Tuberculosis incidence in Portugal: spatiotemporal clustering

Abstract

Background: The statistics of disease clustering is one of the most important tools for epidemiologists to detect and monitor public health disease patterns. Nowadays, tuberculosis (TB) -an infectious disease caused by the Mycobacterium tuberculosis -presents different (development in populations and antibiotics resistance) patterns and specialists are very concerned with it and its association to several other diseases and factors. Each year, tuberculosis kills about three million people in the world. In particular, it is responsible for the death of more than one-third of HIVinfected people, who prove particularly susceptible due to a decline in their immune defences. The purpose of this study is to determine if there are spatiotemporal tuberculosis incidence clusters in continental Portugal. The presented case study is based on the notification of new tuberculosis cases (disease incidence), between 2000 and 2004. In methodological terms, the spatial scan statistic, used to identify spatiotemporal clusters, was improved by including two new approaches: definition of window sizes in the cluster scanning processes considering empirical mean spatial semivariograms and an independent and posterior validation of identified clusters (based on geostatistical simulations).

Worldwide Spatial and Temporal Distribution of Tuberculosis (TB)

“…Similarly, another study in Portugal showed spatiotemporal clustering of tuberculosis incidence hyperendemic "hotspots" often characterized by crowding [16,19], HIV infection [20], and other social determinants [21]. This finding was supported by study in HIV endemic settings which found that populations with clustering of respiratory contacts experience aggregation of TB cases and high numbers of re-infection events [26].…”

“…The transmission of infectious pathogens from infected to susceptible hosts declines with increasing distance between individuals. TB, like many infectious diseases, is prone to spatial aggregation or clustering [16,17]. A cross-sectional study indicated that GIS-based screening can effectively penetrate populations with high disease burden and poor healthcare access.…”

“…Linkage to care remains challenging and will require creative interventions to impact morbidity [18]. And as TB is known to cluster in hyperendemic "hotspots" often characterized by crowding [16,19], HIV infection [20], and other social determinants [21], new approaches, such as mapping and spatial analysis, may be of value in contributing to basic elements of TB control [22].…”

Retrospective space-time cluster analysis of whooping cough re-emergence in Barcelona, Spain, 2000-2011

“…For example, Nunes (2007) used these techniques to analyse the incidence of tuberculosis in Portugal, while Odoi et al (2004) investigated timespace cluster of giardiasis in Canada. Sugumaran et al (2009) conducted a space-time study for the West Nile Virus using this methodology and Gómez-Barroso et al (2012) investigated a time-space cluster of hepatitis A in Spain using scan method.…”

“…During the last decade, space-time cluster detection in the health field has been particularly strong, e.g. for the evaluation of infectious diseases such as tuberculosis (Nunes, 2007), malaria (Magalhaes and Clements, 2011), West Nile virus (Winters et al, 2010), schistosomiasis (Clements et al, 2009), H5N1 avian flu (Souris et al, 2010) and hepatitis A (Gomez-Barroso et al, 2011), among others. Detection methods for space-time clusters refer to the identification of a higher density of event occurrences in certain places at certain times, a dynamic technique essential for the determination of when, where and to what degree a disease is present (Galazka, 1992;Garcia-Corbeira et al, 2000;Dominguez et al, 2001;ez-Domingo et al, 2004;Dempsey et al, 2009).…”