The status of sub-health assessment methods for young and middle-aged people from the perspective of family doctor contract promotion
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摘要: 家庭医生签约服务是实现社区全生命周期健康管理的重要工作内容，提升非重点人群家庭医生签约率是目前面临的一大挑战。本文聚焦中青年人群，分别对亚健康状态和亚健康评估方法展开梳理，提出以疲劳为主要症状的疾病前亚健康状态评估或成为促进中青年人群签约的突破口。亚健康状态与慢性疾病的发生发展密不可分，将亚健康状态限定为以疲劳为主要症状的疾病前亚健康状态有助于对中青年人群的亚健康状态开展评估。分析现有亚健康状态评估方法优缺点及中青年亚健康评估研究的不足。当前亚健康状态评估以量表和问卷为主，主观性较强，缺乏客观评价指标，此外相比于传统的统计学建模方法，机器学习算法可以处理大样本量、多维度数据，具有更好的评估效能。针对中青年亚健康评估缺乏客观评估指标及方法，建议纳入多维度客观评估指标，运用集成机器学习方法，构建科学可行的疾病前亚健康状态的评估模型，并以慢性疾病临界指标设定模型阈值。以期通过科学有效的疾病前亚健康状态评估，提高中青年人群主动健康意识，并为其提供个性化健康管理服务，满足其对家庭医生服务的需求，从而提高中青年人群家庭医生签约意愿。Abstract: Family doctor contract service is an important part of whole life-cycle health management in community. Improving the contract rate of family doctors for normal groups is a major challenge at present. This paper focuses on young and middle-aged people, and sorts out the sub-health status and assessment methods respectively, and proposes that the pre-disease sub-health status assessment with fatigue as the main symptom may become a breakthrough for promoting contracts signing in the young and middle-aged population. Sub-health status is closely related to the occurrence and development of chronic diseases. It is helpful to evaluate the sub-health status of young and middle-aged people by limiting it to pre-disease sub-health states with fatigue as the main symptom. Secondly, we comparatively analyzed the advantages and disadvantages of existing sub-health assessment methods and the shortcomings of sub-health assessment for young and middle-aged people. We found that the current sub-health status assessment is mainly based on scales and questionnaires, with strong subjectivity and lack of objective evaluation indicators. In addition, compared with traditional statistical modeling methods, machine learning algorithms can process large sample sizes and multi-dimensional data, which has better evaluation efficiency. In view of the lack of objective evaluation indicators and methods in sub-health assessment in young and middle-aged people, it is recommended that multi-dimensional objective evaluation indicators should be included to integrate machine learning methods to construct a scientifically feasible assessment model of pre-disease sub-health status, as well as to set the model threshold with chronic disease critical indicators. Expecting that the scientific and effective pre-disease sub-health status assessment can improve the active health awareness of young and middle-aged people and provide personalized health management services to meet their needs for family doctor services, thereby increasing their willingness to sign family doctor contracts.
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表 1 亚健康评估方法优缺点分析
Table 1. Analysis of advantages and disadvantages of sub-health assessment methods
方法 优点 缺点 总结 量表/问卷 优点：评价内容全面；缺点：主观性评价 症状自评量表 在心理学和临床医学广泛应用 只针对心理评估 CMI 较成熟的健康调查工具 条目繁多 亚健康状态评价问卷 以慢性应激为特点 结构效度有待提高 亚健康状态中医症候调查问卷 涵盖范围较广、内容较全面 对中医临床知识要求较高 Delphi法亚健康评价量表 有一定代表性、专家权威性 有效性待进一步探讨 亚健康中医基本症候特征调查问卷 涵盖范围较广、内容较全面 结构和内容效度难以控制 SHMS V1.0、中国人亚健康状态评估表-3 得到全国多省份人群验证 以自我感受评分为主，条目繁多 亚健康状态中医症候调查表 信度、效度较好 躯体症状对女性的内容效度较低 亚健康状态自评量表 以自我感受评分为主 青少年亚健康多维评定量表 限定青少年人群 其他自制量表 以自我感受评分为主 单一/少数生化指标组合评定 优点：快速简便；缺点：指标单一，评价片面 布氏显微镜诊断仪、多功能超高倍显微分析技术、多媒体显微诊断 “一滴血”检测方便快速 检测维度单一 蛋白质谱诊断 灵敏度高 彩色超声心动图、脉搏信号、脑电图超慢涨落图 方便、快捷、无创 指标解读复杂、对解读者中医临床知识要求高，结果判定待进一步证实 福贝斯远程健康检测系统、热扫描成像技术、量子共振检测仪、人体能量(亚健康)监测仪 无创、快捷、安全 优点：快速、无创、安全；缺点：需进一步研究证实 传统统计学方法 优点：线性小样本分类性能好；缺点：不适用于大样本、多维度、非线性数据 逻辑回归模型 小样本预测准确率较高 易受混杂因素影响 偏最小二乘回归分析 适于高度线性的多因变量 不适用于非线性数据 零频数过多的负二项回归模型 适于零频数过多的数据 仅适用于零频数过多的数据 决策树 适于多指标、多维度复杂数据 易过度拟合 灰色聚类 适于信息不完全的数据、对相似数据聚类 适于指数增长的预测 结构方程模型 可处理潜变量、允许观测变量和潜变量的测量误差 不能验证变量间的因果关系、对样本容量和数据要求较高 可拓关联法 模糊综合评价法 可对个体亚健康类别独立判断 需进一步在更多人群中验证 量化风险指标 当数据较多时评价准确性降低 机器学习方法 优点：适于大样本、高纬度、复杂数据(除支持向量机、自主学习更正；缺点：建模较复杂 支持向量机 在小样本数据模型中表现优异 不适用于大样本数据 随机森林 泛化能力强，运行高效 对属性值较多的特征具有倾斜效应、忽略属性之间的相关性 极限学习机 泛化性能较好、适用于非线性函数、学习速度更快 可能过度拟合、可控性能差、模型易受离群数据的影响 
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