Financial toxicity of breast cancer over the last 30 years: A bibliometrics study and visualization analysis via CiteSpace

Financial Toxicity among Patients with Breast Cancer during the COVID-19 Pandemic in the United States

This study reported the prevalence of financial distress (financial toxicity (FT)) and COVID-19-related economic stress in patients with breast cancer (BC). Patients with BC were recruited from the Ciitizen platform, Breastcancer.org, and patient advocacy groups between 30 March and 6 July 2021. FT was assessed with the COmprehensive Score for financial Toxicity (COST) instrument. COVID-19-related economic stress was assessed with the COVID-19 Stress Scale. Among the 669 patients, the mean age was 51.6 years; 9.4% reported a COVID-19 diagnosis. The prevalence rates of mild and moderate/severe FT were 36.8% and 22.4%, respectively. FT was more prevalent in patients with metastatic versus early BC (p < 0.001). The factors associated with FT included income ≤ USD 49,999 (adjusted odds ratio (adj OR) 6.271, p < 0.0001) and USD 50,000-USD 149,999 (adj OR 2.722, p < 0.0001); aged <50 years (adj OR 3.061, p = 0.0012) and 50-64 years (adj OR 3.444, p = 0.0002); living alone (adj OR 1.603, p = 0.0476); and greater depression severity (adj OR 1.155, p < 0.0001). Black patients (adj OR 2.165, p = 0.0133), patients with income ≤ USD 49,999 (adj OR 1.921, p = 0.0432), or greater depression severity (adj OR 1.090, p < 0.0001) were more likely to experience COVID-19-related economic stress. FT was common in patients with BC, particularly metastatic disease, during COVID-19. Multiple factors, especially lower income and greater depression severity were associated with financial difficulties during COVID-19.

Evaluating cluster analysis techniques in ChatGPT versus R-language with visualizations of author collaborations and keyword cooccurrences on articles in the Journal of Medicine (Baltimore) 2023: Bibliometric analysis

BACKGROUND

Analyses of author collaborations and keyword co-occurrences are frequently used in bibliographic research. However, no studies have introduced a straightforward yet effective approach, such as utilizing ChatGPT with Code Interpreter (ChatGPT_CI) or the R language, for creating cluster-oriented networks. This research aims to compare cluster analysis methods in ChatGPT_CI and R, visualize country-specific author collaborations, and then demonstrate the most effective approach.

METHODS

The research focused on articles and review pieces from Medicine (Baltimore) published in 2023. By August 20, 2023, we had gathered metadata for 1976 articles using the Web of Science core collections. The efficiency and effectiveness of cluster displays between ChatGPT_CI and R were compared by evaluating their time consumption. The best method was then employed to present a series of visualizations of country-specific author collaborations, rooted in social network and cluster analyses. Visualization techniques incorporating network charts, chord diagrams, circle bar plots, circle packing plots, heat dendrograms, dendrograms, and word clouds were demonstrated. We further highlighted the research profiles of 2 prolific authors using timeline visuals.

RESULTS

The research findings include that (1) the most active contributors were China, Nanjing Medical University (China), the Medical School Department, and Dr Chou from Taiwan when considering countries, institutions, departments, and individual authors, respectively; (2) the highest cited articles originated from Medicine (Baltimore) accounting for 4.53%: New England Journal of Medicine, PLOS ONE, LANCET, and The Journal of the American Medical Association, with respective contributions of 3.25%, 2.7%, 2.52%, and 1.54%; (3) visual cluster analysis in R proved to be more efficient and effective than ChatGPT_CI, reducing the time taken from 1 hour to just 3 minutes; (4) 7 cluster-focused networks were crafted using R on a custom platform; and (5) the research trajectories of 2 prominent authors (Dr Brin from the United States and Dr Chow from Taiwan) and articles themes in Medicine 2023 were depicted using timeline visuals.

CONCLUSIONS

This research highlighted the efficient and effective methods for conducting cluster analyses of author collaborations using R. For future related studies, such as keyword co-occurrence analysis, R is recommended as a viable alternative for bibliographic research.

Light at night exposure and risk of breast cancer: a meta-analysis of observational studies

Objective

The aim of this meta-analysis is to evaluate the impact of light at night (LAN) exposure on the risk of breast cancer across varying factors.

Method

We conducted a systematic search of literature up to July 15, 2023, including PubMed, Cochrane Library, and Embase databases, using keywords related to breast cancer and LAN exposure. Cohort study and case-control study literature on night light exposure and breast cancer risk were included. Statistical analyses were performed using Stata software version 17.0. To address heterogeneity among different studies, we employed a random-effects model for analysis and assessed publication bias using funnel plots and Egger's test.

Results

We included 13 case-control and 8 cohort studies with 734,372 participants worldwide. In the Newcastle-Ottawa Scale (NOS) assessments, the average score was 7.43 (ranging from 5 to 9). The overall meta-analysis demonstrated a significant association between exposure to LAN and risk of breast cancer (RR = 1.12; 95% CI: 1.06-1.17; I2 = 31.3%, p < 0.001). In the subgroup analysis, the results of the analysis for study types (case-control studies: RR = 1.16; 95% CI: 1.06-1.27; I2 = 40.4%, p = 0.001; cohort studies: RR = 1.08; 95% CI: 1.04-1.14; I2 = 0.0%, p < 0.001) and the results for light exposure types (outdoor LAN: RR = 1.07; 95% CI: 1.02-1.13; I2 = 30.9%, p = 0.004) are presented. In the analysis conducted for continents, the highest breast cancer risk was observed in the Asian population (Asian: RR = 1.24; 95% CI: 1.15-1.34; I2 = 0.0%, p < 0.001) and in the analysis of estrogen receptor status (ER+: RR = 1.10; 95% CI: 1.03-1.18; I2 = 17.0%, p = 0.005;). We also conducted an analysis on menopausal status and various lifestyles but did not find any statistically significant findings.

Conclusion

Our study demonstrates that LAN exposure is associated with an increased risk of breast cancer, particularly in the Asian population. Among the existing hypotheses, the idea that LAN exposure leads to a decrease in melatonin is widely accepted. However, until the mechanism of this effect is clearly elucidated, it is not recommended to take melatonin supplements for breast cancer prevention without medical advice. We hope to conduct more high-quality research, especially concerning the investigation of other environmental confounding factors, to further advance this field.

Barriers and Facilitators of Adherence to Oral Anticancer Medications Among Women with Breast Cancer: A Qualitative Study

Purpose

Despite the life-saving benefits of oral anticancer medications (OAMs) to women with breast cancer (BC), adherence remains suboptimal and, in many cases, not well documented. The study examined barriers and facilitators of adherence to OAMs among women receiving BC treatment in Nigeria.

Patients and Methods

The study was framed within the World Health Organization (WHO) Multidimensional Model of Adherence. We conducted qualitative in-depth interviews of 16 purposively sampled women in two tertiary hospitals in Southern Nigeria. The interviews were audio-recorded and transcribed verbatim. The interview data were analyzed using the Framework Method.

Results

The key barriers to OAM adherence mentioned were socioeconomic factors (high cost of medication) and therapy-related factors (medication side effects). The key facilitating mechanisms for adherence to OAMs mentioned included; (i) patient-related psychosocial factors such as self-encouragement and self-discipline in sticking to the prescription, taking the medication at a particular time each day, receiving practical support from family members; and (ii) healthcare team/system factors such as obtaining an adequate supply of the medication at the pharmacy.

Conclusion

Barriers and facilitators to OAM adherence are multidimensional. The study findings highlight the potential benefit of a multifaceted intervention (such as patient education and monitoring or strategies promoting cost-containment and side effects management) to optimize adherence. Therefore, our findings may inform the designing and evaluating of context-specific adherence measures and multifaceted intervention strategies targeting key barriers and approaches that enable adherence to enhance patient outcomes.

A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric analysis

BACKGROUND

There are 3 issues in bibliometrics that need to be addressed: The lack of a clear definition for author collaborations in cluster analysis that takes into account collaborations with and without self-connections; The need to develop a simple yet effective clustering algorithm for use in coword analysis, and; The inadequacy of general bibliometrics in regard to comparing research achievements and identifying articles that are worth reading and recommended for readers. The study aimed to put forth a clustering algorithm for cluster analysis (called following leader clustering [FLCA], a follower-leading clustering algorithm), examine the dissimilarities in cluster outcomes when considering collaborations with and without self-connections in cluster analysis, and demonstrate the application of the clustering algorithm in bibliometrics.

METHODS

The study involved a search for articles and review articles published in JMIR Medical Informatics between 2016 and 2022, conducted using the Web of Science core collections. To identify author collaborations (ACs) and themes over the past 7 years, the study utilized the FLCA algorithm. With the 3 objectives of; Comparing the results obtained from scenarios with and without self-connections; Applying the FLCA algorithm in ACs and themes, and; Reporting the findings using traditional bibliometric approaches based on counts and citations, and all plots were created using R.

RESULTS

The study found a significant difference in cluster outcomes between the 2 scenarios with and without self-connections, with a 53.8% overlap (14 out of the top 20 countries in ACs). The top clusters were led by Yonsei University in South Korea, Grang Luo from the US, and model in institutes, authors, and themes over the past 7 years. The top entities with the most publications in JMIR Medical Informatics were the United States, Yonsei University in South Korea, Medical School, and Grang Luo from the US.

CONCLUSION

The FLCA algorithm proposed in this study offers researchers a comprehensive approach to exploring and comprehending the complex connections among authors or keywords. The study suggests that future research on ACs with cluster analysis should employ FLCA and R visualizations.

Analyzing author collaborations by developing a follower-leader clustering algorithm and identifying top co-authoring countries: Cluster analysis

BACKGROUND

This study aimed to explore suitable clustering algorithms for author collaborations (ACs) in bibliometrics and investigate which countries frequently coauthored with others in recent years. To achieve this, the study developed a method called the Follower-Leading Clustering Algorithm (FLCA) and used it to analyze ACs and cowords in the Journal of Medicine (Baltimore) from 2020 to 2022.

METHODS

This study extracted article metadata from the Web of Science and used the statistical software R to implement FLCA, enabling efficient and reproducible analysis of ACs and cowords in bibliometrics. To determine the countries that easily coauthored with other countries, the study observed the top 20 countries each year and visualized the results using network charts, heatmaps with dendrograms, and Venn diagrams. The study also used chord diagrams to demonstrate the use of FLCA on ACs and cowords in Medicine (Baltimore).

RESULTS

The study observed 12,793 articles, including 5081, 4418, and 3294 in 2020, 2021, and 2022, respectively. The results showed that the FLCA algorithm can accurately identify clusters in bibliometrics, and the USA, China, South Korea, Japan, and Spain were the top 5 countries that commonly coauthored with others during 2020 and 2022. Furthermore, the study identified China, Sichuan University, and diagnosis as the leading entities in countries, institutes, and keywords based on ACs and cowords, respectively. The study highlights the advantages of using cluster analysis and visual displays to analyze ACs in Medicine (Baltimore) and their potential application to coword analysis.

CONCLUSION

The proposed FLCA algorithm provides researchers with a comprehensive means to explore and understand the intricate connections between authors or keywords. Therefore, the study recommends the use of FLCA and visualizations with R for future research on ACs with cluster analysis.