ArticleMalaria journal2015
Spatial and space-time clustering of mortality due to malaria in rural Tanzania: evidence from Ifakara and Rufiji Health and Demographic Surveillance System sites.
Article in Malaria journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Spatial and spatio-temporal methods for mapping malaria risk: a systematic review.BMJ global health · 2020Pooled it
- A systematic review and synthesis of the strengths and limitations of measuring malaria mortality through verbal autopsy.Malaria journal · 2017Pooled it
- Integrating vulnerability and hazard in malaria risk mapping: the elimination context of Senegal.BMC infectious diseases · 2025Article
- Where environment and malaria intersect: Exploring the spatio-temporal footprints of Plasmodium falciparum in Tanzania.PloS one · 2025Article
- Clustering of malaria in households in the Greater Mekong Subregion: operational implications for reactive case detection.Malaria journal · 2021Article
- Accuracy of verbal autopsy, clinical data and minimally invasive autopsy in the evaluation of malaria-specific mortality: an observational study.BMJ global health · 2021Observational
- Modeling and mapping the burden of disease in Kenya.Scientific reports · 2018Article
- Predicting spatial spread of rabies in skunk populations using surveillance data reported by the public.PLoS neglected tropical diseases · 2017Article
- Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis.Infectious diseases of poverty · 2016Article
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9 authors.
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Abstract
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.
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