Kassandra I. Alcaraz’s research while affiliated with Johns Hopkins University and other places

Purpose This study sought to identify subgroups of adult cancer survivors with distinct food behavior patterns and to examine group characteristics. Methods Data from adult cancer survivors, ages 20–64, in the 2007–2016 National Health and Nutrition Examination Survey was used to conduct latent class analyses to identify food behavior subgroups, based on five indicator variables. Associations between latent food behavior class membership and key sociodemographic and cancer-related characteristics were examined using adjusted multinomial regression models. Results Four unique food behavior classes were identified and named, characterized by the degree to which they purchased and consumed convenience foods: Home Cooks (48.4% of the sample), Non-Cooks/Eats Out (29.8%), Need Convenience (11.3%), and Quick Food at Home (10.5%). Shorter time since diagnosis was associated with higher adjusted relative risk ratios (ARRR) of belonging to the Non-Cooks/Eats Out class (ARRR 12.4, 95% CI 2.3, 68.0) and the Quick Food at Home class (ARRR 18.6, 95% CI 3.5, 98.4). Older age, lower educational attainment, and larger household size were also associated with a higher likelihood of belonging to the Quick Food at Home class. Conclusion There are certain patterns of food behaviors among cancer survivors, and these patterns are related to both cancer-related and sociodemographic factors. Implications for Cancer Survivors As food purchasing and consumption behaviors are precursors to dietary outcomes, identifying the food behavior profiles of cancer survivors may help providers recognize those who could benefit from dietary interventions or supports and receive the necessary resources that would support patients in improving their diets.

Objective In this study, we examined associations between county-level measures of structural racism and county-level cancer incidence and mortality rates between race groups while accounting for factors associated with cancer rates and county-level measures of environmental burden. Methods To explore this relationship, we conducted multiple linear regression analyses. Data for these analyses came from an index of county-level structural racism and publicly available data on 2015 to 2019 age-adjusted cancer rates from the US Cancer Statistics Data Visualization Tool, 2019 County Health Rankings and Roadmaps, the Environmental Protection Agency’s 2006 to 2010 Environmental Quality Index, and 2015 to 2019 estimates from the US Census American Community Survey. Results County-level structural racism was associated with higher county cancer incidence rates among Black (adjusted incidence rate: 17.4, 95% confidence interval [95% CI]: 9.3, 25.5) and Asian/Pacific Islander populations (adjusted incidence rate: 9.3, 95% CI: 1.8, 16.9) and higher mortality rates for American Indian/Alaskan Native (adjusted mortality rate [AMR]: 17.4, 95% CI: 4.2, 30.6), Black (AMR: 11.9, 95% CI: 8.9, 14.8), and Asian/Pacific Islander (AMR: 4.7, 95% CI: 1.3, 8.1) populations than White populations. Conclusion Our findings highlight the detrimental impact of structural racism on cancer outcomes among minoritized populations. Strategies aiming to mitigate cancer disparities must embed processes to recognize and address systems, policies, laws, and norms that create and reproduce patterns of discrimination.

Background Although racial disparities in lung cancer incidence and mortality have diminished in recent years, lung cancer remains the second most diagnosed cancer among US Black populations. Many factors contributing to disparities in lung cancer are rooted in structural racism. To quantify this relationship, we examined associations between a multidimensional measure of county-level structural racism and county lung cancer incidence and mortality rates among Black populations, while accounting for county levels of environmental quality. Methods We merged 2016-2020 data from the United States Cancer Statistics Data Visualization Tool, a pre-existing county-level structural racism index, the Environmental Protection Agency’s 2006-2010 Environmental Quality Index (EQI), 2023 County Health Rankings, and the 2021 United States Census American Community Survey. We conducted multivariable linear regressions to examine associations between county-level structural racism and county-level lung cancer incidence and mortality rates. Results Among Black males and females, each standard deviation increase in county-level structural racism score was associated with an increase in county-level lung cancer incidence of 6.4 (95% CI: 4.4, 8.5) cases per 100,000 and an increase of 3.3 (95% CI: 2.0, 4.6) lung cancer deaths per 100,000. When examining these associations stratified by sex, larger associations between structural racism and lung cancer rates were observed among Black male populations than among Black females. Conclusion Structural racism contributes to both the number of new lung cancer cases and the number of deaths caused by lung cancer among Black populations. Those aiming to reduce lung cancer cases and deaths should consider addressing racism as a root-cause.

Background: Behavioral digital health interventions (e.g., mobile apps, websites, wearables) have been applied widely to improve health outcomes. However, many groups (e.g., people with low income levels, people who are geographically isolated, older adults) may face obstacles to technology access and use. In addition, research has found that biases and stereotypes can be embedded within digital health interventions. As such, behavioral digital health interventions that intend to improve overall population health may unintentionally widen health-related inequities. Purpose: This commentary offers guidance and strategies to mitigate these risks when using technology as a means for delivering a behavioral health intervention. Methods: A collaborative working group from Society of Behavioral Medicine's Health Equity Special Interest Group developed a framework to center equity in the development, testing and dissemination of behavioral digital health interventions. Results: We introduce Partner, Identify, Demonstrate, Access, Report (PIDAR), a 5-point framework to avoid the creation, perpetuation, and/or widening of health inequities in behavioral digital health work. Conclusions: It is critically important to prioritize equity when conducting digital health research. The PIDAR framework can serve as a guide for behavioral scientists, clinicians and developers.

Disparities in health persist despite the development of innovative and effective behavioral interventions. Both behavioral medicine and implementation science are vital to improving health care and health outcomes, and both can play a critical role in advancing health equity. However, to eliminate health disparities, more research in these areas is needed to ensure disparity-reducing behavioral interventions are continually developed and implemented. This special issue on interventions to promote health equity presents a diverse set of articles focused on implementing behavioral interventions to reduce health disparities. The current article summarizes the special issue and identifies key themes and future considerations. Articles in this special issue report on behavioral medicine intervention studies (including those examining aspects of implementation) as well as implementation science studies with implications for behavioral medicine. Articles discuss community-, provider-, and system-level interventions; implementation processes; and barriers and facilitators to implementation. Also included are commentaries calling for greater prioritization of behavioral medicine and implementation research. As evidenced in this special issue, behavioral medicine is primed to lead the implementation of behavioral interventions in historically marginalized and minoritized populations to advance health equity and improve overall population health.

Purpose Prior cancer research is limited by inconsistencies in defining rurality. The purpose of this study was to describe the prevalence of cancer risk factors and cancer screening behaviors across various county-based rural classification codes, including measures reflecting a continuum, to inform our understanding of cancer disparities according to the extent of rurality. Methods Using an ecological cross-sectional design, we examined differences in cancer risk factors and cancer screening behaviors from the Behavioral Risk Factor Surveillance System and National Health Interview Survey (2008–2013) across rural counties and between rural and urban counties using four rural–urban classification codes for counties and county-equivalents in 2013: U.S. Office of Management and Budget, National Center for Health Statistics, USDA Economic Research Service’s rural–urban continuum codes, and Urban Influence Codes. Results Although a rural-to-urban gradient was not consistently evident across all classification codes, the prevalence of smoking, obesity, physical inactivity, and binge alcohol use increased (all ptrend < 0.03), while colorectal, cervical and breast cancer screening decreased (all ptrend < 0.001) with increasing rurality. Differences in the prevalence of risk factors and screening behaviors across rural areas were greater than differences between rural and urban counties for obesity (2.4% vs. 1.5%), physical activity (2.9% vs. 2.5%), binge alcohol use (3.4% vs. 0.4%), cervical cancer screening (6.8% vs. 4.0%), and colorectal cancer screening (4.4% vs. 3.8%). Conclusions Rural cancer disparities persist across multiple rural–urban classification codes, with marked variation in cancer risk factors and screening evident within rural regions. Focusing only on a rural–urban dichotomy may not sufficiently capture subpopulations of rural residents at greater risk for cancer and cancer-related mortality.

Purpose The current study examined the relationships between religious resources (i.e., certainty of belief in God and attendance at religious services), religious struggle (e.g., belief that cancer is evidence of God’s punishment or abandonment), and physical and mental health-related quality of life (HRQoL), including fear of cancer recurrence (FCR), in a large, geographically and clinically diverse sample of long-term survivors of cancer. Methods Participants were 2021 9-year survivors of cancer from the American Cancer Society’s Study of Cancer Survivors – I. Religious resources included belief in God and attendance at religious services. Items from the Brief RCOPE and the PROMIS Psychosocial Impact of Illness were combined to assess religious struggle. Survivors also completed the Fear of Cancer Recurrence Inventory, SF-12, and Meaning and Peace subscales of the FACIT-Sp. Regression models were used to predict HRQoL and FCR from religious resources and struggle. Results In multivariable models, certain belief in God predicted greater mental HRQoL (B = 1.99, p < .01), and attendance at religious services was associated with greater FCR (B = .80, p < .05) as well as better mental (B = .34, p < .01) and physical (B = .29, p < .05) HRQoL. In addition, religious struggle predicted greater FCR (B = 1.32, p < .001) and poorer mental (B = − .59, p < .001) and physical (B = − .29, p < .001) HRQoL. Many of these relationships were mediated through Meaning. Conclusions With the exception of FCR, religious resources predicted better HRQoL outcomes in these long-term survivors of cancer. Conversely, religious struggle consistently predicted poorer HRQoL, including greater FCR. Implications for Cancer Survivors Given the documented importance of its role in coping with the cancer experience, religion/spirituality should be a consideration in every survivorship care plan. Multidisciplinary assessment and support of religious resources and identification of and referral for religious struggle are needed to ensure the well-being of most long-term survivors of cancer.

Background: Cannabis use is increasing, including among smokers, an at-risk population for cancer. Research is equivocal on whether using cannabis inhibits quitting cigarettes. The current longitudinal study investigated associations between smoking cannabis and subsequently quitting cigarettes. Methods: Participants were 4,535 adult cigarette smokers from a cohort enrolled in the American Cancer Society's Cancer Prevention Study-3 in 2009-2013. Cigarette quitting was assessed on a follow-up survey in 2015-2017, an average of 3.1 years later. Rates of quitting cigarettes at follow-up were examined by retrospectively-assessed baseline cannabis smoking status (never, former, recent), and by frequency of cannabis smoking among recent cannabis smokers (low: <3 days/month; medium: 4-19 days/month; high: >20 days/month). Logistic regression models adjusted for sociodemographic factors, smoking and health-related behaviors, and time between baseline and follow-up. Results: Adjusted cigarette quitting rates at follow-up did not differ significantly by baseline cannabis smoking status [never 36.2%, 95% confidence interval (CI), 34.5%-37.8%; former 34.1%, CI, 31.4%-37.0%; recent 33.6%, CI, 30.1%-37.3%], nor by frequency of cannabis smoking (low 31.4%, CI, 25.6%-37.3%; moderate 36.7%, CI, 30.7%-42.3%; high 34.4%, CI, 28.3%-40.2%) among recent baseline cannabis smokers. In cross-sectional analyses conducted at follow-up the proportion of cigarette smokers intending to quit smoking cigarettes in the next 30 days did not differ by cannabis smoking status (p=0.83). Conclusions: Results do not support the hypothesis that cannabis smoking inhibits quitting cigarette smoking among adults. Impact: Future longitudinal research should include follow-ups of >1 year, and assess effects of intensity/frequency of cannabis use and motivation to quit on smoking cessation.

Cancer survivors’ quality of life (QoL) is consistently shown to be positively impacted by social support from family and friends, including informal caregivers. In contrast, a loved one’s negative response to cancer can diminish survivors’ QoL, and these negative responses can be more impactful than supportive behaviors. Nonetheless, negative caregiver response has not been extensively researched, and few studies have explored the potential interaction of negative caregiver response and perceived social support on survivors’ QoL. Therefore, we examined direct effects of perceived negative caregiver response, and the potential moderating role of social support, on QoL in a population-based sample of cancer survivors (N = 7543) using generalized linear models. Findings indicate that survivors who rated their caregiver’s response to their cancer diagnosis more negatively reported worse physical and mental health, even up to 10 years after their initial cancer diagnosis. Perceived social support was not significantly associated with physical health, but it was positively associated with mental health. However, social support was not shown to moderate the relationship between negative caregiver response and mental health. Findings suggest that positive support from others within a survivor’s social network may not be enough to attenuate the negative effects of their primary caregiver’s unsupportive behaviors. Accordingly, cancer survivorship research and practice must consider the critical role that negative caregiver responses have on survivors’ QoL and develop strategies that focus on the survivor–caregiver dynamic.