F.B. Osei

Spatial and demographic patterns of Cholera in Ashanti region - Ghana

BackgroundCholera has claimed many lives throughout history and it continues to be a global threat, especially in countries in Africa. The disease is listed as one of three internationally quarantinable diseases by the World Health organization, along with plague and yellow fever. Between 1999 and 2005, Africa alone accounted for about 90% of over 1 million reported cholera cases worldwide. In Ghana, there have been over 27000 reported cases since 1999. In one of the affected regions in Ghana, Ashanti region, massive outbreaks and high incidences of cholera have predominated in urban and overcrowded communities.ResultsA GIS based spatial analysis and statistical analysis, carried out to determine clustering of cholera, showed that high cholera rates are clustered around Kumasi Metropolis (the central part of the region), with Morans Index = 0.271 and P < 0.001. Furthermore, A Mantel-Haenszel Chi square for trend analysis reflected a direct spatial relationship between cholera and urbanization (χ2 = 2995.5, P < 0.0001), overcrowding (χ2 = 1757.2, P < 0.0001), and an inverse relationship between cholera and order of neighborhood with Kumasi Metropolis (χ2 = 831.38, P < 0.0001).ConclusionThe results suggest that high urbanization, high overcrowding, and neighborhood with Kumasi Metropolis are the most important predictors of cholera in Ashanti region.

Spatial dependency of V. cholera prevalence on open space refuse dumps in Kumasi, Ghana: a spatial statistical modelling

BackgroundCholera has persisted in Ghana since its introduction in the early 70s. From 1999 to 2005, the Ghana Ministry of Health officially reported a total of 26,924 cases and 620 deaths to the WHO. Etiological studies suggest that the natural habitat of V. cholera is the aquatic environment. Its ability to survive within and outside the aquatic environment makes cholera a complex health problem to manage. Once the disease is introduced in a population, several environmental factors may lead to prolonged transmission and secondary cases. An important environmental factor that predisposes individuals to cholera infection is sanitation. In this study, we exploit the importance of two main spatial measures of sanitation in cholera transmission in an urban city, Kumasi. These are proximity and density of refuse dumps within a community.ResultsA spatial statistical modelling carried out to determine the spatial dependency of cholera prevalence on refuse dumps show that, there is a direct spatial relationship between cholera prevalence and density of refuse dumps, and an inverse spatial relationship between cholera prevalence and distance to refuse dumps. A spatial scan statistics also identified four significant spatial clusters of cholera; a primary cluster with greater than expected cholera prevalence, and three secondary clusters with lower than expected cholera prevalence. A GIS based buffer analysis also showed that the minimum distance within which refuse dumps should not be sited within community centres is 500 m.ConclusionThe results suggest that proximity and density of open space refuse dumps play a contributory role in cholera infection in Kumasi.

Diarrhea Morbidities in Small Areas : Accounting for Non-Stationarity in Sociodemographic Impacts using Bayesian Spatially Varying Coefficient Modelling

Model-based estimation of diarrhea risk and understanding the dependency on sociodemographic factors is important for prioritizing interventions. It is unsuitable to calibrate regression model with a single set of coefficients, especially for large spatial domains. For this purpose, we developed a Bayesian hierarchical varying coefficient model to account for non-stationarity in the covariates. We used the integrated nested Laplace approximation for parameter estimation. Diarrhea morbidities in Ghana motivated our empirical study. Results indicated improvement regarding model fit and epidemiological benefits. The findings highlighted substantial spatial, temporal, and spatio-temporal heterogeneities in both diarrhea risk and the coefficients of the sociodemographic factors. Diarrhea risk in peri-urban and urban districts were 13.2% and 10.8% higher than rural districts, respectively. The varying coefficient model indicated further details, as the coefficients varied across districts. A unit increase in the proportion of inhabitants with unsafe liquid waste disposal was found to increase diarrhea risk by 11.5%, with higher percentages within the south-central parts through to the south-western parts. Districts with safe and unsafe drinking water sources unexpectedly had a similar risk, as were districts with safe and unsafe toilets. The findings show that site-specific interventions need to consider the varying effects of sociodemographic factors.

Evaluating Spatial and Space-Time Clustering of Cholera in Ashanti-Region-Ghana

Basic problems in geographical surveillance for a spatially distributed disease data are the identification of areas of exceptionally high prevalence or clusters, test of their statistical significance, and identification of the reasons behind the elevated prevalence of the disease. Knowledge of the location of high risk areas of diseases and factors leading to such elevated risk is essential to better understand human interaction with its environment, especially when the disease transmission is enhanced by environmental or demographic factors. Cluster analysis provides opportunities for environmental epidemiologist to study associations between demographic and environmental exposures and the spatial distribution of diseases (Myaux et al., 1997; Kulldorff and Nagarwalla, 1995; Besag and Newell, 1991; Kulldorff, 2001; Kulldorff et al., 1998).

Spatial variation and hot-spots of district level diarrhea incidences in Ghana: 2010–2014

BackgroundDiarrhea is a public health menace, especially in developing countries. Knowledge of the biological and anthropogenic characteristics is abundant. However, little is known about its spatial patterns especially in developing countries like Ghana. This study aims to map and explore the spatial variation and hot-spots of district level diarrhea incidences in Ghana.MethodsData on district level incidences of diarrhea from 2010 to 2014 were compiled together with population data. We mapped the relative risks using empirical Bayesian smoothing. The spatial scan statistics was used to detect and map spatial and space-time clusters. Logistic regression was used to explore the relationship between space-time clustering and urbanization strata, i.e. rural, peri-urban, and urban districts.ResultsWe observed substantial variation in the spatial distribution of the relative risk. There was evidence of significant spatial clusters with most of the excess incidences being long-term with only a few being emerging clusters. Space-time clustering was found to be more likely to occur in peri-urban districts than in rural and urban districts.ConclusionThis study has revealed that the excess incidences of diarrhea is spatially clustered with peri-urban districts showing the greatest risk of space-time clustering. More attention should therefore be paid to diarrhea in peri-urban districts. These findings also prompt public health officials to integrate disease mapping and cluster analyses in developing location specific interventions for reducing diarrhea.

Cholera and Spatial Epidemiology

Cholera is an acute intestinal infection caused by the water borne bacteria Vibrio cholerae O1 or O139 (V. cholerae). Infection is mainly through ingestion of contaminated water or food (Kelly, 2001). Approximately 102-103 cells are required to cause severe diarrhea and dehydration (Sack et al., 1998; Hornich et al., 1971). Ingested cholera vibrios from contaminated water or food must pass through the acid stomach before they are able to colonize the upper part of the small intestine. After penetrating the mucus layer, V. cholerae colonizes the epithelial lining of the gut, secreting cholera toxin which affects the small intestine.

Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment

BackgroundSpatial modelling studies of schistosomiasis (SCH) are now commonplace. Covariate values are commonly extracted at survey locations, where infection does not always take place, resulting in an unknown positional exposure mismatch. The present research aims to: (i) describe the nature of the positional exposure mismatch in modelling SCH helminth infections; (ii) delineate exposure areas to correct for such positional mismatch; and (iii) validate exposure areas using human positive cases.MethodsTo delineate exposure areas to Schistosoma japonicum, a spatial Bayesian network (sBN) was constructed. It uses data on exposure risk factors such as: potential sites for snails’ accessibility, geographical distribution of snail infection rate, and cost of the community to access nearby water bodies. Prior and conditional probabilities were obtained from the literature and inserted as weights based on their relative contribution to exposure; these probabilities were then used to calculate joint probabilities of exposure within the sBN.ResultsHigh values of probability of S. japonicum exposure correspond to polygons where snails could potentially be present, for instance in wet soils and areas with low slopes, but also where people can easily access water bodies. Low correlation (R2 = 0.3) was found between the percentage of human cases and the delineated probabilities of exposure when validation buffers are generated over the human cases.ConclusionsThe utility of a probabilistic method for the identification of exposure areas for S. japonicum, with wider application for other water-borne infections, was demonstrated. From a public health perspective, the schistosomiasis exposure sBN developed in this study could be used to guide local schistosomiasis control teams to specific potential areas of exposure, and improve efficiency of mass drug administration campaigns in places where people are likely to be exposed to the infection.

Spatio-temporal analysis of small-area intestinal parasites infections in Ghana

Intestinal parasites infection is a major public health burden in low and middle-income countries. In Ghana, it is amongst the top five morbidities. In order to optimize scarce resources, reliable information on its geographical distribution is needed to guide periodic mass drug administration to populations of high risk. We analyzed district level morbidities of intestinal parasites between 2010 and 2014 using exploratory spatial analysis and geostatistics. We found a significantly positive Moran’s Index of spatial autocorrelation for each year, suggesting that adjoining districts have similar risk levels. Using local Moran’s Index, we found high-high clusters extending towards the Guinea and Sudan Savannah ecological zones, whereas low-low clusters extended within the semi-deciduous forest and transitional ecological zones. Variograms indicated that local and regional scale risk factors modulate the variation of intestinal parasites. Poisson kriging maps showed smoothed spatially varied distribution of intestinal parasites risk. These emphasize the need for a follow-up investigation into the exact determining factors modulating the observed patterns. The findings also underscored the potential of exploratory spatial analysis and geostatistics as tools for visualizing the spatial distribution of small area intestinal worms infections.

Spatio-temporal dynamics of schistosomiasis in Rwanda between 2001 and 2012: Impact of the national neglected tropical disease control programme

Schistosomiasis is recognised as a major public health problem in Rwanda. We aimed to identify the spatio-temporal dynamics of its distribution at a fine-scale spatial resolution and to explore the impact of control programme interventions. Incidence data of Schistosoma mansoni infection at 367 health facilities were obtained for the period 2001-2012. Disease cluster analyses were conducted using spatial scan statistics and geographic information systems. The impact of control interventions was assessed for three distinct sub-periods. Findings demonstrated persisting, emerging and re-emerging clusters of schistosomiasis infection across space and time. The control programme initially caused an abrupt increase in incidence rates during its implementation phase. However, this was followed by declining and disappearing clusters when the programme was fully in place. The findings presented should contribute to a better understanding of the dynamics of schistosomiasis distribution to be used when implementing future control activities, including prevention and elimination efforts.