Suboptimal health state (SHS) is a physical state between health and disease and is characterized by the perception of health complaints, general weakness, and low energy [
Recent years, SHS has become a new public health challenge all over China. The number of people who were reported suboptimal health in the absence of a diagnosable condition increased [
The participants were cluster sampled from six clinical centres participating in this project. The centres are the Beijing Guanghua Hospital Medical Center in Beijing (BJ for short), the Hanzhong People’s Hospital Medical Center in Shanxi Province (SX for short), The Hospital affiliated to Changchun University of Chinese Medicine Medical Center in Jilin Province (JL for short), the Shenzhen Second People’s Hospital in Guangdong Province (GD for short), the Zhenjiang People’s Hospital Medical Center in Jiangsu Province (JS for short), and the Huangshi Aikang Hospital in Hubei Province (HB for short).
The participants from the 6 clinical centres, which were sampled from over 1 million people, consisted of 2807 sub-health samples, in which 1286 were male (45.81% of the total number of cases, age
Characteristics of the samples in different areas.
BJ | SX | JL | GD | JS | HB | |
---|---|---|---|---|---|---|
Sample size | 717 | 452 | 463 | 486 | 563 | 666 |
No(%) of sub | 564 (78.7%) | 418 (58.3%) | 448 (62.5%) | 431(60.1%) | 445 (62.1%) | 501 (69.9%) |
Mean age (SD) of sub | 30.41 ± 0.298 | 33.19 ± 0.39. | 34.13 ± 0.389 | 30.72 ± 0.369 | 33.81 ± 0.446 | 28.78 ± 0.298 |
More than three-month recurring illness state and efficiency decline because of persistent or excessive fatigue; and no major organic diseases and physiological or mental diseases. Case which must strictly meet the previous two criteria should be diagnosed as SHS.
Each case must accord with the SHS diagnostic criteria; age should be from 18 through 49 years; each case must be attached with an informed consent form (ICF) signed by the respondent. Case which must all be consistent with the previous 3 items can be concluded in.
Any case who do not accord with inclusion criteria; Women who are pregnant, breast-feeding, or intend to pregnant; any case who do not sign an informed consent form; any case whose questionnaire [
Consecutive samples with a single center are used in present study. In other words, the participants who met the inclusion criterion while not being rejected for exclusion criterion were all included, for inducing selection bias. Clinical investigators were trained so that they were fully understood the epidemiological survey programs and standard operating procedures. Epidata 3.02 was used to verify the data parallel double-inputted.
A basic structure equation model consists of two components: the measurement model which describes how indicator variables related to the latent variables and the structural model which analyzes the relationships among latent variables. The models proposed were estimated using the AMOS 16.0 program. Confirmatory factor analysis (CFA) was used to construct the measurement model structural mode, by maximum likelihood method to estimate parameters. Goodness of fit for our model was two indices of practical fit: the comparative fit indices (CFIs) and the root mean square error of approximation (RMSEA), which were in wide use and known to be relatively unaffected by sample size [
Flow chart for building SEM of SHS.
Based on results of the summary research and the experts’ counselling, we build the theoretical model for the basic patterns of sub-health state [
Figure
Theoretical model tested using structural equations.
The first step in the structural equation analysis was the construction of the measurement model. The initial measurement model was constructed on the understanding of patterns transfer regulation in SHS. The factor loadings of the indicators of the latent construct “Qi deficiency pattern” were all higher than 0.60, the two inverse items (x12 and x02) excepted. The indicator with the highest load for this construct was myasthenia of limbs. This indicates that the latent variable adequately predicted the variability of the observed variable (Figure
Standardized coefficients of the structural model obtained for the SHS were presented in Table
The standardized coefficients of the structural model.
Effects | Estimate | |
---|---|---|
Y4 dampness syndrome | .822 | |
Y3 fire syndrome | .577 | |
Y2 stagnation syndrome | .520 | |
Y3 fire syndrome | .407 | |
Y2 Stagnation syndrome | .351 |
In the same way, the direct effect of Qi deficiency pattern on myasthenia of limbs was of the highest magnitude (value of estimate is 0.686), and then on fatigue (value of estimate is 0.664). This implied that for each variation of one standard deviation in Qi deficiency pattern there was a significant increase of 0.686 standard deviation in myasthenia of limbs and of 0.664 standard deviation in fatigue. The fit of our model provided a middle fit to our data with CFI = 0.851 and RMSEA = 0.075. All of the paths in the final model were highly significant. The final model was represented in Figure
Shows the factor loadings of the measurement model.
Effects | Estimate | |
---|---|---|
x03 myasthenia of limbs | 0.686 | |
x01 fatigue | 0.664 | |
x19 disinclination to say | 0.649 | |
x04 short breath | 0.632 | |
x12 inferiority | −0.143 | |
x02 degree of fatigue | −0.149 | |
x41 vexation | 0.689 | |
x36 dry pharynx | 0.623 | |
x44 swollen sore throat | 0.554 | |
x35 bitter taste of mouth | 0.549 | |
x39 constipation | 0.525 | |
x40 deep-colored urine | 0.508 | |
x28 deprementia | 0.721 | |
x30 nervous | 0.717 | |
x32 be apt to breathe | 0.669 | |
x31 anxiety | 0.644 | |
x33 hypochondriac distension and pain | 0.585 | |
x34 abdominal distension and pain | 0.571 | |
x47 dizziness | 0.731 | |
x49 limpness | 0.722 | |
x48 sticky mouth | 0.629 | |
x50 drainage difficulty | 0.585 |
Structural equation model of SHS.
TCM pattern is a generalization of various symptoms and signs occurring in a certain stage of a disease, investigating causes, pathogenesis, pathological manifestation, location, and nature of disease. Pattern is an abstraction idea based on the symptoms or signs. It is similar to latent variable which should be quantified and made objective. Pattern identification is a method of thinking which provides evidence for treatment by synthesizing and analyzing clinical data and differentiating patterns on the basis of TCM theories.
Structural equation modelling integrates the idea of factor analysis, correlation analysis, and regression analysis. It can inference on the direct and indirect effects among variables [
The results of this study indicate that the SHS model provided middle fit to the data obtained from a large cross-sectional clinical epidemiological investigation. It would be helpful to know for both clinical and research purposes, for example, which variable (symptom) is important to the SHS pattern identification.
Our findings were consistent with the theory of TCM pattern. Effects of Qi deficiency pattern on dampness pattern (0.822) were greater than those on stagnation pattern (0.351). The fact of Qi deficiency of spleen leading to dampness pattern was more obvious than the fact of Qi deficiency of liver leading to stagnation pattern, which was related to the fact of Qi deficiency of spleen being more popular than Qi deficiency of liver and consistent with the fact of liver stagnation and Qi deficiency of spleen pattern being the popular pattern of SHS [
Furthermore, to a certain degree, the study presented here revealed that the weights of symptoms in the respective pattern represent importance to the pattern identification in SHS. The symptoms of different patterns showed the specific standardized factor loadings, which indicate the weights in their respective patterns and the exact diagnosis of patterns. The exogenous variable “Qi deficiency pattern” was composed of 6 directly observed variables, fatigue, degree of fatigue, weakness, shortness of breath, lazy speech, and dizziness. The variable “stagnation pattern” was measured with 7 indicators, emotional depression, irritability, nervousness, anxiety, often heaving a deep sigh, hypochondriac pain, and the lower abdomen pain. In the main symptoms of stagnation pattern, the load coefficient of emotional depression and nervousness was higher than that of hypochondriac pain and lower abdomen pain. It was shown that emotional symptoms for diagnosis of stagnation pattern had greater weight. That was different from the other stagnation patterns of diseases; hypochondriac pain and lower abdomen pain had the greater weight [
One of the limitations of this study was that all variables were assessed using questionnaires [
In conclusion, we have demonstrated that the use of SEM enables us to find and support the impossible cause-effect relationship between latent variables (patterns) and measurable variables (symptoms) in SHS. The study contributed to a theoretical framework, which had implications for the diagnosis points of SHS. To a certain degree, the weights of symptoms in the respective pattern represented importance to the pattern identification in SHS. It was shown that emotional symptoms for diagnosis of stagnation pattern have greater weight in SHS.
The author’s declare that they have no conflict of interests.
L. M. Wang carried out many of the experiments and drafted the paper. Y. Li and D. H. Yi analyzed and interpreted the data. X. Zhao, H. T. Cui, and X. L. Wu performed some of the experiments and contributed to the drafting of the paper. J. X. Chen was involved in the conception and design of the study and the supervision of experiments and contributed to its correction. All authors read the manuscript, contributed to its correction, and approved the final version. L. M. Wang, X. Zhao, Y. Li, and D. H. Yi contributed equally to this work.
This work was supported by Hi-Tech Research and Development Program of China (863 Program) (2008AA02Z406), China National Funds for Distinguished Young Scientists (30825046), Program for Innovative Research Team in Beijing University of Chinese Medicine (2011CXTD-07), and MOE Project of Key Research Institute of Humanities and Social Sciences in Universities (2009JJD910002).