Pulmonary tuberculosis in São Luis, State of Maranhão, Brazil: space and space-time risk clusters for death (2008-2012)

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

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.

Definition of persistent areas with increased dengue risk by detecting clusters in populations with differing mobility and immunity in Rio de Janeiro, Brazil

Dengue is a re-emerging arbovirus infection of major epidemiological importance. The detection of dengue clusters is an important epidemiological surveillance strategy, contributing to better allocation of control measures and prioritizing areas that are subject to increased risk of transmission. Studies involving human populations with low mobility are scarce, and the current study thus aims to investigate the presence of persistent dengue clusters in the city of Rio de Janeiro, Brazil, in populations with different mobility and immunity. Epidemiological data on dengue were obtained from the Brazilian Ministry of Health. Areas of increased risk were defined by the space-time scan statistical method and analysis of persistence with use of map algebra. For both study populations, the clusters that were identified did not show spatial concordance, except in years when both presented the same immunological profile. Their persistent clusters were located mostly in the West Zone of city. The clusters of the two study populations only displayed spatial concordance in years with similar immune profiles, which confirms the confounding role of immunity and supports the use of populations with high percentages of susceptible individuals when designing territory-based dengue studies. The space-time similarity between the areas of persistent risk in both populations suggests that the West Zone, a region with disorderly urban growth and low mean income, shows the highest risk of dengue transmission. The definition of persistent dengue clusters contributes to the improvement of dengue control strategies and territorial planning.

Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review

BACKGROUND

Tuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden.

METHODS

We conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017. The protocol for this systematic review was prospectively registered with PROSPERO ( CRD42016036655 ).

RESULTS

We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff's spatial scan statistic followed by local Moran's I and Getis and Ord's local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined.

CONCLUSIONS

A range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control.

Spatial distribution of tuberculosis in indigenous and non-indigenous populations in the state of Pará, Brazil, 2005-2013

Abstract Objective: To analyze the incidence of tuberculosis in indigenous and non-indigenous residents in the state of Pará from 2005-2013. Method: An ecological study was performed with data from SINAN, stratified for the 13 existing Regional Health Centers in Pará. The tuberculosis incidence rates were calculated for indigenous and non-indigenous populations in the 13 regions and maps were prepared to visualize the magnitude of the occurrence of tuberculosis. Results: Significant differences in the incidence of tuberculosis were found among non-indigenous and indigenous populations, reaching 7,812/100,000 inhabitants and 118/100,000 inhabitants respectively. Conclusion: Tuberculosis was distributed heterogeneously among the indigenous and non-indigenous populations. Moreover, it was possible to identify areas with high risk for this disease. It is important to note that knowledge about priority areas for tuberculosis control can help health service management to improve indicators that assess this disease and to develop different policies for indigenous peoples.