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

doi:10.1186/s12936-015-0905-y

. 2015 Sep 26:14:369.

doi: 10.1186/s12936-015-0905-y.

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

Majige Selemani^{1

2}, Sigilbert Mrema³, Amri Shamte⁴, Josephine Shabani⁵, Michael J Mahande⁶, Karen Yeates⁷, Amina S Msengwa⁸, Maurice C Y Mbago⁹, Angelina M Lutambi¹⁰

Affiliations

¹ Department of Statistics, University of Dar es Salaam, P.O. Box 35047, Dar es Salaam, Tanzania. mselemani@ihi.or.tz.
² Ifakara Health Institute, (IHI), Plot 463, Kiko Avenue, Off Old Bagamoyo Road, Mikocheni, P.O Box 78373, Dar es Salaam, Tanzania. mselemani@ihi.or.tz.
³ Ifakara Health Institute, (IHI), Plot 463, Kiko Avenue, Off Old Bagamoyo Road, Mikocheni, P.O Box 78373, Dar es Salaam, Tanzania. smrema@ihi.or.tz.
⁴ Ifakara Health Institute, (IHI), Plot 463, Kiko Avenue, Off Old Bagamoyo Road, Mikocheni, P.O Box 78373, Dar es Salaam, Tanzania. samri@ihi.or.tz.
⁵ Ifakara Health Institute, (IHI), Plot 463, Kiko Avenue, Off Old Bagamoyo Road, Mikocheni, P.O Box 78373, Dar es Salaam, Tanzania. jshabani@ihi.or.tz.
⁶ Department of Epidemiology and Applied Biostatistics, Kilimanjaro Christian Medical University College, Moshi, Tanzania. jmmahande@gmail.com.
⁷ Department of Medicine, Queen's University, 94 Stuart Street, Kingston, Canada. yeatesk@queensu.ca.
⁸ Department of Statistics, University of Dar es Salaam, P.O. Box 35047, Dar es Salaam, Tanzania. msengwaa@yahoo.com.
⁹ Department of Statistics, University of Dar es Salaam, P.O. Box 35047, Dar es Salaam, Tanzania. mmbago@udsm.ac.tz.
¹⁰ Ifakara Health Institute, (IHI), Plot 463, Kiko Avenue, Off Old Bagamoyo Road, Mikocheni, P.O Box 78373, Dar es Salaam, Tanzania. alutambi@ihi.or.tz.

PMID: 26409483
PMCID: PMC4583746
DOI: 10.1186/s12936-015-0905-y

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

Majige Selemani et al. Malar J. 2015.

. 2015 Sep 26:14:369.

doi: 10.1186/s12936-015-0905-y.

Authors

Majige Selemani^{1

2}, Sigilbert Mrema³, Amri Shamte⁴, Josephine Shabani⁵, Michael J Mahande⁶, Karen Yeates⁷, Amina S Msengwa⁸, Maurice C Y Mbago⁹, Angelina M Lutambi¹⁰

Affiliations

¹ Department of Statistics, University of Dar es Salaam, P.O. Box 35047, Dar es Salaam, Tanzania. mselemani@ihi.or.tz.
² Ifakara Health Institute, (IHI), Plot 463, Kiko Avenue, Off Old Bagamoyo Road, Mikocheni, P.O Box 78373, Dar es Salaam, Tanzania. mselemani@ihi.or.tz.
³ Ifakara Health Institute, (IHI), Plot 463, Kiko Avenue, Off Old Bagamoyo Road, Mikocheni, P.O Box 78373, Dar es Salaam, Tanzania. smrema@ihi.or.tz.
⁴ Ifakara Health Institute, (IHI), Plot 463, Kiko Avenue, Off Old Bagamoyo Road, Mikocheni, P.O Box 78373, Dar es Salaam, Tanzania. samri@ihi.or.tz.
⁵ Ifakara Health Institute, (IHI), Plot 463, Kiko Avenue, Off Old Bagamoyo Road, Mikocheni, P.O Box 78373, Dar es Salaam, Tanzania. jshabani@ihi.or.tz.
⁶ Department of Epidemiology and Applied Biostatistics, Kilimanjaro Christian Medical University College, Moshi, Tanzania. jmmahande@gmail.com.
⁷ Department of Medicine, Queen's University, 94 Stuart Street, Kingston, Canada. yeatesk@queensu.ca.
⁸ Department of Statistics, University of Dar es Salaam, P.O. Box 35047, Dar es Salaam, Tanzania. msengwaa@yahoo.com.
⁹ Department of Statistics, University of Dar es Salaam, P.O. Box 35047, Dar es Salaam, Tanzania. mmbago@udsm.ac.tz.
¹⁰ Ifakara Health Institute, (IHI), Plot 463, Kiko Avenue, Off Old Bagamoyo Road, Mikocheni, P.O Box 78373, Dar es Salaam, Tanzania. alutambi@ihi.or.tz.

PMID: 26409483
PMCID: PMC4583746
DOI: 10.1186/s12936-015-0905-y

Abstract

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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Figures

**Fig. 1**
Reference map for Rufiji Health and Demographic Surveillance System site

**Fig. 2**
Reference map for Ifakara Health and Demographic Surveillance System site

**Fig. 3**
Major malaria control interventions in the study areas. a First line malaria treatment policy, b proportion of households with at least one mosquito nets in Rufiji and Ifakara HDSS

**Fig. 4**
Geographical clustering and space time clustering for estimated malaria mortality rate in Rufiji and Ifakara HDSS by village. a For Rufiji HDSS and b for Ifakara HDSS

**Fig. 5**
Malaria deaths and estimates of malaria mortality rate in the study area. a For Rufiji HDSS and b for Ifakara HDSS

See this image and copyright information in PMC

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References

1. WHO. World malaria report. Geneva, Switzerland: World Health Organization; 2012.
1. Tanzania Commission for AIDS (TACAIDS), Zanzibar AIDS Commission (ZAC), National Bureau of Statistics (NBS) O of the CGS and II. Tanzania HIV/AIDS and malaria indicator survey 2011–2012. Dar es Salaam, Tanzania: United Republic of Tanzania; 2013.
1. Snow RW, Guerra CA, Noor AM, Myint HY, Hay SI. The global distribution of clinical episodes of Plasmodium falciparum malaria. Nature. 2005;434:214–217. doi: 10.1038/nature03342. - DOI - PMC - PubMed
1. Guerra CA, Snow RW, Hay SI. Mapping the global extent of malaria in 2005. Trend Parasitol. 2006;22:353–358. doi: 10.1016/j.pt.2006.06.006. - DOI - PMC - PubMed
1. Msengwa AS. Statistical modeling and analysis of malaria risk and child mortality in Tanzania. Tanzania: University of Dar es Salaam; 2010.

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[1] WHO. World malaria report. Geneva, Switzerland: World Health Organization; 2012.

[2] WHO. World malaria report. Geneva, Switzerland: World Health Organization; 2012.

[3] Tanzania Commission for AIDS (TACAIDS), Zanzibar AIDS Commission (ZAC), National Bureau of Statistics (NBS) O of the CGS and II. Tanzania HIV/AIDS and malaria indicator survey 2011–2012. Dar es Salaam, Tanzania: United Republic of Tanzania; 2013.

[4] Tanzania Commission for AIDS (TACAIDS), Zanzibar AIDS Commission (ZAC), National Bureau of Statistics (NBS) O of the CGS and II. Tanzania HIV/AIDS and malaria indicator survey 2011–2012. Dar es Salaam, Tanzania: United Republic of Tanzania; 2013.

[5] Snow RW, Guerra CA, Noor AM, Myint HY, Hay SI. The global distribution of clinical episodes of Plasmodium falciparum malaria. Nature. 2005;434:214–217. doi: 10.1038/nature03342. - DOI - PMC - PubMed

[6] Snow RW, Guerra CA, Noor AM, Myint HY, Hay SI. The global distribution of clinical episodes of Plasmodium falciparum malaria. Nature. 2005;434:214–217. doi: 10.1038/nature03342. - DOI - PMC - PubMed

[7] Guerra CA, Snow RW, Hay SI. Mapping the global extent of malaria in 2005. Trend Parasitol. 2006;22:353–358. doi: 10.1016/j.pt.2006.06.006. - DOI - PMC - PubMed

[8] Guerra CA, Snow RW, Hay SI. Mapping the global extent of malaria in 2005. Trend Parasitol. 2006;22:353–358. doi: 10.1016/j.pt.2006.06.006. - DOI - PMC - PubMed

[9] Msengwa AS. Statistical modeling and analysis of malaria risk and child mortality in Tanzania. Tanzania: University of Dar es Salaam; 2010.

[10] Msengwa AS. Statistical modeling and analysis of malaria risk and child mortality in Tanzania. Tanzania: University of Dar es Salaam; 2010.

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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

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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

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