Evidence map›Paper›PMID 36653231›Full record

ArticleAmerican journal of preventive medicine2023

Birth Cohort‒Specific Smoking Patterns by Family Income in the U.S.

Jihyoun Jeon, Pianpian Cao, Nancy L Fleischer, David T Levy, Theodore R Holford, Rafael Meza, Jamie Tam

Open access · hybridAbstract read
In one paragraph

Article in American journal of preventive medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
3.3field-weighted citation impact, top 7% 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

17 citing papers in PubMed, 19 citations in OpenAlex.

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  14. Mortality Relative Risks by Smoking, Race/Ethnicity, and Education.American journal of preventive medicine · 2023
    Article
  15. Article
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  17. Patterns of Birth Cohort‒Specific Smoking Histories in Brazil.American journal of preventive medicine · 2023
    Article
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

7 authors at 5 institutions in 2 countries.

Jihyoun JeonFrom the Department of Epidemiology, University of Michigan, Ann Arbor, Michigan. Electronic address: jihjeon@umich.edu.
Pianpian CaoFrom the Department of Epidemiology, University of Michigan, Ann Arbor, Michigan.
Nancy L FleischerFrom the Department of Epidemiology, University of Michigan, Ann Arbor, Michigan.
David T LevyDepartment of Oncology, Georgetown University, Washington, District of Columbia.
Theodore R HolfordDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut.
Rafael MezaFrom the Department of Epidemiology, University of Michigan, Ann Arbor, Michigan; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada.
Jamie TamDepartment of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut.
University of Michigan · USBC Cancer Agency · CAGeorgetown University · USYale New Haven Health System · USYale University · US

Funding

Research Project 3: Modeling the Impact of Tobacco Control Policies on Polytobacco Use and Associated Health DisparitiesU54CA229974 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI David Mendez Emilien · 2018 to 2026
$39.2M
Comparative Modeling of Lung Cancer Prevention, Early Detection and Treatment InterventionsU01CA253858 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2020 to 2025
$8.4M
Comparative Modeling of Lung Cancer Prevention and Control PoliciesU01CA199284 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2015 to 2019
$8.4M
NCI NIH HHS U01 CA199284NCI NIH HHS U01 CA253858NCI NIH HHS U54 CA229974
6 · The paper itself

Abstract

introductionIn the U.S., low-income individuals generally smoke more than high-income individuals. However, detailed information about how smoking patterns differ by income, especially differences by birth cohort, is lacking.

methodsUsing the National Health Interview Survey 1983-2018 data, individual family income was calculated as a ratio of the federal poverty level. Missing income data from 1983 to 1996 were imputed using sequential regression multivariate imputation. Age‒period‒cohort models with constrained natural splines were used to estimate annual probabilities of smoking initiation and cessation and smoking prevalence and intensity by gender and birth cohort (1900-2000) for 5 income groups: <100%, 100%-199%, 200%-299%, 300%-399%, and ≥400% of the federal poverty level. Analysis was conducted in 2020-2021.

resultsAcross all income groups, smoking prevalence and initiation probabilities are decreasing by birth cohort, whereas cessation probabilities are increasing. However, relative differences between low- and high-income groups are increasing markedly, such that there were greater declines in prevalence among those in high-income groups in more recent cohorts. Smoking initiation probabilities are lowest in the ≥400% federal poverty level group for males across birth cohorts, whereas for females, this income group has the highest initiation probabilities in older cohorts but the lowest in recent cohorts. People living below the federal poverty level have the lowest cessation probabilities across cohorts.

conclusionsSmoking prevalence has been decreasing in all income groups; however, disparities in smoking by family income are widening in recent birth cohorts. Future studies evaluating smoking disparities should account for cohort differences. Intervention strategies should focus on reducing initiation and improving quit success among low-income groups.

Indexed as

Birth CohortSmoking CessationAgedFemaleHumansIncomeMaleSmokingTobacco Smoking

Identifiers

PMID36653231
PMCPMC11186479
OpenAlexW4317478155

What OpenQuestion holds

Textmetadata
LicenceTDM
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.