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Updated: May 4, 2026

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
Published on: August 25, 2014
Effect of community size on eligibility for early intervention for infants with a neonatal intensive care experience
Holly Roberts1, Howard Needelman, Barbara Jackson
1Munroe-Meyer Institute, University of Nebraska Medical Center, Omaha, Nebraska.
Insights
Infants in rural areas were more likely to be eligible for early intervention (EI) services than those in urban areas. Male infants also had higher eligibility rates for EI services.
Area of Science:
- Pediatric Health
- Public Health Policy
- Developmental Pediatrics
Background:
- Early intervention (EI) services are crucial for infants with developmental delays.
- Understanding factors influencing EI eligibility is vital for equitable access.
- Population density may impact service accessibility and eligibility decisions.
Purpose of the Study:
- To investigate the influence of population density (rural vs. urban) on early intervention (EI) service eligibility for infants.
- To identify demographic factors associated with EI eligibility.
Main Methods:
- A cohort of 356 infants (gestational age <31 weeks) referred for EI evaluation was analyzed.
- Binary logistic regression was used to determine predictors of EI service acceptance.
Main Results:
- Infants in rural areas were significantly more likely to be eligible for EI services (OR=3.007) compared to urban infants.
- Male infants had higher odds of EI eligibility (OR=1.908) than female infants.
- Rural males were more likely to be eligible than urban females.
Conclusions:
- Disparities in EI eligibility between rural and urban settings may be influenced by community support and differing interpretations of criteria.
- Further analysis is needed to assess the cost-effectiveness of EI service delivery in rural versus urban environments.
Purpose:
To determine if population density (rural vs urban) in a child's home community influenced the decision of eligibility for early intervention (EI) services.
Methods:
The sample included 356 infants with a gestational age of <31 weeks referred from a statewide child find program for an evaluation for EI services. A binary logistic regression analysis was completed to determine which variables predicted acceptance into EI services.
Findings:
Infants less than 31 weeks gestation residing in rural areas were more likely to be eligible for EI services than premature infants (ie, <31 weeks gestation) with similar birth characteristics from urban areas. A binary logistic regression analysis revealed an odds ratio for eligibility for EI services among children living in rural areas compared to those living in urban areas was 3.007 (95% CI, 1.497 to 6.040). Additionally, the odds ratio for eligibility for males as compared to females was 1.908 (95% CI, 1.017 to 3.578). Participants who lived in a rural area and were male were more likely to be eligible for EI services than those who lived in urban locations and were female.
Conclusions:
Factors such as community support, experience with high-risk populations, and differences in interpreting eligibility criteria may influence the differences found between the rural and urban populations. Analysis of intervention cost versus effectiveness will be needed to determine whether the system as adopted in the rural versus urban environment is more appropriate for the provision of EI services.
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