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

Elorm Donkor, Matthew Kelly, Cecilia Eliason, Charles Amotoh, Darren J. Gray, Archie C. A. Clements and Kinley Wangdi
Additional contact information
Elorm Donkor: Department of Global Health, Research School of Population Health, Australian National University, Canberra, ACT 2601, Australia
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
Kinley Wangdi: Department of Global Health, Research School of Population Health, Australian National University, Canberra, ACT 2601, Australia

IJERPH, 2021, vol. 18, issue 11, 1-15

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.

Keywords: Ghana; malaria; Bayesian; modelling; climatic (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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