Improving health literacy and stakeholder-directed knowledge of One Health through analysis of readability:a cross sectional infodemiology study

Background:The One Health approach involves collaboration across several sectors,including public health,veterinary and environmental sectors in an integrated manner.These sectors may be disparate and unrelated,however to succeed,all stakeholders need to understand what the other stakeholders are communicating.Likewise,it is important that there is public acceptance and support of One Health approaches,which requires effective communication between professional and institutional organisations and the public.To help aid and facilitate such communication,written materials need to be readable by all stakeholders,in order to communicate effectively.There has been an exponential increase in the publication of papers involving One Health,with<5 per year,in the 2000s,to nearly 500 published in 2023.To date,readability of One Health information has not been scrutinised,nor has it been considered as an integral intervention of One Health policy communication.The aim of this study was therefore to examine readability of public-facing One Health information prepared by 24 global organisations.Methods:Readability was calculated using Readable software,to obtain four readability scores[(i)Flesch Reading Ease(FRE),(ii)Flesch-Kincaid Grade Level(FKGL),(iii)Gunning Fog Index and(iv)SMOG Index]and two text metrics[words/sentence,syllables/word]for 100 sources of One Health information,from four categories[One Health public information;PubMed abstracts;Science in One Health(SOH)abstracts(articles);SOH abstracts(reviews)].Results:Readability of One Health information for the public is poor,not reaching readability reference standards.No information was found that had a readability of less than 9th grade(around 14 years old).Mean values for the FRE and FKGL were(19.4±1.4)(target>60)and(15.6±0.3)(target<8),respectively,with mean words per sentence and syllables per word of 20.5 and 2.0,respectively.Abstracts with“One Health”in the title were more difficult to read than those without“One Health”in the title(FRE:P=0.0337;FKGL:P=0.0087).Comparison of FRE and FKGL readability scores for the four categories of One Health information[One Health public information;PubMed abstracts;SOH abstracts(articles);SOH abstracts(reviews)]showed that SOH abstracts from articles were easier to read than those from SOH reviews.No One Health public-facing information from the 100 sources examined met the FKGL target of8.The most easily read One Health information required a Grade Level of 9th grade(14-15 years old),with a mean Grade Level of 15.5(university/college level).Conclusion:Considerable work is required in making One Health written materials more readable,particularly for children and adolescents(<14 years of age).It is important that any interventions or mitigations taken to support better public understanding of the One Health approach are not ephemeral,but have longer lasting and legacy value.Authors of One Health information should consider using readability calculators when preparing One Health information for their stakeholders,to check the readability of their work,so that the final material is within recommended readability reference parameters,to support the health literacy and stakeholder-directed knowledge of their readers.

Effects of anesthetic depth on postoperative pain and delirium:a meta-analysis of randomized controlled trials with trial sequential analysis

Background:Whether anesthetic depth affects postoperative outcomes remains controversial.This meta-analysis aimed to evaluate the effects of deepvs.light anesthesia on postoperative pain,cognitive function,recovery from anesthesia,complications,and mortality.Methods:PubMed,EMBASE,and Cochrane CENTRAL databases were searched until January 2022 for randomized controlled trials comparing deep and light anesthesia in adult surgical patients.The co-primary outcomes were postoperative pain and delirium(assessed using the confusion assessment method).We conducted a meta-analysis using a random-effects model.We assessed publication bias using the Begg’s rank correlation test and Egger’s linear regression.We evaluated the evidence using the trial sequential analysis and Grading of Recommendations Assessment,Development and Evaluation(GRADE)methodology.We conducted subgroup analyses for pain scores at different postoperative time points and delirium according to cardiac or non-cardiac surgery.Results:A total of 26 trials with 10,743 patients were included.Deep anesthesia compared with light anesthesia(a mean difference in bispectral index of-12 to-11)was associated with lower pain scores at rest at 0 to 1 h postoperatively(weighted mean difference=-0.72,95%confidence interval[CI]=-1.25 to-0.18,P=0.009;moderate-quality evidence)and an increased incidence of postoperative delirium(24.95%vs.15.92%;risk ratio=1.57,95%CI=1.28-1.91,P<0.0001;high-quality evidence).No publication bias was detected.For the exploratory secondary outcomes,deep anesthesia was associated with prolonged postoperative recovery,without affecting neurocognitive outcomes,major complications,or mortality.In the subgroup analyses,the deep anesthesia group had lower pain scores at rest and on movement during 24 h postoperatively,without statistically significant subgroup differences,and deep anesthesia was associated with an increased incidence of delirium after non-cardiac and cardiac surgeries,without statistically significant subgroup differences.Conclusions:Deep anesthesia reduced early postoperative pain but increased postoperative delirium.The current evidence does not support the use of deep anesthesia in clinical practice.

Handwashing quality assessment via deep learning:a modelling study for monitoring compliance and standards in hospitals and communities

Background Hand hygiene can be a simple,inexpensive,and effective method for preventing the spread of infectious diseases.However,a reliable and consistent method for monitoring adherence to the guidelines within and outside healthcare settings is challenging.The aim of this study was to provide an approach for monitoring handwashing compliance and quality in hospitals and communities.Methods We proposed a deep learning algorithm comprising three-dimensional convolutional neural networks(3D CNNs)and used 230 standard handwashing videos recorded by healthcare professionals in the hospital or at home for training and internal validation.An assessment scheme with a probability smoothing method was also proposed to optimize the neural network’s output to identify the handwashing steps,measure the exact duration,and grade the standard level of recognized steps.Twenty-two videos by healthcare professionals in another hospital and 28 videos recorded by civilians in the community were used for external validation.Results Using a deep learning algorithm and an assessment scheme,combined with a probability smoothing method,each handwashing step was recognized(ACC ranged from 90.64%to 98.87%in the hospital and from 87.39%to 96.71%in the community).An assessment scheme measured each step’s exact duration,and the intraclass correlation coefficients were 0.98(95%CI:0.97-0.98)and 0.91(95%CI:0.88-0.93)for the total video duration in the hospital and community,respectively.Furthermore,the system assessed the quality of handwashing,similar to the expert panel(kappa=0.79 in the hospital;kappa=0.65 in the community).Conclusions This work developed an algorithm to directly assess handwashing compliance and quality from videos,which is promising for application in healthcare settings and communities to reduce pathogen transmis-sion.