世界卫生组织健康调查资料的结构方程模型

目的 探索2002年世界卫生组织(WHO)健康调查数据中,人群自述健康水平、实际健康水平、社会人口学特征、危险因素之间的潜在关系,及各调查指标对自述健康和自述健康对实际健康水平的影响程度.方法 数据来源于WHO提供的2002年包含印度、巴西、布基纳法索、匈牙利、尼泊尔、俄罗斯、西班牙、突尼斯、越南等9个国家在内的健康调查数据(包括自我评价部分和健康情景部分)(29 971例).本研究利用健康情景资料对自我评价资料进行校正和消除切点位移偏倚后,用自我评价资料构建自述健康水平、实际健康水平、社会人口学特征、危险因素之间关系的结构方程模型.结果 最终结构方程模型:实际健康水平=0.80×自述健康水平+(-0.04)×社会人口学特征+(-0.08)×危险因素(R2=0.66),自述健康水平=(-0.70)×社会人口学特征+0.10×危险因素(R2=0.55);自述健康水平对实际健康水平的标准化总效应达0.80,人口学特征对自述健康水平和实际健康水平的标准化总效应分别为-0.70与-0.60;自述健康水平的16项指标含8个维度,按照对其影响程度由大到小依次为:活动能力、疼痛不适、睡眠情况、认知、情绪、自理能力、视觉能力、交际活动能力.结论 实际健康水平与自述健康水平之间、自述健康水平与社会人口学特征之间均有较强的线性关系,自述健康水平对实际健康水平有较大影响,2002年WHO健康调查所用16项指标在人群健康评价中占有重要的地位.     

世界卫生组织健康调查资料的结构方程模型

Objective Based on the 2002 WHO health survey data, to explore the latent relationship among self-reported health level, the actual level of health, the social demographic characteristics and the risk factors, and to analyze the influence of the various surveillence indicators on self-reported health and the degree that the self-reported health explained the actual level of health.Methods Field tests for various components of the World health survey were conducted in nine countries during 2002, including India, Brazil, Burkina, Hungary, Nepal, Russia, Spain, Tunisia, and Vietnam (29 971 ).The survey questionnaire included a self-assessment component and anchoring vignette component.The self-assessment component data was adjusted and eliminated the affect of "cut-point bias" by using the anchoring vignette component data,and then was used to build the structural equation model on the relationship among selfreported health level, actual health level, social demographic characteristics and the risk factors.Results In the final structural equation model, "the actual level of health" = 0.80 × "the self-reported health level" +( - 0.04) × "the social demographic characteristics" + ( - 0.08 ) × "the risk factors" ( R2 = 0.66 ), and"the self-reported health level" = ( -0.70) × "the social demographic characteristics" +0.10 × "the risk factors" (R2 = 0.55 ).The standardized total effect of self-reported health to the actual level of health was 0.80 ,and that of the social demographic characteristics to the self-reported health and the actual level of health were - 0.70 and - 0.60, respectively.And the 16 items of self-reported health consisted of8 dimensions; and sorted by the power of impact to the actual health level, they were mobility, pain and discomfort, sleep, cognition, feelings, self-care ability, visual capacity and interpersonal activities.Conclusion There were significant linear correlation relationship between the actual level of health and the self-reported health, as well as between the self-reported health and the social demographic characteristics.And the self-reported 16 items used by the 2002 WHO health survey played an important role in the health evaluation of population.     

医学生心理健康影响因素的结构方程模型分析

目的了解医学生心理健康状况,探讨医学生心理健康的影响因素及其潜变量之间的关系。方法采用分层整群抽样的方法,对某医科大学818名医学生心理健康状况的一般情况进行描述性分析,并拟合结构方程模型。结果经结构方程模型的拟合,抑郁、焦虑、人际关系、精神病性和强迫症状的回归系数较高。ξ2对心理卫生的影响最大,ξ6和ξ3次之。影响医学生心理健康的主要因素为身体状况、生活规律、与他人交流情况等。结论结构方程模型不仅能提示各个因素对医学生心理健康存在的影响,而且对各影响因素与心理健康之间的作用机制有了深入的、数理角度上的证实和理解。     

威海市碘缺乏病健康教育调查

威海市地处山东半岛东部,是我国沿海开放城市之一,总人口247万。1995年被确定为轻度碘缺乏病区,1996年全面落实了食盐加碘为主的综合性干预措施。为了解我市碘缺乏病防治知识普及情况,评价健康教育效果,我们于2001年进行了碘缺乏病健康教育现状调查。 一、抽样及方法 以全市地理位置重心点划分东西南北中五个区域,每个区域随机抽取2个镇,每个镇抽取1所小学、5个村。每所学校抽取五年级学生30名,每个村抽取2名20～50岁家庭妇女为调查对象。 采用全国统一编制的碘缺乏病健康教育现况调查问卷及评分标准。分为家庭主妇组和学生组,内容包     

健康危险因素与个体健康间的结构方程模型

目的探索健康危险因素与个体健康间的潜在关系,以及各潜变量对测量指标的影响。方法利用健康风险评估调查问卷调查西安市某三级甲等医院健康体检人群。构建健康危险因素与个体健康间的结构方程模型,解释不同因子间的相互作用及其对总体健康的效应权重。结果健康行为与健康意识之间呈显著正相关。健康行为对躯体健康的直接效用为0.85,健康意识不仅直接作用躯体健康,而且通过对心理健康的影响对躯体健康产生间接效应,总效应达0.78。结论健康行为、健康意识均是可控的健康危险因素,对躯体健康、心理健康产生一定影响。通过改变这些可控因素可以提高人们整体健康水平。     

上海市华坪社区老年人健康状况和健康行为调查

上海市闵行区是一个城乡结合区,区内老年人占总人口的10.00%左右.其华坪社区方圆4.8km2,人口11万,60岁及以上老年人8千多人,占总人口的7.40%.为了解华坪社区老年人患病等健康状况和老年人参加体育锻炼、社会交往等健康行为,我们进行了该项调查研究.     

大学生健康素养核心信息精确概率劝导模型分析结果评价

目的 以精确概率劝导模型对大学生健康素养核心信息进行健康教育实施前评价,为提高大学生健康教育效力提供依据.方法 采用分阶段目的抽样和方便抽样相结合的方法,在北京、昆明、广州、武汉、南京、哈尔滨和西安7个城市各选取4所不同类型非医学高校的1357名大学生进行调查.使用自编信息评价表,对大学生健康素养核心信息进行评价.结果 大学生不同信息得分存在差异.55条核心信息评价得分最高72.24分,最低62.79分.大学生对健康素养核心信息评价的影响因素为性别、个体对健康知识和对健康相关技能的认知.结论 在大学生健康教育中,应考虑大学生性别的影响,并根据大学生对信息的认同度来设置健康教育信息内容.     

北京市石景山区城市老年人健康现状与健康需求调查

目的：了解北京市石景山区城市老年人生活质量、健康状况及生活方式疾病的现状和面临的卫生问题。方法：在区内随机抽取两个居委会对60岁以上老人用调查表进行面对面调查。结果：调查对象现在吸烟率23．6％，较普通居民低（P＜0．05），健康知识知晓率比普通居民高，吸烟率和高脂摄入率低于普通人群，缺少体育锻炼率明显高于普通人群。身身有伤残和功能障碍的占12．6％；慢性病患病率为76．5％，明显高于全国慢病患病率（17％）。高血压患病率为42．7％，明显高于北京市普通居民（P＝0．0000）。健康咨询、健康体检是老年居民最需要的卫生服务。结论：老年人群生活质量、健康状况面临较严重的问题，应立足社会开展全方位老年服务，广泛开展健康教育和健康促进，才能有效地提高老年人群的健康水平。     

基于结构方程模型的高校艾滋病健康教育实施效果研究

目的采用结构方程模型分析在青年学生中利用新媒体开展艾滋病健康教育的效果,为制定有针对性的高校艾滋病防治策略提供科学依据。方法采用分层随机整群抽样方法,抽取烟台市4所高校2 561名学生,随机分为干预组(1 289名)和对照组(1 272名)开展问卷调查,并对干预组开展形式多样的新媒体艾滋病防治健康教育,利用结构方程模型进行数据分析。结果干预后对照组和干预组学生艾滋病防治知识知晓率分别为85.9%和94.0%(χ2=46.67,P <0.01)。结构方程模型拟合较好,修正后拟合指数分别为RMSEA=0.04,GFI=0.97,AGFI=0.96,IFI=0.97,CFI=0.98。新媒体干预对预防信息和预防动机的直接效应分别为0.30和0.65,信息和动机对预防行为的影响主要是通过行为技巧的间接作用,标化的总体效应分别为0.09和0.29,行为技巧对预防行为的总体效应为0.80,是影响艾滋病预防行为的主要因素。结论利用新媒体在青年学生中开展艾滋病健康教育效果显著。应把动机提升作为开展工作的新突破点,通过信息、动机、行为技巧等方面促进青年学生艾滋病预防行为的发生。     

脑卒中患者健康行为结构方程模型构建研究

目的 构建脑卒中患者健康行为结构方程模型,定量分析模型中影响因素对健康行为作用路径及强度。方法 以知信行模式为框架,构建健康行为结构方程模型,采用最大似然法对初始模型进行拟合,拟合度采用RMSEA、GFI、AGFI、IFI、TLI、CFI进行检验;采取路径系数分析健康知识、信念及行为之间作用路径。结果 整体适配度CMIN/DF<3,GFI、IFI、TLI、CFI均>0.90,AGFI>0.80,RMSEA<0.08;健康知识、信念、行为三个潜变量组合信度均>0.80,聚敛效度均>0.50;健康知识对健康信念直接效应0.45,健康信念对健康行为直接效应0.49,健康知识对健康行为直接效应0.31,健康知识对健康行为间接效应0.22,健康知识对健康行为总效应0.53。结论 模型具有较好整体适配度及内在结构适配度;健康知识及信念对健康行为有直接影响,健康知识对健康行为除直接作用外,还通过健康信念中介作用间接影响健康行为。     

结构方程模型在健康行为改变理论中的应用

目的用结构方程模型分析健康教育和健康促进中涉及到的行为问题中潜在变量之间及潜在变量与显在变量之间的数量关系。方法按健康信念模式设计问卷,对武汉市建筑工地务工人员进行调查,用结构方程模型对数据进行拟合和评估,分析艾滋病相关知识、健康信念、行为的关系。结果行为效益认知、严重性认知、人口特征、易感性认知和行为障碍认知对行为影响的总体效应分别为-0.28,0.26,0.23,-0.05和-0.01。研究对象严重性认知与行为效益认知呈正相关(r=0.8),严重性认知与行为障碍认知之间、行为效益认知与行为障碍认知之间的相关系数分别为-0.42和-0.54。结论应用结构方程模型对健康行为改变理论中涉及的行为问题及其影响因素进行量化分析是可行有效的。     

Socioeconomic status and health status: A study of males in the Canada health survey

The relationships between education/occupation/income and health status have been well documented in the international epidemiological and sociological literature for many years, however, specific studies on the subject are scarce in Canada. Even when relationships have been demonstrated, the reasons for these relationships are much debated. This study presents an analysis of the relationship between socioeconomic status (SES) and health status. The study is based on analysis of data from a sample of nearly 2000 male principal income earners from the 1978 Canada Health Survey. Firstly, is there a relationship between an individual's SES and health status in Canada? Secondly, what aspects of SES--education, occupational status, and/or income--are most important? Thirdly, what are the possible explanations of the observed relationship? That is, is it possible to disaggregate the relationship and thereby infer possible causal mechanisms? The findings indicated a direct positive relationship between SES and health status, i.e. the higher an individual's SES, the better that person's health. The major exception to this was the SES/fitness relationship. In this instance, the higher the SES, the lower the level of fitness. Though age was an important control variable as SES, fitness and illness are age related, the findings relating SES to the health measures remained even when age was controlled for. Of the three SES measures, income was consistently the best correlate of health status. Occupational status showed the most inconsistent relationships with health status. The findings supported both the social causation and social selection hypotheses. That is, social position can have an effect on health status (social causation), while health status can affect one's social position (social selection).     

Modeling the determinants of Medicaid home care payments for children with special health care needs: A structural equation model approach

BackgroundThe management of children with special needs can be very challenging and expensive.ObjectiveTo examine direct and indirect cost drivers of home care expenditures for this vulnerable and expensive population.MethodsWe retrospectively assessed secondary data on children, ages 4–20, receiving Medicaid Personal Care Services (PCS) (n = 2760). A structural equation model assessed direct and indirect effects of several child characteristics, clinical conditions and functional measures on Medicaid home care payments.ResultsThe mean age of children was 12.1 years and approximately 60% were female. Almost half of all subjects reported mild, moderate or severe ID diagnosis. The mean ADL score was 5.27 and about 60% of subjects received some type of rehabilitation services. Caseworkers authorized an average of 25.5 h of PCS support per week. The SEM revealed three groups of costs drivers: indirect, direct and direct + indirect. Cognitive problems, health impairments, and age affect expenditures, but they operate completely through other variables. Other elements accumulate effects (externalizing behaviors, PCS hours, and rehabilitation) and send them on a single path to the dependent variable. A few elements exhibit a relatively complex position in the model by having both significant direct and indirect effects on home care expenditures – medical conditions, intellectual disability, region, and ADL function.ConclusionsThe most important drivers of home care expenditures are variables that have both meaningful direct and indirect effects. The only one of these factors that may be within the sphere of policy change is the difference among costs in different regions.     

运用结构方程模型对卫生监督体系投入产出的综合分析

目的：对卫生监督体系多投入、多产出的变量进行综合分析,探讨结构方程模型在卫生监督领域的应用。方法：结构方程模型。结果：结构方程模型提示,资源配置正向影响能力建设,而能力建设又正向影响职能落实,通径系数分别为1．03和0．74;在现阶段,卫生监督机构的人员数量、人员素质和设备配置率等指标对机构资源配置水平的影响效应较大。结论：提示要加强卫生监督体系的建设,必须确保卫生监督的保障到位;同时提示结构方程模型可用来对卫生系统多变量问复杂关系的探讨。     

Normative data of the SF-12 health survey with validation using postmyocardial infarction patients in the Dutch population

Objective  To gain Dutch population norms for the Short Form-12 (SF-12), a generic health status questionnaire, in a random sample of the general population and to validate these in postmyocardial infarction (MI) patients. Methods  2,301 respondents from the general population and 459 post-MI patients completed the Short Form-36 (SF-36), which was used to calculate SF-12 scores. Results  The SF-12 summary scores correlated highly with SF-36 summary scores, demonstrating that these scores explain the same amount of variance in health status. Significant sex differences (P < .001) existed for both the physical component summary (PCS) and the mental component summary (MCS). Multivariate analysis of variance showed a main effect of age in oblique (PCS-12: P < .001; MCS-12: P < .001) and orthogonally rotated PCS scores (PCS-12_uc: P < .001; MCS-12_uc: P = .07). As expected, post-MI patients reported statistically significant and clinically relevant poorer mental (P < .001) and physical functioning (P < .001). Differences were less pronounced for MCS and PCS derived from orthogonal rotation data. When controlling for covariates, MI did not significantly affect PCS-12_uc anymore in orthogonally rotated data, while PCS-12_uc was affected by fewer covariates compared with PCS-12. Conclusions  This study presents Dutch population norms for the SF-12 in a large random population sample obtained from both oblique and orthogonal PCA rotation methods, revealing systematic differences between the results based on these two methods. Furthermore, this study demonstrates the discriminative validity of the SF-12 by showing that post-MI patients differ significantly from the normative population on PCS-12 scores.     

On the application of structural equation modeling for the construction of a health index

Objective  

The health of an individual is influenced by many factors. These could include factors that are related to the economy and the environment, as well as social and biological factors. Many studies have been carried out to study the effect of these factors on health, in terms of the individual factors or combined factors. The main purpose of this study was to demonstrate the value of structural equation modeling for the construction of an index to describe the health status of an individual.