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Submission Type

Protocol

Keywords

comparative effectiveness, asthma, cohort identification, research networks, SAFTINet, methods, informatics, medical home, Delivery of Health Care, Outcome Assessment (Health Care), federated databases, electronic health records, children, adults, primary care

Abstract

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.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

DOI

10.13063/2327-9214.1032

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