METHODS: In this cross sectional study, the Malay version of SAQLI was administered to 82 OSA patients seen at the OSA Clinic, Hospital Universiti Sains Malaysia prior to their treatment. Additionally, the patients were asked to complete the Malay version of Medical Outcomes Study Short Form (SF-36). Twenty-three patients completed the Malay version of SAQLI again after 1-2 weeks to assess its reliability.
RESULTS: Initial factor analysis of the 40-item Malay version of SAQLI resulted in four factors with eigenvalues >1. All items had factor loadings >0.5 but one of the factors was unstable with only two items. However, both items were maintained due to their high communalities and the analysis was repeated with a forced three factor solution. Variance accounted by the three factors was 78.17% with 9-18 items per factor. All items had primary loadings over 0.5 although the loadings were inconsistent with the proposed construct. The Cronbach's alpha values were very high for all domains, >0.90. The instrument was able to discriminate between patients with mild or moderate and severe OSA. The Malay version of SAQLI correlated positively with the SF-36. The intraclass correlation coefficients for all domains were >0.90.
CONCLUSIONS: In light of these preliminary observations, we concluded that the Malay version of SAQLI has a high degree of internal consistency and concurrent validity albeit demonstrating a slightly different construct than the original version. The responsiveness of the questionnaire to changes in health-related quality of life following OSA treatment is yet to be determined.
METHODS: A multi-stage sampling design was adopted for the study and data collection took place in three phases in 2010, 2011, and 2012 in the Northern region of Malaysia. Face-to-face interviews involved respondents answering both 13 TTO and 15 VAS valuation tasks were carried out. Both additive and multiplicative model specifications were explored using the valuation data. Model performance was evaluated using out-of-sample predictive accuracy by applying the cross-validation technique. The distribution of the model values was also graphically compared on Bland-Altman plots and kernel density distribution curves.
RESULTS: Data from 630 and 611 respondents were included for analyses using TTO and VAS models, respectively. In terms of main-effects specifications, cross-validation results revealed a slight superiority of multiplicative models over its additive counterpart in modelling TTO values. However, both main-effects models had roughly equal predictive accuracy for VAS models. The non-linear multiplicative model with I32 term, MULT7_TTO, performed best for TTO models; while, the linear additive model with N3 term, ADD11_VAS, outperformed the other VAS models. Multiplicative modelling neither altered the dimensional rankings of importance nor did it change the distribution of values of the health states.
CONCLUSION: Using EQ-5D-3L valuation data, multiplicative modelling was shown to improve out-of-sample predictive accuracy of TTO models but not of VAS models.
Materials and Methods: The 500 individuals of both males and females aged 40 years and older with missing posterior teeth and not rehabilitated with any prosthesis were gone through a clinical history, intraoral examination, and anthropometric measurement to get information regarding age, sex, socioeconomic status, missing posterior teeth, and body mass index (BMI). Subjects were divided into five groups according to BMI (underweight > 18.5 kg/m2, normal weight 18.5-23 kg/m2, overweight 23-25 kg/m2, obese without surgery 25-32.5 kg/m2, obese with surgery < 32.5 kg/m2). Multivariate logistic regression was used to adjust data according to age, sex, number of missing posterior teeth, and socioeconomic status.
Results: People with a higher number of tooth loss were more obese. Females with high tooth loss were found to be more obese than male. Low socioeconomic group obese female had significantly higher tooth loss than any other group. No significant relation between age and obesity was found with regard to tooth loss.
Conclusion: The BMI and tooth loss are interrelated. Management of obesity and tooth loss can help to maintain the overall health status.
METHODS: Forty-eight hospital departments were recruited via open call and stratified by country. Departments were assigned to the operational program (intervention) or usual routine (control group). Data for analyses included 36 of these departments and their 5285 patients (median 147 per department; range 29-201), 2529 staff members (70; 10-393), 1750 medical records (50; 50-50), and standards compliance assessments. Follow-up was measured after 1 year. The outcomes were health status, service delivery, and standards compliance.
RESULTS: No health differences between groups were found, but the intervention group had higher identification of lifestyle risk (81% versus 60%, p health effects, the bias, and the limitations should be considered in implementation efforts and further studies.
TRIAL REGISTRATION: ClinicalTrials.gov : NCT01563575. Registered 27 March 2012. https://clinicaltrials.gov/ct2/show/NCT01563575.
STUDY DESIGN: Individual data on SRH and important covariates were obtained for 424,791 European and United States residents, ≥60 years at recruitment (1982-2008), in eight prospective studies in the Consortium on Health and Ageing: Network of Cohorts in Europe and the United States (CHANCES). In each study, adjusted mortality ratios (hazard ratios, HRs) in relation to SRH were calculated and subsequently combined with random-effect meta-analyses.
MAIN OUTCOME MEASURES: All-cause, cardiovascular and cancer mortality.
RESULTS: Within the median 12.5 years of follow-up, 93,014 (22%) deaths occurred. SRH "fair" or "poor" vs. "at-least-good" was associated with increased mortality: HRs 1.46 (95% CI 1·23-1.74) and 2.31 (1.79-2.99), respectively. These associations were evident: for cardiovascular and, to a lesser extent, cancer mortality, and within-study, within-subgroup analyses. Accounting for lifestyle, sociodemographic, somatometric factors and, subsequently, for medical history explained only a modest amount of the unadjusted associations. Factors favourably associated with SRH were: sex (males), age (younger-old), education (high), marital status (married/cohabiting), physical activity (active), body mass index (non-obese), alcohol consumption (low to moderate) and previous morbidity (absence).
CONCLUSION: SRH provides a quick and simple tool for assessing health and identifying groups of elders at risk of early mortality that may be useful also in clinical settings. Modifying determinants of favourably rating health, e.g. by increasing physical activity and/or by eliminating obesity, may be important for older adults to "feel healthy" and "be healthy".
MATERIALS AND METHODS: The EQ- 5D was cross-culturally adapted and translated using an iterative process following standard guidelines. Consenting adult Malay- and Tamil-speaking subjects at a primary care facility in Singapore were interviewed using a questionnaire (including the EQ-5D, a single item assessing global health, the SF-8 and sociodemographic questions) in their respective language versions. Known-groups and convergent construct validity of the EQ-5D was investigated by testing 30 a priori hypotheses per language at attribute and overall levels.
RESULTS: Complete data were obtained for 94 Malay and 78 Indian patients (median age, 54 years and 51 years, respectively). At the attribute level, all 16 hypotheses were fulfilled with several reaching statistical significance (Malay: 4; Tamil: 5). At the overall level, 42 of 44 hypotheses related to the EQ-5D/ EQ-VAS were fulfilled (Malay: 22; Tamil: 20), with 21 reaching statistical significance (Malay: 9; Tamil: 12).
CONCLUSION: In this study among primary care patients, the Singapore Malay and Tamil EQ-5D demonstrated satisfactory known-groups and convergent validity.