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Application of Statistical Methods in Public Health and Medical Research
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Application of Statistical Methods in Public Health and Medical Research

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 12640

Special Issue Editor

Dr. Jahar Bhowmik

E-Mail Website
Guest Editor
Associate Professor, Department of Health Science and Biostatistics, School of Health Sciences, Swinburne University of Technology, Melbourne, Hawthorn, VIC 3122, Australia
Interests: health related data modelling; public health; applied epidemiology; biostatistics; econometrics; mental health; clinical data modelling; clinical nursing; research design and forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions for a Special Issue of the journal the International Journal of Environmental Research and Public Health (application of statistical methods in public health and medical research) dedicated to the application of statistical methods in the broad areas of public health and medical sciences.

Both public health and medical research have a long history of applying statistical methods in solving complex research questions. In due process, many innovations in statistical methodology have arisen and applied extensively across disciplines. 

The aim of this Special Issue is to promote and propagate knowledge in the wider area of public health in order to improve the effectiveness and competence of public health interventions to improve the overall health outcome of populations around the globe. Therefore, this Special Issue welcomes submissions focusing on advanced statistical methods related to diverse analytical issues cropping up in public health and medical research. In particular, the application of innovative statistical methods or analytical strategies that directly address health-related research questions. 

This Special Issue welcomes submissions of original research in the following areas:

  • Biomedical research
  • Clinical medicine
  • Biostatistics
  • Public health
  • Nursing
  • Health data modeling
  • Epidemiology
  • Infectious diseases
  • Non-communicable diseases
  • Psychology
  • Mental health
  • Health systems
  • Health services
  • Health policy
  • Sustainable development goals
  • Health education
  • eHealth
  • Heath technology
  • Women’s health and policy
  • Violence against women
  • Gender equality
  • Intimate partner violence
  • Children healthcare
  • Adolescent health.

We are happy to announce this Special Issue of the International Journal of Environmental Research and Public Health “Application of statistical methods in public health and medical research” and look forward to receiving your submissions.

Dr. Jahar Bhowmik
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. 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

  • public health
  • biomedical
  • epidemiology
  • medicine
  • nursing
  • biostatistics
  • epidemiology
  • intimate partner violence
  • women’s health
  • interpersonal violence

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

Published Papers (3 papers)

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Research

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9 pages, 318 KiB  
Article
Factors Associated with Low Birthweight in Low-and-Middle Income Countries in South Asia
by Ngan Ngo, Jahar Bhowmik and Raaj Kishore Biswas
Int. J. Environ. Res. Public Health 2022, 19(21), 14139; https://doi.org/10.3390/ijerph192114139 - 29 Oct 2022
Cited by 4 | Viewed by 2093
Abstract
Child with Low Birth Weight (LBW) has a higher risk of infant mortality, learning difficulties in childhood due to stunted growth and impaired neurodevelopment, is more likely to develop heart diseases and diabetes in adulthood. This study aimed to evaluate the latest demographic [...] Read more.
Child with Low Birth Weight (LBW) has a higher risk of infant mortality, learning difficulties in childhood due to stunted growth and impaired neurodevelopment, is more likely to develop heart diseases and diabetes in adulthood. This study aimed to evaluate the latest demographic and health surveys (DHSs) across multiple countries in South Asia to determine the factors associated with LBW among these countries. Latest available DHS data across Afghanistan (2015, n = 29,461), Bangladesh (2018, n = 20,127), Nepal (2016, n = 12,862), and Pakistan (2018, n = 15,068) were analysed. Complex survey adjusted generalized linear models were fitted to investigate the association of birth weight with sociodemographic and decision-making factors. Pakistan had the highest proportion of LBW at 18% followed by Afghanistan and Bangladesh at around 14% and Nepal had the lowest (13%). Children born in Pakistan were more likely to have LBW children than Afghanistan (AOR = 2.17, 95% CI = 1.49–3.14). Mothers living in rural areas (AOR = 0.77, 95% CI = 0.61–0.97), with highly educated partners and belonging to richer families were less susceptible to having child with LBW. To reduce 30% LBW in-line with the World Health Organisation’s 2025 goal, policymakers in SA should focus on women in urban areas with low-educated partners belonging to poor households to ease LBW burden. Full article
(This article belongs to the Special Issue Application of Statistical Methods in Public Health and Medical Research)

Review

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24 pages, 1516 KiB  
Review
A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research
by Zemenu Tadesse Tessema, Getayeneh Antehunegn Tesema, Susannah Ahern and Arul Earnest
Int. J. Environ. Res. Public Health 2023, 20(13), 6277; https://doi.org/10.3390/ijerph20136277 - 1 Jul 2023
Cited by 3 | Viewed by 2762
Abstract
Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review [...] Read more.
Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies. Full article
(This article belongs to the Special Issue Application of Statistical Methods in Public Health and Medical Research)
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Other

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24 pages, 1102 KiB  
Systematic Review
A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research
by Getayeneh Antehunegn Tesema, Zemenu Tadesse Tessema, Stephane Heritier, Rob G. Stirling and Arul Earnest
Int. J. Environ. Res. Public Health 2023, 20(7), 5295; https://doi.org/10.3390/ijerph20075295 - 28 Mar 2023
Cited by 11 | Viewed by 4457
Abstract
With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on [...] Read more.
With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on this topic, specifically when such models would be useful. This systematic review therefore aimed at reviewing health research published using joint spatial and spatiotemporal models. A systematic search of published studies that applied joint spatial and spatiotemporal models was performed using six electronic databases without geographic restriction. A search with the developed search terms yielded 4077 studies, from which 43 studies were included for the systematic review, including 15 studies focused on infectious diseases and 11 on cancer. Most of the studies (81.40%) were performed based on the Bayesian framework. Different joint spatial and spatiotemporal models were applied based on the nature of the data, population size, the incidence of outcomes, and assumptions. This review found that when the outcome is rare or the population is small, joint spatial and spatiotemporal models provide better performance by borrowing strength from related health outcomes which have a higher prevalence. A framework for the design, analysis, and reporting of such studies is also needed. Full article
(This article belongs to the Special Issue Application of Statistical Methods in Public Health and Medical Research)
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