The Spatial Epidemiology of Tuberculosis in Gurage Zone, Southern Ethiopia.
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Date
2018-12
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Addis Ababa University
Abstract
Background: The global distribution of tuberculosis is skewed heavily toward low-and-middle
income countries, which accounted for about 87% of all estimated incident cases. Ethiopia is a
low-income country in east Africa that remains highly afflicted by tuberculosis, with varying
degrees of magnitudes across settings. However, there is a dearth of studies clarifying about the
spatial epidemiology of the disease in Ethiopia. Lack of such information may contribute to the
partial effectiveness of tuberculosis control programs.
Objectives: The specific objectives of this study were: 1) to detect spatial and space-time
clustering of tuberculosis, 2) to estimate spatial risk of tuberculosis distribution using limited
spatial datasets, and 3) to identify ecological factors affecting spatial distribution of tuberculosis
in Gurage Zone, Southern Ethiopia.
Methods: The study data were obtained from different sources. Specific objectives 1 and 3
included a total of 15,805 tuberculosis patients diagnosed at health facilities in Gurage Zone
during 2007 to 2016, whereas specific objective 2 included 1,601 patients diagnosed in 2016.
The geo-location and population data were obtained from the Central Statistical Agency of
Ethiopia (specific objectives 1-3). The altitude data were extracted from global digital elevation
model v2 (specific objective 2). The normalized difference vegetation index data were derived
from the moderate resolution imaging spectroradiometer imagery, and the temperature and
rainfall data were obtained from the Meteorological Agency of Ethiopia (specific objective 3).
The global Moran’s I, Kulldorff’s scan and Getis-Ord
statistics were used to analyze purely
spatial and space-time clustering of tuberculosis (specific objective 1). The geostatistical kriging
approach was applied to estimate the spatial risk of tuberculosis distribution (specific objective
2). The spatial panel data analysis was used to estimate the effects of ecological factors on spatial
distribution of tuberculosis prevalence rate (specific objective 3).
Results: The prevalence of tuberculosis varied from 70.4 to 155.3 cases per 100,000 population
in the Gurage Zone during 2007 to 2016. Eleven purely spatial clusters (relative risk: 1.36–14.52,
P-value < 0.001) and three space-time clusters (relative risk: 1.46–2.01, P-value < 0.001) for
high occurrence of tuberculosis were detected. The clusters were mainly concentrated in border
areas of the zone. The predictive accuracies of ordinary cokriging models have improved with
the inclusion of anisotropy, altitude and latitude covariates, the change in detrending pattern
from local to global, and the increase in size of spatial dataset (mean-standardized error = 0, rootxi
mean-square-standardized error = 1, and average-standard error ≈ root-mean-square error). The
spatial risk of tuberculosis was estimated to be higher (i.e., tuberculosis prevalence rate > 100
cases per 100,000 population) at western, northwest, southwest and southeast parts of the study
area, and crossed between high and low at west-central parts. The tuberculosis prevalence rate
observed in a given kebele was determined by both tuberculosis prevalence rate (spatial
autoregressive coefficient = 0.83) and unobserved factors (spatial autocorrelation coefficient = -
0.70) in the neighboring kebeles. By controlling the spatial effects, a 1°C rise in temperature was
associated with an increase in the number of tuberculosis prevalence rate by 0.72, and a 1 person
per square kilometer increase in population density was related to an increase in the number of
tuberculosis prevalence rate by 1.19.
Conclusions: The spatial and space-time clusters for high occurrence of tuberculosis were
mainly concentrated at border areas of the Gurage Zone. The prevalence rate of tuberculosis in a
given kebele was determined by both the prevalence rate of tuberculosis and other unobserved
factors in its neighboring kebeles in the zone, indicating sustained transmission of the disease
within the communities. The spatial risk of tuberculosis distribution between kebeles in the zone
was partly explained by spatial variations in temperature, population density, altitude, and
latitude. The geostatistical kriging approach can be applied to estimate the spatial risk of
tuberculosis distribution in data limited settings.
Recommendations: Tuberculosis control programs should consider the cooperation of
neighboring kebeles in the design and implementation of tuberculosis prevention and control
strategies to interrupt the chain of disease transmission between the communities. Moreover, the
designing of locally effective tuberculosis prevention and control strategies should consider
spatial locations with higher temperature and population density. Further research is required to
evaluate the effectiveness of geographically targeting tuberculosis prevention and control
interventions using the inputs from spatial epidemiological methods.
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Keywords
Ecological factors, Geostatistical kriging approach, Purely spatial clusters, Spacetime clusters, Spatial autocorrelation, Spatial epidemiology, Spatial heterogeneity, Spatial panel data analysis, Tuberculosis distribution