Tools
Tools
Mlacha, Yeromin P, Chaki, Prosper P, Malishee, Alpha D, Mwakalinga, Victoria M, Govella, Nicodem J, Limwagu, Alex J, Paliga, John M, Msellemu, Daniel F, Mageni, Zawadi D, Terlouw, Anja 
ORCID: https://orcid.org/0000-0001-5327-8995, Killeen, Gerry F ORCID: https://orcid.org/0000-0002-8583-8739 and Dongus, Stefan
(2017)
'Fine scale mapping of malaria infection clusters by using routinely collected health 1 facility data in urban Dar es Salaam, Tanzania'. Geospatial Health, Vol 12, Issue 1, p. 294.
|
Text
Geospatial Health_Fine_Scale_Mapping_ accepted_manuscript.pdf - Accepted Version Download (857kB) | Preview |
|
|
Text
Geospatial_Health_2017_12_494.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
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 Jan 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 were deployed to detect spatial clustering of high infection rates. Among 2,407 patients tested for malaria, 46.6% (1,121) 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% (881/2,082) 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.
| Item Type: | Article |
|---|---|
| Subjects: | WA Public Health > Health Problems of Special Population Groups > WA 395 Health in developing countries WA Public Health > Statistics. Surveys > WA 950 Theory or methods of medical statistics. Epidemiologic methods WC Communicable Diseases > Tropical and Parasitic Diseases > WC 750 Malaria |
| Faculty: Department: | Clinical Sciences & International Health > Clinical Sciences Department |
| Digital Object Identifer (DOI): | https://doi.org/10.4081/gh.2017.494 |
| Depositing User: | Stacy Murtagh |
| Date Deposited: | 06 Apr 2017 15:38 |
| Last Modified: | 16 Sep 2019 10:12 |
| URI: | https://archive.lstmed.ac.uk/id/eprint/6779 |
Statistics
Actions (login required)
 |
Edit Item |