Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
Journals
IJERPH
Special Issues
Innovative Statistical Methods in Public Health
ijerph-logo
Submit to IJERPH Review for IJERPH Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Innovative Statistical Methods in Public Health

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 (30 June 2023) | Viewed by 5092

Special Issue Editors

Dr. Sixia Chen

E-Mail Website
Guest Editor
Department of Biostatistics and Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
Interests: missing data analysis; survey sampling; empirical likelihood; machine learning methods; causal inference; biostatistics; American Indian health disparities; tobacco research; cancer prevention and control
Special Issues, Collections and Topics in MDPI journals
Dr. Janis Campbell

E-Mail Website
Guest Editor
Department of Biostatistics and Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
Interests: program evaluation research; health disparities; community cancer prevention and control; cancer surveillance

Special Issue Information

Dear Colleagues,

Public health research is one of the most critical research areas for improving life. Such research is in a high demand following the rise of viruses including COVID-19, continuing HIV, and others. Public health research also serves an important role in, for example, environmental improvement, substance use cessation, disease prevention, physical activity, and healthy eating behavior. Statistics plays a significant role in public health research in drawing rigorous scientific conclusions. To stimulate new application and development of statistical methods in public health research, we initiated this Special Issue, entitled “Innovative Statistical Methods in Public Health”. We welcome articles including meta-analysis, missing data analysis, survey sampling, causal inference, clinical trials, survival analysis, machine learning, Big Data, Bayesian statistics, and other statistical methods with application in public health research. Public health research includes (but is not limited to) substance use research, infectious disease, cardiovascular disease, physical activity, healthy diet, diabetes, obesity, cancer, oral health, and mental health.

Dr. Sixia Chen
Dr. Janis Campbell
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

  • health application
  • health disparities
  • public health
  • statistics
  • statistical methods
  • tobacco research

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

10 pages, 335 KiB  
Article
Empirical Comparison of Imputation Methods for Multivariate Missing Data in Public Health
by Steven Pan and Sixia Chen
Int. J. Environ. Res. Public Health 2023, 20(2), 1524; https://doi.org/10.3390/ijerph20021524 - 14 Jan 2023
Cited by 11 | Viewed by 3197
Abstract
Sample estimates derived from data with missing values may be unreliable and may negatively impact the inferences that researchers make about the underlying population due to nonresponse bias. As a result, imputation is often preferred to listwise deletion in handling multivariate missing data. [...] Read more.
Sample estimates derived from data with missing values may be unreliable and may negatively impact the inferences that researchers make about the underlying population due to nonresponse bias. As a result, imputation is often preferred to listwise deletion in handling multivariate missing data. In this study, we compared three popular imputation methods: sequential multiple imputation, fractional hot-deck imputation, and generalized efficient regression-based imputation with latent processes for handling multivariate missingness under different missing patterns by conducting descriptive and regression analyses on the imputed data and seeing how the estimates differ from those generated from the full sample. Limited Monte Carlo simulation results by using the National Health Nutrition and Examination Survey and Behavioral Risk Factor Surveillance System are presented to demonstrate the effect of each imputation method on reducing bias and increasing efficiency for the parameter estimate of interest for that particular incomplete variable. Although these three methods did not always outperform listwise deletion in our simulated missing patterns, they improved many descriptive and regression estimates when used to impute all incomplete variables at once. Full article
(This article belongs to the Special Issue Innovative Statistical Methods in Public Health)
15 pages, 4503 KiB  
Article
Medical Experts’ Agreement on Risk Assessment Based on All Possible Combinations of the COVID-19 Predictors—A Novel Approach for Public Health Screening and Surveillance
by Mohd Salami Ibrahim, Nyi Nyi Naing, Aniza Abd Aziz, Mokhairi Makhtar, Harmy Mohamed Yusoff, Nor Kamaruzaman Esa, Nor Iza A Rahman, Myat Moe Thwe Aung, San San Oo, Samhani Ismail and Ras Azira Ramli
Int. J. Environ. Res. Public Health 2022, 19(24), 16601; https://doi.org/10.3390/ijerph192416601 - 10 Dec 2022
Cited by 1 | Viewed by 1530
Abstract
During the initial phase of the coronavirus disease 2019 (COVID-19) pandemic, there was a critical need to create a valid and reliable screening and surveillance for university staff and students. Consequently, 11 medical experts participated in this cross-sectional study to judge three risk [...] Read more.
During the initial phase of the coronavirus disease 2019 (COVID-19) pandemic, there was a critical need to create a valid and reliable screening and surveillance for university staff and students. Consequently, 11 medical experts participated in this cross-sectional study to judge three risk categories of either low, medium, or high, for all 1536 possible combinations of 11 key COVID-19 predictors. The independent experts’ judgement on each combination was recorded via a novel dashboard-based rating method which presented combinations of these predictors in a dynamic display within Microsoft Excel. The validated instrument also incorporated an innovative algorithm-derived deduction for efficient rating tasks. The results of the study revealed an ordinal-weighted agreement coefficient of 0.81 (0.79 to 0.82, p-value < 0.001) that reached a substantial class of inferential benchmarking. Meanwhile, on average, the novel algorithm eliminated 76.0% of rating tasks by deducing risk categories based on experts’ ratings for prior combinations. As a result, this study reported a valid, complete, practical, and efficient method for COVID-19 health screening via a reliable combinatorial-based experts’ judgement. The new method to risk assessment may also prove applicable for wider fields of practice whenever a high-stakes decision-making relies on experts’ agreement on combinations of important criteria. Full article
(This article belongs to the Special Issue Innovative Statistical Methods in Public Health)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop