#iBeatCRC: A Community-based Intervention to Increase Early-onset Colorectal Cancer Awareness (#iBeatCRC)

#iBeatCRC: A Community-based Intervention to Increase Early-onset Colorectal Cancer Awareness Using a Sequential Explanatory Mixed Methods Approach

Dr. Rogers' long-term goal is to better understand the etiology of an early-onset colorectal (CRC) diagnosis and to improve long-term survivorship and quality of life for early-onset CRC (EOCRC) survivors globally by studying the burdens accompanying this condition. The goal of this study is to better understand the reasons why people under age 50 in Utah and Wisconsin are being diagnosed with CRC. As a first step, the researchers identified the specific places in Utah and Wisconsin where diagnoses of CRC among younger people are increasing the most. Next, they conducted 1-hour recorded Zoom interviews over phone and/or video with 27 people across the United States diagnosed with CRC when they were under age 50. Thirdly, the researchers plan to create and test a program that will raise the awareness of residents in Utah and Wisconsin of the increasing risk of CRC among residents of the state who are aged under 50. This study is unique as CRC survivors are key to helping drive the study forward.

Colorectal cancer (CRC) is preventable when detected early. Because of effective screening, fewer Americans aged 50 and older are now being diagnosed with CRC or dying from it. Over the past 20 years, however, the number of Americans under age 50 who are diagnosed with CRC has doubled. Health experts estimate that the numbers of younger Americans with CRC will continue to increase rapidly over the next 10 years. The reasons for this increase are poorly understood. In addition, younger people are less likely to be diagnosed with CRC when the disease is still at an early stage. Also, of concern is that among men and women of all ages and all races, African-American men are the most likely to die of CRC. Central hypotheses were: (1) Patients residing in hotspots-counties with high EOCRC incidence/mortality rates-will have significantly worse EOCRC survival juxtaposed to those in other Utah areas, specifically. (2) Rurality and county-level access to health care will contribute to an explanation of EOCRC incidence and survival.

Intervention development will be informed by (1) integrating Aims 1 and 2 findings, (2) Community Action Board [CAB] input, and (3) the Behaviour Change Wheel,48 a step-by-step intervention development approach that identifies and addresses barriers using theory and evidence-based methods. The intervention pilot may be based on a multicomponent media campaign, as endorsed by the Community Preventive Services Taskforce for promoting CRC screening among individuals ≥ age 50. #iBeatCRC may entail both outdoor mass media and online social media. #iBeatCRC will target Utah and Wisconsin hotspots and non-hotspots for comparison, with pre-post-assessment among 17 individuals in each group for both sites.

We will understand the impact psychosocial, lifestyle, and familial aspects play on an EOCRC diagnosis through 20 one-hour interviews with EOCRC patients and survivors.

Utilizing the Behaviour Change Wheel in conjunction with results gathered from Aims 1 and 2 we will develop a theory-driven, multi-media campaign intervention to increase awareness of EOCRC, risk factors, and early detection benefit.

We used quantitative methods to link incidence and mortality data for the years 2000 to 2020 from the Utah Cancer Registry (UCR) and the Utah Population Database (UPDB) to derive county-level estimates of hotspots for early-onset colorectal cancer (EOCRC) incidence and mortality among Utahns aged 18 to 49 years and obtain county-level estimates using our previous geospatial methods. Twenty-nine counties in each state with high EOCRC incidence and/or mortality rates were identified as hotspots. Next, we used UCR-UPDB linked data to determine the independent contributions of (1) geographical, (2) personal, and (3) county-level factors to EOCRC incidence and survival. We performed hierarchical Cox regression models and implemented a generalized R-square analysis to determine the variance explained by each factor.

For Outcome 2, we drew on factors associated with hotspots identified in Objective 1 and our team's prior research to develop an interview guide with six EOCRC advocate-survivors. Using the interview guide, we conducted one-on-one interviews with 27 individuals who received a first diagnosis of CRC at age 18 to 49 years to yield a richer understanding of the impact of psychosocial, lifestyle, and familial aspects on an EOCRC diagnosis. The qualitative data obtained from these interviews was recorded, transcribed, and analyzed using Hatch's methods.

Mean knowledge index score difference of EOCRC general Awareness The intervention will be assessed with a post-test questionnaire among the 17 hotspot and 17 coldspot participants in each state. Preintervention and postintervention mean score differences will be tested using repeated measures ANOVA. Preintervention and postintervention EOCRC awareness change will be analyzed by McNemar's test. P≤0.05 will be considered statistically significant.

Mean knowledge index score difference of EOCRC risk factors The intervention will be assessed with a post-test questionnaire among the above-mentioned 17 hotspot and 17 coldspot participants in each state. Preintervention and postintervention mean score differences will be tested using repeated measures ANOVA. Preintervention and postintervention EOCRC awareness of risk factors change will be analyzed by McNemar's test. P≤0.05 will be considered statistically significant.

Mean knowledge index score difference of EOCRC early detection benefit. The intervention will be assessed with a post-test questionnaire among the above-mentioned 17 hotspot and 17 coldspot participants in each state. Preintervention and postintervention mean score differences will be tested using repeated measures ANOVA. Preintervention and postintervention EOCRC awareness of early detection benefit change will be analyzed by McNemar's test. P≤0.05 will be considered statistically significant

Inclusion Criteria: Aim 1: No recruitment took place (secondary data analysis). These records were used to determine early-onset colorectal cancer hotspots in Utah. Aim 2: 30 one-hour interviews were conducted with EOCRC patients and survivors who (1) resided in the United States, (2) were diagnosed with CRC at 18-49 years of age, (3) had a telephone, and (4) spoke English. Aim 3: Individuals must: (1) reside in Utah or Wisconsin, (2) were diagnosed with CRC at 18-49 years of age, (3) have a telephone, and (4) speak English. Exclusion Criteria: Aim 1: No recruitment took place (secondary data analysis). Aim 2: Individuals were excluded if they (1) did not reside in the United States, (2) were not diagnosed with CRC between 18-49 years of age, (3) did not have a telephone, and (4) did not speak English. Aim 3: Individuals will be excluded if they (1) do not reside in the Utah or Wisconsin, (2) were not diagnosed with CRC between 18-49 years of age, (3) do not have a telephone, and (4) do not speak English.

Sharing de-identified participant data stemming from cognitive interviews, surveys, intervention arms, and post trial interviews.

Rogers CR, Brooks E, Curtin K, De Vera MA, Qeadan F, Rogers TN, Petersen E, Gallagher P, Pesmen C, Johnson W, Henley C, Hickman W, Newcomb E, Korous KM, Handley MA. Protocol for #iBeatCRC: a community-based intervention to increase early-onset colorectal cancer awareness using a sequential explanatory mixed-methods approach. BMJ Open. 2021 Dec 3;11(12):e048959. doi: 10.1136/bmjopen-2021-048959.