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Statistical Methods in Social and Environmental Epidemiology
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Statistical Methods in Social and Environmental Epidemiology

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 (31 August 2023) | Viewed by 3604

Special Issue Editors

Prof. Susumu Ito

E-Mail Website
Guest Editor
High-Tech Research Centre, Kokushikan University, Tokyo 154-8515, Japan
Interests: general physiology; hygiene/public health (respiratory and circulatory system)
Dr. Izumi Kuboyama

E-Mail Website
Guest Editor
Hasuda Yotsuba Hospital, Saitama 349-0131, Japan
Interests: dementia; epidemiology

Special Issue Information

Dear Colleagues,

Statistical methods are a fundamental tool in epidemiological research, and were developed by Pearson, Fisher and others in the 20th century. In the 21st century, with significant advances in computer technology, it is now possible to analyse complex datasets that were previously impossible due to the limitations of human and computer capabilities.

Thanks to well-designed statistical software, basic statistical methods such as the T-test, F-test and χ-square test can now be performed in an instant, and relatively complex analyses that used to be time-consuming, such as multiple logistic regression analysis and covariance structure analysis, can be easily performed by anyone.

This Special Issue will feature articles on the introduction of useful statistical methods, examples of analyses of specific data, theoretical explanations of novel statistic methods, and future perspectives in the field of social and environmental epidemiology.

Contributions on the latest advanced analytical methods are of course welcome, but please provide explanations that are easy to understand and useful for the general epidemiologist who is not familiar with statistics.

Prof. Susumu Ito
Dr. Izumi Kuboyama
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. 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

10 pages, 1605 KiB  
Article
Exploration of the Relationships between Men’s Healthy Life Expectancy in Japan and Regional Variables by Integrating Statistical Learning Methods
by Fumiya Sato and Keiko Nakamura
Int. J. Environ. Res. Public Health 2023, 20(18), 6782; https://doi.org/10.3390/ijerph20186782 - 19 Sep 2023
Cited by 1 | Viewed by 1518
Abstract
A quantitative understanding of the relationship between comprehensive health levels, such as healthy life expectancy and their related factors, through a highly explanatory model is important in both health research and health policy making. In this study, we developed a regression model that [...] Read more.
A quantitative understanding of the relationship between comprehensive health levels, such as healthy life expectancy and their related factors, through a highly explanatory model is important in both health research and health policy making. In this study, we developed a regression model that combines multiple linear regression and a random forest model, exploring the relationship between men’s healthy life expectancy in Japan and regional variables from open sources at the city level as an illustrative case. Optimization of node-splitting in each decision tree was based on the total mean-squared error of multiple regression models in binary-split child nodes. Variations of standardized partial regression coefficients for each city were obtained as the ensemble of multiple trees and visualized on scatter plots. By considering them, interaction terms with piecewise linear functions were exploratorily introduced into a final multiple regression model. The plots showed that the relationship between the healthy life expectancy and the explanatory variables could differ depending on the cities’ characteristics. The procedure implemented here was suggested as a useful exploratory method for flexibly implementing interactions in multiple regression models while maintaining interpretability. Full article
(This article belongs to the Special Issue Statistical Methods in Social and Environmental Epidemiology)
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19 pages, 1361 KiB  
Article
Analysis of the Effectiveness of Public Health Measures on COVID-19 Transmission
by Thiago Christiano Silva, Leandro Anghinoni, Cassia Pereira das Chagas, Liang Zhao and Benjamin Miranda Tabak
Int. J. Environ. Res. Public Health 2023, 20(18), 6758; https://doi.org/10.3390/ijerph20186758 - 14 Sep 2023
Cited by 5 | Viewed by 1567
Abstract
In this study, we investigate the COVID-19 epidemics in Brazilian cities, using early-time approximations of the SIR model in networks and combining the VAR (vector autoregressive) model with machine learning techniques. Different from other works, the underlying network was constructed by inputting real-world [...] Read more.
In this study, we investigate the COVID-19 epidemics in Brazilian cities, using early-time approximations of the SIR model in networks and combining the VAR (vector autoregressive) model with machine learning techniques. Different from other works, the underlying network was constructed by inputting real-world data on local COVID-19 cases reported by Brazilian cities into a regularized VAR model. This model estimates directional COVID-19 transmission channels (connections or links between nodes) of each pair of cities (vertices or nodes) using spectral network analysis. Despite the simple epidemiological model, our predictions align well with the real COVID-19 dynamics across Brazilian municipalities, using data only up until May 2020. Given the rising number of infectious people in Brazil—a possible indicator of a second wave—these early-time approximations could be valuable in gauging the magnitude of the next contagion peak. We further examine the effect of public health policies, including social isolation and mask usage, by creating counterfactual scenarios to quantify the human impact of these public health measures in reducing peak COVID-19 cases. We discover that the effectiveness of social isolation and mask usage varies significantly across cities. We hope our study will support the development of future public health measures. Full article
(This article belongs to the Special Issue Statistical Methods in Social and Environmental Epidemiology)
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