Search

Background: Patients with stroke are frequently transferred between hospitals. This may have implications on the quality of care received by patients; however, it is not well understood how the characteristics of sending and receiving hospitals affect the likelihood of a transfer event. Our objective was to identify hospital characteristics associated with sending and receiving patients with stroke. Methods: Using a comprehensive statewide administrative dataset, including all 78 Massachusetts hospitals, we identified all transfers of patients with ischemic stroke between October 2007 and September 2015 for this observational study. Hospital variables included reputation (US News and World Report ranking), capability (stroke center status, annual stroke volume, and trauma center designation), and institutional affiliation. We included network variables to control for the structure of hospital-to-hospital transfers. We used relational event modeling to account for complex temporal and relational dependencies associated with transfers. This method decomposes a series of patient transfers into a sequence of decisions characterized by transfer initiations and destinations, modeling them using a discrete-choice framework. Results: Among 73 114 ischemic stroke admissions there were 7189 (9.8%) transfers during the study period. After accounting for travel time between hospitals and structural network characteristics, factors associated with increased likelihood of being a receiving hospital (in descending order of relative effect size) included shared hospital affiliation (5.8× higher), teaching hospital status (4.2× higher), stroke center status (4.3× and 3.8× higher when of the same or higher status), and hospitals of the same or higher reputational ranking (1.5× higher). Conclusions: After accounting for distance and structural network characteristics, in descending order of importance, shared hospital affiliation, hospital capabilities, and hospital reputation were im

International literature on individual behavior has shown the importance of the network of relationships binding individuals to the people who are close to them in everyday life. Family and other role relations are important sources of emotional and instrumental support, as well as social companionship. For the Italian scenario, the 2003 Generations and Gender Survey offers some challenges for constructing ego-centered support networks based on reasonable assumptions of the frequency of contacts and residential proximity of respondents with kin, friends and neighbors. Focusing on young Italian adults aged 18–34 years who are single or have a partner, we define two kinds of support networks—the potential support ego network and the effective support family network—with the aim of analyzing the effects of network characteristics (size and composition) on the probability of receiving help. Our findings show that couples received more support and more often than singles. Although singles’ potential support networks were more characterized by no family ties than the ones of partners, the availability of a ‘‘comprehensive’’ network or a network not ‘‘encapsulated’’ only in the family increased the probability of receiving help in both groups. Moreover, gender differences provide evidence of distinct behavior between partners in activating their network for (family) support.

The Stochastic actor-oriented model (SAO) is a statistical model for longitudinal network data. The most often used procedure for the estimation of the parameter of the SAO model is the Method of Moments (MoM), which estimates the parameters using one observed statistic for each estimated parameter. A new set of statistics is defined taking into account the different ways of creating and deleting ties to which a certain effect can contribute. This definition leads to having more than one statistic for a single parameter, i.e. to an over-determined system of equations. Thus, the ordinary MoM cannot be applied. A suitable method then is the Generalized Method of Moments (GMM), an estimation technique mainly used in econometrics, and potentially more efficient than the MoM. Like the regular MoM, the GMM is based on the differences between the expected values of the statistics and their sample counterparts, but the GMM involves the minimization of a quadratic function of these differences rather than setting all differences to 0. This means that an extra problem arises: the determination of a matrix of weights reflecting the different importance and correlations of the statistics involved. An optimization-simulation algorithm is used, following the approach suggested by Gelman (1995) and based on the Newton-Raphson algorithm, to compare the estimators deriving from the MoM and the GMM. Simulation results suggest that the new set of statistics performs better when network observations are close. In fact, in this context the standard errors of the GMM estimators are lower than those of the MoM.