Submit to IJERPH Review for IJERPH Propose a Special Issue

Journal Menu

Journal Browser

Data Science and New Technologies in Public Health

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Public Health Statistics and Risk Assessment".

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 21689

Share This Special Issue

Special Issue Editor

Prof. Dr. Shinichi Tokuno

E-Mail Website1 Website2
Guest Editor

1) Graduate School of Health Innovation, Kanagawa University of Human Services, Kanagawa 238-8522, Japan
2) Voice Analysis and Measurement of Pathophysiology, Dept. of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
Interests: voice biomarker; disaster medicine; stress management; bioengineering; environmental health

Special Issue Information

Dear Colleagues,

Recent technological developments have also affected research in public health. Varied information has been created in databases, and it has become possible to verify it with a huge sample that was unthinkable before. Along with this, analysis methods are also shifting from simple statistical methods to a computer-based analysis method called data science. As a result, for example, the incidence rate is higher than previously thought, or it is proved that previous calculations were correct. In addition, the causal relationship may become clearer than ever.
On the other hand, the development of technology is also changing the way data are collected. Smartphones, which are becoming widespread all over the world, have the same data processing capacity as former personal computers and also have sensors. By combining this with a device such as a wearable device, the amount of data that can be collected will further increase. As a result, it has become possible to replace information that used to rely on subjective data such as questionnaires with objective data.
Therefore, in this Special Issue, we would like to collect new public health knowledge using these new technologies. Submissions on technology research that contribute to public health research are also welcome. With such a premise, recommended topics may include but are not limited to the following:

New findings on public health using big data;
New analysis method on big data for public health;
New findings on public health using new technologies;
New technologies for public health research. (voice biomarkers, wearable devices, and so on).

Prof. Dr. Shinichi Tokuno
Guest Editor

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. International Journal of Environmental Research and Public Health 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 2500 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

data science
big data
voice biomarker
wearable device
smartphone
analysis method

Published Papers (10 papers)

Download All Papers

Research

Jump to: Other

9 pages, 631 KiB

Open AccessArticle

Estimating Depressive Symptom Class from Voice

by Takeshi Takano, Daisuke Mizuguchi, Yasuhiro Omiya, Masakazu Higuchi, Mitsuteru Nakamura, Shuji Shinohara, Shunji Mitsuyoshi, Taku Saito, Aihide Yoshino, Hiroyuki Toda and Shinichi Tokuno

Int. J. Environ. Res. Public Health 2023, 20(5), 3965; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph20053965 - 23 Feb 2023

Cited by 1 | Viewed by 1123

Abstract

Voice-based depression detection methods have been studied worldwide as an objective and easy method to detect depression. Conventional studies estimate the presence or severity of depression. However, an estimation of symptoms is a necessary technique not only to treat depression, but also to relieve patients’ distress. Hence, we studied a method for clustering symptoms from HAM-D scores of depressed patients and by estimating patients in different symptom groups based on acoustic features of their speech. We could separate different symptom groups with an accuracy of 79%. The results suggest that voice from speech can estimate the symptoms associated with depression. Full article

(This article belongs to the Special Issue Data Science and New Technologies in Public Health)

► Show Figures

Figure 1

13 pages, 782 KiB

Open AccessArticle

Distinguish the Severity of Illness Associated with Novel Coronavirus (COVID-19) Infection via Sustained Vowel Speech Features

by Yasuhiro Omiya, Daisuke Mizuguchi and Shinichi Tokuno

Int. J. Environ. Res. Public Health 2023, 20(4), 3415; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph20043415 - 15 Feb 2023

Cited by 1 | Viewed by 1795

Abstract

The authors are currently conducting research on methods to estimate psychiatric and neurological disorders from a voice by focusing on the features of speech. It is empirically known that numerous psychosomatic symptoms appear in voice biomarkers; in this study, we examined the effectiveness of distinguishing changes in the symptoms associated with novel coronavirus infection using speech features. Multiple speech features were extracted from the voice recordings, and, as a countermeasure against overfitting, we selected features using statistical analysis and feature selection methods utilizing pseudo data and built and verified machine learning algorithm models using LightGBM. Applying 5-fold cross-validation, and using three types of sustained vowel sounds of /Ah/, /Eh/, and /Uh/, we achieved a high performance (accuracy and AUC) of over 88% in distinguishing “asymptomatic or mild illness (symptoms)” and “moderate illness 1 (symptoms)”. Accordingly, the results suggest that the proposed index using voice (speech features) can likely be used in distinguishing the symptoms associated with novel coronavirus infection. Full article

(This article belongs to the Special Issue Data Science and New Technologies in Public Health)

► Show Figures

Figure 1

15 pages, 1355 KiB

Open AccessArticle

A Smart Spatial Routing and Accessibility Analysis System for EMS Using Catchment Areas of Voronoi Spatial Model and Time-Based Dijkstra’s Routing Algorithm

by Abdullah Alamri

Int. J. Environ. Res. Public Health 2023, 20(3), 1808; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph20031808 - 18 Jan 2023

Cited by 2 | Viewed by 1300

Abstract

The concept of a catchment area is often used to establish equitable access to essential services such as ambulance emergency medical services. In a time-sensitive environment, taking the wrong decision when there is a need for a short travel time can have serious consequences. In ambulance management, a mistaken dispatch which may result in the late arrival of an ambulance can lead to a life-and-death situation. In addition, finding the optimal route to reach the destination within a minimum amount of time is a significant problem. A spatial routing analysis based on travel times within the emergency services catchment area can quickly find the best routes to emergency points and may overcome this problem. In this study, a smart spatial routing and accessibility analysis system is proposed for EMS using catchment areas of the Voronoi spatial model and time-based Dijkstra’s routing algorithm (TDRA) to support the route analysis of emergencies and to facilitate the dispatch of appropriate units that are able to respond within a reasonable time frame. Our simulation shows that the system can successfully predict and determine the nearest candidate ambulance unit within the catchment area and candidate ambulance services in the adjacent catchment area that has a minimum travel time to the demand point taking TDRA construction into account. Full article

(This article belongs to the Special Issue Data Science and New Technologies in Public Health)

► Show Figures

Figure 1

13 pages, 390 KiB

Open AccessArticle

Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach

by Masakazu Higuchi, Mitsuteru Nakamura, Shuji Shinohara, Yasuhiro Omiya, Takeshi Takano, Daisuke Mizuguchi, Noriaki Sonota, Hiroyuki Toda, Taku Saito, Mirai So, Eiji Takayama, Hiroo Terashi, Shunji Mitsuyoshi and Shinichi Tokuno

Int. J. Environ. Res. Public Health 2022, 19(18), 11397; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191811397 - 10 Sep 2022

Cited by 1 | Viewed by 1962

Abstract

In general, it is common knowledge that people’s feelings are reflected in their voice and facial expressions. This research work focuses on developing techniques for diagnosing depression based on acoustic properties of the voice. In this study, we developed a composite index of vocal acoustic properties that can be used for depression detection. Voice recordings were collected from patients undergoing outpatient treatment for major depressive disorder at a hospital or clinic following a physician’s diagnosis. Numerous features were extracted from the collected audio data using openSMILE software. Furthermore, qualitatively similar features were combined using principal component analysis. The resulting components were incorporated as parameters in a logistic regression based classifier, which achieved a diagnostic accuracy of ~90% on the training set and ~80% on the test set. Lastly, the proposed metric could serve as a new measure for evaluation of major depressive disorder. Full article

(This article belongs to the Special Issue Data Science and New Technologies in Public Health)

► Show Figures

Figure 1

6 pages, 279 KiB

Open AccessCommunication

Efficacy of Clinical Guidelines in Identifying All Japanese Patients with Hereditary Breast and Ovarian Cancer

by Eri Haneda, Ann Sato, Nobuyasu Suganuma, Yoshiko Sebata, Saki Okamoto, Soji Toda, Kaori Kohagura, Yuka Matsubara, Yuko Sugawara, Takashi Yamanaka, Toshinari Yamashita, Satoru Shimizu and Hiroto Narimatsu

Int. J. Environ. Res. Public Health 2022, 19(10), 6182; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19106182 - 19 May 2022

Viewed by 1933

Abstract

Clinical screening using the National Comprehensive Cancer Network (NCCN) testing criteria may fail to identify all patients with hereditary breast and ovarian cancers. Thus, this study aimed to evaluate the strategy of expanding target patients for genetic testing among Japanese patients. We reviewed the medical records of 91 breast cancer patients who underwent genetic testing. Among 91 patients, eight were diagnosed with pathogenic or likely pathogenic variants: BRCA1 (n = 4) and BRCA2 (n = 4). Among 50 patients meeting the testing criteria of the guidelines, 6 (12%) were diagnosed with pathogenic or likely pathogenic variants. The sensitivity and specificity of screening using the testing criteria were 75% and 47%, respectively. Expanding the NCCN criteria to include all women diagnosed with breast cancer aged ≤65 years achieved 88% sensitivity but 8% specificity. The expansion of the NCCN criteria could benefit Japanese patients; however, larger studies are necessary to change clinical practice. Full article

(This article belongs to the Special Issue Data Science and New Technologies in Public Health)

12 pages, 536 KiB

Open AccessArticle

Sleep Satisfaction May Modify the Association between Metabolic Syndrome and BMI, Respectively, and Occupational Stress in Japanese Office Workers

by Helena Pham, Thomas Svensson, Ung-il Chung and Akiko Kishi Svensson

Int. J. Environ. Res. Public Health 2022, 19(9), 5095; https://doi.org/10.3390/ijerph19095095 - 22 Apr 2022

Cited by 2 | Viewed by 1819

Abstract

The association between obesity and psychological stress is ambiguous. The aim is to investigate the association between metabolic syndrome (MetS) and body mass index (BMI), respectively, with occupational stress among Japanese office workers. The study is a secondary analysis of the intervention group from a randomized controlled trial. There are 167 participants included in the analysis. Occupational stress is self-reported using the Brief Job Stress Questionnaire (BJSQ). BMI and the classification of MetS/pre-MetS was based on the participants’ annual health check-up data. The primary exposure is divided into three groups: no MetS, pre-MetS, and MetS in accordance with Japanese guidelines. The secondary exposure, BMI, remains as a continuous variable. Multiple linear regression is implemented. Sensitivity analyses are stratified by sleep satisfaction. Pre-MetS is significantly associated with occupational stress (7.84 points; 95% CI: 0.17, 15.51). Among participants with low sleep satisfaction, pre-MetS (14.09 points; 95% CI: 1.71, 26.48), MetS (14.72 points; 95% CI: 0.93, 28.51), and BMI (2.54 points; 95% CI: 0.05, 4.99) are all significantly associated with occupational stress. No significant associations are observed in participants with high sleep satisfaction. The findings of this study indicate that sleep satisfaction may modify the association between MetS and BMI, respectively, and occupational stress. Full article

(This article belongs to the Special Issue Data Science and New Technologies in Public Health)

► Show Figures

Figure 1

13 pages, 1703 KiB

Open AccessFeature PaperArticle

An Integrated Management System for Noncommunicable Diseases Program Implementation in a Sub-Saharan Setting

by Maria Agata Miselli, Francesco Cavallin, Samwel Marwa, Bruno Ndunguru, Rehema John Itambu, Katunzi Mutalemwa, Monica Rizzi, Giulia Ciccarelli, Simone Conte, Stefano Taddei, Gaetano Azzimonti, Giovanni Putoto and Giovanni Fernando Torelli

Int. J. Environ. Res. Public Health 2021, 18(21), 11619; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182111619 - 04 Nov 2021

Viewed by 2651

Abstract

Morbidity and mortality due to noncommunicable diseases (NCDs) are growing exponentially across Tanzania. The limited availability of dedicated services and the disparity between rural and urban areas represent key factors for the increased burden of NCDs in the country. From March 2019, an integrated management system was started in the Iringa District Council. The system implements an integrated management of hypertension and diabetes between the hospital and the peripheral health centers and introduces the use of paper-based treatment cards. The aim of the study was to present the results of the first 6 months’ roll-out of the system, which included 542 patients. Data showed that 46.1% of patients returned for the reassessment visit (±1 month), more than 98.4% of patients had blood pressure measured and were checked for complication, more than 88.6% of patients had blood sugar tested during follow-up visit, and blood pressure was at target in 42.8% of patients with hypertension and blood sugar in 37.3% of diabetic patients. Most patients who were lost to follow-up or did not reach the targets were those without medical insurance or living in remote peripheries. Our findings suggest that integrated management systems connecting primary health facilities and referral hospitals may be useful in care and follow-up of patients with hypertension and diabetes. Full article

(This article belongs to the Special Issue Data Science and New Technologies in Public Health)

► Show Figures

Figure 1

15 pages, 4187 KiB

Open AccessArticle

Occupational Exposure to Ultrafine Particles in Metal Additive Manufacturing: A Qualitative and Quantitative Risk Assessment

by Marta Sousa, Pedro Arezes and Francisco Silva

Int. J. Environ. Res. Public Health 2021, 18(18), 9788; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18189788 - 17 Sep 2021

Cited by 10 | Viewed by 2837

Abstract

Ultrafine particles (UFPs) can be released unintentionally during metal additive manufacturing (AM). Experts agree on the urgent need to increase the knowledge of the emerging risk of exposure to nanoparticles, although different points of view have arisen on how to do so. This article presents a case study conducted on a metal AM facility, focused on studying the exposure to incidental metallic UFP. It intends to serve as a pilot study on the application of different methodologies to manage this occupational risk, using qualitative and quantitative approaches that have been used to study exposure to engineered nanoparticles. Quantitative data were collected using a condensation particle counter (CPC), showing the maximum particle number concentration in manual cleaning tasks. Additionally, scanning electron microscopy (SEM) and energy dispersive X-ray analyzer (EDS) measurements were performed, showing no significant change in the particles’ chemical composition, size, or surface (rugosity) after printing. A qualitative approach was fulfilled using Control Banding Nanotool 2.0, which revealed different risk bands depending on the tasks performed. This article culminates in a critical analysis regarding the application of these two approaches in order to manage the occupational risk of exposure to incidental nanoparticles, raising the potential of combining both. Full article

(This article belongs to the Special Issue Data Science and New Technologies in Public Health)

► Show Figures

Figure 1

12 pages, 1293 KiB

Open AccessArticle

Depressive Mood Assessment Method Based on Emotion Level Derived from Voice: Comparison of Voice Features of Individuals with Major Depressive Disorders and Healthy Controls

by Shuji Shinohara, Mitsuteru Nakamura, Yasuhiro Omiya, Masakazu Higuchi, Naoki Hagiwara, Shunji Mitsuyoshi, Hiroyuki Toda, Taku Saito, Masaaki Tanichi, Aihide Yoshino and Shinichi Tokuno

Int. J. Environ. Res. Public Health 2021, 18(10), 5435; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18105435 - 19 May 2021

Cited by 9 | Viewed by 3331

Abstract

Background: In many developed countries, mood disorders have become problematic, and the economic loss due to treatment costs and interference with work is immeasurable. Therefore, a simple technique to determine individuals’ depressive state and stress level is desired. Methods: We developed a method to assess specific the psychological issues of individuals with major depressive disorders using emotional components contained in their voice. We propose two indices: vitality, a short-term index, and mental activity, a long-term index capturing trends in vitality. To evaluate our method, we used the voices of healthy individuals (n = 14) and patients with major depression (n = 30). The patients were also assessed by specialists using the Hamilton Rating Scale for Depression (HAM-D). Results: A significant negative correlation existed between the vitality extracted from the voices and HAM-D scores (r = −0.33, p < 0.05). Furthermore, we could discriminate the voice data of healthy individuals and patients with depression with a high accuracy using the vitality indicator (p = 0.0085, area under the curve of the receiver operating characteristic curve = 0.76). Full article

(This article belongs to the Special Issue Data Science and New Technologies in Public Health)

► Show Figures

Figure 1

Other

Jump to: Research

10 pages, 681 KiB

Open AccessStudy Protocol

Does Anti-TNF-α Therapy Affect the Bacteriological Profile of Specimens Collected from Perianal Lesions? A Retrospective Analysis in Patients with Crohn’s Disease

by Jolanta Gruszecka and Rafał Filip

Int. J. Environ. Res. Public Health 2022, 19(5), 2892; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19052892 - 02 Mar 2022

Cited by 3 | Viewed by 1585

Abstract

Anal abscesses and fistulas are potential complications of Crohn’s disease (CD). Chronic immunosuppression, loose stools, and poor wound healing in this population present challenges to the management of perianal diseases. The purpose of the study was to determine the predominant bacterial species found in specimens from perianal lesions causing anal abscess and/or fistula drainage in hospitalized patients, and to compare the number and type of microorganisms isolated from samples taken from patients undergoing biological therapy or traditionally treated. The outcomes of studies of patients treated for anal abscesses or fistulas from 2017 to 2019 were evaluated. Data obtained from medical records included culture and antibiotic sensitivity results of swabs from perianal lesions of isolated microorganisms. A total of 373 swabs were collected from perianal lesions during the analysis period, including 51 (49 positive samples) from patients with CD. The predominant pathogen was Escherichia coli (55%, p < 0.001), the second most common microorganism was Staphylococcus aureus (14.3%, p < 0.001). In vitro susceptibility testing showed E. coli, ESBL (strain with Extended Spectrum Beta-Lactamase) in five cases, S. aureus, MRSA (methicillin-resistant S. aureus -resistant to all beta-lactam antibiotics: penicillins with inhibitors, cephalosporins, monobactams, carbapenems, except for ceftaroline) in one sample. Biologic therapy does not affect the type of microorganisms isolated from perianal abscesses in patients with CD. Full article

(This article belongs to the Special Issue Data Science and New Technologies in Public Health)

► Show Figures