Abstract
BackgroundLung cancer is an important public health issue, particularly among American Indians (AIs). The reported decline in tobacco use for most racial/ethnic groups is not observed among AIs. This project was designed to address the research question, “Why don't more Northern Plains American Indians alter tobacco use behaviors known to increase the risk of cancer?” MethodsGuided by the Theory of Planned Behavior, a multi-component intervention study was implemented. Adult AIs, age 18 years or older and currently smoking, were enrolled. Eligible subjects were randomized to one of 15 groups and exposed to either a MINIMAL or an INTENSE level of 4 intervention components. The intervention was delivered face-to-face or via telephone by Patient Navigators (PN). The primary outcome was self-reported abstinence from smoking verified by carbon monoxide measurement. ResultsAt 18 months post-quit date, 88% of those who were still in the study were abstinent. This included 6% of all participants who enrolled in the study (14/254) and 13% of those who made it to the quit date (14/108). No intervention groups were found to have significant proportions of participants who were abstinent from smoking at the quit date (visit 5) or primary outcome visit (18 months post-quit date, visit 11), but use of pharmacologic support for abstinence was found to be an effective strategy for individuals who continued participation throughout the study. Those who remained in the study received more visits and were more likely to be abstinent. ConclusionsUse of NRT increased the odds of not smoking, as assessed at the 18-month follow-up visit, but no other interventions were found to significantly contribute to abstinence from smoking. Although the intervention protocol included numerous points of contact between CRRs and participants (11 visits) loss to follow-up was extensive with only 16/254 remaining enrolled. Additional research is needed to improve understanding of factors that influence enrollment and retention in smoking cessation interventions for AI and other populations.
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