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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 AM, Toll BA. Implementation of an "opt-out" tobacco treatment program in six hospitals in South Carolina. BMC Health Serv Res 2024; 24:741. [PMID: 38886764 PMCID: PMC11184783 DOI: 10.1186/s12913-024-11205-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 06/13/2024] [Indexed: 06/20/2024] Open
Abstract
OBJECTIVE Describe the screening, referral, and treatment delivery associated with an opt-out tobacco treatment program (TTP) implemented in six hospitals varying in size, rurality and patient populations. METHODS Between March 6, 2021 and December 17, 2021, adult patients (≥ 18 years) admitted to six hospitals affiliated with the Medical University of South Carolina were screened for smoking status. The hospitals ranged in size from 82 to 715 beds. Those currently smoking were automatically referred to one of two tobacco treatment options: 1) Enhanced care (EC) where patients could receive a bedside consult by a trained tobacco treatment specialist plus an automated post-discharge follow-up call designed to connect those smoking to the South Carolina Quitline (SCQL); or 2) Basic care (BC) consisting of the post-discharge follow-up call only. An attempt was made to survey patients at 6-weeks after hospitalization to assess smoking status. RESULTS Smoking prevalence ranged from 14 to 49% across the six hospitals; 6,000 patients were referred to the TTP.The delivery of the bedside consult varied across the hospitals with the lowest in the Charleston hospitals which had the highest caseload of referred patients per specialist. Among patients who received a consult visit during their hospitalization, 50% accepted the consult, 8% opted out, 3% claimed not to be current smokers, and 38% were unavailable at the time of the consult visit. Most of those enrolled in the TTP were long-term daily smokers.Forty-three percent of patients eligible for the automated post-discharge follow-up call answered the call, of those, 61% reported smoking in the past seven days, and of those, 34% accepted the referral to theSCQL. Among the 986 of patients surveyed at 6-weeks after hospitalization quit rates ranged from 20%-30% based on duration of reported cessation and were similar between hospitals and for patients assigned to EC versus BC intervention groups. CONCLUSION Findings demonstrate the broad reach of an opt-out TTP. Elements of treatment delivery can be improved by addressing patient-to-staffing ratios, improving systems to prescribe stop smoking medications for patients at discharge and linking patients to stop smoking services after hospital discharge.
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Affiliation(s)
- K Michael Cummings
- Department of Psychiatry and Behavioral Sciences, HCC Tobacco Control Program, Hollings Cancer Center, Medical University of South Carolina, 86 Jonathan Lucas Street, Charleston, SC, 29425, USA.
| | | | - Avery Roberson
- Department of Psychiatry and Behavioral Sciences, HCC Tobacco Control Program, Hollings Cancer Center, Medical University of South Carolina, 86 Jonathan Lucas Street, Charleston, SC, 29425, USA
| | - Asia A Bliss
- Department of Psychiatry and Behavioral Sciences, HCC Tobacco Control Program, Hollings Cancer Center, Medical University of South Carolina, 86 Jonathan Lucas Street, Charleston, SC, 29425, USA
| | - Emily Likins
- University of Pikeville, Kentucky College of Osteopathic Medicine, Pikeville, USA
| | - Naomi C Brownstein
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, USA
| | - Stephanie Stansell
- Department of Population Health, Medical University of South Carolina, Charleston, USA
| | - Demetress Adams-Ludd
- Department of Population Health, Medical University of South Carolina, Charleston, USA
| | - Bridget Harris
- Department of Population Health, Medical University of South Carolina, Charleston, USA
| | - David Louder
- MUSC Health Alliance, Medical University of South Carolina, Charleston, USA
| | | | - Rami Zebian
- MUSC Health Florence Division, Florence, USA
| | - Alana M Rojewski
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, USA
| | - Benjamin A Toll
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, USA
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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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