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Structured additive regression models with spatial correlation to estimate under-five mortality risk factors in Ethiopia.

Ayele DG, Zewotir TT, Mwambi HG - BMC Public Health (2015)

Bottom Line: With respect to socio-economic factors, the greater the household wealth, the lower the mortality.The model enables simultaneous modeling of possible nonlinear effects of covariates, spatial correlation and heterogeneity.Our findings are relevant because the identified risk factors can be used to provide priority areas for intervention activities by the government to combat under-five mortality in Ethiopia.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag X01, Pietermaritzburg, Scottsville, 3209, South Africa. ayele@ukzn.ac.za.

ABSTRACT

Background: The risk of a child dying before reaching five years of age is highest in Sub-Saharan African countries. But Child mortality rates have shown substantial decline in Ethiopia. It is important to identify factors affecting under-five mortality.

Methods: A structured additive logistic regression model which accounts the spatial correlation was adopted to estimate under-five mortality risk factors. The 2011 Ethiopian Demographic and Health Survey data was used for this study.

Results: The analysis showed that the risk of under-five mortality increases as the family size approaches seven and keeps increasing. With respect to socio-economic factors, the greater the household wealth, the lower the mortality. Moreover, for older mothers, the chance of their child to dying before reaching five is diminishes.

Conclusion: The model enables simultaneous modeling of possible nonlinear effects of covariates, spatial correlation and heterogeneity. Our findings are relevant because the identified risk factors can be used to provide priority areas for intervention activities by the government to combat under-five mortality in Ethiopia.

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Related in: MedlinePlus

Risk map of child mortality A: Predictive risk map of child mortality, B: Standard errors associated with the risk map of child mortality.
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Fig3: Risk map of child mortality A: Predictive risk map of child mortality, B: Standard errors associated with the risk map of child mortality.

Mentions: Using socio-economic, demographic and geographic indicator variables only, child mortality risk map for Ethiopia was generated (Figure 3A). The risk map shows that, in general, Tigray, Afar, Somali and Benshangul-Gumuz regions had the highest risk followed by Amhara region. In Oromia region, the risk was lower compared to other regions. Addia Ababa, Dire Dawa and Harari regions showed lower risk which could be due their being better health facilities in these regions. Figure 3B shows a map of standard errors, indicating that the highest errors are found in the SNNP regions followed by Amhara and Oromiya regions, compared to the rest of the country.Figure 3


Structured additive regression models with spatial correlation to estimate under-five mortality risk factors in Ethiopia.

Ayele DG, Zewotir TT, Mwambi HG - BMC Public Health (2015)

Risk map of child mortality A: Predictive risk map of child mortality, B: Standard errors associated with the risk map of child mortality.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4373119&req=5

Fig3: Risk map of child mortality A: Predictive risk map of child mortality, B: Standard errors associated with the risk map of child mortality.
Mentions: Using socio-economic, demographic and geographic indicator variables only, child mortality risk map for Ethiopia was generated (Figure 3A). The risk map shows that, in general, Tigray, Afar, Somali and Benshangul-Gumuz regions had the highest risk followed by Amhara region. In Oromia region, the risk was lower compared to other regions. Addia Ababa, Dire Dawa and Harari regions showed lower risk which could be due their being better health facilities in these regions. Figure 3B shows a map of standard errors, indicating that the highest errors are found in the SNNP regions followed by Amhara and Oromiya regions, compared to the rest of the country.Figure 3

Bottom Line: With respect to socio-economic factors, the greater the household wealth, the lower the mortality.The model enables simultaneous modeling of possible nonlinear effects of covariates, spatial correlation and heterogeneity.Our findings are relevant because the identified risk factors can be used to provide priority areas for intervention activities by the government to combat under-five mortality in Ethiopia.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag X01, Pietermaritzburg, Scottsville, 3209, South Africa. ayele@ukzn.ac.za.

ABSTRACT

Background: The risk of a child dying before reaching five years of age is highest in Sub-Saharan African countries. But Child mortality rates have shown substantial decline in Ethiopia. It is important to identify factors affecting under-five mortality.

Methods: A structured additive logistic regression model which accounts the spatial correlation was adopted to estimate under-five mortality risk factors. The 2011 Ethiopian Demographic and Health Survey data was used for this study.

Results: The analysis showed that the risk of under-five mortality increases as the family size approaches seven and keeps increasing. With respect to socio-economic factors, the greater the household wealth, the lower the mortality. Moreover, for older mothers, the chance of their child to dying before reaching five is diminishes.

Conclusion: The model enables simultaneous modeling of possible nonlinear effects of covariates, spatial correlation and heterogeneity. Our findings are relevant because the identified risk factors can be used to provide priority areas for intervention activities by the government to combat under-five mortality in Ethiopia.

Show MeSH
Related in: MedlinePlus