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Principal Investigator: Catherine Garrison Velopulos
Organization: UNIVERSITY OF COLORADO DENVER
Fiscal Year: 2024
Award: $315,000
Funding agency: National Institute on Minority Health and Health Disparities
PROJECT SUMMARY/ABSTRACT
The overall goal of our proposed research is to understand why having health care coverage (or eligibility
for health care coverage) is not sufficient to allow equal access to elective surgical care. Despite successful
efforts to expand coverage through Medicaid expansion and the Affordable Care Act (ACA), potentially elective
surgical care is often not addressed until it becomes an emergency. These patients tend to come from
vulnerable populations, who not only present more often for Emergency General Surgery (EGS), but
experience worse outcomes and greater costs. The disparities leading to this presentation in the United States
have been well-described in terms of overall relationship to insurance status, race and income, but deeper data
collection and analysis are desperately needed to identify modifiable factors that can inform interventions
around decreasing emergent presentation in these populations, particularly in regard to health care coverage.
Even in countries with Universal Health Care systems, disparities are noted in how people are able to actually
access those services.
We initially studied this problem in the context of emergent cholecystectomy, one of our most common
presentations of EGS disease. We found that lack of health care coverage was not a major factor; in fact, 86%
of our patients had some type of coverage (29% private, 57% public), and many other social factors led to an
emergency operation. We now seek to expand and explore this in detail for the other EGS conditions defined
by the American Association for the Surgery of Trauma (AAST) using a multiphase mixed method approach.
We will 1) identify modifiable factors for emergent presentation and explore the trajectory of progression to
elective versus emergency surgery using billing data and EHR in a convergent mixed-methods design,
combining quantitative variables with qualitative narrative data, 2) identify and quantify additional modifiable
factors from the patient perspective that are not available in clinical or administrative datasets using an
exploratory sequential design, using identified domains to conduct systematic review and meta-analysis for
quantitative data, and 3) determine which modifiable factor or factors identified will have the greatest impact for
future intervention strategies using Markov modeling.
This proposal will leverage our ability to link data from multiple sources in novel ways, our diverse, robust
general surgery population in a Medicaid expansion state, and Co-Investigators who are expert in their fields of
longitudinal data modeling and mixed methods research. With this data we can model and understand what
influences the persistent disparity in the ability to access elective surgical care despite increased coverage,
and predict which factors contribute the most to the disparities and thus hold potential for the greatest impact.
By identifying actionable modifiable factors, we will ultimately inform effective intervention strategies to prevent
emergent presentation of elective surgical disease.
Terms: <Access to Care><Acute><Admission><Admission activity><Affordable Care Act><American><Appointment><Area><Caring><Categories><Cholecystectomy><Chronic Disease><Chronic Illness><Classification><Clinical><Communication><Confusion><Confusional State><Consultations><Country><Data><Data Analyses><Data Analysis><Data Collection><Data Set><Diagnosis><Disease><Disease Progression><Disorder><Disparities><Disparity><ED visit><ER visit><Early Intervention><Economic Income><Economical Income><Eligibility><Eligibility Determination><Emergencies><Emergency Situation><Emergency care visit><Emergency department visit><Emergency hospital visit><Emergency room visit><Espanol><Event><Fear><Fright><Future><Goals><Health><Health Care Systems><Health Services Accessibility><Healthcare><Healthcare Systems><History><Hospital Admission><Hospitalization><Income><Insurance><Insurance Coverage><Insurance Status><International><Intervention><Intervention Strategies><Interview><Investigators><Language><Link><Manuals><Medicaid><Mental Confusion><Meta-Analysis><Methods><Modeling><Morbidity><Morbidity - disease rate><Obamacare><Operative Procedures><Operative Surgical Procedures><Outcome><Pain><Painful><Pathologic><Patients><Persons><Phase><Population><Primary Care><Privatization><Probability><Procedures><Protocol Screening><Race><Races><Recording of previous events><Research><Research Methodology><Research Methods><Research Personnel><Researchers><Risk><Schedule><Services><Social Service><Social Work><Spanish><Structure><Surgical><Surgical Interventions><Surgical Procedure><Systematics><Time><Transportation><Trauma><United States><Vulnerable Populations><World Health Organization><access to health services><access to services><access to treatment><accessibility to health services><acute care><availability of services><billing data><biopsychosocial><care access><chronic disorder><consultation><cost><data interpretation><data modeling><demographics><design><designing><disability><effective intervention><expectation><experience><gallbladder removal><health care><health service access><health services availability><histories><incomes><innovate><innovation><innovative><interest><interventional strategy><markov model><model of data><model the data><modeling of the data><mortality><multiple data sources><novel><operation><operations><prevent><preventing><prospective><racial><racial background><racial origin><research and methods><service availability><social factors><social vulnerability><surgery><surgical disparities><surgical service><systematic review><treatment access><vulnerable group><vulnerable individual><vulnerable people>