Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Public Health

First Advisor's Name

Alejandro Arrieta

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Gilbert Ramirez

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Nan Hu

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Richard Olson

Fourth Advisor's Committee Title

Committee Member

Keywords

health services research

Date of Defense

6-29-2023

Abstract

Despite higher health risks immigrants often have better health outcomes for all conditions compared to non-immigrants, as observed in the Hispanic Paradox. Primary care is assessed through ambulatory care-sensitive conditions (ACSC) which are conditions that with timely/effective care can prevent disease complications and hospitalizations. In this dissertation we look at the utilization and expenditure on ACSC for immigrants in the U.S. and predict immigration flow following EE using internet data.

We use the Medical Expenditure Panel Survey to assess the utilization and expenditure for healthcare services on ACSC and non-ACSC in emergency, inpatient, and outpatient settings for immigrants compared to U.S.-born citizens using linear regression and generalized linear models respectively. We evaluated utilization of two immigrant groups categorized by their length of stay in the U.S.: new (less than 5 years) and old (5 years or more). We found differences in utilization and expenditure between immigrants and U.S.-born for ACSC and non-ACSC, as well as differences in each clinical setting. We found differences between the groups. The third paper proposes the use of location-specific unique terms from the internet to predict immigration utilizing dynamic factor and linear regression analyses to validate against the U.S. census. We discuss the methods and limitations associated with this approach.

This study's analysis underscores the importance of a targeted approach, considering specific conditions and clinical settings, to address healthcare disparities and improved outcomes for immigrant populations. Accurate prediction of immigration can inform policymakers and healthcare leaders to address equitable healthcare access regardless of immigration status.

Identifier

FIDC011150

ORCID

https://orcid.org/0000-0002-3938-5951

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