Commentary: 20 years online with "Your Disease Risk"

Development of a cancer risk model and mobile health application to inform the public about cancer risks and risk factors

OBJECTIVE

To develop and evaluate a mobile health application, the Cancer Risk Calculator (CRC), aimed at improving public health literacy by providing personalized information on cancer risks and preventive measures.

MATERIALS AND METHODS

The CRC was developed through a comprehensive process involving the identification of necessary content, integration of average cancer risks using data from reliable sources, creation of a novel risk model emphasizing modifiable factors, and the application's development for easy access. The application covers 38 cancer types, 18 subtypes, and approximately 790 risk factors, utilizing data from the Surveillance, Epidemiology, and End Results Program and scientific literature.

RESULTS

CRC offers users personalized risk assessments across a broad range of cancers, emphasizing modifiable risk factors to encourage preventive behaviors. It distinguishes itself by covering more cancer types and risk factors than existing tools, with preliminary user feedback indicating its utility in promoting health literacy and lifestyle changes.

DISCUSSION

The CRC application stands out as an innovative tool in health informatics, significantly enhancing public understanding of cancer risks. Its development underscores the potential of digital health technologies to bolster preventive healthcare strategies through improved health literacy.

CONCLUSION

The Cancer Risk Calculator is a pivotal development in mobile health technology, offering comprehensive and personalized insights into cancer risks and prevention. It serves as a valuable resource for public health education, facilitating informed decisions and lifestyle modifications for cancer prevention.

Essentialism and Exclusion: Racism in Cancer Risk Prediction Models

Cancer risk prediction models have the potential to revolutionize the science and practice of cancer prevention and control by identifying the likelihood that a patient will develop cancer at some point in the future, likely experience more benefit than harm from a given intervention, and survive their cancer for a certain number of years. The ability of risk prediction models to produce estimates that are valid and reliable for people from diverse socio-demographic backgrounds-and consequently their utility for broadening the reach of precision medicine to marginalized populations-depends on ensuring that the risk factors included in the model are represented as thoroughly and as accurately as possible. However, cancer risk prediction models created in the United States have a critical limitation, the origins of which stem from the country's earliest days: they either erroneously treat the social construct of race as an immutable biological factor (ie, they "essentialize" race), or they exclude from the model those socio-contextual factors that are associated with both race and health outcomes. Models that essentialize race and/or exclude socio-contextual factors sometimes incorporate "race corrections" that adjust a patient's risk estimate up or down based on their race. This commentary discusses the origins of race corrections, potential flaws with such corrections, and strategies for developing cohorts for developing risk prediction models that do not essentialize race or exclude key socio-contextual factors. Such models will help move the science of cancer prevention and control towards its goal of eliminating cancer disparities and achieving health equity.

When belongingness backfires: experienced discrimination predicts increased cardiometabolic risk among college students high in social belonging

Research implicates experiences of discrimination in exacerbating cardiometabolic disease (CMD) risk. Belongingness has been suggested as a buffer against the adverse effects of discrimination. However, when discrimination occurs in an environment to which one feels they belong, then the potential benefits of belongingness may dissipate or even exacerbate the effects of discrimination. In the present study, we examined these competing hypotheses on how campus belonging might moderate the relationship between discrimination experienced on campus and CMD risk. College students (n = 160, 60.9% Latino/a/x) reported the frequency of on-campus discrimination and campus belongingness, and then completed items assessing risk for CMD. More frequent discrimination related to higher comparative CMD risk among those who reported high campus belongingness, even after adjusting for relevant covariates. These findings highlight the complicated nature of belongingness in the context of physical health. Future research is needed to better understand the role of environment when considering morbidity among college students.