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      Spatio-Temporal Analysis of Smear-Positive Tuberculosis in the Sidama Zone, Southern Ethiopia

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          Abstract

          Background

          Tuberculosis (TB) is a disease of public health concern, with a varying distribution across settings depending on socio-economic status, HIV burden, availability and performance of the health system. Ethiopia is a country with a high burden of TB, with regional variations in TB case notification rates (CNRs). However, TB program reports are often compiled and reported at higher administrative units that do not show the burden at lower units, so there is limited information about the spatial distribution of the disease. We therefore aim to assess the spatial distribution and presence of the spatio-temporal clustering of the disease in different geographic settings over 10 years in the Sidama Zone in southern Ethiopia.

          Methods

          A retrospective space–time and spatial analysis were carried out at the kebele level (the lowest administrative unit within a district) to identify spatial and space-time clusters of smear-positive pulmonary TB (PTB). Scan statistics, Global Moran’s I, and Getis and Ordi (Gi*) statistics were all used to help analyze the spatial distribution and clusters of the disease across settings.

          Results

          A total of 22,545 smear-positive PTB cases notified over 10 years were used for spatial analysis. In a purely spatial analysis, we identified the most likely cluster of smear-positive PTB in 192 kebeles in eight districts (RR= 2, p<0.001), with 12,155 observed and 8,668 expected cases. The Gi* statistic also identified the clusters in the same areas, and the spatial clusters showed stability in most areas in each year during the study period. The space-time analysis also detected the most likely cluster in 193 kebeles in the same eight districts (RR= 1.92, p<0.001), with 7,584 observed and 4,738 expected cases in 2003-2012.

          Conclusion

          The study found variations in CNRs and significant spatio-temporal clusters of smear-positive PTB in the Sidama Zone. The findings can be used to guide TB control programs to devise effective TB control strategies for the geographic areas characterized by the highest CNRs. Further studies are required to understand the factors associated with clustering based on individual level locations and investigation of cases.

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          Most cited references27

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          Breast cancer clusters in the northeast United States: a geographic analysis.

          High breast cancer mortality rates have been reported in the northeastern part of the United States, with recent attention focused on Long Island, New York. In this study, the authors investigate whether the high breast cancer mortality is evenly spread over the Northeast, in the sense that any observed clusters of deaths can be explained by chance alone, or whether there are clusters of statistical significance. Demographic data and age-specific breast cancer mortality rates for women were obtained for all 244 counties in 11 northeastern states and for the District of Columbia for 1988-1992. A recently developed spatial scan statistic is used, which searches for clusters of cases without specifying their size or location ahead of time, and which tests for their statistical significance while adjusting for the multiple testing inherent in such a procedure. The basic analysis is adjusted for age, with further analyses examining how the results are affected by incorporating race, urbanicity, and parity as confounding variables. There is a statistically significant and geographically broad cluster of breast cancer deaths in the New York City-Philadelphia, Pennsylvania, metropolitan area (p = 0.0001), which has a 7.4% higher mortality rate than the rest of the Northeast. The cluster remains significant when race, urbanicity, and/or parity are included as confounding variables. Four smaller subclusters within this area are also significant on their own strength: Philadelphia with suburbs (p = 0.0001), Long Island (p = 0.0001), central New Jersey (p = 0.0001), and northeastern New Jersey (p = 0.0001). The elevated breast cancer mortality on Long Island might be viewed less as a unique local phenomenon and more as part of a more general situation involving large parts of the New York City-Philadelphia metropolitan area. The several known and hypothesized risk factors for which we could not adjust and that may explain the detected cluster are most notably age at first birth, age at menarche, age at menopause, breastfeeding, genetic mutations, and environmental factors.
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            Tuberculosis transmission patterns in a high-incidence area: a spatial analysis.

            In the Cape Town suburbs of Ravensmead and Uitsig, tuberculosis has reached epidemic levels, with notifications of 1340/100,000 in 1996. These suburbs are characterised by overcrowding, high unemployment and poverty. It is traditionally believed that tuberculosis transmission takes place mainly in households after close contact with an infectious person. Studies have recently linked tuberculosis transmission to locations outside the household, and have associated these places with a particular high-risk lifestyle. Anthropological studies in some suburbs of Cape Town, in which a very high number of local drinking places (shebeens) were identified (17 per km2), have suggested that social drinking is part of such a lifestyle. To investigate various risk factors and places of transmission of tuberculosis using a geographical information system (GIS). The 1128 bacteriologically-proven cases of tuberculosis studied over the period 1993-1998 were investigated using spatial epidemiological techniques of exploratory disease mapping. Point pattern analysis and spatial statistics indicated clustering of cases in the areas of high incidence. Significant associations of tuberculosis notifications were found with unemployment, overcrowding and number of shebeens per enumerator sub-district. High tuberculosis notifications with unemployment and its associated poverty emerged as the strongest association.
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              The social epidemiology of tuberculosis in South Africa: a multilevel analysis.

              Increased risk of tuberculosis is widely recognized to be associated with increased poverty, yet there have been few analyses of the social determinants of tuberculosis, particularly in high-burden settings. We conducted a multilevel analysis of self-reported tuberculosis disease in a nationally representative sample of South Africans based on the 1998 Demographic and Health Survey (DHS). Individual and household-level demographic, behavioral and socioeconomic risk factors were taken from the DHS; data on community-level socioeconomic status (including measures of absolute wealth and income inequality) were derived from the 1996 national census. Of the 13,043 DHS respondents, 0.5% reported having been diagnosed with tuberculosis disease in the past 12 months and 2.8% reported having been diagnosed with tuberculosis disease in their lifetime. In a multivariate model adjusting for demographic and behavioral risk factors, tuberculosis diagnosis was associated with cigarette smoking, alcohol consumption and low body mass index, as well as a lower level of personal education, unemployment and lower household wealth. In a model including individual- and household-level risk factors, high levels of community income inequality were independently associated with increased prevalence of tuberculosis (adjusted odds ratio for lifetime tuberculosis comparing the most unequal quintile to the middle quintile of inequality: 2.37, 95% confidence interval: 1.59-3.53). These results provide novel insights into the socioeconomic determinants of tuberculosis in developing country settings, although the mechanisms through which income inequality may affect tuberculosis disease require further investigation.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                1 June 2015
                2015
                : 10
                : 6
                : e0126369
                Affiliations
                [1 ]Center for International Health, Faculty of Medicine and Dentistry, University of Bergen, Bergen, Norway
                [2 ]Sidama Zone Health Department, Hawassa, Ethiopia
                [3 ]Hawassa University, Hawassa, Ethiopia
                [4 ]Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, United Kingdom
                University of Tennessee, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: MHD DGD BL. Performed the experiments: MHD DGD BL. Analyzed the data: MHD. Contributed reagents/materials/analysis tools: MHD DGD BL. Wrote the paper: MHD DGD BL.

                Article
                PONE-D-14-39021
                10.1371/journal.pone.0126369
                4451210
                26030162
                3eb3b702-b5c8-4327-9b10-0eb0d2cacd29
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 30 August 2014
                : 1 April 2015
                Page count
                Figures: 8, Tables: 6, Pages: 19
                Funding
                The University of Bergen, Centre for International Health funded the study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                All relevant data are available from Figshare, under the DOI: http://dx.doi.org/10.6084/m9.figshare.1378962.

                Uncategorized
                Uncategorized

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