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Healthcare
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Applied Statistics and Data Analysis in Healthcare
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Applied Statistics and Data Analysis in Healthcare

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 3521

Special Issue Editor

Dr. Paul Peters

E-Mail Website
Guest Editor
1. Department of Health Sciences, Carleton University, Ottawa, ON K1S 5B6, Canada
2. Department of Geography and Environmental Studies, Carleton University, Ottawa, ON K1S 5B6, Canada
Interests: rural health; population geography; environmental health; spatial analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, the nature of quantitative empirical work in research on health and care services has changed dramatically. The expansion of complex data sources, the availability of advanced statistical methods, the use of innovative visualization techniques, and the integration of geographic mapping into the mainstream have brought statistics out of the university laboratory and into regular use by public health and policy professionals. In addition, innovative data linkage has facilitated the merging of health administrative data with multiple data sources, including census records, surveys, or other routinely collected databases. These shifts have enabled the application of statistical techniques and data analysis methods to practical problems that seek to explain health inequities and improve accessibility to health and care services.

This Special Issue will focus on the application of statistical techniques and data analysis to model inequities in and access to health and care services. In particular, we are seeking papers that use spatial analysis, geographic mapping, innovative visualization, or linked administrative data. Papers written in partnership with health professionals and trainees will be welcomed.

Dr. Paul Peters
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. Healthcare 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 2700 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

  • applied statistics
  • data analysis
  • data linkage
  • spatial analysis
  • geographic mapping
  • access to health care
  • healthcare inequalities
  • health equity
  • health services research

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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

17 pages, 3203 KiB  
Article
Prioritizing Disease Diagnosis in Neonatal Cohorts through Multivariate Survival Analysis: A Nonparametric Bayesian Approach
by Jangwon Seo, Junhee Seok and Yoojoong Kim
Healthcare 2024, 12(9), 939; https://doi.org/10.3390/healthcare12090939 - 2 May 2024
Cited by 1 | Viewed by 1387
Abstract
Understanding the intricate relationships between diseases is critical for both prevention and recovery. However, there is a lack of suitable methodologies for exploring the precedence relationships within multiple censored time-to-event data, resulting in decreased analytical accuracy. This study introduces the Censored Event Precedence [...] Read more.
Understanding the intricate relationships between diseases is critical for both prevention and recovery. However, there is a lack of suitable methodologies for exploring the precedence relationships within multiple censored time-to-event data, resulting in decreased analytical accuracy. This study introduces the Censored Event Precedence Analysis (CEPA), which is a nonparametric Bayesian approach suitable for understanding the precedence relationships in censored multivariate events. CEPA aims to analyze the precedence relationships between events to predict subsequent occurrences effectively. We applied CEPA to neonatal data from the National Health Insurance Service, identifying the precedence relationships among the seven most commonly diagnosed diseases categorized by the International Classification of Diseases. This analysis revealed a typical diagnostic sequence, starting with respiratory diseases, followed by skin, infectious, digestive, ear, eye, and injury-related diseases. Furthermore, simulation studies were conducted to demonstrate CEPA suitability for censored multivariate datasets compared to traditional models. The performance accuracy reached 76% for uniform distribution and 65% for exponential distribution, showing superior performance in all four tested environments. Therefore, the statistical approach based on CEPA enhances our understanding of disease interrelationships beyond competitive methodologies. By identifying disease precedence with CEPA, we can preempt subsequent disease occurrences and propose a healthcare system based on these relationships. Full article
(This article belongs to the Special Issue Applied Statistics and Data Analysis in Healthcare)
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10 pages, 623 KiB  
Article
Double Disparity of Sexual Minority Status and Rurality in Cardiometabolic Hospitalization Risk: A Secondary Analysis Using Linked Population-Based Data
by Neeru Gupta and Samuel R. Cookson
Healthcare 2023, 11(21), 2854; https://doi.org/10.3390/healthcare11212854 - 30 Oct 2023
Viewed by 1186
Abstract
Studies have shown separately that sexual minority populations generally experience poorer chronic health outcomes compared with those who identify as heterosexual, as do rural populations compared with urban dwellers. This Canadian national observational study explored healthcare patterns at the little-understood intersections of lesbian, [...] Read more.
Studies have shown separately that sexual minority populations generally experience poorer chronic health outcomes compared with those who identify as heterosexual, as do rural populations compared with urban dwellers. This Canadian national observational study explored healthcare patterns at the little-understood intersections of lesbian, gay, or bisexual (LGB) identity with residence in rural and remote communities, beyond chronic disease status. The secondary analysis applied logistic regressions on multiple linked datasets from representative health surveys, administrative hospital records, and a geocoded index of community remoteness to examine differences in the risk of potentially avoidable cardiometabolic-related hospitalization among adults of working age. Among those with an underlying cardiometabolic condition and residing in more rural and remote communities, a significantly higher hospitalization risk was found for LGB-identified persons compared with their heterosexual peers (odds ratio: 4.2; 95% confidence interval: 1.5–11.7), adjusting for sociodemographic characteristics, behavioral risk factors, and primary healthcare access. In models stratified by sex, the association remained significant among gay and bisexual men (5.6; CI: 1.3–24.4) but not among lesbian and bisexual women (3.5; CI: 0.9–13.6). More research is needed leveraging linkable datasets to better understand the complex and multiplicative influences of sexual minority status and rurality on cardiometabolic health to inform equity-enhancing preventive healthcare interventions. Full article
(This article belongs to the Special Issue Applied Statistics and Data Analysis in Healthcare)
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