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

Previous Article Next Article

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

Abstract
Full Text(HTML)
Download PDF

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.

References

[1]	Bloom B, Cohen RA, Freeman G (2009) Summary health statistics for U.S. children: National Health Interview Survey, 2008. Vital Health Stat 10: 1-81.
[2]	Akinbami L (2006) Centers for Disease C, Prevention National Center for Health S. The state of childhood asthma, United States, 1980-2005. Advance data 381: 1-24.
[3]	Asthma & Children Fact Sheet Available: http://www.lung.org/lung-disease/asthma/resources/facts-and-figures/asthma-children-fact-sheet.html. Accessed February 19, 2015, 2014.
[4]	Akinbami LJ, Moorman JE, Garbe PL, et al. (2009) Status of childhood asthma in the United States, 1980-2007. Pediatrics 123: S131-145. doi: 10.1542/peds.2008-2233C
[5]	National Hospital Ambulatory Medical Care Survey: 2010 Emergency Department Summary Tables Available: http://www.cdc.gov/nchs/data/ahcd/nhamcs_emergency/2010_ed_web_tables.pdf. Accessed November 10, 2014.
[6]	Moorman JE, Akinbami LJ, Bailey CM, et al. (2012) National surveillance of asthma: United States, 2001-2010. Vital & health statistics Series 3, Analytical and epidemiological studies/[US Dept of Health and Human Services, Public Health Service, National Center for Health Statistics] 35: 1-67.
[7]	Akinbami LJ, Moorman JE, Bailey C, et al. (2012) Trends in asthma prevalence, health care use, and mortality in the United States, 2001-2010. NCHS Data Brief 94: 1-8.
[8]	Oraka E, Iqbal S, Flanders WD, et al. (2013) Racial and ethnic disparities in current asthma and emergency department visits: findings from the National Health Interview Survey, 2001-2010. Offic J Assoc Care Asthma 50: 488-496. doi: 10.3109/02770903.2013.790417
[9]	Malhotra K, Baltrus P, Zhang S, et al. (2014) Geographic and racial variation in asthma prevalence and emergency department use among Medicaid-enrolled children in 14 southern states. Offic J Assoc Care Asthma 51: 913-921. doi: 10.3109/02770903.2014.930479
[10]	Largent J, Nickerson B, Cooper D, et al. (2012) Paediatric asthma hospital utilization varies by demographic factors and area socio-economic status. Public Health 126: 928-936. doi: 10.1016/j.puhe.2012.04.011
[11]	Ungar WJ, Paterson JM, Gomes T, et al. (2011) Relationship of asthma management, socioeconomic status, and medication insurance characteristics to exacerbation frequency in children with asthma. Ann Allergy Asthma Immunol 106: 17-23. doi: 10.1016/j.anai.2010.10.006
[12]	Flores G, Snowden-Bridon C, Torres S, et al. (2009) Urban minority children with asthma: substantial morbidity, compromised quality and access to specialists, and the importance of poverty and specialty care. Offic J Assoc Care Asthma 46: 392-398. doi: 10.1080/02770900802712971
[13]	Thakur N, Oh SS, Nguyen EA, et al. (2013) Socioeconomic status and childhood asthma in urban minority youths. The GALA II and SAGE II studies. Am J Respir Critical Care Med 188: 1202-1209. doi: 10.1164/rccm.201306-1016OC
[14]	Almqvist C, Pershagen G, Wickman M (2005) Low socioeconomic status as a risk factor for asthma, rhinitis and sensitization at 4 years in a birth cohort. J British Soc Allergy Clin Immuno 35: 612-618. doi: 10.1111/j.1365-2222.2005.02243.x
[15]	Phillips KA, Morrison KR, Andersen R, et al. (1998) Understanding the context of healthcare utilization: assessing environmental and provider-related variables in the behavioral model of utilization. Health Service Res 33: 571-596.
[16]	Social Ecological Model Available: http://www.cdc.gov/cancer/crccp/sem.htm. Accessed April 3, 2016.
[17]	Baja ES, Schwartz JD, Coull BA, et al. (2013) Structural equation modeling of the inflammatory response to traffic air pollution. J Exposure Sci Environ Epidemiol 23: 268-274. doi: 10.1038/jes.2012.106
[18]	Baja ES, Schwartz JD, Coull BA, et al. (2013) Structural equation modeling of parasympathetic and sympathetic response to traffic air pollution in a repeated measures study. Environ Health 12: 81. doi: 10.1186/1476-069X-12-81
[19]	Rhee H, Belyea MJ, Brasch J (2010) Family support and asthma outcomes in adolescents: barriers to adherence as a mediator. J Adolesc Health 47: 472-478. doi: 10.1016/j.jadohealth.2010.03.009
[20]	Annett RD, Turner C, Brody JL, et al. (2010) Using structural equation modeling to understand child and parent perceptions of asthma quality of life. J Pediatr Psychol 35: 870-882. doi: 10.1093/jpepsy/jsp121
[21]	Bentler PM (1992) On the fit of models to covariances and methodology to the Bulletin. Psychological bulletin 112: 400-404. doi: 10.1037/0033-2909.112.3.400
[22]	MacCallum R, Browne M, Sugawara H (1996) Power analysis and determination of sample size for covariance structure modeling. Psychological Methods 1: 130-149. doi: 10.1037/1082-989X.1.2.130
[23]	Kline RB (2005) Principles and Practice of Structural Equation Modeling, Third Edition: Guilford Press.
[24]	Curtis LM, Wolf MS, Weiss KB, et al. (2012) The impact of health literacy and socioeconomic status on asthma disparities. Offic J Assoc Care Asthma 49: 178-183. doi: 10.3109/02770903.2011.648297
[25]	Victorino CC, Gauthier AH (2009) The social determinants of child health: variations across health outcomes-a population-based cross-sectional analysis. BMC Pediatrics 9: 53. doi: 10.1186/1471-2431-9-53
[26]	Smith LA, Hatcher JL, Wertheimer R (2002) The association of childhood asthma with parental employment and welfare receipt. J Am Med Women's Assoc 57: 11-15.
[27]	Kozyrskyj AL, Kendall GE, Jacoby P, et al. (2010) Association between socioeconomic status and the development of asthma: analyses of income trajectories. Am J Public Health 100: 540-546. doi: 10.2105/AJPH.2008.150771
[28]	Bacon SL, Bouchard A, Loucks EB, et al. (2009) Individual-level socioeconomic status is associated with worse asthma morbidity in patients with asthma. Respir Res10: 125.
[29]	Williams DR, Sternthal M, Wright RJ (2009) Social determinants: taking the social context of asthma seriously. Pediatrics 123: S174-184. doi: 10.1542/peds.2008-2233H
[30]	Halterman JS, Aligne CA, Auinger P, et al. (2000) Inadequate therapy for asthma among children in the United States. Pediatrics 105: 272-276.
[31]	Javier JR, Wise PH, Mendoza FS (2007) The relationship of immigrant status with access, utilization, and health status for children with asthma. Offic J Ambul Pediatric Assoc 7: 421-430.
[32]	Brotanek JM, Halterman J, Auinger P, et al. (2015) Inadequate access to care among children with asthma from Spanish-speaking families. J Health Care Poor Underserved 16: 63-73.
[33]	Rust G, Zhang S, McRoy L, et al. (2015) Potential savings from increasing adherence to inhaled corticosteroid therapy in Medicaid-enrolled children. Am J Manage Care 21: 173-180.
[34]	Rust G, Zhang S, Reynolds J (2013) Inhaled corticosteroid adherence and emergency department utilization among Medicaid-enrolled children with asthma. Offic J Assoc Care Asthma 50: 769-775. doi: 10.3109/02770903.2013.799687
[35]	Salam MT, Li YF, Langholz B, et al. (2004) Early-life environmental risk factors for asthma: findings from the Children's Health Study. Environ Health Perspect 112: 760-765.
[36]	Gleason JA, Bielory L, Fagliano JA (2014) Associations between ozone, PM2.5, and four pollen types on emergency department pediatric asthma events during the warm season in New Jersey: a case-crossover study. Environ Res 132: 421-429.
[37]	To T, Shen S, Atenafu EG, et al. (2013) The air quality health index and asthma morbidity: a population-based study. Environ Health Perspect 121: 46-52. doi: 10.1289/ehp.121-a46
[38]	Milligan KL, Matsui E, Sharma H (2016) Asthma in Urban Children: Epidemiology, Environmental Risk Factors, and the Public Health Domain. Curr Allergy Asthma Rep 16: 33. doi: 10.1007/s11882-016-0609-6
[39]	Miles MB, Huberman AM (1994) Qualitative data analysis: An expanded source book, 2nd ed. Newbury Park, CA Sage.
[40]	Keet CA, McCormack MC, Pollack CE, et al. (2015) Neighborhood poverty, urban residence, race/ethnicity, and asthma: Rethinking the inner-city asthma epidemic. J Allergy Clin Immunol 135: 655-662. doi: 10.1016/j.jaci.2014.11.022. doi: 10.1016/j.jaci.2014.11.022
[41]	Gupta RS1, Zhang X, Sharp LK, et al. (2008) Geographic variability in childhood asthma prevalence in Chicago. J Allergy Clin Immunol 121: 639-645. doi: 10.1016/j.jaci.2007.11.036. doi: 10.1016/j.jaci.2007.11.036

Reader Comments

your name:*

Email:*
© 2017 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)