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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 (30 June 2023) | Viewed by 17942

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Special Issue Editor

Prof. Dr. Azizur Rahman

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Guest Editor

Leader of the Statistics and Data Mining Research Group, School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2650, Australia
Interests: bayesian inference, multilevel modelling and big-data analysis; model validation with uncertainty or reliability estimates; socioeconomic, demographic and health research; spatial analysis and small area estimation; statistics, data mining and deep learning; modelling COVID-19 data; public health and epidemiology; policy analysis

Special Issue Information

Dear Colleagues,

In this data-centric era, the COVID-19 pandemic is impacting us in many ways, including our physical and mental health as the individual, community and social system behaviours and wellbeing of citizens at a local, national or global scale. Furthermore, the dynamics of epidemiology, health and medical services are rapidly changing, which warrant a scope of new research to inform and support various organisations and health services worldwide.

Concerning the effects of the sociomediatic data and COVID-19 pandemic on our everyday life, a range of numerous health conditions, including mental health, COVID-19 infections and new variants, post-COVID-19 health problems and other diseases such as diabetes, alcohol and smoking-related health issues, CVDs, malnutrition and obesity and stroke, would increase. In addition, the prevalence of other chronic and non-communicable illnesses, infectious and non-infectious diseases and spatially and environmentally attributed health issues related to air pollution, water and food insecurities and socioeconomic and demographic inequalities could also take a considerable toll on the societies. We also foresee that an increased incidence of any fatal diseases within the general population can put dramatic pressure on the health system, social, economic and business activities and public norms and values. Finally, we also expect a mix of changes in personal and organisational behaviours, including healthcare professionals and their approaches to demand-driven care, decision makers and their dependence on data-driven and evidence-based policies and service providers and their everyday challenges to compete within and between organisation complexities. All of this will most likely lead to an increase in public health burden and societal costs. Moreover, with the geospatial and vaccination disparities, in a post-pandemic world, health and social services are being challenged to respond to what may be another pandemic—that of risk-attributed health difficulties. To do so, the usual modelling and medical statistics-based professional practices will probably have to change, with leading-edge methodologies, tools and techniques to assist in understanding multidisciplinary theoretical and practical research issues within epidemiology, health and medicine fields, including how to understand the individual activities which occur within very complex behavioural, socioeconomic and ecological systems.

Thus, for this Special Issue, we welcome original studies on the worldwide impact of data, models and COVID-19 on population health, including mental health, the responses from political, social and health professionals at the local, state and global stage, and the outcomes at the individuals and community level. Moreover, review studies exploring the crucial links of various epidemiological and health models or their applications in human and animal diseases are also highly welcome. Finally, public health studies and quantitative and qualitative data analysis research will be prioritised to facilitate reliable statistics-based information and implementation of feasible changes for ordinary public health and medical and critical care service practices.

Prof. Dr. Azizur Rahman
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

COVID-19
mental health
methods, tools, and techniques
data analysis
health modelling
epidemiology
health inequalities
spatial health
environmental factors
models
validation

Published Papers (5 papers)

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Research

13 pages, 1282 KiB

Open AccessArticle

Prevalence of Diabetic Retinopathy and Use of Common Oral Hypoglycemic Agents Increase the Risk of Diabetic Nephropathy—A Cross-Sectional Study in Patients with Type 2 Diabetes

by Wei-Ming Luo, Jing-Yang Su, Tong Xu and Zhong-Ze Fang

Int. J. Environ. Res. Public Health 2023, 20(5), 4623; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph20054623 - 06 Mar 2023

Viewed by 1585

Abstract

Objective: This study investigated the effect of amino acid metabolism on the risk of diabetic nephropathy under different conditions of the diabetic retinopathy, and the use of different oral hypoglycemic agents. Methods: This study retrieved 1031 patients with type 2 diabetes from the First Affiliated Hospital of Liaoning Medical University in Jinzhou, which is located in Liaoning Province, China. We conducted a spearman correlation study between diabetic retinopathy and amino acids that have an impact on the prevalence of diabetic nephropathy. Logistic regression was used to analyze the changes of amino acid metabolism in different diabetic retinopathy conditions. Finally, the additive interaction between different drugs and diabetic retinopathy was explored. Results: It is showed that the protective effect of some amino acids on the risk of developing diabetic nephropathy is masked in diabetic retinopathy. Additionally, the additive effect of the combination of different drugs on the risk of diabetic nephropathy was greater than that of any one drug alone. Conclusions: We found that diabetic retinopathy patients have a higher risk of developing diabetic nephropathy than the general type 2 diabetes population. Additionally, the use of oral hypoglycemic agents can also increase the risk of diabetic nephropathy. Full article

(This article belongs to the Special Issue New Insights into Epidemiology, Health, and Medical Statistics)

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13 pages, 2775 KiB

Open AccessArticle

A Novel Metaphor Graph Drawing Method for Multidimensional Data Visualisation and Its Case Study on COVID-19 Vaccination Analysis

by Xin Chi, Jie Hua and Xiao Ren

Int. J. Environ. Res. Public Health 2022, 19(23), 15547; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph192315547 - 23 Nov 2022

Cited by 1 | Viewed by 2132

Abstract

Visualisation techniques have been one of the best data processing and analysis methods in recent decades, and they have assisted in data understanding efforts in various fields. Visualisation techniques for low-dimensional data are well developed and applied in multiple sectors; however, multidimensional data visualisation techniques still present some limitations, such as inaccurate data comparison and perception, exaggerated visual differences, label occlusion, and overlap. This study addresses the pros and cons and proposes a novel graphical drawing method, the multidimensional rose chart. It adopts the design idea of the Nightingale rose chart, but overcomes relevant limitations. The main challenges of this area include the incomplete presentation of multidimensional data, the neglect of the linkage of multiple attributes, the inefficient use of space, and the lack of simplicity of the interface. Contributions include enriching the representations of multidimensional data through the use of colour shades, area, and height sizes to represent values; straightforward data attribute comparisons via graph nesting; and detailed attributes showing the use of specific value labels. To verify the preliminary validity of this method, we imported COVID-19 data into experiments and further compared the final layouts with traditional methods, such as the line chart, bar chart, tree, parallel coordinate chart, and Nightingale rose chart, as well as their structures, functionalities, clear advantages, and disadvantages. The experimental results show that multidimensional rose diagrams perform effectively in presenting multidimensional data when comparing other graph drawing methods in our case, and the outcomes match existing works’ conclusions in related COVID-19 research sectors. This work has the potential to provide a suitable supplemental approach to the multidimensional data analysis. Full article

(This article belongs to the Special Issue New Insights into Epidemiology, Health, and Medical Statistics)

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16 pages, 574 KiB

Open AccessArticle

Development of a Risk Score to Predict Sudden Infant Death Syndrome

by Mounika Polavarapu, Hillary Klonoff-Cohen, Divya Joshi, Praveen Kumar, Ruopeng An and Karin Rosenblatt

Int. J. Environ. Res. Public Health 2022, 19(16), 10270; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191610270 - 18 Aug 2022

Cited by 2 | Viewed by 2705

Abstract

Sudden Infant Death Syndrome (SIDS) is the third leading cause of death among infants younger than one year of age. Effective SIDS prediction models have yet to be developed. Hence, we developed a risk score for SIDS, testing contemporary factors including infant exposure to passive smoke, circumcision, and sleep position along with known risk factors based on 291 SIDS and 242 healthy control infants. The data were retrieved from death certificates, parent interviews, and medical records collected between 1989–1992, prior to the Back to Sleep Campaign. Multivariable logistic regression models were performed to develop a risk score model. Our finalized risk score model included: (i) breastfeeding duration (OR = 13.85, p < 0.001); (ii) family history of SIDS (OR = 4.31, p < 0.001); (iii) low birth weight (OR = 2.74, p = 0.003); (iv) exposure to passive smoking (OR = 2.64, p < 0.001); (v) maternal anemia during pregnancy (OR = 2.07, p = 0.03); and (vi) maternal age <25 years (OR = 1.77, p = 0.01). The area under the curve for the overall model was 0.79, and the sensitivity and specificity were 79% and 63%, respectively. Once this risk score is further validated it could ultimately help physicians identify the high risk infants and counsel parents about modifiable risk factors that are most predictive of SIDS. Full article

(This article belongs to the Special Issue New Insights into Epidemiology, Health, and Medical Statistics)

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18 pages, 417 KiB

Open AccessArticle

Knowledge, Attitude, and Perception of Cancer Patients towards COVID-19 in Pakistan: A Cross-Sectional Study

by Saadullah Khattak, Muhammad Faheem, Bilawal Nawaz, Maqbool Khan, Nazeer Hussain Khan, Nadeem Ullah, Taj Ali Khan, Rahat Ullah Khan, Kashif Syed Haleem, Zhi-Guang Ren, Dong-Dong Wu and Xin-Ying Ji

Int. J. Environ. Res. Public Health 2022, 19(13), 7926; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19137926 - 28 Jun 2022

Cited by 7 | Viewed by 3359

Abstract

Background: Cancer patients, being immunocompromised, are at higher risk of coronavirus disease (COVID-19). The current study determines cancer patients’ knowledge, attitude, perception, and impact of the COVID-19 pandemic. Method: A cross-sectional online survey was conducted in Pakistan from 1 April 2020 to 1 May 2020. The study respondents were cancer patients with ages equal to or greater than 18 years. Following a request for participation, the URL for the survey was distributed on numerous channels. Other social media platforms, including WeChat, WhatsApp, Facebook, Twitter, Instagram, Messenger, and LinkedIn, were used to increase cancer patient interaction. The questionnaire comprised five different sections such as: (1) sociodemographic information, (2) knowledge, (3) attitude, (4) perception, and (5) impact of COVID-19 on cancer patients. Descriptive medical statistics such as frequency, percentage, mean, and standard deviation were used to illustrate the demographic characteristics of the study participants. To compare mean knowledge scores with selected demographic variables, independent sample t-tests and one-way analysis of variance (ANOVA) were used, which are also practical methods in epidemiological, public health and medical research. The cut-off point for statistical significance was set at a p-value of 0.05. Results: More than 300 cancer patients were invited, of which 208 agreed to take part. The response rate was 69.33% (208/300). Gender, marital status, and employment status had a significant association with knowledge scores. Of the total recruited participants, 96% (n = 200) (p < 0.01) knew about COVID-19, and 90% were aware of general symptoms of COVID-19 disease, such as route of transmission and preventive measurements. In total, 94.5% (n = 197) (p < 0.01) were willing to accept isolation if they were infected with COVID-19, and 98% (n = 204) (p < 0.01) had reduced their use of public transportation. More than 90% (n = 188) (p < 0.01) of cancer patients were found to be practicing preventative measures such as using a face mask, keeping social distance, and avoiding handshaking and hugging. Around 94.4% (n = 196) (p < 0.01) of cancer patients had been impacted by, stopped or had changed cancer treatment during this pandemic, resulting in COVID-related anxiety and depression. Conclusion: The included cancer patients exhibited a good level of COVID-19 knowledge, awareness, positive attitude, and perception. Large-scale studies and efforts are needed to raise COVID-19 awareness among less educated and high-risk populations. The present survey indicates that mass-level effective health education initiatives are required for developing countries to improve and reduce the gap between KAP and COVID-19. Full article

(This article belongs to the Special Issue New Insights into Epidemiology, Health, and Medical Statistics)

17 pages, 638 KiB

Open AccessArticle

Impacts of COVID-19 on the Education, Life and Mental Health of Students in Bangladesh

by Fahmida Liza Piya, Sumaiya Amin, Anik Das and Muhammad Ashad Kabir

Int. J. Environ. Res. Public Health 2022, 19(2), 785; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19020785 - 11 Jan 2022

Cited by 13 | Viewed by 6408

Abstract

COVID-19’s unanticipated consequences have resulted in the extended closure of various educational institutions, causing significant hardship to students. Even though many institutions rapidly transitioned to online education programs, various issues have emerged that are impacting many aspects of students’ lives. An online survey was conducted with students of Bangladesh to understand how COVID-19 impacted their study, social and daily activities, plans, and mental health. A total of 409 Bangladeshi students took part in a survey. As a result of the COVID-19 pandemic, 13.7% of all participants are unable to focus on their studies, up from 1.2% previously. More than half of the participants (54%) have spent more time on social media than previously. We found that 45% of the participants have severe to moderate level depression. In addition, 48.6% of the students are experiencing severe to moderate level anxiety. According to our findings, students’ inability to concentrate on their studies, their increased use of social media and electronic communications, changing sleep hours during the pandemic, increased personal care time, and changes in plans are all correlated with their mental health. Full article

(This article belongs to the Special Issue New Insights into Epidemiology, Health, and Medical Statistics)

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