Related Experiment Video

Updated: Mar 24, 2026

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
19:15

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

88.4K

The Epidemiology of Infant Mortality in the Greater Newark, New Jersey Area: A New Look at an Old Problem

Natalie E Roche1, Fatimah Abdul-Hakeem2, Amy L Davidow3

  • 1Department of Obstetrics, Gynecology and Women's Health, Rutgers New Jersey Medical School.

Journal of the National Medical Association
|March 2, 2016
PubMed

Insights

Infant mortality rates (IMR) vary significantly by zip code in Newark, New Jersey. Factors like race, poverty, and lack of prenatal care are linked to higher IMRs, highlighting disparities.

Area of Science:

  • Public Health
  • Epidemiology
  • Health Disparities

Background:

  • Infant mortality rate (IMR) is a critical indicator of national health.
  • In 2006, New Jersey's IMR was 5.5 deaths per 1,000 births, with significant racial disparities (4.4 for Caucasians vs. 11.5 for African Americans).
  • The 2020 U.S. Healthy People target for IMR is 6.0 deaths per 1,000 births.

Purpose of the Study:

  • To determine if infant mortality rates (IMRs) differ by zip code in the greater Newark region.
  • To identify maternal and infant characteristics associated with elevated IMRs.

Main Methods:

  • A descriptive study utilizing New Jersey Department of Health birth certificate data and U.S. Census data.
  • Analysis of IMRs by zip code and by maternal/infant characteristics within the greater Newark area.

Main Results:

  • Infant mortality rates varied significantly by zip code of residence in Newark.
  • Zip codes 07102 and 07105 exhibited the highest and lowest IMRs, respectively.
  • Maternal factors linked to high IMR included lack of prenatal care, single marital status, and non-Hispanic Black race. Low income and high poverty levels were also associated with increased IMRs.

Conclusions:

  • Race/ethnicity, marital status, and zip code significantly impact infant mortality.
  • Poverty and race/ethnicity are strongly associated with increased IMRs and correlate with specific zip codes.
Abstract

Keywords:
Healthy People 2020IMRInfant Mortality Ratehealth disparity

More Related Videos

A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis08:46

A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis

Published on: August 12, 2020

7.1K
Modeling Ascending Vaginal Infection, Preterm Birth, and Neonatal Morbidity in Mice04:18

Modeling Ascending Vaginal Infection, Preterm Birth, and Neonatal Morbidity in Mice

Published on: October 10, 2025

650

Related Concept Videos

Development of the Oral Microbiota01:28

Development of the Oral Microbiota

The establishment of the oral microbiome begins before birth, challenging the long-held belief that the fetal oral cavity is sterile. The presence of oral microbes such as Streptococcus and Fusobacterium in amniotic fluid suggests that microbial exposure may occur in utero, potentially through translocation from the maternal oral or gastrointestinal tract. This early colonization primes the neonatal immune system and sets the stage for subsequent microbial succession. Maternal health,...
14
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.6K
Introduction to Epidemiology01:26

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
2.4K
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K
Prevalence and Incidence01:08

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
2.3K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.2K