Mortality After the Hospitalization of a Spouse

Background: The illness of a spouse can affect the health of a caregiving partner. We examined the association between the hospitalization of a spouse and a partner’s risk of death among elderly people. Methods: We studied 518,240 couples who were enrolled in Medicare in 1993. We used Cox regression analysis and fixed-effects (case–time–control) methods to assess hospitalizations and deaths during nine years of follow-up. Results: Overall, 383,480 husbands (74 percent) and 347,269 wives (67 percent) were hospitalized at least once, and 252,557 husbands (49 percent) and 156,004 wives (30 percent) died. Mortality after the hospitalization of a spouse varied according to the spouse’s diagnosis. Among men, 6.4 percent died within a year after a spouse’s hospitalization for colon cancer, 6.9 percent after a spouse’s hospitalization for stroke, 7.5 percent after a spouse’s hospitalization for psychiatric disease, and 8.6 percent after a spouse’s hospitalization for dementia. Among women, 3.0 percent died within a year after a spouse’s hospitalization for colon cancer, 3.7 percent after a spouse’s hospitalization for stroke, 5.7 percent after a spouse’s hospitalization for psychiatric disease, and 5.0 percent after a spouse’s hospitalization for dementia. After adjustment for measured covariates, the risk of death for men was not significantly higher after a spouse’s hospitalization for colon cancer (hazard ratio, 1.02; 95 percent confidence interval, 0.95 to 1.09) but was higher after hospitalization for stroke (hazard ratio, 1.06; 95 percent confidence interval, 1.03 to 1.09), congestive heart failure (hazard ratio, 1.12; 95 percent confidence interval, 1.07 to 1.16), hip fracture (hazard ratio, 1.15; 95 percent confidence interval, 1.11 to 1.18), psychiatric disease (hazard ratio, 1.19; 95 percent confidence interval, 1.12 to 1.26), or dementia (hazard ratio, 1.22; 95 percent confidence interval, 1.12 to 1.32). For women, the various risks of death after a spouse’s hospitalization were similar. Overall, for men, the risk of death associated with a spouse’s hospitalization was 22 percent of that associated with a spouse’s death (95 percent confidence interval, 17 to 27 percent); for women, the risk was 16 percent of that associated with death (95 percent confidence interval, 8 to 24 percent). Conclusions: Among elderly people hospitalization of a spouse is associated with an increased risk of death, and the effect of the illness of a spouse varies among diagnoses. Such interpersonal health effects have clinical and policy implications for the care of patients and their families.Sociolog

Spreading in Social Systems: Reflections

In this final chapter, we consider the state-of-the-art for spreading in social systems and discuss the future of the field. As part of this reflection, we identify a set of key challenges ahead. The challenges include the following questions: how can we improve the quality, quantity, extent, and accessibility of datasets? How can we extract more information from limited datasets? How can we take individual cognition and decision making processes into account? How can we incorporate other complexity of the real contagion processes? Finally, how can we translate research into positive real-world impact? In the following, we provide more context for each of these open questions.Comment: 7 pages, chapter to appear in "Spreading Dynamics in Social Systems"; Eds. Sune Lehmann and Yong-Yeol Ahn, Springer Natur

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Spreading paths in partially observed social networks

Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using a static, structurally realistic social network as a platform for simulations, we juxtapose three distinct paths: (1) the stochastic path taken by a simulated spreading process from source to target; (2) the topologically shortest path in the fully observed network, and hence the single most likely stochastic path, between the two nodes; and (3) the topologically shortest path in a partially observed network. In a sampled network, how closely does the partially observed shortest path (3) emulate the unobserved spreading path (1)? Although partial observation inflates the length of the shortest path, the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path. We find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path; in fact, partial observation may, counterintuitively, make the path seem shorter than it actually is.Comment: 12 pages, 9 figures, 1 tabl

Computational modelling with uncertainty of frequent users of e-commerce in Spain using an age-group dynamic nonlinear model with varying size population

[EN] Electronic commerce (EC) has numerous advantages. It allows saving time when we purchase an item, offers the possibility of review without depending on the schedules of traditional stores, access to a wider variety and quantity of articles, in many cases, with lower prices, etc. Based upon mathematical epidemiology tenets strongly related to social behavior able to describe the influence of peers, in this paper we propose an age-group dynamic model with population varying size based on a system of difference equations to study the evolution of the frequent users of EC over time in Spain. Using data from surveys retrieved from the Spanish National Statistics Institute, we use and design computational algorithms to perform a probabilistic estimation of the model parameters that allow the model output to capture the data uncertainty. Then, we will be able to perform a precise prediction with uncertainty.This work has been partially supported by the Ministerio de Economia y Competitividad grant MTM2017-89664-P and by the European Union through the Operational Program of the European Regional Development Fund (ERDF)/European Social Fund (ESF) of the Valencian Community 2014-2020, grants GJIDI/2018/A/009 and GJIDI/2018/A/010.Burgos-Simon, C.; Cortés, J.; Martínez-Rodríguez, D.; Villanueva Micó, RJ. (2019). Computational modelling with uncertainty of frequent users of e-commerce in Spain using an age-group dynamic nonlinear model with varying size population. Advances in Complex Systems. 22(4):1950009-1-1950009-17. https://doi.org/10.1142/S0219525919500097S1950009-11950009-17224Bettencourt, L. (1997). Customer voluntary performance: Customers as partners in service delivery. Journal of Retailing, 73(3), 383-406. doi:10.1016/s0022-4359(97)90024-5Brauer, F., & Castillo-Chávez, C. (2001). Mathematical Models in Population Biology and Epidemiology. Texts in Applied Mathematics. doi:10.1007/978-1-4757-3516-1Cortés, J.-C., Lombana, I.-C., & Villanueva, R.-J. (2010). Age-structured mathematical modeling approach to short-term diffusion of electronic commerce in Spain. Mathematical and Computer Modelling, 52(7-8), 1045-1051. doi:10.1016/j.mcm.2010.02.030Hethcote, H. W. (2000). The Mathematics of Infectious Diseases. SIAM Review, 42(4), 599-653. doi:10.1137/s0036144500371907Yanhui, L., & Siming, Z. (2007). Competitive dynamics of e-commerce web sites. Applied Mathematical Modelling, 31(5), 912-919. doi:10.1016/j.apm.2006.03.029Mahajan, V., Muller, E., & Bass, F. M. (1991). New Product Diffusion Models in Marketing: A Review and Directions for Research. Diffusion of Technologies and Social Behavior, 125-177. doi:10.1007/978-3-662-02700-4_6Turban, E., Outland, J., King, D., Lee, J. K., Liang, T.-P., & Turban, D. C. (2018). Electronic Commerce 2018. Springer Texts in Business and Economics. doi:10.1007/978-3-319-58715-

Studying Paths of Participation in Viral Diffusion Process

Authors propose a conceptual model of participation in viral diffusion process composed of four stages: awareness, infection, engagement and action. To verify the model it has been applied and studied in the virtual social chat environment settings. The study investigates the behavioral paths of actions that reflect the stages of participation in the diffusion and presents shortcuts, that lead to the final action, i.e. the attendance in a virtual event. The results show that the participation in each stage of the process increases the probability of reaching the final action. Nevertheless, the majority of users involved in the virtual event did not go through each stage of the process but followed the shortcuts. That suggests that the viral diffusion process is not necessarily a linear sequence of human actions but rather a dynamic system.Comment: In proceedings of the 4th International Conference on Social Informatics, SocInfo 201