Tobacco causes 29% of cancer-related deaths while alcohol causes 5.5% of cancer-related deaths. Reducing the consumption of these cancer-causing products is a special priority area for the National Cancer Institute. While many factors are linked to tobacco and alcohol use, the placement and density of retail outlets within neighborhoods may be one community-level risk factor contributing to greater use of these products. To elucidate associations between tobacco, alcohol, and tobacco and alcohol retail outlets (TRO, ARO, and TARO) and neighborhood disadvantage over a large geographic area, we employed a novel Bayesian index modeling approach to estimate a neighborhood disadvantage index (NDI) and its associations with rates of the three types of retailers across block groups in the state of North Carolina. We used a novel extension of the Bayesian index model to include a shared component for the spatial pattern common to all three types of outlets and NDI effects that varied by outlet type. The shared component identifies areas that are elevated in risk for all outlets. The results showed significant positive associations between neighborhood disadvantage and TROs (relative risk (RR) = 1.12, 95% credible interval (CI = 1.09, 1.14)) and AROs (RR = 1.15, 95% CI = 1.11, 1.17), but the association was greatest for TAROs (RR = 1.21, 95% CI = 1.18, 1.24). The most important variables in the NDI were percent renters (i.e., low home ownership), percent of homes built before 1940 (i.e., old housing stock), and percent without a high school diploma (i.e., low education).

Background: Cancer survivors are able to live much longer today than in the past due to advances in treatment. The promotion of health behaviors is important to address among cancer survivors. Not only can health behaviors reduce the risk of comorbidities, but they may also be key to improving psychological health among cancer survivors, such as psychological distress, perceptions of one’s general health, and distress of cancer mortality (i.e., cancer fatalism). Our overall goal is to 1) determine which individual health behaviors (e.g., fruit consumption, physical activity, and sleep) are associated with psychological health among cancer survivors and 2) determine if the number of health behaviors engaged in is associated with better psychological health among cancer survivors. Methods: Using data from the HINTS 5 Cycle III survey (N = 856 cancer survivors), we compared whether participants who met guidelines for seven health behaviors (e.g., fruit and vegetable consumption, moderate physical activity, sleep duration) had improved psychological health. Multiple ordinal regression models controlling for sociodemographic variables were used to determine if meeting more recommendations was related to psychological health and then specifically which behaviors were of greatest importance to psychological health. Results: Meeting guidelines for sleep quality and moderate physical activity was associated with higher general health and meeting guidelines for vegetable intake and better sleep quality was associated with less psychological distress. Although the associations were not significant, cancer survivors who met more of the health behavior guidelines reported higher general health, lower psychological distress, and less distress about what could be done to lower one’s chances to get cancer. Conclusions: Meeting the guidelines for individual and multiple health behaviors is associated with favorable psychological health among cancer survivors. The findings can contribute to the development of health behavior interventions that focus on multiple health behaviors to improve psychological health and quality of life among cancer survivors.

Assessing public support of health policies designed to reduce cancer risk is important for policy implementation. This study aimed to identify support for cancer prevention policies and factors associated with support. Data were obtained from the Health Information National Trends Survey. Support for three types of cancer prevention policies were evaluated: tobacco, alcohol, and junk food regulations. Linear and logistic regression analyses were employed to assess the relationship between support for the different types of policies and sociodemographic, lifestyle behaviors, and cancer beliefs. Certain policies, such as providing warning labels on cigarettes (69.9% support) and requiring specific health warnings on alcohol containers (65.1% support), were popular. Banning outdoor advertising of alcohol was not popular (34.4% support). There were individual differences associated with policy support. For example, respondents who were 75 years or older (B = 0.61, p < .001) or female (B = 0.14, p < .008) were more likely to support tobacco polices compared to their counterparts (i.e., younger or male). Respondents who identified as politically conservative (B = −0.20, p < .004) or those who endorsed high cancer fatalistic beliefs (e.g., there’s not much you can do to lower your chances of getting cancer, B = −0.07, p < .012) were less likely to support alcohol policies compared to those who were liberal or had lower cancer fatalistic beliefs. Generally, support was high for most policy questions. However, support varied by different individual factors. The findings also highlight that there may be opportunities to increase understanding and awareness about cancer prevention policies, especially among some segments of the population. It is important to assess public support of cancer preventive health policies and to know which factors, such as individual characteristics (e.g., gender and political orientation) health behaviors, and cancer beliefs, are associated with support. In this study, we assessed support of cancer prevention policies and examined different factors possibly associated with support. We analyzed data from the Health Information National Trends Survey, a nationally representative dataset. Since there were multiple questions related to support of tobacco and alcohol policies, we combined each set of questions to create two combined scores, with higher scores indicating more support. We examined the policy related to junk food individually. We found certain policies, such as including warning labels on cigarettes, were popular, with over 60% support. However, there were group differences. For example, women and those aged 55 years or older were more supportive of policies, while politically conservative respondents were less supportive of policies. Beliefs about cancer were also associated with policy support. For example, those who believed “there’s not much you can do to lower your chances of getting cancer” were less supportive compared to those without that belief. These findings may help inform future policy research and public health campaigns.

Introduction: Universities are increasingly considering tobacco-free campus policies to help promote a healthy learning and working environment. We assessed attitudes of students, faculty, and staff, before and after the implementation of a tobacco-free campus policy at a large, urban...

More than 30% of cancer related deaths are related to tobacco or alcohol use. Controlling and restricting access to these cancer-causing products, especially in communities where there is a high prevalence of other cancer risk factors, has the potential to improve population health and reduce the risk of specific cancers associated with these substances in more vulnerable population subgroups. One policy-driven method of reducing access to these cancer-causing substances is to regulate where these products are sold through the placement and density of businesses selling tobacco and alcohol. Previous work has found significant positive associations between tobacco, alcohol, and tobacco and alcohol retail outlets (TRO, ARO, TARO) and a neighborhood disadvantage index (NDI) using Bayesian shared component index modeling, where NDI associations differed across outlet types and relative risks varied by population density (e.g., rural, suburban, urban). In this paper, we used a novel Bayesian index model with spatially varying effects to explore spatial nonstationarity in NDI effects for TROs, AROs, and TAROs across census tracts in North Carolina. The results revealed substantial variation in NDI effects that varied by outlet type. However, all outlet types had strong positive effects in one coastal area. The most important variables in the NDI were percent renters, Black racial segregation, and the percentage of homes built before 1940. Overall, more disadvantaged areas experienced a greater neighborhood burden of outlets selling one or both of alcohol and tobacco.