Background: In recent years, there has been growing concern about prejudice in crowdfunding; however, empirical research remains limited, particularly in the context of medical crowdfunding. This study addresses the pressing issue of racial disparities in medical crowdfunding, with a specific focus on cancer crowdfunding on the GoFundMe platform. Objective: This study aims to investigate racial disparities in cancer crowdfunding using average donation amount, number of donations, and success of the fundraising campaign as outcomes. Methods: Drawing from a substantial data set of 104,809 campaigns in the United States, we used DeepFace facial recognition technology to determine racial identities and used regression models to examine racial factors in crowdfunding performance. We also examined the moderating effect of the proportion of White residents on crowdfunding bias and used 2-tailed t tests to measure the influence of racial anonymity on crowdfunding success. Owing to the large sample size, we set the cutoff for significance at P<.001. Results: In the regression and supplementary analyses, the racial identity of the fundraiser significantly predicted average donations (P<.001), indicating that implicit bias may play a role in donor behavior. Gender (P=.04) and campaign description length (P=.62) did not significantly predict the average donation amounts. The race of the fundraiser was not significantly associated with the number of donations (P=.42). The success rate of cancer crowdfunding campaigns, although generally low (11.77%), showed a significant association with the race of the fundraiser (P<.001). After controlling for the covariates of the fundraiser gender, fundraiser age, local White proportion, length of campaign description, and fundraising goal, the average donation amount to White individuals was 17.68% higher than for Black individuals. Moreover, campaigns that did not disclose racial information demonstrated a marginally higher average donation amount (3.92%) than those identified as persons of color. Furthermore, the racial composition of the fundraiser’s county of residence was found to exert influence (P<.001); counties with a higher proportion of White residents exhibited reduced racial disparities in crowdfunding outcomes. Conclusions: This study contributes to a deeper understanding of racial disparities in cancer crowdfunding. It highlights the impact of racial identity, geographic context, and the potential for implicit bias in donor behavior. As web-based platforms evolve, addressing racial inequality and promoting fairness in health care financing remain critical goals. Insights from this research suggest strategies such as maintaining racial anonymity and ensuring that campaigns provide strong evidence of deservingness. Moreover, broader societal changes are necessary to eliminate the financial distress that drives individuals to seek crowdfunding support.

Background: Twitter has become a dominant source of public health data and a widely used method to investigate and understand public health–related issues internationally. By leveraging big data methodologies to mine Twitter for health-related data at the individual and community levels, scientists can use the data as a rapid and less expensive source for both epidemiological surveillance and studies on human behavior. However, limited reviews have focused on novel applications of language analyses that examine human health and behavior and the surveillance of several emerging diseases, chronic conditions, and risky behaviors. Objective: The primary focus of this scoping review was to provide a comprehensive overview of relevant studies that have used Twitter as a data source in public health research to analyze users’ tweets to identify and understand physical and mental health conditions and remotely monitor the leading causes of mortality related to emerging disease epidemics, chronic diseases, and risk behaviors. Methods: A literature search strategy following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extended guidelines for scoping reviews was used to search specific keywords on Twitter and public health on 5 databases: Web of Science, PubMed, CINAHL, PsycINFO, and Google Scholar. We reviewed the literature comprising peer-reviewed empirical research articles that included original research published in English-language journals between 2008 and 2021. Key information on Twitter data being leveraged for analyzing user language to study physical and mental health and public health surveillance was extracted. Results: A total of 38 articles that focused primarily on Twitter as a data source met the inclusion criteria for review. In total, two themes emerged from the literature: (1) language analysis to identify health threats and physical and mental health understandings about people and societies and (2) public health surveillance related to leading causes of mortality, primarily representing 3 categories (ie, respiratory infections, cardiovascular disease, and COVID-19). The findings suggest that Twitter language data can be mined to detect mental health conditions, disease surveillance, and death rates; identify heart-related content; show how health-related information is shared and discussed; and provide access to users’ opinions and feelings. Conclusions: Twitter analysis shows promise in the field of public health communication and surveillance. It may be essential to use Twitter to supplement more conventional public health surveillance approaches. Twitter can potentially fortify researchers’ ability to collect data in a timely way and improve the early identification of potential health threats. Twitter can also help identify subtle signals in language for understanding physical and mental health conditions.

Lead (Pb) is a potent neurotoxicant, but few studies have evaluated its effect on neurobehavioral measures that can be used in multiple species including humans. We investigated the effect of prenatal and childhood Pb exposure on children’s rate of forgetting using a delayed matching-to-sample (DMTS) task among children 6 to 8 years of age. Blood Pb was measured during pregnancy (second and third trimesters) and at 4 to 6 years of age. A nonlinear modified power function was used to predict the forgetting rates on the DMTS task, using separate models for prenatal and childhood Pb. Higher childhood Pb [median (interquartile range), 1.7 (1.3) (μg/dl)] was associated with a faster rate of forgetting (β = −0.05; 95% confidence interval: −0.09, −0.01). Higher maternal intelligence quotient and child’s age were significantly associated with a slower rate of forgetting. We validated a unique power function statistical approach for rates of forgetting using Pb exposure.

This study examines the associations of eating-related motivation, perceived norms, and their interaction with eating behaviors in emerging adults.Data are from the NEXT Generation Health Study, a ...