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Statistical Methods in Environmental Epidemiology

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 7669

Special Issue Editors

Institut National de la Recherche Scientifique, Centre Eau-Terre-Environnement, 490, rue de la Couronne, Québec, QC G1K 9A9, Canadaa
Interests: statistical methods; machine learning; environmental epidemiology; environment-health warning systems
Institut National de la Recherche Scientifique, Centre Eau-Terre-Environnement, 490, rue de la Couronne, Québec, QC G1K 9A9, Canada
Interests: applied statistics; envirnemental epidemiology; statistical hydrology

Special Issue Information

Dear Colleagues,

Assessing the impact of diverse environmental exposures on public health is not a simple task.

Indeed, the environment and its interaction with human societies is complex, with a number of interacting variables and confounders that modify the impacts. Examples include interactions between variables such as temperature, humidity, and fine particulate matter, as well as a built environment that can create so-called urban heat islands, not to mention climate changes that introduce some uncertainty regarding the evolution of these exposures and their impact on health. Fortunately, the ever-increasing amount of available data and monitored phenomena allow for more and more accurate assessments of the impact of environmental exposures on populations health.

To take full advantage of all the available information, it is important to have powerful statistical methods at one’s disposal. Advances such as distributed lag models and nonlinear regression models allow for significant improvements in our understanding of the impact of environmental exposures such as air pollution and extreme temperatures. Continuing the improvement of methods is crucial to making our response to environmental exposures more realistic. Furthermore, developed methods are also needed to better understand system-based approaches in quantifying the health risks of environmental changes.

The goal of this Special Issue is to invite researchers to propose new statistical perspectives on particular topics. These topics can be particular environmental exposure and public health variables or policies such as warning systems. The focus of submitted papers can either be new methodologies, new ways to tackle issues, or comparisons between concurrent methods based on variety of environmental exposures as well as health issues in different regions of the world.

Dr. Pierre Masselot
Prof. Fateh Chebana
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.

Keywords

  • Environmental epidemiology
  • Statistics
  • Regression methods
  • Machine learning
  • Weather-related health
  • Risk assessment
  • Climate changes
  • Extremes weather effects
  • Air pollution
  • Mortality and morbidity

Published Papers (3 papers)

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Research

13 pages, 3607 KiB  
Article
Data-Enhancement Strategies in Weather-Related Health Studies
by Pierre Masselot, Fateh Chebana, Taha B. M. J. Ouarda, Diane Bélanger and Pierre Gosselin
Int. J. Environ. Res. Public Health 2022, 19(2), 906; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19020906 - 14 Jan 2022
Viewed by 1931
Abstract
Although the relationship between weather and health is widely studied, there are still gaps in this knowledge. The present paper proposes data transformation as a way to address these gaps and discusses four different strategies designed to study particular aspects of a weather–health [...] Read more.
Although the relationship between weather and health is widely studied, there are still gaps in this knowledge. The present paper proposes data transformation as a way to address these gaps and discusses four different strategies designed to study particular aspects of a weather–health relationship, including (i) temporally aggregating the series, (ii) decomposing the different time scales of the data by empirical model decomposition, (iii) disaggregating the exposure series by considering the whole daily temperature curve as a single function, and (iv) considering the whole year of data as a single, continuous function. These four strategies allow studying non-conventional aspects of the mortality-temperature relationship by retrieving non-dominant time scale from data and allow to study the impact of the time of occurrence of particular event. A real-world case study of temperature-related cardiovascular mortality in the city of Montreal, Canada illustrates that these strategies can shed new lights on the relationship and outlines their strengths and weaknesses. A cross-validation comparison shows that the flexibility of functional regression used in strategies (iii) and (iv) allows a good fit of temperature-related mortality. These strategies can help understanding more accurately climate-related health. Full article
(This article belongs to the Special Issue Statistical Methods in Environmental Epidemiology)
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14 pages, 1271 KiB  
Article
Cardiovascular Health Peaks and Meteorological Conditions: A Quantile Regression Approach
by Yohann Moanahere Chiu, Fateh Chebana, Belkacem Abdous, Diane Bélanger and Pierre Gosselin
Int. J. Environ. Res. Public Health 2021, 18(24), 13277; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182413277 - 16 Dec 2021
Cited by 4 | Viewed by 2542
Abstract
Cardiovascular morbidity and mortality are influenced by meteorological conditions, such as temperature or snowfall. Relationships between cardiovascular health and meteorological conditions are usually studied based on specific meteorological events or means. However, those studies bring little to no insight into health peaks and [...] Read more.
Cardiovascular morbidity and mortality are influenced by meteorological conditions, such as temperature or snowfall. Relationships between cardiovascular health and meteorological conditions are usually studied based on specific meteorological events or means. However, those studies bring little to no insight into health peaks and unusual events far from the mean, such as a day with an unusually high number of hospitalizations. Health peaks represent a heavy burden for the public health system; they are, however, usually studied specifically when they occur (e.g., the European 2003 heatwave). Specific analyses are needed, using appropriate statistical tools. Quantile regression can provide such analysis by focusing not only on the conditional median, but on different conditional quantiles of the dependent variable. In particular, high quantiles of a health issue can be treated as health peaks. In this study, quantile regression is used to model the relationships between conditional quantiles of cardiovascular variables and meteorological variables in Montreal (Canada), focusing on health peaks. Results show that meteorological impacts are not constant throughout the conditional quantiles. They are stronger in health peaks compared to quantiles around the median. Results also show that temperature is the main significant variable. This study highlights the fact that classical statistical methods are not appropriate when health peaks are of interest. Quantile regression allows for more precise estimations for health peaks, which could lead to refined public health warnings. Full article
(This article belongs to the Special Issue Statistical Methods in Environmental Epidemiology)
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18 pages, 2135 KiB  
Article
Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City
by Vicente Navarro Valencia, Yamilka Díaz, Juan Miguel Pascale, Maciej F. Boni and Javier E. Sanchez-Galan
Int. J. Environ. Res. Public Health 2021, 18(22), 12108; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182212108 - 18 Nov 2021
Cited by 8 | Viewed by 2356
Abstract
The present analysis uses the data of confirmed incidence of dengue cases in the metropolitan region of Panama from 1999 to 2017 and climatic variables (air temperature, precipitation, and relative humidity) during the same period to determine if there exists a correlation between [...] Read more.
The present analysis uses the data of confirmed incidence of dengue cases in the metropolitan region of Panama from 1999 to 2017 and climatic variables (air temperature, precipitation, and relative humidity) during the same period to determine if there exists a correlation between these variables. In addition, we compare the predictive performance of two regression models (SARIMA, SARIMAX) and a recurrent neural network model (RNN-LSTM) on the dengue incidence series. For this data from 1999–2014 was used for training and the three subsequent years of incidence 2015–2017 were used for prediction. The results show a correlation coefficient between the climatic variables and the incidence of dengue were low but statistical significant. The RMSE and MAPE obtained for the SARIMAX and RNN-LSTM models were 25.76, 108.44 and 26.16, 59.68, which suggest that any of these models can be used to predict new outbreaks. Although, it can be said that there is a limited role of climatic variables in the outputs the models. The value of this work is that it helps understand the behaviour of cases in a tropical setting as is the Metropolitan Region of Panama City, and provides the basis needed for a much needed early alert system for the region. Full article
(This article belongs to the Special Issue Statistical Methods in Environmental Epidemiology)
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