Child Mortality

Narrative

The death of a child is a tragedy for family and friends and a loss to the community. Along with the direct impact of a child’s death to a family, the child mortality rate in a community can be an important indicator for researchers or policymakers.1 A high rate can point to underlying problems, such as poor access to health care, violent neighborhoods, high levels of risk-taking behaviors, or inadequate child supervision.2 It can also point to inequities, for example, in access to behavioral health services, safe places to play, or exposure to environmental toxins.

Since 1999, the overall mortality rate for children aged 1–19 years declined by more than 25 percent to a low of 25.7 per 100,000 in 2011 (Figure 1). The decline was fairly uniform, ranging from 23 to 30 percent across age groups.

Mortality Among Children by Year and Age

Figure 1 Source

According to 2011 data, racial and ethnic disparities persisted in mortality among children. Mortality rates were highest for non-Hispanic American Indian/Alaska Native (47.6 per 100,000) and non-Hispanic Black (37.3 per 100,000) children, while the rates for Hispanic (21.1 per 100,000) and non-Hispanic Asian/Pacific Islander (13.4 per 100,000) children were lowest (Figure 2).

Mortality Among Children by Age and Race/Ethnicity

Figure 2 Source

According to 2011 data, the “All Cause” mortality rate for children aged 1–9 years was 18.3 per 100,000. Most deaths to children in that age group (10.8 per 100,000) were classified as noninjury (i.e., natural causes) followed by unintentional injury (5.9 per 100,000), homicide (1.5 per 100,000), and deaths of undetermined nature (0.2 per 100,000). For adolescents aged 10–19 years, the “All Cause” mortality rate was 75 percent greater (32.0 per 100,000) than that of children 1–9 years of age. Most of the difference could be attributed to the higher mortality rates among 15- to 19-year-old males (68.5 per 100,000) resulting from higher rates of unintentional injury (27.4 per 100,000), homicide (13.0 per 100,000), and suicide (12.9 per 100,000) relative to younger males. The mortality rates for females in all age groups were lower than the rates for males.

Leading causes of death due specifically to intentional and unintentional injury varied by age group. Drowning, homicide, and motor vehicle accidents were predominant in the 1- to 9-year-olds, though their rank order frequency was different for 1- to 4-year-olds (drowning, homicide, and motor vehicle traffic accident) compared to 5- to 9-year-olds (motor vehicle traffic accident, homicide, and drowning). Motor vehicle traffic accidents, suicide, and homicide were the highest ranked leading causes of deaths due to injury for adolescents aged 10–19 years; however, the rates were higher for 15- to 19-year-olds (12.9, 8.3, and 7.8 per 100,000, respectively) compared to 10- to 14-year-olds (2.1, 1.4, and 0.7 per 100,000, respectively).

General societal improvements, advances in medical care, and the introduction of Medicaid have been cited as factors in the longterm decline in child mortality.3 Despite these advances, many states have disproportionately high child and adolescent mortality, and rates among some racial and ethnic groups fall far short of the Healthy People 2020 goals.4 Continued research on mechanisms underlying racial and ethnic disparities and expansion of child fatality review to inform state and local prevention strategies have been suggested.5, 6

Data Sources

Figure 1. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying cause of death 1999–2011. CDC WONDER Online Database, released 2014. Data are from the Multiple Cause of Death Files, 1999–2011.

Figure 2. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying cause of death 1999–2011. CDC WONDER Online Database, released 2014. Data are from the Multiple Cause of Death Files, 1999–2011.

Endnotes

1 Institute of Medicine. Reducing the Burden of Injury: Advancing Prevention and Treatment. Washington, DC: National Academies Press; 1999.

2 U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. National Action Plan for Child Injury Prevention. Atlanta, GA: Centers for Disease Control and Prevention; 2012.

3 Howell E, Decker S, Hogan S, Yemane A, Foster J. Declining child mortality and continuing racial disparities in the era of the Medicaid and SCHIP insurance coverage expansions. American Journal of Public Health. 2010;100(12):2500–2506.

4 Singh GK, Kogan MD. Widening socioeconomic disparities in U.S. childhood mortality, 1969–2000. American Journal of Public Health. 2007;97(9):1658–1665.

5 Brown RL. Epidemiology of injury and the impact of health disparities. Current Opinion in Pediatrics. 2010;22:321–325.

6 Christian CW; Sege RD; Committee on Child Abuse and Neglect; Committee on Injury, Violence, and Poison Prevention; Council on Community Pediatrics. Child fatality review. Pediatrics. 2010;126;592–596.

Data

Statistical Significance Test

Calculate the difference between two estimates:

Calculated Z-Test Result 0.9567433 Not statistically significant

We follow statistical conventions in defining a significant difference by a p-value less than 0.05 where there is a less than 5% probability of observing a difference of that magnitude or greater by chance alone if there were really no difference between estimates. The 95% confidence interval includes a plausible range of values for the observed difference; 95% of random samples would include the true difference with fewer than 5% of random samples failing to capture the true difference.

This website allows comparisons between two estimates using the independent z-test for differences in rates or proportions. This test is appropriate for comparing independent populations across years (e.g., 2011 versus 2012) or subgroups (e.g., Male versus Female) on corresponding measures. To the extent possible, the functionality of this application has limited estimate comparisons based on appropriate use of the independent z-test. However, some tables present subgroup categories within broader categories that will allow comparisons between non-independent populations (e.g., low birth weight and very low birth weight). Users should exercise caution when interpreting these test results, which will frequently overstate statistical significance.

For some tables, the website does not allow for comparisons between two estimates, even though the data represent independent populations. Generally, this is because the standard errors were not publicly available at the time this website was created.

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