Factor analysis of an instrument to measure the impact of disease on daily life

Analysis of Risk Factors in Patients with Subclinical Atherosclerosis and Increased Cardiovascular Risk Using Factor Analysis

Today, there are many parameters used for cardiovascular risk quantification and to identify many of the high-risk subjects; however, many of them do not reflect reality. Modern personalized medicine is the key to fast and effective diagnostics and treatment of cardiovascular diseases. One step towards this goal is a better understanding of connections between numerous risk factors. We used Factor analysis to identify a suitable number of factors on observed data about patients hospitalized in the East Slovak Institute of Cardiovascular Diseases in Košice. The data describes 808 participants cross-identifying symptomatic and coronarography resulting characteristics. We created several clusters of factors. The most significant cluster of factors identified six factors: basic characteristics of the patient; renal parameters and fibrinogen; family predisposition to CVD; personal history of CVD; lifestyle of the patient; and echo and ECG examination results. The factor analysis results confirmed the known findings and recommendations related to CVD. The derivation of new facts concerning the risk factors of CVD will be of interest to further research, focusing, among other things, on explanatory methods.

A Validation Study of the Rainbow Model of Integrated Care-Measurement Tool for Patients in China

Introduction:

The original Rainbow Model of Integrated Care Measurement Tool (RMIC-MT) is based on the Rainbow Model of Integrated Care (RMIC), which provides a comprehensive theoretical framework for integrated care. The aim of this paper is to modify the original patient version of the RMIC-MT for the Chinese primary care context and validate its psychometric properties.

Methods:

The translation and adaptation processes were performed in four steps, forward and back-translation, experts review and pre-testing. We conducted a cross-sectional study with 386 patients with diabetes attending one of 20 community health stations in the Nanshan district. We analyzed the distribution of responses to each item to study the psychometric sensitivity. Exploratory factor analysis with principal axis extraction method was used to assess the construct validity. Confirmation factor analysis was used to evaluate model fit of the modified version. Cronbach’s alpha was used to ascertain the internal consistency reliability.

Results:

During the translation and adaptation process, all 24 items were retained with some detailed modifications. No item was found to have psychometric sensitivity problems. Five factors (person-centeredness, clinical integration, professional integration, team-based coordination, organizational integration) with 15 items were determined by exploratory factor analysis, accounting for 53.51% of the total variance. Good internal consistency was achieved with each item correlated the highest on an assigned subscale and Cronbach’s alpha score of 0.890. Moderately positive associations (r≥ 0.4, p<0.01) between the score of the scale and these correlations indicate good construct validity.

Conclusions:

The results showed initial satisfactory psychometric properties for the validation of the Chinese RMIC-MT patient version. Its application in China will promote the development of people-centered integrated primary care. However, future studies with diverse samples crossing regions would be needed to test its psychometric properties for the various Chinese primary care contexts.

Development and nationwide application of an antibiotic knowledge scale

Misconceptions about antibiotics among the public can potentially lead to their inappropriate use. Currently, there is no antibiotic knowledge assessment tool to address this issue. This study aimed to develop and validate an antibiotic knowledge scale (AKS) and apply this scale to assess public knowledge about antibiotics in China. An initial 18-item AKS was designed and validated among 1180 people recruited in June 2017. After removing redundant items, the reliability and validity of the AKS were examined. Subsequently, a nationwide survey was conducted, and 12,772 people were recruited using multistage sampling and surveyed using the developed AKS. A logistic regression model was used to identify the factors associated with poor knowledge about antibiotics. The final AKS included two screening items and fifteen knowledge evaluation items. Cronbach's alpha, test-retest reliability, and split-half reliability were 0.91, 0.88, and 0.89, respectively. These knowledge evaluation items were loaded in four distinct factors that explained 70.72% of cumulative variance among respondents. Using the developed AKS to assess public knowledge about antibiotics among 12,772 participants, the mean score on the AKS was 7.25 and 67% of participants had poor antibiotic knowledge, which was associated with male gender, rural residence, lower educational level, poor economic status, living in western China, and lacking education on antibiotics. The AKS demonstrated satisfactory reliability and validity in identifying the population with poor antibiotic knowledge. Importantly, the majority of participants had inadequate knowledge about antibiotics. Thus, it is necessary to conduct interventions focusing on improving public knowledge about antibiotics.