NIH funding opportunity – Predicting Behavioral Responses to Population-Level Cancer Control Strategies (R21)

May 17, 2016 by School of Medicine Webmaster

The goal of this funding opportunity announcement (FOA) is to facilitate research to identify individual influences on the effectiveness of population-level strategies that target cancer-related behaviors. Research facilitated by this FOA has the potential to facilitate cancer prevention and control efforts by better targeting current population-level strategies, shaping the development of new strategies, and communicating strategies most effectively.

Population-level cancer control strategies

Population-level public health strategies are often effective in targeting behavior to prevent and control cancer. Such approaches include public and private policy, federal or state regulation, and media campaigns to target modifiable risk factors such as tobacco use, sun safety, physical activity, dietary consumption, HPV vaccination, and non-adherence to medical recommendations. Although the goal of most such public health approaches is to achieve the intended behavior change at a population level, it is important to understand the individual influences (e.g., psychological, social, cultural) that drive behavioral response. An individual’s personal knowledge, attitudes, emotional state, and social environment are significant determinants of behavior change, even in the context of population-level strategies. Increased focus on population-level strategies with precision intervention components highlight the need to understand how, for whom, and in what contexts a given strategy is most effective.

Examples of population-level strategies

Examples of population-level cancer control strategies include: tobacco taxes; smoke-free air laws; tobacco packaging requirements; tanning-bed regulations; menu-labeling; zoning laws and policy; institutionalized HPV vaccination; and cancer screening recommendations. Investigators are encouraged to focus on behavioral responses to U.S. population-level strategies, to leverage population-level strategies from other countries to inform research on behavioral responses to U.S. population-level strategies, or to examine ways in which behavioral response to U.S. population-level strategies may influence engagement with such strategies in the developing world.

Population-level strategies can explicitly target cancer-related behaviors, such as efforts to discourage smoking through taxes. Population-level strategies may also affect cancer-related behaviors more indirectly, as is the case with smoke-free air laws, which were originally intended to protect individuals from secondhand smoke in the workplace but also – in some individuals – facilitate cessation by increasing barriers to smoking. This is an example of an untended positive consequence. Population-level strategies also have the potential to promote an unintended negative consequence. In one prominent example, institutionalized HPV vaccination in Texas was met with resistance from individuals who perceived it as taking away their personal choice. Although the development and availability of the HPV vaccine is a public health triumph, suboptimal uptake across the U.S. reinforces the need to better understand individual influence even in the context of strong population-level interventions.

Psychological, individual, and contextual moderators

A nascent but growing literature suggests that behavioral, psychological, and social science can inform research on responses to population-level strategies, shedding light on when they are most effective and for whom. For example, it is well-established that cigarette taxation is an excellent strategy to reduce the prevalence of smoking. However, continued smoking among subsets of the population suggests this strategy is not universally effective. Emerging evidence suggests that individual differences in responses to stress and threat may shape smoking behavior within the context of state tobacco taxes, predicting up to a 10% differential in quit rates in response to taxes. This highlights the opportunity to intervene on those who respond to stress and threat with reinforced smoking behaviors under high cigarette taxation. This might be accomplished by augmenting tobacco taxes with population-level strategies that may better motivate smoking cessation among those with low stress management resources.

In some cases, population-level strategies may not target those who are most in need of the cancer control intervention. Cancer prevention and control disparities are observed among specific population groups, suggesting that characteristics such as income, education, place of residence may predict responses to population-level strategies. However, sociodemographic factors are not easy to intervene upon (e.g., increasing wealth, access to resources, and improving education). At the same time, individual differences are also influenced by personal preferences, habits, information; and product availability, cost, and placement. A deeper understanding of these individual differences may hold promise to inform precision interventions, in effect identifying what strategies work for whom, and in what context.

In contrast to a one-size-fits-all approach, informing population-level strategies with knowledge of individual influence that predict behavioral responses could reduce unintended negative consequences, maximize unintended positive consequences, and identify individuals in need of additional intervention even in the context of strong population-level strategy implementation. This approach has important implications for the ways these strategies are designed, disseminated, targeted, and augmented (i.e., with complementary policies or interventions).

In sum, it is critical to understand:

  • In what ways do characteristics of individuals shape how they engage with, participate in, use, and respond, to various population-level strategies relevant to cancer control?
  • For whom is a given population-level strategy most effective?
  • For whom does a given population-level strategy unintentionally promote unhealthy (or healthy) behavior, how might negative unintended consequences be ameliorated, and how might unintended positive consequences be leveraged?
 Specific Research Objectives

The goal of this funding opportunity announcement (FOA) is to facilitate research to identify individual influences on the effectiveness of population-level strategies that target cancer-related behaviors. We seek to encourage collaborations among scientists with expertise in health policy research and implementation, as well as investigators in scientific disciplines that have not traditionally conducted cancer or policy research, such as: psychological science (e.g., social, developmental); affective and cognitive neuroscience; judgment and decision-making; consumer behavior and marketing; organizational behavior; sociology, cultural anthropology; behavioral economics; linguistics; and political science.

Research questions of interest include, but are not limited to:

  • How does aversion to impositions on freedom affect responses to strategies perceived to limit autonomy (e.g., mandated/ institutionalized HPV vaccination)?
  • How does impression management, or the desire to maintain a positive social image, influence responses to strategies that influence cancer-related behaviors through changing social norms about behavior (e.g., smoke free policies)
  • How do reactions to stress predict responses to strategies that target behaviors that can be used to manage stress (e.g., smoking targeted by tobacco taxation)?
  • How does the tendency to evaluate all available information when making a shape behavioral response to strategies designed to convey information (e.g., warning labels on cigarette packages, warning labels on tanning beds)?
  • How do individuals particularly focused on promoting good health interpret and respond to policies about prevention (e.g., mandated coverage of preventive healthcare services)?

Projects may involve experimental methods (i.e., behavioral experiments conducted in well-controlled or in applied/field/”real-life” settings). Hypotheses can also be tested by analyses of extant data sources through integrated data analysis, computational modeling, or other big data analytical techniques, like predictive analytics.

Deadlines:  letters of intent are due 30 days before the deadline; October 7, 2016; April 11, 2017; October 10, 2017; April 11, 2018; October 10, 2018; April 11, 2019 (full proposals)


Filed Under: Funding Opportunities