The contribution of spatial analysis to understanding HIV/TB mortality in children: a structural equation modelling approach

Abstract

Background : South Africa accounts for more than a sixth of the global population of people infected with HIV and TB, ranking her highest in HIV/TB co-infection worldwide. Remote areas often bear the greatest burden of morbidity and mortality, yet there are spatial differences within rural settings.

Objectives : The primary aim was to investigate HIV/TB mortality determinants and their spatial distribution in the rural Agincourt sub-district for children aged 1–5 years in 2004. Our secondary aim was to model how the associated factors were interrelated as either underlying or proximate factors of child mortality using pathway analysis based on a Mosley-Chen conceptual framework.

Methods : We conducted a secondary data analysis based on cross-sectional data collected in 2004 from the Agincourt sub-district in rural northeast South Africa. Child HIV/TB death was the outcome measure derived from physician assessed verbal autopsy. Modelling used multiple logit regression models with and without spatial household random effects. Structural equation models were used in modelling the complex relationships between multiple exposures and the outcome (child HIV/TB mortality) as relayed on a conceptual framework.

Results : Fifty-four of 6,692 children aged 1–5 years died of HIV/TB, from a total of 5,084 households. Maternal death had the greatest effect on child HIV/TB mortality (adjusted odds ratio=4.00; 95% confidence interval=1.01–15.80). A protective effect was found in households with better socio-economic status and when the child was older. Spatial models disclosed that the areas which experienced the greatest child HIV/TB mortality were those without any health facility.

Conclusion : Low socio-economic status and maternal deaths impacted indirectly and directly on child mortality, respectively. These factors are major concerns locally and should be used in formulating interventions to reduce child mortality. Spatial prediction maps can guide policy makers to target interventions where they are most needed.

Appendices available online under Reading Tools.

• HIV/TB
• child mortality
• conceptual framework
• spatial analysis
• pathway analysis
• South Africa

Acknowledgements

This work was made possible by doctoral fellowship support from the World Health Organization, Tropical Disease Research (WHO/TDR), and Swiss–South Africa Joint Research Programme. The MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt) is funded by the Wellcome Trust, UK, (grants 058893/Z/99/A, 069683/Z/02/Z and 085477/Z/08/Z), the University of the Witwatersrand and Medical Research Council, South Africa, and the Andrew W Mellon and William and Flora Hewlett Foundations, USA. Special thanks goes to Benn Sartorius for data extraction, David Rees for his valuable review of an early draft, community leaders, field workers, supervisors, and the Agincourt community.

Notes

¹A16=Respiratory tuberculosis; not confirmed bacteriologically or histologically; A17=Tuberculosis of nervous system; A18=Tuberculosis of other organs; A19=Miliary tuberculosis.

²B20=Human immunodeficiency virus (HIV) disease resulting in infectious and parasitic diseases; B21=Human immunodeficiency virus (HIV) disease resulting in malignant neoplasms; B22=Human immunodeficiency virus (HIV) disease resulting in other specified diseases; B23=Human immunodeficiency virus (HIV) disease resulting in other conditions; B24=Unspecified human immunodeficiency virus (HIV) disease.