The objective of this project is to construct linkages across disparate public health data systems using machine learning tools and assess them for bias and equitable representation of subpopulations defined by demographic and socioeconomic factors.
The funded study examines policies that regulate the PO supply, including those related to prescription drug monitoring programs, pain management clinic laws, and prescribing limits. In this supplement, we add a set of complementary opioid policies that affect access to treatment for opioid use disorder (e.g., Medicaid coverage of medication for opioid use disorder).
Scholars would assist with Aim 1 of a new R01 working with our lab and the Health Evaluation and Analytics Laboratory at NYU Wagner.
Congenital heart defects (CHDs) are the most common and resource intensive birth defects managed in the United States (US), affecting ~40,000 births per year in the US. (1) One-year mortality for these children is >10%. It is >30% for children requiring neonatal surgery. (2) Yet there are currently limited data on long-term outcomes and health expenditures for these children. Due to marked heterogeneity in disease subtypes and treatments among CHD patients, the power of single-center studies is limited. Multi-center data are siloed in diagnostic or procedural registries or in-patient databases, or are the product of individual investigations. Administrative data may lack clinical precision, as ICD codes for this population are not based on physiology. Further, data on costs and value typically rely on cost-to-charge ratio based costs, which are highly influenced by hospital accounting.
