On Higher Education Rankings

This post reflects on colleges rankings by the U.S. News & World Report. The main idea is that the rankings are inaccurate and unreliable

Deciphering the link and direction between attention-deficit/hyperactivity disorder symptoms and obesity: Common behavioural or prenatal pathways?

Growing evidence suggests an association between attention-deficit/hyperactivity disorder (ADHD) and obesity, although very little is understood about the nature of this link. The aims of this thesis were to examine the following aspects of the ADHD-obesity association: (1) the directionality of the link from childhood to adolescence, (2) behavioural mediators during childhood and adolescence, and (3) prenatal risk factors common for both disorders. Participants were from the Northern Finland Birth Cohort (NFBC) 1986 (N=9479). Data were obtained on pregnancy and birth factors, and child/adolescent mental health, obesity, and lifestyle factors. Regression analyses showed that ADHD symptoms significantly predicted obesity, rather than in the opposite direction, from childhood to adolescence. Mediation analyses examined potential underlying behavioural factors – physical activity and binge-eating, and showed that physical inactivity mediated the longitudinal ADHD symptom-obesity association. Further, there was a bidirectional, longitudinal association between physical inactivity and ADHD symptoms. ADHD and obesity may share common prenatal risk factors, including prenatal exposure to cortisol. This was studied using a quasi-experimental approach by examining the impact of prenatal exposure to synthetic glucocorticoids (sGC). Results from propensity-score and mixed-effects methods showed that prenatal sGC increased the risk for general psychiatric disturbance and inattention symptoms, but not obesity, in childhood. Placental size may represent another common prenatal contributing factor; placental size was positively associated with behaviour problems, including ADHD symptoms, in child and adolescent boys, but was not associated with obesity. This thesis addresses important unexplored aspects of the association between ADHD and obesity, and provides insight into risk factors for both disorders. The direction of the association was driven from ADHD symptoms to obesity, and physical inactivity was a behavioural mediator underlying the link. Although there was no evidence that both disorders share common prenatal risk, prenatal sGC and placental size were positively associated with ADHD symptoms.Open Acces

The Development and Evaluation of Hospital Pay-for-Performance in Lebanon: Casemix, Readmissions and Patient Perspectives.

Background: Pay-for-performance (P4P) has been widely used in healthcare, but there are few experiences of hospital-based P4P at scale. The evidence of impact from these has been mixed, and there has been increased recognition of the importance of different contexts, designs, incentives and other factors. In 2014, the Lebanese Ministry of Public Health integrated a P4P model for determining hospital reimbursement tiers. In 2018, this model was updated to include a readmissions component, in addition to the preexisting components such as casemix and patient satisfaction. The impact of these interventions was previously undetermined. This also provided an opportunity to contribute to some of the known knowledge gaps regarding hospital P4P. The purpose of this thesis was to describe the development and evaluate the impact of hospital P4P in Lebanon, and ultimately to contribute more broadly to improved design and implementation of value-based healthcare, particularly in limited resource settings.Methods: This thesis uses a mixed methods approach, combining quantitative and qualitative study designs, to conduct four research investigations. The first paper uses descriptive analysis to address how and why hospital P4P was developed in Lebanon. The second and third papers both use an interrupted time series design on data collected from the Ministry hospitalization database. The former uses Newey-OLS regression, and the latter uses Autoregressive Integrated Moving Average models. The second paper analyzes the impact of the 2014 P4P integration on casemix index, and the third paper analyzes the impact of the 2018 model update on readmissions. The fourth paper uses qualitative content analysis on data collected from eight focus groups discussions with patient participants.Results: The Ministry developed hospital P4P after recognizing the limitations of the previous model that had been solely based on accreditation status. Casemix index was included in the P4P model, to improve the appropriateness and fairness of the Ministry-hospitals relation. The analysis of P4P integration impact on casemix included 1,353,025 hospitalizations between 2011 and 2016. This revealed an abrupt increase in casemix among short-stay cases, and a gradual increase in medium-stay cases. Code-level analysis suggested this was attributable to a decrease in unnecessary hospitalizations and improved coding practices. The analysis of P4P impact on readmissions included 1,333,691 hospitalizations across 2011-2019. An abrupt decrease of cholecystectomy and stroke readmissions was found, but not of general and pneumonia readmissions. Our qualitative investigation allowed us to identify six patient perspectives, including satisfaction, health status, perceptions on each of quality, access and health system, and valuing of health, all of central relevance to health systems performance.Conclusion: Hospital P4P in Lebanon led to several positive impacts, including improving the relation between hospitals and the Ministry of Public Health, and providing a tool for continuous development of the health system. The 2014 and 2018 P4P interventions improved system effectiveness and related patient outcomes, by decreasing unnecessary hospitalizations and decreasing some types of readmissions. The Ministry should develop its P4P model to capture the entire spectrum of hospital visits. Using appropriate interrupted time series analysis on readily available data is a useful way to evaluate the effects of health system interventions in contexts with limited resources. Patients in Lebanon highly valued health and supported improving public hospitals and measures to counter the influence of personal connections and money. Health systems canmore widely engage people for their perspectives, and patients can have a fundamental role in shaping the values and functions of a health system

Adaptive and occupancy-based channel selection for unreliable cognitive radio networks

In this paper, we propose an adaptive and occupancy-based channel selection for unreliable cognitive radio networks

Toward Reliable Contention-aware Data Dissemination in Multi-hop Cognitive Radio Ad Hoc Networks

This paper introduces a new channel selection strategy for reliable contentionaware data dissemination in multi-hop cognitive radio network. The key challenge here is to select channels providing a good tradeoff between connectivity and contention. In other words, channels with good opportunities for communication due to (1) low primary radio nodes (PRs) activities, and (2) limited contention of cognitive ratio nodes (CRs) acceding that channel, have to be selected. Thus, by dynamically exploring residual resources on channels and by monitoring the number of CRs on a particular channel, SURF allows building a connected network with limited contention where reliable communication can take place. Through simulations, we study the performance of SURF when compared with three other related approaches. Simulation results confirm that our approach is effective in selecting the best channels for efficient and reliable multi-hop data dissemination

On the power of graph neural networks and the role of the activation function

In this article we present new results about the expressivity of Graph Neural Networks (GNNs). We prove that for any GNN with piecewise polynomial activations, whose architecture size does not grow with the graph input sizes, there exists a pair of non-isomorphic rooted trees of depth two such that the GNN cannot distinguish their root vertex up to an arbitrary number of iterations. The proof relies on tools from the algebra of symmetric polynomials. In contrast, it was already known that unbounded GNNs (those whose size is allowed to change with the graph sizes) with piecewise polynomial activations can distinguish these vertices in only two iterations. Our results imply a strict separation between bounded and unbounded size GNNs, answering an open question formulated by [Grohe, 2021]. We next prove that if one allows activations that are not piecewise polynomial, then in two iterations a single neuron perceptron can distinguish the root vertices of any pair of nonisomorphic trees of depth two (our results hold for activations like the sigmoid, hyperbolic tan and others). This shows how the power of graph neural networks can change drastically if one changes the activation function of the neural networks. The proof of this result utilizes the Lindemann-Weierstrauss theorem from transcendental number theory