Indicator 3.2.2

Indicator 3.2.2: Neonatal mortality rate

Target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births

Goal 3: Ensure healthy lives and promote well-being for all at all ages

Definition:

The neonatal mortality rate (NMR) is the probability that a child born in a specific year or period will die during the first 28 completed days of life if subject to current age-specific mortality rates, expressed per 1000 live births. 

Concepts:

A live birth is the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of the pregnancy, which, after such separation, breathes or shows any other evidence of life—such as beating of the heart, pulsation of the umbilical cord, or definite movement of voluntary muscles—whether or not the umbilical cord has been cut or the placenta is attached. Each product of such a birth is considered a live birth. 

Rationale and Interpretation:

Child mortality, including neonatal mortality rate (NMR), is a key output indicator for child health and well-being, and, more broadly, for social and economic development. It is a closely watched public health indicator because it reflects the access of children and communities to basic health interventions such as vaccination, medical treatment of infectious diseases and adequate nutrition. 

Strictly speaking, the NMR is not rate, since they are not calculated by dividing the number of deaths by the population at risk. Rather, the measures represent the probability of dying by a certain age derived from a life table. 

By 2030, all countries are expected to reduce neonatal mortality to at least as low as 12 per 1000 live births.

Vital registration systems are the preferred source of data because they collect information prospectively and cover the entire population. However, many low and lower middle income countries lack fully functioning vital registration systems that accurately record all births and deaths. Thus, household surveys, such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), and periodic population censuses have become the primary source of data on child mortality in low and lower middle income countries. 

National Statistical Office or the Ministry of Health are mostly involved in generating neonatal mortality data at the national level.

Computation Method:

The direct method requires each child’s date of birth, survival status, and date or age at death. This information is typically found in vital registration systems and in household surveys that collect complete birth histories. A complete birth history records the dates of birth, and, if applicable, the dates of death of all children born to each woman that is interviewed. 

There are different direct methods of calculating neonatal mortality rates. For data from vital registration systems, the calculation of NMR can be derived from the number of deaths of children in the first month during a particular time period is divided by the number of births in that same period. Survey programmes use a synthetic cohort approach, which combines mortality probabilities for small age segments based on real cohort mortality experience into common age. 

Comments and limitations:

Many countries do not have timely and reliable NMR data but rather have differing mortality rates from different sources. Data from different sources may suffer from different errors, for example random errors in sample surveys or systematic errors due to misreporting. Recall errors are common as data are collected retrospectively. As a result, different sources often yield widely different estimates of mortality for a given time period and available data collected by countries are often inconsistent across sources. Therefore, it is important to analyse, reconcile and evaluate all data sources simultaneously for each country. The UN IGME method aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates of neonatal mortality. 

The UN Inter-agency Group for Child Mortality Estimation (UN IGME) uses a Bayesian B-splines model to estimate NMR. The spline regression model is fitted to all empirical data from vital registration systems, population censuses, household surveys and sample registration systems in a country, after data quality assessment. This method models the ratio of neonatal mortality rate / (under-five mortality rate - neonatal mortality rate). The model generates a smooth trend curve that averages over possibly disparate estimates from the different data sources for a country and extrapolates the estimates to a target year. Estimates of NMR are obtained by recombining the estimates of the ratio with UN IGME-estimated under-five mortality rate. UN IGME generates such estimates with uncertainty bounds. The differences between the UN IGME estimates and national official estimates are usually not large if empirical data has good quality. 

For further details on the methodology please refer to:

https://www.demographic-research.org/volumes/vol38/15/38-15.pdf

http://www.childmortality.org/files_v21/download/IGME%20report%202017%20child%20mortality%20final.pdf

http://childmortality.org/files_v21/download/Child%20Mortality%20Estimation%20Explanatory%20Notes.pdf 

Proxy, alternative and additional indicators: N/A

Common disaggregations for mortality indicators includes disaggregation by sex, wealth quintile, residence, and mother’s education. Disaggregated data are not always available. Disaggregation by geographic location is usually at regional level, or the minimum provincial level for survey or census data. Data from well-functioning vital registration systems can provide further geographical breakdowns. 

Neonatal deaths (deaths among live births during the first 28 completed days of life) may be subdivided into early neonatal deaths, occurring during the first 7 days of life, and late neonatal deaths, occurring after the 7th day but before the 28th completed day of life. 

Neonatal mortality rates can be also disaggregated by cause, including preterm birth complications, pneumonia, and diarrhoea.

Official SDG Metadata URL
https://unstats.un.org/sdgs/metadata/files/Metadata-03-02-01.pdf  

Internationally agreed methodology and guideline URL
http://childmortality.org/files_v20/download/IGME%20report%202015%20child%20mortality%20final.pdf http://www.childmortality.org/ 

Other references
UNICEF Briefing Notes on SDG Indicators:

UNICEF. Briefing notes on SDG global indicators related to children. Available at https://data.unicef.org/resources/sdg-global-indicators-related-to-children/

Additional References:
UNICEF (2017). United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Levels & trends in child mortality. Report 2017. New York. Available at: http://www.childmortality.org/files_v21/download/IGME%20report%202017%20child%20mortality%20final.pdf 

Alexander, M and Alkema, L. Global Estimation of Neonatal Mortality using a Bayesian Hierarchical Splines Regression Model. Demographic Research, 2018, 38, 335-372. https://www.demographic-research.org/volumes/vol38/15/38-15.pdf

Alkema L, New J.R. (2014). Global estimation of child mortality using a Bayesian B-spline bias-reduction method. The Annals of Applied Statistics. 2014; 8(4): 2122–2149. Available at: http://arxiv.org/abs/1309.1602

Alkema L, Chao F, You D, Pedersen J, Sawyer CC. (2014). National, regional, and global sex ratios of infant, child, and under-5 mortality and identification of countries with outlying ratios: a systematic assessment. The Lancet Global Health. 2014; 2(9): e521–e530. Available at: http://www.thelancet.com/pdfs/journals/langlo/PIIS2214-109X(14)70280-3.pdf

Pedersen J, Liu J. (2012). Child Mortality Estimation: Appropriate Time Periods for Child Mortality Estimates from Full Birth Histories. Plos Medicine. 2012;9(8). Available at: http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001289

Silva R. (2012). Child Mortality Estimation: Consistency of Under-Five Mortality Rate Estimates Using Full Birth Histories and Summary Birth Histories. Plos Medicine. 2012;9(8). Available at: http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001296

Walker N, Hill K, Zhao FM. (2012). Child Mortality Estimation: Methods Used to Adjust for Bias due to AIDS in Estimating Trends in Under-Five Mortality. Plos Medicine. 2012;9(8). Available at: http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001298 

Country examples
N/A

This document was prepared based on inputs from United Nations Children’s Fund (UNICEF).

For focal point information for this indicator, please visit https://unstats.un.org/sdgs/dataContacts/