Ten health policy challenges for the next 10 years.

Q3 Mathematics Journal of Modern Applied Statistical Methods Pub Date : 2023-06-20 eCollection Date: 2023-07-01 DOI:10.1093/haschl/qxad010
Kathryn A Phillips, Deborah A Marshall, Loren Adler, Jose Figueroa, Simon F Haeder, Rita Hamad, Inmaculada Hernandez, Corrina Moucheraud, Sayeh Nikpay
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Abstract

Health policies and associated research initiatives are constantly evolving and changing. In recent years, there has been a dizzying increase in research on emerging topics such as the implications of changing public and private health payment models, the global impact of pandemics, novel initiatives to tackle the persistence of health inequities, broad efforts to reduce the impact of climate change, the emergence of novel technologies such as whole-genome sequencing and artificial intelligence, and the increase in consumer-directed care. This evolution demands future-thinking research to meet the needs of policymakers in translating science into policy. In this paper, the Health Affairs Scholar editorial team describes "ten health policy challenges for the next 10 years." Each of the ten assertions describes the challenges and steps that can be taken to address those challenges. We focus on issues that are traditionally studied by health services researchers such as cost, access, and quality, but then examine emerging and intersectional topics: equity, income, and justice; technology, pharmaceuticals, markets, and innovation; population health; and global health.

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未来 10 年的十大卫生政策挑战。
卫生政策和相关研究计划在不断发展和变化。近年来,有关新兴课题的研究急剧增加,如不断变化的公共和私人医疗支付模式的影响、大流行病的全球影响、解决长期存在的医疗不公平问题的新举措、减少气候变化影响的广泛努力、全基因组测序和人工智能等新技术的出现,以及消费者指导医疗的增加。这种演变要求开展具有未来思维的研究,以满足决策者将科学转化为政策的需求。在本文中,《健康事务学者》编辑团队介绍了 "未来 10 年的十大健康政策挑战"。这十项论断中的每一项都描述了所面临的挑战以及应对这些挑战可以采取的措施。我们将重点放在医疗服务研究人员传统研究的问题上,如成本、可及性和质量,但也会研究新出现的交叉性话题:公平、收入和公正;技术、药品、市场和创新;人口健康;以及全球健康。
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CiteScore
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5
期刊介绍: The Journal of Modern Applied Statistical Methods is an independent, peer-reviewed, open access journal designed to provide an outlet for the scholarly works of applied nonparametric or parametric statisticians, data analysts, researchers, classical or modern psychometricians, and quantitative or qualitative methodologists/evaluators.
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