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In a newly published study, Assistant Professor Sheng Li and colleagues tested the hypothesis that children in New York City (NYC) with an immigrant parent were more likely to lack health insurance and report poorer parent-rated health compared to those of US-born parents.
This serial, cross-sectional study used existing data from 2009 and 2017 among children under years of age in two NYC health surveys. It included 2,637 children from 2009 and 7,042 children from 2017.
The main outcomes were parent-reported responses for child insurance coverage and child general health status. In multivariable logistic regression models, the team estimated the likelihood of outcomes for children of immigrants compared to those of US-born parents, adjusting for child, parent, and household characteristics.
In 2017, children of immigrant parents were more likely to experience uninsurance than children of US-born parents. Compared to children of US-born parents, children with an immigrant parent were more likely to have a gap in insurance coverage in both 2009 and 2017. Although more children of immigrants had poor/fair health than those of US-born parents in both years, differences were not statistically significant after adjusting for covariates.
These findings among a sample of children eligible for health insurance suggest that policies intended to expand child health care access did not equitably reach children of immigrants, despite their eligibility for health insurance. Tailored interventions for children of immigrants are needed to mitigate disparities in health insurance coverage.
“I appreciate the support from SPH to promote collaborations with researchers from NYC DOHMH and other 12 institutes in tristate area,” says Li. “These collaborations present us as an important academic partner for our community”.
This study is part of the Health Data for New York City (HD4NYC) program. Funded by the Robert Wood Johnson Foundation, HD4NYC is a multi-institutional platform created by The New York Academy of Medicine and NYCDOHMH that advances actionable policy-relevant research to promote health equity. Professor Li introduced innovative machine learning approaches in the HD4NYC program that were applied in the current study as well as recent and ongoing studies.
