Spatio-temporal trends of malaria incidence from 2011 to 2017 and environmental predictors of malaria transmission in Myanmar

Abstract

Background: Myanmar bears the heaviest malaria burden in the Greater Mekong Subregion (GMS). This study assessed the spatio-temporal dynamics and environmental predictors of Plasmodium falciparum and Plasmodium vivax malaria in Myanmar.

Methods: Monthly reports of malaria cases at primary health centers during 2011-2017 were analyzed to describe malaria distribution across Myanmar at the township and state/region levels by spatial autocorrelation (Moran index) and spatio-temporal clustering. Negative binomial generalized additive models identified environmental predictors for falciparum and vivax malaria, respectively.

Results: From 2011 to 2017, there was an apparent reduction in malaria incidence in Myanmar. Malaria incidence peaked in June each year. There were significant spatial autocorrelation and clustering with extreme spatial heterogeneity in malaria cases and test positivity across the nation (P < 0.05). Areas with higher malaria incidence were concentrated along international borders. Primary clusters of P. falciparum persisted in western townships, while clusters of P. vivax shifted geographically over the study period. The primary cluster was detected from January 2011 to December 2013 and covered two states (Sagaing and Kachin). Annual malaria incidence was highest in townships with a mean elevation of 500‒600 m and a high variance in elevation (states with both high and low elevation). There was an apparent linear relationship between the mean normalized difference vegetative index and annual P. falciparum incidence (P < 0.05).

Conclusion: The decreasing trends reflect the significant achievement of malaria control efforts in Myanmar. Prioritizing the allocation of resources to high-risk areas identified in this study can achieve effective disease control.

Keywords: Environmental predictor; Myanmar; Plasmodium falciparum; Plasmodium vivax; Spatial distribution; Spatiotemporal clustering; Temporal clustering.

Fig. 1

A Dynamics of malaria incidence (number of cases per 1000 population) and test positivity among Myanmar residents from 2011 to 2017. B State/region wide malaria incidence rate from 2011 to 2017. C Test positivity of malaria at state/region level from 2011 to 2017. D Monthly incidence of malaria in Myanmar from 2011 to 2016. API Annual parasite incidence; TP Test positivity

Fig. 2

Monthly incidence of malaria at state/region level in Myanmar from 2011 to 2016

Fig. 3

Yearly spatial correlograms of P. falciparum malaria and P. vivax malaria. TP Test positivity; API annual parasite incidence; SEB smoothed empirical Bayesian rates (API that has been smoothed using the SEB approach)

Fig. 4

Maps of estimated P. falciparum malaria incidence (top row) and spatial autocorrelation. Likely clusters are indicated with red and blue circles. Statistical clusters of high and low numbers of cases (from LISA statistics) are indicated in the lower row. The Myanmar map is generated based on the latest Myanmar Information Management Unit (MIMU) shapefiles and administrative unit codes version 9.3 (http://geonode.themimu.info/)

Fig. 5

Maps of estimated P. vivax malaria incidence (top row) and spatial autocorrelation. Likely clusters are indicated with red and blue circles. Statistical clusters of high and low numbers of cases (from LISA statistics) are indicated in the lower row. Myanmar map is generated based on the latest Myanmar Information Management Unit (MIMU) shapefiles and administrative unit codes version 9.3 (http://geonode.themimu.info/)

Fig. 6

Results from the generalized additive model for environmental predictors of P. falciparum and P. vivax cases at the township level. Where a spline and its confidence intervals fall above zero (blue and yellow line in figures), there is a positive association, and where they fall below zero (blue and yellow line in figures), there is a negative association. The x-axis gives the value for the variable in question