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Long SE, Lama Y, D'Angelo H. Digital Communication Inequalities Among U.S. Adults Reporting Current Cigarette Use. Am J Prev Med 2024; 66:307-314. [PMID: 37793558 PMCID: PMC10842098 DOI: 10.1016/j.amepre.2023.09.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 09/26/2023] [Accepted: 09/26/2023] [Indexed: 10/06/2023]
Abstract
INTRODUCTION To reduce tobacco-related health problems, it is critical to reach people who smoke with smoking cessation information and treatment. However, digital communication inequalities may limit access to online information sources. METHODS Digital device ownership, high-speed internet access, and online health information-seeking were examined among adults reporting current smoking in the Health Information National Trends Survey (n=847). Data were collected in 2019 and 2020 and analyzed in 2022. Multivariable logistic regression models examined associations between demographics, digital technology access, and online health information-seeking. RESULTS Only 47.6% (95% CI 39.0%, 56.3%) of adults aged 65+, 54.2% of Black/African American adults (95% CI 37.8%, 69.8%), and 59.6% with high school or less education (95% CI 51.5%, 67.1%) reported high-speed internet access (vs. 74.0% overall, 95% CI 68.9%, 78.6%). Inequalities in device ownership, high-speed internet access, and online health information-seeking were found by education and income. Adults with high school or less education (vs. college or more) had 78% lower odds of digital device ownership (aOR 0.22, 95% CI 0.08, 0.59) and 75% lower odds of high-speed internet access (aOR 0.25, 95% CI 0.09, 0.71). High-speed internet access (vs. no digital device or high-speed internet) was associated with 4.9 times greater odds of online health information-seeking (95% CI 1.81, 13.4). CONCLUSIONS Digital communication inequalities among adults who smoke exist. Understanding digital technology access among lower income populations could inform the development and delivery of interventions and health communication strategies to improve health outcomes among this population.
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Affiliation(s)
| | - Yuki Lama
- National Cancer Institute, Rockville, Maryland
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Cummings KM, Talbot V, Roberson A, Bliss AA, Likins E, Brownstein NC, Stansell S, Adams-Ludd D, Harris B, Louder D, McCutcheon E, Zebian R, Rojewski A, Toll BA. Implementation of an "Opt-Out" Tobacco Treatment Program in Six Hospitals in South Carolina. RESEARCH SQUARE 2023:rs.3.rs-3318088. [PMID: 37720041 PMCID: PMC10503831 DOI: 10.21203/rs.3.rs-3318088/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Objective To describe the implementation an opt-out tobacco treatment program (TTP) in 6 diverse hospitals located in different regions of South Carolina. Methods Between March 8, 2021 and December 17, 2021, adult patients (≥ 18 years) admitted to 6 hospitals affiliated with the Medical University of South Carolina (MUSC) were screened for their cigarette status. Patients who smoked cigarettes were referred to an TTP offering a brief bedside consult and automated post-discharge follow-up calls with an opportunity to receive a referral to the South Carolina Quitline (SCQL). The hospitals included in this study ranged in size from 82 to 715 beds with diverse patient populations. Herein, we report on the results of screening and referring patients to the TTP, delivery of smoking cessation treatments, and patient smoking status assessed in a sample of patients followed 6-weeks after discharge from the hospital. Results Smoking prevalence ranged from 14-49% across the 6 hospitals. Among eligible patients reached, 85.6% accepted the bedside consult. Only 3.4% of patients reached were deemed ineligible because they claimed not to be currently smoking cigarettes. The automated post-discharge follow-up calls were answered by 43% of patients, with about a third of those who had relapsed back to smoking accepting the offer of a referral to the SCQL. Overall, about half of the 6,000 patients referred to the TTP received some type of treatment. Self-reported smoking abstinence rates assessed 6-weeks after discharge were similar across the five acute care hospitals ranging from about 20-30%. Conclusion The findings demonstrate the broad reach of implementing an opt-out TTP for patients in hospitals of varying size, rurality and patient populations.
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Taylor KL, Williams RM, Li T, Luta G, Smith L, Davis KM, Stanton C, Niaura R, Abrams D, Lobo T, Mandelblatt J, Jayasekera J, Meza R, Jeon J, Cao P, Anderson ED. A Randomized Trial of Telephone-Based Smoking Cessation Treatment in the Lung Cancer Screening Setting. J Natl Cancer Inst 2022; 114:1410-1419. [PMID: 35818122 DOI: 10.1093/jnci/djac127] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/06/2022] [Accepted: 06/28/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Lung cancer mortality is reduced via low-dose CT screening and treatment of early-stage disease. Evidence-based smoking cessation treatment in the lung screening setting can further reduce mortality. We report the results of a cessation trial from the NCI's SCALE collaboration. METHODS Eligible patients (N = 818) aged 50-80 were randomized (May 2017-January 2021) to the Intensive vs. Minimal arms (8 vs. 3 phone sessions plus 8 vs. 2 weeks of nicotine patches, respectively). Bio-verified (primary) and self-reported 7-day abstinence rates were assessed 3-, 6-, and 12-months post-randomization. Logistic regression analyses evaluated the effects of study arm. All statistical tests were two-sided. RESULTS Participants reported 48.0 (SD = 17.2) pack-years and 51.6% were not ready to quit in < 30 days. Self-reported 3-month quit rates were significantly higher in the Intensive vs. Minimal arm (14.3% vs. 7.9%; OR = 2.00, 95% confidence interval [CI] = 1.26,3.18). Bio-verified abstinence was lower but with similar relative differences between arms (9.1% vs. 3.9%; OR = 2.70, 95% CI = 1.44, 5.08). Compared to the Minimal arm, the Intensive arm was more effective among those with greater nicotine dependence (OR = 3.47, 95% CI = 1.55, 7.76), normal screening results (OR = 2.58, 95% CI = 1.32, 5.03), high engagement in counseling (OR = 3.03, 95% CI = 1.50, 6.14) and patch use (OR = 2.81, 95% CI = 1.39, 5.68). Abstinence rates did not differ significantly between arms at 6-months (OR = 1.2, 95% CI = 0.68, 2.11) or 12-months (OR = 1.4, 95% CI = 0.82, 2.42). CONCLUSIONS Delivering intensive telephone counseling and nicotine replacement with lung screening is an effective strategy to increase short-term smoking cessation. Methods to maintain short-term effects are needed. Even with modest quit rates, integrating cessation treatment into lung screening programs may have a large impact on tobacco-related mortality.
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Affiliation(s)
- Kathryn L Taylor
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Randi M Williams
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Tengfei Li
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA
| | - George Luta
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA
| | - Laney Smith
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Kimberly M Davis
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | | | - Raymond Niaura
- School of Global Public Health, New York University, NY, NY, USA
| | - David Abrams
- School of Global Public Health, New York University, NY, NY, USA
| | - Tania Lobo
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Jeanne Mandelblatt
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Jinani Jayasekera
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Rafael Meza
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Pianpian Cao
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Eric D Anderson
- Department of Pulmonary and Sleep Medicine, Georgetown University Medical Center, Washington, DC, USA
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