Abstract:

Background: Cholera epidemics is a serious health concern in low and middle income countries (LMICs); to better target interventions, the integration of existing initiatives with Geographical Information System (GIS) technology offers opportunities for surveillance, monitoring, and exploring risk factors. GIS has proven to be successful tool in improving health in different parts of world including LMICs and this technology enables analysis of information within geographical spatial context. Data is presented in terms of maps and users are able to establish conclusions about geographical relationship. Building on a number of studies that have documented the biological aspects to understand the spatial distribution of cholera.

Methods: This was a cross-sectional secondary data, and using geographical and spatial distribution of suspected cholera cases in Ilemela and Nyamagana Municipals from August 2015 – April 2016. All patients from camps were samples to provide a detailed picture of the problem. Data collection involved two phases: 1) data collection from patients’ mainly socio-demographic data; and 2) geospatial mapping of confirmed cholera cases using latitudes (Eastings) and Longitudes (Northings) of the reported residency of the patient.

Results: The spatial pattern of distribution of suspected cholera cases revealed the lack of well confined hot spots for the disease. Among suspected cholera cases (n=878, only (n=466, 52.6%) underwent laboratory investigation and (n=366, 41.6%) were positive. There was no association between socio demographic characteristics and cholera cases, while time spent at cholera camp and water sources showed associated with cholera outbreak.

Conclusion: Patterns of cholera disease outbreak in the study area revealed the lack of well confined hot spots for the disease. However, some areas are more prone with more cases as compared to others, indicating hot spots do exist. From the spatial maps one can conclude that each of the affected area reveled district geospatial pattern, which differ largely from the other. In this regard therefore the study suggests that decentralized strategy, which would focus the intervention on each heavily affected area, should be considered.