Journal of Medicine, Surgery, and Public Health最新文献

{"title":"Artificial Intelligence And Cancer Care in Africa","authors":"Adewunmi Akingbola ,&nbsp;Abiodun Adegbesan ,&nbsp;Olajide Ojo ,&nbsp;Jessica Urowoli Otumara ,&nbsp;Uthman Hassan Alao","doi":"10.1016/j.glmedi.2024.100132","DOIUrl":"10.1016/j.glmedi.2024.100132","url":null,"abstract":"<div><p>AI's potential to revolutionize oncology through enhanced diagnostics, treatment planning, and patient monitoring is well-documented globally. However, in Africa, its adoption has been slower, albeit steadily progressing. This commentary explores the integration of artificial Intelligence in cancer care across Africa, assessing its current state, challenges and future directions. It highlights significant AI innovations in cancer diagnostics, such as DataPathology, PapsAI, MinoHealth, and Hurone AI, which utilize AI for tissue analysis, cervical cell imaging, disease forecasting, and remote patient monitoring. Despite these advancements, several challenges impede AI's full integration into African healthcare systems. Key issues include data privacy and security, algorithm bias, and insufficient regulatory frameworks. The review emphasizes the necessity of robust data protection policies, representative datasets to mitigate biases, and clear guidelines for AI deployment tailored to the African context. Emerging AI technologies in Africa, such as AI-enhanced telemedicine, mobile health applications, predictive analytics, and virtual tumor boards, show promise in overcoming geographic and resource limitations. These innovations can facilitate remote consultations, continuous patient monitoring, and multidisciplinary collaborations, thereby improving cancer care accessibility and outcomes. Conclusively, recommendations for enhancing AI integration in African cancer care, including investing in data infrastructure, capacity building for healthcare professionals, and fostering international collaborations are discussed. Addressing ethical and regulatory challenges is crucial to ensure responsible and effective use of AI technologies. By leveraging AI, Africa can significantly improve cancer care delivery, reduce mortality rates, and enhance patient quality of life.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100132"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000859/pdfft?md5=7dfc5fec196e71461d03d515f44efe55&pid=1-s2.0-S2949916X24000859-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141990684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The rising threat of counterfeit GLP-1 receptor agonists: Implications for public health","authors":"Abdur Rehman,&nbsp;Abdulqadir J. Nashwan","doi":"10.1016/j.glmedi.2024.100136","DOIUrl":"10.1016/j.glmedi.2024.100136","url":null,"abstract":"<div><p>The rising demand for GLP-1 receptor agonists (GLP-1RAs), effective treatments for type 2 diabetes and obesity, has inadvertently led to a proliferation of counterfeit versions. This letter to the editor highlights the significant public health challenges posed by counterfeit GLP-1RAs, including severe risks to patient safety, economic impacts, and the erosion of public trust in the healthcare system. Counterfeit GLP-1RAs often contain incorrect dosages, harmful ingredients, or entirely lack the active ingredients, leading to ineffective treatment and potentially life-threatening complications such as hyperglycemia and cardiovascular issues. The economic burden of counterfeit drugs is also considerable, with healthcare systems incurring substantial costs in managing complications from these illegitimate medications, including hospitalizations and increased surveillance efforts. The drivers of this counterfeit drug problem include regulatory gaps, inadequate enforcement, and the expanding market demand due to rising rates of diabetes and obesity. In conclusion, the proliferation of counterfeit GLP-1RAs represents a critical threat to global health, underscoring the need for comprehensive measures to safeguard the integrity of the pharmaceutical supply chain and ensure patient safety. Addressing this issue requires a multifaceted approach that integrates regulatory oversight, technological innovation, and public education to mitigate the risks posed by counterfeit drugs and restore public trust in the healthcare system.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000896/pdfft?md5=0be073421461f3d89291c6db187dd7ad&pid=1-s2.0-S2949916X24000896-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Scalpels to Algorithms: The Risk of Dependence on Artificial Intelligence in Surgery","authors":"Abiodun Adegbesan,&nbsp;Adewunmi Akingbola,&nbsp;Olusola Aremu,&nbsp;Olajumoke Adewole,&nbsp;John Chukwuemeka Amamdikwa,&nbsp;Uchechukwu Shagaya","doi":"10.1016/j.glmedi.2024.100140","DOIUrl":"10.1016/j.glmedi.2024.100140","url":null,"abstract":"<div><div>Artificial Intelligence (AI) is transforming surgery, advancing robotic-assisted procedures, preoperative risk prediction, and intraoperative decision-making. However, increasing reliance on AI raises concerns, particularly regarding the potential deskilling of surgeons and overdependence on algorithmic recommendations. This over-reliance risks diminishing surgeons' skills, increasing surgical errors, and undermining their decision-making autonomy. The \"black-box\" nature of many AI systems also presents ethical challenges, as surgeons may feel pressured to follow AI-driven recommendations without fully understanding the underlying logic. Additionally, AI biases from inadequate datasets can result in misdiagnoses and worsen healthcare disparities. While AI offers immense promise, a cautious approach is vital to prevent overdependence. Ensuring that AI enhances rather than replaces human skills in surgery is critical to maintaining patient safety. Ongoing research, ethical considerations, and robust legal frameworks are essential for guiding AI's integration into surgical practice. Surgeons must receive comprehensive training to incorporate AI into their work without compromising clinical judgment. This letter emphasizes the need for clear guidelines, thorough surgeon training, and transparent AI systems to address the risks associated with AI dependence. By taking these steps, healthcare systems can harness the benefits of AI while preserving the essential human aspects of surgical care.</div></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence for Pediatric Emergency Medicine","authors":"Mohammed Alsabri ,&nbsp;Nicholas Aderinto ,&nbsp;Marina Ramzy Mourid ,&nbsp;Fatima Laique ,&nbsp;Salina Zhang ,&nbsp;Noha S. Shaban ,&nbsp;Abdalhakim Shubietah ,&nbsp;Luis L. Gamboa","doi":"10.1016/j.glmedi.2024.100137","DOIUrl":"10.1016/j.glmedi.2024.100137","url":null,"abstract":"<div><p>Pediatric Emergency Medicine (PEM) addresses the unique needs of children in emergencies. This subspecialty faces significant challenges, including the need for specialized training, patient crowding, and the demand for timely and accurate management. Artificial Intelligence (AI) presents promising solutions by enhancing diagnostic precision and operational efficiency. This review examines current trends and prospects of AI in PEM, focusing on its applications, benefits, challenges, and transformative potential. The review highlights AI’s role in overcoming PEM challenges and its future opportunities. Key AI applications in PEM include early sepsis detection, improving triage accuracy, predicting injuries, and supporting diagnostics. AI models show significant potential in forecasting clinical outcomes, optimizing resource management, and improving patient care. Despite these benefits, challenges remain, including the need for specialized training for physicians and the integration of AI systems into clinical practice. Yet, AI holds considerable promise for advancing PEM through enhanced diagnostic tools, more efficient patient management, and improved clinical decision support. Continued advancements and collaborations between AI researchers and pediatric emergency practitioners are essential to fully realize AI’s potential in this field.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000902/pdfft?md5=c25608f5f3bd62a26321340e3e2b4894&pid=1-s2.0-S2949916X24000902-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The promise of artificial intelligence and internet of things in oral cancer detection","authors":"Amol S. Dhane","doi":"10.1016/j.glmedi.2024.100130","DOIUrl":"10.1016/j.glmedi.2024.100130","url":null,"abstract":"<div><p>The significance of artificial intelligence (AI) and the internet of things (IoT) in improving oral cancer detection is critically assessed in this letter. Oral cancer is a major worldwide health concern that is frequently detected at a late stage, resulting in a poor prognosis. AI techniques, in particular machine learning and deep learning models, show great promise for accurately assessing digital images and histopathology slides, assisting physicians in risk assessment and early identification. Furthermore, real-time monitoring and surveillance are made possible by IoT-enabled devices, which gather important patient data for the early identification of indications of oral cancer. Furthermore, the performance and efficacy of diagnosis have been improved by developments in image processing algorithms, which helps to avoid delayed diagnosis. Big data analytics and the application of salivary biomarkers enhance early detection initiatives. To battle oral cancer, a variety of AI and IoT strategies are being investigated, in addition to other AI uses. Although encouraging developments, application in clinical practice will not be successful unless issues with validation, standardization, data privacy and regulatory compliance are resolved. Working together, healthcare stakeholders can promote innovation, validate techniques and get over current obstacles. To reduce the prevalence of oral cancer, future directions include the creation of multimodal imaging methods and their incorporation into population-based screening initiatives. We can move closer to early detection, individualized therapy and prevention of oral cancer by utilizing AI and IoT, which will ultimately improve patient outcomes.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100130"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000835/pdfft?md5=a566ff140095815c83fe54567839c4e1&pid=1-s2.0-S2949916X24000835-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141963315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of artificial intelligence in enhancing nurses' work-life balance","authors":"Moustaq Karim Khan Rony ,&nbsp;Daifallah M. Alrazeeni ,&nbsp;Fazila Akter ,&nbsp;Latifun Nesa ,&nbsp;Dipak Chandra Das ,&nbsp;Muhammad Join Uddin ,&nbsp;Jeni Begum ,&nbsp;Most. Tahmina Khatun ,&nbsp;Md. Abdun Noor ,&nbsp;Sumon Ahmad ,&nbsp;Sabren Mukta Tanha ,&nbsp;Tuli Rani Deb ,&nbsp;Mst. Rina Parvin","doi":"10.1016/j.glmedi.2024.100135","DOIUrl":"10.1016/j.glmedi.2024.100135","url":null,"abstract":"<div><p>Nursing, a cornerstone of healthcare, is a profession characterized by its dedication to patient well-being. However, the demanding nature of nursing often takes a toll on work-life balance. This commentary investigates how artificial intelligence (AI) could significantly impact the healthcare sector, particularly by enhancing the work-life balance of nurses. It highlights how AI can greatly lessen administrative tasks, improve clinical decision-making, and support remote patient monitoring, ultimately helping nurses achieve a more balanced work-life dynamic. The advancement of AI in healthcare presents a strong opportunity to improve nurses' work-life balance. Our comprehensive conceptual framework illustrates how AI can transform nursing practice, offering nurses newfound efficiency and flexibility. By responsibly integrating AI technologies, healthcare institutions can empower nurses to excel in their roles while enjoying a more sustainable work-life equilibrium. This commentary serves as a roadmap for embracing the potential of AI, not as a replacement for nurses, but as a valuable ally in fostering a better future for both nurses and the patients they serve.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000884/pdfft?md5=8a9d33afadf21b22bcaef0ba4a53449d&pid=1-s2.0-S2949916X24000884-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142097896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for hearing loss prevention, diagnosis, and management","authors":"Jehad Feras AlSamhori ,&nbsp;Abdel Rahman Feras AlSamhori ,&nbsp;Rama Mezyad Amourah ,&nbsp;Yara AlQadi ,&nbsp;Zina Wael Koro ,&nbsp;Toleen Ramzi Abdallah Haddad ,&nbsp;Ahmad Feras AlSamhori ,&nbsp;Diala Kakish ,&nbsp;Maya Jamal Kawwa ,&nbsp;Margaret Zuriekat ,&nbsp;Abdulqadir J. Nashwan","doi":"10.1016/j.glmedi.2024.100133","DOIUrl":"10.1016/j.glmedi.2024.100133","url":null,"abstract":"<div><p>This paper explores the transformative impact of artificial intelligence (AI), particularly machine learning (ML), on diagnosing and treating hearing loss, which affects over 5% of the global population across all ages and demographics. AI encompasses various applications, from natural language processing models like ChatGPT to image recognition systems; however, this paper focuses on ML, a subfield of AI that can revolutionize audiology by enhancing early detection, formulating personalized rehabilitation plans, and integrating electronic health records for streamlined patient care. The integration of ML into audiometry, termed \"computational audiology,\" allows for automated, accurate hearing tests. AI algorithms can process vast data sets, provide detailed audiograms, and facilitate early detection of hearing impairments. Research shows ML's effectiveness in classifying audiograms, conducting automated audiometry, and predicting hearing loss based on noise exposure and genetics. These advancements suggest that AI can make audiological diagnostics and treatment more accessible and efficient. The future of audiology lies in the seamless integration of AI technologies. Collaborative efforts between audiologists, AI experts, and individuals with hearing loss are essential to overcome challenges and leverage AI's full potential. Continued research and development will enhance AI applications in audiology, improving patient outcomes and quality of life worldwide.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100133"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000860/pdfft?md5=0ce34d0abea03ea27c334d59f1f1c016&pid=1-s2.0-S2949916X24000860-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards equitable renal care: Strategies for enhancing kidney transplantation in Africa","authors":"Ikponmwosa Jude Ogieuhi,&nbsp;Nicholas Aderinto,&nbsp;Gbolahan Olatunji,&nbsp;Emmanuel Kokori,&nbsp;Adetola Emmanuel Babalola,&nbsp;Komolafe Babajide Ayodeji,&nbsp;Ajekiigbe Victor Oluwatomiwa,&nbsp;Muhammadul-Awwal Irodatullah Bisola,&nbsp;Ibukunoluwa V. Ishola,&nbsp;Ojabo Rebecca,&nbsp;Irene Ojapah","doi":"10.1016/j.glmedi.2024.100131","DOIUrl":"10.1016/j.glmedi.2024.100131","url":null,"abstract":"<div><p>Chronic kidney disease (CKD) is defined as the presence of kidney damage persisting for 3 months or more. Kidney transplantation stands as a vital intervention for individuals grappling with end-stage renal disease (ESRD) in Africa, offering the promise of extended life and improved quality of life. However, numerous challenges hinder its widespread implementation across the continent. This paper explored kidney transplantation in Africa, aiming to illuminate key strategies for bridging gaps and building pathways to enhanced renal care. There is a disproportionate burden of CKD on the region's population. Therefore, there is a critical need for early diagnosis and intervention. This paper outlines comprehensive strategies for improving kidney transplantation in Africa. Results indicate that financial support systems, infrastructure enhancement, public awareness campaigns, and legal frameworks are essential for addressing renal care barriers. Active measures such as government subsidy programs, international funding collaboration, and engagement with community leaders are highlighted as effective approaches. Drawing from global standards and best practices, the paper shows the importance of tailored approaches that address Africa's unique socio-economic and healthcare landscape. By leveraging collaborative efforts, regulatory frameworks, and public engagement, African nations can overcome barriers to kidney transplantation and pave the way for equitable access to life-saving treatment.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100131"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000847/pdfft?md5=29f2b5f3a952f9a293fd2fcb82443306&pid=1-s2.0-S2949916X24000847-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141997211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sex differences in post-COVID ageusia/anosmia in the United States","authors":"R. Constance Wiener ,&nbsp;Christopher Waters ,&nbsp;Ruchi Bhandari","doi":"10.1016/j.glmedi.2024.100129","DOIUrl":"10.1016/j.glmedi.2024.100129","url":null,"abstract":"<div><p>Post-COVID Conditions (PCC) involve persistent symptoms associated with COVID-19 that continue beyond four weeks of initial infection. Sex has been shown to be related with COVID-19 severity and symptoms. The purpose of this study is to assess the loss of taste (ageusia) or smell (anosmia) among U.S. residents who had PCC and examine its specific association with sex. The data source for this cross-sectional study was 2022 Behavioral Risk Factor Surveillance System (BRFSS), a U.S. national dataset. Participants were included if they had COVID-19, reported experiencing PCC, and identified their primary PCC symptom. Overall, 23,824 participants were included in the study. In multivariable logistic regression analysis, adjusted odds ratio for males compared to females was 1.18 (95 % CI: 1.03–1.35; p=0.0165). Participants who were aged &lt;50 years as compared with those who were aged ≥50 years, non-Hispanic white as compared with non-Hispanic black, had BMI ≤ 25 as compared with BMI ≥ 30, had no reported chronic condition as compared with those who did report a chronic condition, and had ≤high school education as compared with those who had &gt; high school education had higher odds of reporting PCC-related ageusia/anosmia in this multivariable model. Among people with PCC, males had an 18 % higher odds of reporting PCC-related ageusia/anosmia as their primary symptom of PCC compared to females. Findings from our study can help identify patients affected by PCC-related ageusia/anosmia who would benefit from early referral for supportive care, such as counseling or interventions that can alleviate this dysfunction.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000823/pdfft?md5=b0768d1497eb93973692c546ef1bbeef&pid=1-s2.0-S2949916X24000823-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anxiety and depression among college students in the post-COVID-19 phase","authors":"Gillian Gottlieb ,&nbsp;Corrin Sullivan ,&nbsp;Dale Netski ,&nbsp;Kavita Batra","doi":"10.1016/j.glmedi.2024.100128","DOIUrl":"10.1016/j.glmedi.2024.100128","url":null,"abstract":"<div><p>Stress is prevalent in the lives of college students, which may manifest into anxiety and depression, especially after life-altering events, such as the COVID-19 pandemic. The goal of this study was to assess the post-pandemic presence and severities of anxiety and depression among the current population of college students at a minority-serving institution using a psychometrically valid 37-item questionnaire. The Generalized Anxiety Disorder 7 Scale (GAD-7) and the Center for Epidemiological Studies Depression Scale (CES-D) were used to assess anxiety and depression, respectively. Univariate and bivariate statistical tests were utilized to analyze the data. A total of 41 students completed the survey, of which 29 (70.8 %) demonstrated minimal to mild anxiety and 12 (29.2 %) demonstrated moderate to severe anxiety. Among respondents, 26 (63.4 %) demonstrated depressive symptoms, and 15 (36.6 %) did not demonstrate any depressive symptoms. There were significantly higher anxiety scores among undergraduate students (p = 0.013) and those who have encountered barriers to identifying mental health resources (p = 0.03). In addition, marginally significant anxiety scores were found among students who have used mental health resources (p = 0.05). There were also significantly higher depression scores among undergraduate students (p = 0.005), those who have encountered barriers to identifying mental health resources (p = 0.02), and 18–22-year-olds (p = 0.01). As time has progressed since the height of the COVID-19 pandemic, further research is needed to discern whether anxiety and depression symptoms have improved or worsened in college students.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000811/pdfft?md5=648c13a4166191fd80da6e0574bf5cee&pid=1-s2.0-S2949916X24000811-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141712904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}