Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial-temporal models

Victor A Alegana; Peter M Atkinson; Jim A Wright; Richard Kamwi; Petrina Uusiku; Stark Katokele; Robert W Snow; Abdisalan M Noor

doi:10.1016/j.sste.2013.09.001

Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial-temporal models

Spat Spatiotemporal Epidemiol. 2013 Dec:7:25-36. doi: 10.1016/j.sste.2013.09.001. Epub 2013 Sep 17.

Authors

Victor A Alegana¹, Peter M Atkinson, Jim A Wright, Richard Kamwi, Petrina Uusiku, Stark Katokele, Robert W Snow, Abdisalan M Noor

Affiliation

¹ Malaria Public Health Department, KEMRI-Wellcome Trust-University of Oxford Collaborative Programme, P.O. Box 43640, 00100 GPO Nairobi, Kenya; Centre for Geographical Health Research, Geography and Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK. Electronic address: vaa1g10@soton.ac.uk.

Abstract

As malaria transmission declines, it becomes increasingly important to monitor changes in malaria incidence rather than prevalence. Here, a spatio-temporal model was used to identify constituencies with high malaria incidence to guide malaria control. Malaria cases were assembled across all age groups along with several environmental covariates. A Bayesian conditional-autoregressive model was used to model the spatial and temporal variation of incidence after adjusting for test positivity rates and health facility utilisation. Of the 144,744 malaria cases recorded in Namibia in 2009, 134,851 were suspected and 9893 were parasitologically confirmed. The mean annual incidence based on the Bayesian model predictions was 13 cases per 1000 population with the highest incidence predicted for constituencies bordering Angola and Zambia. The smoothed maps of incidence highlight trends in disease incidence. For Namibia, the 2009 maps provide a baseline for monitoring the targets of pre-elimination.

Keywords: ACD; CAR; CPO; Conditional-autoregressive; DIC; ESRI; EVI; Environmental System Research Institute; GF; GIS; GMRF; GPS; GRUMP; Gaussian field; Gaussian markov random field; Global Rural and Urban Mapping Project; HMIS; Health Management Information System; INLA; Integrated Nested Laplace Approximation; JAXA; Japan Aerospace Exploration Agency; MAUP; MCMC; MODIS; MODerate-resolution Imaging Spectro-radiometer; Malaria; Markov Chain Monte Carlo; Ministry of Health and Social Services; MoHSS; Modifiable Areal Unit Problem; NASA; NVBDCP; Namibia; National Aeronautics and Space Administration; National Vector-Borne and Disease Control Programme; PCD; PHS; RDT; Rapid Diagnostic Test; SPA; Service Provision Assessments; Spatio-temporal; TRMM; TSI; Tropical Rainfall Measuring Mission; WHO; World Health Organisation; ZIP; Zero-Inflated Poisson; active case detection; conditional auto-regressive; conditional predictive ordinate; deviance information criterion; enhanced vegetation index; geographic information system; global positioning system; passive case detection; public health sector; temperature suitability index.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem*
Humans
Malaria / epidemiology*
Models, Statistical*
Namibia / epidemiology
Spatio-Temporal Analysis

Abstract

Publication types

MeSH terms

Grants and funding