Reevaluating health metrics:Unraveling the limitations of disabilityadjusted life years as an indicator in disease burden assessment

In 1993,the World Bank released a global report on the efficacy of health promotion,introducing the disability-adjusted life years(DALY)as a novel indicator.The DALY,a composite metric incorporating temporal and qualitative data,is grounded in preferences regarding disability status.This review delineates the algorithm used to calculate the value of the proposed DALY synthetic indicator and elucidates key methodological challenges associated with its application.In contrast to the quality-adjusted life years approach,derived from multi-attribute utility theory,the DALY stands as an independent synthetic indicator that adopts the assumptions of the Time Trade Off utility technique to define Disability Weights.Claiming to rely on no mathematical or economic theory,DALY users appear to have exempted themselves from verifying whether this indicator meets the classical properties required of all indicators,notably content validity,reliability,specificity,and sensitivity.The DALY concept emerged primarily to facilitate comparisons of the health impacts of various diseases globally within the framework of the Global Burden of Disease initiative,leading to numerous publications in international literature.Despite widespread adoption,the DALY synthetic indicator has prompted significant methodological concerns since its inception,manifesting in inconsistent and non-reproducible results.Given the substantial diffusion of the DALY indicator and its critical role in health impact assessments,a reassessment is warranted.This reconsideration is imperative for enhancing the robustness and reliability of public health decisionmaking processes.

Physiology and health assessment,risk balance,and model for endstage liver disease scores:Postoperative outcome of liver transplantation

BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To provide fair organ distribution,predictive mortality scores have been developed.AIM To compare the Acute Physiology and Chronic Health Evaluation IV(APACHE IV),balance of risk(BAR),and model for end-stage liver disease(MELD)scores as predictors of mortality.METHODS Retrospective cohort study,which included 283 adult patients in the postoperative period of deceased donor liver transplantation from 2014 to 2018.RESULTS The transplant recipients were mainly male,with a mean age of 58.1 years.Donors were mostly male,with a mean age of 41.6 years.The median cold ischemia time was 3.1 hours,and the median intensive care unit stay was 5 days.For APACHE IV,a mean of 59.6 was found,BAR 10.7,and MELD 24.2.The 28-day mortality rate was 9.5%,and at 90 days,it was 3.5%.The 28-day mortality prediction for APACHE IV was very good[area under the curve(AUC):0.85,P<0.001,95%CI:0.76-0.94],P<0.001,BAR(AUC:0.70,P<0.001,95%CI:0.58–0.81),and MELD(AUC:0.66,P<0.006,95%CI:0.55-0.78),P<0.008.At 90 days,the data for APACHE IV were very good(AUC:0.80,P<0.001,95%CI:0.71–0.90)and moderate for BAR and MELD,respectively,(AUC:0.66,P<0.004,95%CI:0.55–0.77),(AUC:0.62,P<0.026,95%CI:0.51–0.72).All showed good discrimination between deaths and survivors.As for the best value for liver transplantation,it was significant only for APACHE IV(P<0.001).CONCLUSION The APACHE IV assessment score was more accurate than BAR and MELD in predicting mortality in deceased donor liver transplant recipients.

Transfer Learning Empowered Skin Diseases Detection in Children

Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)rays.If left untreated,these diseases can have severe consequences and spread,especially among children.Early detection is crucial to prevent their spread and improve a patient’s chances of recovery.Dermatology,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and others.This study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the summer.The method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and ringworm.The proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,respectively.This illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.

A spatio-temporal multi-scale fusion algorithm for pine wood nematode disease tree detection

Pine wood nematode infection is a devastating disease.Unmanned aerial vehicle(UAV)remote sensing enables timely and precise monitoring.However,UAV aerial images are challenged by small target size and complex sur-face backgrounds which hinder their effectiveness in moni-toring.To address these challenges,based on the analysis and optimization of UAV remote sensing images,this study developed a spatio-temporal multi-scale fusion algorithm for disease detection.The multi-head,self-attention mechanism is incorporated to address the issue of excessive features generated by complex surface backgrounds in UAV images.This enables adaptive feature control to suppress redundant information and boost the model’s feature extraction capa-bilities.The SPD-Conv module was introduced to address the problem of loss of small target feature information dur-ing feature extraction,enhancing the preservation of key features.Additionally,the gather-and-distribute mechanism was implemented to augment the model’s multi-scale feature fusion capacity,preventing the loss of local details during fusion and enriching small target feature information.This study established a dataset of pine wood nematode disease in the Huangshan area using DJI(DJ-Innovations)UAVs.The results show that the accuracy of the proposed model with spatio-temporal multi-scale fusion reached 78.5%,6.6%higher than that of the benchmark model.Building upon the timeliness and flexibility of UAV remote sensing,the pro-posed model effectively addressed the challenges of detect-ing small and medium-size targets in complex backgrounds,thereby enhancing the detection efficiency for pine wood nematode disease.This facilitates early preemptive preser-vation of diseased trees,augments the overall monitoring proficiency of pine wood nematode diseases,and supplies technical aid for proficient monitoring.

A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.

Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.

Survey and Detection of Disease and Pest Occurrence in Citrus Nurseries

[Objectives]The paper was to ascertain the prevalence of diseases and pests in a range of citrus nurseries situated in Guangdong Province and its neighboring provinces.[Methods]Citrus diseases and pests were systematically investigated,and citrus leaf samples were randomly collected from 15 citrus nurseries across 8 regions in Guangdong Province and its neighboring provinces.Quantitative polymerase chain reaction(qPCR)and reverse transcription polymerase chain reaction(RT-PCR)techniques were employed to detect diseases in the collected samples.Additionally,root and substrate samples were obtained,and root-knot nematodes were isolated using the Baermann funnel method.[Results]The positive detection rate of citrus huanglongbing(HLB)was recorded at 3%,indicating an increase in attention towards this disease compared to 2013.Additionally,the positive detection rate for citrus bacterial canker disease(CBCD)was found to be 16.5%.It was observed that the majority of nurseries with positive samples employed open field rearing practices without the use of mesh chambers,and the primary source of scions was self-propagation.The detection rate of citrus tristeza virus(CTV)was found to be the highest,with a positive detection rate of 63%,and the prevalence in disease-bearing nurseries reached as high as 90%.In comparison to 2013,there had been no improvement in the condition of seedlings affected by CTV.The positive detection rate of citrus yellow vein clearing virus(CYVCV)was found to be 38%,with 70%of the surveyed nurseries exhibiting the disease.The citrus varieties identified as carriers of the disease included‘Qicheng’,‘Shatangju’,‘Wogan’,and‘Gonggan’.Nematodes were isolated from the matrix and roots of seedlings grown in both container and open field environments.The susceptibility of container seedlings to nematodes was found to be 36.4%,while the susceptibility of open field seedlings was 38.6%.Statistical analysis indicated no significant difference in susceptibility between the two groups.[Conclusions]The disease detection rates associated with various seedling rearing methods and citrus varieties exhibited notable variability.Open field seedlings without the protection of mesh chambers demonstrated a higher susceptibility to disease.Additionally,the types of infectious diseases varied among the different citrus varieties.

Detection and Identification of Phytoplasma of Cleome rutidosperma in Areca Palm Yellow Leaf Disease Field

[Objectives]The paper was to detect and identify the phytoplasma of Cleome rutidosperma in areca palm yellow leaf disease(YLD)field in Wenchang City,Hainan Province,China.[Methods]The nested PCR technique was employed to amplify the phytoplasma 16S rDNA of C.rutidosperma samples,followed by sequence analysis.Concurrently,this study examined C.rutidosperma in YLD field,collecting symptomatic leaves for phytoplasma detection.[Results]The 16S rDNA sequence of the C.rutidosperma witches'-broom phytoplasma was found to be identical to that of the HNWC5 strain associated with areca palm yellows phytoplasma,leading to the identification of this phytoplasma as belonging to the 16SrII-A subgroup.Field investigations revealed a higher incidence of C.rutidosperma in areca palm fields,with symptoms of leaf yellows observed in six of these fields.Quantitative PCR(qPCR)analysis confirmed the presence of phytoplasma infection in these instances.[Conclusions]Through the analysis of geographical distribution,sequence alignment,and field occurrence data,a significant correlation has been identified between witches'broom disease and YLD.It is proposed that the former may act as an intermediate host for the areca palm yellows phytoplasma.

Diagnostic Value of the Padua Score Combined with Thrombotic Biomarker Tissue Plasminogen Activator Inhibitor-1 (tPAI-1) Detection for the Risk of Deep Vein Thrombosis in Patients with Pulmonary Heart Disease

This study explores the diagnostic value of combining the Padua score with the thrombotic biomarker tissue plasminogen activator inhibitor-1(tPAI-1)for assessing the risk of deep vein thrombosis(DVT)in patients with pulmonary heart disease.These patients often exhibit symptoms similar to venous thrombosis,such as dyspnea and bilateral lower limb swelling,complicating differential diagnosis.The Padua Prediction Score assesses the risk of venous thromboembolism(VTE)in hospitalized patients,while tPAI-1,a key fibrinolytic system inhibitor,indicates a hypercoagulable state.Clinical data from hospitalized patients with cor pulmonale were retrospectively analyzed.ROC curves compared the diagnostic value of the Padua score,tPAI-1 levels,and their combined model for predicting DVT risk.Results showed that tPAI-1 levels were significantly higher in DVT patients compared to non-DVT patients.The Padua score demonstrated a sensitivity of 82.61%and a specificity of 55.26%at a cutoff value of 3.The combined model had a significantly higher AUC than the Padua score alone,indicating better discriminatory ability in diagnosing DVT risk.The combination of the Padua score and tPAI-1 detection significantly improves the accuracy of diagnosing DVT risk in patients with pulmonary heart disease,reducing missed and incorrect diagnoses.This study provides a comprehensive assessment tool for clinicians,enhancing the diagnosis and treatment of patients with cor pulmonale complicated by DVT.Future research should validate these findings in larger samples and explore additional thrombotic biomarkers to optimize the predictive model.

Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System

Cardiovascular diseases are the most common cause of death worldwide over the last few decades in the developed as well as underdeveloped and developing countries. Early detection of cardiac diseases and continuous supervision of clinicians can reduce the mortality rate. However, accurate detection of heart diseases in all cases and consultation of a patient for 24 hours by a doctor is not available since it requires more sapience, time and expertise. In this?study, a tentative design of a cloud-based heart disease prediction system had been proposed to detect impending heart disease using Machine learning techniques. For the accurate detection of the heart disease, an efficient machine learning technique should be used which had been derived from a distinctive analysis among several machine learning algorithms in a Java Based Open Access Data Mining Platform, WEKA. The proposed algorithm was validated using two widely used open-access database, where 10-fold cross-validation is applied in order to analyze the performance of heart disease detection. An accuracy level of 97.53% accuracy was found from the SVM algorithm along with sensitivity and specificity of 97.50% and 94.94%respectively. Moreover, to monitor the heart disease patient round-the-clock by his/her caretaker/doctor, a real-time patient monitoring system was developed and presented using Arduino, capable of sensing some real-time parameters such as body temperature, blood pressure, humidity, heartbeat. The developed system can transmit the recorded data to a central server which are updated every 10 seconds. As a result, the doctors can visualize the patient’s real-time sensor data by using the application and start live video streaming if instant medication is required. Another important feature of the proposed system was that as soon as any real-time parameter of the patient exceeds the threshold, the prescribed doctor is notified at once through GSM technology.

Decoding molecular mechanisms:brain aging and Alzheimer's disease

The complex morphological,anatomical,physiological,and chemical mechanisms within the aging brain have been the hot topic of research for centuries.The aging process alters the brain structure that affects functions and cognitions,but the worsening of such processes contributes to the pathogenesis of neurodegenerative disorders,such as Alzheimer's disease.Beyond these observable,mild morphological shifts,significant functional modifications in neurotransmission and neuronal activity critically influence the aging brain.Understanding these changes is important for maintaining cognitive health,especially given the increasing prevalence of age-related conditions that affect cognition.This review aims to explore the age-induced changes in brain plasticity and molecular processes,differentiating normal aging from the pathogenesis of Alzheimer's disease,thereby providing insights into predicting the risk of dementia,particularly Alzheimer's disease.

Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel

Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.

Report on Cardiovascular Health and Diseases in China 2023:An Updated Summary

Since 1990,China has made considerable progress in resolving the problem of“treatment difficulty”of cardiovascular diseases(CVD).The prevalent unhealthy lifestyle among Chinese residents has exposed a massive proportion of the population to CVD risk factors,and this situation is further worsened due to the accelerated aging population in China.CVD remains one of the greatest threats to the health of Chinese residents.In terms of the proportions of disease mortality among urban and rural residents in China,CVD has persistently ranked first.In 2021,CVD accounted for 48.98%and 47.35%of deaths in rural and urban areas,respectively.Two out of every five deaths can be attributed to CVD.To implement a national policy“focusing on the primary health institute and emphasizing prevention”and truly achieve a shift of CVD prevention and treatment from hospitals to communities,the National Center for Cardiovascular Diseases has organized experts from relevant fields across China to compile the“Report on Cardiovascular Health and Diseases in China”annually since 2005.The 2024 report is established based on representative,published,and high-quality big-data research results from cross-sectional and cohort population epidemiological surveys,randomized controlled clinical trials,large sample registry studies,and typical community prevention and treatment cases,along with data from some projects undertaken by the National Center for Cardiovascular Diseases.These firsthand data not only enrich the content of the current report but also provide a more timely and comprehensive reflection of the status of CVD prevention and treatment in China.

Health Systems Strengthening to Tackle the Global Burden of Pediatric and Congenital Heart Disease: A Diagonal Approach

1 Background Congenital heart disease(CHD)is the most common major congenital anomaly,affecting approximately one in every 100 live births[1].Among congenital anomalies,66%of preventable deaths are due to CHD,and 58%of the avertable morbidity and mortality due to congenital anomalies would result from scaling congenital heart surgery services[2].Every year,nearly 300,000 children and adults die from CHD,the majority of whom live in low-and middle-income countries(LMICs)[3].Approximately 49%of all individuals with CHD will require surgical or interventional care at some point in their lifetime[4];as a result of advances in access to and the delivery of such services,over 95%of children born with CHD in high-income countries now live into adulthood[3].Here,adults have surpassed children in the number of CHD cases at a ratio of 2:1[5].

Deep Learning-Driven Anomaly Detection for IoMT-Based Smart Healthcare Systems

The Internet of Medical Things(IoMT)is an emerging technology that combines the Internet of Things(IoT)into the healthcare sector,which brings remarkable benefits to facilitate remote patient monitoring and reduce treatment costs.As IoMT devices become more scalable,Smart Healthcare Systems(SHS)have become increasingly vulnerable to cyberattacks.Intrusion Detection Systems(IDS)play a crucial role in maintaining network security.An IDS monitors systems or networks for suspicious activities or potential threats,safeguarding internal networks.This paper presents the development of an IDS based on deep learning techniques utilizing benchmark datasets.We propose a multilayer perceptron-based framework for intrusion detection within the smart healthcare domain.The primary objective of our work is to protect smart healthcare devices and networks from malicious attacks and security risks.We employ the NSL-KDD and UNSW-NB15 intrusion detection datasets to evaluate our proposed security framework.The proposed framework achieved an accuracy of 95.0674%,surpassing that of comparable deep learning models in smart healthcare while also reducing the false positive rate.Experimental results indicate the feasibility of using a multilayer perceptron,achieving superior performance against cybersecurity threats in the smart healthcare domain.

Evaluation of dry eye disease symptomatology and mental health status among patients with different COVID-19 statuses

AIM:To evaluate dry eye disease(DED)symptomatology and mental health status in different COVID-19 patients.METHODS:A cross-sectional observational design was used.Totally 123 eligible adults(46.34%of men,age range,18-59y)with COVID-19 included in the study from August to November,2022.Ocular Surface Disease Index(OSDI),Five-item Dry Eye Questionnaire(DEQ-5),Hospital Anxiety and Depression Scale(HADS),and Pittsburgh Sleep Quality Index(PSQI)were used in this study.RESULTS:OSDI scores were 6.82(1.25,15.91)in asymptomatic carriers,7.35(2.50,18.38)in mild cases,and 16.67(4.43,28.04)in recurrent cases,with 30.00%,35.56%,and 57.89%,respectively evaluated as having DED symptoms(χ2=7.049,P=0.029).DEQ-5 score varied from 2.00(0,6.00)in asymptomatic carriers,3.00(0,8.00)in mild cases,and 8.00(5.00,10.00)in recurrent cases,with 27.50%,33.33%,and 55.26%,respectively assessed as having DED symptoms(χ2=8.532,P=0.014).The prevalence of clinical anxiety(50.00%)and depression(47.37%)symptoms were also significantly higher in patients with recurrent infection(χ2=24.541,P<0.001;χ2=30.871,P<0.001).Recurrent infection was a risk factor for high OSDI scores[odds ratio,2.562;95%confidence interval(CI),1.631-7.979;P=0.033]and DEQ-5 scores(odds ratio,3.353;95%CI,1.038-8.834;P=0.043),whereas having a fixed occupation was a protective factor for OSDI scores(odds ratio,0.088;95%CI,0.022-0.360;P=0.001)and DEQ-5 scores(odds ratio,0.126;95%CI,0.039-0.405;P=0.001).CONCLUSION:Patients with recurrent COVID-19 have more severe symptoms of DED,anxiety,and depression.

Impact of Social Determinants of Health on Self-Perceived Resilience: An Exploratory Study of Two Cohorts of Adults with Congenital Heart Disease

Social determinants of health(SDOH)affect quality of life.We investigated SDOH impacts on self-perceived resilience among people with adult congenital heart disease(ACHD).Secondary analysis of data from two com-plementary studies:a survey study conducted May 2021–June 2022 and a qualitative study conducted June 2020–August 2021.Resilience was assessed through CD-RISC10 score(range 0–40,higher scores reflect greater self-perceived resilience)and interview responses.Sociodemographic and SDOH(education,employment,living situa-tion,monetary stability,financial dependency,area deprivation index)data were collected by healthcare record review and self-report.We used linear regression with robust standard errors to analyze survey data and performed a thematic analysis of interview data.Survey participants(N=127)mean age was 42±14 years;51%were female,87%white.ACHD was moderate(75%)or complex(25%);41%functional class C or D.Resilience(mean 30±7)varied by monetary stability:compared to people with difficulty paying bills,resilience was 15.0 points higher(95%CI:6.9–23.1,p<0.001)for people reporting having enough money and 14.2 points higher(95%CI:5.9–22.4,p=0.001)for those reporting just enough money.Interview participants’(N=25)mean age was 32 years(range 22–44);52%were female,72%white.ACHD was moderate(56%)or complex(44%);76%functional class C or D.Participants discussed factors affecting resilience aligned with each of the major SDOH,prominently,economic stability and healthcare access and quality.Financial stability may be important for supporting self-perceived resi-lience in ACHD.This knowledge can inform the development of resilience interventions for this population.

Postpartum quality of life and mental health in women with heart disease:Integrated clinical communication and treatment

BACKGROUND Postpartum quality of life(QoL)in women with heart disease has been neglected.AIM To improve clinical communication and treatment,we integrated medical data and subjective characteristics to study postpartum QoL concerns.METHODS The study assessed QoL 6 wk after birth using the 12-Item Short-Form Health Survey.The Edinburgh Postnatal Depression Scale,Cardiac Anxiety Questionnaire,European Heart Failure Self-Care Behavior Scale,and a self-designed questionnaire based on earlier research were also used to assess patient characteristics.Patient data were collected.Prediction models were created using multiple linear regression.RESULTS This retrospective study examined postpartum QoL in 105 cardiac patients.Postpartum QoL scores were lower(90.69±13.82)than those of women without heart disease,with physical component scores(41.09±9.91)lower than mental component scores(49.60±14.87).Postpartum depression(33.3%),moderate anxiety(37.14%),pregnancy concerns(57.14%),offspring heart problems(57.14%),and life expectancy worries(48.6%)were all prevalent.No previous cardiac surgery,multiparity,higher sadness and cardiac anxiety,and fear of unfavorable pregnancy outcomes were strongly related to lower QoL(R^(2)=0.525).CONCLUSION Postpartum QoL is linked to physical and mental health in women with heart disease.Our study emphasizes the need for healthcare workers to recognize the unique characteristics of these women while developing and implementing comprehensive management approaches during their maternity care.

Observation on the Effect of Health Education in Health Management of Chronic Disease Patients

Objective: By the end of 2021, the aging rate of China’s population is 18.9%, and the prevalence rate of chronic diseases in the elderly population is increasing year by year, and chronic diseases have become the main causes of death and health threats of Chinese residents. Therefore, how to manage this huge group well is crucial. This paper analyzes the value of health education in the process of health management for patients with chronic diseases. Methods: 102 patients with chronic diseases treated from January 2021 to December 2021 were divided into control group and experimental group by random number table method. The control group was given routine health management while the experimental group was given health education based on the control group, and the implementation effect was analyzed. Results: After management, the scores of chronic disease knowledge in the experimental group were significantly higher than those in the control group, and the dimensions of ESCA were higher than those in the control group, and P < 0.05;Conclusion: The implementation of health education in the process of chronic disease health management is helpful to improve patients’ self-care ability and better control disease progression.

The Interventional Capacity of Community Health Volunteers for Screening and Linkages of Non-Communicable Diseases in Nyeri County, Kenya

Non-communicable diseases (NCDs) account for 63% of mortalities. Approximately 80% of these NCD-related deaths occur in LMICs. A quasi-experimental study utilizing a non-equivalent pre-and post-test was conducted from May 2022 to March 2023 with 370 study participants. Multistage cluster and random sampling were used to select ten community units, and therefore, 150 CHVs were chosen for the control unit, and 150 were used to form the interventional group. Data was collected from the KOBO app. Six (6) homogenous FGDs comprised ten members, and 10 KII were conducted across study sites. Quantitative data was analyzed using SPSS version 28.0, and qualitative data was audio-recorded, transcribed, and analyzed via N-Vivo 12. The study shows that 59.3% of respondents have minimal information, and 92.7% (n = 139) have no clear understanding of NCDs, with a pre-intervention capacity of 48.8%. Independent sample t-test showed a significant difference in capacity from a pre-intervention average of 48.75 (SD ± 5.7)%, which increased to 68.28 (SD ± 7.6)%, p < 0.001. A well-designed community interventional model plays a pivotal role in grassroots healthcare delivery but requires optimization for NCD management.