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您的检索式：作者名="Hongdao Meng"
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| 1 | Family structure and support for the oldest old:A cross-sectional study in Dujiangyan,China显示文摘Objective:In China,adult children have traditionally provided support for their aged parents.This model of care is increasingly challenged by the decline of the extended family.Recently,China’s decades-long one-child policy has eased.We examine the association of family size and composition with family support for nonagenarians and centenarians.Methods:Data were obtained through a questionnaire survey of 619 nonagenarians and cen-tenarians in Dujiangyan and were analyzed by chi-square tests,Student t tests,and multivariate logistic regression models.Results:This study revealed that an increased number of offspring might provide financial support for older people,but this does not necessarily translate into physical and emotional care.Conclusion:It is clear that although alternative sources of support for older people are in-creasingly needed,in China the family will continue to play an important role in supporting older people. | Xiaohui Ren Hongdao Meng Chaojie Liu Jinhui Wu Birong Dong Ningxiu Li | 2015 | Family Medicine and Community Health2015，3，4： | 2 |
| 2 | Heterogeneous length-of-stay modeling of post-acute care residents in the nursing home with competing discharge dispositions显示文摘Post-acute care(PAC)residents in nursing homes(NHs)are recently hospitalized patients with medically complex diagnoses,ranging from severe orthopedic injuries to cardiovascular diseases.A major role of NHs is to maximize restoration of PAC residents during their NH stays with desirable discharge outcomes,such as higher community discharge likelihood and lower re/hospitalization risk.Accurate prediction of the PAC residents’length-of-stay(LOS)with multiple discharge dispositions(e.g.,community discharge and re/hospitalization)will allow NH management groups to stratify NH residents based on their individualized risk in realizing personalized and resident-centered NH care delivery.Due to the highly heterogeneous health conditions of PAC residents and their multiple types of correlated discharge dispositions,developing an accurate prediction model becomes challenging.Existing predictive analytics methods,such as distribution-/regression-based methods and machine learning methods,either fail to incorporate varied individual characteristics comprehensively or ignore multiple discharge dispositions.In this work,a data-driven predictive analytics approach is considered to jointly predict the individualized re/hospitalization risk and community discharge likelihood over time in the presence of varied residents’characteristics.A sampling algorithm is further developed to generate accurate predictive samples for a heterogeneous population of PAC residents in an NH and facilitate facility-level performance evaluation.A real case study using large-scale NH data is provided to demonstrate the superior prediction performance of the proposed work at individual and facility levels through comprehensive comparison with a large number of existing prediction methods as benchmarks.The developed analytics tools will allow NH management groups to identify the most at-risk residents by providing them with more proactive and focused care to improve resident outcomes. | Nazmus SAKIB Xuxue SUN Nan KONG Chris MASTERSON Hongdao MENG Kelly SMITH Mingyang LI | 2022 | Frontiers of Engineering Management2022，9，4： | 0 |
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