METHODOLOGY: A cross-sectional study involved consented adult patients with ≥ 1 dental implant. Two calibrated operators examined the patients. BoP was outcome variable and peri-implant gingival biotype was principal predictor variable. The effects of site, implant, and patient level factors on BoP were assessed using a multilevel logistic regression model.
RESULTS: Eighty patients for a total of 119 implants and 714 sites were included in the study. Bleeding on probing was observed in 42 implants (35.29%) with a significant higher risk observed in presence of gingival recession, thin peri-implant gingival biotype, duration of implant placement, smokers, and male patients.
CONCLUSION: Peri-implant bleeding on probing was associated with site specific, implant, and patient level factors.
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.
METHODS: Data provided by the Fukuoka Prefecture Latter-stage Elderly Insurance Association was used. There were 11,821 patients aged ≥65 years with acute coronary syndromes who were identified from 2015 to 2017. Three-level multilevel logistic regression analyses were performed to quantify the small-area and hospital variations, as well as, to identify the determinants of PCI use.
RESULTS: The results showed significant variation (δ2 = 0.744) and increased PCI use (MOR = 2.425) at the hospital level. After controlling patient- and hospital-level characteristics, a large proportional change in cluster variance was found at the hospital level (PCV 14.7%). Fixed-effect estimation results showed that females, patients aged ≥80 years old, hypertension and dyslipidemia had significant association with the use of PCI. Hospitals with high physician density had a significantly positive relationship with PCI use.
CONCLUSIONS: Patients receiving care in hospitals located in small areas have equitable access to PCI. Hospital-level variation might be originated from the oversupply of physicians. A balanced number of physicians and beds should be taken into consideration during healthcare allocation. A treatment process guideline on PCI targeting older patients is also needed to ensure a more equitable access for healthcare resources.
METHODS: A random sample of digital panoramic radiographs from the database of a dental hospital was evaluated. Two calibrated examiners (κ ≥ 0.89) assessed the technical quality of the root fillings and the radiographic periapical health status by using the periapical index. Descriptive statistical analysis was carried out, followed by multilevel modeling by using tooth-level and patient-level predictors. Model fit information was obtained, and the findings of the best-fit model were reported.
RESULTS: A total of 6409 teeth were included in the analysis. The predicted probability of a tooth having AP was 0.42%. There was a statistically significant variability between patients (P Multilevel modeling is a valid and efficient statistical method in analyzing AP data. The predicted probability of a tooth having AP was generally small, but there was great variation between individuals. Posterior teeth and those with poor quality root filling were found to be more frequently associated with AP. On the patient level, advancing age was a factor significantly associated with AP.
METHODS: Data were derived from four waves of nationally representative Bangladesh Demographic and Health Survey (BDHS) conducted between 2004 and 2014. Rate of change analysis was used to calculate the average annual rate of increase in CS from 2004 to 2014, by socio-demographic categories. Multi-level logistic regression was used to identify the socio-demographic predictors of CS in a cross-sectional analysis of the 2014 BDHS data.
RESULT: CS rates increased from 3.5% in 2004 to 23% in 2014. The average annual rate of increase in CS was higher among women of advanced maternal age (≥35 years), urban areas, and relatively high socio-economic status; with higher education, and who regularly accessed antenatal services. The multi-level logistic regression model indicated that lower (≤19) and advanced maternal age (≥35), urban location, relatively high socio-economic status, higher education, birth of few children (≤2), antenatal healthcare visits, overweight or obese were the key factors associated with increased utilization of CS. Underweight was a protective factor for CS.
CONCLUSION: The use of CS has increased considerably in Bangladesh over the survey years. This rising trend and the risk of having CS vary significantly across regions and socio-economic status. Very high use of CS among women of relatively high socio-economic status and substantial urban-rural difference call for public awareness and practice guideline enforcement aimed at optimizing the use of CS.
Methods: A cross-sectional study was conducted among students at their first year at university in Europe, Asia, the Western Pacific, and Latin and North America. Data were obtained through a self-administered questionnaire, including questions on sociodemographic characteristics, depressive symptoms, and social capital. The simplified Beck's Depression Inventory was used to measure the severity of depressive symptoms. Social capital was assessed using items drawn from the World Bank Integrated Questionnaire to Measure Social Capital. Multilevel analyses were conducted to determine the relationship between social capital and depressive symptoms, adjusting for individual covariates (e.g., perceived stress) and country-level characteristics (e.g., economic development).
Results: Among 4228 students, 48% presented clinically relevant depressive symptoms. Lower levels of cognitive (OR: 1.82, 95% CI: 1.44-2.29) and behavioral social capital (OR: 1.51, 95% CI: 1.29-1.76) were significantly associated with depressive symptoms. The likelihood of having depressive symptoms was also significantly higher among those living in regions with lower levels of social capital.
Conclusion: The study demonstrates that lower levels of individual and macro-level social capital contribute to clinically relevant depressive symptoms among university students. Increasing social capital may mitigate depressive symptoms in college students.
METHODS: Data for this study was extracted from the 2011 Bangladesh Demographic and Health Survey (BDHS-2011). In this survey, data was collected using a two-stage stratified cluster sampling approach. The chi-square test and a two-level logistic regression model were used for further analysis.
RESULTS: Data from 2231 children aged 6-59 months were included for analysis. The prevalence of child anemia was noted to be 52.10%. Among these anemic children, 48.40% where from urban environment and 53.90% were from rural areas. The prevalence of mild, moderate and severe anemia among children was 57.10, 41.40 and 1.50% respectively. The two-level logistic regression model revealed that the following factors were associated with childhood anemia: children of anemic mothers (p