Browsing by Author "Mahande, Michael J."

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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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    The Effect of Mother’s Age and Other Related Factors On Neonatal Survival Associated With First And Second Birth In Rural, Tanzania: Evidence From Ifakara Health and Demographic Surveillance System in Rural Tanzania
    (2014-07) Selemani, Majige; Mwanyangala, Mathew A.; Mrema, Sigilbert; Shamte, Amri; PhD, Dan K.; Mkopi, Abdallah; Mahande, Michael J.; Rose, Nathan
    Background With a view to improve neonatal survival, data on birth outcomes are critical for planning maternal and child health care services. We present information on neonatal survival from Ifakara Health and Demographic Surveillance System (HDSS) in Tanzania, regarding the influence of mother’s age and other related factors on neonatal survival of first and second births. Methods The study conducted analysis using longitudinal health and demographic data collected from Ifakara HDSS in parts of Kilombero and Ulanga districts in Morogoro region. The analysis included first and second live births that occurred within six years (2004–2009) and the unit of observation was a live birth. A logistic regression model was used to assess the influence of socio-demographic factors on neonates’ survival. Results A total of 18,139 first and second live births were analyzed. We found neonatal mortality rate of 32 per 1000 live births (95% CI: 29/1000-34/1000). Results from logistic regression model indicated increase in risk of neonatal mortality among neonates those born to young mothers aged 13–19 years compared with those whose mother‘s aged 20–34 years (aOR = 1.64, 95% CI = 1.34-2.02). We also found that neonates in second birth order were more likely to die than those in first birth order (aOR = 1.85:95%CI = 1.52-2.26). The risk of neonatal mortality among offspring of women who had a partner co-resident was 18% times lower as compared with offspring of mothers without a partner co-resident in the household (aOR = 0.82: 95%CI = 0.66-0.98). Short birth interval (<33 months) was associated with increased risk of neonatal mortality (aOR = 1.50, 95% CI =1.16-1.96) compared with long birth interval (> = 33 months). Male born neonates were found to have an increased risk (aOR = 1.34, 95% CI =1.13- 1.58) of neonatal mortality as compared to their female counterparts. Conclusions Delaying the age at first birth may be a valuable strategy to promote and improve neonatal he
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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.