Academic literature on the topic 'Demographic profile'

Abstract Introduction Sleep value, the worth placed on sleep, is an important dimension of sleep health. Using the Sleep Valuation Item Bank (SVIB) we have previously found associations between sleep value factors and demographic, sleep, and psychological variables. The patterns of sleep value hint that there may be distinct sleep value profiles. In a nationally representative sample of 454 participants, we sought to identify these profiles and explore whether they differ in demographic features. Methods Participants ages 18-85 (M = 45) completed an online Qualtrics survey that included demographic variables and the SVIB. The factor structure of the SVIB was confirmed using factor analysis. Latent profile analyses with the confirmed factors were conducted and multiple models were compared to identify the best-fitting sleep value profiles. Multiple regression was used to explore whether these profiles differed in age, gender, marital status, income, education, having dependents, and race. Results Five sleep value factors were confirmed: sleep wanting, liking, devaluing, prioritizing, and preferring. Latent profile analysis found evidence for 5 classes, or sleep value profiles: (1) an indifferent profile who neither valued nor devalued their sleep who made up 25% of the sample, (2) sleep prioritizers who scheduled their lives around sleep made up 27% of the sample, (3) sleep devaluers made up 26%, (4) an ambivalent profile who highly valued and devalued sleep made up 15%, and (5) on 7% were sleep valuers. Those with the indifferent profile tended to be older (p < .001). Those with the ambivalent profile were more likely to be working-age adults (p < .001) and male (p < .05). Conclusion This study found distinct profiles of sleep value that differ in terms of demographics. Only 7% of our nationally representative sample had a profile suggesting they value sleep. The vast majority had an indifferent, ambivalent, or devaluing profile. Understanding how the patterns of sleep value develop and inform sleep-related behavior is an important avenue for future research. These findings may help personalize messaging for sleep health promotion. Support (if any)

Objectives: Population-based studies have focused on patients’ rendering of orthodontic treatment due to their malocclusion and medical needs. However, there is a scarcity of data from national sources on the prevalence of orthodontic visits and associated patient characteristics, as well as their effect on dental health. This study evaluated the demographic differences of orthodontic patients and examined the association between orthodontics use and risk of periodontal disease and oral surgical intervention. Methods: This study used data collected from the 2019 Medical Expenditure Panel Survey. Descriptive statistics were used to evaluate socio-demographics and covariates by the use of orthodontics. Chi-square tests were used to assess demographic differences among respondents who stated whether or not they used orthodontics. Logistic regression models were then used to examine the association of orthodontics and oral health outcomes. Results: The sample size was 12,422, of which 491 respondents indicated a usage of orthodontics. There were significant differences among demographic variables (p < 0.05) that included 61.1% females, 60.8% Whites, 67.6% participants under the age of 18 years old, and a family total income of $100,000 or more (52.7%). After controlling for socio-demographics and covariates, individuals who used orthodontics were less likely to have periodontal disease (AOR = 0.623, 95% CI = 0.610 to 0.637) and tooth extractions (AOR = 0.071, 95% CI = 0.070 to 0.073) than those who did not. Conclusions: Our findings indicate orthodontics usage was higher in females, younger patients, and Whites, highlighting the possible demographic disparities in orthodontics use. Additionally, those who used orthodontics were less likely to have oral health issues.

India has several socially disadvantaged communities and Scheduled Tribes are the most deprived ones. In Census 2011 the Government of India identified 10,42,81,034 citizens as Scheduled Tribes constituting 8.6% of the total population of the India (As per 2001 Census, it was 8.2% of the total population of India). In India, each state is practically equivalent to a country with its own specific socio-economic level, different ethnic groups, food habits, health infrastructures and communication facilities. Kodagu is one such district located on the eastern slopes of the Western Ghats in Karnataka and is home to many communities with diverse ethnic origins, and tribes such as the Yeravas, Kurubas and Kudiyas, who are of hunter-gatherers of forest origin. The present study focuses on the socio demographic and economic conditions of the Yerava, Jenu Kuruba and Malekudiya tribes. There have been changes especially in the social and economic life of these tribal people though, differences were found in their social, demographic and economic conditions.