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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: closed (28 February 2023) | Viewed by 13733

Special Issue 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 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

  • 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 (3 papers)

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Research

9 pages, 3624 KiB  
Article
Forecasting HFMD Cases Using Weather Variables and Google Search Queries in Sabah, Malaysia
by Vivek Jason Jayaraj and Victor Chee Wai Hoe
Int. J. Environ. Res. Public Health 2022, 19(24), 16880; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph192416880 - 15 Dec 2022
Cited by 4 | Viewed by 1232
Abstract
HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department, climatic [...] Read more.
HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department, climatic predictors from the Malaysia Meteorological Department, and Google search trends from the Google trends platform between the years 2010–2018 were utilized. Cross-correlations were estimated in building a seasonal auto-regressive moving average (SARIMA) model with external regressors, directed by measuring the model fit. The selected variables were then validated using test data utilizing validation metrics such as the mean average percentage error (MAPE). Google search trends evinced moderate positive correlations to the HFMD cases (r0–6weeks: 0.47–0.56), with temperature revealing weaker positive correlations (r0–3weeks: 0.17–0.22), with the association being most intense at 0–1 weeks. The SARIMA model, with regressors of mean temperature at lag 0 and Google search trends at lag 1, was the best-performing model. It provided the most stable predictions across the four-week period and produced the most accurate predictions two weeks in advance (RMSE = 18.77, MAPE = 0.242). Trajectorial forecasting oscillations of the model are stable up to four weeks in advance, with accuracy being the highest two weeks prior, suggesting its possible usefulness in outbreak preparedness. Full article
(This article belongs to the Special Issue Statistical Methods with Applications in Human Health and Disease)
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15 pages, 684 KiB  
Article
Cohen’s Kappa Coefficient as a Measure to Assess Classification Improvement following the Addition of a New Marker to a Regression Model
by Barbara Więckowska, Katarzyna B. Kubiak, Paulina Jóźwiak, Wacław Moryson and Barbara Stawińska-Witoszyńska
Int. J. Environ. Res. Public Health 2022, 19(16), 10213; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191610213 - 17 Aug 2022
Cited by 8 | Viewed by 2866
Abstract
The need to search for new measures describing the classification of a logistic regression model stems from the difficulty in searching for previously unknown factors that predict the occurrence of a disease. A classification quality assessment can be performed by testing the change [...] Read more.
The need to search for new measures describing the classification of a logistic regression model stems from the difficulty in searching for previously unknown factors that predict the occurrence of a disease. A classification quality assessment can be performed by testing the change in the area under the receiver operating characteristic curve (AUC). Another approach is to use the Net Reclassification Improvement (NRI), which is based on a comparison between the predicted risk, determined on the basis of the basic model, and the predicted risk that comes from the model enriched with an additional factor. In this paper, we draw attention to Cohen’s Kappa coefficient, which examines the actual agreement in the correction of a random agreement. We proposed to extend this coefficient so that it may be used to detect the quality of a logistic regression model reclassification. The results provided by Kappa‘s reclassification were compared with the results obtained using NRI. The random variables’ distribution attached to the model on the classification change, measured by NRI, Kappa, and AUC, was presented. A simulation study was conducted on the basis of a cohort containing 3971 Poles obtained during the implementation of a lower limb atherosclerosis prevention program. Full article
(This article belongs to the Special Issue Statistical Methods with Applications in Human Health and Disease)
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17 pages, 928 KiB  
Article
Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches
by Ram D. Joshi and Chandra K. Dhakal
Int. J. Environ. Res. Public Health 2021, 18(14), 7346; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18147346 - 09 Jul 2021
Cited by 73 | Viewed by 8767
Abstract
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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