Psychometric Properties of Suboptimal Health Status Instruments: A Systematic Review

Intrarelationships between suboptimal health status and anxiety symptoms: A network analysis

BACKGROUND

Suboptimal health status is a global public health concern of worldwide academic interest, which is an intermediate health status between health and illness. The purpose of the survey is to investigate the relationship between anxiety statuses and suboptimal health status and to identify the central symptoms and bridge symptoms.

METHODS

This study recruited 26,010 participants aged <60 from a cross-sectional study in China in 2022. General Anxiety Disorder-7 (GAD-7) and suboptimal health status short form (SHSQ-9) were used to quantify the levels of anxiety and suboptimal health symptoms, respectively. The network analysis method by the R program was used to judge the central and bridge symptoms. The Network Comparison Test (NCT) was used to investigate the network differences by gender, place of residence, and age in the population.

RESULTS

In this survey, the prevalence of anxiety symptoms, SHS, and comorbidities was 50.7 %, 54.8 %, and 38.5 %, respectively. "Decreased responsiveness", "Shortness of breath", "Uncontrollable worry" were the nodes with the highest expected influence. "Irritable", "Exhausted" were the two symptom nodes with the highest expected bridge influence in the network. There were significant differences in network structure among different subgroup networks.

LIMITATIONS

Unable to study the causal relationship and dynamic changes among variables. Anxiety and sub-health were self-rated and may be limited by memory bias.

CONCLUSIONS

Interventions targeting central symptoms and bridge nodes may be expected to improve suboptimal health status and anxiety in Chinese residents. Researchers can build symptom networks for different populations to capture symptom relationships.

Analyzing Longitudinal Health Screening Data with Feature Ensemble and Machine Learning Techniques: Investigating Diagnostic Risk Factors of Metabolic Syndrome for Chronic Kidney Disease Stages 3a to 3b

Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models-Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost-each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them.

Assessing suboptimal health status in the Saudi population: Translation and validation of the SHSQ-25 questionnaire

Background

Suboptimal Health Status (SHS) is realised as a vital feature for improving global health. However, the Arabian world does not have a validated instrument for screening SHS in their population. Therefore, the study aimed to evaluate the psychometric properties of Arabic-translated SHS (ASHSQ-25) in the Saudi Arabian population.

Methods

We conducted a cross-sectional study among the conveniently sampled 1590 participants from the Saudi population (with a 97.4% response rate). The data was gathered through an online survey and then exported into SPSS and AMOS version 26.0 for analysis. Mann-Whitney and Kruskal-Wallis tests were used to identify the median difference between demographic groups. The one-tailed 90% upper limit of SHS scores was chosen as the cut-off criteria for SHS. Reliability and confirmatory analysis were performed for the psychometric evaluation of ASHSQ-25 in the Saudi Arabian context.

Results

This study demonstrates that the ASHSQ-25 has good internal consistency, interclass correlation coefficient (ICC) = 0.92; 95% confidence interval (CI) = 0.91-0.93) and reliability (Cronbach's α = 0.92). The confirmatory factor analysis (CFA) results indicated a good fit of the databased on the CMIN/degrees of freedom (df) = 4.461, comparative fit index (CFI) = 0.94, Tucker Lewis index (TLI) = 0.93, and Root Mean Square Error of Approximation (RMSEA) = 0.05. The result factor loadings for each item were high (≥ 0.55), except for one item from the immune system subscale. The SHS cut-off point for ASHSQ-25 was 33, leading to a 23.7% prevalence of SHS.

Conclusions

This study reveals that ASHSQ-25 has appropriate internal consistency and structural validity to assess SHS in an Arabic-speaking population; therefore, it is recommended.