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Special Issue "Analysis Approaches for Disease Prevention and Health Promotion"

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

Deadline for manuscript submissions: 30 July 2024 | Viewed by 3933

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Special Issue Editors

Dr. Lihua Li

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Guest Editor

Department of Polulation Health Science and policy, Icahn school of Medicine at Mount Sinai, New York, NY 10029, USA
Interests: evaluation of implemented interventional program for quality improvement of healthcare delivery; patient’s healthcare utilization using health economics; panel data analysis, and time series analysis

Dr. Bian Liu

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Guest Editor

Department of Polulation Health Science and policy, Icahn school of Medicine at Mount Sinai NY, New York, NY 10029, USA
Interests: studying exposure to environmental stressors throughout the lifetime, and their related health effects and health services utilization, using large databases and statistics

Dr. Liangyuan Hu

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Guest Editor

Department of Biostatistics and Epidemiology, Rutgers University, Piscataway, NJ 08854, USA
Interests: development of statistical methods for causal inference, statistical machine learning, missing data, Bayesian statistics and EHR data

Special Issue Information

Dear Colleagues,

The importance of health promotion through appropriate and innovative modeling is being increasingly recognized. Health promotion, as defined by World Health Organization, “enables people to increase control over their own health. It covers a wide range of social and environmental interventions that are designed to benefit and protect individual people’s health and quality of life by addressing and preventing the root causes of ill health, not just focusing on treatment and cure.” These interventions can include health policies, such as the Affordable Care Act, with the key goal being to expand Medicaid coverage for low-income Americans; the implementation of a cancer screening program, such as the National Breast and Cervical Cancer Early Detection Program (NBCCEDP), established by the Centers for Disease Control and Prevention (CDC) more than 25 years ago to connect low-income, uninsured, and underserved populations to preventive screening services; or behavioral change, such as smoking cessation programs to encourage tobacco users to quit smoking.

Research regarding the choice and application of appropriate and innovative analysis approaches in health promotion remains limited. These analysis approaches are characterized by interdisciplinary methods used in the fields of environment, geography, epidemiology, statistics, public health, health economics, etc. These methods are often used in other fields, yet are under-recognized or under-used in health promotion research. Such methods include difference-in-differences (DID) modeling, variable selection analysis using machine learning techniques, time series analysis, geo-spatial analysis, and microsimulation.

This Special Issue of IJERPH invites papers with a focus on the development and/or application of interdisciplinary analysis approaches in health promotion research. We accept original research, methodological papers, position papers, brief reports, and commentaries.

Dr. Lihua Li
Dr. Bian Liu
Dr. Liangyuan Hu
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 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 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

disease prevention
health promotion
health intervention
analysis approach
statistical modeling
impact analysis

Published Papers (3 papers)

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Research

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Open AccessArticle

Sequential Multiple Imputation for Real-World Health-Related Quality of Life Missing Data after Bariatric Surgery

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Int. J. Environ. Res. Public Health 2022, 19(17), 10827; https://doi.org/10.3390/ijerph191710827 - 30 Aug 2022

Cited by 1 | Viewed by 1199

Abstract

One of the main challenges for the successful implementation of health-related quality of life (HRQoL) assessments is missing data. The current study examined the feasibility and validity of a sequential multiple imputation (MI) method to deal with missing values in the longitudinal HRQoL data from the Scandinavian Obesity Surgery Registry. All patients in the SOReg who received bariatric surgery between 1 January 2011 and 31 March 2019 (n = 47,653) were included for the descriptive analysis and missingness pattern exploration. The patients who had completed the short-form 36 (SF-36) at baseline (year 0), and one-, two-, and five-year follow-ups were included (n = 3957) for the missingness pattern simulation and the sequential MI analysis. Eleven items of the SF-36 were selected to create the six domains of SF-6D, and the SF-6D utility index of each patient was calculated accordingly. The multiply-imputed variables in previous year were used as input to impute the missing values in later years. The performance of the sequential MI was evaluated by comparing the actual values with the imputed values of the selected SF-36 items and index at all four time points. At the baseline and year 1, where missing proportions were about 20% and 40%, respectively, there were no statistically significant discrepancies between the distributions of the actual and imputed responses (all p-values > 0.05). In year 2, where the missing proportion was about 60%, distributions of the actual and imputed responses were consistent in 9 of the 11 SF-36 items. However, in year 5, where the missing proportion was about 80%, no consistency was found between the actual and imputed responses in any of the SF-36 items. Relatively high missing proportions in HRQoL data are common in clinical registries, which brings a challenge to analyzing the HRQoL of longitudinal cohorts. The experimental sequential multiple imputation method adopted in the current study might be an ideal strategy for handling missing data (even though the follow-up survey had a missing proportion of 60%), avoiding significant information waste in the multivariate analysis. However, the imputations for data with higher missing proportions warrant more research. Full article

(This article belongs to the Special Issue Analysis Approaches for Disease Prevention and Health Promotion)

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Open AccessReview

Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series

Liangyuan Hu

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Lihua Li

Int. J. Environ. Res. Public Health 2022, 19(23), 16080; https://doi.org/10.3390/ijerph192316080 - 01 Dec 2022

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Abstract

Tree-based machine learning methods have gained traction in the statistical and data science fields. They have been shown to provide better solutions to various research questions than traditional analysis approaches. To encourage the uptake of tree-based methods in health research, we review the methodological fundamentals of three key tree-based machine learning methods: random forests, extreme gradient boosting and Bayesian additive regression trees. We further conduct a series of case studies to illustrate how these methods can be properly used to solve important health research problems in four domains: variable selection, estimation of causal effects, propensity score weighting and missing data. We exposit that the central idea of using ensemble tree methods for these research questions is accurate prediction via flexible modeling. We applied ensemble trees methods to select important predictors for the presence of postoperative respiratory complication among early stage lung cancer patients with resectable tumors. We then demonstrated how to use these methods to estimate the causal effects of popular surgical approaches on postoperative respiratory complications among lung cancer patients. Using the same data, we further implemented the methods to accurately estimate the inverse probability weights for a propensity score analysis of the comparative effectiveness of the surgical approaches. Finally, we demonstrated how random forests can be used to impute missing data using the Study of Women’s Health Across the Nation data set. To conclude, the tree-based methods are a flexible tool and should be properly used for health investigations. Full article

(This article belongs to the Special Issue Analysis Approaches for Disease Prevention and Health Promotion)

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Open AccessBrief Report

A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations

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Int. J. Environ. Res. Public Health 2022, 19(22), 14903; https://doi.org/10.3390/ijerph192214903 - 12 Nov 2022

Cited by 1 | Viewed by 797

Abstract

Personalized medicine requires an understanding of treatment effect heterogeneity. Evolving toward causal evidence for scenarios not studied in randomized trials necessitates a methodology using real-world evidence. Herein, we demonstrate a methodology that generates causal effects, assesses the heterogeneity of the effects and adjusts for the clustered nature of the data. This study uses a state-of-the-art machine learning survival model, riAFT-BART, to draw causal inferences about individual survival treatment effects, while accounting for the variability in institutional effects; further, it proposes a data-driven approach to agnostically (as opposed to a priori hypotheses) ascertain which subgroups exhibit an enhanced treatment effect from which intervention, relative to global evidence—average treatment effects measured at the population level. Comprehensive simulations show the advantages of the proposed method in terms of bias, efficiency and precision in estimating heterogeneous causal effects. The empirically validated method was then used to analyze the National Cancer Database. Full article

(This article belongs to the Special Issue Analysis Approaches for Disease Prevention and Health Promotion)

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Special Issue "Analysis Approaches for Disease Prevention and Health Promotion"

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