A spatial model to quantify the mortality impact of service delivery in Sub-Saharan Africa: an ecological design utilizing data from South Africa

International Journal of Health Geographics

Benn KD Sartorius¹  Kurt Sartorius² 

[1] School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa;School of Accountancy, Faculty of Commerce, Law and Management, University of the Witwatersrand, Johannesburg, South Africa

关键词: Population density;    Income inequality;    Poverty;    Mortality;    Service delivery;   

Others  :  810249
DOI  :  10.1186/1476-072X-12-8

 received in 2012-11-02, accepted in 2013-02-13,  发布年份 2013

【 摘 要 】

Background

Sub Saharan Africa is confronted with a wide range of interlinked health and economic problems that include high levels of mortality and poor service delivery. The objective of the paper is to develop a spatial model for Sub-Saharan Africa that can quantify the mortality impact of (poor) service delivery at sub-district level in order to integrate related health and local level policy interventions. In this regard, an expanded composite service delivery index was developed, and the data were analysed using a Bayesian Poisson spatial model.

Results

The results indicate significant differences in the risk of mortality and poor service delivery at sub-district level. In particular, the results indicate clusters of high mortality and poor service delivery in two of the bigger, poorer provinces with large rural communities. Conversely, two of the wealthier provinces have lower levels of mortality and higher levels of service delivery, but income inequality is more widespread. The bivariate and multivariate models, moreover, reflect significant positive linkages (p < 0.01) between increased mortality and poor service delivery after adjusting for HIV/AIDS, income inequality, population density and the protective influence of metropolitan areas. Finally, the hypothesized provision of a basket of services reduced the mortality rate in South Africa’s 248 sub-districts by an average of 5.3 (0.3-15.4) deaths per 1000.

Conclusion

The results indicate that the model can accurately plot mortality and service delivery “hotspots’ at sub-district level, as well as explain their associations and causality. A mortality reduction index shows that mortality in the highest risk sub-districts can be reduced by as much as 15.4 deaths per 1000 by providing a range of basic services. The ability to use the model in a wider SSA context and elsewhere is also feasible given the innovative use of available databases. Finally, the paper illustrates the importance of developing policy in SSA that can simultaneously solve both economic and health problems.

【 授权许可】

   
2013 Sartorius and Sartorius; licensee BioMed Central Ltd.

【 图 表 】

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