METHODS: Data from the World Health Survey conducted in 2002-2004, across 70 low-, middle- and high-income countries was used. Participants aged 18 years and over were selected using multistage, stratified cluster sampling. BMI was used as outcome variable. The potential determinants of individual-level BMI were participants' sex, age, marital-status, education, occupation, household-wealth and location(rural/urban) at the individual-level. The country-level factors used were average national income (GNI-PPP) and income inequality (Gini-index). A two-level random-intercepts and fixed-slopes model structure with individuals nested within countries was fitted, treating BMI as a continuous outcome.
RESULTS: The weighted mean BMI and standard-error of the 206,266 people from 70-countries was 23.90 (4.84). All the low-income countries were below the 25.0 mean BMI level and most of the high-income countries were above. All wealthier quintiles of household-wealth had higher scores in BMI than lowest quintile. Each USD10000 increase in GNI-PPP was associated with a 0.4 unit increase in BMI. The Gini-index was not associated with BMI. All these variables explained 28.1% of country-level, 4.9% of individual-level and 7.7% of total variance in BMI. The cross-level interaction effect between GNI-PPP and household-wealth was significant. BMI increased as the GNI-PPP increased in first four quintiles of household-wealth. However, the BMI of the wealthiest people decreased as the GNI-PPP increased.
CONCLUSION: Both individual-level and country-level factors made an independent contribution to the BMI of the people. Household-wealth and national-income had significant interaction effects.
METHODS: Behavioural Risk Factors Surveillance System data were used to estimate the weight the US population needed to lose to achieve a BMI
METHODS: Data from World Health Survey conducted in 2002-2004 in low-middle- and high-income countries were used. Participants aged 18 years and over were selected using multistage, stratified cluster sampling. BMI was used as an outcome variable. Culture of the countries was measured using Hofstede's cultural dimensions: Uncertainty avoidance, individualism, Power Distance and masculinity. The potential determinants of individual-level BMI were participants' sex, age, marital status, education, occupation as well as household-wealth and location (rural/urban) at the individual-level. The country-level factors used were average national income (GNI-PPP), income inequality (Gini-index) and Hofstede's cultural dimensions. A two-level random-intercepts and fixed-slopes model structure with individuals nested within countries were fitted, treating BMI as a continuous outcome variable.
RESULTS: A sample of 156,192 people from 53 countries was included in this analysis. The design-based (weighted) mean BMI (SE) in these 53 countries was 23.95(0.08). Uncertainty avoidance (UAI) and individualism (IDV) were significantly associated with BMI, showing that people in more individualistic or high uncertainty avoidance countries had higher BMI than collectivist or low uncertainty avoidance ones. This model explained that one unit increase in UAI or IDV was associated with 0.03 unit increase in BMI. Power distance and masculinity were not associated with BMI of the people. National level Income was also significantly associated with individual-level BMI.
CONCLUSION: National culture has a substantial association with BMI of the individuals in the country. This association is important for understanding the pattern of obesity or overweight across different cultures and countries. It is also important to recognise the importance of the association of culture and BMI in developing public health interventions to reduce obesity or overweight.
MATERIAL AND METHODS: Thirty-four patients (mean age 60.70 ± 8.7 years) received telescopic crown or locator attachments for ISOD and completed OHIP-14 (Malaysian version) and DS questionnaires, at baseline (T0 ) with new conventional complete dentures (CCD) and 3 months (T1 ) and 3 years (T2 ) after ISOD conversion. Mandibular bone volume was calculated from cone beam computed tomography (CBCT) datasets using Mimics software. Mean changes (MC) in OHIP-14 and DS at intervals were analyzed using the Wilcoxon signed-rank test and effect size (ES). The association of bone volume, implant attachment type, and other patient variables with the change in OHIP-14 and DS were determined using multivariate linear regression analysis.
RESULTS: The MC in OHIP-14 and DS scores from T0 to T1 and T2 showed significant improvement with moderate and large ES, respectively. Regression analyses for the change in OHIP-14 score from T0 to T2 showed significant association with implant attachment type (P = 0.043), bone volume (P = 0.004), and baseline OHIP-14 (P = 0.001), while for DS, the association was only significant with baseline DS score (P = 0.001).
CONCLUSION: Improvement in patients' OHRQoL and satisfaction with ISOD was associated with their baseline ratings. Mandibular bone volume had a stronger association for improvement in OHRQoL compared to type of attachment.
METHODS: National representative data from the 2009 Adult Dental Health Survey, United Kingdom, were used in this study. Periodontal disease severity was measured using periodontal pocket depth and categorized into three groups: pocket depth up to 3.5, 3.5-5.5 and more than 5.5 mm. OHRQoL was measured using the Oral Health Impact Profile-14 (OHIP-14) scores. Bivariate and multivariable Zero-inflated Poisson regression analysis was used.
RESULTS: A total of 6378 participants was analysed in this study. Periodontal pocketing was significantly associated with higher OHIP-14 scores. Participants with periodontal pocket depths >3.5 mm had a significantly higher prevalence for functional limitation, physical pain and social disability than participants with pocket depths of less than 3.5 mm. Participants with periodontal pocket depth(s) >5.5 mm had significantly higher OFOVO prevalence in all the domains of OHIP-14 except handicap domain than participants with pocket depth(s) <3.5 mm.
PARTICIPANTS:
CONCLUSION: This study showed that for a nationally representative sample of the United Kingdom population, periodontal disease was significantly associated with the domains of OHRQoL.
METHODS: A subset of elderly (≥65year) participants from the UK Adult Dental Health Survey 2009 data was used. OHRQoL was assessed by means of the OHIP-14 additive score. The number of missing teeth; presence of active caries, dental pain, root caries, tooth wear, periodontal pockets>4mm, loss of attachment>9mm; having PUFA>0 (presence of severely decayed teeth with visible pulpal involvement, ulceration caused by dislocated tooth fragments, fistula and abscess); and wearing a denture were used as predictor variables. Age, gender, marital status, education level, occupation and presence of any long standing illness were used as control variables. Multivariate zero-inflated Poisson regression analysis was performed using R-project statistical software.
RESULTS: A total of 1277 elderly participants were included. The weighted mean(SE) OHIP-14 score of these participants was 2.95 (0.17). Having active caries (IRR=1.37, CI=1.25;1.50), PUFA>0 (IRR=1.17, CI=1.05;1.31), dental pain (IRR=1.34, CI=1.20;1.50), and wearing dentures (IRR=1.30, CI=1.17;1.44), were significantly positively associated with OHIP-14 score. Having periodontal pockets>4mm, at least one bleeding site, and anterior tooth wear were not significantly associated with the OHIP-14 score.
CONCLUSION: Whereas previous research has suggested a moderate relationship between oral disease and quality of life in this large scale survey of older adults, the presence of active caries and the presence of one or more of the PUFA indicators are associated with impaired oral health related quality of life in older adults, but not indicators of periodontal status. The implication of this is that whilst focussing on prevention of disease, there is an ongoing need for oral health screening and treatment in this group.