DESIGN: Descriptive, cross-sectional survey.
METHODS: The research method employed in this study is characterized as a methodological study. Self-reported survey data were collected among community-dwelling older adults with chronic diseases residing in suburban counties in China. Including the following psychometric characteristics, item analysis was performed using the decision value method and Pearson's correlation analysis. Content validity was assessed through expert panel evaluation. The internal consistency of the questionnaire was determined by calculating Cronbach's alpha coefficient and corrected item-total correlation. Additionally, confirmatory factor analysis (CFA) was utilized to assess the construct validity of the ACPRS-C.
RESULTS: A total of 228 older adults participated in this psychometric study from August to October 2023. The item content validity index ranged from 0.80 to 1.00, while the scale content validity index was 0.945. The scale demonstrated excellent internal consistency (Cronbach's alpha = 0.931), and the correlation between items and total score was satisfactory. The structural validity was deemed robust (CFA model fit: chi-square/df = 1.121, comparative fit index = 0.992).
CONCLUSION: The ACPRS-C is a scale with strong psychometric properties to assess the ACP readiness within the context of community-dwelling older adults with chronic diseases residing in suburban counties in China. Its reliability and validity hold considerable significance for both research and clinical practice.
METHODS: From 18,933 college students who took part in two surveys 12 months apart, 4,006 participants who reported adverse childhood experiences were screened. Cross-sectional network comparisons and cross-lagged panel network (CLPN) analysis characterized interactions among CPTSD symptoms.
RESULTS: In the cross-sectional networks, feeling like a failure and avoid activities reminiscent of the trauma were the central symptoms. Takes long time to calm down and exaggerated startle are important bridge symptoms in the two networks respectively. The comparison of cross-sectional networks indicates that the global network strength was stable. The findings of the CLPN model reveal that feel worthless and feel like a failure had the highest "out" expected influence; exaggerated startle and avoid thoughts and feelings about the trauma had the highest "in" expected influence.
CONCLUSIONS: By conducting cross-sectional network analyses, the study illuminated the attributes of CPTSD networks across various time points. Additionally, the CLPN analysis uncovered the longitudinal patterns of CPTSD symptoms.
METHODS: This is a cross-sectional study using multiple logistic regression to identify predictors of elevated CRP among pre-treatment, newly diagnosed BCa patients. Studied variables were socio-demographic and medical characteristics, anthropometric measurements [body weight, Body Mass Index, body fat percentage, fat mass/fat free mass ratio, muscle mass, visceral fat], biochemical parameters [albumin, hemoglobin, white blood cell (WBC), neutrophil, lymphocyte], energy-adjusted Dietary Inflammatory Index, handgrip strength (HGS), scored Patient Generated-Subjective Global Assessment, physical activity level and perceived stress scale (PSS).
RESULTS: A total of 105 participants took part in this study. Significant predictors of elevated CRP were body fat percentage (OR 1.222; 95 % CI 1.099-1.358; p < 0.001), PSS (OR 1.120; 95 % CI 1.026-1.223; p = 0.011), low vs normal HGS (OR 41.928; 95 % CI 2.155-815.728; p = 0.014), albumin (OR 0.779; 95 % CI 0.632-0.960; p = 0.019), and WBC (OR 1.418; 95% CI 1.024-1.963; p = 0.036).
CONCLUSION: Overall, predictors of elevated CRP in pre-treatment, newly diagnosed BCa population were body fat percentage, PSS, HGS category, albumin and WBC.
METHODS: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds.
FINDINGS: The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles.
INTERPRETATION: Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere.
FUNDING: Bill & Melinda Gates Foundation.
METHODS: A single centre, latin-square cross-over, double masked, randomized controlled clinical trial was conducted on 45 chronic generalized gingivitis subjects who were chosen from the dental clinic of MAHSA University, Malaysia. A total of 45 subjects were randomly assigned into one of the three different groups (n = 15 each) using a computer-generated random allocation sequence: Group A Propolis mouthwash; Group B Chlorhexidine mouthwash; and Group C Placebo mouthwash. Supragingival plaque and gingival inflammation were assessed by full mouth Plaque index (PI) and gingival index (GI) at baseline and after 21 days. The study was divided into three phases, each phase lasted for 21 days separated by a washout period of 15 days in between them. Groups A, B and C were treated with 0.2% Propolis, Chlorhexidine, and Placebo mouthwash, respectively, in phase I. The study subjects were instructed to use the assigned mouthwash twice daily for 1 min for 21 days. On day 22nd, the subjects were recalled for measurement of PI and GI. After phase I, mouthwash was crossed over as dictated by the Latin square design in phase II and III.
RESULTS: At baseline, intergroup comparison revealed no statistically significant difference between Groups A, B and C (p > 0.05). On day 21, one-way ANOVA revealed statistically significant difference between the three groups for PI (p
METHODS: An intermediary trainee was subjected to an 8-week structured self-practice program. The program was divided into 2 parts of nonbeating and beating practices with a minimum number of timed anastomoses. Each part was followed by an assessment using an objective skills assessment tool score. The beating-heart simulator was built using motorized toy blocks connected wirelessly to a smartphone application. This was coded to enable rate selection. A junior consultant was compared to the subject at the end of the program. Both were tasked to perform 1 coronary anastomosis for both off-pump coronary artery bypass (OPCAB) and minimally invasive CAB (MICS) setup. The primary outcomes were anastomotic time and score compared with the junior consultant. Secondary outcomes were progression of anastomotic time and score throughout the program.
RESULTS: Overall performance of the studied subject approached the performance of the junior consultant in terms of time (OPCAB, 489 vs 605 s; MICS, 712 vs 652 s) and scores (OPCAB, 21 vs 20.7; MICS, 19 vs 20.6). There were inverse correlations between anastomosis time and number of practices for both nonbeating and beating anastomoses. Overall improvement was observed in terms of assessment scoring by 26.6%.
CONCLUSIONS: A structured self-practice program using an affordable and accessible simulator was able to help trainees overcome the MICS anastomosis learning curve quicker when introduced earlier. This may encourage earlier adoption of MICS among surgeons.