(Presented as a poster at the 2023 American Association for Cancer Research (AACR) Annual Meeting)



John Charles A. Lacson, MS, PhD1*, Youngchul Kim, PhD2, Richard G. Roetzheim, MD3, Steven K. Sutton, MS, PhD2, Susan T. Vadaparampil, MPH, MS, PhD4, Brenda Soto-Torres, MPHE, PhD5, Peter A. Kanetsky, MPH, PhD1



1 Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, US

2 Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, US

3 Department of Family Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, US

4 Department of Health and Behavioral Outcomes, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, US

5 Public Health Program, Ponce Health Sciences University, Ponce, Puerto Rico, USA

* Poster presenter, email: JohnCharles.Lacson@moffitt.org




  • Hispanics are underserved in precision medicine – they are less likely to get referred to and to undergo genetic testing and are more likely to have variants of unknown significance1-5.
  • Although Hispanics generally have low awareness of genetic testing services1, those who are aware of such testing perceive several benefits from and report positive attitudes about testing6-8.
  • Modest successes in improving preventive behavior, particularly for skin cancer prevention, have been observed from precision prevention trials among both Hispanic and non-Hispanic participants that directly provided genetic testing results together with genetics-based prevention guidelines to participants9-12.
  • Recall is a fundamental psychological mechanism underlying how people receive and act upon health recommendations; better recall is associated with greater adherence to treatment and improved health outcomes13,14.
  • There have been no studies that describe recall of genetic testing results among Hispanics.
  • Genetic variants in MC1R are markers of susceptibility to skin cancer, including basal cell carcinoma, squamous cell carcinoma, and melanoma, among individuals of European ancestry15,16, including Hispanics and Puerto Ricans specifically17.
  • Hypotheses:
    • Participants at higher risk are more likely to correctly recall their MC1R risk compared to participants at average risk.
    • Better recall at the 3-month follow-up than the 9-month follow-up because of time-related memory decay18.



Materials and Methods

This study reports secondary analyses of data from a previously published randomized controlled intervention trial12.


Participants and setting

  • Self-identified Hispanic participants were recruited from 8 primary care clinics and community health centers in Tampa, Florida and Ponce, Puerto Rico from September 2018 to January 2020.
  • At least 18 years (Tampa) or 21 years (Ponce) of age.
  • Exclusion criteria: having a full-body skin examination within the past year, a previous diagnosis of melanoma, or two or more diagnoses of non-melanoma skin cancer.
  • Both English- and Spanish-language study materials were available for participants.
  • Bilingual and bicultural research staff were available to interact with participants.
  • After providing written informed consent.
  • Participants provided a saliva sample for DNA isolation and MC1R sequencing, completed a baseline questionnaire, and were randomized in 1:1 ratio within MC1R risk stratum (average or higher) to receive precision prevention or generic prevention materials (standard).


Precision prevention materials

  • Intervention arm: precision prevention materials with participant’s MC1R risk category (average or higher risk), genetics-based prevention guidelines, facts and figures about skin cancer.
  • Carriage of specific MC1R variant(s) was not provided.
  • Standard arm: generic prevention materials (without genetic information) and skin exam guidelines adapted from the American Academy of Dermatology (AAD)19.


Questionnaire assessments

  • Baseline questionnaire (completed before intervention materials were provided) collected information on age, sex, marital status, education, family history of basal cell carcinoma and/or squamous cell carcinoma, family history of melanoma, and family history of other cancers.
  • The following measures, which have been described in detail previously12,20, were also assessed: health numeracy, health literacy, absolute and comparative chance of getting skin cancer (including melanoma, squamous cell carcinoma, and basal cell carcinoma), recent concern and worry about skin cancer, perceived severity, self-efficacy, response efficacy, skin cancer worry , and recent distress about skin cancer.
  • Supplemental questionnaire, answered by 80% of participants, collected information on pigmentation characteristics, cancer fatalism21, and familism22.
  • Baseline questionnaire also assessed 11 prevention activities over the past 12 months via a standardized survey of sun exposure and sun protection behaviors23:
    • frequency (never, rarely, sometimes, often, always) of each of five sun protection behaviors:
      • 1) using sunscreen
      • 2) wearing sunglasses
      • 3) seeking shade or using an umbrella while outside
      • 4) wearing a shirt with sleeves
      • 5) wearing a hat
    • 6) number of red or painful sunburns
    • 7) frequency (never, rarely, sometimes, often, always) of intentional outdoor tanning
    • 8) number of intentional indoor tanning occurrences
    • 9) number of hours spent outside between 10 a.m. and 4 p.m. separately for weekdays and weekends
    • 10) number of skin examinations performed by oneself or partner
    • 11) skin examination (yes/no) performed by a health provider.
  • At 3- and 9-months post-intervention, these 11 prevention activities were reassessed to be used as outcomes to estimate intervention efficacy.
  • At 3- and 9-months post-intervention, participants in the intervention arm were asked to recall their MC1R genetic risk category (“Yes, it was average risk”; “Yes, it was high risk”; or “No, I don’t recall”).


Statistical analyses

Predictors of risk recall

  • Baseline measures of pigmentation phenotypes, demographics, family history of skin and cancers, psychosocial constructs, and prevention activities, and the completion of the telephone follow-up were considered as potential predictors of risk recall.
  • Risk recall was modeled as a binary variable using logistic regression.
  • Due to small numbers, those who misremembered their genetic risk were grouped with those who did not recall.
  • The univariate association with each potential predictor was assessed using appropriate statistical tests (chi-square, t-, Wilcoxon rank sum, Kruskal-Wallis, and ANOVA F- tests), separately for recall at 3 and 9 months after baseline.
  • Separately for each MC1R risk category, a backwards stepwise regression was conducted on variables with p<0.20 to build a multivariate logistic regression model.
  • A final parsimonious model was selected by minimizing the Akaike Information Criterion (AIC), which optimizes model fit rather than relying on a specified statistical threshold to select individual predictors24.
  • MASS and radiant packages in R software (ver 4.1.0, R Foundation for Statistical Computing, Vienna, Austria, RRID:SCR_001905) and RStudio (ver 1.4.1717, RStudio Team, Boston, MA, RRID:SCR_000432) 

Assessment of intervention effects

  • Intervention effects were reassessed for all trial participants (n=920) using previously published methods11, stratified by MC1R risk category and risk recall reported at 3 months.
  • Generalized estimating equations (GEE) to determine the intervention effect on each primary prevention activity measured at 3 and 9 months.
  • Adjusted for the baseline measure of the outcome, predictors of outcome, imbalanced baseline variables and predictors of missingness for return of the 3- and 9-month assessments.
  • We summed the number of sun protection behaviors practiced often or always and treated this as an additional outcome (assumed to have a normal distribution).
  • Each of the five sun protection behaviors were examined as binary outcomes (often or always vs. sometimes, rarely, or never) and were modelled using a logit link function.
  • All GEEs used an unstructured correlation relationship of repeated measurements.
  • Derived binary variables to indicate ever having a skin examination over the study period, separately for those conducted by a health professional or by self/partner.
    • Multivariate logistic regression to estimate ORs stratified by recall at 3 months.
    • Participants who reported having a skin exam at baseline and those who did not return their 9-month survey were excluded from these analyses, precluding inclusion of missingness predictors.
  • SAS ver. 9.4 (SAS Institute, Cary, NC, USA, RRID:SCR_008567)




  • At 9 months, participants at higher risk were three times less likely to correctly recall versus misremember (OR=0.31, 95%CI:0.10-0.95).

 Bar chart showing genetic risk recall at 3 and 9 months post-intervention




Table 1. Baseline predictors (p<0.05) of correct risk recall common across the 3- and 9-month follow-ups.



MC1R average risk





MC1R higher risk





 Table showing intervention effects for primary and secondary prevention outcome measures by genetic risk recall and MC1R risk category




  • Consistent with previous studies, participants at higher risk were less likely to correctly recall than participants at average risk.
  • Higher educational attainment was a consistent predictor of correct recall among both participants at average and higher risk
  • Future studies to further minimize literacy and numeracy demands of their intervention materials and perhaps provide additional support to individuals with lower educational attainment.
  • Lastly, findings presented here and in our previous study of recall among non-Hispanic Whites suggest that those who correctly recalled had better intervention outcomes than those who did not recall or misremembered.



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