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Predictors of Tobacco Cessation Among American Indian/Alaska Native Adults Enrolled in a State Quitline.

Nicole P YuanJennifer Schultz De La RosaUma S NairMelanie L Bell
Published in: Substance use & misuse (2019)
Background: High rates of smoking are documented among some American Indian and Alaska Native (AI/AN) communities, with potential variability by region and urban/rural settings. Quitlines are a cost-effective strategy for providing evidence-based cessation treatment, but little is known about the effectiveness of quitline services for the AI/AN population. Objectives: This study compared demographic characteristics, tobacco use, and cessation and program utilization behaviors between AI/AN (n = 297) and Non-Hispanic White (NHW; n = 13,497) quitline callers. The study also identified predictors of 30-day cessation at 7-month follow-up among AI/AN callers and determined if predictors were different between AI/AN and NHW callers. Methods: Data from callers to the Arizona Smokers' Helpline between January 2011 and June 2016 were analyzed. Results: At enrollment, AI/AN callers were less likely to use tobacco daily and were less dependent on nicotine compared to NHW callers. Both groups reported similar rates of 30-day cessation at 7-month follow-up (37.3% and 39.7% for AI/AN and NHW callers, respectively). For AI/AN callers, 30-day cessation was significantly associated with tobacco cessation medication use (OR = 2.24, 95% CI: 1.02-4.93), number of coaching sessions (OR = 1.14, 95% CI: 1.04-1.26), and other smokers in the home (OR = 0.41, 95% CI: 0.19-0.91). The effect of other smokers in the home was significantly different between AI/AN and NHW callers (p = .007). Conclusions: Different individual characteristics and predictors of cessation among AI/AN callers compared to NHW callers were documented. Findings may be used to inform the development of culturally-tailored strategies and protocols for AI/AN quitline callers.
Keyphrases
  • artificial intelligence
  • smoking cessation
  • big data
  • machine learning
  • healthcare
  • deep learning
  • randomized controlled trial
  • systematic review
  • primary care
  • physical activity
  • mental health
  • risk assessment