Browsing by Author "Yeates, Karen"

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    Assessing The Effects of Mosquito Nets on Malaria Mortality Using a Space Time Model: A Case Study of Rufiji And Ifakara Health And Demographic Surveillance System Sites In Rural Tanzania
    (2016-11) Selemani, Majige; Msengwa, Amina; Mrema, Sigilbert; Shamte, Amri; Mahande, Michael J.; Yeates, Karen; Mbago, Maurice; Lutambi, Angelina M.
    Background: Although malaria decline has been observed in most sub-Saharan African countries, the disease still represents a significant public health burden in Tanzania. There are contradictions on the effect of ownership of at least one mosquito net at household on malaria mortality. This study presents a Bayesian modelling framework for the analysis of the effect of ownership of at least one mosquito net at household on malaria mortality with environmental factors as confounder variables. Methods: The analysis used longitudinal data collected in Rufiji and Ifakara Health Demographic Surveillance System (HDSS) sites for the period of 1999-2011 and 2002-2012, respectively. Bayesian framework modelling approach using integrated nested laplace approximation (INLA) package in R software was used. The space time models were established to assess the effect of ownership of mosquito net on malaria mortality in 58 villages in the study area. Results: The results show that an increase of 10 % in ownership of mosquito nets at village level had an average of 5.2 % decrease inall age malaria deaths (IRR = 0.948, 95 % CI = 0.917, 0.977) in Rufiji HDSS and 12.1 % decrease in all age malaria deaths (IRR = 0.879, 95 % CI = 0.806, 0.959) in Ifakara HDSS. In children under 5 years, results show an average of 5.4 % decrease of malaria deaths (IRR = 0.946, 95 % CI = 0.909, 0.982) in Rufiji HDSS and 10 % decrease of malaria deaths (IRR = 0.899, 95 % CI = 0.816, 0.995) in Ifakara HDSS. Model comparison show that model with spatial and temporal random effects was the best fitting model compared to other models without spatial and temporal, and with spatial-temporal interaction effects. Conclusion: This modelling framework is appropriate and provides useful approaches to understanding the effect of mosquito nets for targeting malaria control intervention. Furthermore, ownership of mosquito nets at household showed a significant impact on malaria mortality.
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    Assessing the Effects of Mosquito Nets on Malaria Mortality Using a Space Time Model: A Case Study of Rufiji and Ifakara Health and Demographic Surveillance System Sites in Rural Tanzania
    (BioMed Central, 2016) Selemani, Majige; Msengwa, Amina S.; Mrema, Sigilbert; Shamte, Amri; Mahande, Michael J.; Yeates, Karen; Mbago, Maurice C. Y.; Lutambi, Angelina M.
    Background: Although malaria decline has been observed in most sub-Saharan African countries, the disease still represents a significant public health burden in Tanzania. There are contradictions on the effect of ownership of at least one mosquito net at household on malaria mortality. This study presents a Bayesian modelling framework for the analysis of the effect of ownership of at least one mosquito net at household on malaria mortality with environmental factors as confounder variables. Methods: The analysis used longitudinal data collected in Rufiji and Ifakara Health Demographic Surveillance System (HDSS) sites for the period of 1999–2011 and 2002–2012, respectively. Bayesian framework modelling approach using integrated nested laplace approximation (INLA) package in R software was used. The space time models were established to assess the effect of ownership of mosquito net on malaria mortality in 58 villages in the study area. Results: The results show that an increase of 10 % in ownership of mosquito nets at village level had an average of 5.2 % decrease inall age malaria deaths (IRR = 0.948, 95 % CI = 0.917, 0.977) in Rufiji HDSS and 12.1 % decrease in all age malaria deaths (IRR = 0.879, 95 % CI = 0.806, 0.959) in Ifakara HDSS. In children under 5 years, results show an average of 5.4 % decrease of malaria deaths (IRR = 0.946, 95 % CI = 0.909, 0.982) in Rufiji HDSS and 10 % decrease of malaria deaths (IRR = 0.899, 95 % CI = 0.816, 0.995) in Ifakara HDSS. Model comparison show that model with spatial and temporal random effects was the best fitting model compared to other models without spatial and temporal, and with spatial–temporal interaction effects. Conclusion: This modelling framework is appropriate and provides useful approaches to understanding the effect of mosquito nets for targeting malaria control intervention. Furthermore, ownership of mosquito nets at household showed a significant impact on malaria mortality.
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    Spatial and Space-Time Clustering of Mortality Due To Malaria in Rural Tanzania: Evidence from Ifakara and Rufiji Health and Demographic Surveillance System Sites
    (BioMed Central, 2015) Selemani, Majige; Mrema, Sigilbert; Shamte, Amri; Shabani, Josephine; Mahande, Michael J.; Yeates, Karen; Msengwa, Amina S.; Mbago, Maurice C. Y.; Lutambi, Angelina M.
    Background: Although, 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. Methods: The 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. Results: A 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. Conclusions: Clustering 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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    Spatial and Space-Time Clustering of Mortality Due to Malaria in Rural Tanzania: Evidence From Ifakara And Rufiji Health And Demographic Surveillance System Sites
    (2015-09) Selemani, Majige; Mrema, Sigilbert; Shamte, Amri; Shabani, Josephine; Mahande, Michael J.; Yeates, Karen; Msengwa, Amina; Mbago, Maurice; Lutambi, Angelina M.
    Background: Although, 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. Methods: The analysis used longitudinal data collected in Rufiji and Ifakara Health Demographic Surveillance System (HDSS) sites for the period 1999 to 2011 and 2002 to 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 SaTScanTM, 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. Results: A total of 11,462 deaths in 33 villages and 9,328 deaths in 25 villages in Rufiji and Ifakara HDSS respectively were recorded. Overall, 2,699(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. Conclusions: Clustering 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.