Since the current testing and treatment of symptomatic chronic disease is considered a delayed response, it has become generally accepted that early detection provides better treatment options and ensures better quality of life (1, 2). Targeting at-risk individuals is critical, as they can be counselled and provided with prophylactic therapies that can potentially reduce or prevent their risk (1, 2). To achieve this, researchers have resorted to using health assessment or screening instruments or tools, usually subjective questionnaires, to measure individual’s dietary habits (3), physical activities (4) and work productivity (5). Although reliance and usage of such questionnaires have promoted clinical diagnosis and lifestyle modifications, their clinical relevance has been eclipsed by the cumbersome and ambiguous nature of some of the questions, the time required to complete the questionnaire and the challenges of interpreting the results. For these reasons, a more streamlined and targeted instrument is required.
Over the last few years, some advances in research have been made in the design of robust screening instruments, giving rise to the widely used Suboptimal Health Status Questionnaire-25 (SHSQ-25) (6-8). Popularly articulated and operationalised in 2009, the SHSQ-25 has had a leverage over the existing instruments due to its simplicity, clearly described questions and the simple scoring system (9, 10). Importantly, it encapsulates questions that comprehensively capture multiple indicators of good health, including fatigue, the cardiovascular system, the immune system, mental status and digestive tract (6, 9, 11). When completed, SHSQ-25 can reveal individuals who may be experiencing poor health that cannot be traced to a particular disease, referred to as Suboptimal Health Status (SHS) (6, 10-12).
SHS represents an intervening state, prior to chronic disease, that is often hallmarked by a lack of vitality, body weakness and loss of appetite (9, 13). It has become a major public health concern worldwide, as its link to different chronic diseases traverses across multiple populations (6, 8, 9, 12, 14, 15). Among the mainland Chinese, SHS was found to be associated with commonly known cardiovascular risk factors including psychosocial stress (10, 16), physical inactivity, increased blood pressure, plasma glucose and abnormal lipid profiles (9, 11). In a Russian population, SHS was associated with endothelial dysfunction (7) and among Ghanaians, it was a precursor to type II diabetes mellitus (7, 12). Following their analyses of hematobiochemical, sociodemographic and clinical data, Anto et al., (15) indicated the presence of SHS before preeclampsia among pregnant women in Ghana (15). Among Chinese youths, SHS was associated with altered intestinal microbiota (17). Furthermore, its association with objective markers including plasma cortisol, mRNA expression of glucocorticoid receptor α/β (10), plasma metabolites (13), N-glycosylation profiles (14), telomere length (18) and oxidative stress (19) as well as angiogenic growth mediators (19) have been reported.
Despite its widespread applications, studies that explore the psychometric properties of the SHSQ-25 are inadequate. The first and only study to date, that tested the validity and reliability of SHSQ25 was conducted in a Chinese population (6). In this study, they applied statistical methods such as test-retest reliability, internal consistency, convergent validity, along with factor and exploratory analysis to show that SHS-Q25 is capable of detecting SHS (6). Although this study highlights some psychometric testing, its construct validity has not been evaluated outside of China. This information is critically important because the relative validity and reliability of tools may not be the same in different populations, especially an African population such as Ghana.
On the one hand, Ghanaians in urban cities share similarities with Chinese in terms of urbanisation, increased work stress and pressures from home (20, 21). As such, the prevalence of SHS might be the same in both countries. On the other hand, Ghanaians have different genetic composition, varied job types, climatic conditions, different cultures and dietary differences that may make them susceptible to SHS or even a particular chronic disease (20). In addition, the extent of interactions and correlations between the metrics or components in each of the five domains have not been properly reported. Taken together, these constitute a significant research interstice and provide a justification for this present study.
Following on our previous studies (6, 8, 10, 22), with the goal of exploring the cross-national comparability of SHSQ-25 and emphasising on the robustness of the SHSQ-25, this current study aims to investigate the aspects of construct validity of the SHS-Q25 by applying a Structural Equation Model (SEM) to determine the interactions between SHS subscales in a Ghanaian population.