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A Bayesian Spatio-Temporal Analysis of Malaria in the Greater Accra Region of Ghana from 2015 to 2019

Author

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  • Elorm Donkor

    (Department of Global Health, Research School of Population Health, Australian National University, Canberra, ACT 2601, Australia
    Greater Accra Regional Health Directorate, Ghana Health Service, P.O. Box 184, Accra, Ghana)

  • Matthew Kelly

    (Department of Global Health, Research School of Population Health, Australian National University, Canberra, ACT 2601, Australia)

  • Cecilia Eliason

    (Department of Adult Health, School of Nursing and Midwifery, College of Health Sciences, University of Ghana, Legon, P.O. Box LG43, Accra, Ghana)

  • Charles Amotoh

    (Greater Accra Regional Health Directorate, Ghana Health Service, P.O. Box 184, Accra, Ghana)

  • Darren J. Gray

    (Department of Global Health, Research School of Population Health, Australian National University, Canberra, ACT 2601, Australia)

  • Archie C. A. Clements

    (Faculty of Health Sciences, Curtin University, Perth, WA 6102, Australia
    Telethon Kids Institute, Nedlands, WA 6009, Australia)

  • Kinley Wangdi

    (Department of Global Health, Research School of Population Health, Australian National University, Canberra, ACT 2601, Australia)

Abstract

The Greater Accra Region is the smallest of the 16 administrative regions in Ghana. It is highly populated and characterized by tropical climatic conditions. Although efforts towards malaria control in Ghana have had positive impacts, malaria remains in the top five diseases reported at healthcare facilities within the Greater Accra Region. To further accelerate progress, analysis of regionally generated data is needed to inform control and management measures at this level. This study aimed to examine the climatic drivers of malaria transmission in the Greater Accra Region and identify inter-district variation in malaria burden. Monthly malaria cases for the Greater Accra Region were obtained from the Ghanaian District Health Information and Management System. Malaria cases were decomposed using seasonal-trend decomposition, based on locally weighted regression to analyze seasonality. A negative binomial regression model with a conditional autoregressive prior structure was used to quantify associations between climatic variables and malaria risk and spatial dependence. Posterior parameters were estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. A total of 1,105,370 malaria cases were recorded in the region from 2015 to 2019. The overall malaria incidence for the region was approximately 47 per 1000 population. Malaria transmission was highly seasonal with an irregular inter-annual pattern. Monthly malaria case incidence was found to decrease by 2.3% (95% credible interval: 0.7–4.2%) for each 1 °C increase in monthly minimum temperature. Only five districts located in the south-central part of the region had a malaria incidence rate lower than the regional average at >95% probability level. The distribution of malaria cases was heterogeneous, seasonal, and significantly associated with climatic variables. Targeted malaria control and prevention in high-risk districts at the appropriate time points could result in a significant reduction in malaria transmission in the Greater Accra Region.

Suggested Citation

  • Elorm Donkor & Matthew Kelly & Cecilia Eliason & Charles Amotoh & Darren J. Gray & Archie C. A. Clements & Kinley Wangdi, 2021. "A Bayesian Spatio-Temporal Analysis of Malaria in the Greater Accra Region of Ghana from 2015 to 2019," IJERPH, MDPI, vol. 18(11), pages 1-15, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:6080-:d:569126
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    References listed on IDEAS

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    1. M. C. Thomson & F. J. Doblas-Reyes & S. J. Mason & R. Hagedorn & S. J. Connor & T. Phindela & A. P. Morse & T. N. Palmer, 2006. "Malaria early warnings based on seasonal climate forecasts from multi-model ensembles," Nature, Nature, vol. 439(7076), pages 576-579, February.
    2. Shaowei Sang & Shaohua Gu & Peng Bi & Weizhong Yang & Zhicong Yang & Lei Xu & Jun Yang & Xiaobo Liu & Tong Jiang & Haixia Wu & Cordia Chu & Qiyong Liu, 2015. "Predicting Unprecedented Dengue Outbreak Using Imported Cases and Climatic Factors in Guangzhou, 2014," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(5), pages 1-12, May.
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    Cited by:

    1. Koh Kawaguchi & Elorm Donkor & Aparna Lal & Matthew Kelly & Kinley Wangdi, 2022. "Distribution and Risk Factors of Malaria in the Greater Accra Region in Ghana," IJERPH, MDPI, vol. 19(19), pages 1-13, September.
    2. Zemenu Tadesse Tessema & Getayeneh Antehunegn Tesema & Susannah Ahern & Arul Earnest, 2023. "A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research," IJERPH, MDPI, vol. 20(13), pages 1-24, July.

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