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Deng S, Zhang C, Guo X, Lv H, Fan Y, Wang Z, Luo D, Duan X, Sun X, Wang F. Gaps in the Utilization of Community Health Services for the Elderly Population in Rural Areas of Mainland China: A Systematic Review Based on Cross-Sectional Investigations. Health Serv Insights 2022; 15:11786329221134352. [PMID: 36330309 PMCID: PMC9623352 DOI: 10.1177/11786329221134352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 10/03/2022] [Indexed: 11/05/2022] Open
Abstract
Background While the community health service system was initially established in rural areas of mainland China, the utilization of community health service resources by the rural elderly population is not clear. Methods Cross-sectional studies on the utilization of community health services by the rural elderly population in mainland China published within the last 10 years were retrieved from the China National Knowledge Infrastructure (CNKI), Wan Fang, Medline and Web of Science (WOS) databases. The quality of the article was evaluated by the Critical Appraisal Skills Programme (CASP), and obstacles to the utilization of community health services by the rural elderly population were analyzed based on the Andersen model. Results Twenty-four studies were analyzed, and 3 gaps were found. (i) The cognition of rural elderly residents does not match the current health security system. (ii) There is a gap between the supply of health service resources in rural communities and the health needs of the elderly residents in these areas. (iii) The health services provided by rural primary health service institutions are not targeted. Conclusions In mainland China, the provision of community health services to the rural elderly population has improved significantly. However, several factors from the individual level to the system level lead to low levels of access and utilization. This finding means that under the leadership of the government, it is necessary to integrate the strength of multiple departments to cooperate in improving the welfare system, policy publicity, health education, financial support, system guarantees and resource exchange and sharing for the elderly population in rural areas and to jointly promote community health services for the elderly population in rural areas.
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Affiliation(s)
- Shanshan Deng
- School of Nursing, Bengbu Medical
College, Bengbu, China
| | - Chenchen Zhang
- School of Nursing, Bengbu Medical
College, Bengbu, China
| | - Xing Guo
- School of Nursing, Bengbu Medical
College, Bengbu, China
| | - Hekai Lv
- School of Health Management, Bengbu
Medical College, Bengbu, China
| | - Yanyan Fan
- School of Nursing, Bengbu Medical
College, Bengbu, China
| | - Zhuoxin Wang
- School of Nursing, Bengbu Medical
College, Bengbu, China
| | - Dan Luo
- School of Public Foundation, Bengbu
Medical College, Bengbu, China
| | - Xiaoxia Duan
- School of Nursing, Bengbu Medical
College, Bengbu, China
| | - Xinying Sun
- Department of Social Medicine and
Health Education, School of Public Health, Peking University, Beijing, China
| | - Fuzhi Wang
- Department of Social Medicine and
Health Education, School of Public Health, Peking University, Beijing, China,School of Health Management, Innovation
Team of Health Information Management and Application Research (BYKC201913), Bengbu
Medical College, Bengbu, China,Fuzhi Wang, School of Health Management,
Innovation Team of Health Information Management and Application Research
(BYKC201913), Bengbu Medical College, Bengbu 233000, China; Department of Social
Medicine and Health Education, School of Public Health, Peking University,
Beijing, China.
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Aouni J, Bacro JN, Toulemonde G, Colin P, Darchy L. Utility-Based Dose Selection for Phase II Dose-Finding Studies. Ther Innov Regul Sci 2021; 55:818-840. [PMID: 33851358 DOI: 10.1007/s43441-021-00273-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 02/26/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND AND OBJECTIVES Dose selection is a key feature of clinical development. Poor dose selection has been recognized as a major driver of development failure in late phase. It usually involves both efficacy and safety criteria. The objective of this paper is to develop and implement a novel fully Bayesian statistical framework to optimize the dose selection process by maximizing the expected utility in phase III. METHODS The success probability is characterized by means of a utility function with two components, one for efficacy and one for safety. Each component refers to a dose-response model. Moreover, a sequential design (with futility and efficacy rules at the interim analysis) is compared to a fixed design in order to allow one to hasten the decision to perform the late phase study. Operating characteristics of this approach are extensively assessed by simulations under a wide range of dose-response scenarios. RESULTS AND CONCLUSIONS Simulation results illustrate the difficulty of simultaneously estimating two complex dose-response models with enough accuracy to properly rank doses using an utility function combining the two. The probability of making the good decision increases with the sample size. For some scenarios, the sequential design has good properties: with a quite large probability of study termination at interim analysis, it enables to reduce the sample size while maintaining the properties of the fixed design.
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Affiliation(s)
- Jihane Aouni
- Sanofi, Research and Development, 91385, Chilly-Mazarin, France. .,IMAG, Univ Montpellier, CNRS, Montpellier, France.
| | | | - Gwladys Toulemonde
- IMAG, Univ Montpellier, CNRS, Montpellier, France.,Lemon, INRIA, Montpellier Cedex 5, France
| | - Pierre Colin
- Sanofi, Research and Development, 91385, Chilly-Mazarin, France
| | - Loic Darchy
- Sanofi, Research and Development, 91385, Chilly-Mazarin, France
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Takahashi A, Suzuki T. Bayesian optimization for estimating the maximum tolerated dose in Phase I clinical trials. Contemp Clin Trials Commun 2021; 21:100753. [PMID: 33681528 PMCID: PMC7910500 DOI: 10.1016/j.conctc.2021.100753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 11/26/2020] [Accepted: 02/09/2021] [Indexed: 11/26/2022] Open
Abstract
We introduce a Bayesian optimization method for estimating the maximum tolerated dose in this article. A number of parametric model-based methods have been proposed to estimate the maximum tolerated dose; however, parametric model-based methods need an assumption that dose-toxicity relationships follow specific theoretical models. This assumption potentially leads to suboptimal dose selections if the dose-toxicity curve is misspecified. Our proposed method is based on a Bayesian optimization framework for finding a global optimizer of unknown functions that are expensive to evaluate while using very few function evaluations. It models dose-toxicity relationships with a nonparametric model; therefore, a more flexible estimation can be realized compared with existing parametric model-based methods. Also, most existing methods rely on point estimates of dose-toxicity curves in their dose selections. In contrast, our proposed method exploits a probabilistic model for an unknown function to determine the next dose candidate without ignoring the uncertainty of posterior while imposing some dose-escalation limitations. We investigate the operating characteristics of our proposed method by comparing them with those of the Bayesian-based continual reassessment method and two different nonparametric methods. Simulation results suggest that our proposed method works successfully in terms of selections of the maximum tolerated dose correctly and safe dose allocations.
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Affiliation(s)
- Ami Takahashi
- Department of Mathematical and Computing Science, School of Computing, Tokyo Institute of Technology, Tokyo, Japan.,Biometrics and Data Management, Clinical Statistics, Pfizer R&D Japan, Tokyo, Japan
| | - Taiji Suzuki
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.,Center for Advanced Intelligence Project, RIKEN, Japan
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