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A comparison of classifications for geographic location and their associations with tobacco use among US adults

  • Jenny E. Ozga
  • , Andrea Milstred
  • , Melissa D. Blank
  • , Mary Kay Rayens
  • , Brittney Keller-Hamilton
  • , Megan E. Roberts
  • , Seth Himelhoch
  • , Cassandra A. Stanton

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Purpose: This study compared two classifications of rurality and their associations with cigarette, e-cigarette, and smokeless tobacco (SLT) use among a nationally representative sample of 31,196 US adults. Methods: Data from Wave 1 of the Population Assessment of Tobacco and Health Study. Weighted descriptive statistics and multivariable logistic regressions assessed whether two classifications of rurality were differentially associated with past 30-day (P30D) cigarette, e-cigarette, or SLT use in separate models. Classifications were (1) the US Census Bureau's classification as urban/non-urban; and (2) the National Center for Education Statistic (NCES)’s classification as urban/suburban/town/rural. This study is reported in accordance with STROBE guidelines. Findings: With the Census Bureau classification, 79.3% were in urban areas. With the NCES classification, 34.3% were in urban, 35.1% in suburban, 9.4% in town, and 21.1% in rural areas. With the Census Bureau classification, non-urban (vs. urban) residence was associated with reduced odds of e-cigarette use (AOR = 0.79; 95% CI = 0.70–0.88) and increased odds of SLT use (AOR = 2.32; 95% CI = 1.97–2.72). With the NCES classification with urban as reference, rural residence was associated with reduced odds of e-cigarette use (AOR = 0.77; 95% CI = 0.75–0.98); both town (AOR = 2.16; 95% CI = 1.69–2.78) and rural (AOR = 2.75; 95% CI = 2.16, 3.48) were associated with increased odds of SLT use. Location was not associated with cigarette use for either classification. Conclusions: Location was similarly associated with P30D e-cigarette and SLT use across both classifications in adjusted models. The use of classifications with more categories may be beneficial to understand nuanced location differences in tobacco use.

Original languageEnglish
Article numbere70070
JournalJournal of Rural Health
Volume41
Issue number3
DOIs
StatePublished - Jun 1 2025

Bibliographical note

© 2025 National Rural Health Association.

Funding

Research reported in this publication was supported by grant numbers U54DA036151 from the National Institute of Drug Abuse [NIDA] and Food & Drug Administration [FDA] Center for Tobacco Products [CTP]. Additional funding for authors was provided by the Centers for Disease Control and Prevention [CDC] by grant number U48DP006391 to the West Virginia Prevention Research Center [author MDB], NIDA K01DA055696 and NCI R01CA289551 [author BKH], NIDA and FDA CTP U54DA058256 [authors SH and MKR], NCI R01CA273206 and NCI and FDA CTP U54CA287392 [author MER]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, FDA, or CDC.

FundersFunder number
National Institutes of Health (NIH)
National Institute on Drug Abuse
U.S. Food and Drug Administration
Centers for Disease Control and PreventionU48DP006391, K01DA055696
National Childhood Cancer Registry – National Cancer InstituteCTP U54CA287392, CTP U54DA058256, R01CA289551, R01CA273206

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    Keywords

    • measurement
    • rural
    • suburban
    • tobacco
    • urban

    ASJC Scopus subject areas

    • Public Health, Environmental and Occupational Health

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