Int J Health Geogr - Infant mortality in South Africa--distribution, associations and policy implications, 2007: an ecological spatial analysis.


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CKGROUND: Many sub-Saharan countries are confronted with persistently high levels of infant mortality because of the impact of a range of biological and social determinants. In particular, infant mortality has increased in sub-Saharan Africa in recent decades due to the HIV/AIDS epidemic. The geographic distribution of health problems and their relationship to potential risk factors can be invaluable for cost effective intervention planning. 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 inform policy intervention. In particular, the paper identifies and maps high risk clusters of infant mortality, as well as examines the impact of a range of determinants on infant mortality. A Bayesian approach is used to quantify the spatial risk of infant mortality, as well as significant associations (given spatial correlation between neighbouring areas) between infant mortality and a range of determinants. The most attributable determinants in each sub-district are calculated based on a combination of prevalence and model risk factor coefficient estimates. This integrated small area approach can be adapted and applied in other high burden settings to assist intervention planning and targeting.RESULTS: Infant mortality remains high in South Africa with seemingly little reduction since previous estimates in the early 2000's. Results showed marked geographical differences in infant mortality risk between provinces as well as within provinces as well as significantly higher risk in specific sub-districts and provinces. A number of determinants were found to have a significant adverse influence on infant mortality at the sub-district level. Following multivariable adjustment increasing maternal mortality, antenatal HIV prevalence, previous sibling mortality and male infant gender remained significantly associated with increased infant mortality risk. Of these antenatal HIV sero-prevalence, previous sibling mortality and maternal mortality were found to be the most attributable respectively.CONCLUSIONS: This study demonstrates the usefulness of advanced spatial analysis to both quantify excess infant mortality risk at the lowest administrative unit, as well as the use of Bayesian modelling to quantify determinant significance given spatial correlation. The "novel" integration of determinant prevalence at the sub-district and coefficient estimates to estimate attributable fractions further elucidates the "high impact" factors in particular areas and has considerable potential to be applied in other locations. The usefulness of the paper, therefore, not only suggests where to intervene geographically, but also what specific interventions policy makers should prioritize in order to reduce the infant mortality burden in specific administration areas.

Resumo Limpo

ckground mani subsaharan countri confront persist high level infant mortal impact rang biolog social determin particular infant mortal increas subsaharan africa recent decad due hivaid epidem geograph distribut health problem relationship potenti risk factor can invalu cost effect intervent plan object paper determin map spatial natur infant mortal south africa sub district level order inform polici intervent particular paper identifi map high risk cluster infant mortal well examin impact rang determin infant mortal bayesian approach use quantifi spatial risk infant mortal well signific associ given spatial correl neighbour area infant mortal rang determin attribut determin subdistrict calcul base combin preval model risk factor coeffici estim integr small area approach can adapt appli high burden set assist intervent plan targetingresult infant mortal remain high south africa seem littl reduct sinc previous estim earli s result show mark geograph differ infant mortal risk provinc well within provinc well signific higher risk specif subdistrict provinc number determin found signific advers influenc infant mortal subdistrict level follow multivari adjust increas matern mortal antenat hiv preval previous sibl mortal male infant gender remain signific associ increas infant mortal risk antenat hiv seropreval previous sibl mortal matern mortal found attribut respectivelyconclus studi demonstr use advanc spatial analysi quantifi excess infant mortal risk lowest administr unit well use bayesian model quantifi determin signific given spatial correl novel integr determin preval subdistrict coeffici estim estim attribut fraction elucid high impact factor particular area consider potenti appli locat use paper therefor suggest interven geograph also specif intervent polici maker priorit order reduc infant mortal burden specif administr area

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