Related Experiment Video
Updated: Aug 13, 2026

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
Published on: January 12, 2018
Racial and gender differences in the viability of extremely low birth weight infants: a population-based study
Steven B Morse1, Samuel S Wu, Changxing Ma
1Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA. morsesb@peds.ufl.edu
Insights
This study reveals significant race and gender disparities in the 1-year survival of extremely low birth weight (ELBW) infants. Black female infants showed the highest survival odds compared to white male infants.
Area of Science:
- Neonatalogy
- Pediatric Survival Analysis
- Public Health
Background:
- Extremely low birth weight (ELBW) infants face significant survival challenges.
- Existing survival models often lack race- and gender-specific data.
- Accurate prediction of survival is crucial for clinical care and family counseling.
Purpose of the Study:
- To develop a race- and gender-specific predictive model for 1-year survival in ELBW infants.
- To utilize population-based data for robust model development.
- To identify demographic factors influencing ELBW infant survival.
Main Methods:
- Analysis of birth and death certificates for 5076 ELBW infants (300-1000 g) born in Florida (1996-2000).
- Application of semiparametric, multivariate, logistic regression.
- Comparison of survival rates across race/gender groups using odds ratios (ORs).
Main Results:
- Overall 1-year survival for ELBW infants remained stable (60-62%) from 1996-2000.
- Survival rates varied significantly by birth weight, with <14% survival for infants <=500g and >85% for infants >800g.
- Female infants (OR: 1.7) and Black infants (OR: 1.3) demonstrated higher survival odds compared to males and white infants, respectively. Black female infants had 2.1 times greater odds of survival than white male infants.
Conclusions:
- Significant race and gender disparities exist in 1-year survival rates for ELBW infants.
- The interaction between race and gender significantly impacts ELBW infant survival.
- Findings can aid clinicians in patient care and facilitate informed discussions with families regarding prognosis.
Objective:
The purpose of this study is to provide a race- and gender-specific model for predicting 1-year survival rates for extremely low birth weight (ELBW) infants by using population-based data.
Methods:
Birth and death certificates were analyzed for all children (N = 5076) with birth weights between 300 g and 1000 g who were born in Florida between 1996 and 2000. Semiparametric, multivariate, logistic regression analysis was used to model 1-year survival probabilities as a function of birth weight, gestational age, mother's race, and infant's gender. Estimated survival rates among different race/gender groups were compared by using odds ratios (ORs).
Results:
One-year survival rates for 5076 ELBW infants born between 1996 and 2000 did not change during the 5-year period (60-62%). The survival rate at < or = 500 g was < or = 14% (n = 716). Survival rates at 501 to 600 g and 601 to 700 g were 36% and 62%, respectively. The survival rate reached > 85% for infants of > 800 g. Modeling indicated a survival advantage for female infants, compared with male infants (OR: 1.7; 95% confidence interval: 1.5-1.9), and for black infants, compared with white infants (OR: 1.3; 95% confidence interval: 1.1-1.5). Black female infants had 2.1 greater odds of survival than did white male infants.
Conclusions:
This population-based study highlights the significant race and gender differences in 1-year survival rates for ELBW infants, as well as the interactions of these 2 factors. These findings can assist obstetricians and neonatologists not only in the care of ELBW infants but also in frank discussions with families.
Related Concept Videos
Regression Toward the Mean
Testing a Claim about Mean: Known Population SD
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...