Article(id=1324070982817808647, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324070892967428793, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202509378, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1758556800000, receivedDateStr=2025-09-23, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793612722353, onlineDateStr=2026-11-02, pubDate=1766592000000, pubDateStr=2025-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793612722353, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793612722353, creator=13701087609, updateTime=1793612722353, updator=13701087609, issue=Issue{id=1324070892967428793, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='24', pageStart='4417', pageEnd='4608', issueExtLink='null', onlineDate='null', pubDate='1766592000000', pubDateStr='2025-12-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793612700931, creator='13701087609', updateTime=1793612772549, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324071193430602023, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324070892967428793, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324071193430602024, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324070892967428793, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=4507, endPage=4515, ext={EN=ArticleExt(id=1324070984734605576, articleId=1324070982817808647, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Progress in research of comprehensive measurement indicators and methods for healthy life expectancy value, columnId=1228016567846367388, journalTitle=Modern Preventive Medicine, columnName=Health Policy and Management, runingTitle=null, highlight=null, articleAbstract=

Healthy life expectancy (HLE) is a key indicator for measuring population health levels. Its value estimation is significant for optimizing public health resource allocation, promoting health equity, and addressing active aging. Current main valuation methods include: the human capital approach, which reflects the future value of income; the willingness to pay method, which quantifies individual health risk preferences; and the economic growth model method, which reflects comprehensive value dimensions. This article reviews existing main valuation methods and their application research, systematically summarizes the advantages, limitations, and applicable scenarios of different methods, and provides support for constructing a multi-dimensional HLE valuation system to facilitate more precise evidence-based decision-making for resource allocation and policy interventions under the Healthy China strategy.

, authors=Yu-xuan ZHAO1, 2, Hong-yu LAI1, 2, Shi-yong WU3, Jie PAN1, 2, authorsList=Yu-xuan ZHAO, Hong-yu LAI, Shi-yong WU, Jie PAN, authorCompany=null, correspAuthors=Shi-yong WU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1324070986626236695, articleId=1324070982817808647, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=健康预期寿命价值综合测度指标与方法研究进展, columnId=1228016568014139551, journalTitle=现代预防医学, columnName=卫生政策与管理, runingTitle=null, highlight=null, articleAbstract=

健康预期寿命(HLE)是衡量人群健康水平的核心指标之一,其价值测算对优化公共卫生资源配置、促进健康公平及积极应对老龄化具有重要意义。当前主流测算方法包括:体现未来收入价值的人力资本法,量化个体健康风险偏好的意愿支付法和体现价值综合维度的经济增长模型法。本文对现有主流价值测算方法及其应用研究展开综述,系统梳理不同方法的优势、局限及其适用场景,为构建HLE多维价值测算体系,健康中国战略下资源配置与政策干预的更精准循证决策提供支持。

, authors=赵羽宣1, 2, 赖泓宇1, 2, 吴士勇3, 潘杰1, 2, authorsList=赵羽宣, 赖泓宇, 吴士勇, 潘杰, authorCompany=null, correspAuthors=吴士勇, authorNote=

赵羽宣(2000—),女,硕士在读,研究方向:健康经济学,数智医疗

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吴士勇,E-mail:
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Comparison of HLE value dimensions, core focus, and application examples

, figureFileSmall=null, figureFileBig=null, tableContent=
价值维度价值阐述核心关注应用举例
社会维度HLE对广泛社会产生的非经济性积极影响,通常难以完全用货币衡量但显著提升社会生活质量、促进健康公平等。社会福祉、健康公平基于HLE对最佳延迟退休年龄测算[12];HLE促进健康老龄化,提升老年人口社会参与及养老服务提升[13]
经济维度HLE在市场交易中能够直接或间接影响生产要素所产生的货币价值或对财富积累、生产效率、成本节约的贡献。人力资本、生产效率、经济增长健康改善能够提高人力资本水平[14];人口健康预期寿命提高也有助于改进劳动生产率,进而提高全要素生产率[15]
个人维度HLE对于个体的重要性、意义和效用,高度依赖于个人的需求、价值观、信仰和生活背景等。个人意愿、效用最大化、统计生命价值测算老年人主观健康效应[16];个人对于不同风险降低的支付偏好意愿[17-18]
), ArticleFig(id=1324070990778597697, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324070982817808647, language=CN, label=表1, caption=

HLE价值维度阐述、核心关注及其应用举例的对比

, figureFileSmall=null, figureFileBig=null, tableContent=
价值维度价值阐述核心关注应用举例
社会维度HLE对广泛社会产生的非经济性积极影响,通常难以完全用货币衡量但显著提升社会生活质量、促进健康公平等。社会福祉、健康公平基于HLE对最佳延迟退休年龄测算[12];HLE促进健康老龄化,提升老年人口社会参与及养老服务提升[13]
经济维度HLE在市场交易中能够直接或间接影响生产要素所产生的货币价值或对财富积累、生产效率、成本节约的贡献。人力资本、生产效率、经济增长健康改善能够提高人力资本水平[14];人口健康预期寿命提高也有助于改进劳动生产率,进而提高全要素生产率[15]
个人维度HLE对于个体的重要性、意义和效用,高度依赖于个人的需求、价值观、信仰和生活背景等。个人意愿、效用最大化、统计生命价值测算老年人主观健康效应[16];个人对于不同风险降低的支付偏好意愿[17-18]
), ArticleFig(id=1324070990854095170, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324070982817808647, language=EN, label=Table 2, caption=

Analysis of HLE value mechanism pathways within economic growth models

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路径经济影响
人口增长[55,58]LE/HLE增加→结合人口结构特征、经济发展水平和发展阶段→经济增长
健康人力资本[55,58]LE/HLE增加→健康改善减少发病和残疾→提升劳动力数量与质量→增强工作投入和技能积累→创新转化效率提高→经济增长
储蓄率提升[52,59]LE/HLE增加→储蓄动机上升(老龄化程度/社会保障程度)→资本积累→经济增长
教育增长[60]LE/HLE增加→教育投资增加→劳动力素质提高→ 全要素生产率提升→经济增长
闲暇[61-65]LE/HLE增加→闲暇对劳动替代效应扩大→ 人力资本积累→经济增长
), ArticleFig(id=1324070990929592643, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324070982817808647, language=CN, label=表2, caption=

经济增长模型中HLE价值机制路径解析表

, figureFileSmall=null, figureFileBig=null, tableContent=
路径经济影响
人口增长[55,58]LE/HLE增加→结合人口结构特征、经济发展水平和发展阶段→经济增长
健康人力资本[55,58]LE/HLE增加→健康改善减少发病和残疾→提升劳动力数量与质量→增强工作投入和技能积累→创新转化效率提高→经济增长
储蓄率提升[52,59]LE/HLE增加→储蓄动机上升(老龄化程度/社会保障程度)→资本积累→经济增长
教育增长[60]LE/HLE增加→教育投资增加→劳动力素质提高→ 全要素生产率提升→经济增长
闲暇[61-65]LE/HLE增加→闲暇对劳动替代效应扩大→ 人力资本积累→经济增长
), ArticleFig(id=1324070991017673028, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324070982817808647, language=EN, label=Table 3, caption=

Evaluation of the strengths, weaknesses, and application contexts of three measurement methods

, figureFileSmall=null, figureFileBig=null, tableContent=
方法优势局限性适用场景
人力资本法数据易得、计算简单;直接反映个体劳动力成本与产出忽略非市场价值;伦理问题:低估低收入群体价值,歧视性;仅关注市场产出,忽略个人偏好司法赔偿[27];灾害损失评估[28]
意愿支付法
显示性偏好法(RP)基于真实市场行为;规避假设偏差和认知负担难以测量非市场物品价值;无法适应动态场景;混杂变量控制困难劳动力市场风险补偿(CWD法)[41-42]
陈述性偏好法(SP)可测算非市场价值;方法灵活(CVM/DCE)假设偏差,认知负担高;范围不敏感导致VSL偏差卫生政策评估[66];非市场物品估值[44]
双维度健康效用评估模型将健康改善分为寿命延长与质量提升,基于终身效用最大化框架模型依赖贴现率假设,对参数敏感老龄化经济价值研究、健康干预长期收益评估[46]
校准补偿变量法(CCV)修正非边际风险变化下的VSL偏差,分解收入与基线风险效应计算复杂度高,需精细数据重大公共卫生政策[9,51]
经济增长模型法反映宏观系统效应;揭示HLE对经济增长的综合路径模型复杂度高;动态效应存在滞后性;异质性受国家发展阶段影响国家健康战略成本效益分析[55];健康人力资本与经济增长关系研究[52-54]
), ArticleFig(id=1324070991122530629, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324070982817808647, language=CN, label=表3, caption=

三类测算方法的优势、局限性和适用场景分析

, figureFileSmall=null, figureFileBig=null, tableContent=
方法优势局限性适用场景
人力资本法数据易得、计算简单;直接反映个体劳动力成本与产出忽略非市场价值;伦理问题:低估低收入群体价值,歧视性;仅关注市场产出,忽略个人偏好司法赔偿[27];灾害损失评估[28]
意愿支付法
显示性偏好法(RP)基于真实市场行为;规避假设偏差和认知负担难以测量非市场物品价值;无法适应动态场景;混杂变量控制困难劳动力市场风险补偿(CWD法)[41-42]
陈述性偏好法(SP)可测算非市场价值;方法灵活(CVM/DCE)假设偏差,认知负担高;范围不敏感导致VSL偏差卫生政策评估[66];非市场物品估值[44]
双维度健康效用评估模型将健康改善分为寿命延长与质量提升,基于终身效用最大化框架模型依赖贴现率假设,对参数敏感老龄化经济价值研究、健康干预长期收益评估[46]
校准补偿变量法(CCV)修正非边际风险变化下的VSL偏差,分解收入与基线风险效应计算复杂度高,需精细数据重大公共卫生政策[9,51]
经济增长模型法反映宏观系统效应;揭示HLE对经济增长的综合路径模型复杂度高;动态效应存在滞后性;异质性受国家发展阶段影响国家健康战略成本效益分析[55];健康人力资本与经济增长关系研究[52-54]
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健康预期寿命价值综合测度指标与方法研究进展
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赵羽宣 1, 2 , 赖泓宇 1, 2 , 吴士勇 3 , 潘杰 1, 2
现代预防医学 | 卫生政策与管理 2025,52(24): 4507-4515
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现代预防医学 |卫生政策与管理 2025 , 52 (24) : 4507 -4515
健康预期寿命价值综合测度指标与方法研究进展
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赵羽宣1, 2, 赖泓宇1, 2, 吴士勇3 , 潘杰1, 2
作者信息
  • 1.四川大学华西公共卫生学院/四川大学华西第四医院,成都 610041
  • 2.四川大学健康城市发展研究中心/西部农村卫生发展研究中心
  • 3.国家卫生健康委统计信息中心
通讯作者:
吴士勇,E-mail:
作者简介:

赵羽宣(2000—),女,硕士在读,研究方向:健康经济学,数智医疗

Progress in research of comprehensive measurement indicators and methods for healthy life expectancy value
Yu-xuan ZHAO1, 2, Hong-yu LAI1, 2, Shi-yong WU3 , Jie PAN1, 2
Affiliations
  • West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2025-12-25 doi: 10.20043/j.cnki.MPM.202509378
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健康预期寿命(HLE)是衡量人群健康水平的核心指标之一,其价值测算对优化公共卫生资源配置、促进健康公平及积极应对老龄化具有重要意义。当前主流测算方法包括:体现未来收入价值的人力资本法,量化个体健康风险偏好的意愿支付法和体现价值综合维度的经济增长模型法。本文对现有主流价值测算方法及其应用研究展开综述,系统梳理不同方法的优势、局限及其适用场景,为构建HLE多维价值测算体系,健康中国战略下资源配置与政策干预的更精准循证决策提供支持。

健康预期寿命  /  价值测算  /  人力资本法  /  经济增长模型法  /  意愿支付法

Healthy life expectancy (HLE) is a key indicator for measuring population health levels. Its value estimation is significant for optimizing public health resource allocation, promoting health equity, and addressing active aging. Current main valuation methods include: the human capital approach, which reflects the future value of income; the willingness to pay method, which quantifies individual health risk preferences; and the economic growth model method, which reflects comprehensive value dimensions. This article reviews existing main valuation methods and their application research, systematically summarizes the advantages, limitations, and applicable scenarios of different methods, and provides support for constructing a multi-dimensional HLE valuation system to facilitate more precise evidence-based decision-making for resource allocation and policy interventions under the Healthy China strategy.

Healthy life expectancy  /  Value estimation  /  Human capital method  /  Economic growth model method  /  Willingness to pay method
赵羽宣, 赖泓宇, 吴士勇, 潘杰. 健康预期寿命价值综合测度指标与方法研究进展. 现代预防医学, 2025 , 52 (24) : 4507 -4515 . DOI: 10.20043/j.cnki.MPM.202509378
Yu-xuan ZHAO, Hong-yu LAI, Shi-yong WU, Jie PAN. Progress in research of comprehensive measurement indicators and methods for healthy life expectancy value[J]. Modern Preventive Medicine, 2025 , 52 (24) : 4507 -4515 . DOI: 10.20043/j.cnki.MPM.202509378
随着全球人均预期寿命(Life Expectancy, LE)持续增长,如何提升延长寿命中健康状态的比例已成为一个重要研究问题。因此,健康预期寿命(Healthy Life Expectancy, HLE)逐渐受到重视。根据《2025年世界卫生统计》监测数据[1],2000-2021年全球LE从66.8岁增至71.4岁,HLE从58.1岁增至61.9岁。然而,寿命延长也伴随着一系列挑战,如带病生存期延长[2],人口老龄化[3]与慢性病负担加重[4]等问题,并进一步引发劳动力健康损耗[5]、医疗财政压力[6]和社保体系承压[7]等社会经济挑战。2016年,《“健康中国2030”规划纲要》中将HLE正式纳入国家战略,并将其作为衡量健康政策与制度绩效的重要指标。这一举措标志着我国向“以人民健康为中心”的战略转变,为后续公共卫生干预、医疗服务体系优化及政策评估提供了新的衡量维度。
基于上述趋势,世界银行于2024年发布《释放健康长寿的力量》报告,并指出通过量化健康与长寿的价值可以评估全生命周期健康投资的经济效益,为政策制定者提供具有成本效益的决策依据,从而推动中低收入国家降低慢性非传染性疾病负担和延长HLE,并释放人力资本潜力[8]。因此,亟需对HLE价值进行系统和科学地量化测度,一方面,来引导健康政策精准投资,在人口老龄化进程中同步提升生存质量与经济效益;另一方面,通过HLE价值测度来引导资源向重点人群倾斜,缓解慢性病造成的“代际贫困陷阱”,促进健康公平和医疗可及性。然而,现有研究多聚焦于LE价值或可避免死亡价值的测算[9],对HLE价值测算体系的构建关注不足,尤其缺乏适配中国发展需求的本地化HLE测算体系构建。
综上,本文将对现有主流价值测算方法及其应用研究展开综述,系统梳理不同方法的优势、局限及其适用场景,解析人力资本法、意愿支付法和经济增长模型在HLE价值测算场景的应用潜力,为构建科学系统的HLE价值测算体系、优化健康投资决策提供参考。
HLE作为衡量人群健康水平的核心指标,其价值具有显著的多维特征,不仅体现在促进生产力提升、减少医疗及社会福利负担等对经济系统的实质性贡献[10];还深刻塑造着社会结构,如增强健康公平性、改善家庭福祉等;同时更直接关联个人福祉,涵盖全生命周期内生活质量改善、主观幸福感提升等深层需求[11]。不同层面的HLE价值阐述、核心关注及应用举例如表1。
HLE价值测算是提升公共卫生资源配置效率的核心依据。通过量化单位HLE增加所带来的经济价值,决策者可以对比不同干预措施,优先支持具有成本效益和成本效果的项目,从而避免资源错配。2024年世界银行在《释放健康长寿的力量》报告中,利用统计生命价值(value of statistical life, VSL)来评估健康投资对减贫、性别平等与经济增长的多重效益,为政策优先序制定提供了实证锚点[8]。
HLE价值测算是应对人口老龄化的数据支柱。在人口结构转型背景下,HLE与LE的差值可以一定程度地反映失能负担与照护成本规模。精准测算HLE价值可支撑预测老年长期照护需求总量及结构、科学规划养老服务体系和设计财政可持续的养老保障制度三大关键决策。实证研究表明,60岁以上群体仅占全球人口的13%,却贡献了34%的健康经济价值[9],凸显HLE价值测算对老龄化社会人力资本评估的重要性。
HLE价值测算通过揭示群体间健康差异及其社会经济代价,成为促进健康公平的伦理工具。通过对比不同地域、收入人群的HLE价值,来量化城乡健康不平等程度;通过识别弱势群体需求,为靶向政策设计提供依据。Chang等[9]研究发现,传统死因列表法直接忽略70-75岁以上高龄群体,导致对健康负担的低估。而HLE价值测算包含全年龄段数据,一定程度上保证测算结果公平和客观。
在HLE价值测算研究通常基于两个分析层面:一是健康效益测量,如质量调整生命年(Quality-Adjusted Life Year,QALY)[19]、伤残调整生命年(Disability-Adjusted Life Year,DALY)[20]与等值健康预期寿命年(Equal Value of Life Years,evLY)[21]等指标。这些方法的核心目标是对健康结果进行标准化和可比化的量化,用于卫生经济学评价与资源配置的健康效益比较;二是经济价值测算(economic valuation),即将健康或寿命的变化直接转化为货币价值,用于反映健康改善对社会财富积累、生产效率提升与福利变化的经济意义。
本文聚焦于后者,即健康预期寿命的经济价值测算方法。当前主流价值测算方法包括人力资本法、意愿支付法及经济增长模型法。人力资本法基于未来收入评估价值;意愿支付法通过统计生命价值等指标量化个体健康偏好反映价值;经济增长模型法解析HLE影响人口、教育等多领域路径体现价值的综合维度。
人力资本法(Human Capital Approach,HCA)是指依据个人创造财富的能力,将个人生命的价值定义为其市场生产力,并根据其未来创造的预期价值来衡量人的生命价值。人力资本法用于衡量生命价值已有较长历史。1672年,由英国古典经济学家William Petty[22]在《政治算术》中首次提出了经济意义上的生命价值概念。1776年Adam Smith[23]将人的生产和劳动能力视为市场中的资本,并运用到经济学理论中。1853年英国经济学家William Farr[24]为确保征税公平性,提出应按个人财富比例征税。他通过评估个人服务的价值来评估个人财富。法尔给出个人服务的价值即生命价值的计量模型如公式(1)所示:
其中,V表示全社会的总生命价值,W0表示全社会的总收入,Y0表示全社会总消费,Q0表示总人口。
1885年,Jacob Grimm首次将人的生命价值应用到人寿保险领域。他认为保险的基础是人的生命价值,而个人生命的货币价值即个人未来收入的现值。1924年,美国保险学家Huebner[25]在著作《人寿保险经济学》中对生命价值在人寿保险领域的应用做了系统的研究。他指出,人寿和健康保险角度下人类生命价值应定义为不包括被保险人自己花费的和税收部分的资本价值总额。1946年,美国保险学家Louis Dublin和Alfred Lotka[26]则主要根据保险人治疗疾病和死亡前的经济费用测算生命价值。
人力资本法反映个人不同年龄段的生命价值和生存期间的净收益。由于这种算法直接反映了个体劳动力成本、产出和消费,并主要集中在个人的未来净收人上,在早期测算生命价值的研究中较为流行[27]。人力资本法曾主要应用于国家司法赔偿的测算,如2001年美国政府对“9·11”恐怖事件受害人赔偿[27],2014年Boris Porfiriev[28]以2010年俄罗斯森林火灾为例评估灾害造成的人员损失等。
但是,现有涉及健康收益与生命价值的研究却较少使用人力资本法来测算生命价值,这是因为人力资本法对生命价值的测算存在一定局限性。首先,人力资本法将人的价值等同于其未来市场收入的现值,有研究认为这本质上是衡量个人作为“生产单位”对社会的生命价值,而非个人对自己生命和健康的主观评价或福利变化[29]。现代福利经济学认为,人力资本法忽略了个人偏好和效用,只关注市场产出[30-31]。其次,人力资本法系统性地低估那些市场收入低或没有市场收入的人群的生命价值,这导致政策资源分配严重偏向高收入群体,被认为存在一定的伦理和公平性问题。同时人力资本法没有考虑生命的非市场价值,如个人享受闲暇、家庭情感维系、社区贡献等带来的效用[32-33]。因上述局限性,人力资本法在主流经济学理论和发达国家的政策应用与研究中已被较少使用。
意愿支付法(Willingness to Pay,WTP)是指人们基于个体行为或直接询问个体为降低死亡风险愿意支付的金额[34]。1962年,Dreze[35]首次提出用支付意愿方法评估死亡风险降低的价值。继1968年Schelling[30]的开创性论文发表后,这一概念广为人知。
意愿支付法测算价值的核心指标包括统计生命价值(Value of Statistical Life, VSL)、统计生命年价值(Value of Statistical Life Year, VSLY),及每质量调整生命年价值(Willingness-To-Pay for a QALY, WTP-Q),具体如下:
VSL计算个人愿意为每一单位死亡风险所支付的金额数,代表一个人剩余LE(预期寿命)所有年份的总价值,如公式(2)所示:
上式中,ΔWTP为个体意愿支付的金额变化,Δπ为单位死亡风险率变化。1993年Viscusi [29]在研究中探讨如何通过经济学方法量化生命和健康风险的价值,使用VSL为公共政策提供成本效益分析依据。
为了考虑政策改善措施潜在受益者之间LE的差异,研究者使用VSLY这一指标来反映按照一定贴现率分摊得到剩余LE每年的生命价值[36],如公式(3)所示:
上式中,L为LE,ρ为贴现率。实证发现VSL可能随年龄增长而保持不变或增加,这一结论说明个人的VSLY并非恒定不变,而是随年龄增长而增加[37]。
20世纪80年代健康经济学家George Torrance[19]提出质量调整生命年(Quality-Adjusted Life Year,QALY)后,基于VSL模型为QALY赋予货币价值,即WTP-Q的测算方法如公式(4)所示,
上式中,LQ为QALY调整的LE,ρ为贴现率。2015年Ryen和Svensson[38]系统回顾了24项实证研究,探讨人们对QALY变动带来的意愿支付变化规律,研究显示WTP-Q非单一常量,其受到健康获益性质、评估方法及增益规模共同影响。2017年Hammitt[39]通过对急性和慢性疾病研究发现WTP对疾病持续时间和严重程度敏感性是非线性变化,与QALYs对健康改善持续时间是线性比例变化的假设并不适配,提出未来研究需整合QALY和WTP的信息以构建更优的WTP-Q估值框架。因此,WTP-Q是可变的,并受多种因素影响,如果决策者机械地、不加区分地使用一个固定的、全国统一的金额作为阈值来判断所有干预措施的成本效益,可能会导致错误的或不公平的决策。
现有意愿支付法的主流实施方法主要有显示性偏好法(Revealed Preference, RP)和陈述性偏好法(Stated Preference, SP)。显示性偏好法(RP)是通过观察个体在真实世界中的行为和选择,并对该行为带来的健康效益进行估值[40]。实现该方法的常用方式是补偿性工资差异法(compensating wage differential method,CWD),CWD基于补偿性工资理论,该理论由Adam Smith在《国富论》[23]中首次提出。该方法估算出劳动力市场为了忍耐工作中负面特征(尤其是健康和安全风险)而愿意接受的额外工资补偿,从而来衡量人们对自己“健康”的估值。
2003年,Viscusi等[32]用CWD系统测算了部分发达国家及发展中国家的VSL,研究结果指出发展中国家的VSL被低估。他们认为提出使用VSL评估各国政策效果时应使用本国收入水平校准。2013年Qin等[41]基于边际工资回归模型,采用CWD估算中国劳动者因工作导致的额外死亡风险所对应的补偿性工资差异,推导出中国及不同地区VSL的估计值。2021年,Keller等[42]对不同领域中VSL估值研究进行系统综述,在纳入的43篇RP研究中,有74%研究基于CWD。
陈述性偏好法(SP)是另一个现有WTP研究中使用较为广泛的测算方法[43]。该方法通过直接询问个体,来获取他们对非市场物品、服务或特定属性变化的偏好信息,并据此估算这些物品、服务或属性的货币价值。实现该方法的常用方式是条件估值法(contingent valuation method,CVM)与离散选择实验(discrete choice experiment,DCE)。CVM通过直接询问个人愿意为商品数量的变化支付或接受多少金额,或者在变化未实施的情况下,他们要求多少补偿金额。DCE要求个人对不同选项进行排序、评分或选择,从而推断出产品或服务的效用。2024年,Wang等[44]对1998-2019年中国相关研究主要使用SP估算受访者对降低死亡风险的支付意愿进行系统回顾,并使用元回归分析识别影响VSL的关键因素如收入、风险类型及风险降低幅度等。
总体而言,尽管SP和RP方法为非市场价值的估计提供有力参考,但是它们在不同实际应用场景下的价值测算各有优劣。RP基于真实市场决策如工资选择、消费行为等,规避了假设偏差和认知负担,但RP对非市场物品、动态场景、混杂变量控制的适应性弱,在实际应用中受到可观测和测量风险态度场景的限制,通常难以实现将“风险小幅变化的估值”转化为“完整生命估值” [40];而SP则可以测算非市场价值,方法灵活性较高,但存在假设偏差及认知负担同时,在使用CVM时会出现受试者因范围不敏感而导致VSL估算有偏差[45]。
随着意愿支付法在多领域使用,研究者基于领域特点及研究主题对意愿支付方法做出不同的拓展和改进。
一方面,2006年,Murphy等[46]基于SP的VSL模型,结合个人效用最大化理论构建双维度健康效用评估模型,将健康改善分为两类:延长寿命G(t)的改善和生活质量H(t)的改善。延长寿命之所以有价值,是因为人们可以更长时间地享受商品和闲暇带来的效用;而生活质量的改善则通过提高既定商品和闲暇消费下的效用水平来体现价值。该模型基于Arthur[47]和Rosen[48]关于LE的分析,假设人们为健康服务所支付的意愿是基于对终身效用的最大化来决定的。对于年龄为a的个体而言,其剩余的预期终身效用为如公式(5)及公式(6)所示:
其中,ρ为贴现率,c(t)为消费,l(t)闲暇时间。
上式中,λ(τ,G(τ))为瞬时死亡(风险)率,其中τ表示时间点,为从年龄a活到t的生存率。
公式(1)和(2)为生命年价值、剩余生命价值以及健康改善时这些价值的变化提供了精确的表达式。LE的提高会增加剩余生命的价值,进而提高人们对进一步健康改善的意愿支付。2021年,Scott等[10]在对健康长寿的经济价值评估研究中,使用双维度健康效用评估模型评估LE延长、健康状况改善和针对衰老的治疗的经济价值,揭示健康改善的长期收益和疾病间的互补效应,为政策制定提供新依据。
另一方面,在评估降低死亡风险所带来的益处时,对于较小的风险变化,可以通过将VSL与预期的死亡人数减少量相乘来近似计算出收益,同时保持VSL的估计值不变,计算生存与财富之间的边际替代率[32,49]。然而,当风险变化较大时,边际补偿可能会有很大差异,这种方法难以保证测算的准确性[50]。针对上述方法挑战,2024年Diego等[50]构建校准补偿变量法(Calibrated Contingent Valuation,CCV),该方法基于传统VSL方法在政策干预导致非边际风险变化中产生的系统性偏差,分解收入效应和基线风险效应,通过校准补偿变异,修正该偏差,从而更准确的估计VSL,为癌症防控、新冠干预等重大公共卫生政策提供更精准的成本效益分析工具。2024年,Chang等[9]基于CCV方法计算全球六个地区的可避免死亡率的经济价值,并将该模型应用于计算慢性病的可避免死亡研究中[51]。
宏观视角下,HLE是社会发展水平、社会治理能力和社会保障水平的健康体现,反映当前我国健康软实力。现有研究通过量化HLE在不同发展阶段国家中,经由人口、储蓄、教育、闲暇等中介路径对经济增长的差异化贡献,可间接识别HLE价值及其相对重要性[52]。
2007年,Well[53]将健康视为影响国家收入的直接决定因素,在保持物质资本、教育水平、制度质量等其他因素不变的情况下,探讨健康状况改善如何使劳动者能够更努力、更高效地工作。2017年,贺磊等[54]研究发现LE是通过某些中间经济变量对经济增长产生影响,其中储蓄和人力资本是两个关键的变量。2020年,张颖熙等[52]提出使用HLE来衡量健康人力资本,可以更准确地解释健康改进如何通过提高劳动者的劳动供给或劳动效率而促进技术进步和经济增长。2024年,Fumagalli等[55]聚焦2000年后采用面板数据、工具变量等可信因果识别策略的跨国及单一国家研究,系统梳理健康通过经济、行为和人口渠道影响经济增长的机制,揭示多数研究支持健康对经济增长的正向效应,但效应大小和方向受样本国家发展阶段、健康维度及模型设定等因素影响而存在异质性。
综上所述,HLE增长主要通过人口、储蓄、教育、闲暇等中介路径对经济增长产生贡献,具体路径机制见表2。
对于HLE价值的测算,人力资本法、意愿支付法及经济增长模型法在不同领域场景的应用有各自的优点及局限性,具体见表3。
尽管现有主流方法为HLE价值测算奠定了基础,其在理论完善与实践应用中仍面临一系列亟待解决的挑战。
现有方法主要聚焦于死亡风险降低的价值评估。对于失能、慢性病痛苦等非致死健康状态造成的巨大社会经济负担和个体效用损失,尚缺乏成熟、被广泛接受的测算方法,导致该部分健康损失的价值在整体HLE价值评估中被系统性低估。其中,关于VSLY是否应随年龄调整以及如何调整,学界尚未达成共识。传统观点认为VSLY随剩余寿命缩短而下降,但有实证研究发现VSLY随年龄增长而保持稳定甚至增加[67]。2023年,Hammitt[17]通过生存曲线扰动研究进一步揭示了该问题的复杂性,表明价值评估对生存曲线形态变化敏感。该结论为理解年龄效应增加了新的维度的同时,也为对不同年龄群体的干预措施政策评估带来一定挑战。
其次,由于HLE价值涵盖经济、社会与个人多维层面,当前方法各有侧重。人力资本法主要捕捉经济维度,意愿支付法侧重反映个人维度,经济增长模型法试图从宏观层面解析综合影响路径。现有研究多独立应用某类方法或简单并列不同维度结果,缺乏深度的协同分析机制。因此,构建统一协调框架,形成较全面的HLE价值指标,这仍然是测算方法研究的重大挑战之一。
最后,直接将高收入国家估算的VSL值应用于发展中国家会产生偏差[51]。发展中国家的收入水平、风险认知、文化价值观以及社会保障体系完善程度,均会深刻影响个体的风险偏好与支付意愿。忽略这些因素将导致价值测算结果严重失真。意愿支付法中的SP易受问卷设计、文化背景和受访者理解能力的影响。RP则依赖成熟的劳动力或产品市场数据,在缺乏相关市场或数据质量不佳的地区应用受限。经济增长模型法对国家发展阶段、制度环境等宏观条件高度敏感,需结合不同因素开展更具针对性的研究。
在中国语境下的HLE价值测算,除面对以上方法论的局限外,还存在若干独特的结构性困境:首先,人口老龄化呈现“未富先老”和“快速老龄化”的双重特征。我国在中等收入阶段便进入老龄化社会,健康投资与经济承载力之间存在显著张力,使得HLE延长带来的经济回报与财政可持续性面临双重压力[68-69]。其次,城乡二元结构导致健康资源配置及健康收益分布存在显著差异,农村地区医疗基础薄弱、健康产出与投入效率偏低,造成HLE测算中城乡群体间价值评估的系统性偏差[70-71]。同时,劳动力市场中非标准就业比例较高,收入与劳动健康产出的真实数据可得性不足,限制了人力资本法与意愿支付法的准确应用,也增加了模型参数不确定性[72-73]。最后,中国社会保障体系尚在完善过程中,健康投资的回报机制与风险保障体系不同于西方发达国家,居民健康偏好与风险态度受文化因素深刻影响,这使得VSL、VSLY等指标的参数转化及校准需进行本土化修正[74-75]。
综合前文分析可知,HLE的经济价值测算在卫生经济学研究与健康政策决策中具有重要应用潜力。本文在系统梳理主流测算方法的基础上,对其理论逻辑、适用范围与局限性进行了比较评述,并进一步分析了不同方法在中国情境下的应用困境与未来改进方向。
首先,中国在积极应对人口老龄化建设中,面临的难点之一是如何使人群更健康得长寿?精准测算HLE价值,可以用来评估HLE延长对降低医疗照护成本、减轻社会保障体系压力的贡献,能够直观揭示慢性病管理和失能预防等高成本效益干预措施的战略价值,为设计优化“医养结合”服务路径提供量化依据。
其次,HLE价值尚未在我国卫生健康领域得到充分体现。国际上已有探索将HLE指标纳入卫生政策与服务评价体系,如欧洲在医药体系中实践的“基于适应症的差别定价”(Indication-Based Pricing, IBP)[76],尝试将不同疾病下的HLE改善纳入价值评估。未来,我国可探索将HLE价值引入药品谈判、基本公共卫生服务、慢病综合防治及医保支付方式改革等多个环节,建立更全面的疾病与干预特异性价值评估体系,从而更系统地反映健康产出与社会经济价值。
此外,为克服现有方法的局限并推动HLE价值测算方法的深入改进,未来研究可进一步关注:第一,系统研究年龄、文化、社会经济地位等因素对VSL/VSLY的影响机制,开发适应场景需求的测算模型;第二,探索构建HLE价值测算标准化体系,建立中国本土化的、基于实证研究的VSL/VSLY基准值及其调整参数,为政策评估提供可靠的本土化价值参照;第三,探索制定多维度HLE价值的评估指南与报告规范,促进不同研究结果的可比性与整合应用,致力于发展能将个人、经济和社会维度进行科学整合的综合性HLE价值评估框架;第四,未来研究应当开发能够捕捉共病状态及其交互作用对HLE价值影响的复杂模型,以更真实地反映健康干预的综合效益;最后,利用人工智能和大数据分析技术,提升对HLE价值及其影响因素的动态预测能力,探索HLE价值随人口结构、疾病谱、技术进步等因素变化的长期趋势,为前瞻性政策制定提供支撑。
目前,测算HLE价值的相关研究主要集中于发达国家,发展中国家及低收入国家较为欠缺。中国在HLE价值测算领域的研究尚处于起步阶段[77-79],亟需构建本土化的HLE价值测算方法与标准,以及覆盖不同区域、年龄组和疾病类型的测算体系,实现精细化、多维度的HLE价值评估。未来研究可进一步以更系统的理论为指导,将健康资本理论与多层社会生态框架相融合,构建贯通“健康资本—社会制度—宏观经济”多层逻辑的HLE价值分析路径。Grossman健康资本模型及其后续拓展揭示了“个体健康积累—人力资本提升—劳动供给与生产效率—经济增长”的传导链条,为从个体健康行为延伸至宏观经济发展提供了可量化的理论基础[80]。Bronfenbrenner生态系统理论强调健康与福祉受到个体—家庭与社区—社会制度与宏观政策多层结构的共同影响,与HLE“个人—社会—经济”多维价值属性高度契合[81]。在此基础上,未来研究可结合多水平数据及其配套分析框架,将个体健康效用、风险偏好与劳动行为,社区与制度环境差异,以及人口与经济结构等多层信息纳入统一分析体系,更全面地刻画健康改善的价值传导机制及其经济社会效应。国际健康与政策研究领域已广泛采用多水平模型探索健康差异与资源配置[82-83],并在医疗服务利用、健康不平等及健康资本效应等研究中展现出较强的解释力与应用潜力[10,51]。多水平数据与模型的融合应用,有望推动HLE价值评估从指标测算走向机制识别与情境化解释,进一步增强HLE价值测算的层次性、结构性与政策洞见。通过持续深化这些方向的研究,有望构建更加科学、精准以及符合中国实际的HLE价值综合测算指标与方法,为提升国民健康水平、优化健康资源配置和完善健康相关政策提供强有力的循证决策支持。
  • 国家重点研发项目(2022YFC3600800)
  • 四川大学华西公共卫生学院/华西第四医院健康新质生产力研究所课题(HN240202A)
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2025年第52卷第24期
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doi: 10.20043/j.cnki.MPM.202509378
  • 接收时间:2025-09-23
  • 首发时间:2026-11-02
  • 出版时间:2025-12-25
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  • 收稿日期:2025-09-23
基金
国家重点研发项目(2022YFC3600800)
四川大学华西公共卫生学院/华西第四医院健康新质生产力研究所课题(HN240202A)
作者信息
    1.四川大学华西公共卫生学院/四川大学华西第四医院,成都 610041
    2.四川大学健康城市发展研究中心/西部农村卫生发展研究中心
    3.国家卫生健康委统计信息中心

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吴士勇,E-mail:
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https://castjournals.cast.org.cn/joweb/xdyfyx/CN/10.20043/j.cnki.MPM.202509378
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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