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. 2020 Feb;32(2):223-229.
doi: 10.1080/09540121.2019.1626340. Epub 2019 Jun 7.

Delivery and implementation of an algorithm for smoking cessation treatment for people living with HIV and AIDS

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Delivery and implementation of an algorithm for smoking cessation treatment for people living with HIV and AIDS

Karen L Cropsey et al. AIDS Care. 2020 Feb.

Abstract

Compared to the general population, persons living with HIV (PLWH) have higher rates of tobacco use and an increased risk of morbidity from tobacco-related diseases. We conducted a single-arm pilot study of the real-world feasibility of integrating a smoking cessation decisional algorithm within routine clinic visits to engage non-treatment-seeking smokers in smoking cessation therapies. Smokers had an initial study visit during routine care followed by phone contacts at one and three months. Participants completed a baseline survey, followed by the algorithm which resulted in a recommendation for a smoking cessation medication, which was prescribed during the visit. Follow-up phone surveys assessed changes in smoking behavior and use of cessation medications at 1 and 3 months. Participants' (N = 60) self-reported smoking decreased from a baseline average of 14.4 cigarettes/day to 7.1 cigarettes/day at 3 months (p = .001). Nicotine dependence (FTND) decreased from 5.6 at baseline to 3.6 at 3 months (p < .001). Twenty-seven (45%) made a 24-h quit attempt and 39 (65%) used cessation medication. Insurance prior-authorization delayed medication receipt for seven participants and insurance denial occurred for one. Motivational status did not significantly influence outcomes. The algorithm was successful in engaging participants to use cessation medications and change smoking behaviors.

Keywords: Decisional aid; HIV; smoking cessation.

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Conflict of interest statement

Declaration of Interest

Funding and Declarations of Interest for all authors: This work was supported through the Southern Consortium of the National Institute on Drug Abuse (NIDA) Clinical Trials Network (CTN) (UG1 DA 013727). The findings and conclusions herein are those of the authors and do not necessarily represent the official position of NIDA or CTN. All authors report no grant funding outside the support listed above for the submitted work.

Figures

Figure 1.
Figure 1.
Algorithm Schematic

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