Changes in Harm Perception for E-Cigarettes Among Youth in the United States, 2014-2019.

PURPOSE: To evaluate the trend of harm perception for e-cigarettes and the trend of the association between harm perception for e-cigarettes and for cigarettes among US youth from 2014 to 2019. DESIGN, SETTING AND SUBJECTS: The National Youth Tobacco Survey is an annual, cross-sectional, school-based survey done among youth selected using three-stage probability sampling. ANALYSIS: Data were drawn from the 2014 to 2019 Surveys. A Multinomial logistic regression model was used to assess the association between harm perception for e-cigarettes and harm perception for cigarettes for each year. RESULTS: The percentage of youth who perceived e-cigarettes as harmless decreased from 2014 to 2019 (17.2% to 5.8%). From 2015 to 2018, the percentage of smokers who perceived e-cigarettes as a little harmful increased (33.6% to 41.2%). The positive association between harm perception for e-cigarettes and harm perception for cigarettes became stronger with time. In 2014, the odds of perceiving e-cigarettes as harmless relative to very harmful were 19.55 times greater for youth who perceived cigarettes as harmless, compared to those who perceived cigarettes as very harmful (OR = 19.55; 95% CI: 14.19-26.94). These odds increased to 77.65 times in 2019 (OR = 77.65; 95% CI: 41.48-107.85). CONCLUSION: This study suggests a stronger relationship between perceived harm of cigarettes and e-cigarettes with time. Interventions to prevent smoking have the potential to change e-cigarette use.

Facilitators of Multisector Collaboration for Delivering Cancer Control Interventions in Rural Communities: A Descriptive Qualitative Study.

PURPOSE AND OBJECTIVES: Multisector collaboration is a widely promoted strategy to increase equitable availability, access, and use of healthy foods, safe places for physical activity, social supports, and preventive health care services. Yet fewer studies and resources exist for collaboration among governmental and nongovernmental agencies to address public problems in rural areas, despite an excess burden of risk factors for cancer morbidity and mortality. We aimed to learn about cancer prevention activities and collaboration facilitators among rural informal interagency networks. EVALUATION METHODS: In 2020, researchers conducted semistructured interviews with staff from rural public health and social services agencies, community health centers, and extension offices. Agency staff were from 5 service areas across 27 rural counties in Missouri and Illinois with high poverty rates and excess cancer risks and mortality. We conducted a thematic analysis to code interview transcripts and identify key themes. RESULTS: Exchanging information, cohosting annual or one-time events, and promoting other agencies' services and programs were the most commonly described collaborative activities among the 32 participants interviewed. Participants indicated a desire to improve collaborations by writing more grants together to codevelop ongoing prevention programs and further share resources. Participants expressed needs to increase community outreach, improve referral systems, and expand screenings. We identified 5 facilitator themes: commitment to address community needs, mutual willingness to collaborate, long-standing relationships, smaller community structures, and necessity of leveraging limited resources. Challenges included lack of funding and time, long travel distances, competing priorities, difficulty replacing staff in remote communities, and jurisdictional boundaries. Although the COVID-19 pandemic further limited staff availability for collaboration, participants noted benefits of remote collaborative meetings. IMPLICATIONS FOR PUBLIC HEALTH: Rural areas need consistent funding and other resources to support health-improving multisector initiatives. Existing strengths found in the rural underresourced areas can facilitate multisector collaborations for cancer prevention, including long-standing relationships, small community structures, and the need to leverage limited resources.

Mapping the Lay of the Land: Using Interactive Network Analytic Tools for Collaboration in Rural Cancer Prevention and Control.

BACKGROUND: Cancer mortality rates in the United States are higher in rural than urban areas, especially for colorectal cancer. Modifiable cancer risks (e.g., tobacco use, obesity) are more prevalent among U.S. rural than urban residents. Social network analyses are common, yet rural informal collaborative networks for cancer prevention and control and practitioner uses of network findings are less well understood. METHODS: In five service areas in rural Missouri and Illinois, we conducted a network survey of informal multisector networks among agencies that address cancer risk (N = 152 individuals). The survey asked about contact, collaborative activities, and referrals. We calculated descriptive network statistics and disseminated network visualizations with rural agencies through infographics and interactive Network Navigator platforms. We also collected feedback on uses of network findings from agency staff (N = 14). RESULTS: Service areas had more connections (average degree) for exchanging information than for more time-intensive collaborative activities of co-developing and sustaining ongoing services and programs, and co-developing and sharing resources. On average, collaborative activities were not dependent on just a few agencies to bridge gaps to hold networks together. Users found the network images and information useful for identifying gaps, planning which relationships to establish or enhance to strengthen certain collaborative activities and cross-referrals, and showing network strengths to current and potential funders. CONCLUSIONS: Rural informal cancer prevention and control networks in this study are highly connected and largely decentralized. IMPACT: Disseminating network findings help ensure usefulness to rural health and social service practitioners who address cancer risks.

Building the Next Generation of Researchers: Mentored Training in Dissemination and Implementation Science.

PROBLEM: Dissemination and implementation (D&I) science provides the tools needed to close the gap between known intervention strategies and their effective application. The authors report on the Mentored Training for Dissemination and Implementation Research in Cancer (MT-DIRC) program-a D&I training program for postdoctoral or early-career cancer prevention and control scholars. APPROACH: MT-DIRC was a 2-year training institute in which fellows attended 2 annual Summer Institutes and other conferences and received didactic, group, and individual instruction; individualized mentoring; and other supports (e.g., pilot funding). A quasi-experimental design compared changes in 3 areas: mentoring, skills, and network composition. To evaluate mentoring and D&I skills, data from fellows on their mentors' mentoring competencies, their perspectives on the importance of and satisfaction with mentoring priority areas, and their self-rated skills in D&I competency domains were collected. Network composition data were collected from faculty and fellows for 3 core social network domains: contact, mentoring, and collaboration. Paired t tests (mentoring), linear mixed models (skills), and descriptive analyses (network composition) were performed. OUTCOMES: Mentors were rated as highly competent across all mentoring competencies, and each mentoring priority area showed reductions in gaps between satisfaction and importance between the 6 and 18 months post-first Summer Institute. Fellows' self-rated skills in D&I competencies improved significantly in all domains over time (range: 42.5%-52.9% increase from baseline to 18 months post-first Summer Institute). Mentorship and collaboration networks grew over time, with the highest number of collaboration network ties for scholarly manuscripts (n = 199) in 2018 and for research projects (n = 160) in 2019. NEXT STEPS: Building on study findings and existing literature, mentored training of scholars is an important approach for building D&I skills and networks, and thus to better applying the vast amount of available intervention evidence to benefit cancer control.

The Translational Science Benefits Model: A New Framework for Assessing the Health and Societal Benefits of Clinical and Translational Sciences.

We report the development of the Translational Science Benefits Model (TSBM), a framework designed to support institutional assessment of clinical and translational research outcomes to measure clinical and community health impacts beyond bibliometric measures. The TSBM includes 30 specific and potentially measurable indicators that reflect benefits that accrue from clinical and translational science research such as products, system characteristics, or activities. Development of the TSBM was based on literature review, a modified Delphi method, and in-house expert panel feedback. Three case studies illustrate the feasibility and face validity of the TSBM for identification of clinical and community health impacts that result from translational science activities. Future plans for the TSBM include further pilot testing and a resource library that will be freely available for evaluators, translational scientists, and academic institutions who wish to implement the TSBM framework in their own evaluation efforts.

Multisector Health Policy Networks in 15 Large US Cities.

CONTEXT: Local health departments (LHDs) have historically not prioritized policy development, although it is one of the 3 core areas they address. One strategy that may influence policy in LHD jurisdictions is the formation of partnerships across sectors to work together on local public health policy. DESIGN: We used a network approach to examine LHD local health policy partnerships across 15 large cities from the Big Cities Health Coalition. SETTING/PARTICIPANTS: We surveyed the health departments and their partners about their working relationships in 5 policy areas: core local funding, tobacco control, obesity and chronic disease, violence and injury prevention, and infant mortality. OUTCOME MEASURES: Drawing on prior literature linking network structures with performance, we examined network density, transitivity, centralization and centrality, member diversity, and assortativity of ties. RESULTS: Networks included an average of 21.8 organizations. Nonprofits and government agencies made up the largest proportions of the networks, with 28.8% and 21.7% of network members, whereas for-profits and foundations made up the smallest proportions in all of the networks, with just 1.2% and 2.4% on average. Mean values of density, transitivity, diversity, assortativity, centralization, and centrality showed similarity across policy areas and most LHDs. The tobacco control and obesity/chronic disease networks were densest and most diverse, whereas the infant mortality policy networks were the most centralized and had the highest assortativity. Core local funding policy networks had lower scores than other policy area networks by most network measures. CONCLUSION: Urban LHDs partner with organizations from diverse sectors to conduct local public health policy work. Network structures are similar across policy areas jurisdictions. Obesity and chronic disease, tobacco control, and infant mortality networks had structures consistent with higher performing networks, whereas core local funding networks had structures consistent with lower performing networks.

An Examination of Two Policy Networks Involved in Advancing Smokefree Policy Initiatives.

This study examines smokefree policy networks in two cities­Kansas City and St. Louis, Missouri­one that was successful in achieving widespread policy success, and one that was not. Descriptive social network analyses and visual network mapping were used to compare importance and contact relationships among actors involved in the smokefree policy initiatives. In Kansas City, where policy adoption was achieved, there was a higher level of connectivity among members, with network members being in contact with an average of more than five people, compared to just over two people for the St. Louis network. For both cities, despite being recognized as important, politicians were in contact with the fewest number of people. Results highlight the critical need to actively engage a variety of stakeholders when attempting city wide public health policy change. As evident by the success in smokefree policy adoption throughout Kansas City compared to St. Louis, closer linkages and continued communication among stakeholders including the media, coalitions, public health agencies, policymakers, and other partners are essential if we are to advance and broaden the impact of public health policy. Results indicate that the presence of champions, or those that play leadership roles in actively promoting policy by linking individuals and organizations, play an important role in advancing public health policy. Those working in public health should examine their level of engagement with the policy process and implement strategies for improving that engagement through relationship building and ongoing interactions with a variety of stakeholders, including policymakers.

Social network analysis of public health programs to measure partnership.

In order to prevent chronic diseases, community-based programs are encouraged to take an ecological approach to public health promotion and involve many diverse partners. Little is known about measuring partnership in implementing public health strategies. We collected data from 23 Missouri communities in early 2012 that received funding from three separate programs to prevent obesity and/or reduce tobacco use. While all of these funding programs encourage partnership, only the Social Innovation for Missouri (SIM) program included a focus on building community capacity and enhancing collaboration. Social network analysis techniques were used to understand contact and collaboration networks in community organizations. Measurements of average degree, density, degree centralization, and betweenness centralization were calculated for each network. Because of the various sizes of the networks, we conducted comparative analyses with and without adjustment for network size. SIM programs had increased measurements of average degree for partner collaboration and larger networks. When controlling for network size, SIM groups had higher measures of network density and lower measures of degree centralization and betweenness centralization. SIM collaboration networks were more dense and less centralized, indicating increased partnership. The methods described in this paper can be used to compare partnership in community networks of various sizes. Further research is necessary to define causal mechanisms of partnership development and their relationship to public health outcomes.

Using organizational network analysis to plan cancer screening programs for vulnerable populations.

OBJECTIVES: We examined relationships among organizations in a cancer screening network to inform the development of interventions to improve cancer screening for South Asians living in the Peel region of Ontario. METHODS: From April to July 2012, we surveyed decision-makers, program managers, and program staff in 22 organizations in the South Asian cancer screening network in the Peel region. We used a network analytic approach to evaluate density (range = 0%-100%, number of ties among organizations in the network expressed as a percentage of all possible ties), centralization (range = 0-1, the extent of variability in centrality), and node characteristics for the communication, collaboration, and referral networks. RESULTS: Density was similar across communication (15%), collaboration (17%), and referral (19%) networks. Centralization was greater in the collaboration network (0.30) than the communication network (0.24), and degree centralization was greater in the inbound (0.42) than the outbound (0.37) referral network. Diverse organizations were central to the networks. CONCLUSIONS: Certain organizations were unexpectedly important to the South Asian cancer screening network. Program planning was informed by identifying opportunities to strengthen linkages between key organizations and to leverage existing ties.

Network influences on dissemination of evidence-based guidelines in state tobacco control programs.

Little is known regarding the social network relationships that influence dissemination of evidence-based public health practices and policies. In public health, it is critical that evidence-based guidelines, such as the Centers for Disease Control and Prevention's Best Practices for Comprehensive Tobacco Control Programs, are effectively and efficiently disseminated to intended stakeholders. To determine the organizational and network predictors of dissemination among state tobacco control programs, interviews with members of tobacco control networks across eight states were conducted between August 2009 and September 2010. Measures included partner attributes (e.g., agency type) and relationships among network members (frequency of contact, extent of collaboration, and dissemination of Best Practices). Exponential random graph modeling was used to examine attribute and structural predictors of collaboration and dissemination among partners in each network. Although density and centralization of dissemination ties varied across states, network analyses revealed a consistent prediction pattern across all eight states. State tobacco control dissemination networks were less dense but more centralized compared with organizational contact and collaboration networks. Tobacco control partners in each state were more likely to disseminate the Best Practices guidelines if they also had existing contact and collaboration relationships with one another. Evidence-based guidelines in public health need to be efficiently and broadly disseminated if we hope to translate science into practice. This study suggests that funders, advocacy groups, and public health agencies can take advantage of existing public health organizational relationships to support the communication and dissemination of evidence-based practices and policies.

Exempting casinos from the Smoke-free Illinois Act will not bring patrons back: they never left.

OBJECTIVE: The Smoke-free Illinois Act was implemented in January 2008, one month after the beginning of a national recession. In December 2010, the Illinois legislature proposed new legislation that would provide an exemption for casinos from the act until neighbouring states also implement smoke-free casino policies. Lobbyists and gaming commission representatives argued that Illinois casinos were losing patrons to casinos in neighbouring states that allow smoking. This study examined the influence of the act on casino admissions in Illinois and neighbouring states in light of the economy. METHODS: A multilevel model was developed to examine monthly casino admissions from January 2007 to December 2008. RESULTS: There was no difference in changes in admissions across the four states over time after accounting for the economic downturn. CONCLUSIONS: The Smoke-Free Illinois Act did not have a detectable effect on Illinois casino admissions.

Interpersonal influence among public health leaders in the United States department of health and human services.

BACKGROUND: In public health, interpersonal influence has been identified as an important factor in the spread of health information, and in understanding and changing health behaviors. However, little is known about influence in public health leadership. Influence is important in leadership settings, where public health professionals contribute to national policy and practice agendas. Drawing on social theory and recent advances in statistical network modeling, we examined influence in a network of tobacco control leaders at the United States Department of Health and Human Services (DHHS). DESIGN AND METHODS: Fifty-four tobacco control leaders across all 11 agencies in the DHHS were identified; 49 (91%) responded to a web-based survey. Participants were asked about communication with other tobacco control leaders, who influenced their work, and general job characteristics. Exponential random graph modeling was used to develop a network model of influence accounting for characteristics of individuals, their relationships, and global network structures. RESULTS: Higher job ranks, more experience in tobacco control, and more time devoted to tobacco control each week increased the likelihood of influence nomination, as did more frequent communication between network members. Being in the same agency and working the same number of hours per week were positively associated with mutual influence nominations. Controlling for these characteristics, the network also exhibited patterns associated with influential clusters of network members. CONCLUSIONS: Findings from this unique study provide a perspective on influence within a government agency that both helps to understand decision-making and also can serve to inform organizational efforts that allow for more effective structuring of leadership.

Demographic and geographic differences in exposure to secondhand smoke in Missouri workplaces, 2007-2008.

INTRODUCTION: African Americans, Hispanics, service and blue-collar workers, and residents of rural areas are among those facing higher rates of workplace secondhand smoke exposure in states without smokefree workplace laws. Consequently, these groups also experience more negative health effects resulting from secondhand smoke exposure. The objective of this study was to examine disparities in workplace secondhand smoke exposure in a state without a comprehensive statewide smokefree workplace law and to use this information in considering a statewide law. METHODS: We developed a logistic multilevel model by using data from a 2007-2008 county-level study to account for individual and county-level differences in workplace secondhand smoke exposure. We included sex, age, race, annual income, education level, smoking status, and rural or urban residence as predictors of workplace secondhand smoke exposure. RESULTS: Factors significantly associated with increased exposure to workplace secondhand smoke were male sex, lower education levels, lower income, living in a small rural or isolated area, and current smoking. For example, although the overall rate of workplace exposure in Missouri is 11.5%, our model predicts that among young white men with low incomes and limited education living in small rural areas, 40% of nonsmokers and 56% of smokers may be exposed to secondhand smoke at work. CONCLUSION: Significant disparities exist in workplace secondhand smoke exposure across Missouri. A statewide smokefree workplace law would protect all citizens from workplace secondhand smoke exposure.

Referrals among cancer services organizations serving underserved cancer patients in an urban area.

Significant racial, socioeconomic, and geographic disparities exist nationwide in cancer screenings, treatments, and outcomes. Differences in health and social service provision and utilization may contribute to or exacerbate these disparities. We evaluated the composition and structure of a referral network of organizations providing services to underserved cancer patients in an urban area in 2007. We observed a need for increased awareness building among provider organizations, broader geographic coverage among organizations, and increased utilization of tobacco cessation and financial assistance services.

Systems analysis of collaboration in 5 national tobacco control networks.

OBJECTIVES: We studied 5 members of the National Network Consortium on Tobacco Control in Priority Populations. These networks, which consist of governmental and nongovernmental organizations, targeted lesbian, gay, bisexual, and transgender persons; Asian Americans, Native Hawaiians, and Pacific Islanders; American Indians and Alaska Natives; African Americans; and persons with low socioeconomic status, respectively. METHODS: We used statistical network analysis modeling to examine collaboration among these national networks in 2007. RESULTS: Network size and composition varied, but all 5 networks had extensive interorganizational collaboration. Location and work area were significant predictors of collaboration among network members in all 5 networks. Organizations were more likely to collaborate with their network's lead agency; collaborations with other agencies were more likely if they were geographically close. Collaboration was perceived to be important for achieving the goals of the national network. CONCLUSIONS: The similarity of collaboration patterns across the 5 networks suggests common underlying partnership formation processes. Statistical network modeling promises to be a useful tool for understanding how public health systems such as networks and coalitions can be used to improve the nation's health.