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Medical Home Characteristics and Asthma Control: A Prospective, Observational Cohort Study Protocol

BACKGROUND: This paper describes the methods for an observational comparative effectiveness research study designed to test the association between practice-level medical home characteristics and asthma control in children and adults receiving care in safety-net primary care practices. METHODS: This...

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Autores principales: Sills, Marion R., Kwan, Bethany M., Yawn, Barbara P., Sauer, Brian C., Fairclough, Diane L., Federico, Monica J., Juarez-Colunga, Elizabeth, Schilling, Lisa M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AcademyHealth 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4371502/
https://www.ncbi.nlm.nih.gov/pubmed/25848577
http://dx.doi.org/10.13063/2327-9214.1032
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author Sills, Marion R.
Kwan, Bethany M.
Yawn, Barbara P.
Sauer, Brian C.
Fairclough, Diane L.
Federico, Monica J.
Juarez-Colunga, Elizabeth
Schilling, Lisa M.
author_facet Sills, Marion R.
Kwan, Bethany M.
Yawn, Barbara P.
Sauer, Brian C.
Fairclough, Diane L.
Federico, Monica J.
Juarez-Colunga, Elizabeth
Schilling, Lisa M.
author_sort Sills, Marion R.
collection PubMed
description BACKGROUND: This paper describes the methods for an observational comparative effectiveness research study designed to test the association between practice-level medical home characteristics and asthma control in children and adults receiving care in safety-net primary care practices. METHODS: This is a prospective, longitudinal cohort study, utilizing survey methodologies and secondary analysis of existing structured clinical, administrative, and claims data. The Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) is a safety net-oriented, primary care practice-based research network, with federated databases containing electronic health record (EHR) and Medicaid claims data. Data from approximately 20,000 patients from 50 practices in four healthcare organizations will be included. Practice-level medical home characteristics will be correlated with patient-level asthma outcomes, controlling for potential confounding variables, using a clustered design. Linear and non-linear mixed models will be used for analysis. Study inception was July 1, 2012. A causal graph theory approach was used to guide covariate selection to control for bias and confounding. DISCUSSION: Strengths of this design include a priori specification of hypotheses and methods, a large sample of patients with asthma cared for in safety-net practices, the study of real-world variations in the implementation of the medical home concept, and the innovative use of a combination of claims data, patient-reported data, clinical data from EHRs, and practice-level surveys. We address limitations in causal inference using theory, design and analysis.
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spelling pubmed-43715022015-04-06 Medical Home Characteristics and Asthma Control: A Prospective, Observational Cohort Study Protocol Sills, Marion R. Kwan, Bethany M. Yawn, Barbara P. Sauer, Brian C. Fairclough, Diane L. Federico, Monica J. Juarez-Colunga, Elizabeth Schilling, Lisa M. EGEMS (Wash DC) Methods BACKGROUND: This paper describes the methods for an observational comparative effectiveness research study designed to test the association between practice-level medical home characteristics and asthma control in children and adults receiving care in safety-net primary care practices. METHODS: This is a prospective, longitudinal cohort study, utilizing survey methodologies and secondary analysis of existing structured clinical, administrative, and claims data. The Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) is a safety net-oriented, primary care practice-based research network, with federated databases containing electronic health record (EHR) and Medicaid claims data. Data from approximately 20,000 patients from 50 practices in four healthcare organizations will be included. Practice-level medical home characteristics will be correlated with patient-level asthma outcomes, controlling for potential confounding variables, using a clustered design. Linear and non-linear mixed models will be used for analysis. Study inception was July 1, 2012. A causal graph theory approach was used to guide covariate selection to control for bias and confounding. DISCUSSION: Strengths of this design include a priori specification of hypotheses and methods, a large sample of patients with asthma cared for in safety-net practices, the study of real-world variations in the implementation of the medical home concept, and the innovative use of a combination of claims data, patient-reported data, clinical data from EHRs, and practice-level surveys. We address limitations in causal inference using theory, design and analysis. AcademyHealth 2013-12-18 /pmc/articles/PMC4371502/ /pubmed/25848577 http://dx.doi.org/10.13063/2327-9214.1032 Text en All eGEMs publications are licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License http://creativecommons.org/licenses/by-nc-nd/3.0/
spellingShingle Methods
Sills, Marion R.
Kwan, Bethany M.
Yawn, Barbara P.
Sauer, Brian C.
Fairclough, Diane L.
Federico, Monica J.
Juarez-Colunga, Elizabeth
Schilling, Lisa M.
Medical Home Characteristics and Asthma Control: A Prospective, Observational Cohort Study Protocol
title Medical Home Characteristics and Asthma Control: A Prospective, Observational Cohort Study Protocol
title_full Medical Home Characteristics and Asthma Control: A Prospective, Observational Cohort Study Protocol
title_fullStr Medical Home Characteristics and Asthma Control: A Prospective, Observational Cohort Study Protocol
title_full_unstemmed Medical Home Characteristics and Asthma Control: A Prospective, Observational Cohort Study Protocol
title_short Medical Home Characteristics and Asthma Control: A Prospective, Observational Cohort Study Protocol
title_sort medical home characteristics and asthma control: a prospective, observational cohort study protocol
topic Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4371502/
https://www.ncbi.nlm.nih.gov/pubmed/25848577
http://dx.doi.org/10.13063/2327-9214.1032
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