Detection of risk clusters for deaths due to tuberculosis specifically in areas of southern Brazil where the disease was supposedly a non-problem

Abstract

Background Tuberculosis (TB) is the infectious disease that kills the most people worldwide. The use of geoepidemiological techniques to demonstrate the dynamics of the disease in vulnerable communities is essential for its control. Thus, this study aimed to identify risk clusters for TB deaths and their variation over time. Methods This ecological study considered cases of TB deaths in residents of Londrina, Brazil between 2008 and 2015. We used standard, isotonic scan statistics for the detection of spatial risk clusters. The Poisson discrete model was adopted with the high and low rates option used for 10, 30 and 50% of the population at risk, with circular format windows and 999 replications considered the maximum cluster size. Getis-Ord Gi* (Gi*) statistics were used to diagnose hotspot areas for TB mortality. Kernel density was used to identify whether the clusters changed over time. Results For the standard version, spatial risk clusters for 10, 30 and 50% of the exposed population were 4.9 (95% CI 2.6–9.4), 3.2 (95% CI: 2.1–5.7) and 3.2 (95% CI: 2.1–5.7), respectively. For the isotonic spatial statistics, the risk clusters for 10, 30 and 50% of the exposed population were 2.8 (95% CI: 1.5–5.1), 2.7 (95% CI: 1.6–4.4), 2.2 (95% CI: 1.4–3.9), respectively. All risk clusters were located in the eastern and northern regions of the municipality. Additionally, through Gi*, hotspot areas were identified in the eastern and western regions. Conclusions There were important risk areas for tuberculosis mortality in the eastern and northern regions of the municipality. Risk clusters for tuberculosis deaths were observed in areas where TB mortality was supposedly a non-problem. The isotonic and Gi* statistics were more sensitive for the detection of clusters in areas with a low number of cases; however, their applicability in public health is still restricted.

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

“…In this study, the population (kernel) density per area (m 2 ) was positively associated with the risk of TB, thus indicating that a one-unit increase per area would greatly increase the risk of TB, as shown by the spatial analysis of TB cases conducted by Murakami et al [85] and Alves et al [86] in Japan and Brazil, respectively. Six high-risk clusters of high-density regions were found through the kernel density estimation, whereas the rest of the regions presented low-risk areas.…”

“…[ 85 ] and Alves et al. [ 86 ] in Japan and Brazil, respectively. Six high-risk clusters of high-density regions were found through the kernel density estimation, whereas the rest of the regions presented low-risk areas.…”

“The devil is in the detail”: geographical inequalities of femicides in Ecuador

“…The isotonic scan statistic produces several steps corresponding to the multiple circular spatial windows [ 23 ]. Those circles were centralized in the initially identified canton as a centre of the cluster, and the steps in the risk function allowed us to verify the differences of the case intensity within the high-risk areas [ 24 ]. Thus, the highest rate is within the central circle and continues decreasing until the outer circle [ 23 , 24 ].…”

“…This approach has demonstrated to be a proper method to analyze the spatial clustering of territorial subunits with rare events [ 24 ]. The femicide cases by canton occurred between 2018 and 2019, and the mean estimated female population by canton according to the different age ranges was considered in the analysis.…”

Identification of risk areas as a method of surveillance of dengue cases

“…The discrete Poisson model will be used with requirements of non-overlapping geographical clusters, clusters with a circular shape, 999 replications, and the size of the exposed population will be stipulated by the Gini coefficient released by the software itself. In this model, the number of cases is compared to the baseline population data, and the expected number of cases in each unit is proportional to the size of the population at risk [Alves et al 2019].…”