Use of prospective hospital surveillance data to define spatiotemporal heterogeneity of malaria risk in coastal Kenya

Malaria Journal

Research

Charles H. King¹  Peter L. Mungai¹  Francis Mutuku²  Uriel Kitron³  Donal Bisanzio⁴  Angelle D. LaBeaud⁵  Hajara Busaidy⁶  Dunstan Mukoko⁷  Jackson Muinde⁸ 

[1] Center for Global Health and Diseases, Case Western Reserve University, Cleveland, OH, USA;Department of Environment and Health Sciences, Technical University of Mombasa, Mombasa, Kenya;Department of Environmental Sciences, Emory University, Atlanta, GA, USA;Department of Environmental Sciences, Emory University, Atlanta, GA, USA;Department of Zoology, University of Oxford, Oxford, UK;Department of Pediatrics, Stanford University, Stanford, CA, USA;Ministry of Health Kwale County, Kwale, Mombasa, Kenya;Vector-Borne Diseases Control Unit, Ministry of Health, Nairobi, Kenya;Vector-Borne Diseases Control Unit, Msambweni County Referral Hospital, Kwale, Msambweni, Kenya;

关键词: Malaria/epidemiology;    Malaria/statistics and numerical data;    Distribution/spatial;    Public health surveillance;    Spatiotemporal analysis;    Geographic mapping;    Geographic information systems;    Kenya;   

DOI  :  10.1186/s12936-015-1006-7

 received in 2015-08-26, accepted in 2015-11-19,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundMalaria in coastal Kenya shows spatial heterogeneity and seasonality, which are important factors to account for when planning an effective control system. Routinely collected data at health facilities can be used as a cost-effective method to acquire information on malaria risk for large areas. Here, data collected at one specific hospital in coastal Kenya were used to assess the ability of such passive surveillance to capture spatiotemporal heterogeneity of malaria and effectiveness of an augmented control system.MethodsFever cases were tested for malaria at Msambweni sub-County Referral Hospital, Kwale County, Kenya, from October 2012 to March 2015. Remote sensing data were used to classify the development level of each monitored community and to identify the presence of rice fields nearby. An entomological study was performed to acquire data on the seasonality of malaria vectors in the study area. Rainfall data were obtained from a weather station located in proximity of the study area. Spatial analysis was applied to investigate spatial patterns of malarial and non-malarial fever cases. A space–time Bayesian model was performed to evaluate risk factors and identify locations at high malaria risk. Vector seasonality was analysed using a generalized additive mixed model (GAMM).ResultsAmong the 25,779 tested febrile cases, 28.7 % were positive for Plasmodium infection. Malarial and non-malarial fever cases showed a marked spatial heterogeneity. High risk of malaria was linked to patient age, community development level and presence of rice fields. The peak of malaria prevalence was recorded close to rainy seasons, which correspond to periods of high vector abundance. Results from the Bayesian model identified areas with significantly high malaria risk. The model also showed that the low prevalence of malaria recorded during late 2012 and early 2013 was associated with a large-scale bed net distribution initiative in the study area during mid-2012.ConclusionsThe results indicate that the use of passive surveillance was an effective method to detect spatiotemporal patterns of malaria risk in coastal Kenya. Furthermore, it was possible to estimate the impact of extensive bed net distribution on malaria prevalence among local fever cases over time. Passive surveillance based on georeferenced malaria testing is an important tool that control agencies can use to improve the effectiveness of interventions targeting malaria (and other causes of fever) in such high-risk locations.

【 授权许可】

CC BY   
© Bisanzio et al. 2015