Modeling peer effect modification by network strength: The diffusion of implantable cardioverter defibrillators in the US hospital network

Abstract

We develop methodology that allows peer effects (also referred to as social influence and contagion) to be modified by the structural importance of the focal actor's position in the network. The methodology is first developed for a single peer effect and then extended to simultaneously model multiple peer‐effects and their modifications by the structural importance of the focal actor. This work is motivated by the diffusion of implantable cardioverter defibrillators (ICDs) in patients with congestive heart failure across a cardiovascular disease patient‐sharing network of United States hospitals. We apply the general methodology to estimate peer effects for the adoption of capability to implant ICDs, the number of ICD implants performed by hospitals that are capable, and the number of patients referred to other hospitals by noncapable hospitals. Applying our novel methodology to study ICD diffusion across hospitals, we find evidence that exposure to ICD‐capable peer hospitals is strongly associated with the chance a hospital becomes ICD‐capable and that the direction and magnitude of the association is extensively modified by the strength of that hospital's position in the network, even after controlling for effects of geography. Therefore, interhospital networks, rather than geography per se, may explain key patterns of regional variations in healthcare utilization.

The association of insurance plan characteristics with physician patient-sharing network structure

“…First, our analysis is based in Massachusetts, which may limit generalizability. However, most network analysis is done in highly local areas (e.g., hospital referral regions) (Landon et al 2012) or using a single insurer (Trogdon et al 2019) and so using a state-level dataset is of value to measure both geographically close and distant patient-sharing patterns (Geissler et al 2020a;O'Malley et al 2020). We include fixed effects for patient 3-digit ZIP code to account for geographic differences in patient care and physician patient-sharing network measures in areas with fewer physicians.…”

Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients

“…Consequently, analyses focused on the New England region are considered to be based on a sufficient magnitude and richness of data to be informative and have a realistic chance of detecting effects of clinical significance. Following the approach introduced in Moen et al (2016) andO'Malley et al (2020), for each pair of physicians we compute the weighted edges between physicians by summing the geometric means of the number of visits the same patient made to each physician in the pair across all patients suffering one of these four cancer types. To clarify, let a ijl and a ikh denote the number Table 4 Bias, mean squared error (MSE), and 95% coverage rates (Rate) of ρ using the uniform (0, 1) prior (Pos Unif ), N(0.36, 0.7 2 ) prior (Norm) for ρ and half Cauchy prior (HC) for ω and assume an improper flat prior for α in model ( 2)…”

Constructing within and between hospital physician social networks for modeling physician research participation

“…By construction, A is a weighted network with weights corresponding to the number of shared patients between the two physicians. However, by using a function other than the indicator or step function, different edge weights may be easily determined; for example, the geometric mean (z ik , z jk ) 1/2 has also been used previously [19]. For some computations we will use the binarized network, B = [b ij ] , formed by applying a threshold rule to A, such as b ij = I(a ij > a low ) where a low is a non-negative number (e.g., a low = 0 for any patient-sharing, a low = 100 for a 100 shared patient minimum threshold to constitute a network edge).…”