Despite the Millennium Development Project's aims to reduce infant and child mortality, this problem remains a challenge in sub-Saharan Africa. The infant mortality rate (IMR), moreover, has worsened in many of these countries reversing the gains achieved in the previous century . In 1990, for example, there was a 20-fold difference (180 versus 9 deaths per 1000 live births) in IMR between sub-Saharan African and industrialized countries. By 2000, this difference had increased to 29-fold with IMR's of 175 and 6 per 1000 respectively  largely as a result of the prevalence of HIV/AIDS in sub-Saharan Africa . Southern Africa, in particular, has been significantly compromised by the HIV/AIDS epidemic both directly through vertical HIV transmission, and indirectly, through maternal death and the absence of a primary care giver .
Material deprivation is widespread in many sub-Saharan countries. In recent times, the combined effect of material deprivation and HIV/AIDS has negatively impacted on infant mortality. The interactive relationship between HIV/AIDS and material deprivation is illustrated by a combination of increased healthcare costs and a reduced ability to generate income . Furthermore, a wide range of socio-economic variables influence material deprivation including ethnicity, female literacy, maternal mortality and household size. In addition, a lack of social support, unemployment, poor nutrition, access to water, transport and the distance to the nearest healthcare facility are aggravating factors . An important variable that measures relative material deprivation in terms of income inequality, namely, the Gini-Coefficient, has also been established as a key determinant of infant mortality .
Reducing infant mortality requires a range of investments that include increased health sector spending, improving health systems functioning, and "through socioeconomic progress to improve nutrition, housing, hygiene, education, gender equality, and human rights" . However which investment to make, given resource constraints is not clear. Not only is it not clear which interventions to prioritise, but also whom or where to target the interventions. Reliable statistics on mortality, its causes and trends are in high demand for assessing the global and regional health situation. Reliable mortality data are a prerequisite for planning health interventions, yet such data are often not available in developing countries, particularly in sub-Saharan Africa (SSA). In the absence of such data, alternate data sources need to be utilized to address these gaps and inform progress towards the Millennium Development Goals.
The geographic distribution of health problems such as mortality is also not uniformly distributed and aggregated poverty and health statistics do often not describe the variations in mortality experienced within regions of countries . The IMR, in particular, can vary significantly between geographic locations, as well as across the urban rural divide . In South Africa, the incidence of infant mortality differs widely across race groups and provinces . The differential IMR rate is also reflected in unequal socioeconomic status (SES) and access to services and facilities that vary widely across the nine provinces .
Population- wide interventions are costly to implement and it is often necessary to target high risk areas . Investigating the distribution and determinants of adverse health outcomes, therefore, can usefully inform more focused and cost effective interventions. In particular, the targeting of high risk health clusters or sub-districts can inform policy planning . Spatial analysis is an important tool in epidemiology to detect possible sources of heterogeneity, spatial incidence or patterns . The potential of spatial analysis is reinforced by the increasing availability of geographically indexed population level data such as mortality, as well as advances in computation methods using GIS systems. Spatial analysis, moreover, can be applied to health data in small area studies , as well as to imperfect data, often the case in Africa, through the use of space and time geo-statistics .
The objective of this paper is to determine and map the spatial nature of infant mortality in South Africa at a sub district level in order to identify high risk areas and inform policy interventions. In particular, we identify and map high risk clusters of infant mortality, as well as examine the impact of a range of social determinants of infant mortality including maternal health, provincial antenatal HIV sero-prevalence, socio-economic inequality and access to services and facilities at the sub-district level.
In South Africa, little research has focused on spatial differences in mortality at the municipal/sub district level. The identification, targeting and quantification of factors contributing to sub-district level mortality can contribute to more focused public health interventions in South Africa, as well as many other developing countries confronted with similar problems . This paper makes a primary contribution in the health domain by developing a more integrated argument for the determinants of infant mortality in a developing country context . The paper also makes a contribution by using Bayesian spatial modelling to determine infant mortality at the sub-district or small area level thus extending the conclusions of advanced spatial modelling for public health intervention  to interface with service delivery and other indicators. Finally, the paper contributes to the public sector domain by suggesting the use of an infant mortality indicator which can be used as a proxy for the delivery of basic services that influence material deprivation.