青少年多维度健康结局综合评价方法的研究进展

Research progress in comprehensive evaluation methods for multidimensional health outcomes in adolescents

摘要

青春期是身心快速发展的关键阶段,该时期健康状况对个体生命历程健康结局具有深远影响。随着健康内涵的不断拓展,青少年健康评价也趋于身体、心理、行为等多维度健康结局的综合评价。本文系统梳理了国内外有关青少年健康评价指标体系的研究进展,重点阐述用于多维度健康结局综合评价的方法研究与应用进展,包括聚类分析、潜在类别与潜在剖面分析等用于识别健康模式(分类变量)的方法,和主成分分析、因子分析、客观赋权法、层次分析法、模糊综合评价、逼近理想解排序法以及秩和比法等用于构建健康综合评分(连续变量)的方法；以及各种方法的各自特点,可从不同角度揭示青少年多维度健康结局间的潜在结构与相互作用关系以及其健康综合水平,但同时也显示存在着评价结果稳定性不足、缺乏标准化流程、部分方法缺乏对应统计程序等问题。此外,对青少年多维度健康结局综合评价方法的未来发展方向如优化算法鲁棒性、加强多源数据整合、发展动态评估模型等方面提出了相应建议,以期为构建更加具有卫生经济学意义的精准干预相关措施和制定健康政策等提供新思路,助力实现“健康中国2030”全人群、全生命周期健康管理目标。

Abstract

Adolescence is a critical period of rapid physical and mental development, during which health status profoundly affects lifelong health outcomes. With the continuous expansion of the concept of health, adolescent health assessment has increasingly shifted toward comprehensive evaluation of multidimensional health outcomes. This paper systematically reviews the research progress in adolescent health evaluation indicator systems at home and abroad. It focuses on methods for comprehensive evaluation of multidimensional health outcomes, including approaches for identifying health patterns such as cluster analysis and latent class or profile analysis, as well as methods for constructing composite health scores, including principal component analysis, factor analysis, objective weighting methods, analytic hierarchy process, fuzzy comprehensive evaluation, technique for order preference by similarity to ideal solution (TOPSIS), and rank sum ratio (RSR) methods. Each of these methods has unique strengths and can reveal the underlying structure and overall health status of adolescents from different perspectives. However, challenges remain, such as lack of standardized procedures and absence of corresponding statistical software for some methods. Future work should focus on improving algorithm robustness, enhancing integration of multi-source data, and developing dynamic evaluation models to support precise interventions and health policy formulation, helping realize the goal of “Healthy China 2030” regarding health management for the entire population and across the full life cycle.

关键词

青少年 / 多维健康结局 / 综合评价方法 / 健康模式识别 / 健康评分构建

Key words

adolescent / multidimensional health outcome / comprehensive evaluation method / health pattern identification / construction of health scores

参考文献

[1] World Health Organization. Adolescent health[EB/OL]. (2024—11—26) [2025—04—07]. https://www.who.int/health—topics/adolescent—health/#tab=tab_1.

[2] SAWYER S M, AFIFI R A, BEARINGER L H, et al. Adolescence: a foundation for future health[J]. Lancet, 2012, 379(9826): 1630-1640.

[3] WEIHRAUCH—BLÜHER S, SCHWARZ P, KLUSMANN J H. Childhood obesity: increased risk for cardiometabolic disease and cancer in adulthood[J]. Metabolism, 2019, 92: 147-152.

[4] BUNDY D A P, DE SILVA N, HORTON S, et al. Investment in child and adolescent health and development: key messages from disease control priorities, 3rd edition[J]. Lancet, 2018, 391(10121): 687-699.

[5] PATTON G C, SAWYER S M, SANTELLI J S, et al. Our future: a Lancet Commission on adolescent health and wellbeing[J]. Lancet, 2016, 387(10036): 2423-2478.

[6] RODGERS R F, SLATER A, GORDON C S, et al. A biopsychosocial model of social media use and body image concerns, disordered eating, and muscle—building behaviors among adolescent girls and boys[J]. J Youth Adolesc, 2020, 49(2): 399-409.

[7] NWOSU E, MAKWAMBENI P, HERSTAD S H, et al. Longitudinal relationship between adolescents' mental health, energy balance—related behavior, and anthropometric changes[J]. Obes Rev, 2023, 24(S2): e13629.

[8] 颜艳, 王彤. 医学统计学[M]. 北京: 人民卫生出版社, 2020.

[9] YAN Y, WANG T. Medical statistics[M]. Beijing: People's Medical Publishing House, 2020.

[10] RAVENS—SIEBERER U, GOSCH A, ABEL T, et al. Quality of life in children and adolescents: a European public health perspective[J]. Soz Praventivmed, 2001, 46(5): 294-302.

[11] RAVENS—SIEBERER U, HERDMAN M, DEVINE J, et al. The European KIDSCREEN approach to measure quality of life and well—being in children: development, current application, and future advances[J]. Qual Life Res, 2014, 23(3): 791-803.

[12] RAVENS—SIEBERER U, GOSCH A, RAJMIL L, et al. The KIDSCREEN—52 quality of life measure for children and adolescents: psychometric results from a cross—cultural survey in 13 European countries[J]. Value in Health, 2008, 11(4): 645-658.

[13] BUDLER L C, PAJNKIHAR M, RAVENS—SIEBERER U, et al. The KIDSCREEN—27 scale: translation and validation study of the Slovenian version[J]. Health Qual Life Outcomes, 2022, 20(1): 67.

[14] RAVENS SIEBERER U, ERHART M, RAJMIL L, et al. Reliability, construct and criterion validity of the KIDSCREEN—10 score: a short measure for children and adolescents' well—being and health—related quality of life[J]. Qual Life Res, 2010, 19(10): 1487-1500.

[15] CDC National Center for Health Statistics [EB/OL]. National Survey of Children's Health[EB/OL]. (2022—02—22)[2026—05—08]. https://archive.cdc.gov/www_cdc_gov/nchs/slaits/nsch.htm.

[16] VARNI J W, SEID M, RODE C A. The PedsQL: measurement model for the pediatric quality of life inventory[J]. Med Care, 1999, 37(2): 126-139.

[17] IRWIN D E, STUCKY B D, THISSEN D, et al. Sampling plan and patient characteristics of the PROMIS pediatrics large—scale survey[J]. Qual Life Res, 2010, 19(4): 585-594.

[18] 秦文哲. 山东省青少年健康状况综合评价与时空分析研究———基于2014—2018年面板数据分析[D]. 济南: 山东大学, 2019.

[19] QIN W Z. Comprehensive evaluation and spatiotemporal analysis study on adolescents health of Shandong province: based on panel data analysis[D]. Jinan: Shandong University, 2019.

[20] 张新宇, 徐慧琼, 王陈芳, 等 . 熵权逼近理想解排序法综合评价安徽省四地区学生体质健康[J]. 中华预防医学杂志, 2023, 57(7): 997-1003.

[21] ZHANG X Y, XU H Q, WANG C F, et al. Application of entropy weight TOPSIS comprehensive method in the evaluation of students' physical health level[J]. Chin J Prev Med, 2023, 57(7): 997-1003.

[22] 刘励. 儿童青少年体质健康的综合评价及影响因素研究[D]. 武汉: 华中科技大学, 2009.

[23] LIU L. A study on synthetical evaluation of children and adolescents health and its influencing factors[D]. Wuhan: Huazhong University of Science and Technology, 2009.

[24] 中华人民共和国国家卫生健康委员会. 儿童青少年发育水平的综合评价[EB/OL]. (2015—04—07) [2025—06—05]. https://www.nhc.gov.cn/wjw/pqt/201504/3661756c241b46329dbc6ad73eba0bd1.shtml.

[25] XU R B, SONG Y, HU P J, et al. Towards comprehensive national surveillance for adolescent health in China: priority indicators and current data gaps[J]. J Adolesc Health, 2020, 67(5): S14-S23.

[26] IMRAN SHAFI M, CHAUDHRY M, MONTERO E C, et al. A review of approaches for rapid data clustering: challenges, opportunities, and future directions[J]. IEEE Access, 2024, 12: 138086-138120.

[27] OTTEVAERE C, HUYBRECHTS I, BENSER J, et al. Clustering patterns of physical activity, sedentary and dietary behavior among European adolescents: the HELENA study[J]. BMC Public Health, 2011, 11: 328.

[28] KUNDU L R, AL MASUD A, ISLAM Z, et al. Clustering of health risk behaviors among school—going adolescents in Mymensingh District, Bangladesh[J]. BMC Public Health, 2023, 23(1): 1850.

[29] ESTER M, KRIEGEL H P, SANDER J, et al, A density—based algorithm for discovering clusters in large spatial databases with noise[C]// Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Portland, Oregon: AAAI Press, 2009: 836-841.

[30] NICOLET A, ASSOULINE D, LE POGAM M A, et al. Exploring patient multimorbidity and complexity using health insurance claims data: a cluster analysis approach[J]. JMIR Med Inform, 2022, 10(4): e34274.

[31] ARAVINDAKSHAN M R, MAITY S K, PAUL A, et al. Distinct pathoclinical clusters among patients with uncontrolled type 2 diabetes: results from a prospective study in rural India[J]. BMJ Open Diabetes Res Care, 2022, 10(1): e002654.

[32] SCRUCCA L, FOP M, MURPHY T B, et al. Mclust 5: clustering, classification and density estimation using Gaussian finite mixture models[J]. R J, 2016, 8(1): 289-317.

[33] MUN E Y, WINDLE M, SCHAINKER L M. A model—based cluster analysis approach to adolescent problem behaviors and young adult outcomes[J]. Dev Psychopathol, 2008, 20(1): 291-318.

[34] HAHSLER M, PIEKENBROCK M, DORAN D. Dbscan: fast density—based clustering with R[J]. J Stat Softw, 2019, 91(1): 1-30.

[35] 赵喜迎, 江宇, 刘鹏. 基于聚类分析和BP人工神经网络的中学生体质健康综合评价模型研究[J]. 体育科技, 2022, 43(6): 33-36.

[36] ZHAO X Y, JIANG Y, LIU P. A comprehensive evaluation model of secondary school students' physical health based on cluster analysis and back propagation (BP) artificial neural network[J]. Sport Science and Technology, 2022, 43(6): 33-36.

[37] TSOI K K F, CHAN N B, YIU K K L, et al. Machine learning clustering for blood pressure variability applied to systolic blood pressure intervention trial (SPRINT) and the Hong Kong community cohort[J]. Hypertension, 2020, 76(2): 569-576.

[38] TEIN J Y, COXE S, CHAM H. Statistical power to detect the correct number of classes in latent profile analysis[J]. Struct Equ Modeling, 2013, 20(4): 640-657.

[39] 任高跃, 詹雨欣, 张瑾, 等 . 抑郁障碍青少年健康危险行为潜在类别与童年期虐待类型的关联[J]. 军事护理, 2025, 42(5): 63-67.

[40] REN G Y, ZHAN Y X, ZHANG J, et al. Association between latent classes of health risk behaviors and childhood maltreatment types in adolescents with depressive disorder[J]. Mil Nurs, 2025, 42(5): 63-67.

[41] LIU F, YANG D, LIU Y G, et al. Use of latent profile analysis and K—means clustering to identify student anxiety profiles[J]. BMC Psychiatry, 2022, 22(1): 12.

[42] LI X, LI J T, YE S, et al. Mental health profiles and correlates among Chinese adolescents: a latent profile analysis[J]. Public Health, 2026, 251: 106121.

[43] ESSAU C A, DE LA TORRE—LUQUE A. Comorbidity profile of mental disorders among adolescents: a latent class analysis[J]. Psychiatry Res, 2019, 278: 228-234.

[44] LINZER D A, LEWIS J B. poLCA: an R package for polytomous variable latent class analysis[J]. J Stat Softw, 2011, 42(10): 1-29.

[45] JOSHUA M R, PATRICK N B, DANIEL J A, et al. tidyLPA: an R package to easily carry out latent profile analysis(LPA) using open—source or commercial software[J]. J Open Source Softw, 2018, 3: 978.

[46] PETERSON M D, LIU D, IGLAYREGER H B, et al. Principal component analysis reveals gender—specific predictors of cardiometabolic risk in 6th graders[J]. Cardiovasc Diabetol, 2012, 11: 146.

[47] LJUBICIC M L, MADSEN A, JUUL A, et al. The application of principal component analysis on clinical and biochemical parameters exemplified in children with congenital adrenal hyperplasia[J]. Front Endocrinol (Lausanne), 2021, 12: 652888.

[48] 李华, 李加鹏, 陈海春. 福建省16～18岁听障青少年体质健康评价模型构建研究[J]. 泉州师范学院学报, 2021, 39(4): 36-43.

[49] LI H, LI J P, CHEN H C. A study on the construction of physical health evaluation model for hearing impaired adolescents aged 16—18 in Fujian province[J]. Journal of Quanzhou Normal University, 2021, 39(4): 36-43.

[50] ZHU Y X, TIAN D Z, YAN F. Effectiveness of entropy weight method in decision—making[J]. Math Probl Eng, 2020, 2020(1): 3564835.

[51] DIAKOULAKI D, MAVROTAS G, PAPAYANNAKIS L. Determining objective weights in multiple criteria problems: the critic method[J]. Comput Oper Res, 1995, 22(7): 763-770.

[52] 程嘉星. 基于层次分析法—熵权法的医疗设备指标优化与评价[J]. 中国医疗设备, 2023, 38(12): 137-143.

[53] CHENG J X. Optimization and evaluation of medical equipment indicator based on AHP and entropy weight method[J]. China Med Devices, 2023, 38(12): 137-143.

[54] WU M X, ZHANG Z, YAN W J, et al. A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement[J]. PLoS One, 2022, 17(1): e0262261.

[55] ZAVADSKAS E K, PODVEZKO V. Integrated determination of objective criteria weights in MCDM[J]. Int J Inf Technol Decis Mak, 2016, 15(2): 267-283.

[56] NAJAFI A, NEMATI A, ASHRAFZADEH M, et al. Multiple—criteria decision making, feature selection, and deep learning: a golden triangle for heart disease identification[J]. Eng Appl Artif Intell, 2023, 125: 106662.

[57] SAATY T L. How to make a decision: the analytic hierarchy process[J]. Eur J Oper Res, 1990, 48(1): 9-26.

[58] ALHARBE N R. Soft computing analysis of the factors associated with stress, anxiety, and depression[J]. BMC Public Health, 2025, 25(1): 1415.

[59] 徐慧琼. 青少年简明健康指数的建立与实证研究[D]. 合肥: 安徽医科大学, 2023.

[60] XU H Q. The establishment of concise health index for adolescents: an empirical study[D]. Hefei: Anhui Medical University, 2023.

[61] 吴汉荣, 卢珊. 模糊数学法综合评价中学生体质与健康状况的应用[J]. 中国学校卫生, 2005, 26(3): 177-178.

[62] WU H R, LU S. Application of fuzzy mathematics method in comprehensive evaluation of physical fitness and health status of middle school students[J]. Chinese Journal of School Health, 2005, 26(3): 177-178.

[63] WANG T. Research on fuzzy comprehensive evaluation index system of mental health education for college students[J]. J Healthc Eng, 2022, 2022: 7106926.

[64] 刘毅, 陈阳阳, 杨磊明, 等 . 基于模糊综合评价法的高校学生体质健康测试结果分析[J]. 体育科技, 2017, 38(3): 142-144.

[65] LIU Y, CHEN Y Y, YANG L M, et al. Analysis of college students' constitution and health test results based on fuzzy synthetic evaluation method[J]. Sport Science and Technology, 2017, 38(3): 142-144.

[66] 黄玉荣. 基于模糊综合评价的眉山市中学生体质健康分析研究[D]. 成都: 成都体育学院, 2024.

[67] HUANG Y R. Analysis of physical health of middle school students in Meishan City based on fuzzy comprehensive evaluation[D]. Chengdu: Chengdu Sport University, 2024.

[68] LIMA J F, PATIÑO LEÓN A, ORELLANA M, et al. Evaluating the impact of membership functions and defuzzification methods in a fuzzy system: case of air quality levels[J]. Applied Sciences, 1934, 15(4): 1934.

[69] CHAKRABORTY S. TOPSIS and Modified TOPSIS: a comparative analysis[J]. Decision Analytics Journal, 2022, 2: 100021.

[70] IBRAHIM I, MANSOR N H, BIDIN J. Factors affecting mental illness and social stress in students using fuzzy TOPSIS[J]. Journal of computing research and innovation, 2022, 7(2): 88-100.

[71] XIANG L, YAMADA M, FENG W, et al. Spatial variations and influencing factors of cumulative health deficit index of elderly in China[J]. J Health Popul Nutr, 2023, 42(1): 66.

[72] 田凤调. 秩和比法及其应用[J]. 中国医师杂志, 2002, 4(2): 115-119.

[73] TIAN F D. Rank sum radio and its application[J]. Journal of Chinese Physician, 2002, 4(2): 115-119.

[74] 卜清清. 基于熵权TOPSIS法结合 RSR 法的我国居民健康水平综合评价[J]. 统计学应用, 2024, 13(1): 133-140.

[75] BU Q Q. Comprehensive evaluation on the health status of residents of 31 provinces in mainland china with entropy weight TOPSIS and RSR methods[J]. Stat Appl, 2024, 13(1): 133-140.

基金资助

国家自然科学基金资助项目(82103867)

中央高校基本科研业务费专项资金资助项目(xxj032025066)

陕西省重点研发计划项目(2024SF-YBXM-310)

陕西省创新能力支撑计划项目(2023-CX-PT-47)