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Special Issue "Bayesian Spatial Modelling of Global Public Health Issues based on Complex Survey Data"

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 October 2020).

Special Issue Editors

Prof. Dr. Ngianga-Bakwin Kandala
E-Mail Website
Guest Editor
Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
Interests: Bayesian modelling and diseases mapping; statistical methods applied to epidemiology; survival analysis; longitudinal data analysis; meta-analysis; Bayesian spatial Analysis; Health Economics and Health Technology Assessment
Prof. Dr. Saverio Stranges
E-Mail Website
Guest Editor
Department of Epidemiology and Biostatistics, Western University, London, ON N6A 5C1, Canada
Interests: epidemiology and prevention of chronic disease and aging; dietary patterns; sleep behaviors; social determinants of health; cardio-metabolic disease

Special Issue Information

Dear Colleagues,

The Special Issue will try to bring together many applications of the Bayesian approach on global health issues (HIV, TB, HSV-2, malaria, hypertension, diabetes, malnutrition, Female Genital Mutilation (FGM), and obesity) using different data sources from complex household survey data.

Hierarchical spatial modeling is a common and useful approach for modeling complex spatially correlated data in many settings in epidemiology, public health, and development studies. Most of the data collected by many governments through surveys and sentinel surveillance are georeferenced by districts, counties, provinces or other administrative units.

Because of the complexity of factors associated with survival and health, traditional measures such as household socioeconomic and education may require supplementation with types of data that are both novel and less conventional. Statistical techniques that incorporate spatial analysis using a combination of data sources and spatial covariates offer such a possibility, though broadening the view of environment at both the macrolevel and the microlevel may be required to fully understand the scope of such influences.

Papers focusing on spatial Bayesian models based on complex surveys data will be invited in this Special Issue. A Bayesian framework based on Markov chain Monte Carlo (MCMC) simulation techniques will be encouraged. The models cover a number of well-known model classes as special cases, including generalized additive models (Hastie and Tibshirani 1990), generalized additive mixed models (Lin and Zhang 1999), geoadditive models (Kammann and Wand 2003), varying coefficient models (Hastie and Tibshirani 1993), and geographically weighted regression (Fotheringham, Brunsdon and Charlton (2002).

Prof. Ngianga-Bakwin Kandala
Prof. Dr. Saverio Stranges
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 papers will be 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 2300 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

  • global public health issues
  • HIV
  • TB
  • HSV-2
  • malaria, hypertension
  • diabetes
  • malnutrition
  • Female Genital Mutilation (FGM) and obesity
  • Environment, Bayesian Spatial modelling
  • complex survey data

Published Papers (1 paper)

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Research

Article
Mapping the Burden of Hypertension in South Africa: A Comparative Analysis of the National 2012 SANHANES and the 2016 Demographic and Health Survey
Int. J. Environ. Res. Public Health 2021, 18(10), 5445; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18105445 - 19 May 2021
Viewed by 712
Abstract
This study investigates the provincial variation in hypertension prevalence in South Africa in 2012 and 2016, adjusting for individual level demographic, behavioural and socio-economic variables, while allowing for spatial autocorrelation and adjusting simultaneously for the hierarchical data structure and risk factors. Data were [...] Read more.
This study investigates the provincial variation in hypertension prevalence in South Africa in 2012 and 2016, adjusting for individual level demographic, behavioural and socio-economic variables, while allowing for spatial autocorrelation and adjusting simultaneously for the hierarchical data structure and risk factors. Data were analysed from participants aged ≥15 years from the South African National Health and Nutrition Examination Survey (SANHANES) 2012 and the South African Demographic and Health Survey (DHS) 2016. Hypertension was defined as blood pressure ≥ 140/90 mmHg or self-reported health professional diagnosis or on antihypertensive medication. Bayesian geo-additive regression modelling investigated the association of various socio-economic factors on the prevalence of hypertension across South Africa’s nine provinces while controlling for the latent effects of geographical location. Hypertension prevalence was 38.4% in the SANHANES in 2012 and 48.2% in the DHS in 2016. The risk of hypertension was significantly high in KwaZulu-Natal and Mpumalanga in the 2016 DHS, despite being previously nonsignificant in the SANHANES 2012. In both survey years, hypertension was significantly higher among males, the coloured population group, urban participants and those with self-reported high blood cholesterol. The odds of hypertension increased non-linearly with age, body mass index (BMI), waist circumference. The findings can inform decision making regarding the allocation of public resources to the most affected areas of the population. Full article
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