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Review Special Issue Series: Recent Advances in Epidemiology & Public...
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Review Special Issue Series: Recent Advances in Epidemiology & Public Health

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Epidemiology & Public Health".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1780

Special Issue Editors

Prof. Dr. Francisco Guillen-Grima

E-Mail Website
Guest Editor
1. Department of Preventive Medicine, Clinica Universidad de Navarra, University of Navarra, 31008 Pamplona, Spain
2. Navarra Medical Research Institute (IdiSNA), 31008 Pamplona, Spain
3. Center for Biomedical Research Network Epidemiology and Public Health, (CIBERESP), 28029 Madrid, Spain
4. Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain
Interests: epidemiology; preventive medicine; public health; methodology
Prof. Dr. María M. Morales Suárez-Varela

E-Mail Website
Guest Editor
1. Department of Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine, Universitat de Valencia, 46100 Burjassot, Spain
2. Center for Biomedical Research Network Epidemiology and Public Health, (CIBERESP), 28029 Madrid, Spain
Interests: epidemiology; public health; preventive medicine; cancer; nutrition; maternal and fetal health
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Agustín Llopis-González

E-Mail Website
Guest Editor
1. Department of Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine, Universitat de Valencia, 46100 Burjassot, Spain
2. Center for Biomedical Research Network Epidemiology and Public Health, (CIBERESP), 28029 Madrid, Spain
Interests: epidemiology; public health; preventive medicine; cancer; nutrition; maternal and fetal health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In a time where Clinical Epidemiology & Public Health are seeing critical developments, our understanding of diseases, prevention, and clinical care is expanding. This Review Special Issue Series: "Recent Advances in Clinical Epidemiology & Public Health" is committed to presenting an exclusive collection of comprehensive reviews. This Special Issue will primarily focus on clinical aspects, accepting only submissions pertaining to clinical Epidemiology and Public Health.

The Special Issue will concentrate on important advancements in infectious and chronic disease epidemiology that directly pertain to clinical practice. It will explore new methodologies, surveillance techniques, and preventive strategies that have clinical applications. Specific areas of interest include:

- Disease Prevention: Understanding and implementing measures for the prevention of various diseases.

- Epidemiology of Chronic or Acute Diseases: Exploration of the occurrence, distribution, and control of chronic and acute diseases in clinical settings.

- Hospital Infection Control: Examination of strategies and best practices to prevent and control infections within hospital environments.

- Etiology and Risk Factors of Diseases: Analysis of the cause and factors contributing to diseases to inform clinical intervention and prevention.

- Hospital Mortality: Investigation of mortality rates and related factors within hospital settings.

- Application of Artificial Intelligence to Clinical Epidemiology and Public Health: Utilization of AI technologies in enhancing clinical epidemiology research and public health initiatives.

- Clinical Aspects of Aging: Exploration of aging processes, age-related diseases, and preventive interventions within clinical contexts.

- Clinical Insights into Women's Health: Specific examination of health concerns related to women, including reproductive epidemiology, maternal health, and gender-specific clinical care.

- Clinical Nutrition Insights: Examination of dietary patterns and the impact of nutrition on clinical health and well-being.

- Physical Activity in Clinical Practice: Investigation of the role of physical activity in disease prevention, mental health, and well-being within clinical settings.

By focusing exclusively on clinical aspects, this Special Issue aims to provide a nuanced insight into the complexities and dynamics of Clinical Epidemiology & Public Health. We aim to promote dialogue, collaboration, and innovation among clinicians, researchers, and policymakers through critical examination and thoughtful synthesis.

Prof. Dr. Francisco Guillen-Grima
Prof. Dr. María M. Morales Suárez-Varela
Prof. Dr. Agustín Llopis-González
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • clinical epidemiology
  • public health
  • infectious diseases
  • chronic diseases
  • disease prevention
  • hospital infection control
  • etiology and risk factors of diseases
  • hospital mortality
  • application of artificial intelligence
  • clinical aspects of aging
  • women's health
  • clinical nutrition
  • physical activity in clinical practice

Published Papers (3 papers)

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Research

11 pages, 271 KiB  
Article
COVID-19 Posttraumatic Stress Disorder and Mental Health among Spanish Adolescents: SESSAMO Project
by Nora Yárnoz-Goñi, Adriana Goñi-Sarriés, Azucena Díez-Suárez, Guillermo Pírez, Leticia Morata-Sampaio and Almudena Sánchez-Villegas
J. Clin. Med. 2024, 13(11), 3114; https://0-doi-org.brum.beds.ac.uk/10.3390/jcm13113114 - 26 May 2024
Viewed by 260
Abstract
Background: Child and adolescent mental health problems have increased after the COVID-19 pandemic. The objective of this study was to establish the association of the presence and intensity of posttraumatic stress due to COVID-19 with the presence of (1) self-harm and suicide [...] Read more.
Background: Child and adolescent mental health problems have increased after the COVID-19 pandemic. The objective of this study was to establish the association of the presence and intensity of posttraumatic stress due to COVID-19 with the presence of (1) self-harm and suicide risk, (2) depressive and anxious symptoms, (3) eating disorders and (4) problematic Internet and video game use. Methods: A cross-sectional analysis was performed on a sample of second–fourth grade secondary school students (14 to 16 years old) from Navarra and the Canary Islands recruited at the SESSAMO project. Validated questionnaires were used to assess the intensity of posttraumatic stress due to COVID-19, risk of suicide and presence of self-harm, symptoms of mental disorder and problematic use of the Internet and video games. Results: Out of 1423 participants analyzed, those with the highest level of posttraumatic stress showed a significant increase in the risk of suicide (OR = 5.18; 95% CI = 2.96–9.05) and in the presence of eating disorder symptoms (OR = 3.93; 95% CI = 2.21–7.00), and higher anxiety and depression scores (b coefficient for anxiety = 11.1; CI = 9.7–12.5; for depression = 13.0; CI = 11.5–14.5) as compared to those with the lowest level. Participants with a high level of posttraumatic stress were almost 10 times more likely to present problematic video game use (OR = 9.49; 95% CI = 3.13–28.82). Conclusions: Years after the pandemic, posttraumatic stress derived from it continues to impact the mental health of adolescents. Further long-term research is needed, as well as close follow-up and intervention in this population. Full article
(This article belongs to the Special Issue Review Special Issue Series: Recent Advances in Epidemiology & Public Health)
11 pages, 876 KiB  
Article
Musculoskeletal Diseases as the Most Prevalent Component of Multimorbidity: A Population-Based Study
by Nina Rajovic, Slavisa Zagorac, Andja Cirkovic, Bojana Matejic, Danilo Jeremic, Radica Tasic, Jelena Cumic, Srdjan Masic, Jovana Grupkovic, Vekoslav Mitrovic, Natasa Milic and Boris Gluscevic
J. Clin. Med. 2024, 13(11), 3089; https://0-doi-org.brum.beds.ac.uk/10.3390/jcm13113089 - 24 May 2024
Viewed by 313
Abstract
Background/Objectives: Due to their high frequency, common risk factors, and similar pathogenic mechanisms, musculoskeletal disorders (MSDs) are more likely to occur with other chronic illnesses, making them a “component disorder“ of multimorbidity. Our objective was to assess the prevalence of multimorbidity and [...] Read more.
Background/Objectives: Due to their high frequency, common risk factors, and similar pathogenic mechanisms, musculoskeletal disorders (MSDs) are more likely to occur with other chronic illnesses, making them a “component disorder“ of multimorbidity. Our objective was to assess the prevalence of multimorbidity and to identify the most common clusters of diagnosis within multimorbidity states, with the primary hypothesis that the most common clusters of multimorbidity are MSDs. Methods: The current study employed data from a population-based 2019 European Health Interview Survey (EHIS). Multimorbidity was defined as a ≥2 diagnosis from the list of 17 chronic non-communicable diseases, and to define clusters, the statistical method of hierarchical cluster analysis (HCA) was performed. Results: Out of 13,178 respondents, multimorbidity was present among 4398 (33.4%). The HCA method yielded six multimorbidity clusters representing the most common diagnoses. The primary multimorbidity cluster, which was prevalent among both genders, age groups, incomes per capita, and statistical regions, consisted of three diagnoses: (1) lower spine deformity or other chronic back problem (back pain), (2) cervical deformity or other chronic problem with the cervical spine, and (3) osteoarthritis. Conclusions: Given the influence of musculoskeletal disorders on multimorbidity, it is imperative to implement appropriate measures to assist patients in relieving the physical discomfort and pain they endure. Public health information, programs, and campaigns should be utilized to promote a healthy lifestyle. Policymakers should prioritize the prevention of MSDs by encouraging increased physical activity and a healthy diet, as well as focusing on improving functional abilities. Full article
(This article belongs to the Special Issue Review Special Issue Series: Recent Advances in Epidemiology & Public Health)
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29 pages, 3745 KiB  
Article
Infodemiology of Influenza-like Illness: Utilizing Google Trends’ Big Data for Epidemic Surveillance
by Dong-Her Shih, Yi-Huei Wu, Ting-Wei Wu, Shu-Chi Chang and Ming-Hung Shih
J. Clin. Med. 2024, 13(7), 1946; https://0-doi-org.brum.beds.ac.uk/10.3390/jcm13071946 - 27 Mar 2024
Viewed by 656
Abstract
Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during [...] Read more.
Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for “fever” and “cough” were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences. Full article
(This article belongs to the Special Issue Review Special Issue Series: Recent Advances in Epidemiology & Public Health)
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