Spatio-temporal patterns of under-five mortality in Matlab HDSS in rural Bangladesh
Zahirul Haq, M.
Kim Streatfield, Peter
- Publisher: CoAction Publishing
Global Health Action,
(issn: 1654-9716, eissn: 1654-9880)
Matlab | children | mortality | Supplement 1, 2010 | INDEPTH Mortality Clustering Supplement | clustering | Clustering; Children; Mortality; Rural; Bangladesh. | Public Health; population Health | Bangladesh
Background: Knowledge of spatial and temporal distributions of mortality and morbidity is important to prioritise areas for adjusting the public health system where people need services most. A Health and Demographic Surveillance System (HDSS) plays an important role where accurate national vital events are not available in identifying areas and periods with excess mortality risks. Methods: The HDSS in Matlab, a rural area of Bangladesh, provided data on yearly number of deaths and children aged below 5 years for each of 90 villages during 1998-2007, along with village location points, longitudes and latitudes. Kulldorff ’s space-time scan statistic was used to identify villages and periods that experienced high mortality risks in the HDSS area with a statistical significance of pB0.001. Logistic regression was conducted to examine if village-level education and economic status explained village-level mortality risks. Results: There were 3,434 deaths among children aged below 5 years in the HDSS area during 1998-2007 with an average yearly rate of 13 deaths per 1,000 under-five child-years. The mortality rate showed a declining trend with high concentration in 1998-2002, but not in 2003-2007. Two clusters of villages had significantly higher mortality risks in 1998-2002, but not later, and the mortality risks in the high-risk clusters reduced little, but remained significant after controlling for adult education and economic status at village level. Conclusions: Spatial clustering of childhood mortality observed during 1998-2002 had disappeared in subsequent years with a decline in mortality rates. Space-time scanning helps identify high-risk areas and periods to enhance public health actions. Keywords: clustering; children; mortality; Matlab; Bangladesh (Published: 30 August 2010) Citation: Global Health Action Supplement 1, 2010. DOI: 10.3402/gha.v3i0.5252