Predictors of caregiver burden across the home-based palliative care trajectory in Ontario, Canada

Family caregivers of patients enrolled in home-based palliative care programmes provide unpaid care and assistance with daily activities to terminally ill family members. Caregivers often experience caregiver burden, which is an important predictor of anxiety and depression that can extend into bereavement. We conducted a longitudinal, prospective cohort study to comprehensively assess modifiable and non-modifiable patient and caregiver factors that account for caregiver burden over the palliative care trajectory. Caregivers (n = 327) of patients with malignant neoplasm were recruited from two dedicated home-based palliative care programmes in Southern Ontario, Canada from 1 July 2010 to 31 August 2012. Data were obtained from bi-weekly telephone interviews with caregivers from study admission until death, and from palliative care programme and home-care agency databases. Information collected comprised patient and caregiver demographics, utilisation of privately and publicly financed resources, patient clinical status and caregiver burden. The average age of the caregivers was 59.0 years (SD: 13.2), and almost 70% were female. Caregiver burden increased over time in a non-linear fashion from study admission to patient death. Increased monthly unpaid care-giving time costs, monthly public personal support worker costs, emergency department visits and low patient functional status were associated with higher caregiver burden. Greater use of hospice care was associated with lower burden. Female caregivers tended to report more burden compared to men as death approached, and burden was higher when patients were male. Low patient functional status was the strongest predictor of burden. Understanding the influence of modifiable and non-modifiable factors on the experience of burden over the palliative trajectory is essential for the development and targeting of programmes and policies to support family caregivers and reduce burden. Supporting caregivers can have benefits such as improved caregiver health outcomes, and enhancing their ability to meet care-giving demands, thereby potentially allowing for longer patient care in the home setting.

Uses of Agent-Based Modeling for Health Communication: the TELL ME Case Study

Government communication is an important management tool during a public health crisis, but understanding its impact is difficult. Strategies may be adjusted in reaction to developments on the ground and it is challenging to evaluate the impact of communication separately from other crisis management activities. Agent-based modeling is a well-established research tool in social science to respond to similar challenges. However, there have been few such models in public health. We use the example of the TELL ME agent-based model to consider ways in which a non-predictive policy model can assist policy makers. This model concerns individuals' protective behaviors in response to an epidemic, and the communication that influences such behavior. Drawing on findings from stakeholder workshops and the results of the model itself, we suggest such a model can be useful: (i) as a teaching tool, (ii) to test theory, and (iii) to inform data collection. We also plot a path for development of similar models that could assist with communication planning for epidemics.

Abnormal Pedestrian Trajectory analysis based on arbitrary-length clustering

This papers examines the use of trajectory distance measures and clustering techniques to define normal
and abnormal trajectories in the context of pedestrian tracking in public spaces. In order to detect abnormal
trajectories, what is meant by a normal trajectory in a given scene is firstly defined. Then every trajectory
that deviates from this normality is classified as abnormal. By combining Dynamic Time Warping and a
modified K-Means algorithms for arbitrary-length data series, we have developed an algorithm for trajectory
clustering and abnormality detection. The final system performs with an overall accuracy of 83% and 75%
when tested in two different standard datasets.

A Collaborative Multiagent Framework based on Online Risk-Aware Planning and Decision-Making

Planning is an essential process in teams of multiple agents pursuing a common goal. When the effects of actions undertaken by agents are uncertain, evaluating the potential risk of such actions alongside their utility might lead to more rational decisions upon planning. This challenge has been recently tackled for single agent settings, yet domains with multiple agents that present diverse viewpoints towards risk still necessitate comprehensive decision making mechanisms that balance the utility and risk of actions. In this work, we propose a novel collaborative multi-agent planning framework that integrates (i) a team-level online planner under uncertainty that extends the classical UCT approximate algorithm, and (ii) a preference modeling and multicriteria group decision making approach that allows agents to find accepted and rational solutions for planning problems, predicated on the attitude each agent adopts towards risk. When utilised in risk-pervaded scenarios, the proposed framework can reduce the cost of reaching the common goal sought and increase effectiveness, before making collective decisions by appropriately balancing risk and utility of actions.