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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 (15 June 2022) | Viewed by 17152

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Special Issue Editor

Prof. Dr. Antonio Palazón-Bru

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Guest Editor

Department of Clinical Medicine, Miguel Hernández University, 03550 Alicante, Spain
Interests: predictive models; systematic reviews; meta-analysis; screening; cardiovascular diseases; cancer

Special Issue Information

Dear Colleagues,

We are organizing a Special Issue on the use of predictive models to benefit public health for the International Journal of Environmental Research and Public Health. This journal is peer-reviewed and publishes articles in the interdisciplinary area of environmental health sciences and public health. Predictive models estimate probability of an event through the use of information on certain risk factors for that event. They are being increasingly used to assist healthcare professionals in making decisions that are more likely to avoid a specific event, with the consequent positive impact on public health. This Special Issue is focused on the construction and validation of predictive models with original data. Some possible topics are listed below, though other topics are also welcome:

COVID-19 research
Cardiovascular diseases and their risk factors
Infectious diseases
Environmental research
Screening tests
Hospital emergencies
Health promotion
Forecasting

Prof. Dr. Antonio Palazón-Bru
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

predictive models
forecasting
public health
environment
screening
prognosis
risk assessment

Published Papers (7 papers)

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Research

17 pages, 902 KiB

Open AccessArticle

A Predictive Model for Abnormal Bone Density in Male Underground Coal Mine Workers

by Ziwei Zheng, Yuanyu Chen, Yongzhong Yang, Rui Meng, Zhikang Si, Xuelin Wang, Hui Wang and Jianhui Wu

Int. J. Environ. Res. Public Health 2022, 19(15), 9165; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19159165 - 27 Jul 2022

Cited by 1 | Viewed by 1450

Abstract

The dark and humid environment of underground coal mines had a detrimental effect on workers’ skeletal health. Optimal risk prediction models can protect the skeletal health of coal miners by identifying those at risk of abnormal bone density as early as possible. A total of 3695 male underground workers who attended occupational health physical examination in a coal mine in Hebei, China, from July to August 2018 were included in this study. The predictor variables were identified through single-factor analysis and literature review. Three prediction models, Logistic Regression, CNN and XG Boost, were developed to evaluate the prediction performance. The training set results showed that the sensitivity of Logistic Regression, XG Boost and CNN models was 74.687, 82.058, 70.620, the specificity was 80.986, 89.448, 91.866, the F1 scores was 0.618, 0.919, 0.740, the Brier scores was 0.153, 0.040, 0.156, and the Calibration-in-the-large was 0.104, 0.020, 0.076, respectively, XG Boost outperformed the other two models. Similar results were obtained for the test set and validation set. A two-by-two comparison of the area under the ROC curve (AUC) of the three models showed that the XG Boost model had the best prediction performance. The XG Boost model had a high application value and outperformed the CNN and Logistic regression models in prediction. Full article

(This article belongs to the Special Issue Predictive Models That Can Impact Public Health)

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12 pages, 2622 KiB

Open AccessArticle

Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia

by Cia Vei Tan, Sarbhan Singh, Chee Herng Lai, Ahmed Syahmi Syafiq Md Zamri, Sarat Chandra Dass, Tahir Bin Aris, Hishamshah Mohd Ibrahim and Balvinder Singh Gill

Int. J. Environ. Res. Public Health 2022, 19(3), 1504; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19031504 - 28 Jan 2022

Cited by 13 | Viewed by 2441

Abstract

With many countries experiencing a resurgence in COVID-19 cases, it is important to forecast disease trends to enable effective planning and implementation of control measures. This study aims to develop Seasonal Autoregressive Integrated Moving Average (SARIMA) models using 593 data points and smoothened case and covariate time-series data to generate a 28-day forecast of COVID-19 case trends during the third wave in Malaysia. SARIMA models were developed using COVID-19 case data sourced from the Ministry of Health Malaysia’s official website. Model training and validation was conducted from 22 January 2020 to 5 September 2021 using daily COVID-19 case data. The SARIMA model with the lowest root mean square error (RMSE), mean absolute percentage error (MAE) and Bayesian information criterion (BIC) was selected to generate forecasts from 6 September to 3 October 2021. The best SARIMA model with a RMSE = 73.374, MAE = 39.716 and BIC = 8.656 showed a downward trend of COVID-19 cases during the forecast period, wherein the observed daily cases were within the forecast range. The majority (89%) of the difference between the forecasted and observed values was well within a deviation range of 25%. Based on this work, we conclude that SARIMA models developed in this paper using 593 data points and smoothened data and sensitive covariates can generate accurate forecast of COVID-19 case trends. Full article

(This article belongs to the Special Issue Predictive Models That Can Impact Public Health)

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18 pages, 1989 KiB

Open AccessArticle

Predicting Continuity of Asthma Care Using a Machine Learning Model: Retrospective Cohort Study

by Yao Tong, Beilei Lin, Gang Chen and Zhenxiang Zhang

Int. J. Environ. Res. Public Health 2022, 19(3), 1237; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19031237 - 22 Jan 2022

Cited by 4 | Viewed by 1927

Abstract

Continuity of care (COC) has been shown to possess numerous health benefits for chronic diseases. Specifically, the establishment of its level can facilitate clinical decision-making and enhanced allocation of healthcare resources. However, the use of a generalizable predictive methodology to determine the COC in patients has been underinvestigated. To fill this research gap, this study aimed to develop a machine learning model to predict the future COC of asthma patients and explore the associated factors. We included 31,724 adult outpatients with asthma who received care from the University of Washington Medicine between 2011 and 2018, and examined 138 features to build the machine learning model. Following the 10-fold cross-validations, the proposed model yielded an accuracy of 88.20%, an average area under the receiver operating characteristic curve of 0.96, and an average F1 score of 0.86. Further analysis revealed that the severity of asthma, comorbidities, insurance, and age were highly correlated with the COC of patients with asthma. This study used predictive methods to obtain the COC of patients, and our excellent modeling strategy achieved high performance. After further optimization, the model could facilitate future clinical decisions, hospital management, and improve outcomes. Full article

(This article belongs to the Special Issue Predictive Models That Can Impact Public Health)

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27 pages, 3906 KiB

Open AccessArticle

A Predictive Model of Pandemic Disaster Fear Caused by Coronavirus (COVID-19): Implications for Decision-Makers

by Vladimir M. Cvetković, Neda Nikolić, Adem Ocal, Jovana Martinović and Aleksandar Dragašević

Int. J. Environ. Res. Public Health 2022, 19(2), 652; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19020652 - 07 Jan 2022

Cited by 7 | Viewed by 2697

Abstract

This paper presents quantitative research results regarding a predictive model of pandemic disaster fear caused by the coronavirus disease (COVİD-19). The aim of this paper was to establish the level and impact of certain demographic and socioeconomic characteristics on pandemic disaster fear caused by the coronavirus (COVID-19). The research was conducted using a questionnaire that was provided and then collected online for 1226 respondents during May 2021. A closed, five-point Likert scale was used to create the structured questionnaire. The first section of the questionnaire included research questions about the participants’ socioeconomic and demographic characteristics, while the second section included issue questions about fear caused by COVID-19. The results of multivariate regression analyses showed the most important predictor for fear of COVID-19 to be gender, followed by age and education level. Furthermore, the results of t-tests showed statistically significant differences between men and women in terms of different aspects of pandemic disaster fear caused by the coronavirus disease. Our results have several significant public health implications. Women who were more educated and knowledgeable, married, and older, reported a greater fear of the outbreak at various levels. Decision-makers can use these findings to identify better strategic opportunities for pandemic disaster risk management. Full article

(This article belongs to the Special Issue Predictive Models That Can Impact Public Health)

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13 pages, 11876 KiB

Open AccessArticle

The Effect of Local and Global Interventions on Epidemic Spreading

by Jiarui Fan, Haifeng Du, Yang Wang and Xiaochen He

Int. J. Environ. Res. Public Health 2021, 18(23), 12627; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182312627 - 30 Nov 2021

Cited by 2 | Viewed by 1647

Abstract

Epidemic spreading causes severe challenges to the global public health system, and global and local interventions are considered an effective way to contain such spreading, including school closures (local), border control (global), etc. However, there is little study on comparing the efficiency of global and local interventions on epidemic spreading. Here, we develop a new model based on the Susceptible-Exposed-Infectious-Recovered (SEIR) model with an additional compartment called “quarantine status”. We simulate various kinds of outbreaks and interventions. Firstly, we predict, consistent with previous studies, interventions reduce epidemic spreading to 16% of its normal level. Moreover, we compare the effect of global and local interventions and find that local interventions are more effective than global ones. We then study the relationships between incubation period and interventions, finding that early implementation of rigorous intervention significantly reduced the scale of the epidemic. Strikingly, we suggest a Pareto optimal in the intervention when resources were limited. Finally, we show that combining global and local interventions is the most effective way to contain the pandemic spreading if initially infected individuals are concentrated in localized regions. Our work deepens our understandings of the role of interventions on the pandemic, and informs an actionable strategy to contain it. Full article

(This article belongs to the Special Issue Predictive Models That Can Impact Public Health)

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12 pages, 2176 KiB

Open AccessArticle

Importance of Geospatial Heterogeneity in Chronic Disease Burden for Policy Planning in an Urban Setting Using a Case Study of Singapore

by Ken Wei Tan, Joel R. Koo, Jue Tao Lim, Alex R. Cook and Borame L. Dickens

Int. J. Environ. Res. Public Health 2021, 18(9), 4406; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18094406 - 21 Apr 2021

Viewed by 3243

Abstract

Chronic disease burdens continue to rise in highly dense urban environments where clustering of type II diabetes mellitus, acute myocardial infarction, stroke, or any combination of these three conditions is occurring. Many individuals suffering from these conditions will require longer-term care and access to clinics which specialize in managing their illness. With Singapore as a case study, we utilized census data in an agent-modeling approach at an individual level to estimate prevalence in 2020 and found high-risk clusters with >14,000 type II diabetes mellitus cases and 2000–2500 estimated stroke cases. For comorbidities, 10% of those with type II diabetes mellitus had a past acute myocardial infarction episode, while 6% had a past stroke. The western region of Singapore had the highest number of high-risk individuals at 173,000 with at least one chronic condition, followed by the east at 169,000 and the north with the least at 137,000. Such estimates can assist in healthcare resource planning, which requires these spatial distributions for evidence-based policymaking and to investigate why such heterogeneities exist. The methodologies presented can be utilized within any urban setting where census data exists. Full article

(This article belongs to the Special Issue Predictive Models That Can Impact Public Health)

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13 pages, 1237 KiB

Open AccessArticle

Development, and Internal, and External Validation of a Scoring System to Predict 30-Day Mortality after Having a Traffic Accident Traveling by Private Car or Van: An Analysis of 164,790 Subjects and 79,664 Accidents

by Antonio Palazón-Bru, María José Prieto-Castelló, David Manuel Folgado-de la Rosa, Ana Macanás-Martínez, Emma Mares-García, María de los Ángeles Carbonell-Torregrosa, Vicente Francisco Gil-Guillén, Antonio Cardona-Llorens and Dolores Marhuenda-Amorós

Int. J. Environ. Res. Public Health 2020, 17(24), 9518; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17249518 - 18 Dec 2020

Cited by 1 | Viewed by 2024

Abstract

Predictive factors for fatal traffic accidents have been determined, but not addressed collectively through a predictive model to help determine the probability of mortality and thereby ascertain key points for intervening and decreasing that probability. Data on all road traffic accidents with victims involving a private car or van occurring in Spain in 2015 (164,790 subjects and 79,664 accidents) were analyzed, evaluating 30-day mortality following the accident. As candidate predictors of mortality, variables associated with the accident (weekend, time, number of vehicles, road, brightness, and weather) associated with the vehicle (type and age of vehicle, and other types of vehicles in the accident) and associated with individuals (gender, age, seat belt, and position in the vehicle) were examined. The sample was divided into two groups. In one group, a logistic regression model adapted to a points system was constructed and internally validated, and in the other group the model was externally validated. The points system obtained good discrimination and calibration in both the internal and the external validation. Consequently, a simple tool is available to determine the risk of mortality following a traffic accident, which could be validated in other countries. Full article

(This article belongs to the Special Issue Predictive Models That Can Impact Public Health)

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