Prevalence and spatial distributions of trachomatous inflammation-follicular among children aged 1-9 years in rural areas of Yilmana Densa and Gonji Kolela districts, Northwestern Ethiopia

Abstract

Background: Trachoma is the world's major infectious cause of blindness, responsible for blinding 1.9 million people, including 1.2 million irreversibly. It is still endemic predominantly in sub-Saharan Africa, including Ethiopia. Five or more follicles in the upper tarsal conjunctiva measuring at least 0.5 mm indicate trachomatous inflammation-follicular (TF) disease. No previous study determined the prevalence of TF, and it had not been determined for the study area to satisfy adequate geospatial representation/spatial distribution of TF among children 1-9 years old. These study findings can help programmers understand the prevalence of TF and identify the villages in the study area where TF will be clustered to implement appropriate intervention strategies to support the current trachoma control and elimination program and to help achieve SDG Goal 3 target 3.3 and Goal 6. Therefore, this study addressed those gaps by identifying TF's prevalence and spatial distribution using spatial analytical techniques and models in Yilmana Densa and Gonji Kolela Districts.

Methods: The study utilizes spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters. The global Moran's I statistic was used to evaluate the global spatial autocorrelation of TF prevalence. The Gi_Bin field was computed in hot spot spatial analysis, independent of the False Discovery Rate correction (FDR), to detect important hot spots and cold spots. Bins of +/-3, +/-2, and +/-1 indicated statistically significant clustering of the TF distribution with 99%, 95%, and 90% confidence levels, respectively. However, non-significant TF clusters were identified with a 0 bin.

Results: This study found that the prevalence of TF was 17.8% (95% CI: 15.3-20.2%). From spatial analytical techniques and models, the global spatial autocorrelation analysis based on feature locations and attribute values revealed a clustering of TF among children aged 1-9 years across the study area (Global Moran's I = 0.849, p-value < 0.0001). In hot spot spatial analysis, fourteen hot spot clusters were detected. Eight clusters were detected as significantly clustered from those fourteen hot spot areas at the 99% confidence level. The study also found that the distribution of TF was not spatially random. It was clustered at the village levels and showed strong spatial patterns. It was affected by different locations based on sociodemographic, environmental, and behavioral factors. It was more clustered in Gonji Kolela District compared to Yilmana Densa District. This study showed that trachoma is a family-based disease.

Conclusion: TF was found to be higher than the WHO recommended threshold of 10% to say that trachoma is a severe public health problem to conduct MDA and eliminate trachoma as a Public Health problem in a community when the prevalence of TF is less than 5%. The results of the study may be used to support the current trachoma control and elimination program, and to help achieve SDG Goal 3 target 3.3 and Goal 6. Intervention against TF may also have an impact on poverty (SDG1) and hunger (SDG2), may improve education (SDG4), work, and economic growth (SDG8). These will be helpful to decide whether the Yilmana Densa and Gonji Kolela Districts meet VISION 2020, "The Right to Sight" (elimination of the major causes of avoidable blindness), an initiative launched in Ethiopia in September 2002. It is recommended that coordinated work on implementing the WHO endorsed SAFE strategy in particular, and enhancing the overall living conditions of the community be given a high priority.

Keywords: Global Moran’s I; Gonji Kolela; Hotspot analysis; Local getis-ord-statistic; Spatial distribution; Trachomatous inflammation-follicular; Yilmana Densa.

Fig. 1

Map of the study area for the prevalence and spatial distribution of TF among children aged 1–9 years, Northwestern Ethiopia, from February 15–April 15, 2024

Fig. 2

Prevalence of TF among children aged 1–9 years across the study area, Northwestern Ethiopia, from February 15–April 15, 2024

Fig. 3

Spatial auto-correlation of TF based on feature locations and attribute values across the study area, Northwestern Ethiopia, from February 15–April 15, 2024. Given the z-score of 4.31064019883, there is a less than 1% likelihood that this clustered pattern could be the result of random chance

Fig. 4

Spatial clusters (Local Moran I) of TF across the study area, Northwestern Ethiopia, from February 15–April 15, 2024

Fig. 5

Clustering level of TF across the study areas, Northwestern Ethiopia, from February 15–April 15, 2024. Given the z-score of 2.67189785292, there is a less than 1% likelihood that this high-clustered pattern could be the result of random chance

Fig. 6

Hotspots of TF in Yilmana Densa District at village level, Northwestern Ethiopia, from February 15–April 15, 2024

Fig. 7

Hotspots of TF in Gonji Kolela District at village levels, Northwestern Ethiopia, from February 15–April 15, 2024

Fig. 8

Hotspots of TF among children aged 1–9 years across the study area at village levels Northwestern Ethiopia, from February 15–April 15, 2024