Spatial and space–time clustering of mortality due to malaria in rural Tanzania: evidence from Ifakara and Rufiji Health and Demographic Surveillance System sites

Malaria Journal

Research

Michael J. Mahande¹  Karen Yeates²  Amina S. Msengwa³  Maurice C. Y. Mbago³  Majige Selemani⁴  Sigilbert Mrema⁵  Amri Shamte⁵  Angelina M. Lutambi⁵  Josephine Shabani⁵ 

[1] Department of Epidemiology and Applied Biostatistics, Kilimanjaro Christian Medical University College, Moshi, Tanzania;Department of Medicine, Queen’s University, 94 Stuart Street, Kingston, Canada;Department of Statistics, University of Dar es Salaam, P.O. Box 35047, Dar es Salaam, Tanzania;Department of Statistics, University of Dar es Salaam, P.O. Box 35047, Dar es Salaam, Tanzania;Ifakara Health Institute, (IHI), P.O Box 78373, Plot 463, Kiko Avenue, Off Old Bagamoyo Road, Mikocheni, Dar es Salaam, Tanzania;Ifakara Health Institute, (IHI), P.O Box 78373, Plot 463, Kiko Avenue, Off Old Bagamoyo Road, Mikocheni, Dar es Salaam, Tanzania;

关键词: Spatial methods;    Malaria mortality;    Clustering;   

DOI  :  10.1186/s12936-015-0905-y

 received in 2015-04-24, accepted in 2015-09-14,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundAlthough, malaria control interventions are widely implemented to eliminate malaria disease, malaria is still a public health problem in Tanzania. Understanding the risk factors, spatial and space–time clustering for malaria deaths is essential for targeting malaria interventions and effective control measures. In this study, spatial methods were used to identify local malaria mortality clustering using verbal autopsy data.MethodsThe analysis used longitudinal data collected in Rufiji and Ifakara Health Demographic Surveillance System (HDSS) sites for the period 1999–2011 and 2002–2012, respectively. Two models were used. The first was a non-spatial model where logistic regression was used to determine a household’s characteristic or an individual’s risk of malaria deaths. The second was a spatial Poisson model applied to estimate spatial clustering of malaria mortality using SaTScan™, with age as a covariate. ArcGIS Geographical Information System software was used to map the estimates obtained to show clustering and the variations related to malaria mortality.ResultsA total of 11,462 deaths in 33 villages and 9328 deaths in 25 villages in Rufiji and Ifakara HDSS, respectively were recorded. Overall, 2699 (24 %) of the malaria deaths in Rufiji and 1596 (17.1 %) in Ifakara were recorded during the study period. Children under five had higher odds of dying from malaria compared with their elderly counterparts aged five and above for Rufiji (AOR = 2.05, 95 % CI = 1.87–2.25), and Ifakara (AOR = 2.33, 95 % CI = 2.05–2.66), respectively. In addition, ownership of mosquito net had a protective effect against dying with malaria in both HDSS sites. Moreover, villages with consistently significant malaria mortality clusters were detected in both HDSS sites during the study period.ConclusionsClustering of malaria mortality indicates heterogeneity in risk. Improving targeted malaria control and treatment interventions to high risk clusters may lead to the reduction of malaria deaths at the household and probably at country level. Furthermore, ownership of mosquito nets and age appeared to be important predictors for malaria deaths.

【 授权许可】

CC BY   
© Selemani et al. 2015