Hot spot or not: a comparison of spatial statistical methods to predict prospective malaria infections

Malaria Journal

Research

Chris Drakeley¹  Daniel Chandramohan¹  Colin J Sutherland¹  Sharan Atwal¹  Brian Greenwood¹  Nahla B Gadalla¹  Teun Bousema²  Simon Hemelaar²  Gibson Kibiki³  Jacklin F Mosha⁴  Hugh JW Sturrock⁵  Roland D Gosling⁵  Joelle M Brown⁶ 

[1] Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK;Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK;Department of Medical Microbiology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands;Kilimanjaro Clinical Research Institute and Kilimanjaro Christian Medical College, Kilimanjaro, Moshi, Tanzania;National Institute for Medical Research (NIMR), Mwanza Medical Research Centre, Mwanza, Tanzania;The Global Health Group, University of California, San Francisco, CA, USA;The Global Health Group, University of California, San Francisco, CA, USA;Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA;

关键词: Spatial methods;    Malaria;    Transmission;    Hotspots;    Micro-epidemiology;    Serology;    PCR;    Africa;    Plasmodium falciparum;   

DOI  :  10.1186/1475-2875-13-53

 received in 2013-12-06, accepted in 2014-02-06,  发布年份 2014

来源: Springer

【 摘 要 】

BackgroundWithin affected communities, Plasmodium falciparum infections may be skewed in distribution such that single or small clusters of households consistently harbour a disproportionate number of infected individuals throughout the year. Identifying these hotspots of malaria transmission would permit targeting of interventions and a more rapid reduction in malaria burden across the whole community. This study set out to compare different statistical methods of hotspot detection (SaTScan, kernel smoothing, weighted local prevalence) using different indicators (PCR positivity, AMA-1 and MSP-1 antibodies) for prediction of infection the following year.MethodsTwo full surveys of four villages in Mwanza, Tanzania were completed over consecutive years, 2010-2011. In both surveys, infection was assessed using nested polymerase chain reaction (nPCR). In addition in 2010, serologic markers (AMA-1 and MSP-119 antibodies) of exposure were assessed. Baseline clustering of infection and serological markers were assessed using three geospatial methods: spatial scan statistics, kernel analysis and weighted local prevalence analysis. Methods were compared in their ability to predict infection in the second year of the study using random effects logistic regression models, and comparisons of the area under the receiver operating curve (AUC) for each model. Sensitivity analysis was conducted to explore the effect of varying radius size for the kernel and weighted local prevalence methods and maximum population size for the spatial scan statistic.ResultsGuided by AUC values, the kernel method and spatial scan statistics appeared to be more predictive of infection in the following year. Hotspots of PCR-detected infection and seropositivity to AMA-1 were predictive of subsequent infection. For the kernel method, a 1 km window was optimal. Similarly, allowing hotspots to contain up to 50% of the population was a better predictor of infection in the second year using spatial scan statistics than smaller maximum population sizes.ConclusionsClusters of AMA-1 seroprevalence or parasite prevalence that are predictive of infection a year later can be identified using geospatial models. Kernel smoothing using a 1 km window and spatial scan statistics both provided accurate prediction of future infection.

【 授权许可】

CC BY   
© Mosha et al.; licensee BioMed Central Ltd. 2014