Published in:
Open Access 01-12-2014 | Research
Global infant mortality trends and attributable determinants – an ecological study using data from 192 countries for the period 1990–2011
Published in: Population Health Metrics | Issue 1/2014
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Background
Infant mortality rate (IMR) is regarded as an important indicator of population health. IMR rates vary substantially with the highest found in sub-Saharan Africa (SSA) compared to the lowest in Europe. Identifying spatial disparities in IMR and quantifying attributable risk factors is essential for policymakers when tailoring time-appropriate interventions at a global, regional, and country level.
Methods
Data for 192 countries were extracted from the World Bank Development Indicator database for the period 1990–2011. Spatial clustering was used to identify significant higher-risk IMR countries. A robust ecological generalized linear negative binomial regression model was used to quantify risk factors and associated decomposition values (Shapley).
Results
Significant reductions were observed in IMR for all of the World Health Organization regions for the period 1990–2011 except for SSA, which indicated a reversal of this trend in the 1990s due to HIV. Significant high-risk clustering of IMR is also indicated in SSA countries and parts of Asia. Maternal mortality (survival), lack of water and sanitation and female education were confirmed as prominent and high attributable risk factors for IMR. Distinct temporal changes in the attributability of these factors were observed, as well as significant heterogeneity with regards to the most attributable factor by region and country.
Conclusions
Our study suggests that maternal mortality is the most prominent attributable risk factor for infant mortality, followed by lack of access to sanitation, lack of access to water, and lower female education. Variation exists across regions and countries with regards to the most attributable factor. Our study also suggests significant underestimation of IMR in regions known for poorer data quality. The results will aid policymakers in re-tailoring time-appropriate interventions to more effectively reduce IMR in line with Millennium Development Goal 4.