Fine scale mapping of malaria infection clusters by using routinely collected health facility data in urban Dar es Salaam, Tanzania

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Yeromin P. Mlacha
Prosper P. Chaki
Alpha D. Malishee
Victoria M. Mwakalinga
Nicodem J. Govella
Alex J. Limwagu
John M. Paliga
Daniel F. Msellemu
Zawadi D. Mageni
Dianne J. Terlouw
Gerry F. Killeen
Stefan Dongus *
(*) Corresponding Author:
Stefan Dongus | stefan.dongus@unibas.ch

Abstract

This study investigated whether passively collected routine health facility data can be used for mapping spatial heterogeneities in malaria transmission at the level of local government housing cluster administrative units in Dar es Salaam, Tanzania. From June 2012 to January 2013, residential locations of patients tested for malaria at a public health facility were traced based on their local leaders’ names and geo-referencing the point locations of these leaders’ houses. Geographic information systems (GIS) were used to visualise the spatial distribution of malaria infection rates. Spatial scan statistics was deployed to detect spatial clustering of high infection rates. Among 2407 patients tested for malaria, 46.6% (1121) could be traced to their 411 different residential housing clusters. One small spatially aggregated cluster of neighbourhoods with high prevalence was identified. While the home residence housing cluster leader was unambiguously identified for 73.8% (240/325) of malaria-positive patients, only 42.3% (881/2082) of those with negative test results were successfully traced. It was concluded that recording simple points of reference during routine health facility visits can be used for mapping malaria infection burden on very fine geographic scales, potentially offering a feasible approach to rational geographic targeting of malaria control interventions. However, in order to tap the full potential of this approach, it would be necessary to optimise patient tracing success and eliminate biases by blinding personnel to test results.

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