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Application of Statistical Methods in Chronic and Communicable Conditions
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Application of Statistical Methods in Chronic and Communicable Conditions

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 4696

Special Issue Editors

Dr. Paola Berchialla

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Guest Editor
Center for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torin, 10043 Turin, Italy
Interests: bayesian statistics; machine learning; clinical epidemiology; precision medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Veronica Sciannameo

E-Mail Website
Guest Editor
Center for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torin, 10043 Turin, Italy
Interests: machine learning; biostatistics; risk analysis; epidemiology; real world data
Dr. Giulia Lorenzoni

E-Mail Website
Guest Editor
Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy
Interests: clinical epidemiology; public health; biostatistics; clinical prediction models

Special Issue Information

Dear Colleagues, 

Chronic diseases, such as cancer, diabetes, and cardiovascular diseases, are well known to be the leading cause of morbidity and mortality in the Western world. These chronic diseases often coexist with endemic infectious diseases such as hepatitis, tuberculosis, or HIV. Better knowledge of the epidemiology of chronic and communicable diseases is necessary in order to develop interventions tailored to their prevention, and to better allocate healthcare resources. 

Clinical trials and real-world studies provide complementary knowledge in such fields to support the clinical decision-making process in managing chronic and communicable diseases. Traditional statistical models lead to biologically plausible and easily interpretable estimates. On the other hand, heterogeneity in clinical manifestations and the progression of chronic diseases pose challenges for the risk stratification of patients. Similarly, heterogeneity between individuals in infectious disease transmission patterns requires techniques that can cope with clustered patterns, minimize the impact of missing data, and incorporate mixed-resolution data. In these situations, advanced statistical methods are needed. For example, machine learning is a very flexible and promising approach, free from assumptions, which can accurately discover hidden patterns by analyzing large structured and unstructured datasets with high accuracy in classification, prediction, and risk estimation, but often has low interpretability. 

This Special Issue invites research papers addressing various statistical methodological aspects in the context of chronic and communicable diseases. We encourage the submission of works proposing statistical method comparison, machine learning and forecasting approaches, as well the analysis of statistical problems and new approaches related to chronic and communicable diseases.

We welcome original research papers, both in clinical trials and real-world settings, simulation studies, health economics, and data analysis, as well as systematic reviews and meta-analysis.

Dr. Paola Berchialla
Dr. Veronica Sciannameo
Dr. Giulia Lorenzoni
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

  • chronic diseases
  • communicable diseases
  • COVID-19
  • statistical methods
  • causal inference
  • machine learning
  • precision medicine
  • simulation studies

Published Papers (2 papers)

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Research

13 pages, 505 KiB  
Article
Assessment of Glucose Lowering Medications’ Effectiveness for Cardiovascular Clinical Risk Management of Real-World Patients with Type 2 Diabetes: Targeted Maximum Likelihood Estimation under Model Misspecification and Missing Outcomes
by Veronica Sciannameo, Gian Paolo Fadini, Daniele Bottigliengo, Angelo Avogaro, Ileana Baldi, Dario Gregori and Paola Berchialla
Int. J. Environ. Res. Public Health 2022, 19(22), 14825; https://doi.org/10.3390/ijerph192214825 - 11 Nov 2022
Cited by 2 | Viewed by 1451
Abstract
The results from many cardiovascular (CV) outcome trials suggest that glucose lowering medications (GLMs) are effective for the CV clinical risk management of type 2 diabetes (T2D) patients. The aim of this study is to compare the effectiveness of two GLMs (SGLT2i and [...] Read more.
The results from many cardiovascular (CV) outcome trials suggest that glucose lowering medications (GLMs) are effective for the CV clinical risk management of type 2 diabetes (T2D) patients. The aim of this study is to compare the effectiveness of two GLMs (SGLT2i and GLP-1RA) for the CV clinical risk management of T2D patients in a real-world setting, by simultaneously reducing glycated hemoglobin, body weight, and systolic blood pressure. Data from the real-world Italian multicenter retrospective study Dapagliflozin Real World evideNce in Type 2 Diabetes (DARWINT 2D) are analyzed. Different statistical approaches are compared to deal with the real-world-associated issues, which can arise from model misspecification, nonrandomized treatment assignment, and a high percentage of missingness in the outcome, and can potentially bias the marginal treatment effect (MTE) estimate and thus have an influence on the clinical risk management of patients. We compare the logistic regression (LR), propensity score (PS)-based methods, and the targeted maximum likelihood estimator (TMLE), which allows for the use of machine learning (ML) models. Furthermore, a simulation study is performed, resembling the structure of the conditional dependencies among the main variables in DARWIN-T2D. LR and PS methods do not underline any difference in the effectiveness regarding the attainment of combined CV risk factor goals between the two treatments. TMLE suggests instead that dapagliflozin is significantly more effective than GLP-1RA for the CV risk management of T2D patients. The results from the simulation study suggest that TMLE has the lowest bias and SE for the estimate of the MTE. Full article
(This article belongs to the Special Issue Application of Statistical Methods in Chronic and Communicable Conditions)
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13 pages, 373 KiB  
Article
An Overview of Strategies to Improve Vaccination Compliance before and during the COVID-19 Pandemic
by Lorena Charrier, Jacopo Garlasco, Robin Thomas, Paolo Gardois, Marco Bo and Carla Maria Zotti
Int. J. Environ. Res. Public Health 2022, 19(17), 11044; https://doi.org/10.3390/ijerph191711044 - 3 Sep 2022
Cited by 9 | Viewed by 2715
Abstract
The debate on vaccination mandate was fuelled over the past two years by the COVID-19 pandemic. This study aimed at overviewing vaccination strategies and corresponding vaccine coverages for childhood vaccinations before the pandemic and for SARS-CoV-2 in high-income countries. A qualitative comparison was [...] Read more.
The debate on vaccination mandate was fuelled over the past two years by the COVID-19 pandemic. This study aimed at overviewing vaccination strategies and corresponding vaccine coverages for childhood vaccinations before the pandemic and for SARS-CoV-2 in high-income countries. A qualitative comparison was also performed between the two contexts: unlike for childhood vaccinations, only one European country (Austria) imposed generalised COVID-19 mandates, most countries preferring targeted mandates for higher-risk categories (Italy, Greece) or workers in key public services (Finland, Australia, New Zealand, UK, Germany). Many countries (Norway, Sweden, Netherlands, Portugal, Spain) confirmed their traditional voluntary vaccination approach also for COVID-19, while others (Slovenia and Hungary), historically relying on compulsory vaccination strategies, surprisingly opted for voluntary SARS-CoV-2 vaccination, with unsatisfactory results in terms of immunisation rates. However, no tangible relationship was generally found between vaccination policies and immunisation coverages: data show that, unlike some countries with mandates, countries where vaccinations are merely recommended could achieve higher coverages, even beyond the recommended 95% threshold. The COVID-19 experience has enriched pre-existent vaccination strategy debates by adding interesting elements concerning attitudes towards vaccines in a novel and unexplored context. Interpreting the available results by considering the different cultural contexts and vaccine hesitancy determinants can help to better understand the complexity of the relationship between policies and achieved coverages. Full article
(This article belongs to the Special Issue Application of Statistical Methods in Chronic and Communicable Conditions)
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