| dc.creator |
Selemani, Majige |
|
| dc.creator |
Mrema, Sigilbert |
|
| dc.creator |
Shamte, Amri |
|
| dc.creator |
Shabani, Josephine |
|
| dc.creator |
Mahande, Michael J. |
|
| dc.creator |
Yeates, Karen |
|
| dc.creator |
Msengwa, Amina S. |
|
| dc.creator |
Mbago, Maurice C. Y. |
|
| dc.creator |
Lutambi, Angelina M. |
|
| dc.date |
2016-05-18T15:08:18Z |
|
| dc.date |
2016-05-18T15:08:18Z |
|
| dc.date |
2015 |
|
| dc.date.accessioned |
2018-03-27T09:13:20Z |
|
| dc.date.available |
2018-03-27T09:13:20Z |
|
| dc.identifier |
Selemani, M., Mrema, S., Shamte, A., Shabani, J., Mahande, M.J., Yeates, K., Msengwa, A.S., Mbago, M.C. and Lutambi, A.M., 2015. 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, 14(1), pp.1-15. |
|
| dc.identifier |
http://hdl.handle.net/20.500.11810/2138 |
|
| dc.identifier |
10.1186/s12936-015-0905-y |
|
| dc.identifier.uri |
http://hdl.handle.net/20.500.11810/2138 |
|
| dc.description |
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. |
|
| dc.language |
en |
|
| dc.publisher |
BioMed Central |
|
| dc.subject |
Spatial methods |
|
| dc.subject |
Malaria mortality |
|
| dc.subject |
Clustering |
|
| dc.title |
Spatial and Space-Time Clustering of Mortality Due To Malaria in Rural Tanzania: Evidence from Ifakara and Rufiji Health and Demographic Surveillance System Sites |
|
| dc.type |
Journal Article, Peer Reviewed |
|