Special Issue "Statistical Methods with Applications in Human Health and Disease"

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: 31 December 2021.

Special Issue Editor

Dr. Pedro Femia-Marzo
E-Mail Website
Guest Editor
Department of Statistics, Faculty of Health Sciences, University of Granada, 18016 Granada, Spain
Interests: biostatistics; coding; mathematical and statistical modeling; bioinformatics

Special Issue Information

Dear Colleagues,

In a broad sense, biostatistics is the branch of statistics that centres on the development and use of statistical methods to address problems that arise in biology. Nowadays, talking about biology supposes considering a vast area of knowledge, and biostatistics accompanies all of these fields. In particular, human biology and medicine have made this discipline their own, in such a way that biostatistics is often conceived as the set of methods that solve the problems that arise in biomedical research. This Special Issue of IJERPH intends to address some of the subjects that appear in this biomedical facet of biostatistics.

Any manuscripts related to the development of statistical methods with applications in human health and disease are welcome. Nevertheless, let us consider some topics of particular interest.

Nowadays, our society is experiencing what it means to live through a pandemic firsthand. Mathematical modelling has proven to be a handy tool to predict the evolution of infections and to decide the best policy of action. Formal models can be built from different points of view, namely deterministic, stochastic, and statistical approaches. Although it is a more complex task, the stochastic approach is more realistic than the classical deterministic one. Moreover, the statistical approach is based on the observed data. Both stochastic and statistical modelling of the evolution of the infectious disease and the interaction of pathogen–organism are of interest in this Issue, particularly, the estimation strategies of formal parameters from observable data.

Medical diagnosis is another area in need of attention. Diagnosis is often challenging. On the one hand, the predictive values of the diagnostic procedures depend on the prevalence of the illness. On the other hand, many indexes of the disease can be difficult to be observed and quantified, so that, frequently, the diagnosis relies on agreement among specialists. Moreover, diagnosis sometimes requires categorization of continuous scales through cutpoints. This procedure is sometimes far from optimal, and frequently grey zones can appear where the diagnosis can be fuzzy.

Related to diagnostics is measuring the effect size due to the exposition of risk factors or interventions. In the presence of comorbidities or specific demographic characteristics, effect modification is related to the concept of interaction. Frequently, studying interactions relies on non-standard and intricate designs. Risk evaluation and strategies of data acquisition are also topics of interest.

Dr. Pedro Femia-Marzo
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 papers will be 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 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 2300 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.


  • biostatistics
  • statistical modelling in health research
  • stochastic models in medicine
  • parameter estimation in epidemic and illness processes
  • diagnostic test
  • cutpoints
  • agreement between raters
  • effect size assessing
  • interactions
  • risk factor
  • data acquisition and analysis in public health

Published Papers (1 paper)

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Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches
Int. J. Environ. Res. Public Health 2021, 18(14), 7346; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18147346 - 09 Jul 2021
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Diabetes mellitus is one of the most common human diseases worldwide and may cause several health-related complications. It is responsible for considerable morbidity, mortality, and economic loss. A timely diagnosis and prediction of this disease could provide patients with an opportunity to take [...] Read more.
Diabetes mellitus is one of the most common human diseases worldwide and may cause several health-related complications. It is responsible for considerable morbidity, mortality, and economic loss. A timely diagnosis and prediction of this disease could provide patients with an opportunity to take the appropriate preventive and treatment strategies. To improve the understanding of risk factors, we predict type 2 diabetes for Pima Indian women utilizing a logistic regression model and decision tree—a machine learning algorithm. Our analysis finds five main predictors of type 2 diabetes: glucose, pregnancy, body mass index (BMI), diabetes pedigree function, and age. We further explore a classification tree to complement and validate our analysis. The six-fold classification tree indicates glucose, BMI, and age are important factors, while the ten-node tree implies glucose, BMI, pregnancy, diabetes pedigree function, and age as the significant predictors. Our preferred specification yields a prediction accuracy of 78.26% and a cross-validation error rate of 21.74%. We argue that our model can be applied to make a reasonable prediction of type 2 diabetes, and could potentially be used to complement existing preventive measures to curb the incidence of diabetes and reduce associated costs. Full article
(This article belongs to the Special Issue Statistical Methods with Applications in Human Health and Disease)
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