Sleep Health: A New Frontier for Public Health

Ding Zou^1,#, Shiwei Liu²

1 Center for Sleep and Vigilance Disorders, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden;

2 Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.

^# Corresponding author: Ding Zou, zou.ding@lungall.gu.se.

Sleep is a fundamental biological necessity that remains underrecognized in global health policy. This perspective argues that sleep health constitutes a core pillar of public health and a key determinant of health equity. Moving beyond a deficit-based focus on clinical sleep disorders, an integrated sleep health framework highlights the social, environmental, and structural factors shaping population sleep patterns. Incorporating multidimensional sleep metrics into population health surveillance and public health agendas, aligned with the American Heart Association’s Life’s Essential 8 and the calls from the World Sleep Society Global Sleep Health Taskforce, can help address the “sleep gap” in the society. Prioritizing sleep health is therefore essential to reducing chronic disease burden and improving population-level resilience and health outcomes. This perspective paper synthesizes current scientific evidence to argue that sleep health is not merely a matter of individual wellness, but a foundational pillar of public health and a key driver of health equity. By shifting from a narrow emphasis on clinical sleep disorders to a holistic public health framework, sleep health can be more effectively integrated into population-level strategies, strengthening chronic disease prevention and management.

睡眠健康：公共卫生新前沿

邹丁^1,#，刘世炜²

1 睡眠和觉醒障碍中心，临床医学系，沙卡林斯卡医学院，哥德堡大学，哥德堡，瑞典；

2 中国疾病预防控制中心（中国预防医学科学院），北京，中国。

^# 通信作者: 邹丁，zou.ding@lungall.gu.se。

作为一种不可或缺的生理需求，睡眠在全球卫生政策中长期未能获得应有的重视。睡眠健康是公共卫生的核心基石，也是健康公平的关键因素。构建全面的睡眠健康框架，需要将关注点从单纯的临床睡眠障碍这一“缺陷视角”，转向影响人群睡眠模式的社会、环境及结构性因素。将多维度睡眠指标纳入人群健康监测和公共卫生议程，使其与美国心脏协会的“生命八要素”以及世界睡眠学会全球睡眠健康工作组的呼吁相呼应，将有助于缩小社会中的“睡眠鸿沟”。同时，将睡眠健康置于优先地位，对于减轻慢性病负担、提高人群的健康韧性及改善健康结局具有重要意义。本文综合现有科学证据，旨在阐明：睡眠健康不仅关乎个人身心福祉，更是公共卫生的重要基石与推动健康公平的关键力量。将睡眠健康的视角从临床睡眠疾患扩展至公共卫生层面，有助于将睡眠健康有效融入群体战略规划，从而强化慢性病的预防与管理，提高全民的幸福感。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2026.053

Sleep Patterns of Chinese Aged 15 and Above with Different Characteristics — China, 2024

Yingchen Sang¹; Xinying Zeng¹; Ying Liu¹; Youjiao Wang¹; Zhiping Peng²; Shiwei Liu^1,#

1. Tobacco Control Office, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China;

2. NO 984 Hospital of PLA, Beijing, China.

^# Corresponding author: Shiwei Liu, liusw@chinacdc.cn.

Sleep is fundamental to human health, yet comprehensive data characterizing sleep patterns across China's diverse population remain scarce. This national study systematically assessed sleep behaviors among Chinese residents aged 15 years and older through a population-based cross-sectional survey conducted in 2024. Using multi-stage stratified cluster random sampling, trained investigators collected data on sleep duration, sleep latency, bedtime, and wake-up time via standardized questionnaires. Statistical analyses incorporated sampling weights and stratification to ensure population representativeness. The population-weighted mean sleep duration was 7.24 (95% CI: 7.16, 7.32) hours, with a mean bedtime of 22:08 (21:58, 22:18), a mean wake-up time of 6:18 (6:06, 6:30), and a mean sleep latency of 27.45 (26.39, 28.51) minutes. Age-stratified analyses revealed notable sex differences: among adults aged 18–44 years, females slept longer than males (7.66 vs. 7.49 hours), whereas among those aged 45–64 years, females slept less (6.82 vs. 6.97 hours). Rural adolescents slept longer than their urban counterparts (8.39 vs. 8.00 hours), and both education level and occupation independently influenced sleep patterns. Together, these findings reveal meaningful demographic disparities in sleep behavior across China and establish an essential evidence base for developing targeted public health strategies and sleep health promotion initiatives for specific subpopulations.

中国15岁及以上居民睡眠模式特征研究 — 中国，2024年

桑莹宸¹，曾新颖¹，刘影¹，王友娇¹，彭志平²，刘世炜^1,#

1. 控烟办公室，中国疾病预防控制中心（中国预防医学科学院），北京，中国；

2. 中国人民解放军勤联保障部队第九八四医院，北京，中国。

^# 通信作者：刘世炜，liusw@chinacdc.cn。

睡眠健康已成为重要的公共卫生议题，但国内尚缺乏涵盖不同人口学特征群体睡眠模式的全国代表性调查数据。本研究基于2024年全国人口横断面调查，系统评估了我国15岁及以上居民的睡眠模式。研究采用多阶段分层整群随机抽样，通过标准化问卷收集受访者的睡眠时长、入睡潜伏期、就寝及起床时间。统计分析引入了复杂抽样权重以确保人群代表性，并运用分层分析评估不同人口学特征群体的睡眠模式。结果显示，2024年我国15岁及以上居民加权平均睡眠时长为7.24（95% CI：7.16, 7.32）小时。平均就寝与起床时间分别为22:08（21:58, 22:18）和6:18（6:06, 6:30），平均入睡潜伏期为27.45（26.39, 28.51）分钟。年龄分层分析显示，在18–44岁人群中，女性睡眠时间长于男性[7.66 (7.59, 7.73)小时 vs. 7.49 (7.41, 7.57)小时]；而在45–64岁人群中，女性睡眠时间较短[6.82 (6.72, 6.92)小时 vs. 6.97 (6.90, 7.04)小时]。农村青少年睡眠时间长于城市青少年[8.39 (8.14, 8.64)小时 vs. 8.00 (7.78, 8.22)小时]。此外，不同受教育程度与职业人群的与睡眠时长及节律存在显著差异。本研究揭示了中国不同人口学特征群体在睡眠模式上的分布差异，为制定公共卫生策略及特定人群的睡眠健康促进提供了坚实的实证依据。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2026.054

Sleep Quality and the Influencing Factors in Older Adults Aged 65 Years and Above — 6 PLADs, China, 2025

Xin Gao¹; Jinglei Wang¹; Xiaojie Li²; Xin Zheng¹; Xiaolei Zhu¹; Shiwei Liu³; Jianqiang Lai^1,#
1. Office of NCD and Ageing Health Management, Chinese Center for Disease Control and Prevention & Chinese Academy of PreventiveMedicine, Beijing, China;

2. Office of Science and Technology, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China;

3. Tobacco Control Office, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.

^# Corresponding authors: Jianqiang Lai, laijq@chinacdc.cn.

Sleep quality among older adults critically influences overall health, yet research on this topic remains limited in the Chinese context. This study analyzed data from the 2025 follow-up of the Healthy Aging and Elderly Longevity Survey (HAELS), spanning six provincial-level administrative divisions in China, to evaluate sleep quality among older adults. Using chi-square tests for subgroup comparisons and multivariable logistic regression to identify factors associated with poor sleep quality, the study enrolled 4,631 participants. The prevalence of poor sleep quality was alarmingly high at 48.39% (95% CI: 46.95%, 49.83%). Independent risk factors included female sex (OR=1.45, 95% CI: 1.26, 1.68), rural residency (OR=1.33, 95% CI: 1.16, 1.52), hypertension (OR=1.24, 95% CI: 1.10, 1.40), chronic digestive system diseases (OR=1.85, 95% CI: 1.55, 2.20), chronic urinary system diseases (OR=1.44, 95% CI: 1.19, 1.74), and depressive symptoms (OR=2.35, 95% CI: 2.03, 2.72). Among the multidimensional sleep problems assessed, inadequate total sleep duration had the highest detection rate at 41.44% (95% CI: 40.01%, 42.87%), followed by prolonged sleep onset latency at 35.95% (95% CI: 34.57%, 37.35%) and reduced sleep efficiency at 30.79% (95% CI: 29.46%, 32.14%). These findings underscore the high prevalence of poor sleep quality among older adults in China and reveal significant disparities across demographic and health subgroups. Enhanced screening and targeted intervention strategies are urgently needed for high-risk groups, including women, rural residents, and older adults with depression or chronic diseases.

65岁及以上老年人睡眠质量及其影响因素分析 — 6省份，中国， 2025年

高欣¹，王静雷¹，李晓杰²，郑新¹，朱晓磊¹，刘世炜³，赖建强^1,#

1. 慢病和老龄健康管理处，中国疾病预防控制中心（中国预防医学科学院），北京，中国；

2. 科技处，中国疾病预防控制中心（中国预防医学科学院），北京，中国；

3. 控烟办公室，中国疾病预防控制中心（中国预防医学科学院），北京，中国。

^# 通信作者：赖建强，laijq@chinacdc.cn。

老年人睡眠质量是健康的重要影响因素，然而，中国关于该领域的研究仍然有限。本研究利用涵盖中国六个省级行政区的健康老龄化纵向调查（HAELS）随访数据（2025年），评估老年人的睡眠质量。研究采用卡方检验进行亚组比较，并采用多变量逻辑回归分析来确定与睡眠质量差相关的因素，共纳入4,631名参与者。研究结果显示，睡眠质量差的流行率高达48.39%（95% CI：46.95%，49.83%）。独立危险因素包括女性（OR=1.45，95% CI：1.26，1.68）、农村居住地（OR=1.33，95% CI：1.16，1.52）、高血压（OR=1.24，95% CI：1.10，1.40）、慢性消化系统疾病（OR=1.85，95% CI：1.55，2.20）、慢性泌尿系统疾病（OR=1.44，95% CI：1.19，1.74）以及抑郁症状（OR=2.35，95% CI：2.03，2.72）。在所评估的多维睡眠问题中，总睡眠时间问题的检出率最高，为41.44%（95% CI：40.01%，42.87%），其次是入睡潜伏期（35.95%，95% CI：34.57%，37.35%），以及睡眠效率（30.79%，95% CI：29.46%，32.14%）。本研究发现了在中国老年人中睡眠质量差的高流行率，及不同人口学和健康亚组之间的显著差异。亟需针对高危人群，包括女性、农村居民以及患有抑郁症或慢性病的老年人，加强筛查和干预。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2026.055

Stage-Specific Lifestyle Effects on the Dynamic Transitions of Metabolic Multimorbidity — Jiangsu Province, China, 2019–2024

Yang Ding^1,&, Xuan Zhu^1,2,&, Wentao Yao^1,3,&, Xinyuan Ge¹, Jiahao Zhang¹, Jing Lu^4,5, Jingyi Fan⁶, Pei Qin^4,5, Chengxiao Yu^4,5,7, Yun Wang⁶, Xuehui Wang^3,8, Qingning Duan⁹, Lihua Cheng^2,10, Chen Zhou^2,10, Qun Zhang⁴, Ci Song^1,3,4, Hongxia Ma^1,#, Hongbing Shen^1,#.

1. Department of Epidemiology, China International Cooperation Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China.

2. Changzhou Medical Center, Nanjing Medical University, Changzhou City, Jiangsu Province, China.

3. Wuxi Medical Center, Nanjing Medical University, Wuxi City, Jiangsu Province, China.

4. Health Management Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing City, Jiangsu Province, China.

5. Department of Health Management, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China.

6. Health Management Center, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou City, Jiangsu Province, China.

7. Taizhou Medical Center, Nanjing Medical University, Taizhou City, Jiangsu Province, China.

8. The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi City, Jiangsu Province, China.

9. The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou City, Jiangsu Province, China.

10. The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou City, Jiangsu Province, China.

^& Joint first authors.

^# Corresponding authors: Hongbin Shen, hbshen@njmu.edu.cn; Hongxia Ma, hongxiama@njmu.edu.cn.

Metabolic multimorbidity is increasingly prevalent in China and closely linked to modifiable lifestyle behaviors, yet the dynamic transitions between metabolic disease states and the stage-specific effects of lifestyle factors remain poorly characterized. We enrolled 9,673 participants from the Health Omics Preventive Examination Program (April 2019–December 2024), each with at least two follow-up visits (median follow-up: 3.07 years; interquartile range: 1.09 years). To evaluate how eight lifestyle factors—smoking, alcohol consumption, physical activity, sleep duration, dietary oil intake, taste preference, and two dietary pattern scores—influence metabolic disease transitions, we applied a Markov multi-state model. At baseline, the prevalence of metabolic multimorbidity was 24.14%, with males substantially more affected than females (40.92% vs. 9.70%; P<0.05). Among single metabolic diseases, hepatic steatosis carried the greatest risk of progressing to multimorbidity. Lifestyle factors demonstrated clear stage-specific associations: smoking (HR=2.44, 95% CI: 1.63, 3.65) and a high-salt diet (HR=2.28, 95% CI: 1.46, 3.55) accelerated disease progression, while regular physical activity (HR=0.58, 95% CI: 0.40, 0.84) reduced the likelihood of transitioning from a single disease to multimorbidity. These associations varied meaningfully by age and sex. Collectively, these findings offer compelling evidence in support of early, stage-specific lifestyle interventions to prevent metabolic multimorbidity in China.

生活方式在代谢性共病动态状态转移中的阶段特异性作用 — 江苏省，中国，2019–2024年

丁杨^1,&，朱炫^1,2,&，姚闻涛^1,3,&，葛心媛¹，张佳浩¹，鲁璟^4,5，范静依⁶，钦佩^4,5，余成啸^4,5,7，王允⁶，王学慧^3,8，段庆宁⁹，程立华^2,10，周辰^2,10，张群⁴，宋词^1,3,4，马红霞^1,#，沈洪兵^1,#

1. 南京医科大学公共卫生学院流行病学系，环境与人类健康中国国际合作中心，全球健康中心，南京市，江苏省，中国；

2. 南京医科大学常州医学中心，常州市，江苏省，中国；

3. 南京医科大学无锡医学中心，无锡市，江苏省，中国；

4. 南京医科大学第一附属医院健康管理中心，南京市，江苏省，中国；

5. 南京医科大学公共卫生学院全球健康中心健康管理学系，南京市，江苏省，中国；

6. 南京医科大学附属苏州医院（苏州市立医院）健康管理中心，南京医科大学姑苏学院，苏州市，江苏省，中国；

7. 南京医科大学泰州医学中心，泰州市，江苏省，中国；

8. 南京医科大学附属无锡人民医院，无锡人民医院，无锡市，江苏省，中国；

9. 南京医科大学附属泰州人民医院，泰州市，江苏省，中国；

10. 南京医科大学附属常州第二人民医院，常州市，江苏省，中国。

^& 共同第一作者。

^# 通信作者：沈洪兵，hbshen@njmu.edu.cn；马红霞，hongxiama@njmu.edu.cn。

代谢性共病在中国人群中的患病率持续上升，并与多种可改变的生活方式因素密切相关。然而，代谢性疾病在不同疾病状态之间的动态转移过程以及生活方式因素在不同疾病阶段中的作用仍缺乏系统研究。本研究纳入来自希望健康队列（Health Omics Preventive Examination Program）2019年4月至2024年12月期间的9,673名研究对象，所有研究对象均至少完成2次随访（中位随访时间为3.07年，四分位距为1.09年）。采用马尔可夫多状态模型评估8种生活方式因素对代谢性疾病状态转移的影响，包括吸烟、饮酒、体力活动、睡眠时长、食用油摄入量、口味偏好以及两种膳食模式评分。结果显示，基线时研究人群代谢性共病患病率为24.14%，且男性显著高于女性（40.92% vs. 9.70%，P<0.05）。与其他单一代谢性疾病相比，脂肪肝进展为代谢性共病的风险更高。生活方式因素对疾病转移呈现阶段特异性影响：吸烟（HR=2.44，95% CI：1.63, 3.65）和高盐饮食（HR=2.28，95% CI：1.46, 3.55）与从未患代谢性疾病直接进展为代谢性共病间存在正向关联，而规律体育锻炼（HR=0.58，95% CI：0.40, 0.84）与由单一疾病向共病状态转移的风险降低有关。上述关联在不同年龄和性别人群中存在差异。本研究结果为在中国人群中制定早期、分阶段的生活方式干预策略以预防代谢性共病提供了重要科学依据。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2026.056

Temporal Trends and Characteristics of Immunization Consultation Hotline Calls — Suzhou City, Jiangsu Province, China, 2018–2024

Yunyan Zhang¹, Jinling Gao¹, Lin Luan¹, Juan Xu¹, Benfeng Zheng¹, Wenyu Wang¹, Na Liu^1,2,#

1. Immunization Program Department, Suzhou Center for Disease Control and Prevention, Suzhou City, Jiangsu Province, China;

2. National Immunization Program, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.

^# Corresponding author: Na Liu, liuna@szcdc.cn.

Vaccination remains a topic of widespread public concern. To ensure that professionals could deliver accurate information to the public directly and efficiently, Suzhou established a dedicated immunization program consultation hotline in 2018. To date, comprehensive, long-term standardized statistical analyses of immunization consultation hotlines in China remain scarce. This study analyzes temporal trends, category characteristics, and shifts in core public concerns regarding immunization consultations in Suzhou from 2018 to 2024, offering evidence to support the optimization of local public health service allocation. Dedicated professionals answered all calls in real time within a designated room (operating hours: 9:00–17:00), and the full content of each call was recorded. Natural language processing (NLP) was applied for text preprocessing, categorical feature definition, frequency counting, and systematic analysis in Microsoft Excel. Descriptive statistics were performed and figures were generated using Python 3.12.1. A total of 76,154 valid records were collected. Annual call volume peaked at 15,365 in 2021 before declining by 80% to 3,025 in 2024. Monthly call volumes were highest between May and September and lowest in January and February. The most common consultation category was Assessment form-related inquiries (24,911; 32.71%), followed by vaccination services (15,387; 20.21%) and vaccination policies (15,026; 19.73%). The most frequently consulted vaccines were the human papillomavirus vaccine (HPV) and the rabies vaccine (RV). The hotline served as a direct communication channel between the public and government, accurately reflecting dynamic shifts in public immunization demands while providing actionable support for immunization program efforts. The evolving pattern of public vaccination concerns demonstrates measurable improvements in the quality and efficiency of immunization program work in Suzhou.

免疫咨询热线电话的时间趋势与特征分析 — 苏州市，江苏省，中国，2018–2024年

张云艳¹，高金玲¹，栾琳¹，徐娟¹，郑本锋¹，王文瑜¹，刘娜^2,1,#

1.苏州市疾病预防控制中心，免疫规划科，苏州市，江苏省，中国；

2.免疫规划中心，中国疾病预防控制中心（中国预防医学科学院），北京，中国。

^# 通信作者：刘娜，liuna@szcdc.cn。

预防接种是全民普遍关注的热点。电话咨询可直接高效地向公众提供专业信息，苏州市自2018年起设立了专门的免疫规划咨询热线。本研究分析了2018年至2024年间苏州免疫规划咨询热线电话的时间趋势、类别特征及核心关注点的变化，为优化地方公共卫生服务资源配置提供依据。热线电话由专业人员在专用房间内接听（工作日: 9:00-17:00），所有通话内容均被记录。采用自然语言处理（NLP）进行文本预处理，定义分类数据特征、统计频率并在Microsoft Excel中进行系统分析。使用Python 3.12.1进行描述性统计并制作图表。共收集到76,154条有效记录。年度呼叫量在2021年达到峰值15,365次，随后在2024年下降80%至3,025次。月度呼叫量在5月至9月期间达到峰值，而1月至2月记录的呼叫量最低。最常见的咨询类型为评估表相关（24,911次，32.71%），其次为疫苗接种服务（15,387次，20.21%）和疫苗接种政策（15,026次，19.73%）。最常咨询的疫苗为人乳头瘤病毒疫苗（HPV）和狂犬病疫苗（RV）。该热线作为政府与公众沟通的渠道，能够准确反映动态的公众免疫需求，为免疫规划工作提供支持。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2026.057

Development and Application of a Metabolic Health Index for the Chinese Population Aged 18 Years and Above

Yujing Zhang^1,&; Xiaoyan Fan^2,&; Tianhao Zhang¹; Junjing Wang³; Wei Wang²; Juntao Kan³; Rong Luo²; Yizhun Zhu⁴; Zhenping Zhao^1#

1. National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China

2. Amway (China) Co., Ltd., Guangzhou City, Guangdong Province, China

3. Nutrilite Health Institute, Shanghai, China.

4. School of Pharmacy, Macau University of Science and Technology, Macau Special Administrative Region, China.

^& Joint first authors.

^# Corresponding author: Zhenping Zhao, zhaozhenping@ncncd.chinacdc.cn.

Metabolic health status plays a central role in the development of a broad spectrum of chronic diseases. Yet existing assessment approaches rely on comparatively complex measurement techniques, which constrains their widespread adoption and limits their utility in population-based metabolic health management. We employed a two-phase approach to construct a metabolic health index. In the first phase, we conducted a Delphi consultation to define and prioritize key metabolic health indicators. In the second phase, we drew on data from the China Chronic Disease and Risk Factor Surveillance, linked to the national mortality surveillance system, and built Cox proportional hazards models to estimate indicator-specific weights. Using these weights, we developed the Metabolic Health Index (MHI) and established a classification system to stratify metabolic health subtypes. We subsequently analyzed MHI score distributions and subtype prevalence by sex, age group, geographic area, and occupation. The Delphi process identified 11 indicators for inclusion in the model: age, smoking, alcohol consumption, body-mass index, waist-to-height ratio, fasting blood glucose, blood pressure, blood lipids, serum uric acid, metabolic-associated fatty liver disease, and family history of metabolic diseases. Applying the classification system, we identified 13 metabolic health subtypes across 7 categories. MHI scores declined with age, and females consistently outscored males across all age groups. Among males, higher educational attainment was paradoxically associated with lower metabolic health levels relative to those with lower educational backgrounds. Across regions and occupational groups alike, the distribution of metabolic health broadly followed an age-related trajectory. By translating routinely available health data into a unified, interpretable metric, the MHI offers a pragmatic pathway for bridging epidemiological evidence and public health action — particularly in resource-limited settings.

18岁以上人群代谢健康指数的构建与应用

张钰婧^1,&; 范笑嫣^2,&; 张天昊¹; 王珺璟³; 王为²; 阚君陶³; 罗蓉²; 朱依谆⁴; 赵振平^1,#

1. 慢性非传染性疾病预防控制中心，中国疾病预防控制中心（中国预防医学科学院），北京，中国

2. 安利（中国）日用品有限公司，广州，广东省，中国

3. 纽崔莱健康研究所，上海，中国

4. 澳门科技大学药学院，澳门特别行政区，中国。

^&共同第一作者。

^# 通信作者：赵振平，zhaozhenping@ncncd.chinacdc.cn。

代谢健康状况在多种慢性疾病的发生发展中起着核心作用。然而，现有的评估方法依赖于较为复杂的测量手段，这限制了其在人群代谢健康管理工作中的推广与应用。本研究通过两个阶段构建代谢健康指数。第一阶段利用德尔菲咨询筛选并确定核心代谢健康指标；第二阶段基于中国慢性病及危险因素监测数据与全国死因监测系统关联，构建Cox比例风险模型估算各指标权重建立代谢健康指数，并进行代谢健康分型。在此基础上，分析不同性别、年龄组、地区和职业人群的代谢健康指数得分及亚型分布。经德尔菲咨询，最终确定11项核心指标：年龄、吸烟、饮酒、体质指数、腰围身高比、空腹血糖、血压、血脂、尿酸、代谢相关脂肪性肝病及代谢性疾病家族史。据此建立代谢健康分型体系7大类共13种亚型。代谢健康指数得分随年龄增长呈下降趋势，且各年龄段女性得分均高于男性。在男性人群中，受教育程度较高者代谢健康水平反而低于受教育程度较低者。不同地区及职业人群的代谢健康分布总体呈现随年龄变化的趋势。代谢健康指数将人群健康数据转化为统一可解读的量化指标，为资源有限地区将流行病学证据转化为公共卫生实践提供了可行方案。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2026.058