# Linking State Medicaid and Congenital Heart Surgical Registry Data: Building Capacity to Assess Disparities in Longitudinal Outcomes and Value for Children with Congenital Heart Disease

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2021 · $388,654

## Abstract

Project Summary
Congenital heart defects are the most common and resource intensive birth defects managed in the United
States with high morbidity and mortality. They are estimated to effect over 1 million US children and 1.4 million
US adults and to result in ~200,000 life-years lost and >$6 billion in inpatient acute care costs per year.
Significant disparities are known to exist in short-term outcomes and resource utilization. We have shown that
children from low income neighborhoods have 18% higher risk-adjusted odds of in-hospital mortality, have 7%
longer lengths-of-stay, and require 7% more perioperative resources than do children from higher income
neighborhoods, even after controlling for the effects of race, public versus private insurance provider, and the
hospitals at which these children ultimately receive surgical repair. Research on long-term outcomes and
health expenditures is limited and mechanisms driving health inequities remain unknown. Greater than 50% of
children with congenital heart disease are covered by Medicaid. We have partnered with the NY Department
of Health and have clean, validated data for all Medicaid patient encounters over 15 years across NY State.
We have developed methods of abstracting and linking locally-held clinical registry data to Medicaid files and
have built an interdisciplinary team of pediatric cardiologists, cardiac surgeons, health services researchers,
health economists, and NY Department of Health senior leadership to identify dimensions of healthcare access
that could be targeted through center- or state-level interventions to reduce inequities. We propose linking
pediatric cardiac surgical clinical registry data from across NY State, longitudinal Medicaid claims, Census
Bureau data, geocoded data, and the National Death Index. We will use these data to accomplish the
following Aims: 1) We will define 10-year risk-stratified, multi-dimensional outcomes (mortality and morbidities)
and associated health expenditures for children undergoing congenital heart surgery across New York State;
2) we will develop novel longitudinal risk models for children with congenital heart disease that adjust for social
determinants of health, and 3) we will test dimensions of healthcare access as modifiable drivers of health
inequities among children in New York with congenital heart disease on Medicaid. Achieving our aims would
1) establish a comprehensive, population-based resource for longitudinal outcomes and health expenditure
research, inclusive of not only in-patient data, but also out-patient, emergency room, pharmacy, rehabilitation,
home healthcare, education records, and neighborhood-level social determinants of health for nearly half of all
children undergoing congenital heart surgery in NY State, 2) generate a methodology for investigations on
long-term outcomes and value applicable across multiple populations, 3) assess constructs of access as
modifiable mediators of social determinants of health, an...

## Key facts

- **NIH application ID:** 10079025
- **Project number:** 5R01HL150044-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Brett Romeo Anderson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $388,654
- **Award type:** 5
- **Project period:** 2020-01-05 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10079025

## Citation

> US National Institutes of Health, RePORTER application 10079025, Linking State Medicaid and Congenital Heart Surgical Registry Data: Building Capacity to Assess Disparities in Longitudinal Outcomes and Value for Children with Congenital Heart Disease (5R01HL150044-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10079025. Licensed CC0.

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