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Margit Szermer

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Aug 2, 2024, 11:21:12 PM8/2/24

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The SF-8 is a short form of the SF-36 Health Survey, which is used for generic assessment of physical and mental aspects of health-related quality of life (HRQoL). Each of the 8 dimensions of the SF-36 is covered by a single item in the SF-8. The aim of the study was to examine the latent model structure of the SF-8.

In the SF-8, each item reflects mainly general HRQoL (general factor) as well as one of the three components physical, mental, and overall health. The findings suggest in particular that the evaluation of the information of the SF-8 items can be validly supplemented by a general value HRQoL.

To provide a time-efficient screening of physical and mental aspects of HRQoL the SF-8 has been developed. In the SF-8 each of the 8 SF-36 dimensions is represented by a single item [6]. In their original study Ware et al. [8] applied a principal component analysis (PCA) to identify the factorial structure of the SF-8 (see Fig. 1; full model). Factor loadings were allowed for all 8 single items on each of the two uncorrelated constructs PCS and MCS. Nevertheless, both constructs proved to be mainly represented by 6 items. The physical component PCS reflects Physical Functioning, Physical Role Functioning, Bodily Pain, General Health and Vitality. The mental component MCS mainly represents the facets Social Functioning, Mental Health, Emotional Role Functioning, General Health and Vitality [8]. Accordingly, General Health and Vitality proved to be germane indicators of both underlying constructs PCS and MCS (see Fig. 1; restricted model structure).

Wang et al. [9] as well as Lang et al. [10] used a confirmatory factor analytical (CFA) approach to investigate the underlying latent structure of the SF-8. In CFA models, a theory-based specification is made for each item to which latent variable it is assigned. CFA models assuming between-item-multidimensionality (BIM) require that each item loads on only one factor. Wang et al. [9] as well as Lang et al. [10] identified a three dimensional BIM structure as the best fitting model in Chinese samples. The third factor Overall Health is reflected by the item pair General Health and Vitality (see Fig. 2; 3-DIM). Lang et al. [10] emphasize that this result for the SF-8 is consistent with studies on the SF-36, which have shown a third component of General Well-Being besides Physical and Mental Health to be relevant [11,12,13,14,15].

Furthermore, Lang et al. [10] found a two-dimensional CFA model to be acceptable (see Fig. 2; 2-DIM). Nevertheless, the item Vitality showed a noticeably weak item-total correlation. On closer examination of the data reported by Lang et al. [10], this seems quite reasonable: the item Vitality is closely related to the item General Health, which is clearly assigned to Physical Health in the two-factor model. Accordingly, Vitality should be considered as an indicator of Physical Health rather than Mental Health. This model structure corresponds exactly to the structure that Hann and Reeves [16] found valid for the SF-36. In Fig. 2 the 2-DIM-Modified model represents the corresponding latent model structure.

For the SF-36 [16, 17], the SF-12 [18, 19] and the SF-8 [9, 10] the underlying constructs proved to be highly correlated. Nevertheless, the assumption that a general component is reflected in all SF-items could not be confirmed (unidimensional model; see Fig. 2: 1-DIM), because multifactorial models provided a better data fit.

Bi-factor models consider the answer to each item to be determined by two information components simultaneously (with2in-item-multidimensionality; WIM; [20, 21]). Regarding the construct HRQoL, each item has to be assigned to a general (i.e. general HRQol) and a specific latent variable (i.e. physical, mental or overall). As shown in Fig. 2, three bi-factor models can be defined for the SF-8 by combining the single factor model (1-DIM; left) with one of the three multi-dimensional models (2-DIM, 2-DIMMOD, 3-DIM; right). Accordingly, the response to each item reflects the general HRQoL on the one hand and an physical, mental or overall aspect on the other [22,23,24]. Chen, West and Sousa [25] pointed out, that bi-factor models generally provide a reasonable alternative model approach, if highly related domains comprise the general multifaceted construct of interest. The assumption that the general characteristic HRQoL value is included in the answers to each item of an HRQoL scale is in concordance with the underlying theoretical assumptions regarding HRQoL [2].

Knowing the underlying model structure is a prerequisite to validly interpret and use the information of the SF-8 items for diagnostic and evaluative purposes. Hence, the central aim of the present study was to comparatively evaluate the factor structures underlying the SF-8 items. The specific aims were:

The research institute USUMA provided weighting factors (γi) for each participant. These weighting factors (γi) can be used to correct violations of representativeness with regard to central socio-demographic characteristics (i.e. state, gender and age). The weights correct the increased selection probability of individuals in small households and the distortions due to the lack of participation of randomly selected individuals. Members of groups that are underrepresented (vs. overrepresented) in the sample receive a weight greater (vs. smaller) than 1, ensuring that the corrected actual values correspond to the target values in the population. These corrective weighting factors were used to determine the univariate distributions and correlation statistics.

Alternatively, models assuming each item to be indicative for only one of the underlying latent factors (BIM) also showed a good data fit (Fig. 2, Table 4). Assuming BIM, the best CFA model fit has been identified for the three-factor model structure (3-DIM) reported by Lang et al. [10]. The third factor, Overall Health, is formed by the two items General Health Perception and Vitality.

For the two-dimensional BIM definition, the assignment of the item Vitality to the physical factor in the model 2-DIM-Modified lead to an improved data fit. This is in accordance with the results of Lang et al. [10] in a representative Chinese population. Lang et al. [10]) discuss these results as particularly characteristic for the Asian region (see also: [9, 11,12,13, 15]) in comparison to European and US-American data. The findings reported in the present paper provide evidence that cultural differences should not be assumed as the main cause for differences in the reported findings. The well-founded CFA approach of Lang et al. [10] yields very similar results in the Chinese population as the CFA approach in the data presented here for Germany. Differences to earlier analyses in the United States [8, 38], thus seem to be due to the CFA approach.

For the short versions SF-12 and SF-8, high correlations of the Physical)PCS) and Mental Component Summary (MCS) are reported in the literature [8,9,10, 19]. Despite this high correlation of Physical Health and Mental Health, a general factor HRQoL has not yet been considered when evaluating the SF-8. The underlying assumption of the BIM is: Each item exclusively covers either a physical or mental aspect. If the bi-factor approach (WIM assumption) is applied, a fundamentally different model is assumed. Bi-factor models allow the information of the SF-8 items to be determined by general HRQoL. Our findings showed a clearly better data fit for the bi-factor models (Table 5). Note, that in these models Physical and Mental as well as Overall Health are assumed to be uncorrelated components. The correlation of the single items assigned to different facets is completely modeled by the general factor HRQoL. In the bi-factor models our results showed, that the general HRQoL dominantly determines the variance of all items. WIM thus represents a plausible and statistically superior model assumption, which opens a completely new view on the structure of the SF-8 [22, 23, 25]: The SF-8 primarily measures a general HRQoL component. Assuming a dominant principal component HRQoL for the items of SF-8 is further supported by the results of a PCA: Only the eigenvalue 5.11 of the first component is greater than 1. This first component explains a very high amount of the item variances: 63.40%.

Accordingly, a SF-8 overall score can be determined, which represents HRQoL across physical and mental facets. This approach thus represents a psychometrically well-founded alternative to existing evaluation approaches for scale variants of the SF family. The suitability should also be tested for the SF-12 and SF-36.

The model estimates were calculated using both the ML algorithm as well as the WLSMV algorithm. Generally, the global fit measures (especially χ2, CFI and SRMR) indicated a better model fit for the WLSMV estimates. The poorer fit for the ML estimates was expected because of strong violations of the normal distribution in the analyzed norm data set. The WLSMV algorithm is methodologically superior to alternative modeling approaches when the underlying latent correlation structure is analyzed. WLSMW prevents underestimation of correlations due to asymmetric data distributions and categorical data format [30, 33, 34]. Accordingly, applying the WLSMV algorithm leads to factor loadings and explained item variances being higher. The validity of all modeling results is systematically attenuated when the ML approach is used in the case of clearly non-normal distributed data [27, 28].

Some limitations of the study must be considered in order to correctly assess the study results. We focused on the dimensional structure of the SF-8, without analyzing further clinimetric characteristics of the instrument [39]. Clinimetrics emphasizes, that each assessment has to be evaluated regarding its suitability for specific purposes in clinical practice comprehensively. In addition to our study results, it would be particularly important to find out to what extent the individual items as well as the scale scores of the SF-8 are able to reflect clinically relevant changes in health status validly over time. In addition, future research should focus on how the SF-8 can be embedded in an overall assessment to address individual patient needs in treatment planning and to sensitively evaluate clinically significant changes [40, 41].