Understanding spatial, temporal, and spatiotemporal variations of chronic respiratory disease (CRD) is significant for the early prevention of the disease and efficient allocation of scarce healthcare resources. However, this is not well known in the Ethiopian context, particularly in charcoal-producing areas. Thus, this study aimed to assess the spatial, temporal, and spatiotemporal variations of CRD in northwest Ethiopia from 2012/13 to 2018/19. This study was conducted in three zones (Awi, East Gojjam, and West Gojjam), Amhara Region, Ethiopia. These zones are known for charcoal production. Data on CRD among those aged 15 years and above were obtained from 44 districts for seven years from the three zonal health departments' annual records. Scan statistics with a discrete Poisson model was used to identify statistically significant clusters using SaTScan version 10.1. Global Moran's I and Getis-Ord Gi statistics were employed to assess the spatial heterogeneity and hot spot areas of CRD respectively. To display maps of cluster locations, ArcGIS version 10.8 was used. The annual morbidity rate of CRD was remarkably varied from 305 to 731per 100,000 population in 2012/13 and 2018/19 respectively. The highest annual morbidity rate, 6508/100,000 population, was observed in the Awi zone. The global spatial autocorrelation (Moran's I = 0.2772, Z-score = 3.6597, and P-value < 0.001) shows a strong clustered distribution. The most likely purely spatial and spatiotemporal clusters were detected in eleven districts, and eight of these were in the Awi zone. The cluster center/radius was 10.967700 N, 36.925500 E/ 48.37 km, and RR = 4.37, p-value < 0.001. People living in the highest risk areas had a 4.37 times greater chance of developing CRD than those in the lowest risk areas. The Getis-Ord Gi hot spot analysis detected that hot spot clusters were primarily located in the northwest and southwest parts of the study area. The spatial, temporal, and spatiotemporal variations of CRD were non-random and clustered in high charcoal production areas. The identified high-risk clusters are the priority areas for target intervention. Further research is recommended to identify risk factors for higher clusters of CRD morbidity in these areas.
Gebeyehu et al. (Wed,) studied this question.