Epidemiology and Spatial Analysis of Malaria in the Northern Peruvian Amazon

Abstract

A retrospective surveillance study was conducted to examine the micro-geographic variation of malaria incidence in three malaria-endemic communities in the Northern Peruvian Amazon. The annual malaria risk rate (per 100) ranged from 38% to 47% for Plasmodium vivax and from 15% to 18% for P. falciparum. Spatial clusters were found for P. vivax in Padre Cocha, Manacamiri, and Zungaro Cocha, and for P. falciparum only in Padre Cocha. Spatial-temporal clusters showed that the highest monthly number of P. vivax cases varied every year from December to March in 1996-1997 and from February to June in 1998-1999, and for P. falciparum from November to April in 1996-1997 and from January to April in 1998-1999. Our results suggest a constant presence of high-risk areas (hot spots) for malaria infection in periods with high or low malaria incidence. Modest targeted control efforts directed at identified high-risk areas may have significant impact on malaria transmission in this region.

Deforestation and Malaria on the Amazon Frontier: Larval Clustering of Anopheles darlingi (Diptera: Culicidae) Determines Focal Distribution of Malaria

“…42 Very focal high-risk areas of malaria transmission in the Peruvian Amazon were mentioned to be in close proximity to secondary forest-covered larval habitats in a riverine community. 43 The association of proximity to the fringe and malaria has been previously mentioned in the Machadinho Colonization Project, in Rondônia State, 44 where it was observed that settlers living at distances > 50 m from the forest had an OR of 0.4043 of being infected with malaria, that is, there was an OR of 2.47 and a 60% increased rise in malaria odds for settlers living closer to the forest fringe. Malaria has also been associated with forest-related activities, 11 such as land clearing, which appear to increase the contact with forest fringes and A. darlingi.…”

Unstable Malaria Transmission in the Southern Peruvian Amazon and Its Association with Gold Mining, Madre de Dios, 2001–2012

“…Microgeographic clustering of malaria has been previously identified in the northern Peruvian Amazon, where gold mining activities are minimal. 50,51 This spatial heterogeneity has been linked to the location of vector breeding sites and clustering of human reservoirs. 24 In our study, proximity to the Interoceanic Highway was associated with a lower risk for malaria transmission, which may be related to the destruction of vector breeding site.…”

A scoping review of spatial cluster analysis techniques for point-event data

“…Spatial weights refer to the measure of spatial neighbourhood relationships applied to spatial cluster analysis methods. In our review the most common issue with regard to spatial weights concerned the spatial scan statistic and its inability to detect irregularly shaped clusters (Brooker et al, 2004;Bautista et al, 2006;Chaix et al, 2006;Wheeler, 2007;Huang et al, 2009;Tanser et al, 2009). Since early developments of the spatial scan statistic options have been added to the software that deal with this issue and other scanning window methods have been developed (Tango and Takahashi, 2005;Duczmal et al, 2011), however no reviewed papers applied them.…”

“…Over half of papers in this review applied the spatial scan statistic to examine the spatial patterns of address location data (Andrade et al, 2004;Brooker et al, 2004;Han et al, 2004;Polack et al, 2005;Bautista et al, 2006;Chaix et al, 2006;Ernst et al, 2006;Pollack et al, 2006;Sarkar et al, 2007;Wheeler, 2007;Pasma, 2008;Warden, 2008;Huang et al, 2009;Meliker et al, 2009;Tanser et al, 2009;Epp et al, 2010;Ngowi et al, 2010;Poljak et al, 2010;Westercamp et al, 2010;Winskill et al, 2011). Of the reviewed articles, 83% applied a Bernoulli model spatial scan statistic to case-control data; two of those articles also used other models in SatScan that can be applied to address location data, the discrete normal continuous model ) and the discrete poisson continuous model (Ngowi et al, 2010), ordinal model (Westercamp et al, 2010) and the multinomial model (Westercamp et al, 2010).…”