Background: Studies of noncutaneous and cutaneous malignancies support the hypothesis that poor risk-perception status contributes to health disparity.
Objective: We evaluated skin cancer (SC) risk perceptions across race and other demographic markers using the Health Information National Trends Survey (HINTS) and compared them to discover differences in perception that may contribute to the disparities in SC diagnosis and treatment.
Methods: Respondents with no history of SC were randomly selected to answer questions assessing perceived risk and knowledge of preventive strategies of SC. Logistic regression was performed to identify associations between perceptions of SC and demographic variables including self-described race, age, sex, education, income, and health insurance status.
Results: Blacks, the elderly, and people with less education perceived themselves as at lower risk of developing SC. They, along with Hispanics, were also more likely to believe that one cannot lower their SC risk and that there are so many different recommendations on how to prevent SC that it makes it difficult to know which ones to follow. Lower education also correlated with greater reluctance to have a skin examination.
Limitations: HINTS is a cross-sectional instrument, thus it only provides a snapshot of SC perceptions.
Conclusion: Uncertainty and altered perceptions are more common in the SC risk perceptions of ethnic minorities, the elderly, and those with less education. These are the same groups that are subject to disparities in SC outcomes. Educational programs directed at these demographic groups may help to reduce the SC-related health disparities.
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http://dx.doi.org/10.1016/j.jaad.2011.05.021 | DOI Listing |
JMIR Form Res
December 2024
Pharmacy Department, Gold Coast Hospital and Health Service, Southport, Australia.
Background: Artificial intelligence (AI) has the potential to address growing logistical and economic pressures on the health care system by reducing risk, increasing productivity, and improving patient safety; however, implementing digital health technologies can be disruptive. Workforce perception is a powerful indicator of technology use and acceptance, however, there is little research available on the perceptions of allied health professionals (AHPs) toward AI in health care.
Objective: This study aimed to explore AHP perceptions of AI and the opportunities and challenges for its use in health care delivery.
Math Biosci
January 2025
Biocomplexity Institute, University of Virginia, VA, USA; Department of Computer Science, University of Virginia, VA, USA.
Public health interventions reduce infection risk, while imposing significant costs on both individuals and the society. Interventions can also lead to behavioral changes, as individuals weigh the cost and benefits of avoiding infection. Aggregate epidemiological models typically focus on the population-level consequences of interventions, often not incorporating the mechanisms driving behavioral adaptations associated with interventions compliance.
View Article and Find Full Text PDFPLoS One
January 2025
School of Economics & Management, Beijing Information Science & Technology University, Beijing, China.
E-commerce faces challenges such as content homogenization and high perceived risk among users. This paper aims to predict perceived risk in different contexts by analyzing review content and website information. Based on a dataset containing 262,752 online reviews, we employ the KeyBERT-TextCNN model to extract thematic features from the review content.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
The Jackson Laboratory, Bar Harbor, ME, USA.
Background: Alzheimer's disease (AD) and AD-related dementias (ADRD) are modulated by gene-environment (GxE) interactions across the lifespan. Variants of specific genes increase AD risk and synergize with lifetime exposure to environmental toxicants ("exposome"), including neurotoxic metals (lead, Pb; cadmium, Cd) and metalloid (As). These metal/metalloid toxicants readily enter the body (e.
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