448.FindingsWe identified 41 eligible studies (24 from North America, 16 from Europe, and one from China) that assessed combinations of the following MPOWER policies: smoke-free legislation (n=35), tobacco taxation (n=11), and smoking cessation services (n=3). Risk of bias was low in 23 studies, moderate in 16, and high in two. Implementation of smoke-free legislation was associated with reductions in rates of preterm birth (–3·77% [95% CI −6·37 to −1·16]; ten studies, 27 530 183 individuals), rates of hospital attendance for asthma exacerbations (–9·83% [–16·62 to −3·04]; five studies, 684 826 events), and rates of hospital attendance for all respiratory tract infections (–3·45% [–4·64 to −2·25]; two studies, 1 681 020 events) and for lower respiratory tract infections (–18·48% [–32·79 to −4·17]; three studies, 887 414 events). Associations appeared to be stronger when comprehensive smoke-free laws were implemented than when partial smoke-free laws were implemented. Among two studies assessing the association between smoke-free legislation and perinatal mortality, one showed significant reductions in stillbirth and neonatal mortality but did not report the overall effect on perinatal mortality, while the other showed no change in perinatal mortality. Meta-analysis of studies on other MPOWER policies was not possible; all four studies on increasing tobacco taxation and one of two on offering disadvantaged pregnant women help to quit smoking that reported on our primary outcomes had positive findings. Assessment of publication bias was only possible for studies assessing the association between smoke-free legislation and preterm birth, showing some degree of bias.InterpretationSmoke-free legislation is associated with substantial benefits to child health. The majority of studies on other MPOWER policies also indicated a positive effect. These findings provide strong support for implementation of such policies comprehensively across the world.FundingChief Scientist Office Scotland, Farr Institute, Netherlands Lung Foundation, Erasmus...

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Section: Methodsmentioning

confidence: 99%

Effect of tobacco control policies on perinatal and child health: a systematic review and meta-analysis

Faber

Kumar

Mackenbach

et al. 2017

The Lancet Public Health

Self Cite

164

130

View full text Add to dashboard Cite

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“…Long noncoding RNAs (lncRNAs) do not encode proteins and were once regarded as transcriptional noise or cloning artifacts. 3 , 4 According to recent research, lncRNAs are thought to be regulators of cancer development, which make them a promising target for cancer treatment. 5 , 6 MALAT1, with a length of over 8,000 nucleotides, is a highly conserved lncRNA that is derived from chromosome 11q13.…”

Section: Introductionmentioning

confidence: 99%

Clinical significance and prospective molecular mechanism of MALAT1 in pancreatic cancer exploration: a comprehensive study based on the GeneChip, GEO, Oncomine, and TCGA databases

Xie¹,

Dang²,

Wei³

et al. 2017

OTT

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PurposeLong noncoding RNAs (lncRNAs) are known to function as regulators in the development and occurrence of various tumors. MALAT1 is a highly conserved lncRNA and has vital functions in diverse tumors, including pancreatic cancer (PC). However, the underlying molecular regulatory mechanism involved in the occurrence and development of PC remains largely unknown. Thus, it is important to explore MALAT1 in PC and elucidate its function, which might offer a new perspective for clinical diagnosis and therapy.MethodsFirst, we used the Gene Expression Omnibus, Oncomine, and The Cancer Genome Atlas databases to determine the clinical diagnostic and prognostic values of MALAT1. We next used our own GeneChip and The Cancer Genome Atlas database to collect the possible target genes of MALAT1 and further utilized a bioinformatics analysis to explore the underlying significant pathways that might be crucial in PC. Finally, we identified several key target genes of MALAT1 and hope to offer references for future research.ResultsWe found that the expression of MALAT1 was significantly elevated in patients with PC. A receiver operating characteristics curve analysis showed a moderate diagnostic value (area under the curve =0.75, sensitivity =0.66, specificity =0.72). A total of 224 important overlapping genes were collected, and six hub genes (CCND1, MAPK8, VEGFA, FOS, CDH1, and HSP90AA1) were identified, of which CCND1, MAPK8, and VEGFA, are important genes in PC. Several pathways, including the mTOR signaling pathway, pathways in cancer, and the MAPK signaling pathway, were suggested to be the vital MALAT1 pathways in PC.ConclusionMALAT1 is suggested to be a promising diagnostic biomarker in PC. Six hub genes (CCND1, MAPK8, VEGFA, FOS, CDH1, and HSP90AA1), and specifically CCND1, MAPK8, and VEGFA, might be key MALAT1 target genes in PC. Due to their possible clinical significance in PC, several pathways, such as the mTOR signaling pathway, pathways in cancer, and the MAPK signaling pathway, are worthy of further study.

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“…For example, tobacco control policies might be expected to have an immediate impact on maternal smoking levels but a lag of approximately nine months before any impact on small for gestational age births and a much longer lag before any impact on lung cancer. (30)…”

Section: Immediate or Lagged Effectsmentioning

confidence: 99%

A methodological framework for model selection in interrupted time series studies

Bernal

Soumerai

Gasparrini

2018

Journal of Clinical Epidemiology

149

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Interrupted time series (ITS) is a powerful and increasingly popular design for evaluating public health and health service interventions. The design involves analyzing trends in the outcome of interest and estimating the change in trend following an intervention relative to the counterfactual (the expected ongoing trend if the intervention had not occurred). There are two key components to modeling this effect: first, defining the counterfactual; second, defining the type of effect that the intervention is expected to have on the outcome, known as the impact model. The counterfactual is defined by extrapolating the underlying trends observed before the intervention to the postintervention period. In doing this, authors must consider the preintervention period that will be included, any time-varying confounders, whether trends may vary within different subgroups of the population and whether trends are linear or nonlinear. Defining the impact model involves specifying the parameters that model the intervention, including for instance whether to allow for an abrupt level change or a gradual slope change, whether to allow for a lag before any effect on the outcome, whether to allow a transition period during which the intervention is being implemented, and whether a ceiling or floor effect might be expected. Inappropriate model specification can bias the results of an ITS analysis and using a model that is not closely tailored to the intervention or testing multiple models increases the risk of false positives being detected. It is important that authors use substantive knowledge to customize their ITS model a priori to the intervention and outcome under study. Where there is uncertainty in model specification, authors should consider using separate data sources to define the intervention, running limited sensitivity analyses or undertaking initial exploratory studies.

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Tobacco control policies and perinatal and child health: a systematic review and meta-analysis protocol

Cited by 6 publications

References 23 publications

Effect of tobacco control policies on perinatal and child health: a systematic review and meta-analysis

Effect of tobacco control policies on perinatal and child health: a systematic review and meta-analysis

Clinical significance and prospective molecular mechanism of MALAT1 in pancreatic cancer exploration: a comprehensive study based on the GeneChip, GEO, Oncomine, and TCGA databases

A methodological framework for model selection in interrupted time series studies

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