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

<p>Abstract</p> <p>Background</p> <p>Cholera has persisted in Ghana since its introduction in the early 70's. 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 <it>V. cholera </it>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.</p> <p>Results</p> <p>A 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.</p> <p>Conclusion</p> <p>The results suggest that proximity and density of open space refuse dumps play a contributory role in cholera infection in Kumasi.</p

Spatial and demographic patterns of Cholera in Ashanti region - Ghana

Abstract Background Cholera 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. Results A 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 Moran's Index = 0.271 and P Chi square for trend analysis reflected a direct spatial relationship between cholera and urbanization (χ2 = 2995.5, P χ2 = 1757.2, P χ2 = 831.38, P Conclusion The results suggest that high urbanization, high overcrowding, and neighborhood with Kumasi Metropolis are the most important predictors of cholera in Ashanti region.</p

Spatial dependency of Buruli ulcer prevalence on arsenic-enriched domains in Amansie West District, Ghana: implications for arsenic mediation in Mycobacterium ulcerans infection

BACKGROUND: In 1998, the World Health Organization recognized Buruli ulcer (BU), a human skin disease caused by Mycobacterium ulcerans (MU), as the third most prevalent mycobacterial disease. In Ghana, there have been more than 2000 reported cases in the last ten years; outbreaks have occurred in at least 90 of its 110 administrative districts. In one of the worst affected districts, Amansie West, there are arsenic-enriched surface environments resulting from the oxidation of arsenic-bearing minerals, occurring naturally in mineral deposits. RESULTS: Proximity analysis, carried out to determine spatial relationships between BU-affected areas and arsenic-enriched farmlands and arsenic-enriched drainage channels in the Amansie West District, showed that mean BU prevalence in settlements along arsenic-enriched drainages and within arsenic-enriched farmlands is greater than elsewhere. Furthermore, mean BU prevalence is greater along arsenic-enriched drainages than within arsenic-enriched farmlands. CONCLUSION: The results suggest that arsenic in the environment may play a contributory role in MU infection

Bayesian structured additive regression modeling of epidemic data: application to cholera

Abstract Background A significant interest in spatial epidemiology lies in identifying associated risk factors which enhances the risk of infection. Most studies, however, make no, or limited use of the spatial structure of the data, as well as possible nonlinear effects of the risk factors. Methods We develop a Bayesian Structured Additive Regression model for cholera epidemic data. Model estimation and inference is based on fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulations. The model is applied to cholera epidemic data in the Kumasi Metropolis, Ghana. Proximity to refuse dumps, density of refuse dumps, and proximity to potential cholera reservoirs were modeled as continuous functions; presence of slum settlers and population density were modeled as fixed effects, whereas spatial references to the communities were modeled as structured and unstructured spatial effects. Results We observe that the risk of cholera is associated with slum settlements and high population density. The risk of cholera is equal and lower for communities with fewer refuse dumps, but variable and higher for communities with more refuse dumps. The risk is also lower for communities distant from refuse dumps and potential cholera reservoirs. The results also indicate distinct spatial variation in the risk of cholera infection. Conclusion The study highlights the usefulness of Bayesian semi-parametric regression model analyzing public health data. These findings could serve as novel information to help health planners and policy makers in making effective decisions to control or prevent cholera epidemics.</p

Hierarchical Bayesian modeling of the space - time diffusion patterns of cholera epidemic in Kumasi, Ghana

This study analyses the joint effects of the two transmission routes of cholera on the space-time diffusion dynamics. Statistical models are developed and presented to investigate the transmission network routes of cholera diffusion. A hierarchical Bayesian modelling approach is employed for a joint analysis of nonlinear effects of continuous covariates, spatially structured variation, and unstructured heterogeneity. Proximity to primary case locations and population density serve as continuous covariates. Reference to communities is modelled as a spatial effect. The study applied to the Kumasi area in Ghana shows that communities proximal to primary case locations are infected relatively early during the epidemics, with more remote communities infected at later dates. Similarly, more populous communities are infected relatively early and less populous communities at later dates. The rate of infection increases almost linearly with population density. A non systematic relation occurs between the rate of infection and proximity to primary case locations. It is discussed how these findings could serve as significant information to help health planners and policy makers in making effective decisions to limit cholera epidemics

Hierarchical Bayesian modeling of the space-time diffusion patterns of cholera epidemic in Kumasi, Ghana

This study analyses the joint effects of the two transmission routes of cholera on the space-time diffusion dynamics. Statistical models are developed and presented to investigate the transmission network routes of cholera diffusion. A hierarchical Bayesian modelling approach is employed for a joint analysis of nonlinear effects of continuous covariates, spatially structured variation, and unstructured heterogeneity. Proximity to primary case locations and population density serve as continuous covariates. Reference to communities is modelled as a spatial effect. The study applied to the Kumasi area in Ghana shows that communities proximal to primary case locations are infected relatively early during the epidemics, with more remote communities infected at later dates. Similarly, more populous communities are infected relatively early and less populous communities at later dates. The rate of infection increases almost linearly with population density. A non systematic relation occurs between the rate of infection and proximity to primary case locations. It is discussed how these findings could serve as significant information to help health planners and policy makers in making effective decisions to limit cholera epidemics