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Space-time variations in child mortality in a rural South African population with high HIV prevalence (2000-2014)

Thursday, 9th of November 2017 Print

Space-time variations in child mortality in a rural South African population with high HIV prevalence (2000-2014)

Tlou B1,2Sartorius B1,2Tanser F1,2,3.

 

1 Discipline of Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa.

2 Africa Centre for Health and Population Studies, University of KwaZulu-Natal, Mtubatuba, South Africa.

3 Centre for the AIDS Programme of Research in South Africa-CAPRISA, University of KwaZulu-Natal, Durban, South Africa.

Abstract below; full text is at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0182478

 

OBJECTIVE:

The aim of the study was to identify the key determinants of child mortality hot-spots in space and time.

METHODS:

Comprehensive population-based mortality data collected between 2000 and 2014 by the Africa Centre Demographic Information System located in the UMkhanyakude District of KwaZulu-Natal Province, South Africa, was analysed. We assigned all mortality events and person-time of observation for children <5 years of age to an exact homestead of residence (mapped to <2m accuracy as part of the DSA platform). Using these exact locations, both the Kulldorff and Tango spatial scan statistics for regular and irregular shaped cluster detection were used to identify clusters of childhood mortalityevents in both space and time.

FINDINGS:

Of the 49 986 children aged < 5 years who resided in the study area between 2000 and 2014, 2010 (4.0%) died. Childhood mortality decreased by 80% over the period from >20 per 1000 person-years in 2001-2003 to 4 per 1000 person-years in 2014. The two scanning spatial techniques identified two high-risk clusters for child mortality along the eastern border of the study site near the national highway, with a relative risk of 2.10 and 1.91 respectively.

CONCLUSIONS:

The high-risk communities detected in this work, and the differential risk factor profile of these communities, can assist public health professionals to identify similar populations in other parts of rural South Africa. Identifying child mortalityhot-spots will potentially guide policy interventions in rural, resource-limited settings.

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