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Article(id=1324077862772409012, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077716676424398, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202504468, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1745596800000, receivedDateStr=2025-04-26, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614362662, onlineDateStr=2026-11-02, pubDate=1773072000000, pubDateStr=2026-03-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614362662, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614362662, creator=13701087609, updateTime=1793614362662, updator=13701087609, issue=Issue{id=1324077716676424398, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='5', pageStart='769', pageEnd='960', issueExtLink='null', onlineDate='null', pubDate='1773072000000', pubDateStr='2026-03-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614327830, creator='13701087609', updateTime=1793614418028, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324078095132655884, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077716676424398, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324078095132655885, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077716676424398, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=809, endPage=814, ext={EN=ArticleExt(id=1324077864542405301, articleId=1324077862772409012, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Evaluation of the health benefits of simulated interventions to residential greenness exposure on hypertension prevalence, columnId=1228016570660745413, journalTitle=Modern Preventive Medicine, columnName=Environmental and Occupational Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To quantify the health benefits of simulated interventions to residential greenness exposure on the prevalence of hypertension (HTN), and to provide scientific evidence for the construction of a reasonable and effective healthy city planning system.

Methods

Based on the normalized difference vegetation index (NDVI) exposure levels at the county level in China, population distribution, and NDVI-HTN relative risk data, the Miettinen formula was used to evaluate the degree of reduction in HTN prevalence caused by equal amounts, static, dynamic and modified intervention NDVI.

Results

The number of prevalent HTN cases decreased by 13.99% (13.97%-14.01%), 25.97% (25.92%-26.02%) and 36.00% (35.88%-36.11%)for every 0.10, 0.20, or 0.30 units increase of NDVI exposure in the study population. When NDVI was elevated to the maximum observation value (0.82) or above P75, 20.84% (17.11%-24.58%) and 6.21% (5.82%-6.59%) of HTN cases could be prevented.

Conclusion

All four greenness intervention schemes can reduce the prevalence of HTN. Among them, the equal intervention effect is better. In the future, a multi-dimensional and collaborative hypertension prevention and control strategy could be developed.

, authors=Ke-jun CHEN1, Xin-yu HAN2, Yong-xin YANG1, Xing ZHAO3, Peng WANG1, authorsList=Ke-jun CHEN, Xin-yu HAN, Yong-xin YANG, Xing ZHAO, Peng WANG, authorCompany=null, correspAuthors=Peng WANG, 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=1324077865859416766, articleId=1324077862772409012, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=模拟干预居住地绿色空间暴露对高血压患病率的收益评估, columnId=1228016571273113811, journalTitle=现代预防医学, columnName=环境与职业卫生, runingTitle=null, highlight=null, articleAbstract=
目的

量化模拟干预居住地绿色空间暴露对高血压患病的健康收益，为构建合理有效的健康城市规划体系提供科学证据。

方法

基于中国区县级归一化植被指数(the normalized difference vegetation index,NDVI)暴露水平、人口分布及NDVI-高血压相对风险数据，运用Miettinen公式，评估等量、静态、动态和修正干预NDVI导致高血压患病率降低的程度。

结果

研究人群NDVI暴露每提升0.10、0.20和0.30个单位时，高血压病例数可对应减少13.99%（13.97%~14.01%）、25.97%（25.92%~26.02%）和36.00%（35.88%~36.11%）。将NDVI提升到最大观察值（0.82）或≥P75时，可预防20.84%（17.11%~24.58%）和6.21%（5.82%~6.59%）的高血压患病。

结论

四种绿色空间干预方案均可降低HTN患病率，其中等量干预效应更好，未来可形成多元协同的高血压防控模式。

, authors=陈柯君1, 韩欣雨2, 杨勇新1, 赵星3, 汪鹏1, authorsList=陈柯君, 韩欣雨, 杨勇新, 赵星, 汪鹏, authorCompany=null, correspAuthors=汪鹏, authorNote=

陈柯君（1999—），女，硕士，助理工程师，研究方向：流行病与卫生统计学

, correspAuthorsNote=
汪鹏，E-mail:
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陈柯君（1999—），女，硕士，助理工程师，研究方向：流行病与卫生统计学

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Proceedings of the National Academy of Sciences of the United States of America, 2019, 116(32):15883-15888., articleTitle=Effects of policy-driven hypothetical air pollutant interventions on childhood asthma incidence in southern California, refAbstract=null)], funds=[Fund(id=1324077871672722164, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, awardId=CSTB2022TIAD-KPX0167, language=CN, fundingSource=发热门诊智能化诊疗服务体系研究与应用(CSTB2022TIAD-KPX0167), fundOrder=null, country=null), Fund(id=1324077873400775413, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, awardId=null, language=CN, fundingSource=重庆市技术创新与应用发展专项, fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324077866123657919, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, xref=1., ext=[AuthorCompanyExt(id=1324077866132046528, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, companyId=1324077866123657919, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Center for Medical Big Data and Artificial Intelligence, The First Hospital Affiliated Army Medical University, Chongqing 400038, China), AuthorCompanyExt(id=1324077866144629441, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, companyId=1324077866123657919, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.陆军军医大学第一附属医院医学大数据与人工智能中心，重庆 400038)]), AuthorCompany(id=1324077866232709826, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, xref=2., ext=[AuthorCompanyExt(id=1324077866245292739, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, companyId=1324077866232709826, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.南方医科大学南方医院肾内科)]), AuthorCompany(id=1324077866345956036, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, xref=3., ext=[AuthorCompanyExt(id=1324077866354344645, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, companyId=1324077866345956036, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系)])], figs=[ArticleFig(id=1324077870309573352, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, language=EN, label=Figure 1, caption=Flowchart for estimating the impact of simulated NDVI interventions on hypertension prevalence, figureFileSmall=YWVSsIB6X6yQcspsZk25dQ==, figureFileBig=aXcUH6Hs6si16i3F+AfCXg==, tableContent=null), ArticleFig(id=1324077870389265129, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, language=CN, label=图1, caption=估算模拟干预NDVI对高血压患病影响的流程图, figureFileSmall=YWVSsIB6X6yQcspsZk25dQ==, figureFileBig=aXcUH6Hs6si16i3F+AfCXg==, tableContent=null), ArticleFig(id=1324077870607368938, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, language=EN, label=Table 1, caption=

NDVI and population characteristics by county in China, 2022—2024

, figureFileSmall=null, figureFileBig=null, tableContent=
分布NDVI人口数量
均数0.50481 450
标准差0.16455 628
P10.058 454
P250.40206 914
P500.50375 206
P750.63646 457
P1000.8210 466 625
), ArticleFig(id=1324077870687060715, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, language=CN, label=表1, caption=

2022—2024年全国各区县NDVI和人口数量特征

, figureFileSmall=null, figureFileBig=null, tableContent=
分布NDVI人口数量
均数0.50481 450
标准差0.16455 628
P10.058 454
P250.40206 914
P500.50375 206
P750.63646 457
P1000.8210 466 625
), ArticleFig(id=1324077870783529708, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, language=EN, label=Table 2, caption=

Population-weighted exposure characteristics of NDVI in China, 2022—2024

, figureFileSmall=null, figureFileBig=null, tableContent=
分布NDVI
均数0.51
标准差0.13
P10.05
P250.42
P500.51
P750.61
P1000.82
), ArticleFig(id=1324077870875804397, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, language=CN, label=表2, caption=

2022—2024年全国人口加权的NDVI暴露特征

, figureFileSmall=null, figureFileBig=null, tableContent=
分布NDVI
均数0.51
标准差0.13
P10.05
P250.42
P500.51
P750.61
P1000.82
), ArticleFig(id=1324077870947107566, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, language=EN, label=Table 3, caption=

Exposure-response relationship between NDVI and HTN

, figureFileSmall=null, figureFileBig=null, tableContent=
类型未调整尺度调整尺度
RRu (95%CI)RRc (95%CI)
原始0.91 (0.85～0.98)0.060.95 (0.90～0.99)0.10
转换后0.86 (0.76～0.97)0.100.95 (0.90～0.99)0.10
), ArticleFig(id=1324077871043576559, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, language=CN, label=表3, caption=

NDVI与高血压暴露-反应关系

, figureFileSmall=null, figureFileBig=null, tableContent=
类型未调整尺度调整尺度
RRu (95%CI)RRc (95%CI)
原始0.91 (0.85～0.98)0.060.95 (0.90～0.99)0.10
转换后0.86 (0.76～0.97)0.100.95 (0.90～0.99)0.10
), ArticleFig(id=1324077871119074032, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, language=EN, label=Table 4, caption=

Simulated effects of NDVI interventions on HTN prevalence

, figureFileSmall=null, figureFileBig=null, tableContent=
干预方案转换后RRGIF (%)
估计值 (95%CI)估计值 (95%CI)
调整0.95 (0.90～0.99)
未调整0.86 (0.76～0.97)
等量干预
0.1013.99 (13.97～14.01)
0.1520.22 (20.19～20.25)
0.2025.97 (25.92～26.02)
0.2531.24 (31.16～31.31)
0.3036.00 (35.88～36.11)
静态干预
P10020.84 (17.11～24.58)
P755.61 (5.19～6.03)
P501.14 (0.79～1.48)
P25-3.55 (-3.91～-3.20)
动态干预
P756.21 (5.82～6.59)
P503.11 (2.87～3.36)
P251.18 (1.04～1.31)
修正干预
5%1.26 (1.24～1.28)
10%2.51 (2.47～2.55)
15%3.75 (3.69～3.81)
20%4.98 (4.90～5.06)
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模拟干预NDVI对高血压患病的影响

, figureFileSmall=null, figureFileBig=null, tableContent=
干预方案转换后RRGIF (%)
估计值 (95%CI)估计值 (95%CI)
调整0.95 (0.90～0.99)
未调整0.86 (0.76～0.97)
等量干预
0.1013.99 (13.97～14.01)
0.1520.22 (20.19～20.25)
0.2025.97 (25.92～26.02)
0.2531.24 (31.16～31.31)
0.3036.00 (35.88～36.11)
静态干预
P10020.84 (17.11～24.58)
P755.61 (5.19～6.03)
P501.14 (0.79～1.48)
P25-3.55 (-3.91～-3.20)
动态干预
P756.21 (5.82～6.59)
P503.11 (2.87～3.36)
P251.18 (1.04～1.31)
修正干预
5%1.26 (1.24～1.28)
10%2.51 (2.47～2.55)
15%3.75 (3.69～3.81)
20%4.98 (4.90～5.06)
), ArticleFig(id=1324077871358149362, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, language=EN, label=Table 5, caption=

Simulated effects of NDVI interventions on HTN prevalence in sensitivity analyses

, figureFileSmall=null, figureFileBig=null, tableContent=
干预方案转换后RRGIF (%)
估计值 (95%CI)估计值 (95%CI)
调整0.96 (0.94~0.99)
未调整0.88 (0.86~0.90)
等量干预
0.1011.99 (11.98～12.01)
0.1517.43 (17.40～17.45)
0.2022.50 (22.45～22.54)
0.2527.19 (27.13～27.26)
0.3031.49 (31.38～31.59)
静态干预
P10016.87 (13.74～20.00)
P754.37 (4.04～4.69)
P500.78 (0.51～1.05)
P25-2.95 (-3.23～-2.67)
动态干预
P754.89 (4.58～5.19)
P502.43 (2.24～2.63)
P250.92 (0.82～1.02)
修正干预
5%1.00 (0.99～1.02)
10%2.00 (1.97～2.03)
15%2.99 (2.95～3.04)
20%3.98 (3.92～4.04)
), ArticleFig(id=1324077871488172787, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077862772409012, language=CN, label=表5, caption=

敏感性分析中模拟干预NDVI对高血压患病的影响

, figureFileSmall=null, figureFileBig=null, tableContent=
干预方案转换后RRGIF (%)
估计值 (95%CI)估计值 (95%CI)
调整0.96 (0.94~0.99)
未调整0.88 (0.86~0.90)
等量干预
0.1011.99 (11.98～12.01)
0.1517.43 (17.40～17.45)
0.2022.50 (22.45～22.54)
0.2527.19 (27.13～27.26)
0.3031.49 (31.38～31.59)
静态干预
P10016.87 (13.74～20.00)
P754.37 (4.04～4.69)
P500.78 (0.51～1.05)
P25-2.95 (-3.23～-2.67)
动态干预
P754.89 (4.58～5.19)
P502.43 (2.24～2.63)
P250.92 (0.82～1.02)
修正干预
5%1.00 (0.99～1.02)
10%2.00 (1.97～2.03)
15%2.99 (2.95～3.04)
20%3.98 (3.92～4.04)
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模拟干预居住地绿色空间暴露对高血压患病率的收益评估
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陈柯君 1 , 韩欣雨 2 , 杨勇新 1 , 赵星 3 , 汪鹏 1
现代预防医学 | 环境与职业卫生 2026,53(5): 809-814
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现代预防医学 |环境与职业卫生 2026 , 53 (5) : 809 -814
模拟干预居住地绿色空间暴露对高血压患病率的收益评估
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陈柯君（1999—），女，硕士，助理工程师，研究方向：流行病与卫生统计学

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陈柯君1, 韩欣雨2, 杨勇新1, 赵星3, 汪鹏1
作者信息
  • 1.陆军军医大学第一附属医院医学大数据与人工智能中心，重庆 400038
  • 2.南方医科大学南方医院肾内科
  • 3.四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系
通讯作者:
汪鹏，E-mail:
作者简介:

陈柯君（1999—），女，硕士，助理工程师，研究方向：流行病与卫生统计学

Evaluation of the health benefits of simulated interventions to residential greenness exposure on hypertension prevalence
Ke-jun CHEN1, Xin-yu HAN2, Yong-xin YANG1, Xing ZHAO3, Peng WANG1
Affiliations
  • Center for Medical Big Data and Artificial Intelligence, The First Hospital Affiliated Army Medical University, Chongqing 400038, China
出版时间: 2026-03-10 doi: 10.20043/j.cnki.MPM.202504468
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摘要
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目的

量化模拟干预居住地绿色空间暴露对高血压患病的健康收益，为构建合理有效的健康城市规划体系提供科学证据。

方法

基于中国区县级归一化植被指数(the normalized difference vegetation index,NDVI)暴露水平、人口分布及NDVI-高血压相对风险数据，运用Miettinen公式，评估等量、静态、动态和修正干预NDVI导致高血压患病率降低的程度。

结果

研究人群NDVI暴露每提升0.10、0.20和0.30个单位时，高血压病例数可对应减少13.99%（13.97%~14.01%）、25.97%（25.92%~26.02%）和36.00%（35.88%~36.11%）。将NDVI提升到最大观察值（0.82）或≥P75时，可预防20.84%（17.11%~24.58%）和6.21%（5.82%~6.59%）的高血压患病。

结论

四种绿色空间干预方案均可降低HTN患病率，其中等量干预效应更好，未来可形成多元协同的高血压防控模式。

绿地  /  高血压  /  模拟干预  /  Miettinen公式
Objective

To quantify the health benefits of simulated interventions to residential greenness exposure on the prevalence of hypertension (HTN), and to provide scientific evidence for the construction of a reasonable and effective healthy city planning system.

Methods

Based on the normalized difference vegetation index (NDVI) exposure levels at the county level in China, population distribution, and NDVI-HTN relative risk data, the Miettinen formula was used to evaluate the degree of reduction in HTN prevalence caused by equal amounts, static, dynamic and modified intervention NDVI.

Results

The number of prevalent HTN cases decreased by 13.99% (13.97%-14.01%), 25.97% (25.92%-26.02%) and 36.00% (35.88%-36.11%)for every 0.10, 0.20, or 0.30 units increase of NDVI exposure in the study population. When NDVI was elevated to the maximum observation value (0.82) or above P75, 20.84% (17.11%-24.58%) and 6.21% (5.82%-6.59%) of HTN cases could be prevented.

Conclusion

All four greenness intervention schemes can reduce the prevalence of HTN. Among them, the equal intervention effect is better. In the future, a multi-dimensional and collaborative hypertension prevention and control strategy could be developed.

Greenness  /  Hypertension  /  Simulated intervention  /  Miettinen formula
陈柯君, 韩欣雨, 杨勇新, 赵星, 汪鹏. 模拟干预居住地绿色空间暴露对高血压患病率的收益评估. 现代预防医学, 2026 , 53 (5) : 809 -814 . DOI: 10.20043/j.cnki.MPM.202504468
Ke-jun CHEN, Xin-yu HAN, Yong-xin YANG, Xing ZHAO, Peng WANG. Evaluation of the health benefits of simulated interventions to residential greenness exposure on hypertension prevalence[J]. Modern Preventive Medicine, 2026 , 53 (5) : 809 -814 . DOI: 10.20043/j.cnki.MPM.202504468
正文
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高血压是全球心血管疾病和过早死亡的主要危险因素之一[1]。据估计，全球范围内，2017年因收缩压异常升高而导致的死亡病例高达1 040万例（占总死亡人数的30.4%）[1]。与此同时，由高血压造成的经济损失约占全球整体医疗保健支出的10%[2]。多项全国性调查表明，中国成年人高血压患病率约为27.9%[3]，且逐年上升。面对这一挑战，世界卫生组织（World Health Organization, WHO）将降低高血压患病率作为非传染性疾病防控的重要目标之一，设定了2030年患病率下降33%的目标[4]。鉴于此，采取切实有效的干预措施，以遏制高血压的进一步蔓延，已成为各国公共卫生领域亟待解决的关键问题。
高血压的病因包括遗传、环境和个人行为等因素[5]。其中，居住地绿色空间作为一个重要的生活环境因素，其对高血压的影响受到了极大的关注[6]。有研究发现，居住地绿化可能会降低高血压的患病率[1,7]。但这些研究仅局限于探索绿色环境暴露和高血压的关联性，对改善居住地绿色环境暴露对高血压患病的健康效益的评估不足。本研究基于Miettinen公式，量化模拟干预居住地绿色空间暴露水平对高血压患病率的影响，旨在为健康城市规划提供科学参考，从而显著降低高血压疾病负担。
1　资料与方法
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1.1　研究数据
1.1.1　NDVI暴露
归一化植被指数（the normalized difference vegetation index, NDVI）主要反映植被在红光波段（red band, RED）和可见光波段（near-infrared,NIR）的反射值与土壤背景值的差异[1]。其计算公式为[7]：
本研究利用基于卫星影像的植被指数——NDVI来评估居住地绿色空间暴露[7]。理论上，NDVI的有效取值范围为-1~1,负值表示存在独立的水、云或雪；值在0左右表示贫瘠区域，即岩石、沙子或建筑物；正值表示草地或灌木林。数值越接近1代表植被越茂密[7]。
本研究NDVI暴露数据源自美国国家航天航空局提供的有关中分辨率成像光谱仪卫星监测的公共数据库（MOD13A3）[8]（https://search.earthdata.nasa.gov/search），其空间分辨率为1km。为区分绿地暴露和生活在邻近水域的潜在影响，本文基于原始的栅格数据，将NDVI负值视为缺失值。在此基础上，按照中国区县级行政边界进行求平均数处理得到区县统计口径的逐年NDVI平均值。计算2022—2024年的年平均NDVI水平作为暴露。
1.1.2　人口数量
本研究采用的人口数据来源于官方发布的2020年第七次全国人口普查（简称“七普”）区县级常住人口统计数据（中国经济社会大数据研究平台[9]，https://data.cnki.net/yearbook/）。考虑到七普数据采集时点（2020年11月1日）与研究基准年（2025年）的时间跨度，以及国家统计局公布的近几年间中国人口自然增长率持续走低（年均增长率<0.3‰）的客观现实，认为最新各区县人口规模普查结果对研究期间区域人口分布特征仍具有代表性，因此未对基础人口数据进行动态调整。
1.1.3　NDVI-高血压暴露-反应关系
既往研究表明，NDVI的升高与高血压患病风险降低存在关联。本文的调整和未调整风险比分别来自一项系统综述和一项中国人群研究[7,10]中NDVI分辨率为1 km的结果，参照相关文献，当效应量较小时，使用优势比近似替代相对风险比（risk ratio, RR）[6]。为确保数据尺度的一致性，基于线性暴露-反应关系的理论假设，运用以下公式将原始数据转换为NDVI每增加0.1单位所对应的相对风险值RR及其95%置信区间（confidence interval, CI）[6]：
其中RRoriginal为原始文献的风险比数据，original increment对应原始尺度，converted increment为0.1。经过转换，未调整RR表示为RRu，调整RR为RRc。
根据以上获得的各区县NDVI暴露和人口数据，将任一数据缺失的区县排除，最终纳入2 779个区县的人群参与后续分析。
1.2　统计分析
1.2.1　描述性分析
统计描述各区县NDVI暴露和人口的分布范围、平均水平等。为更准确地展示人口分布对NDVI暴露的影响，计算人口数量加权的NDVI特征值。通过计算，描述转换前后的NDVI与高血压患病风险之间的暴露-反应关系，以符合后续分析的需求。
1.2.2　模拟干预效应评估
1.2.2.1　干预效应衡量指标
由于暴露因素为连续型变量，本文使用人群归因分数（population attributable fraction, PAF）的扩展概念——广义影响分数（generalized impact fraction, GIF）[11]来评估模拟干预效应。该指标定义为将当前的暴露水平改变至某一特定感兴趣的水平时，能够减少的人群中疾病病例的比例[11]。
1.2.2.2　干预方案
本文设置如下四种干预方案：等量、静态、动态和修正干预。（1）等量干预：指同时将研究人群的NDVI暴露水平增加相同值，即同时增加0.10、0.15、0.20、0.25和0.30，若增加后值>1，则取1；（2）静态干预：指将所有观察值控制在某个固定值，即研究人群NDVI观察值的第100、第75、第50和第25百分位数（P100、P75、P50和P25）；（3）动态干预：指将低于界值的观察值提升至界值，大于等于界值的不变，即以研究人群暴露水平的第75、第50、第25百分位数作为界值进行干预；（4）修正干预：指干预后的暴露水平取决于观察值，即将研究人群的NDVI暴露值提升（1-观察值）5%、10%、15%或20%。
1.2.2.3　干预效应估计
人群模拟干预效应方法学中，Miettinen提出的估算公式考虑了暴露因素的人群分布和相对风险[12]，从而更准确地评估了某种暴露因素对于特定健康结果的影响[13]。基于前文所获取的数据，运用如下Miettinen公式对多种干预方案的广义影响分数（GIF）进行估算[13]，：
式中，x为观察值，P(x)表示人群中暴露于x水平的人的比例，即某区县的人口数量/所有区县总人口数量。RRU(x)=RRu(x-cutoff)/10为未调整混杂变量的相对风险比，RRC(x)=RRc(x-cutoff)/10表示调整混杂变量的相对风险比[14]，RRU和RRC来自于前文，cutoff表示相应干预方案的界值。GIF对比了自然情况下和暴露于特定感兴趣水平时人群中的疾病患病率。95% CI是在线性暴露-反应关系的理论假设下[15]，通过1 000次蒙特卡罗模拟估算得出。模拟干预效应估计流程见图1。
1.2.3　敏感性分析
本研究还进行了如下敏感性分析以验证结果的稳定性：改变暴露-反应关系数据来源，选取一项基于中国社区人群研究得到的未调整和调整风险比数据[16]。原始数据为NDVI每0.15个单位增量所对应的风险比，未调整风险比为0.83（0.80~0.86)，调整风险比是0.94（0.91~0.98）。
本研究分析均在R 4.4.2中进行，采用双侧检验，检验水准α=0.05。
2　结果
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2.1　各区县NDVI暴露和人口数量分布情况
各区县2022—2024年平均NDVI均值为0.50（标准差=0.16），数值分布区间为0.05~0.82。超过半数（50%）区县的常住人口规模低于38万人，见表1。经人口数量加权的三年平均NDVI均值提升至0.51（标准差=0.13），P1、P25、P50、P75和P100分别是0.05、0.42、0.51、0.61和0.82，见表2。上述百分位数值已被确定为相应干预方案的划分依据。
2.2　NDVI与高血压暴露-反应关系
未调整协变量模型中，NDVI每增加0.06，高血压患病风险比降低0.912（0.847~0.982），将NDVI尺度转换为0.1时，其对应的RR（95% CI）为0.86（0.76~0.97）。调整协变量模型结果显示，NDVI每增加0.1个单位，患病风险比为0.95（0.90~0.99）。见表3。
2.3　模拟干预NDVI对高血压患病的影响
当研究人群NDVI暴露量分别增加0.10、0.15、0.20、0.25和0.30个单位时，高血压病例数将对应减少13.99%（13.97%～14.01%）、20.22%（20.19%～20.25%）、25.97%（25.92%～26.02%）、31.24%（31.16%～31.31%）和36.00%（35.88%～36.11%）。提升NDVI暴露到人群最大值（0.82）时，高血压患病的潜在可预防比例为20.84%（17.11%～24.58%）。控制NDVI为P75、P50时，对应的GIF为5.61%（5.19%～6.03%）和1.14%（0.79%～1.48%）。而控制NDVI暴露为P25时，可能引发高血压病例数增加3.55%（3.91%～3.20%）。动态干预方案中，以NDVI的P75、P50、P25为调控基准可分别预防6.21%（5.82%～6.59%）、3.11%（2.87%～3.36%）和1.18%（1.04%～1.31%）的高血压发生。修正干预方案进一步揭示，若全体NDVI暴露水平提升（1-观察值）5%，可减少1.26%（1.24%～1.28%）的高血压患病，且干预效应随调控强度增强而递增，如提升比例为20%，GIF为4.98%（4.90%～5.06%）。见表4。
2.4　敏感性分析结果
等量干预方案下，提升NDVI暴露0.10、0.15、0.20、0.25和0.30个单位时分别可预防11.99%（11.98%～12.01%）、17.43%（17.40%～17.45%）、22.50%（22.45%～22.54%）、27.19%（27.13%～27.26%）和31.49%（31.38%～31.59%）的病例，其他各方案GIF值也与主分析结果相似。见表5。
3　讨论
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本研究基于Miettinen公式，估算了模拟干预NDVI水平对高血压患病率的影响。研究结果发现四种干预方案均可使高血压患病率降低，其中等量干预的效果更好，降低病例比例为10%～35%，其他方案可减少5%左右或20%的病例。
在本研究中，对各区县三年平均NDVI以及常住人口分布情况进行了统计，结果表明，我国不同区县之间植被覆盖状况存在显著差异。西部地区NDVI值较低，可能由于这些区域植被覆盖较少，或者面临一些生态环境问题，如土地荒漠化、恶劣气候对植被的破坏等；而NDVI值较高的地区，则可能拥有较好的自然植被覆盖或成功的生态建设成果，例如大面积的森林、绿地等。这种差异不仅反映了区域自然条件的多样性，也可能与各地的经济发展模式、生态保护政策等因素密切相关。此外，不同区县的人口明显分布不均衡，当引入人口数量加权后，三年平均NDVI均值提升至0.51，标准差减小至0.13，各分位数数值也发生了变化，这一变化意味着人口密度与高植被覆盖存在某种正向程度的关联，揭示了绿地的潜在健康效益。在全球城市化进程以及对建筑环境规划需求日益迫切的背景下，绿地所带来的诸多益处已得到广泛报道。如，一项在中国开展的横断面研究表示居住地绿化程度与高血压之间的负向关联[7]。2019年发表的一项针对中国33个社区，24 845名成年人的大规模队列研究[16]发现，在500米缓冲区范围内，NDVI每增加一个四分位数间距，高血压的患病风险降低5%[16]。Jia等人[17]使用1 944名40岁以上的参与者的数据探索绿色和身体活动对血压的协同影响，研究发现，与生活在绿化面积较少地区的参与者（NDVI的第一个四分位数）相比，居住在绿化面积较多地区的研究对象的OR为0.20（0.07～0.37）（40～60岁）和0.65（0.28～0.89）（60岁以上）[17]。台湾的一项纵向队列研究发现，NDVI每增加0.1个单位，患高血压的风险就会降低24%[1]。目前已有若干研究报告了绿色环境对高血压有益影响的潜在机制。研究发现，居住环境的绿化能够通过消除对心脏健康不利的有害环境因素（例如，环境空气污染物和高温）来降低高血压的患病风险[18]。不同地理背景下的近期研究也表明，居住环境的绿化与微生物多样性有关[19-20]，这可能通过产生有益代谢物来改善心脏代谢健康（例如血压水平等）[21]。同时，绿化能够通过减少心理社会压力和促进社会凝聚力与互动来降低包括糖皮质激素、肾上腺素和去甲肾上腺素在内的应激激素的水平[22-23]。这些应激激素已被证实与血压水平的升高有关。此外，绿化还能够调节交感神经/副交感神经的活动并稳定自主神经系统[22,24]，从而降低高血压的风险。可见改善居住地绿化以减少高血压疾病负担具有很大的应用价值。
近年来，越来越多的研究开始关注模拟干预风险因素对人群健康的影响，主要集中于饮酒和空气污染物暴露。基于参数G formula方法的队列研究量化了模拟干预饮酒对肝脂肪变性的影响[25]，结果证明由持续重度饮酒转变为中度饮酒可使脂肪肝指数（fatty liver index, FLI）降低2.280（3.016～1.544），并且红酒可能是减缓肝脂肪变性的更好选择。一项前瞻性队列研究探索了模拟干预PM2.5和O3对哮喘症状的健康效益[26]，该研究估计得到PM2.5减少一分位数对应的GIF为4.38%（1.42%～7.25%），O3对应的GIF为2.86%（0.17%～5.50%）。中国地区O3的疾病负担分析，采用了从队列研究中获得的暴露效应关系来估计臭氧暴露导致的长期心血管疾病死亡率负担[27]，计算了改变O3暴露浓度后相应的PAF。Garcia等人[28]也研究了不同方式干预PM2.5和NO2对哮喘的效应，当NO2满足低于20 ppb（parts per billion）的标准时[28]，估计可使儿童哮喘发病率降低20%（11%～27%）。本研究在采用静态、动态和修正干预三个模拟干预方案基础上，增加了等量干预方案，以提供更多的视角来量化干预效应。在未来的环境健康领域发展工作中，为环境政策制定者提供了切实可行的方向指引，即可采取综合性干预方案，特别聚焦等量干预以更有效地降低疾病负担。例如，在资源允许的情况下，将等量干预作为绿色空间暴露干预方案的首选。合理布局公园的面积、植被种类和分布密度，确保不同区县的居民都能获得相对均等的绿色暴露改善量；当资源有限时，结合其他干预方案，优先针对绿化程度较低的城市进行规划，增加街道绿植、保护周边农田等，以提升城市绿化暴露至合理目标。
本研究存在一定的局限性：第一，研究中所采用的人口数据来源于2021年中国第七次全国人口普查的常住人口统计，未将流动人口因素纳入考量范围；第二，受数据限制，本研究基于既往研究结果进行分析，未直接采用个体层面的数据；第三，研究采用了各区县的环境绿地暴露水平来近似替代个体的暴露水平，这种替代方法可能会引入一定程度的偏差。
综上所述，四种NDVI暴露干预方案均可降低高血压患病率，其中等量干预效果最明显。以上研究结果量化了提升居住地绿色暴露水平对公众心血管健康的积极影响，为制定高效、创新的健康城市规划措施以改善人群健康提供了科学依据和实践方向。
基金
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  • 发热门诊智能化诊疗服务体系研究与应用(CSTB2022TIAD-KPX0167)
  • 重庆市技术创新与应用发展专项
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2026年第53卷第5期
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doi: 10.20043/j.cnki.MPM.202504468
  • 接收时间：2025-04-26
  • 首发时间：2026-11-02
  • 出版时间：2026-03-10
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  • 收稿日期：2025-04-26
基金
发热门诊智能化诊疗服务体系研究与应用(CSTB2022TIAD-KPX0167)
重庆市技术创新与应用发展专项
作者信息
    1.陆军军医大学第一附属医院医学大数据与人工智能中心，重庆 400038
    2.南方医科大学南方医院肾内科
    3.四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系

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