Assessment of the general public's knowledge about rheumatic diseases: evidence from a Portuguese population-based survey

Background. To identify incorrect beliefs and common knowledge about rheumatic diseases in the general population. Methods. Participants were selected during the follow-up of a representative cohort of adult population of Porto, Portugal; 1626 participants completed a questionnaire that included general knowledge items about rheumatic diseases. Discrete and continuous latent variable models were used to identify knowledge flaws and the target groups. Odds ratios (OR) estimated by multinomial logistic regression, and 95% confidence intervals (95%CI) were computed to evaluate magnitude of associations. Results. A continuous latent variable model identified two dimensions: one related to general beliefs (latent 1) and another concerning characteristics, treatment and impact of rheumatic diseases (latent 2). A 3-class latent variable model refined these results: the first class presented the lowest probabilities of correct answer for items associated with the first latent (mean of 39%), and the second class presented the lowest probabilities of correct answer for items with the second latent (mean of 62%). The third class showed the highest probability of a correct answer for almost all the items (mean of 79%). The age and sex standardized prevalence of the classes was 25.7%, 30.8% and 43.5%. Taking class 2 as reference, class 1 was positively associated with the presence of rheumatic diseases (OR = 2.79; CI95% = (2.10-3.70)), with females (OR = 1.28 CI95% = (0.99-1.67)) and older individuals (OR = 1.04; CI95% = (1.03-1.05)), and was negatively associated with education (OR = 0.84; CI95% = (0.81-0.86)); class 3 was positively associated with education (OR = 1.03; CI95% = (1.00-1.05)) and the presence of rheumatic diseases (OR = 1.29; CI95% = (0.97-1.70)). Conclusions. There are several knowledge flaws about rheumatic diseases in the general public. One out of four participants considered false general beliefs as true and approximately 30% did not have detailed knowledge on rheumatic disease. Higher education and the presence of disease contributed positively to the overall knowledge. These results suggest some degree of effectiveness of patient education, either conducted by health professionals or self-driven. © 2010 Severo et al; licensee BioMed Central Ltd

Multiple Deprivation, Severity and Latent Sub-Groups:Advantages of Factor Mixture Modelling for Analysing Material Deprivation

Material deprivation is represented in different forms and manifestations. Two individuals with the same deprivation score (i.e. number of deprivations), for instance, are likely to be unable to afford or access entirely or partially different sets of goods and services, while one individual may fail to purchase clothes and consumer durables and another one may lack access to healthcare and be deprived of adequate housing . As such, the number of possible patterns or combinations of multiple deprivation become increasingly complex for a higher number of indicators. Given this difficulty, there is interest in poverty research in understanding multiple deprivation, as this analysis might lead to the identification of meaningful population sub-groups that could be the subjects of specific policies. This article applies a factor mixture model (FMM) to a real dataset and discusses its conceptual and empirical advantages and disadvantages with respect to other methods that have been used in poverty research . The exercise suggests that FMM is based on more sensible assumptions (i.e. deprivation covary within each class), provides valuable information with which to understand multiple deprivation and is useful to understand severity of deprivation and the additive properties of deprivation indicators

Exploratory factor analysis of self-reported symptoms in a large, population-based military cohort

<p>Abstract</p> <p>Background</p> <p>US military engagements have consistently raised concern over the array of health outcomes experienced by service members postdeployment. Exploratory factor analysis has been used in studies of 1991 Gulf War-related illnesses, and may increase understanding of symptoms and health outcomes associated with current military conflicts in Iraq and Afghanistan. The objective of this study was to use exploratory factor analysis to describe the correlations among numerous physical and psychological symptoms in terms of a smaller number of unobserved variables or factors.</p> <p>Methods</p> <p>The Millennium Cohort Study collects extensive self-reported health data from a large, population-based military cohort, providing a unique opportunity to investigate the interrelationships of numerous physical and psychological symptoms among US military personnel. This study used data from the Millennium Cohort Study, a large, population-based military cohort. Exploratory factor analysis was used to examine the covariance structure of symptoms reported by approximately 50,000 cohort members during 2004-2006. Analyses incorporated 89 symptoms, including responses to several validated instruments embedded in the questionnaire. Techniques accommodated the categorical and sometimes incomplete nature of the survey data.</p> <p>Results</p> <p>A 14-factor model accounted for 60 percent of the total variance in symptoms data and included factors related to several physical, psychological, and behavioral constructs. A notable finding was that many factors appeared to load in accordance with symptom co-location within the survey instrument, highlighting the difficulty in disassociating the effects of question content, location, and response format on factor structure.</p> <p>Conclusions</p> <p>This study demonstrates the potential strengths and weaknesses of exploratory factor analysis to heighten understanding of the complex associations among symptoms. Further research is needed to investigate the relationship between factor analytic results and survey structure, as well as to assess the relationship between factor scores and key exposure variables.</p

The Australian Racism, Acceptance, and Cultural-Ethnocentrism Scale (RACES): item response theory findings

BACKGROUND: Racism and associated discrimination are pervasive and persistent challenges with multiple cumulative deleterious effects contributing to inequities in various health outcomes. Globally, research over the past decade has shown consistent associations between racism and negative health concerns. Such research confirms that race endures as one of the strongest predictors of poor health. Due to the lack of validated Australian measures of racist attitudes, RACES (Racism, Acceptance, and Cultural-Ethnocentrism Scale) was developed. METHODS: Here, we examine RACES’ psychometric properties, including the latent structure, utilising Item Response Theory (IRT). Unidimensional and Multidimensional Rating Scale Model (RSM) Rasch analyses were utilised with 296 Victorian primary school students and 182 adolescents and 220 adults from the Australian community. RESULTS: RACES was demonstrated to be a robust 24-item three-dimensional scale of Accepting Attitudes (12 items), Racist Attitudes (8 items), and Ethnocentric Attitudes (4 items). RSM Rasch analyses provide strong support for the instrument as a robust measure of racist attitudes in the Australian context, and for the overall factorial and construct validity of RACES across primary school children, adolescents, and adults. CONCLUSIONS: RACES provides a reliable and valid measure that can be utilised across the lifespan to evaluate attitudes towards all racial, ethnic, cultural, and religious groups. A core function of RACES is to assess the effectiveness of interventions to reduce community levels of racism and in turn inequities in health outcomes within Australia. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12939-016-0338-4) contains supplementary material, which is available to authorized users

Applying modern psychometric techniques to melodic discrimination testing: Item response theory, computerised adaptive testing, and automatic item generation

Modern psychometric theory provides many useful tools for ability testing, such as item response theory, computerised adaptive testing, and automatic item generation. However, these techniques have yet to be integrated into mainstream psychological practice. This is unfortunate, because modern psychometric techniques can bring many benefits, including sophisticated reliability measures, improved construct validity, avoidance of exposure effects, and improved efficiency. In the present research we therefore use these techniques to develop a new test of a well-studied psychological capacity: melodic discrimination, the ability to detect differences between melodies. We calibrate and validate this test in a series of studies. Studies 1 and 2 respectively calibrate and validate an initial test version, while Studies 3 and 4 calibrate and validate an updated test version incorporating additional easy items. The results support the new test’s viability, with evidence for strong reliability and construct validity. We discuss how these modern psychometric techniques may also be profitably applied to other areas of music psychology and psychological science in general

The Effects Of Estimator Choice And Weighting Strategies On Confirmatory Factor Analysis With Stratified Samples

Survey researchers often design stratified sampling strategies to target specific subpopulations within the larger population. This stratification can influence the population parameter estimates from these samples because they are not simple random samples of the population. There are three typical estimation options that account for the effects of this stratification in latent variable models: unweighted maximum likelihood, weighted maximum likelihood, and pseudo-maximum likelihood estimation. This paper examines the effects of these procedures on parameter estimates, standard errors, and fit statistics in Lisrel 8.7 (Jöreskog & Sörbom, 2004) and Mplus 3.0 (Muthén & Muthén, 2004). Options using several estimation methods will be compared to pseudo-maximum likelihood estimation. Results indicated the choice of estimation technique does not have a substantial effect on confirmatory factor analysis parameter estimates in large samples. However, standard errors of those parameter estimates and RMSEA values for assessing of model fit can be substantially affected by estimation technique