Evidence map›Paper›PMID 34262067›Full record

ArticleScientific reports2021

Exposure-lag response of smoking prevalence on lung cancer incidence using a distributed lag non-linear model.

Daniel Robert Smith, Alireza Behzadnia, Rabbiaatul Addawiyah Imawana, Muzammil Nahaboo Solim, Michaela Louise Goodson

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
0.8field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 18 citations in OpenAlex.

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  11. Light at night and lung cancer risk: A worldwide interdisciplinary and time-series study.Chinese medical journal pulmonary and critical care medicine · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Daniel Robert SmithNewcastle University Medicine Malaysia, No. 1, Jalan Sarjana 1, Kota Ilmu, EduCity@Iskandar, 79200, Iskandar Puteri, Johor, Malaysia. dr.daniel.robert.smith@gmail.com.ORCID 0000-0002-3916-8041
Alireza BehzadniaHistopathology Department, Leeds Teaching Hospital, NHS Trust, Beckett Street, Leeds, LS9 7TF, West Yorkshire, UK.
Rabbiaatul Addawiyah ImawanaNewcastle University Medicine Malaysia, No. 1, Jalan Sarjana 1, Kota Ilmu, EduCity@Iskandar, 79200, Iskandar Puteri, Johor, Malaysia.
Muzammil Nahaboo SolimThe James Cook University Hospital, Marton Road, Middlesbrough, TS4 3BW, UK.
Michaela Louise GoodsonNewcastle University Medicine Malaysia, No. 1, Jalan Sarjana 1, Kota Ilmu, EduCity@Iskandar, 79200, Iskandar Puteri, Johor, Malaysia.
Newcastle University Medicine Malaysia · MYJames Cook University Hospital · GBLeeds Teaching Hospitals NHS Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The prevalence of smokers is a major driver of lung cancer incidence in a population, though the "exposure-lag" effects are ill-defined. Here we present a multi-country ecological modelling study using a 30-year smoking prevalence history to quantify the exposure-lag response. To model the temporal dependency between smoking prevalence and lung cancer incidence, we used a distributed lag non-linear model (DLNM), controlling for gender, age group, country, outcome year, and population at risk, and presented the effects as the incidence rate ratio (IRR) and cumulative incidence rate ratio (IRR

Indexed as

AdultAgedFemaleHumansIncidenceLung NeoplasmsMaleMiddle AgedModels, StatisticalNonlinear DynamicsPrevalenceSmoking

Identifiers

PMID34262067
PMCPMC8280159
OpenAlexW3182821875

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.