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Purpose Sleep disturbance and cancer-related fatigue (CRF) are among the most commonly reported symptoms associated with breast cancer and its treatment. This study identified symptom cluster groups of breast cancer patients based on multidimensional assessment of sleep disturbance and CRF prior to and during chemotherapy. Methods Participants were 152 women with stage I–IIIA breast cancer. Data were collected before chemotherapy (T1) and during the final week of the fourth chemotherapy cycle (T2). Latent profile analysis was used to derive groups of patients at each timepoint who scored similarly on percent of the day/night asleep per actigraphy, the Pittsburgh Sleep Quality Index global score, and the five subscales of the Multidimensional Fatigue Symptom Inventory-Short Form. Bivariate logistic regression evaluated if sociodemographic/medical characteristics at T1 were associated with group membership at each timepoint. Results Three groups (Fatigued with sleep complaints, Average, Minimal symptoms) were identified at T1, and five groups (Severely fatigued with poor sleep, Emotionally fatigued with average sleep, Physically fatigued with average sleep, Average, Minimal symptoms) at T2. The majority of individuals in a group characterized by more severe symptoms at T1 were also in a more severe symptom group at T2. Sociodemographic/medical variables at T1 were significantly associated with group membership at T1 and T2. Conclusions This study identified groups of breast cancer patients with differentially severe sleep disturbance and CRF symptom profiles prior to and during chemotherapy. Identifying groups with different symptom management needs and distinguishing groups by baseline sociodemographic/medical variables can identify patients at risk for greater symptom burden.

Simulation models of tobacco use behavior are useful analytic tools for projecting rates of tobacco use over time and identifying priority areas for intervention. This paper presents the Tobacco Use Individual-level Simulation and Tracking (TwIST) Model, an individual-based simulation model of tobacco use in the adult US population. We describe the model structure, data sources and parameters, and, in addition to future projections, compare modeled estimates of smoking prevalence to those from established surveys. The simulated population and model parameter estimates are informed by the Population Assessment of Tobacco and Health Study and other nationally representative datasets. To simulate tobacco use over time, we estimated 2 nd order Markov models using multinomial logistic regression. To validate the model, we compared model estimates of tobacco use to data from three national surveys. The model estimates adult cigarette smoking rates will decline from a prevalence of 12.4% (95% uncertainty interval (95% UI): 12.2–12.8%) in 2020 to 9.6% (95% UI: 9.3–9.9%), 9.1% (95% UI: 8.9–9.4%), and 8.7% (95% UI: 8.5–9.0%) in 2030, 2040, and 2050, respectively. From 2020 through 2050, adults living in poverty are estimated to have a cigarette smoking rate 2.1–2.3 times higher than individuals living above the poverty line. The prevalence of menthol cigarette use will decline at a slower rate than the prevalence of non-menthol cigarette use (21% vs. 38% decline). Model projections of cigarette smoking prevalence typically fall within the 95% confidence intervals of prevalence estimates across three national surveys. Overall, the TwIST Model projects cigarette smoking prevalence rates that are similar to real-world estimates. If tobacco use continues based on current patterns, income-based disparities in smoking will persist and a growing proportion of individuals who smoke will use menthol cigarettes, which are known to be harder to quit.

PurposeAssessing factors associated with being up-to-date with colorectal cancer (CRC) screening is important for identifying populations for which targeted interventions may be needed.MethodsThis study used Medicare and private insurance claims data for residents of North Carolina to identify up-to-date status in the 10th year of continuous enrollment in the claims data and in available subsequent years. USPSTF guidelines were used to define up-to-date status for multiple recommended modalities. Area Health Resources Files provided geographic and health care service provider data at the county level. A generalized estimating equation logistic regression model was used to examine the association between individual- and county-level characteristics and being up-to-date with CRC screening.ResultsFrom 2012–2016, 75% of the sample (n = 274,660) age 59–75 was up-to-date. We identified several individual- (e.g., sex, age, insurance type, recent visit with a primary care provider, distance to nearest endoscopy facility, insurance type) and county-level (e.g., percentage of residents with a high school education, without insurance, and unemployed) predictors of being up-to-date. For example, individuals had higher odds of being up-to-date if they were age 73–75 as compared to age 59 [OR: 1.12 (1.09, 1.15)], and if living in counties with more primary care physicians [OR: 1.03 (1.01, 1.06)].ConclusionThis study identified 12 individual- and county-level demographic characteristics related to being up-to-date with screening to inform how interventions may optimally be targeted.

Reducing racial and socioeconomic inequities in smoking has been declared a priority for tobacco control in the USA for several decades. Yet despite the rhetoric, these inequities persist and some have actually worsened over time. Although tobacco companies have targeted racially and ethnically diverse and lower-income tobacco users, which substantially contributes to these disparities, less attention has been given to the role of individuals and organisations within the tobacco control movement who have allowed progress in eliminating disparities to stagnate. We examine the failure of tobacco control professionals to ensure the widespread adoption of equity-focused tobacco control strategies. Review of major US tobacco control reports found that the focus on equity often stops after describing inequities in tobacco use. We suggest ways to advance equity in tobacco control in the USA. These recommendations fall across five categories: surveillance, interventions, funding, accountability and addressing root causes. Policy interventions that will have a pro-equity impact on smoking and related disease should be prioritised. Funding should be designated to tobacco control activities focused on eliminating racial and socioeconomic inequities in smoking, and tobacco control programmes should be held accountable for meeting equity-related goals.

The African American Tobacco Control Leadership Council (AATCLC) is an advocacy group that works to inform the direction of tobacco control policy and priorities in the USA. This article narrates the AATCLC’s work advocating for a comprehensive, flavoured tobacco product sales ban in San Francisco, California. Recommendations for tobacco control advocates and lessons learned from their work are provided. The article concludes by discussing conditions necessary to enact the policy. These include having a dedicated advocacy team, community support, a policy sponsor, and clear and repeated messaging that is responsive to community concerns.

ObjectivesDevelop and use a causal loop diagram (CLD) of smoking among racial/ethnic minority and lower-income groups to anticipate the intended and unintended effects of tobacco control policies.MethodsWe developed a CLD to elucidate connections between individual, environmental and structural causes of racial/ethnic and socioeconomic disparities in smoking. The CLD was informed by a review of conceptual and empirical models of smoking, fundamental cause and social stress theories and 19 qualitative interviews with tobacco control stakeholders. The CLD was then used to examine the potential impacts of three tobacco control policies.ResultsThe CLD includes 24 constructs encompassing individual (eg, risk perceptions), environmental (eg, marketing) and structural (eg, systemic racism) factors associated with smoking. Evaluations of tobacco control policies using the CLD identified potential unintended consequences that may maintain smoking disparities. For example, the intent of a smoke-free policy for public housing is to reduce smoking among residents. Our CLD suggests that the policy may reduce smoking among residents by reducing smoking among family/friends, which subsequently reduces pro-smoking norms and perceptions of tobacco use as low risk. On the other hand, some residents who smoke may violate the policy. Policy violations may result in financial strain and/or housing instability, which increases stress and reduces feelings of control, thus having the unintended consequence of increasing smoking.ConclusionsThe CLD may be used to support stakeholder engagement in action planning and to identify non-traditional partners and approaches for tobacco control.

In this issue of AJPH, Reynolds et al. (p. 1540) describe the process of passing the first Tobacco 21 (T21) policy that raised the legal age for the sale oftobacco products to 21 years in Needham, Massachusetts, in 2003 and the subsequent spread of T21 policies across the United States. T21 policies have proliferated faster than any other retail tobacco control policy and the reasons behind their rise are worth examining because they may offer clues for promoting other worthy policies that have low adoption. We use diffusion ofinnovation1 and multiple streams theories2 to explain the rise of T21 policies. Why has T21 spread so rapidly compared with other policies? At least 475 localities have passed T21, and 17 states have adopted the policy (two of which go in effect in 2020 or 2021).3 Not a single state has put in place other retail-focused policies such as minimum floor prices for tobacco products, bans on tobacco promotions, limits on tobacco retailer density, or banning menthol tobacco sales. Only one state, Massachusetts, has a ban on tobacco sales in pharmacies.

Reducing racial/ethnic disparities in smoking is a priority for state tobacco control programs. We investigated disparities in cigarette use by race/ethnicity, as well as trends in cigarette use across racial/ethnic groups from 2011 to 2018 in 50 US states and the District of Columbia. We used data from the Behavioral Risk Factor Surveillance System. In each state, smoking prevalence and corresponding 95% CIs were estimated for each racial/ethnic group in 2011, 2014, and 2018. We used logistic regression models to examine state-specific linear and quadratic time trends in smoking prevalence from 2011 to 2018. Racial/ethnic disparities in smoking prevalence varied across states. From 2011 to 2018, compared with White adults, the odds of smoking were lower among Black adults in 14 states (odds ratio [OR] range, 0.58-0.91) and were higher in 9 states (OR range, 1.10-1.98); no differences were found in the odds of smoking in 13 states. Compared with White adults, the odds of smoking were lower among Hispanic adults in most states (OR range, 0.33-0.84) and were typically higher among Other adults (OR range, 1.19-2.44). Significant interactions between year and race/ethnicity were found in 4 states, indicating that time trends varied across racial/ethnic groups. In states with differential time trends, the decline in the odds of smoking was typically greater among Black, Hispanic, and Other adults compared with White adults. Some progress in reducing racial/ethnic disparities in smoking has been made, but additional efforts are needed to eliminate racial/ethnic disparities in smoking.

BackgroundThere is evidence that the cheapest cigarettes cost even less in neighbourhoods with higher proportions of youth, racial/ethnic minorities and low-income residents. This study examined the relationship between the price of the cheapest cigarette pack and neighbourhood demographics in a representative sample of tobacco retailers in the USA.MethodsData collectors recorded the price of the cheapest cigarette pack (regardless of brand) in 2069 retailers in 2015. Multilevel linear modelling examined the relationship between price and store neighbourhood (census tract) characteristics, specifically median household income and percentage of youth, Black, Asian/Pacific Islander and Hispanic residents.ResultsAverage price for the cheapest pack was $5.17 (SD=1.73) and it was discounted in 19.7% of stores. The price was $0.04 less for each SD increase in the percentage of youth and $0.22 less in neighbourhoods with the lowest as compared with the highest median household incomes. Excluding excise taxes, the average price was $2.48 (SD=0.85), and associations with neighbourhood demographics were similar.ConclusionThe cheapest cigarettes cost significantly less in neighbourhoods with a greater percentage of youth and lower median household income. Non-tax mechanisms to increase price, such as minimum price laws and restrictions on discounts/coupons, may increase cheap cigarette prices.

We evaluated the unique contribution of the two subscales of the Multigroup Ethnic Identity Measure-Revised (MEIM-R), Exploration and Commitment, to mental and behavioral health outcomes among non-Hispanic White, ethnic minority, and mixed-race college students. Monoracial ethnic minority and mixed-race students reported higher Exploration scores in comparison to monoracial non-Hispanic White students. Monoracial ethnic minority students reported higher Commitment scores in comparison to monoracial non-Hispanic White and mixed-race students. Among the total sample, higher Exploration scores were associated with greater anxiety symptoms, suggesting that ethnic identity exploration may result in heightened levels of distress.