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Topical Collection "Public Health Surveillance and Infectious Disease Control"

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A topical collection in International Journal of Environmental Research and Public Health (ISSN 1660-4601). This collection belongs to the section "Global Health".

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Editors

Prof. Dr. Giovanni Improta

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Collection Editor

Department of Public Health, University of Naples Federico II, 80134 Naples, Italy
Interests: bioengineering; public health; healthcare decision making; machine learning and data mining for healthcare; modeling and analysis of biomedical data; health technology assessment; quality improvement in healthcare; lean six sigma; biomedical signal processing and analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Emma Montella

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Collection Editor

Department of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, Italy
Interests: public health; preventive medicine; epidemiology

Dr. Giovanni Boccia

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Collection Editor

Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, 84081 Salerno, Italy
Interests: public health; occupational medicine; health economics; preventive medicine; epidemiology; environmental health and food control
Special Issues, Collections and Topics in MDPI journals

Dr. Ilaria Loperto

E-Mail Website
Collection Editor

Department of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, Italy
Interests: public health; preventive medicine; epidemiology; lean six sigma

Topical Collection Information

Dear Colleagues,

Never before has interest in infectious diseases reached such a high level as today. The particular historical period we are experiencing has placed before us the power that viruses and bacteria can have over the health of the population. According to the World Health Organization (WHO), the diseases hitherto identified as being at epidemic risk are only warning signs of a new era of potentially rapidly spreading epidemics that will put the national health systems of most of the countries of the world in serious crisis.

It is easy to understand how all this can influence not only health, but also the economy. It becomes very important to be able to identify, using the new knowledge at our disposal, tools of interest in terms of global health, specifically regarding infectious diseases. However, infections represent a problem and a considerable cost even at the lowest level, such as that of individual hospitals. Being able to manage and limit them therefore becomes of strategic interest for hospital management.

Given the complexity of the field of interest, Global Health requires a transdisciplinary and multi-methodological approach, which makes use of the contribution of many sciences including economics, engineering, and biomedicine.

We therefore welcome all articles, systematic reviews, and other original productions that address some of the core research topics related to this Topical Collection, including but not limited to:

Global infectious diseases;
Hospital infections and healthcare acquired infections;
Community infections;
Surveillance and control.

Dr. Giovanni Improta
Dr. Emma Montella
Dr. Giovanni Boccia
Dr. Ilaria Loperto
Collection 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 collection 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 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 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

Global Health
infectious diseases
hospital infections
community infections
surveillance and control

Published Papers (7 papers)

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2023

Jump to: 2022

Open AccessArticle

Microbial Air Contamination in a Dental Setting Environment and Ultrasonic Scaling in Periodontally Healthy Subjects: An Observational Study

and

Int. J. Environ. Res. Public Health 2023, 20(3), 2710; https://doi.org/10.3390/ijerph20032710 - 03 Feb 2023

Cited by 3 | Viewed by 958

Abstract

The risk of microbial air contamination in a dental setting, especially during aerosol-generating dental procedures (AGDPs), has long been recognized, becoming even more relevant during the COVID-19 pandemic. However, individual pathogens were rarely studied, and microbial loads were measured heterogeneously, often using low-sensitivity methods. Therefore, the present study aimed to assess microbial air contamination in the dental environment, identify the microorganisms involved, and determine their count by active air sampling at the beginning (T0), during (T1), and at the end (T2) of ultrasonic scaling in systemically and periodontally healthy subjects. Air microbial contamination was detected at T0 in all samples, regardless of whether the sample was collected from patients treated first or later; predominantly Gram-positive bacteria, including Staphylococcus and Bacillus spp. and a minority of fungi, were identified. The number of bacterial colonies at T1 was higher, although the species found were similar to that found during the T0 sampling, whereby Gram-positive bacteria, mainly Streptococcus spp., were identified. Air samples collected at T2 showed a decrease in bacterial load compared to the previous sampling. Further research should investigate the levels and patterns of the microbial contamination of air, people, and the environment in dental settings via ultrasonic scaling and other AGDPs and identify the microorganisms involved to perform the procedure- and patient-related risk assessment and provide appropriate recommendations for aerosol infection control. Full article

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2022

Jump to: 2023

Open AccessArticle

Re-Emergence of Dengue Serotype 3 in the Context of a Large Religious Gathering Event in Touba, Senegal

Int. J. Environ. Res. Public Health 2022, 19(24), 16912; https://doi.org/10.3390/ijerph192416912 - 16 Dec 2022

Cited by 1 | Viewed by 977

Abstract

Dengue virus (DENV) was detected in Senegal in 1979 for the first time. Since 2017, unprecedented frequent outbreaks of DENV were noticed yearly. In this context, epidemiological and molecular evolution data are paramount to decipher the virus diffusion route. In the current study, we focused on a dengue outbreak which occurred in Senegal in 2018 in the context of a large religious gathering with 263 confirmed DENV cases out of 832 collected samples, including 25 life-threatening cases and 2 deaths. It was characterized by a co-circulation of dengue serotypes 1 and 3. Phylogenetic analysis based on the E gene revealed that the main detected serotype in Touba was DENV-3 and belonged to Genotype III. Bayesian phylogeographic analysis was performed and suggested one viral introduction around 2017.07 (95% HPD = 2016.61–2017.57) followed by cryptic circulation before the identification of the first case on 1 October 2018. DENV-3 strains are phylogenetically related, with strong phylogenetic links between strains retrieved from Burkina Faso and other West African countries. These phylogenetic data substantiate epidemiological data of the origin of DENV-3 and its spread between African countries and subsequent diffusion after religious mass events. The study also highlighted the usefulness of a mobile laboratory during the outbreak response, allowing rapid diagnosis and resulting in improved patient management. Full article

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Open AccessArticle

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study

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Int. J. Environ. Res. Public Health 2022, 19(16), 10021; https://doi.org/10.3390/ijerph191610021 - 14 Aug 2022

Cited by 1 | Viewed by 913

Abstract

Background: Surgical site infections (SSIs) have a major role in the evolution of medical care. Despite centuries of medical progress, the management of surgical infection remains a pressing concern. Nowadays, the SSIs continue to be an important factor able to increase the hospitalization duration, cost, and risk of death, in fact, the SSIs are a leading cause of morbidity and mortality in modern health care. Methods: A study based on statistical test and logistic regression for unveiling the association between SSIs and different risk factors was carried out. Successively, a predictive analysis of SSIs on the basis of risk factors was performed. Results: The obtained data demonstrated that the level of surgery contamination impacts significantly on the infection rate. In addition, data also reveals that the length of postoperative hospital stay increases the rate of surgical infections. Finally, the postoperative length of stay, surgery department and the antibiotic prophylaxis with 2 or more antibiotics are a significant predictor for the development of infection. Conclusions: The data report that the type of surgery department and antibiotic prophylaxis there are a statistically significant predictor of SSIs. Moreover, KNN model better handle the imbalanced dataset (48 infected and 3983 healthy), observing highest accuracy value. Full article

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Open AccessArticle

Diagnostic Accuracy of Various Immunochromatographic Tests for NS1 Antigen and IgM Antibodies Detection in Acute Dengue Virus Infection

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Int. J. Environ. Res. Public Health 2022, 19(14), 8756; https://doi.org/10.3390/ijerph19148756 - 19 Jul 2022

Cited by 1 | Viewed by 1709

Abstract

Introduction: Rapid diagnostic tests (RDTs) were evaluated, in this paper, for their utility as a reliable test, using resource-constrained studies. In most studies, NS1 antigen and immunoglobulin M (IgM)-based immunochromatographic tests (ICTs) were considered for acute phase detection. We aimed to evaluate the diagnostic accuracy of NS1, IgM, and NS1/IgM-based ICTs to detect acute dengue virus (DENV) infection in dengue-endemic regions. Methods: Studies were electronically identified using the following databases: MEDLINE, Embase, Cochrane Library, Web of Science, and CINAHL Plus. Keywords including dengue, rapid diagnostic test, immunochromatography, sensitivity, specificity, and diagnosis were applied across databases. In total, 15 studies were included. Quality assessment of the included studies was performed using the QUADAS-2 tool. All statistical analyses were conducted using RevMan, MedCalc, and SPSS software. Results: The studies revealed a total of 4135 individuals, originating largely from the Americas and Asia. The prevalence of DENV cases was 53.8%. Pooled sensitivities vs. specificities for NS1 (only), IgM (only) and combined NS1/IgM were 70.97% vs. 94.73%, 40.32% vs. 93.01%, and 78.62% vs. 88.47%, respectively. Diagnostic odds ratio (DOR) of DENV for NS1 ICTs was 43.95 (95% CI: 36.61–52.78), for IgM only ICTs was 8.99 (95% CI: 7.25–11.16), and for NS1/IgM ICTs was 28.22 (95% CI: 24.18–32.95). ELISA ICTs yielded a DOR of 21.36, 95% CI: 17.08–26.741. RT-PCR had a DOR of 40.43, 95% CI: 23.3–71.2. Heterogeneity tests for subgroup analysis by ICT manufacturers for NS1 ICTs revealed an χ² finding of 158.818 (df = 8), p < 0.001, whereas for IgM ICTs, the χ² finding was 21.698 (df = 5), p < 0.001. Conclusion: NS1-based ICTs had the highest diagnostic accuracy in acute phases of DENV infection. Certain factors influenced the pooled sensitivity, including ICT manufacturers, nature of the infection, reference method (RT-PCR), and serotypes. Prospective studies may examine the best strategy for incorporating ICTs for dengue diagnosis. Full article

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Open AccessArticle

Knowledge, Attitude, and Practices towards Dengue Fever among University Students of Dhaka City, Bangladesh

Md Mostafizur Rahman

Saadmaan Jubayer Khan

Md. Alim Al Raji Rumi

Mohammed Sadman Sakib

Masrur Abdul Quader

Md. Nafee-Ul-Islam Bhuiyan

Farzana Rahman

Edris Alam

and

Abu Reza Md. Towfiqul Islam

Int. J. Environ. Res. Public Health 2022, 19(7), 4023; https://doi.org/10.3390/ijerph19074023 - 28 Mar 2022

Cited by 5 | Viewed by 3729

Abstract

Dhaka has become the worst affected city in Bangladesh regarding dengue fever (DF). A large number of university students are residing in this city with a high DF risk. This cross-sectional study was conducted to assess the DF status and responses among these students through their Knowledge, Attitude, and Practices (KAP) survey. A total of 625 students participated in an online self-reported survey. Statistical analyses were performed to assess the status and KAP regarding DF. University students from the city perceived their living places as moderately safe (45.28%) against DF, whereas about 20% reported their DF infection history. Some of these students had exemplary DF knowledge (66.72%), attitude (89.28%), and practices (68.32%). However, many of them were also observed with a lack of knowledge about this disease’s infectious behavior, recognizing Aedes mosquito breeding sites, multiple infection cases, and the risk of DF viral infection during pregnancy. Fair correlations (p < 0.001) were determined in the KAP domain. Gender, residential unit, major, and dengue-relevant subjects were found to be significant predictors (p < 0.05) of KAP level in the univariate analysis. Major subject and residential units remained significant predictors of overall KAP level in further multiple analysis. This study revealed the urgency of infectious disease-related subjects and the relevant demonstration into the university curriculum. The study’s findings can assist the university, government and non-governmental organizations, and the health and social workers to prepare a comprehensive dengue response and preparedness plan. Full article

Open AccessArticle

A Fuzzy Inference System for the Assessment of Indoor Air Quality in an Operating Room to Prevent Surgical Site Infection

Ylenia Colella

Antonio Saverio Valente

Lucia Rossano

Teresa Angela Trunfio

Antonella Fiorillo

and

Giovanni Improta

Int. J. Environ. Res. Public Health 2022, 19(6), 3533; https://doi.org/10.3390/ijerph19063533 - 16 Mar 2022

Cited by 11 | Viewed by 1711

Abstract

Indoor air quality in hospital operating rooms is of great concern for the prevention of surgical site infections (SSI). A wide range of relevant medical and engineering literature has shown that the reduction in air contamination can be achieved by introducing a more efficient set of controls of HVAC systems and exploiting alarms and monitoring systems that allow having a clear report of the internal air status level. In this paper, an operating room air quality monitoring system based on a fuzzy decision support system has been proposed in order to help hospital staff responsible to guarantee a safe environment. The goal of the work is to reduce the airborne contamination in order to optimize the surgical environment, thus preventing the occurrence of SSI and reducing the related mortality rate. The advantage of FIS is that the evaluation of the air quality is based on easy-to-find input data established on the best combination of parameters and level of alert. Compared to other literature works, the proposed approach based on the FIS has been designed to take into account also the movement of clinicians in the operating room in order to monitor unauthorized paths. The test of the proposed strategy has been executed by exploiting data collected by ad-hoc sensors placed inside a real operating block during the experimental activities of the “Bacterial Infections Post Surgery” Project (BIPS). Results show that the system is capable to return risk values with extreme precision. Full article

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Open AccessArticle

Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study

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Int. J. Environ. Res. Public Health 2022, 19(5), 2498; https://doi.org/10.3390/ijerph19052498 - 22 Feb 2022

Cited by 19 | Viewed by 1508

Abstract

Background: Neonatal infections represent one of the six main types of healthcare-associated infections and have resulted in increasing mortality rates in recent years due to preterm births or problems arising from childbirth. Although advances in obstetrics and technologies have minimized the number of deaths related to birth, different challenges have emerged in identifying the main factors affecting mortality and morbidity. Dataset characterization: We investigated healthcare-associated infections in a cohort of 1203 patients at the level III Neonatal Intensive Care Unit (ICU) of the “Federico II” University Hospital in Naples from 2016 to 2020 (60 months). Methods: The present paper used statistical analyses and logistic regression to identify an association between healthcare-associated blood stream infection (HABSIs) and the available risk factors in neonates and prevent their spread. We designed a supervised approach to predict whether a patient suffered from HABSI using seven different artificial intelligence models. Results: We analyzed a cohort of 1203 patients and found that birthweight and central line catheterization days were the most important predictors of suffering from HABSI. Conclusions: Our statistical analyses showed that birthweight and central line catheterization days were significant predictors of suffering from HABSI. Patients suffering from HABSI had lower gestational age and birthweight, which led to longer hospitalization and umbilical and central line catheterization days than non-HABSI neonates. The predictive analysis achieved the highest Area Under Curve (AUC), accuracy and F1-macro score in the prediction of HABSIs using Logistic Regression (LR) and Multi-layer Perceptron (MLP) models, which better resolved the imbalanced dataset (65 infected and 1038 healthy). Full article

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Topical Collection "Public Health Surveillance and Infectious Disease Control"

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Published Papers (7 papers)

2023

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2022

Jump to: 2023

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