Factors Associated with Asthma ED Visit Rates among Medicaid-enrolled Children: A Structural Equation Modeling Approach

Luceta McRoy; George Rust; Junjun Xu; Luceta McRoy; George Rust; Junjun Xu

doi:10.3934/medsci.2017.1.71

AIMS Medical Science

2017, Volume 4, Issue 1: 71-82. doi: 10.3934/medsci.2017.1.71

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Research article

Factors Associated with Asthma ED Visit Rates among Medicaid-enrolled Children: A Structural Equation Modeling Approach

1.
Southern Adventist University, Department of Business, Collegedale, TN 37363;
2.
Center for Medicine and Public Health, Florida State University College of Medicine, 1115 W. Call Street, Tallahassee, FL 32304;
3.
National Center for Primary Care, Morehouse School of Medicine Atlanta, GA 30310

Received: 04 November 2016 Accepted: 06 February 2017 Published: 10 February 2017

Background: Asthma is one of the leading causes of emergency department visits and school absenteeism among school-aged children in the United States, but there is significant
local-area variation in emergency department visit rates, as well as significant differences across racial-ethnic groups. Analysis: We first calculated emergency department (ED) visit rates among Medicaid-enrolled children age 5–12 with asthma using a multi-state dataset. We then performed exploratory factor analysis using over 226 variables to assess whether they clustered around three county-level conceptual factors (socioeconomic status, healthcare capacity, and air quality) thought to be associated with variation in asthma ED visit rates. Measured variables (including ED visit rate as the outcome of interest) were then standardized and tested in a simple conceptual model through confirmatory factor analysis. Results: County-level (contextual) variables did cluster around factors declared a priori in the conceptual model. Structural equation models connecting the ED visit rates to socioeconomic status, air quality, and healthcare system professional capacity factors (consistent with our conceptual framework) converged on a solution and achieved a reasonable goodness of fit on confirmatory factor analysis. Conclusion: Confirmatory factor analysis offers an approach for quantitatively testing conceptual models of local-area variation and racial disparities in
asthma-related emergency department use.
- Asthma,
- Local-area variation,
- emergency department,
- Medicaid,
- structural equation modeling,
- conceptual model,
- social determinants
Citation: Luceta McRoy, George Rust, Junjun Xu. Factors Associated with Asthma ED Visit Rates among Medicaid-enrolled Children: A Structural Equation Modeling Approach[J]. AIMS Medical Science, 2017, 4(1): 71-82. doi: 10.3934/medsci.2017.1.71

Related Papers:

Abstract

Background: Asthma is one of the leading causes of emergency department visits and school absenteeism among school-aged children in the United States, but there is significant
local-area variation in emergency department visit rates, as well as significant differences across racial-ethnic groups. Analysis: We first calculated emergency department (ED) visit rates among Medicaid-enrolled children age 5–12 with asthma using a multi-state dataset. We then performed exploratory factor analysis using over 226 variables to assess whether they clustered around three county-level conceptual factors (socioeconomic status, healthcare capacity, and air quality) thought to be associated with variation in asthma ED visit rates. Measured variables (including ED visit rate as the outcome of interest) were then standardized and tested in a simple conceptual model through confirmatory factor analysis. Results: County-level (contextual) variables did cluster around factors declared a priori in the conceptual model. Structural equation models connecting the ED visit rates to socioeconomic status, air quality, and healthcare system professional capacity factors (consistent with our conceptual framework) converged on a solution and achieved a reasonable goodness of fit on confirmatory factor analysis. Conclusion: Confirmatory factor analysis offers an approach for quantitatively testing conceptual models of local-area variation and racial disparities in
asthma-related emergency department use.

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