Social network analysis of illicit organ trading networks: The Medicus case

Organ trafficking has been receiving more attention in recent years as its association with transnational crime organizations became evident. Most of the academic studies available on this topic are qualitative case studies, descriptively analyzing the nature of the crime and the agents involved. These studies often highlight the unique nature of organ trafficking, which is the involvement of medical service providers in the network. There have been, however, no effort made to examine the connections between medical service providers and other agents in the network in a quantitative fashion. This study presents unique quantitative data extracted from the “Medicus case”, a well-documented court case involving kidney trafficking that surfaced in Pristina, Kosovo, in 2008. Social Network Analysis (SNA) was employed to quantitatively analyze the structure and characteristics of the kidney trafficking network. The results reveal that there was a significant variation in the level of involvement in kidney trafficking both across and within different types of agents. Notably, medical staff, facilities, and brokers played vital roles in the kidney trafficking network. Moreover, kidney sellers held a more prominent role than kidney buyers, with certain sellers playing particularly influential roles. In sum, this study demonstrates the promise of SNA as a tool for understanding kidney trafficking networks, and that further research is warranted to fully explore its potential in this field

Towards a threshold climate for emergency lower respiratory hospital admissions

Identification of ‘cut-points’ or thresholds of climate factors would play a crucial role in alerting risks of climate change and providing guidance to policymakers. This study investigated a ‘Climate Threshold’ for emergency hospital admissions of chronic lower respiratory diseases by using a distributed lag non-linear model (DLNM). We analysed a unique longitudinal dataset (10 years, 2000–2009) on emergency hospital admissions, climate, and pollution factors for the Greater London. Our study extends existing work on this topic by considering non-linearity, lag effects between climate factors and disease exposure within the DLNM model considering B-spline as smoothing technique. The final model also considered natural cubic splines of time since exposure and ‘day of the week’ as confounding factors. The results of DLNM indicated a significant improvement in model fitting compared to a typical GLM model. The final model identified the thresholds of several climate factors including: high temperature (≥≥27 °C), low relative humidity (≤≤ 40%), high Pm10 level (≥≥70-µg/m3), low wind speed (≤≤ 2 knots) and high rainfall (≥≥30 mm). Beyond the threshold values, a significantly higher number of emergency admissions due to lower respiratory problems would be expected within the following 2–3 days after the climate shift in the Greater London. The approach will be useful to initiate ‘region and disease specific’ climate mitigation plans. It will help identify spatial hot spots and the most sensitive areas and population due to climate change, and will eventually lead towards a diversified health warning system tailored to specific climate zones and populations

A queueing network model with blocking: Analysis of congested patients flows in mental health systems

The current trend toward downsizing and closing of state mental health institutions has led to an over-utilization of many local mental health facilities. This problem has often been exacerbated by a shortage of long-stay psychiatric hospitals and community-type accommodations. Many patients are spending extra days in unnecessarily intensive facilities, leading to congestion of these facilities. In the present study, an open queueing network model with blocking is applied to analyze such congestion processes. Here “blocking” denotes situations where patients are turned away from accommodations to which they are referred, and are thus forced to remain in their present facilities until space becomes available. Although traditional queueing models have been used in numerous healthcare studies, the inclusion of blocking is rarely found. This study specifically examines blocking in interrelated mental health facilities, including (a) acute hospitals (where patients wait to enter extended acute hospitals), (b) extended acute hospitals (where patients wait to enter residential facilities), and (c) residential facilities (where patients wait to enter supported housing). The Philadelphia mental health system is used as a case study. This system has experienced severe congestion problems since 1992. In the theoretical portion of this study, a queueing network model of the Philadelphia system is constructed and analyzed both in terms of steady-states behavior and transient behavior via numerical simulations. These theoretical results are then compared with the observed congestion levels in Philadelphia. The single most important finding is that, in contrast to popular perception, system congestion is not always a simple cumulative effect of shortages across all facility types. In the Philadelphia case, it is shown that system-wide congestion is due primarily to shortages in one specific facility type, namely, supported housing. Here the shortage of supported housing in Philadelphia has created “upstream blocking” of patients at both extended acute hospitals and residential facilities. Perhaps the most important policy implication of this analysis is that removal of such facility-specific bottlenecks may often be the most cost-efficient way to reduce congestion in the system as a whole

A queueing network model with blocking: Analysis of congested patients flows in mental health systems

The current trend toward downsizing and closing of state mental health institutions has led to an over-utilization of many local mental health facilities. This problem has often been exacerbated by a shortage of long-stay psychiatric hospitals and community-type accommodations. Many patients are spending extra days in unnecessarily intensive facilities, leading to congestion of these facilities. In the present study, an open queueing network model with blocking is applied to analyze such congestion processes. Here “blocking” denotes situations where patients are turned away from accommodations to which they are referred, and are thus forced to remain in their present facilities until space becomes available. Although traditional queueing models have been used in numerous healthcare studies, the inclusion of blocking is rarely found. This study specifically examines blocking in interrelated mental health facilities, including (a) acute hospitals (where patients wait to enter extended acute hospitals), (b) extended acute hospitals (where patients wait to enter residential facilities), and (c) residential facilities (where patients wait to enter supported housing). The Philadelphia mental health system is used as a case study. This system has experienced severe congestion problems since 1992. In the theoretical portion of this study, a queueing network model of the Philadelphia system is constructed and analyzed both in terms of steady-states behavior and transient behavior via numerical simulations. These theoretical results are then compared with the observed congestion levels in Philadelphia. The single most important finding is that, in contrast to popular perception, system congestion is not always a simple cumulative effect of shortages across all facility types. In the Philadelphia case, it is shown that system-wide congestion is due primarily to shortages in one specific facility type, namely, supported housing. Here the shortage of supported housing in Philadelphia has created “upstream blocking” of patients at both extended acute hospitals and residential facilities. Perhaps the most important policy implication of this analysis is that removal of such facility-specific bottlenecks may often be the most cost-efficient way to reduce congestion in the system as a whole

Living on a plot of land as a tenure choice: The case of Panama

In this paper, we use analyze data from a survey of over thirteen hundred household housing-tenures in Panama. Our objective is to identify the features which determine whether households in a developing country such as Panama choose to rent or to buy housing properties, or alternatively to seek somewhat alternative tenure arrangements. In particular, we investigate the common characteristic of Panamanian households undertaking plot purchases with a view to future building. In order to analyze these alternative tenure arrangements we develop a series of log-linear models, in which dichotomous rent-versus-buy models are extended to include the possibility of plot purchasing with a view to future building. The extended models including plot purchases are seen to be superior to the dichotomous rent-versus-buy model in identifying which household characteristics are associated with particular housing-tenure decisions

Network Contagion vs. Spatial Contagion: The Diffusion of EHR Incentive Programs in Physician Networks

The present study supported the network contagion theory that healthcare providers are more likely to adopt the Medicare and Medicaid Electronic Health Record (EHR) incentive program when their direct relations have more prior adopters. Spatial contagion, however, exhibits an opposite finding that healthcare providers geographically surrounded by more prior adopters are less likely to adopt the EHR incentive program. When taking both network contagion and spatial contagion into account, healthcare providers connected with more prior adopters within 30 miles are more likely to adopt the EHR incentive program. The findings enrich our understanding of how network contagion influences the diffusion of EHR incentive programs and how spatial contagion moderates the effects of network contagion on the diffusion of the EHR incentive programs

Environmental regulation effect on health poverty in China

How does government spending on environmental protection benefit people's health? The current paper analyzed 2010 and 2018 data from the China Family Panel Studies (CFPS) database to measure the impact of province-level environmental regulations on the health of local population. The study also applied the Alkire Foster method to develop the multidimensional health poverty (MHP) score, a new index intended to measure the health status of individuals in a holistic manner. Our results indicated that more fiscal spending on environmental regulation could improve health of the local population, especially among low-income population living in the rural areas. Further, the size of health benefit differs by the type of environmental regulation. More specifically, regulations focusing on preventing environmental pollution can achieve more sizable health benefits than remedial ones. Finally, fine inhalable particle (PM2.5) has the largest mediating effect on the relationship between environmental regulation and public health. These results provide several policy implications, which highlight the importance of: scaling up fiscal environmental expenditure and optimizing the structure of environmental expenditure with more emphasis on rural areas where more low-income population are located; shifting from ex-post accountability to ex-ante prevention; and strengthening regional cooperation in environmental protection among local governments, and establishing a cross-regional coordination mechanism