Suchergebnisse

Rural, tobacco-growing areas are disproportionately affected by tobacco use, secondhand smoke, and weak policies. The study determined whether overall strength of Resources, Capacity and Efforts in tobacco control predicts readiness for smoke-free policy in rural communities, controlling for county population size and pounds of tobacco produced. This was a correlational, cross-sectional analysis of data from key informants (n = 148) and elected officials (n = 83) from 30 rural counties who participated in telephone interviews examining smoke-free policy. Six dimensions of community readiness (knowledge, leadership, resources, community climate, existing smoke-free policies, and political climate) were identified and summed to assess overall readiness for smoke-free policy. General strength of overall Resources, Capacity and Efforts in tobacco control at the county level was measured. Readiness for smoke-free policy was lower in communities with higher smoking rates, higher tobacco production, and smaller population. Efforts related to general tobacco control (i.e., media advocacy, training and technical assistance) predicted readiness for local smoke-free policy development (standardized β=.35, p=.05), controlling for county population size and pounds of tobacco produced. Given that small, rural tobacco-growing communities are least ready for smoke-free policy change, tailoring and testing culturally sensitive approaches that account for this tobacco-growing heritage are warranted.

The aim of this longitudinal study was to evaluate the long-term effects of providing a therapeutic conversation intervention, based on Family Systems Nursing, to family caregivers of a close relative with advanced cancer over the period before and during bereavement. To prevent adverse outcomes, caregivers need ongoing support that begins pre-loss and extends into the post-loss period. This study employed a one-group pre-test, post-test quasi-experimental design. Twenty-four caregivers participated in two intervention trials conducted over a 42-month period, receiving two intervention sessions pre-loss (Trial 1) and one intervention session post-loss (Trial 2). Significant decreases in anxiety and stress were noted over the three post-loss assessments. The final post-loss stress outcome was significantly lower than the first pre-loss score. For the depression score, there was not a significant change over time within the pre- or post-loss period. The findings provide evidence of decreasing anxiety and stress following the implementation of an extended family nursing intervention for bereaved family caregivers.

Purpose. The study investigated the relationships among local smoke-free public policies, county-level quitline call rate, and adult smoking status. Design. A retrospective cross-sectional examination of demographic characteristics, smoking status of Kentuckians, and data from the Kentucky Tobacco Quitline were used to investigate the relationship of local smoke-free ordinances or Board of Health regulations together with county-level quitline use rates and population-level adult smoking status. Setting. One hundred and four Kentucky counties. Subjects. The sample was comprised of 14,184 Kentucky participants with complete demographic information collected from the 2009–2010 Behavioral Risk Factor Surveillance System (BRFSS). Measures. Individual-level demographics and smoking status from the BRFSS; county-level urban/rural status; quitline rates; and smoke-free policy status. Analysis. Given the hierarchical structure of the dataset, with BRFSS respondents nested within county, multilevel modeling was used to determine the predictors of smoking status. Results. For every 1-unit increase in the county-level call rate the likelihood of current smoking status decreased by 9%. Compared to those living in communities without a policy, those in communities with a smoke-free public policy were 18% less likely to be current smokers. Limitations include quitline call rate as the sole indicator of cessation demand, as well as the cross-sectional design. Conclusion. Communities with smoke-free policies and higher rates of quitline use have lower rates of adult smoking.

Purpose. The purpose of this study was to use the Community Readiness Model to examine local smoke-free policy development. Design, Setting, and Subjects. A descriptive, cross-sectional design was used to assess 64 Kentucky communities. Dimensions of readiness included a community's knowledge of the problem and existing voluntary smoke-free policies; leadership for policy development; resources for policy development; climate surrounding policy development; existing voluntary policy efforts; and political climate for policy development. Dimension scores were summed to identify one of six overall readiness stages: (1) unawareness; (2) vague awareness; (3) preplanning; (4) preparation; (5) initiation; and (6) endorsement. Analyses. Correlations between dimensions and overall readiness scores were evaluated. One-way analysis of variance was used to evaluate regional trends, and multiple regression was used to assess the influence of sociodemographic/political variables on policy readiness. Results. The knowledge dimension rated highest, and community climate rated lowest. Most communities were in the lower stages of readiness. No relationship was found between overall readiness and region (F[4,59] = 1.17; p > .05); nor were there regional differences among dimension scores. Smaller communities were less ready for local policy development than larger ones (adjusted R2 = .25; p = .003). Conclusions. The Community Readiness Model is appropriate for understanding local policy development, and it provides advocates with information that may prove helpful in advancing smoke-free policy. (Am J Health Promot 2008;23[2]:112–120.)

Purpose: To examine the association between strength of policy and self-reported tobacco use behavior, controlling for demographic characteristics, polytobacco use, knowledge of campus tobacco policy, and perceived policy compliance by others. Design: Cross-sectional, online survey. Setting: Ten participating State University of New York (SUNY) campuses; 5 with designated smoking/tobacco use areas and 5 with 100% tobacco-free policies. Subjects: Convenience sample of students from SUNY campuses: only tobacco users (N = 576 students) included for analysis. Measures: Items assessing tobacco use behaviors on campus, policy knowledge, and observation of others using tobacco on campus. Analysis: T tests and chi-square tests of association used to compare responses between tobacco users across campuses. Generalized estimating equations modeling used to evaluate predictors of tobacco use on campus; model estimated with students nested within campus. Results: Those on campuses without a comprehensive policy were more likely to report (in the past week) having seen others smoke on campus (98% vs 69%, P < .001), having personally smoked on campus (65% vs 36%, P < .001), and seeing others use tobacco products on campus (88% vs 67%, P < .001), compared to those on tobacco-free campuses. Conclusion: Tobacco-free campus policies provide numerous protective factors for tobacco users and nonusers. However, compliance strategies are imperative for intended policy success.

BACKGROUND. Almost 2,000 campuses have adopted tobacco-free (TF) policies across the United States. However, there is not a systematic process to help campuses implement successful policies. Readiness assessments can help provide assistance as campuses work to implement successful policies. PURPOSE. We assessed readiness for TF policies among campuses of a statewide university system and determined factors associated with readiness. PARTICIPANTS. Stakeholders from 23 campuses without 100% TF policies were invited to participate in an online survey. METHOD. System administrators provided contacts for five to eight stakeholders per campus. Included in the analysis were 10 of 23 campuses (43.5%) with at least three stakeholders completing the survey. RESULTS. Of the 10 campuses, one was in Preplanning, five were in Preparation, and four were in the Initiation stage of readiness. Political Climate was the highest scored dimension on seven campuses (0.74–1.0); Resources was the lowest on eight campuses (0.0–0.67). Campus size and county rurality were each associated with one dimension score. CONCLUSIONS. Despite being part of a statewide university system, campuses are in varying stages of readiness for TF policies. Stage-based strategies to advance TF campus policies must be implemented to set campuses up for successful policies.

Purpose: Examine predictors of short- and long-term adherence to a tobacco-free policy prohibiting all tobacco use inside and outdoors on Executive Branch property in Kentucky. Design: Cross-sectional online surveys administered at 2 time points. Setting: Commonwealth of Kentucky. Participants: 27 000 employees of the Kentucky Executive Branch were invited to participate in 2 online surveys: March 2015, 4 months postpolicy (N = 4854) and August 2015, 9 months postpolicy (N = 3522). Intervention: Executive order prohibiting tobacco use on Executive property implemented November 2014. Measures: Tobacco use, tobacco users' adherence to the tobacco-free policy, and personal characteristics were assessed. Analysis: Two separate logistic regression models used to determine predictors of short- and long-term adherence to the policy. Results: In the short term, employees who had seen others violate the policy ( P < .001) and had more tobacco-using friends ( P = .020) were less likely to adhere to the policy. At 9 months after the policy, employees who were older ( P = .038) and those who had seen others violate the policy ( P < .001) were less likely to report adherence to the policy. Tobacco-using employees who worked in a county with a comprehensive smoke-free ordinance ( P = .047) were more likely to adhere to the policy. Conclusion: This study indicates the need for strong policy compliance procedures, employer-based tobacco treatment programs accompanying tobacco-free policy change, and smoke-free workplace laws to promote prohealth norms.

Purpose. To assess workplace tobacco use prevention and cessation policies in manufacturing facilities and explore factors associated with tobacco policies and practices in the tobacco-growing state of Kentucky. Design. Cross-sectional, descriptive, correlational design. Setting. Telephone survey of Kentucky manufacturing facilities. Subjects. A total of 437 human resource managers (77% participation rate). Measures. Telephone interviews by trained local health department staff to assess indoor and outdoor smoking policies, sale of cigarettes on company property, and provision of cessation and prevention programs. Results. Nearly seven in 10 manufacturing facilities had a written smoking policy, but only 43% banned indoor smoking. About one-fourth of companies reimbursed for cessation treatment and/or provided cessation resources. Companies with unions were more likely than those without unions to provide cessation resources but were also more likely to allow indoor smoking. Although large companies had more than two and a half times the odds as small companies to have a written smoking policy, they were more likely to allow cigarette sales on company property. Conclusion. Despite the importance of smoke-free policies in the workplace, most manufacturing facilities surveyed allowed indoor smoking and few helped smokers quit. Companies with unions were more likely to cater to their smoking employees. Manufacturing facilities provide an opportunity to protect large numbers of adult workers from the hazards of secondhand smoke and to provide quit assistance for smokers.

Older adults in long-term care (LTC) facilities suffer disproportionately from health conditions caused or worsened by secondhand smoke. Long-term care facilities in many states and municipalities permit smoking. Americans for Nonsmokers' Rights compiles data on smoke-free policies only in institutional facilities (e.g., nursing homes), but not in transitional (e.g., independent living) or community-based settings (e.g., adult day). A cross-sectional, observational study was conducted of smoke-free policies using cluster random sampling in Kentucky to compare differences in policy location of coverage and strength of smoke-free policies in institutional, transitional, and community-based LTC facilities by rural/urban status. Online or phone surveys of LTC administrators representing 306 facilities were conducted. Of the facilities sampled, 35.5% were institutional, 33.4% transitional, 25.1% community-based, and 6.0% multi-type. Only one in five (19.6%) facilities restricted smoking indoors and outdoors. Only 17.3% of the policies were comprehensive (i.e., prohibiting use of all tobacco products by all persons living, frequenting, or working in LTC facilities). Compared to transitional facilities, institutional and community-based facilities were more likely to have comprehensive policies and restrict smoking indoors and outdoors. Facilities located in rural communities were less likely to restrict smoking indoors or outdoors and less likely to have comprehensive smoke-free policies, reflecting a disparity in policy protections. Strong, consistent smoke-free policies and policy enforcement are needed to reduce the disparity in smoke-free protections for older adults, LTC employees, and visitors. More research is needed to investigate the best strategies for implementing and enforcing policies that completely restrict smoking in all LTC facilities.

Purpose: To examine the short-term impact of a personalized environmental report-back intervention to reduce home exposure to tobacco smoke and radon on perception of synergistic risk for lung cancer. Radon-induced lung cancer is more common among those exposed to tobacco smoke. Design: Randomized controlled trial. Setting: Primary care clinics and a pharmacy waiting area at a University Medical Center in the Southeastern United States and community events. Participants: Five hundred sixty adult homeowners and renters (3-month follow-up, n = 334). Intervention: Personalized environmental report back. Measures: Single-item synergistic risk perception measure using 5-point Likert-type scale. Analysis: Change in synergistic risk from baseline to 3 months was evaluated using a generalized estimating equation model containing main effects of treatment group and time. Covariates in the model included age, gender, education, and home smoking status. Results: For treatment and control groups combined, there was a significant increase in perception of synergistic risk from baseline to 3 months, but the study groups did not differ. There was no association between perceived synergistic risk and whether or not there were smokers at home. Conclusion: Learning about combined risks for lung cancer, with or without dual home screening for secondhand smoke and radon and environmental report-back, may enhance perceived risk for combined environmental exposures. Evaluation of perceived synergistic risk with a single item is a study limitation.

Purpose. To test the effects of a population-based self-efficacy message card campaign on compliance with a tobacco-free campus policy. Setting. This study was conducted at a large public university in the South. Subjects. Three hundred twelve observational periods at 39 campus sites. Intervention. The message card campaign, based on the Theory of Planned Behavior, consisted of distributing approximately 6000 efficacy-enhancing cards over 3 days. The intent of the cards was to increase awareness of the policy and resources available to help individuals stop smoking or remain comfortable while on campus. Measures. Policy compliance was measured using the Tobacco-Free Compliance Assessment Tool. Field notes were used to assess campaign reaction. Analysis. Mann-Whitney U-test was conducted to compare number of cigarette butts in hot spot areas before and after the intervention. A rate ratio was also calculated using Geographic Information System (GIS) mapping software to compare cigarette butts collected per day before and after the intervention at each of the study sites. Results. The median number of cigarette butts per day after the intervention was significantly lower than before the intervention (1.9 vs. 4.7, χ2: 8.1, p = .004). Eighteen sites (66.6%) had a post-pre ratio of .11 to .75, indicating a decrease in cigarette butts per day. Conclusion. An efficacy-enhancing message card campaign shows promise in improving compliance with tobacco-free campus policies.

The purpose was to determine factors associated with rural communities' political readiness to enact smoke-free laws. Data from baseline assessment of a longitudinal intervention study to promote smoke-free policy in rural Kentucky communities; key informants (n=144) and elected officials (n=83) from 29 counties participated in cross-sectional telephone interviews. Controlling for population size and county-level smoking rate, the following factors predicted elected officials' perception of the likelihood of a local smoke-free law passing in the next 12 months: 1) support from the local board of health; 2) support from local leaders; and 3) smoke-free hospitals. Communities with lower adult smoking prevalence were more ready for smoke-free laws. Rural health advocates can increase political readiness for smoke-free laws by educating and engaging Board of Health members and local leaders, promoting the voluntary adoption of smoke-free policies in rural hospitals, and investing in effective population-based approaches to evidence-based tobacco treatment in rural communities.

Purpose. To compare the retail distribution and density per population of electronic and conventional cigarettes in smoke-free communities with and without e-cigarette restrictions. Design. A cross-sectional study with field observations of retail tobacco stores. Setting. Two Central Kentucky counties with 100% smoke-free workplace regulations; counties selected on the basis of whether e-cigarette use was restricted. Subjects. Fifty-seven tobacco retailers in two counties, including conventional retailers and stand-alone e-cigarette stores. Measures. Type and location of store and products sold; addresses of stores and schools geocoded with ArcGIS. Analysis. Bivariate comparisons between counties, rates and confidence intervals for frequency of tobacco retailers and e-cigarette stores per population. Results. Fifty-three percent of tobacco retailers sold e-cigarettes. E-cigarette availability did not differ by whether smoke-free regulation covered e-cigarettes. Rates of tobacco retailers and e-cigarette distributors per 10,000 were 8.29 and 4.40, respectively, in the two-county area. Of the 40 schools, 88% had a tobacco retailer and 68% had an e-cigarette distributor within 1 mile. Conclusion. In this exploratory study, e-cigarette use restriction was not related to store availability. For a relatively new product, e-cigarettes were readily available in retail outlets and close to schools.

Purpose: Adolescent tobacco use is higher in rural than in urban areas. While e-cigarette use is increasing rapidly among this age group, differences in prevalence between rural versus urban populations for this relatively novel product have not been explored. The purpose is to investigate whether location of school (rural–urban) is associated with e-cigarette use and dual use (defined as the use of both e-cigarettes and conventional cigarettes) among high school students. Design: Cross-sectional survey obtained using a stratified, 3-stage cluster sample design. Setting: United States. Participants: A nationally representative sample of US high school students (N = 11 053) who completed the 2014 National Youth Tobacco Survey (NYTS); slightly more than half were urban (54%). Measures: The NYTS measures tobacco-related knowledge, attitudes, and use behavior and demographics of students in the United States. Analysis: Weighted logistic regression assessed the relationships of urban–rural location with current e-cigarette use and dual use, adjusting for demographic factors, perceived risk, and social norms. Results: There were clear differences in patterns of adolescent e-cigarette and cigarette use in rural versus urban areas. Social norms and perceptions may play a role in understanding these differences. Conclusion: Urban youth current cigarette smokers were nearly twice as likely as rural cigarette smokers to also use e-cigarettes. Reasons for urban–rural differences need to be taken into account when designing prevention programs and policy changes.