Purpose: This paper aimed to explore the relationship between the different factors, especially health insurance, and the availability of long-term care (LTC) services, among the disabled elderly. Methods: Based on the data of China Health and Retirement Longitudinal Study (CHARLS), the logistic regression model was utilized to evaluate the influence of the different factors, especially health insurance, on the availability of long-term care services. Results: Our findings show some interesting results. Firstly, the findings suggest that informal long-term care (LTC) services for elderly persons with disabilities heavily depend on a family member from different health insurance groups. About 80.733% of the disabled elderly depend on a family member as their primary caregivers. Secondly, other influence factors such as income and area of residence were also significantly related to the availability of long-term rental services. Thirdly, Health insurance is a very important factor influencing the availability of Long-term care services both in urban and rural areas (p<0.001) but Income is the most interesting variable. Conclusion: Based on our results, the growth and integration of formal long-term care (LTC) services should be facilitated. Firstly, policymakers can encourage formal long-term care (LTC) services from a variety of sources to work together to increase overall supply capability. Secondly, the long-term living security needs of people who do not have health insurance should be regulated through subsidies according to the economic status.

Chronic diseases among the elderly and their huge economic burden on family have caught much attention from economists and sociologists over the past decade in China. This study measured the economic burden of elderly chronic disease (ECD) in families using the China Health and Retirement Longitudinal Study (CHARLS) data set from Peking University (China). We studied some aspects of this burden, including health-service utilization, out-of-pocket expenditure on inpatient and outpatient, total family expenditures on items, and labor force participation rates of family members, etc. Some interesting things were found, for example, the additional annual expenditure on inpatient care (per member) in ECD-families was 37 to 45 percent of the annual expenditure in the control group; the labor-force participation rate in ECD-families was 2.4 to 3.3 percent of points lower than in the control group.

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Dynamic network pruning achieves runtime acceleration by dynamically determining the inference paths based on different inputs. However, previous methods directly generate continuous decision values for each weight channel, which cannot reflect a clear and interpretable pruning process. In this paper, we propose to explicitly model the discrete weight channel selections, which encourages more diverse weights utilization, and achieves more sparse runtime inference paths. Meanwhile, with the help of interpretable layerwise channel selections in the dynamic network, we can visualize the network decision paths explicitly for model interpretability. We observe that there are clear differences in the layerwise decisions between normal and adversarial examples. Therefore, we propose a novel adversarial example detection algorithm by discriminating the runtime decision features. Experiments show that our dynamic network achieves higher prediction accuracy under the similar computing budgets on CIFAR10 and ImageNet datasets compared to traditional static pruning methods and other dynamic pruning approaches. The proposed adversarial detection algorithm can significantly improve the state-of-the-art detection rate across multiple attacks, which provides an opportunity to build an interpretable and robust model.

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