Evidence map›Paper›PMID 38406660›Full record

ArticleTobacco induced diseases2024

Trend estimation of sub-national level daily smoking prevalence by age and sex in Australia.

Sumonkanti Das, Bernard Baffour, Alice Richardson

Abstract read
In one paragraph

Article in Tobacco induced diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Smoking Among Healthcare Professionals in Australia: A Scoping Review.International journal of environmental research and public health · 2025
    Article
  3. Article
4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Sumonkanti DasSchool of Demography, ANU College of Arts and Social Sciences, The Australian National University, Canberra, Australia.
Bernard BaffourSchool of Demography, ANU College of Arts and Social Sciences, The Australian National University, Canberra, Australia.
Alice RichardsonStatistical Support Network, The Australian National University, Canberra, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDespite that the smoking prevalence has considerably declined in Australia after successful public health strategies over many decades, smoking is still the leading cause of preventable diseases and death in Australia. These declines have not occurred consistently across all geographical-demographic domains. In order to provide an evidence base for monitoring the trend towards the goal of reducing smoking across all domains in Australia, this study aims to estimate trends of smoking prevalence for small domains cross-classified by seven age groups (18-24, 25-29, 30-39, 40-49, 50-59, 60-69, and ≥70 years), two genders, and eight states and territories over twenty years (2001-2021).

methodsDirect estimates of smoking prevalence for the target small domains were calculated from the micro-data of the Australian National Health Surveys conducted in seven rounds during 2001-2021. The obtained direct estimates were then used as input for developing time-series models expressed in a hierarchical Bayesian structure as a form of small-area estimation. The developed models borrow cross-sectional, temporal, and spatial strength in such a way that they can interpolate smoking levels in the non-survey years for all detailed level small domains. Smoothed trends of smoking prevalence for higher aggregation levels are obtained by aggregation of the detailed level trend predictions.

resultsModel-based small area estimators provide consistent and reasonable smoothed trends at both detailed and higher aggregation levels. Results show that the national-level trend exhibits a steeper linear decline over the study period, from 24% in 2001 to 12% in 2021, with a considerable gender difference of around 5% over the period, with males reporting a higher prevalence. Improved model-based estimates at the state level and by age also show steady declines in trends except for the Northern Territory (still above 20%) and older age groups 60-69 and ≥70 years (declining trends remain stable after 2012).

conclusionsThe findings of the study identify the geographical-demographic groups that had poor improvement over the period 2001-2021, and are still behind the target of achieving lower smoking prevalence. These, in turn, will help health researchers and policymakers deliver targeted programs to the most vulnerable, enabling the nation to meet its health goals in a timely way.

Indexed as

hierarchical Bayesian approachjoinpoint regressionnational health surveysmall area estimationtime-series model

Identifiers

PMID38406660
PMCPMC10885685

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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.