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

A Scoping Review of Multilevel Patient-Sharing Network Measures in Health Services Research

Social network analysis is the study of the structure of relationships between social entities. Access to health care administrative datasets has facilitated use of "patient-sharing networks" to infer relationships between health care providers based on the extent to which they have encounters with common patients. The structure and nature of patient-sharing relationships can reflect observed or latent aspects of health care delivery systems, such as collaboration and influence. We conducted a scoping review of peer-reviewed studies that derived patient-sharing network measure(s) in the analyses. There were 134 papers included in the full-text review. We identified and created a centralized resource of 118 measures and uncovered three major themes captured by them: Influential and Key Players, Care Coordination and Teamwork, and Network Structure and Access to Care. Researchers may use this review to inform their use of patient-sharing network measures and to guide the development of novel measures.

Developing and Comparing Four Families of Bayesian Network Autocorrelation Models for Binary Outcomes: Estimating Peer Effects Involving Adoption of Medical Technologies

Despite the extensive use of network autocorrelation models in social network analysis, network autocorrelation models for binary dependent variables have received surprisingly scant attention. In this paper, we develop four network autocorrelation models for a binary random variable defined by whether the peer effect (also termed social influence or contagion) acts on latent continuous outcomes leading to an indirect effect under a normal or a logistic distribution or on the probability of the observed outcome itself under a probit or a logit link function defining a direct effect to account for interdependence between outcomes. For all models, we use a Bayesian approach for model estimation under a uniform prior on a transformed peer effect parameter ( ρ $\rho$ ) designed to enhance model computation and compare results to those under the uniform prior for ρ $\rho$ . We use simulation to assess the performance of Bayesian point and interval estimators for each of the four models when the model that generated the data is used for estimation (precision assessment) and when each of the other three models instead generated the data (robustness assessment). We construct a United States New England region patient-sharing hospital network and apply the four network autocorrelation models to study the adoption of robotic surgery, a new medical technology, among hospitals using a cohort of United States Medicare beneficiaries in 2016 and 2017. Finally, we develop a deviance information criterion for each of the four models to compare their fit to the observed data and use posterior predictive p-values to assess the models' ability to recover specified features of the data. The results find that although the indirect peer effect of the propensity of peer hospital adoption on that of the focal hospital is positive under both latent response autocorrelation models, the direct peer effect of the peer hospital's probability of adopting robotic surgery on the probability of the focal hospital adopting robotic surgery decreases under both mean autocorrelation data models. However, neither of these associations is statistically significant.

Exploiting relationship directionality to enhance statistical modeling of peer-influence across social networks

Risky-prescribing is the excessive or inappropriate prescription of drugs that singly or in combination pose significant risks of adverse health outcomes. In the United States, prescribing of opioids and other "risky" drugs is a national public health concern. We use a novel data framework-a directed network connecting physicians who encounter the same patients in a sequence of visits-to investigate if risky-prescribing diffuses across physicians through a process of peer-influence. Using a shared-patient network of 10 661 Ohio-based physicians constructed from Medicare claims data over 2014-2015, we extract information on the order in which patients encountered physicians to derive a directed patient-sharing network. This enables the novel decomposition of peer-effects of a medical practice such as risky-prescribing into directional (outbound and inbound) and bidirectional (mutual) relationship components. Using this framework, we develop models of peer-effects for contagion in risky-prescribing behavior as well as spillover effects. The latter is measured in terms of adverse health events suspected to be related to risky-prescribing in patients of peer-physicians. Estimated peer-effects were strongest when the patient-sharing relationship was mutual as opposed to directional. Using simulations we confirmed that our modeling and estimation strategies allows simultaneous estimation of each type of peer-effect (mutual and directional) with accuracy and precision. We also show that failing to account for these distinct mechanisms (a form of model mis-specification) produces misleading results, demonstrating the importance of retaining directional information in the construction of physician shared-patient networks. These findings suggest network-based interventions for reducing risky-prescribing.

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

When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. In such a situation, a patient's own hospital can be thought of as a mediator between the effects of peer hospitals and their outcome. However, as in mediation analyses, there may be interest in allowing the effects of peer units to directly impact patients of other units. To accommodate these possibilities, we develop two hierarchical network autocorrelation models that allow for direct and indirect peer effects between hospitals when modeling individual outcomes of the patients cared for at the hospitals. A Bayesian approach is used for model estimation while a simulation study assesses the performance of the models and sensitivity of results to different prior distributions. We construct a United States New England region patient-sharing hospital network and apply newly developed Bayesian hierarchical models to study the diffusion of robotic surgery and hospital peer effects in patient outcomes using a cohort of United States Medicare beneficiaries in 2016 and 2017. The comparative fit of models to the data is assessed using Deviance information criteria tailored to hierarchical models that include peer effects as latent variables.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41109-024-00627-1.

Bayesian Hierarchical Network Autocorrelation Models for Estimating Direct and Indirect Effects of Peer Hospitals on Outcomes of Hospitalized Patients

When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. In such a situation, a patient's own hospital can be thought of as a mediator between the effects of peer hospitals and their outcome. However, as in mediation analyses, there may be interest in allowing the effects of peer units to directly impact patients of other units. To accommodate these possibilities, we develop two hierarchical network autocorrelation models that allow for direct and indirect peer effect pathways between hospitals when modeling individual outcomes of the patients cared for at the hospitals. A Bayesian approach is used for model estimation while a simulation study is used to assess the performance of the models and sensitivity of results to different prior distributions. We construct a United States New England region patient-sharing hospital network and apply our Bayesian hierarchical models to study the diffusion of robotic surgery and hospital peer effects in patient outcomes using a cohort of United States Medicare beneficiaries in 2016 and 2017. The comparative fit of models to the data is assessed using Deviance information criteria tailored to hierarchical models that include peer effects as latent variables.

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

BACKGROUND

Physician participation in clinical trials is essential for the progress of modern medicine. However, the demand for physician research partners is outpacing physicians' interest in participating in scientific studies. Understanding the factors that influence physician participation in research is crucial to addressing this gap.

METHODS

In this study, we used a physician's social network, as constructed from patient billing data, to study if the research choices of a physician's immediate peers influence their likelihood to participate in scientific research. We analyzed data from 348 physicians across 40 hospitals. We used logistic regression models to examine the relationship between a physician's participation in clinical trials and the participation of their social network peers, adjusting for age, years of employment, and influences from other hospital facilities.

RESULTS

We found that the likelihood of a physician participating in clinical trials increased dramatically with the proportion of their social network-defined colleagues at their primary hospital who were participating ([Formula: see text] for a 1% increase in the proportion of participating peers, [Formula: see text]). Additionally, physicians who work regularly at multiple facilities were more likely to participate ([Formula: see text], [Formula: see text]) and increasingly so as the extent to which they have social network ties to colleagues at hospitals other than their primary hospital increases ([Formula: see text], [Formula: see text]). These findings suggest an inter-hospital peer participation process.

CONCLUSION

Our study provides evidence that the social structure of a physician's work-life is associated with their decision to participate in scientific research. The results suggest that interventions aimed at increasing physician participation in clinical trials could leverage the social networks of physicians to encourage participation. By identifying factors that influence physician participation in research, we can work towards closing the gap between the demand for physician research partners and the number of physicians willing to participate in scientific studies.

The diffusion of health care fraud: A bipartite network analysis

Many studies have examined the diffusion of health care innovation but less is known about the diffusion of health care fraud. In this paper, we consider the diffusion of potentially fraudulent Medicare home health care billing in the United States during 2002-16, with a focus on the 21 hospital referral regions (HRRs) covered by local Department of Justice (DOJ) anti-fraud "strike force" offices. We hypothesize that patient-sharing across home health care agencies (HHAs) provides a mechanism for the rapid diffusion of fraudulent strategies. We measure such activity using a novel bipartite mixture (or BMIX) network index, which captures patient sharing across multiple agencies and thus conveys more information about the diffusion process than conventional unipartite network measures. Using a complete population of fee-for-service Medicare claims data, we first find a remarkable increase in home health care activity between 2002 and 2009 in many regions targeted by the DOJ; average billing per Medicare enrollee in McAllen TX and Miami increased by $2127 and $2422 compared to just an average $289 increase in other HRRs not targeted by the DOJ. Second, we establish that the HRR-level BMIX (but not other network measures) was a strong predictor of above-average home health care expenditures across HRRs. Third, within HRRs, agencies sharing more patients with other agencies were predicted to increase billing. Finally, the initial 2002 BMIX index was a strong predictor of subsequent changes in HRR-level home health billing during 2002-9. These results highlight the importance of bipartite network structure in diffusion and in infection and contagion models more generally.

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

Professional and social connections among physicians impact patient outcomes, but little is known about how characteristics of insurance plans are associated with physician patient-sharing network structure. We use information from commercially insured enrollees in the 2011 Massachusetts All Payer Claims Database to construct and examine the structure of the physician patient-sharing network using standard and novel social network measures. Using regression analysis, we examine the association of physician patient-sharing network measures with an indicator of whether a patient is enrolled in a health maintenance organization (HMO) or preferred provider organization (PPO), controlling for patient and insurer characteristics and observed health status. We find patients enrolled in HMOs see physicians who are more central and densely embedded in the patient-sharing network. We find HMO patients see PCPs who refer to specialists who are less globally central, even as these specialists are more locally central. Our analysis shows there are small but significant differences in physician patient-sharing network as experienced by patients with HMO versus PPO insurance. Understanding connections between physicians is essential and, similar to previous findings, our results suggest policy choices in the insurance and delivery system that change physician connectivity may have important implications for healthcare delivery, utilization and costs.

The impact of sampling patients on measuring physician patient-sharing networks using Medicare data

OBJECTIVE

To investigate the impact of sampling patients on descriptive characteristics of physician patient-sharing networks.

DATA SOURCES

Medicare claims data from 10 hospital referral regions (HRRs) in the United States in 2010.

STUDY DESIGN

We form a sampling frame consisting of the full cohort of patients (Medicare enrollees) with claims in the 2010 calendar year from the selected HRRs. For each sampling fraction, we form samples of patients from which a physician ("patient-sharing") network is constructed in which an edge between two physicians depicts that at least one patient in the sample encountered both of those physicians. The network is summarized using 18 network measures. For each network measure and sampling fraction, we compare the values determined from the sample and the full cohort of patients. Finally, we assess the sampling fraction that is needed to measure each network measure to specified levels of accuracy.

DATA COLLECTION/EXTRACTION METHODS

We utilized administrative claims from the traditional (fee-for-service) Medicare.

PRINCIPAL FINDINGS

We found that measures of physician degree (the number of ties to other physicians) in the network and physician centrality (importance or prominence in the network) are learned quickly in the sense that a small sampling fraction suffices to accurately compute the measure. At the network level, network density (the proportion of possible edges that are present) was learned quickly while measures based on more complex configurations (subnetworks involving multiple actors) are learned relatively slowly with relative rates of learning depending on network size (the number of nodes).

CONCLUSIONS

The sampling fraction applied to Medicare patients has a highly heterogeneous effect across different network measures on the extent to which sample-based network measures resemble those evaluated using the full cohort. Even random sampling of patients may yield physician networks that distort descriptive features of the network based on the full cohort, potentially resulting in biased results.