We explore two statistical methods that infer social network structures and statistically test those structures for change over time: regression-based differential network analysis (R-DNA) and information theory-based differential network analysis (I-DNA). RDNA is adapted from bioinformatics and I-DNA employs reconstructability analysis. Both methods are used to analyze Medicaid claims data from one-year periods before and after the formation of the Health Share of Oregon Coordinated Care Organization (CCO). We hypothesized that Health Share’s CCO formation would be followed by several changes in the healthcare delivery network.
Application of R-DNA and I-DNA to claims data involves three steps: (a) the inference of billing provider networks, (b) measurement of a ‘distance’ between networks before and after Health Share’s CCO formation, and (c) statistical testing of this distance by resampling. Both methods afford what is akin to a network-level t-test for significant network difference between two time periods.
Results did not conform to our hypotheses: 1) Network connectivity consolidated after Health Share’s formation, producing stronger connections within the network’s core and weaker ones throughout its periphery. 2) Changes in connectivity between primary and specialty care sectors reveal an increase in patients who received neither type of care, which may indicate increased obstacles to access. 3) Changes between primary and mental/ behavioral care sectors reveal a decrease in the number of patients receiving both types of care, which may suggest that referrals did not increase as expected.
We conclude that both I-DNA and R-DNA are useful for inferring social network structures, for descriptively exploring the types of change that occur in them over time, and for testing whether those changes are statistically significant.
Teresa is a healthcare research analyst at OCHIN, a nonprofit healthcare innovation center, where she does research on health policy and healthcare utilization patterns among the nation’s safety net. She recently defended her Systems Science PhD dissertation on the above topic.
Bioinformatics | Computer Sciences | Medicine and Health Sciences
© Copyright the author(s)
This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
The purpose of this statement is to help the public understand how this Item may be used. When there is a (non-standard) License or contract that governs re-use of the associated Item, this statement only summarizes the effects of some of its terms. It is not a License, and should not be used to license your Work. To license your own Work, use a License offered at https://creativecommons.org/
Schmidt, Teresa D., "Statistical Analysis of Social Network Change" (2020). Systems Science Friday Noon Seminar Series. 1.