Narrow Results

Use of alternative medicine among the public has increased rapidly over the last few decades. Studies on alternative health care have been conducted on the general public and on major ethnic groups, but not on Korean Americans, who are one of the fastest growing Asian populations in the United States. This study was a cross-sectional survey of 143 Korean Americans in the Greater New York Metropolitan area; all of them were users of alternative medicine. Inter-correlations of hypothetical demographic predictors and preferences for alternative medicine were obtained. Multiple logistic regression analyses were used to identify factors influencing alternative medicine preferences. Most of the respondents answered that they preferred alternative medicine because of philosophical congruence, i.e. they found alternative therapies compatible with their values, world views, spiritual/religious philosophies, and beliefs regarding the nature and meaning of health and illness. The results show that younger, well-educated respondents, with significant acculturation level, and have stayed in the US for a relatively short period of time preferred alternative medicine. The information derived from this and similar studies may be of potential value to the biomedical community, governmental agencies, insurance companies and managed care organizations.

The Central Provident Fund (CPF) system has worked well for majority of Singaporeans who are able to work consistently over their life cycle and have made prudent housing choices. However, the inherent structure of CPF, which is based purely on contributions, is unable to address retirement adequacy for its vulnerable members. Adding a means-tested non-contributory basic pillar to the system will make the system more inclusive. This paper studies the pension cost of a targeted old-age income support system for needy elderly to help meet their basic living expenses. A Lee–Carter stochastic model is used to forecast the elderly population. Pension costs depend on coverage and benefit levels and the cost of living adjustments. The viability of a basic retirement support scheme would also depend on the speed of ageing and the rate of economic growth.

Demographics have significant effects on disease spread in populations and the topological evolution of the underlying networks that represent the populations. In the context of network-based epidemic modeling, Markov chain-based approach and pairwise approximation are two powerful tools — the former can capture stochastic effects of disease transmission dynamics and the latter can characterize the dynamical correlations in each pair of connected individuals. However, to our best knowledge, the study on epidemic spreading in networks relying on these two techniques is still lacking. To fill this gap, in this paper, a deterministic pairwise susceptible–infected–susceptible (SIS) epidemic model with demographics on complex networks with arbitrary degree distributions is studied based on a continuous time conditional Markov chain. This deterministic model is rigorously derived — using the moment generating function — from the Kolmogorov differential equations for the evolution of individuals and pairs. It is found that demographics will induce the extinction of the disease by reducing the basic reproduction number or lowering the epidemic prevalence after the disease prevails. Moreover, due to the demographical effects, the resulting network tends to a homogeneous network with a degree distribution similar to Poisson distribution, irrespective of the initial network structure. Additionally, we find excellent agreement between numerical solutions and individual-based stochastic simulations using both Erdös–Renyi (ER) random and Barabási–Albert (BA) scale-free initial networks. Our results may provide new insights on the understanding of the influence of demographics on epidemic dynamics and network evolution.

Developing and marketing innovations is a major challenge for organisations. Thus, a large body of research has analysed factors influencing innovation adoption and identified passive innovation resistance (PIR) as a crucial inhibitor. However, extant studies failed to examine whether predictors and consequences vary for the two different types of PIR — cognitive and situational PIR. This research strives to address this gap by analysing data of 238 consumers using PLS structural equation modelling. With respect to predictors, corresponding results show that cognitive PIR is strongly enhanced by neuroticism and increases with age, while it is reduced by openness to experience. Situational PIR is reduced by openness to experience and is lower among men. The results also highlight that cognitive and situational PIR substantially differ with respect to consequences. Cognitive PIR represents a strong inhibitor for behavioural innovations, whereas situational PIR is a strong inhibitor of functional innovations.