Nursing Graduate Program in Public Health Nursing, University of São Paulo at Ribeirão Preto Nursing College, 3900 Avenida dos Bandeirantes, São Paulo, Brazil.
Maternal-Infant and Public Health Nursing Department, University of São Paulo at Ribeirão Preto College of Nursing, Av dos Bandeirantes 3900, Ribeirão Preto, São Paulo, 14040-902, Brazil.
BMC Infect Dis. 2019 Jul 17;19(1):628. doi: 10.1186/s12879-019-4263-1.
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.
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.
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.
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.
结核病（TB）是全球导致死亡人数最多的传染病。利用地理流行病学技术来展示疾病在脆弱社区中的动态变化对于控制结核病至关重要。因此，本研究旨在确定结核病死亡的风险集群及其随时间的变化。
本生态研究考虑了巴西隆德里纳市 2008 年至 2015 年居民的结核病死亡病例。我们使用标准的、等容扫描统计方法来检测空间风险集群。采用泊松离散模型，使用高和低速率选项，以 10%、30%和 50%的风险人群为基础，圆形格式窗口，最大集群大小为 999 次复制。使用 Getis-Ord Gi*（Gi*）统计量诊断结核病死亡率的热点区域。核密度用于确定集群是否随时间发生变化。
对于标准版本，暴露人群的 10%、30%和 50%的空间风险集群分别为 4.9（95%CI 2.6-9.4）、3.2（95%CI 2.1-5.7）和 3.2（95%CI 2.1-5.7）。对于等容空间统计，暴露人群的 10%、30%和 50%的风险集群分别为 2.8（95%CI 1.5-5.1）、2.7（95%CI 1.6-4.4）和 2.2（95%CI 1.4-3.9）。所有风险集群均位于该市的东部和北部地区。此外，通过 Gi*，还确定了东部和西部地区的热点区域。
该市东部和北部地区存在结核病死亡的重要风险区域。结核病死亡的风险集群出现在本应不存在结核病问题的地区。等容和 Gi*统计数据对于检测低病例数地区的集群更为敏感；然而，它们在公共卫生中的应用仍然受到限制。
