MATERIALS AND METHODS: This cross-sectional study comprised 2,000 schoolchildren aged 6-12 years. Sleep-disordered breathing symptoms were assessed with Arabic version of Pediatric Sleep Questionnaire (PSQ). Overweight/obesity was evaluated using body mass index (BMI) and their association with SDB was tested using a regression analysis model.
RESULTS: Overall, 23% of children were at high risk of SDB. Prevalence of habitual snoring was 15.9% and sleep apnea 4%. Boys were at higher risk of SDB than girls (p = 0.026), while age had no effect (p = 0.254). High-risk SDB had a strong association with sleep symptoms compared to low-risk SDB (p < 0.05). Sleep-disordered breathing increased significantly in overweight and obese children (p = 0.017 and p < 0.001, respectively).
CONCLUSION: Around 23% Saudi schoolchildren are at risk of SDB. Related symptoms were strongly associated with high risk of SDB. Overweight and obesity had a strong and progressive association with SDB.
CLINICAL SIGNIFICANCE: The results will help in identifying children at high risk of developing SDB and plan for early intervention to avoid the progression of SDB later in life.
MATERIALS AND METHODS: This prospective cross-sectional study comprised 78 growing children in the age range of 11-14 years with polysomnography (PSG)-proven OSA and 86 non-OSA corresponding controls. BMI, tonsil size (Friedman grading scale), and Mallampati score were determined for both groups, and related differences were assessed with a t-test, while their independent association with OSA severity was tested with a regression analysis. Statistical significance was set at p <0.05.
RESULTS: Male gender, BMI, tonsil size, and Mallampati score were significantly higher in the OSA group (p < 0.05). A significant correlation was recorded between the Mallampati score and OSA severity (p < 0.01), but not with BMI or tonsil size (p > 0.05). For every 1-point increase in the Mallampati scale, the apnea-hypopnea index (AHI) increased by more than five events per hour in the bivariate analysis and by more than three events per hour in the multivariate analysis.
CONCLUSION: Male gender, increased BMI, high tonsil, and Mallampati scores were clinical indicators of the presence of OSA. However, only Mallampati scale had a significant association with OSA severity. Clinical diagnostic indicators should be established and encouraged especially in community-based studies.
CLINICAL SIGNIFICANCE: Clinical diagnostic indicators are very useful in examining and screening children who are at risk of developing OSA as PSG is expensive and unsuitable for universal use in the pediatric population.
DESIGN/METHODOLOGY/APPROACH: The authors adopted a quantitative and qualitative approach, i.e., a self-administered questionnaire, unstructured and a semi-structured interview, which were used to collect the data. A questionnaire was distributed to Bahraini residents selected randomly. The framework was based on the technology acceptance model (TAM) and theory of reasoned action (TRA). Important variables from both the TAM model and TRA theory were extracted and jointly used to build the research model.
FINDINGS: The findings indicated that the most factors affecting e-health adoption are trust, health literacy and attitude. Additionally, people in the private and government sectors understand e-health benefits.
PRACTICAL IMPLICATIONS: If healthcare professionals understand the factors affecting e-health system adoption from an individual and organisational perspective, then nurses, pharmacists and others will be more conscious about e-health and its adoption status.
ORIGINALITY/VALUE: E-health system adoption has become increasingly important to governments, individuals, and researchers in recent years. A novel research framework, based on TAM and TRA, was used to produce a new integrated model.
METHODS: We used ten years combined mortality statistics from 2005 to 2014 and Welsh Index of Multiple Deprivation rankings for each lower super output area. After accounting for the population's age, the number of deaths in Hospital, Hospice, Home, Care Home, Psychiatric Units, and Elsewhere were compared across deprivation quintiles.
RESULTS: Distribution of place of death was found to be concentrated in three places - hospital (60%), home (21%) and care home (13%). Results from this study shows a high number of hospital deaths, especially for more deprived areas, despite being the least preferred place of death.
CONCLUSION: This is the first Welsh study investigating place of death in relation to deprivation, which could be of major importance to academics, end of life care providers and policy makers interested in to reduce health care inequality in Wales.