Yi Zeng, Zhenglian Wang, Qiushi Feng, Danan Gu, Junni Zhang, Wei Tang, Kenneth Land
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In section 4, we demonstrate that the ProFamy approach provides an adequate and highly feasible modelling framework to extend probabilistic households and living arrangement projections (PHPs), in which the population size/structure projection outcomes are in consistence with those of probabilistic population projections (PPPs) released by United Nations Population Division (UNPD). In the last Section, we discuss and recommend applying the user-friendly R package DemoRates of ProFamy software to estimate rural/urban (or race)-sex-age-specific standard schedules and the demographic summary measures, to conduct analyses and projections, such as single-parent households, caregivers, and care needs/costs for disabled older adults, age-friendly housing and households-based energy demands, etc. for healthy aging and sustainable development studies. Finally, we discuss the prospects of our ongoing international collaborative research project to substantially extend ProFamy cohort-component method from deterministic into probabilistic households and living arrangement projection (PHPs). 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引用次数: 0
摘要
在这篇评论中,我们首先简要回顾家庭和生活安排预测的重要用途,以及进行家庭预测的主要方法类型。在第二和第三部分中,我们总结了profamily扩展队列组件方法/软件用于家庭和生活安排预测的基本思想、所需数据、评估和应用;我们强调将profamily方法和软件从确定性扩展到概率性的家庭和生活安排预测的重要性。在第4节中,我们证明profamily方法提供了一个充分且高度可行的建模框架来扩展概率家庭和生活安排预测(PHPs),其中人口规模/结构预测结果与联合国人口司(UNPD)发布的概率人口预测(PPPs)一致。在最后一节中,我们讨论并建议使用用户友好的R软件包demoates of profamily软件来估计农村/城市(或种族)-性别-年龄特定的标准时间表和人口统计汇总措施,以进行分析和预测,例如单亲家庭,照顾者,残疾老年人的护理需求/成本,老年人友好住房和家庭能源需求等,以进行健康老龄化和可持续发展研究。最后,我们讨论了我们正在进行的国际合作研究项目的前景,将profamily队列成分方法从确定性扩展到概率家庭和生活安排预测(PHPs)。与profamily确定性预测方法相比,PHPs产生了2021-2100年概率预测家庭和生活安排的许多额外结果,这些结果具有不确定性区间,这对健康老龄化和可持续发展研究至关重要。
Extend the ProFamy cohort-component method to conduct probabilistic households and living arrangement projections.
In this commentary, we first briefly review the significant utilities of household and living arrangement projections and the main types of methods for conducting household projections. In the second and third sections, we summarize basic ideas, data needed, assessments and applications of ProFamy extended cohort-component methods/software for households and living arrangement projections; and we emphasize the importance to extend the ProFamy methods and software from deterministic to probabilistic households and living arrangement projections. In section 4, we demonstrate that the ProFamy approach provides an adequate and highly feasible modelling framework to extend probabilistic households and living arrangement projections (PHPs), in which the population size/structure projection outcomes are in consistence with those of probabilistic population projections (PPPs) released by United Nations Population Division (UNPD). In the last Section, we discuss and recommend applying the user-friendly R package DemoRates of ProFamy software to estimate rural/urban (or race)-sex-age-specific standard schedules and the demographic summary measures, to conduct analyses and projections, such as single-parent households, caregivers, and care needs/costs for disabled older adults, age-friendly housing and households-based energy demands, etc. for healthy aging and sustainable development studies. Finally, we discuss the prospects of our ongoing international collaborative research project to substantially extend ProFamy cohort-component method from deterministic into probabilistic households and living arrangement projection (PHPs). As compared with ProFamy deterministic projection method, the PHPs produces a lot of additional outcomes of probabilistically projected households and living arrangements in 2021-2100 with uncertainty intervals that are crucial for healthy aging and sustainable development studies.