Geographic Patterns of Malaria in the Brong Ahafo Region of Ghana

Research Article

Ghana. Austin J Public Health Epidemiol. 2015; 2(2): 1020.

# Geographic Patterns of Malaria in the Brong Ahafo Region of Ghana

Osei FB¹* and Yibile MM²

¹Department of Mathematics and Statistics, University of Energy and Natural Resources, Africa

²Faculty of Public Health and Allied Sciences, Catholic University College of Ghana-Fiapre, Africa

*Corresponding author: Frank Badu Osei, Department of Mathematics and Statistics, University of Energy and Natural Resources, Africa

Received: February 05, 2015; Accepted: September 15, 2015; Published: September 22, 2015

## Abstract

Knowledge of the geographical distribution of disease incidence is useful to assess the need for geographical variation in health resource location and planning. Many disease mapping methods have been developed, yet most of these methods are computationally intensive and the results are somewhat challenging for non-statisticians to understand. This study explores the simplicity of the application of the empirical Bayesian’s method in disease mapping. The methodology is illustrated by mapping the yearly geographical distribution of district level malaria incidences from 2008 to 2011 in the Brong/Ahafo region of Ghana. The results translate into a malaria distribution maps with persistent clustering of high incidence rates at the western parts whiles low rates has persisted at the eastern parts of the study area. These maps would be useful to inform on public health provision about the success or failure of interventions, and to make hypothesis or to provide evidences concerning disease etiology.

Keywords: Malaria; Brong Ahafo Region of Ghana; Empirical Bayesians

## Background

Spatial epidemiology seeks to study the spatial/geographical distribution of disease incidences and its relationship to potential risk factors [1-3]. One important aspect of spatial epidemiology is disease mapping which seeks to display incidence rates geographically [4-6]. Mapping the geographical distribution of a disease is useful to inform on public health provision about the success or failure of interventions, and to make hypothesis or to provide evidences concerning disease etiology. Such studies are essential for epidemiologist and health officials to implicitly understand the population’s interaction with its environment and make appropriate decisions. Since the quality of decision-making relies on an accurate quantification of risks from observed rates, disease map should be based on smoothed estimates, clean of any random noise and any artifacts of population variation [5-7].

The development of disease mapping methods has progressed considerably [5,8,9]. Governs [10] discusses the limitations of various disease mapping methods as a result of heterogeneous population distributions. Most of these methods [11-13] have been developed within a fully Bayesian framework which require iterative procedures, such as Markov Chain Monte Carlo (MCMC). Thus, these methods and are computer intensive and require fine-tuning, which makes their application and interpretation challenging for non-statisticians. Unlike the fully Bayesian method, the empirical Bayesian methods neglect the variability associated with the parameter estimation and allow only computation of approximate standard errors for the risk. Thus, they are easier to implement, less challenging to interpret results for no-statisticians, and are favored by practitioners.

Malaria has continued to be a public health burden and a great threat globally [14,15]. The burden of malaria has, for decades, been a major public health concern in Ghana. In this study, GIS and Exploratory Spatial Data Analysis (ESDA) techniques are used to study the geographical patterns of malaria for the districts in the Brong Ahafo Region of Ghana (Figure 1). This region is the second largest region of the 10 regions of Ghana. The population of the region is 2,310,983 with a terrestrial size of 39,557 square kilometers which is nearly 16.6% of the total land size of Ghana [16]. The region has a tropical climate, with high temperatures averaging 23.9oC and a double maxima rainfall pattern. Rainfall ranges, from an average of 1000mm in the northern parts to 1400 mm in the southern parts. The region lies in the forest zone and is a major cocoa and timber producing area. The region has two main vegetation types, the moist semi-deciduous forest, mostly in the southern and southeastern parts, and the guinea savannah woodland, which is predominant in the northern and northeastern parts of the region. The region has 22 administrative districts based on which spatial analysis is conducted [16].