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Emerging Role of Bioinformatics Tools and Software in Evolution of...
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Emerging Role of Bioinformatics Tools and Software in Evolution of Epidemiology

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Epidemiology".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 13495

Special Issue Editors

Dr. Giampiero Mazzaglia

E-Mail Website
Guest Editor
Research Centre on Public Health (CESP), University of Milan-Bicocca, 20900 Monza, Italy
Interests: public health; health service research; drug/vaccine safety; comparative effectiveness; surveillance; pharmacoepidemiology; network databases; risk assessment; risk management
Special Issues, Collections and Topics in MDPI journals
Dr. Rosa Gini

E-Mail Website
Guest Editor
Agenzia regionale di sanità della Toscana, Osservatorio di epidemiologia, Florence, Italy
Interests: drug safety; primary care; chronic disease management; databases; health services research

Special Issue Information

Dear Colleagues,

The increasing availability of health-related information originating from disparate electronic sources (including social media) has resulted in a growing need of new methods and tools for data analysis and synthesis. Additionally, advances in electronic reporting and interoperability of claims or clinical databases across different healthcare systems have put pressure on the integration of informatics, statistics, epidemiology, and public health disciplines to translate these advances into reliable scientific evidence. This pressure has been particularly active for surveillance systems, including those associated with infectious disease control and vaccine/drug safety monitoring. This Special Issue of the Journal of Personalized Medicine aims to highlight the current state of the science and showcase some of the latest example in the field of multiple database exploitation for public health purposes. Studies may also include those that explore the impact of the COVID-19 pandemic on healthcare services and health-related events using clinical and population-based approach.

Prof. Giampiero Mazzaglia
Dr. Rosa Gini
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 Personalized Medicine is an international peer-reviewed open access monthly 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

  • Multi-Database studies
  • Real-World data
  • Observational studies
  • Drug/vaccine surveillance
  • COVID-19
  • Healthcare services
  • Public health

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Published Papers (4 papers)

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Research

10 pages, 1163 KiB  
Article
Antidepressants Drug Use during COVID-19 Waves in the Tuscan General Population: An Interrupted Time-Series Analysis
by Ippazio Cosimo Antonazzo, Carla Fornari, Sandy Maumus-Robert, Eleonora Cei, Olga Paoletti, Pietro Ferrara, Sara Conti, Paolo Angelo Cortesi, Lorenzo Giovanni Mantovani, Rosa Gini and Giampiero Mazzaglia
J. Pers. Med. 2022, 12(2), 178; https://doi.org/10.3390/jpm12020178 - 28 Jan 2022
Cited by 10 | Viewed by 2725
Abstract
In Italy, during the COVID-19 waves two lockdowns were implemented to prevent virus diffusion in the general population. Data on antidepressant (AD) use in these periods are still scarce. This study aimed at exploring the impact of COVID-19 lockdowns on prevalence and incidence [...] Read more.
In Italy, during the COVID-19 waves two lockdowns were implemented to prevent virus diffusion in the general population. Data on antidepressant (AD) use in these periods are still scarce. This study aimed at exploring the impact of COVID-19 lockdowns on prevalence and incidence of antidepressant drug use in the general population. A population-based study using the healthcare administrative database of Tuscany was performed. We selected a dynamic cohort of subjects with at least one ADs dispensing from 1 January 2018 to 27 December 2020. The weekly prevalence and incidence of drug use were estimated across different segments: pre-lockdown (1 January 2018–8 March 2020), first lockdown (9 March 2020–15 June 2020), post-first lockdown (16 June 2020–15 November 2020) and second lockdown (16 November 2020–27 December 2020). An interrupted time-series analysis was used to assess the effect of lockdowns on the observed outcomes. Compared to the pre-lockdown we observed an abrupt reduction of ADs incidence (Incidence-Ratio: 0.82; 95% Confidence-Intervals: 0.74–0.91) and a slight weekly decrease of prevalence (Prevalence-Ratio: 0.997; 0.996–0.999). During the post-first lockdown AD use increased, with higher incidence- and similar prevalence values compared with those expected in the absence of the outbreak. This pandemic has impacted AD drug use in the general population with potential rebound effects during the period between waves. This calls for future studies aimed at exploring the mid–long term effects of this phenomenon. Full article
(This article belongs to the Special Issue Emerging Role of Bioinformatics Tools and Software in Evolution of Epidemiology)
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13 pages, 2119 KiB  
Article
Colchicine Use and Risks of Stroke Recurrence in Acute Non-Cardiogenic Ischemic Stroke Patients: A Population-Based Cohort Study
by Chi-Hung Liu, Yu-Sheng Lin, Pi-Shan Sung, Yi-Chia Wei, Ting-Yu Chang, Tsong-Hai Lee, Ching-Yu Lee and Yan-Rong Li
J. Pers. Med. 2021, 11(9), 935; https://doi.org/10.3390/jpm11090935 - 19 Sep 2021
Cited by 5 | Viewed by 2720
Abstract
Background: The objective is to study whether the cardiovascular protective effects of colchicines could be applied to non-cardiogenic ischemic stroke (IS) patients. Patients and Methods: Non-cardiogenic IS patients were identified from the National Health Insurance Research Database. Eligible patients were divided into chronic [...] Read more.
Background: The objective is to study whether the cardiovascular protective effects of colchicines could be applied to non-cardiogenic ischemic stroke (IS) patients. Patients and Methods: Non-cardiogenic IS patients were identified from the National Health Insurance Research Database. Eligible patients were divided into chronic and non-chronic use categories based on their long-term status of colchicine use. The non-chronic use category was subdivided into (1) non-user and (2) new user groups while the chronic use category was divided into (3) former user and (4) long-term user groups according to the patient’s recent status of colchicine use. Inverse probability of treatment weights for propensity scores was used to balance the baseline characteristics. The primary outcome was recurrent IS, which was compared within the non-chronic use and chronic use categories. Results: In the non-chronic use category, the number of patients was 355,498 and 912 in the non-user and new user groups, respectively. In the chronic use category, the number of patients was 4737 and 4354 in the former user and long-term user groups, respectively. In the non-chronic use category, patients in the new user group had a marginally lower risk of recurrent IS at 6-months (subdistribution hazard ratio [SHR], 0.95; 95% confidence interval [CI], 0.94–0.97) and 2-years (SHR, 0.92; 95% CI, 0.91–0.93) follow up. In the chronic use category, patients in the long-term user group also had a marginally lower risk of recurrent IS at 6-months (SHR, 0.87; 95% CI, 0.86–0.88) and 2-years (SHR, 0.87; 95% CI, 0.86–0.88) follow up. The effect of colchicine on the reduced risk of recurrent IS was more favorable in patients who also used statins. Conclusions: Recent colchicine use in acute non-cardiogenic IS patients is associated with marginal fewer incidences of recurrent IS. Patients with concurrent statin use may have more profound protective effects. Full article
(This article belongs to the Special Issue Emerging Role of Bioinformatics Tools and Software in Evolution of Epidemiology)
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18 pages, 1489 KiB  
Article
Deep Learning Algorithm for Management of Diabetes Mellitus via Electrocardiogram-Based Glycated Hemoglobin (ECG-HbA1c): A Retrospective Cohort Study
by Chin-Sheng Lin, Yung-Tsai Lee, Wen-Hui Fang, Yu-Sheng Lou, Feng-Chih Kuo, Chia-Cheng Lee and Chin Lin
J. Pers. Med. 2021, 11(8), 725; https://doi.org/10.3390/jpm11080725 - 27 Jul 2021
Cited by 22 | Viewed by 3713
Abstract
Background: glycated hemoglobin (HbA1c) provides information on diabetes mellitus (DM) management. Electrocardiography (ECG) is a noninvasive test of cardiac activity that has been determined to be related to DM and its complications. This study developed a deep learning model (DLM) to estimate HbA1c [...] Read more.
Background: glycated hemoglobin (HbA1c) provides information on diabetes mellitus (DM) management. Electrocardiography (ECG) is a noninvasive test of cardiac activity that has been determined to be related to DM and its complications. This study developed a deep learning model (DLM) to estimate HbA1c via ECG. Methods: there were 104,823 ECGs with corresponding HbA1c or fasting glucose which were utilized to train a DLM for calculating ECG-HbA1c. Next, 1539 cases from outpatient departments and health examination centers provided 2190 ECGs for initial validation, and another 3293 cases with their first ECGs were employed to analyze its contributions to DM management. The primary analysis was used to distinguish patients with and without mild to severe DM, and the secondary analysis was to explore the predictive value of ECG-HbA1c for future complications, which included all-cause mortality, new-onset chronic kidney disease (CKD), and new-onset heart failure (HF). Results: we used a gender/age-matching strategy to train a DLM to achieve the best AUCs of 0.8255 with a sensitivity of 71.9% and specificity of 77.7% in a follow-up cohort with correlation of 0.496 and mean absolute errors of 1.230. The stratified analysis shows that DM presented in patients with fewer comorbidities was significantly more likely to be detected by ECG-HbA1c. Patients with higher ECG-HbA1c under the same Lab-HbA1c exhibited worse physical conditions. Of interest, ECG-HbA1c may contribute to the mortality (gender/age adjusted hazard ratio (HR): 1.53, 95% conference interval (CI): 1.08–2.17), new-onset CKD (HR: 1.56, 95% CI: 1.30–1.87), and new-onset HF (HR: 1.51, 95% CI: 1.13–2.01) independently of Lab-HbA1c. An additional impact of ECG-HbA1c on the risk of all-cause mortality (C-index: 0.831 to 0.835, p < 0.05), new-onset CKD (C-index: 0.735 to 0.745, p < 0.01), and new-onset HF (C-index: 0.793 to 0.796, p < 0.05) were observed in full adjustment models. Conclusion: the ECG-HbA1c could be considered as a novel biomarker for screening DM and predicting the progression of DM and its complications. Full article
(This article belongs to the Special Issue Emerging Role of Bioinformatics Tools and Software in Evolution of Epidemiology)
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15 pages, 1926 KiB  
Article
Superiority of Mild Interventions against COVID-19 on Public Health and Economic Measures
by Makoto Niwa, Yasushi Hara, Yusuke Matsuo, Hodaka Narita, Yeongjoo Lim, Shintaro Sengoku and Kota Kodama
J. Pers. Med. 2021, 11(8), 719; https://doi.org/10.3390/jpm11080719 - 26 Jul 2021
Cited by 3 | Viewed by 3476
Abstract
(1) Background: During the global spread of COVID-19, Japan has been among the top countries to maintain a relatively low number of infections, despite implementing limited institutional interventions and its high population density. This study investigated how limited intervention policies have affected public [...] Read more.
(1) Background: During the global spread of COVID-19, Japan has been among the top countries to maintain a relatively low number of infections, despite implementing limited institutional interventions and its high population density. This study investigated how limited intervention policies have affected public health and economic conditions in the COVID-19 context and aimed to gain insight into the effective and sustainable measures against new infectious diseases in densely inhabited areas. (2) Methods: A system dynamics approach was employed. Qualitative causal loop analysis and stock and quantitative flow model analysis were performed, using a Tokyo Metropolitan area dataset. (3) Results: A causal loop analysis suggested that there were risks in prematurely terminating such interventions. Based on this result and the subsequent quantitative modeling, we found that the short-term effectiveness of a short-term pre-emptive stay-at-home request caused a resurgence in the number of positive cases, whereas an additional request provided a limited negative add-on effect for economic measures (e.g., number of electronic word-of-mouth communications and restaurant visits). (4) Conclusions: These findings suggest the superiority of a mild and continuous intervention as a long-term countermeasure under epidemic pressures when compared with strong intermittent interventions. Full article
(This article belongs to the Special Issue Emerging Role of Bioinformatics Tools and Software in Evolution of Epidemiology)
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