Development and Implementation of a Predictive Model of Documentation Status to Examine Factors Related to Health Outcomes for Undocumented Immigrants
type: CreativeWork
description: Background: The United States (US) is home to more than 8.6 million undocumented Hispanic immigrants. These individuals face significant barriers to accessing health services and are a subpopulation on whom relatively little research has been done. Undocumented Hispanics are faced with unique vulnerabilities, and research on them should be done thoughtfully and with care. One proposed method of investigating health outcomes for this subpopulation, and the factors that influence these outcomes, is utilizing predictive modeling for documentation status. Objectives: This dissertation is composed of three studies that aim to: 1) Synthesize the literature to assess health outcomes of undocumented immigrants with at least one chronic condition; 2) Create and internally validate a predictive model of documentation status utilizing two data sets by comparing demographic and health characteristics of individuals alongside their known documentation status; and 3) Utilize the predictive model on a prospective cohort of essential workers – in which documentation status is unknown – to examine healthcare utilization for the treatment of SARS-CoV-2 infections by predicted undocumented status. Methods: Aim one is explored through a scoping review of the literature related to chronic disease outcomes for undocumented Hispanic immigrants compared to documented immigrants or US-born citizens, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses checklist for scoping reviews (PRISMA-ScR). A systematic search was conducted on October 10, 2019 in five databases (PubMed, MEDLINE, Web of Science, Embase, and CINAHL), with supplementary searches up to June 24, 2023. Studies were examined by two reviewers, and those found to be quantitative studies conducted in the US, comparing health outcomes for undocumented Hispanic immigrants aged 18+ compared to either or both documented Hispanic immigrants or US-born citizens were included. Aim two is examined by the devel…
abstract: Background: The United States (US) is home to more than 8.6 million undocumented Hispanic immigrants. These individuals face significant barriers to accessing health services and are a subpopulation on whom relatively little research has been done. Undocumented Hispanics are faced with unique vulnerabilities, and research on them should be done thoughtfully and with care. One proposed method of investigating health outcomes for this subpopulation, and the factors that influence these outcomes, is utilizing predictive modeling for documentation status. Objectives: This dissertation is composed of three studies that aim to: 1) Synthesize the literature to assess health outcomes of undocumented immigrants with at least one chronic condition; 2) Create and internally validate a predictive model of documentation status utilizing two data sets by comparing demographic and health characteristics of individuals alongside their known documentation status; and 3) Utilize the predictive model on a prospective cohort of essential workers – in which documentation status is unknown – to examine healthcare utilization for the treatment of SARS-CoV-2 infections by predicted undocumented status. Methods: Aim one is explored through a scoping review of the literature related to chronic disease outcomes for undocumented Hispanic immigrants compared to documented immigrants or US-born citizens, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses checklist for scoping reviews (PRISMA-ScR). A systematic search was conducted on October 10, 2019 in five databases (PubMed, MEDLINE, Web of Science, Embase, and CINAHL), with supplementary searches up to June 24, 2023. Studies were examined by two reviewers, and those found to be quantitative studies conducted in the US, comparing health outcomes for undocumented Hispanic immigrants aged 18+ compared to either or both documented Hispanic immigrants or US-born citizens were included. Aim two is examined by the devel…
author: Rivers, Patrick Sullivan
language: en
keywords: Machine Learning, Predictive Modeling, Public Health, Undocumented Immigrants
license: http://rightsstatements.org/vocab/InC/1.0/
date published: 2023
date modified: 2026-07-24T00:56:34Z
provider: University of Arizona
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: The University of Arizona.

PropertyValue
property ID: Handle
value: http://hdl.handle.net/10150/669749