Examining the social networks of older adults receiving informal or formal care: a systematic review.

PURPOSE: To address the care needs of older adults, it is important to identify and understand the forms of care support older adults received. This systematic review aims to examine the social networks of older adults receiving informal or formal care and the factors that influenced their networks. METHODS: A systematic review was conducted by searching six databases from inception to January 31, 2023. The review included primary studies focusing on older adults receiving long-term care, encompassing both informal and formal care. To assess the risk of bias in the included studies, validated appraisal tools specifically designed for different study types were utilized. Network analysis was employed to identify the grouping of study concepts, which subsequently formed the foundation for describing themes through narrative synthesis. RESULTS: We identified 121 studies relating to the formal and informal care of older adults' networks. A variety of social ties were examined by included studies. The most commonly examined sources of care support were family members (such as children and spouses) and friends. Several factors were consistently reported to influence the provision of informal care, including the intensity of networks, reciprocity, and geographical proximity. In terms of formal care utilization, older age and poor health status were found to be associated with increased use of healthcare services. Additionally, physical limitations and cognitive impairment were identified as factors contributing to decreased social engagement. CONCLUSION: This review found that older people were embedded within a diverse network. The findings of this review emphasize the importance of recognizing and incorporating the diversity of social networks in care plans and policies to enhance the effectiveness of interventions and improve the overall well-being of older adults.

Opinion Dynamics Explain Price Formation in Prediction Markets.

Prediction markets are heralded as powerful forecasting tools, but models that describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong to a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable of replicating the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, which is an exchange platform whose data have never been used before to validate models of opinion diffusion.

Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups.

BACKGROUND: Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data. METHODS: We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC. FINDINGS: Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations. INTERPRETATION: Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity. FUNDING: National Institute for Health and Care Research.

Resisting Influence: How the Strength of Predispositions to Resist Control Can Change Strategies for Optimal Opinion Control in the Voter Model.

In this paper, we investigate influence maximization, or optimal opinion control, in a modified version of the two-state voter dynamics in which a native state and a controlled or influenced state are accounted for. We include agent predispositions to resist influence in the form of a probability q with which agents spontaneously switch back to the native state when in the controlled state. We argue that in contrast to the original voter model, optimal control in this setting depends on q: For low strength of predispositions q, optimal control should focus on hub nodes, but for large q, optimal control can be achieved by focusing on the lowest degree nodes. We investigate this transition between hub and low-degree node control for heterogeneous undirected networks and give analytical and numerical arguments for the existence of two control regimes.