Spatial clustering of notified tuberculosis in Ethiopia: A nationwide study

Alene, Kefyalew Addis; Clements, Archie C. A.

doi:10.1371/journal.pone.0221027

Cited by 23 publications

(32 citation statements)

References 18 publications

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“…The spatial heterogeneity in the prevalence of TB among people living with HIV was significantly associated with underlying variability in area-level socio-economic variables such as low wealth index, low adult literacy rate, and close distance to the international border. The observed association between the prevalence of TB and socio-economic factors has already been described in the existing literature [25–27]. A high prevalence of TB has previously been reported among people with low wealth quintile and low educational status in several countries such as South Africa [28], Kenya [29], China [30], and Brazil [31].…”

Section: Discussionmentioning

confidence: 77%

Spatial patterns of tuberculosis and HIV co-infection in Ethiopia

et al. 2019

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BackgroundTuberculosis (TB) and human immunodeficiency virus (HIV) are the leading causes of infectious-disease-related deaths in Ethiopia, but little is known about their spatial distribution across the country. The aim of this study was to investigate the spatial patterns of TB and HIV co-infection in Ethiopia at the district level.MethodsWe conducted an ecological study using TB and HIV data reported from all regions of Ethiopia through the national Health Management Information System (HMIS), between June 2015 and June 2017. Spatial clustering was assessed using Moran’s I statistic and Getis-Ord statistic. Spatial binomial regression models were constructed separately for the prevalence of TB among people living with HIV and for the prevalence of HIV among TB patients, with and without spatial components using a Bayesian approach.ResultsA total of 1,830,880 HIV and 192,359 TB patients were included in the analysis. The prevalence of HIV among TB patients was 7.34%; hotspots were observed in districts located in Amhara, Afar, and Gambela regions, and cold spots were observed in Oromiya and Southern Nations, Nationalities, and People (SNNP) regions. The prevalence of TB among people living with HIV varied from 0.7% in Oromia region to 14.5% in Afar region. Hotspots of TB prevalence among people living with HIV were observed in districts located in Gambela, Afar, Somali, and Oromiya regions; whereas the cold spots were observed in districts located in Amhara and Tigray regions. The ecological-level factors associated with the prevalence of TB among people living with HIV were low wealth index (OR: 1.49; 95% CrI: 1.05, 2.05), low adult literacy rate (OR: 0.67; 95% CrI: 0.46, 0.94), and distance to an international border (OR: 0.61; 95% CrI: 0.40, 0.91). The factors associated with the prevalence of HIV among TB patients were poor health care access (OR: 0.76; 95% CrI: 0.59, 0.95), low wealth index (OR: 1.31; 95% CrI: 1.01, 1.67), and low adult literacy rate (OR: 1.37; 95% CrI: 1.03, 1.78).ConclusionOur study provides evidence for geographic clustering of TB/HIV co-infection in Ethiopia. Health care access, proximity to international borders, and demographic factors such as low wealth index and adult literacy were significantly associated with the prevalence of TB/HIV co-infection.

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Section: Discussionmentioning

confidence: 77%

Spatial patterns of tuberculosis and HIV co-infection in Ethiopia

et al. 2019

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“…However, studies from Africa and Ethiopia using TB notification data aggregated at larger areas and populations, to assess the spatial distribution of the disease have poor resolution. Moreover, several of the previous studies conducted at broader geographic level did not exclude cases who came from neighbouring areas diagnosed and treated in the reporting health facilities and could have underestimated or overestimated the true disease burden [29,30].…”

Section: Introductionmentioning

confidence: 99%

Identifying geographical heterogeneity of pulmonary tuberculosis in southern Ethiopia: a method to identify clustering for targeted interventions

Dangisso

Datiko

Lindtjørn

2020

Global Health Action

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Background: Previous studies from Ethiopia detected disease clustering using broader geographic settings, but limited information exists on the spatial distribution of the disease using residential locations. An assessment of predictors of spatial variations of TB at community level could fill the knowledge gaps, and helps in devising tailored interventions to improve TB control. Objective: To assess the pattern of spatial distribution of pulmonary tuberculosis (PTB) based on geographic locations of individual cases in the Dale district and Yirga Alem town in southern Ethiopia. Methods: The socio-demographic characteristics of PTB cases were collected using a structured questionnaire, and spatial information was collected using geographic position systems. We carried out Getis and Ord (Gi*) statistics and scan statistics to explore the pattern of spatial clusters of PTB cases, and geographically weighted regression (GWR) was used to assess the spatial heterogeneities in relationship between predictor variables and PTB case notification rates (CNRs). Results: The distribution of PTB varied by enumeration areas within the kebeles, and we identified areas with significant hotspots in various areas ineach year. In GWR analysis, the disease distribution showed a geographic heterogeneity (non-stationarity) in relation to physical access (distance to TB control facilities) and population density (AICc = 5591, R 2 = 0.3359, adjusted R 2 = 0.2671). The model explained 27% of the variability in PTB CNRs (local R 2 ranged from 0.0002-0.4248 between enumeration areas). The GWR analysis showed that areas with high PTB CNRs had better physical accessibility to TB control facilities and high population density. The effect of physical access on PTB CNRs changed after the coverage of TB control facilities was improved. Conclusion: We report a varying distribution of PTB in small and different areas over 10 years. Spatial and temporal analysis of disease distribution can be used to identify areas with a high burden of disease and predictors of clustering, which helps in making policy decisions and devising targeted interventions.

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“…Research findings from different parts of Ethiopia [ 2 – 6 ] showed the presence of high TB burdens with varied magnitude across its regions and woredas (third level administrative hierarchy in Ethiopia). Recent studies that used the applications of geographic information system (GIS) in the analysis of TB data exhibited that TB incidence and prevalence in Ethiopia have spatial and space-time variations [ 7 – 11 ].…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal clustering of notified pulmonary tuberculosis and its predictors in East Gojjam Zone, Northwest Ethiopia

2021

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Background Tuberculosis (TB) remains a key health menace in Ethiopia and its districts. This study aimed to assess the spatial-temporal clustering of notified pulmonary TB (PTB) cases in East Gojjam Zone, Northwest Ethiopia. Methods A retrospective study was conducted among all PTB cases reported from 2013–2019. Case notification rates (CNRs) of PTB cases at Kebele (the lowest administrative unit), woreda, and zone levels were estimated. The PTB clustering was done using global Moran’s I statistics on Arc GIS 10.6. We used Kulldorff SaTScan 9.6 with a discrete Poisson model to identify statistically significant spatial-temporal clustering of PTB cases at Kebele level. Similarly, a negative binomial regression analysis was used to identify factors associated with the incidence of PTB cases at kebele level. Results A total of 5340 (52%) smear-positive and 4928 (48%) smear-negative PTB cases were analyzed. The overall mean CNR of PTB cases at zone, woreda and Kebele levels were 58(47–69), 82(56–204), and 69(36–347) per 100,000 population, respectively. The purely spatial cluster analysis identified eight most likely clusters (one for overall and one per year for seven reporting years) and 47 secondary clusters. Similarly, the space-time scan analysis identified one most likely and seven secondary clusters. The purely temporal analysis also detected one most likely cluster from 2013–2015. Rural residence, distance from the nearest health facility, and poor TB service readiness were factors (p-value <0.05) to PTB incidence at kebele level. Conclusion The distribution of PTB cases was clustered. The PTB CNR was low and showed a decreasing trend during the reporting periods. Rural residence, distance from the health facilities, and poor facility readiness were factors of PTB incidence. Improving accessibility and readiness of health facilities mainly to rural and hotspot areas is vital to increase case detection and reduce TB transmission.

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Spatial clustering of notified tuberculosis in Ethiopia: A nationwide study

Cited by 23 publications

References 18 publications

Spatial patterns of tuberculosis and HIV co-infection in Ethiopia

Spatial patterns of tuberculosis and HIV co-infection in Ethiopia

Identifying geographical heterogeneity of pulmonary tuberculosis in southern Ethiopia: a method to identify clustering for targeted interventions

Spatial-temporal clustering of notified pulmonary tuberculosis and its predictors in East Gojjam Zone, Northwest Ethiopia

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