The Use of Traditional, Complementary, and Integrative Medicine in Cancer: Data-Mining Study of 1 Million Web-Based Posts From Health Forums and Social Media Platforms.

BACKGROUND: Patients with cancer are increasingly using forums and social media platforms to access health information and share their experiences, particularly in the use of traditional, complementary, and integrative medicine (TCIM). Despite the popularity of TCIM among patients with cancer, few related studies have used data from these web-based sources to explore the use of TCIM among patients with cancer. OBJECTIVE: This study leveraged multiple forums and social media platforms to explore patients' use, interest, and perception of TCIM for cancer care. METHODS: Posts (in English) related to TCIM were collected from Facebook, Twitter, Reddit, and 16 health forums from inception until February 2022. Both manual assessments and natural language processing were performed. Descriptive analyses were performed to explore the most commonly discussed TCIM modalities for each symptom and cancer type. Sentiment analyses were performed to measure the polarity of each post or comment, and themes were identified from posts with positive and negative sentiments. TCIM modalities that are emerging or recommended in the guidelines were identified a priori. Exploratory topic-modeling analyses with latent Dirichlet allocation were conducted to investigate the patients' perceptions of these modalities. RESULTS: Among the 1,620,755 posts available, cancer-related symptoms, such as pain (10/10, 100% cancer types), anxiety and depression (9/10, 90%), and poor sleep (9/10, 90%), were commonly discussed. Cannabis was among the most frequently discussed TCIM modalities for pain in 7 (70%) out of 10 cancer types, as well as nausea and vomiting, loss of appetite, anxiety and depression, and poor sleep. A total of 7 positive and 7 negative themes were also identified. The positive themes included TCIM, making symptoms manageable, and reducing the need for medication and their side effects. The belief that TCIM and conventional treatments were not mutually exclusive and intolerance to conventional treatment may facilitate TCIM use. Conversely, TCIM was viewed as leading to patients' refusal of conventional treatment or delays in diagnosis and treatment. Doctors' ignorance regarding TCIM and the lack of information provided about TCIM may be barriers to its use. Exploratory analyses showed that TCIM recommendations were well discussed among patients; however, these modalities were also used for many other indications. Other notable topics included concerns about the legalization of cannabis, acupressure techniques, and positive experiences of meditation. CONCLUSIONS: Using machine learning techniques, social media and health forums provide a valuable resource for patient-generated data regarding the pattern of use and patients' perceptions of TCIM. Such information will help clarify patients' needs and concerns and provide directions for research on integrating TCIM into cancer care. Our results also suggest that effective communication about TCIM should be achieved and that doctors should be more open-minded to actively discuss TCIM use with their patients.

Racial Health Disparities and Black Heterogeneity in COVID-19: A Case Study of Miami Gardens.

The COVID-19 pandemic is a critical public health concern that has disproportionately affected the Black community in the United States. The purpose of this study was to examine the risk and protective factors faced by residents in the City of Miami Gardens during the COVID-19 pandemic, with emphases placed on racial health disparities and Black heterogeneity. Using convenience and snowball sampling, quantitative and qualitative data for this study were collected via an anonymous online questionnaire using QuestionPro. Survey links were distributed by e-mail invitations with assistance from city officials to the residents of this predominantly Black city in Florida (n = 83). Descriptive statistics and relevant qualitative responses are presented. Furthermore, a machine learning (ML) approach was used to select the most critical variables that characterized the two racial groups (Black versus non-Black participants) based on four ML feature selectors. Study findings offered important and interesting insights. Specifically, despite the greater prevalence of adopting measures to protect themselves and others from COVID-19, Black participants were more susceptible to activities that increased their COVID-19 risk levels. In addition, their rate of infection, particularly among the Afro-Caribbean ethnic group, was reported to be higher, indicating the need to further investigate the underlying conditions and root causes (including vaccine hesitancy and refusal) that contribute to their greater health disparities.