Infant mortality rate why is it important

Some of the leading causes of infant mortality—e. California requires newborn screenings for potentially fatal genetic diseases, as early identification and treatment can help avert long-term health consequences and even death 1.

Public and institutional policy also can address risk and protective factors for SIDS and preterm births by ensuring that women are in good health before conception, avoid smoking and substance use while pregnant, and forgo elective deliveries before 39 weeks of pregnancy, as well as broader strategies that address social determinants of health 2, 3. For more policy ideas and research on this topic, see kidsdata.

Also see policy implications on kidsdata. California Department of Public Health. Newborn Screening Program. Richards, J. Infant mortality toolkit. SIDS and other sleep-related infant deaths: Updated recommendations for a safe infant sleeping environment. Pediatrics, 5 , e Centers for Disease Control and Prevention Lapses in efforts to reduce infant mortality and associated contributing factors can lead to a slowing of and even a reversal of the decline in mortality rates in coming years.

Our model suggests that significant underestimation underreporting of IMR occurred in some countries, particularly in SSA. Reliable infant and child mortality data are critical for planning health interventions and assessing progress, yet such data are often not available or reliable in developing countries [ 36 ], especially in SSA [ 37 ].

This study also makes a contribution by more accurately estimating IMR in the presence of underreporting in areas with known higher IMR risk that have poor data sources.

Much of the WDI data comes from individual member countries and is compiled by internationally recognized organizations [ 13 ]. However, the quality of global data still depends on how well the individual national systems perform. We therefore cannot discount that differential data quality by country may have affected our findings.

The World Bank has worked to help developing countries improve national statistical systems and hence the quality of their data. Therefore, changes in data quality with time may also affect the observed temporal trends. Substantial missing data for certain key indicators e. AF p can be incorrectly overestimated if confounding is not taken into account i.

This can occur for the formula used in this study if unadjusted risk coefficients are utilized. We have tried to limit this potential bias by use of multivariable adjusted risk coefficients. A recent study based on data from countries suggested that suboptimal breastfeeding may rank higher as a risk factor for child mortality than poor water and sanitation [ 38 ]. Thus we cannot also discount the potential impact or contribution of such a missing indicator in our analyses.

Future applications of our proposed framework should include this and other potentially key missing indicators. This study contributes to our understanding of the global burden of infant mortality and disaggregation to the country level with regards to associated high-impact determinants for policy tailoring.

Maternal mortality survival appeared to be the most prominent risk factor for infant mortality, followed by lack of access to sanitation, female education, and lack of access to water. Substantial heterogeneity exists across regions and countries with regards to the most important factor. The model also suggests that there is potentially significant underestimation of IMR in regions known for poorer data quality.

The framework will potentially aid policymakers in retailoring time-appropriate interventions to more effectively reduce IMR and associated high-risk indicators in the post-Millennium Development Goal era and thus potentially build on momentum garnered for associated determinants during this era. BS and KS contributed to the conception and design, interpretation of data, and drafting of the manuscript. BS contributed to the acquisition of data and analysis. BS and KS have given final approval of the version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Both authors read and approved the final manuscript. In general significant clustering of high attributability decomposition values for all selected determinants was observed Figure 7 a-d below , especially in SSA in general. Descending top 50 decomposition values by selected determinant and country, as well as spatial clustering of these determinants, — [Note: dark grey represents significant spatial clustering of high decomposition values].

Lack of Sanitation: African countries again showed the highest attributably of IMR due to lack of sanitation Figure 7 b , as well as strong spatial clustering of high decomposition values due lack of sanitation.

However, high and significant spatial clustering attributably due to sanitation was also in South America and parts of Central Asia. Lack of Water: The distribution of significant clustering of high decomposition importance of lack of access to water was almost identical to that of lack of access to sanitation, particularly in SSA, central Asia, and parts of South America Figure 7 c.

Maternal Mortality: Significant spatial concentration of high contiguous decomposition values for maternal mortality as a determinant of infant mortality was largely concentrated in SSA Figure 7 d with sporadic clustering in parts of the Middle East and Central Asia.

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Lancet , S Download references. We acknowledge the World Bank for compiling and making freely available the WDI database, without which this study would not have been possible. You can also search for this author in PubMed Google Scholar.

Correspondence to Benn KD Sartorius. This article is published under license to BioMed Central Ltd. Reprints and Permissions. Sartorius, B. Global infant mortality trends and attributable determinants — an ecological study using data from countries for the period — Popul Health Metrics 12, 29 Almost 21, infants died in the United States in The five leading causes of infant death in were:.

Healthy People external icon provides science-based, year national objectives for improving the health of all Americans. One of the Healthy People objectives is to reduce the rate of all infant deaths. In , 15 states met the Healthy People target of 5.

Geographically, infant mortality rates in were highest among states in the south. CDC is committed to improving birth outcomes. This requires public health agencies working together with health care providers, communities, and other partners to reduce infant mortality in the United States. This joint approach can help address the social, behavioral, and health risk factors that contribute to infant mortality and affect birth outcomes. Skip directly to site content Skip directly to page options Skip directly to A-Z link.