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

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
  • Yeromin P. Mlacha
    Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, Dar es Salaam, Tanzania; Vector Biology Department, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
  • Prosper P. Chaki
    Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, Dar es Salaam, Tanzania, United Republic of
  • Alpha D. Malishee
    Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, Dar es Salaam; College of Information and Communication Technologies, University of Dar es Salaam, Dar es Salaam, Tanzania, United Republic of
  • Victoria M. Mwakalinga
    Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, Dar es Salaam, Tanzania; School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
  • Nicodem J. Govella
    Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, Dar es Salaam, Tanzania, United Republic of
  • Alex J. Limwagu
    Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, Dar es Salaam, Tanzania, United Republic of
  • John M. Paliga
    Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, Dar es Salaam, Tanzania, United Republic of
  • Daniel F. Msellemu
    Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, Dar es Salaam, Tanzania, United Republic of
  • Zawadi D. Mageni
    Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, Dar es Salaam, Tanzania, United Republic of
  • Dianne J. Terlouw
    Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK; Malawi-Liverpool-Wellcome Trust Clinical Research Programme, College of Medicine, Blantyre, Malawi
  • Gerry F. Killeen
    Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, Dar es Salaam, Tanzania; Vector Biology Department, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
  • Stefan Dongus http://orcid.org/0000-0002-2761-5596
    Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, Dar es Salaam, Tanzania; Vector Biology Department, Liverpool School of Tropical Medicine, Liverpool, UK; Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel; University of Basel, Basel, Switzerland | 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.

Keywords

Malaria; Spatial heterogeneity; Hot spots; GIS; Tanzania

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Submitted: 2016-07-06 16:50:31
Published: 2017-05-11 11:22:30
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Copyright (c) 2017 Yeromin P Mlacha, Prosper P Chaki, Alpha D Malishee, Victoria M Mwakalinga, John M Paliga, Alex J Limwagu, Nicodem J Govella, Daniel F Msellemu, Zawadi D Mageni, Dianne J Terlouw, Gerry F Killeen, Stefan Dongus

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