Cross-border malaria drivers and risk factors on the Brazil-Venezuela border between 2016 and 2018

Abstract

Globally, cross-border importation of malaria has become a challenge to malaria elimination. The border areas between Brazil and Venezuela have experienced high numbers of imported cases due to increased population movement and migration out of Venezuela. This study aimed to identify risk factors for imported malaria and delineate imported malaria hotspots in Roraima, Brazil and Bolivar, Venezuela between 2016 and 2018. Data on malaria surveillance cases from Roraima, Brazil and Bolivar, Venezuela from 2016 to 2018 were obtained from national surveillance systems: the Brazilian Malaria Epidemiology Surveillance Information System (SIVEP-Malaria), the Venezuelan Ministry of Health and other non-government organizations. A multivariable logistic regression model was used to identify the risk factors for imported malaria. Spatial autocorrelation in malaria incidence was explored using Getis-Ord (Gi*) statistics. During the study period, there were 11,270 (24.3%) and 4072 (0.7%) imported malaria cases in Roraima, Brazil and Bolivar, Venezuela, respectively. In the multivariable logistic regression for Roraima, men were 28% less likely to be an imported case compared to women (Adjusted Odds Ratio [AOR] = 0.72; 95% confidence interval [CI] 0.665, 0.781). Ages 20-29 and 30-39 were 90% (AOR = 1.90; 95% CI 1.649, 2.181) and 54% (AOR = 1.54; 95% CI 1.331, 1.782) more likely to be an imported case compared to the 0-9 year age group, respectively. Imported cases were 197 times (AOR = 197.03; 95% CI 175.094, 221.712) more likely to occur in miners than those working in agriculture and domestic work. In Bolivar, cases aged 10-19 (AOR = 1.75; 95% CI 1.389, 2.192), 20-29 (AOR = 2.48; 95% CI 1.957, 3.144), and 30-39 (AOR = 2.29; 95% CI 1.803, 2.913) were at higher risk of being an imported case than those in the 0-9 year old group, with older age groups having a slightly higher risk compared to Roraima. Compared to agriculture and domestic workers, tourism, timber and fishing workers (AOR = 6.38; 95% CI 4.393, 9.254) and miners (AOR = 7.03; 95% CI 4.903, 10.092) were between six and seven times more likely to be an imported case. Spatial analysis showed the risk was higher along the international border in the municipalities of Roraima, Brazil. To achieve malaria elimination, cross-border populations in the hotspot municipalities will need targeted intervention strategies tailored to occupation, age and mobility status. Furthermore, all stakeholders, including implementers, policymakers, and donors, should support and explore the introduction of novel approaches to address these hard-to-reach populations with the most cost-effective interventions.