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IJERPH
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Editorial Board Members' Collection Series: Modeling Disease Risk and Outcomes
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Editorial Board Members' Collection Series: Modeling Disease Risk and Outcomes

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Infectious Diseases, Chronic Diseases, and Disease Prevention".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 5548

Special Issue Editors

Dr. Matteo Malvezzi

E-Mail Website
Guest Editor
Department of Clinical Sciences and Community Health, University of Milan, 20122 Milano, MI, Italy
Interests: cancer; epidemiology; public health; statistics; statistical modelling
Prof. Dr. Kenneth C. Land

E-Mail Website
Guest Editor
Department of Sociology, Duke University, Durham, NC 27708, USA
Interests: mathematical and statistical model; social and demographic processes; contemporary social trends; quality-of-life measurement; demography; criminology

Special Issue Information

Dear Colleagues,

We are pleased to announce this Collection titled, “Editorial Board Members' Collection Series: Modeling Disease Risk and Outcomes”. This Issue will be a collection of papers from researchers invited by the Editorial Board Members. The aim is to provide a venue for networking and communication between IJERPH and scholars in the field of modeling disease risk and outcomes. All papers will be fully open access upon publication after peer review.

Dr. Matteo Malvezzi
Prof. Dr. Kenneth C. Land
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

  • biodemography
  • socioeconomic covariates
  • temporal trends
  • cancer
  • epidemiology
  • public health
  • statistics
  • statistical modeling

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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

9 pages, 347 KiB  
Article
Strategies of Modelling Incident Outcomes Using Cox Regression to Estimate the Population Attributable Risk
by Marlien Pieters, Iolanthe M. Kruger, Herculina S. Kruger, Yolandi Breet, Sarah J. Moss, Andries van Oort, Petra Bester and Cristian Ricci
Int. J. Environ. Res. Public Health 2023, 20(14), 6417; https://doi.org/10.3390/ijerph20146417 - 20 Jul 2023
Cited by 1 | Viewed by 1878
Abstract
When the Cox model is applied, some recommendations about the choice of the time metric and the model’s structure are often disregarded along with the proportionality of risk assumption. Moreover, most of the published studies fail to frame the real impact of a [...] Read more.
When the Cox model is applied, some recommendations about the choice of the time metric and the model’s structure are often disregarded along with the proportionality of risk assumption. Moreover, most of the published studies fail to frame the real impact of a risk factor in the target population. Our aim was to show how modelling strategies affected Cox model assumptions. Furthermore, we showed how the Cox modelling strategies affected the population attributable risk (PAR). Our work is based on data collected in the North-West Province, one of the two PURE study centres in South Africa. The Cox model was used to estimate the hazard ratio (HR) of mortality for all causes in relation to smoking, alcohol use, physical inactivity, and hypertension. Firstly, we used a Cox model with time to event as the underlying time variable. Secondly, we used a Cox model with age to event as the underlying time variable. Finally, the second model was implemented with age classes and sex as strata variables. Mutually adjusted models were also investigated. A statistical test to the multiplicative interaction term the exposures and the log transformed time to event metric was used to assess the proportionality of risk assumption. The model’s fitting was investigated by means of the Akaike Information Criteria (AIC). Models with age as the underlying time variable with age and sex as strata variables had enhanced validity of the risk proportionality assumption and better fitting. The PAR for a specific modifiable risk factor can be defined more accurately in mutually adjusted models allowing better public health decisions. This is not necessarily true when correlated modifiable risk factors are considered. Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Modeling Disease Risk and Outcomes)
16 pages, 2024 KiB  
Article
Defining the South African Acute Respiratory Infectious Disease Season
by Ogone Motlogeloa, Jennifer M. Fitchett and Neville Sweijd
Int. J. Environ. Res. Public Health 2023, 20(2), 1074; https://doi.org/10.3390/ijerph20021074 - 7 Jan 2023
Cited by 3 | Viewed by 3117
Abstract
The acute respiratory infectious disease season, or colloquially the “flu season”, is defined as the annually recurring period characterized by the prevalence of an outbreak of acute respiratory infectious diseases. It has been widely agreed that this season spans the winter period globally, [...] Read more.
The acute respiratory infectious disease season, or colloquially the “flu season”, is defined as the annually recurring period characterized by the prevalence of an outbreak of acute respiratory infectious diseases. It has been widely agreed that this season spans the winter period globally, but the precise timing or intensity of the season onset in South Africa is not well defined. This limits the efficacy of the public health sector to vaccinate for influenza timeously and for health facilities to synchronize efficiently for an increase in cases. This study explores the statistical intensity thresholds in defining this season to determine the start and finish date of the acute respiratory infectious disease season in South Africa. Two sets of data were utilized: public-sector hospitalization data that included laboratory-tested RSV and influenza cases and private-sector medical insurance claims under ICD 10 codes J111, J118, J110, and J00. Using the intensity threshold methodology proposed by the US CDC in 2017, various thresholds were tested for alignment with the nineteen-week flu season as proposed by the South African NICD. This resulted in varying thresholds for each province. The respiratory disease season commences in May and ends in September. These findings were seen in hospitalization cases and medical insurance claim cases, particularly with influenza-positive cases in Baragwanath hospital for the year 2019. These statistically determined intensity thresholds and timing of the acute respiratory infectious disease season allow for improved surveillance and preparedness among the public and private healthcare. Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Modeling Disease Risk and Outcomes)
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