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. 2017 Jan 16:7:40350.
doi: 10.1038/srep40350.

Spatio-temporal analysis of malaria incidence in the Peruvian Amazon Region between 2002 and 2013

Veronica Soto-Calle  1 , Angel Rosas-Aguirre  1   2 , Alejandro Llanos-Cuentas  1 , Emmanuel Abatih  3 , Redgi DeDeken  3 , Hugo Rodriguez  4 , Anna Rosanas-Urgell  3 , Dionicia Gamboa  5 , Umberto D Alessandro  6   7   8 , Annette Erhart  6   8 , Niko Speybroeck  2
Affiliations

Spatio-temporal analysis of malaria incidence in the Peruvian Amazon Region between 2002 and 2013

Veronica Soto-Calle et al. Sci Rep. .

Abstract

Malaria remains a major public health problem in the Peruvian Amazon where the persistence of high-risk transmission areas (hotspots) challenges the current malaria control strategies. This study aimed at identifying significant space-time clusters of malaria incidence in Loreto region 2002-2013 and to determine significant changes across years in relation to the control measures applied. Poisson regression and purely temporal, spatial, and space-time analyses were conducted. Three significantly different periods in terms of annual incidence rates (AIR) were identified, overlapping respectively with the pre-, during, and post- implementation control activities supported by PAMAFRO project. The most likely space-time clusters of malaria incidence for P. vivax and P. falciparum corresponded to the pre- and first two years of the PAMAFRO project and were situated in the northern districts of Loreto, while secondary clusters were identified in eastern and southern districts with the latest onset and the shortest duration of PAMAFRO interventions. Malaria in Loreto was highly heterogeneous at geographical level and over time. Importantly, the excellent achievements obtained during 5 years of intensified control efforts totally vanished in only 2 to 3 years after the end of the program, calling for sustained political and financial commitment for the success of malaria elimination as ultimate goal.

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Figures

Figure 1
Figure 1. Map of Loreto Region with provinces and respective districts.
Map generated with QGIS 2.16 (QGIS Development Team, 2016. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://www.qgis.org/) Districts’ names: 1: Balsapuerto, 2: Jeberos, 3: Lagunas, 4: Santa Cruz, 5: Teniente Cesar Lopez, 6: Yurimaguas, 7: Barranca, 8: Cahuapanas, 9: Manseriche, 10: Morona, 11: Pastaza, 12: Nauta, 13: Parinari, 14: Tigre, 15: Trompeteros, 16: Urarinas, 17: Pebas, 18: Ramon Castilla, 19: San Pablo, 20: Yavari, 21: Alto Nanay, 22: Fernando Lores, 23: Indiana, 24: SanJuan, 25: Las Amazonas, 26: Mazan, 27: Napo, 28: Punchana, 29: Putumayo, 30: Torres Causana, 31: Iquitos, 32: Belen, 33: Yaquerana, 34: AltoTapiche, 35: Capelo, 36: Emilio San Martin, 37: Jenaro Herrera, 38: Maquia, 39: Puinahua, 40:Requena, 41: Saquena, 42: Soplin, 43: Tapiche, 44: Contamana, 45: Inahuaya, 46: Padre Marquez,47: Pampa Hermosa, 48: Sarayacu, 49: Vargas Guerra.
Figure 2
Figure 2. Annual (A) and monthly (B) malaria (overall and per species) incidence per 1000 population in Loreto Region: 2002–2013.
Numbers in the timeline differentiate three periods: 1) Pre-PAMAFRO period (Jan. 2002–Sept. 2005): control activities essentially based on passive case detection (PCD), and the response to malaria outbreaks with focalized active detection of symptomatic cases (ACD) and indoor residual spraying; 2) PAMAFRO period (Oct. 2005–Dec. 2010): intensified comprehensive control strategies including the strengthening of malaria diagnosis and detection, improvement of malaria case-management, use of insecticide treated nets, and encouragement of community participation in environmental management; 3) Post-PAMAFRO period (Jan. 2011–Dec. 2013): control activities essentially limited to passive case detection (PCD) at health facilities and irregular ACD in communities with high incidence of malaria cases.
Figure 3
Figure 3. Annual overall (P. vivax and P. falciparum) malaria incidence and adjusted most significant spatial clusters at district level in Loreto Region: 2002–2013 (using Satscan purely spatial analysis).
Map generated with QGIS 2.16 (QGIS Development Team, 2016. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://www.qgis.org/).
Figure 4
Figure 4. Annual P. vivax incidence and adjusted significant most likely spatial clusters at district level in Loreto Region: 2002–2013 (using Satscan purely spatial analysis).
Map generated with QGIS 2.16 (QGIS Development Team, 2016. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://www.qgis.org/).
Figure 5
Figure 5. Annual P. falciparum incidence and adjusted most likely significant spatial clusters at district level in Loreto Region: 2002–2013 (using Satscan purely spatial analysis).
Map generated with QGIS 2.16 (QGIS Development Team, 2016. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://www.qgis.org/).
Figure 6
Figure 6. Main malaria control interventions by district and year implemented during the PAMAFRO project in Loreto Region.
See this image and copyright information in PMC

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