IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i8p4442-d788751.html
   My bibliography  Save this article

Bayesian Hierarchical Modelling of Historical Data of the South African Coal Mining Industry for Compliance Testing

Author

Listed:
  • Felix Made

    (School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa
    Global Biostatistics and Programming, Pharmaceutical Product Development, Thermo Fisher Scientific, Woodmead, Johannesburg 2191, South Africa)

  • Ngianga-Bakwin Kandala

    (School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa
    Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
    Département de la Santé Communautaire, Institut Supérieur des Techniques Médicales de Kinshasa, Kinshasa XI, Mont Ngafula, Kinshasa B.P. 774, Congo)

  • Derk Brouwer

    (School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa)

Abstract

Bayesian hierarchical framework for exposure data compliance testing is highly recommended in occupational hygiene. However, it has not been used for coal dust exposure compliance testing in South Africa (SA). The Bayesian analysis incorporates prior information, which increases solid decision making regarding risk management. This study compared the posterior 95th percentile (P95) of the Bayesian non-informative and informative prior from historical data relative to the occupational exposure limit (OEL) and exposure categories, and the South African Mining Industry Code of Practice (SAMI CoP) approach. A total of nine homogenous exposure groups (HEGs) with a combined 243 coal mine workers’ coal dust exposure data were included in this study. Bayesian framework with Markov chain Monte Carlo (MCMC) simulation to draw a full P95 posterior distribution relative to the OEL was used to investigate compliance. We obtained prior information from historical data and employed non-informative prior distribution to generate the posterior findings. The findings were compared to the SAMI CoP. The SAMI CoP 90th percentile (P90) indicated that one HEG was compliant (below the OEL), while none of the HEGs in the Bayesian methods were compliant. The analysis using non-informative prior indicated a higher variability of exposure than the informative prior according to the posterior GSD. The median P95 from the non-informative prior were slightly lower with wider 95% credible intervals (CrI) than the informative prior. All the HEGs in both Bayesian approaches were in exposure category four (poorly controlled), with the posterior probabilities slightly lower in the non-informative uniform prior distribution. All the methods mainly indicated non-compliance from the HEGs. The non-informative prior, however, showed a possible potential of allocating HEGs to a lower exposure category, but with high uncertainty compared to the informative prior distribution from historical data. Bayesian statistics with informative prior derived from historical data should be highly encouraged in coal dust overexposure assessments in South Africa for correct decision making.

Suggested Citation

  • Felix Made & Ngianga-Bakwin Kandala & Derk Brouwer, 2022. "Bayesian Hierarchical Modelling of Historical Data of the South African Coal Mining Industry for Compliance Testing," IJERPH, MDPI, vol. 19(8), pages 1-11, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:8:p:4442-:d:788751
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/8/4442/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/8/4442/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Uris Lantz C Baldos & Frederi G Viens & Thomas W Hertel & Keith O Fuglie, 2019. "R&D Spending, Knowledge Capital, and Agricultural Productivity Growth: A Bayesian Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(1), pages 291-310.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Felix Made & Ngianga-Bakwin Kandala & Derk Brouwer, 2023. "Bayesian Hierarchical Framework from Expert Elicitation in the South African Coal Mining Industry for Compliance Testing," IJERPH, MDPI, vol. 20(3), pages 1-15, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lachaud, Michée A. & Bravo-Ureta, Boris E., 2022. "A Bayesian statistical analysis of return to agricultural R&D investment in Latin America: Implications for food security," Technology in Society, Elsevier, vol. 70(C).
    2. Hertel, By Thomas W. & Baldos, Uris L.C. & Fuglie, Keith O., 2020. "Trade in technology: A potential solution to the food security challenges of the 21st century," European Economic Review, Elsevier, vol. 127(C).
    3. Michał Gazdecki & Grzegorz Leszczyński & Marek Zieliński, 2021. "Food Sector as an Interactive Business World: A Framework for Research on Innovations," Energies, MDPI, vol. 14(11), pages 1-19, June.
    4. Ryota Nakatani, 2024. "Food companies' productivity dynamics: Exploring the role of intangible assets," Agribusiness, John Wiley & Sons, Ltd., vol. 40(1), pages 185-226, January.
    5. Felix Made & Ngianga-Bakwin Kandala & Derk Brouwer, 2023. "Bayesian Hierarchical Framework from Expert Elicitation in the South African Coal Mining Industry for Compliance Testing," IJERPH, MDPI, vol. 20(3), pages 1-15, January.
    6. Youngjune Kim & Ji Yong Lee, 2020. "Effects of Government Payments on Agricultural Productivity: The Case of South Korea," Sustainability, MDPI, vol. 12(9), pages 1-11, April.
    7. Hertel, Thomas W. & de Lima, Cicero Z., 2020. "Viewpoint: Climate impacts on agriculture: Searching for keys under the streetlight," Food Policy, Elsevier, vol. 95(C).
    8. Hassan, Samir Ul & Khanday, Shafi Ahmad & Ahmad, Masroor & Mishra, Biswambhara & Rymbai, Motika Sinha, 2022. "A Historical Cum Empirical Overview of Agriculture Spending and Output Nexus in India," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 14(3), September.
    9. Nilsson, Pia & Bommarco, Riccardo & Hansson, Helena & Kuns, Brian & Schaak, Henning, 2022. "Farm performance and input self-sufficiency increases with functional crop diversity on Swedish farms," Ecological Economics, Elsevier, vol. 198(C).
    10. Alejandro Nin‐Pratt, 2021. "Agricultural R&D investment intensity: A misleading conventional measure and a new intensity index," Agricultural Economics, International Association of Agricultural Economists, vol. 52(2), pages 317-328, March.
    11. Arita, Shawn & Cooper, Joseph C. & Gerlt, Scott & Meyer, Seth D. & Thompson, Wyatt & Westhoff, Patrick, 2021. "Agricultural Supply Response under Extreme Market Events and Policy Shocks," 2021 Annual Meeting, August 1-3, Austin, Texas 313930, Agricultural and Applied Economics Association.
    12. Thompson, Wyatt & Dewbre, Joe & Pieralli, Simone & Schroeder, Kateryna & Pérez Domínguez, Ignacio & Westhoff, Patrick, 2019. "Long-term crop productivity response and its interaction with cereal markets and energy prices," Food Policy, Elsevier, vol. 84(C), pages 1-9.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:8:p:4442-:d:788751. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.