Steps Toward Scalability: Illustrations From A Smoke-Free Homes Program

HEALTH EDUCATION & BEHAVIOR(2019)

引用 3|浏览29
暂无评分
摘要
Scalable interventions remain effective across a range of real-world settings and can be modified to fit organizational and community context. "Smoke-Free Homes: Some Things are Better Outside" has been effective in promoting smoke-free home rules in low-income households in efficacy, effectiveness, generalizability, and dissemination studies. Using data from a dissemination study in collaboration with five 2-1-1 call centers in Ohio, Florida, Oklahoma, and Alabama (n = 2,345 households), this article examines key dimensions of scalability, including effectiveness by subpopulation, secondary outcomes, identification of core elements driving effectiveness, and cost-effectiveness. Evaluated by 2-1-1 staff using a pre-post design with self-reported outcomes at 2 months postbaseline, the program was equally effective for men and women, across education levels, with varying number of smokers in the home, and whether children were present in the home or not. It was more effective for nonsmokers, those who smoked fewer cigarettes per day, and African Americans. Creating a smoke-free home was associated with a new smoke-free vehicle rule (odds ratio [OR] = 3.38, confidence interval [CI 2.58, 4.42]), decreased exposure to secondhand smoke among nonsmokers (b = -2.33, p < .0001), and increased cessation among smokers (OR = 5.8, CI [3.81, 8.81]). Use of each program component was significantly associated with success in creating a smoke-free home. Using an intent-to-treat effect size of 40.1%, program benefits from 5 years of health care savings exceed program costs yielding a net savings of $9,633 for delivery to 100 households. Cost effectiveness, subpopulation analyses, and identification of core elements can help in assessing the scalability potential of research-tested interventions such as this smoke-free homes program.
更多
查看译文
关键词
cost-effectiveness, scalability, smoke-free homes, tobacco control
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要