Effective Health Care Interventions Research Articles

An Ensemble Machine Learning and Data Mining Approach to Enhance Stroke Prediction.

Stroke poses a significant health threat, affecting millions annually. Early and precise prediction is crucial to providing effective preventive healthcare interventions. This study applied an ensemble machine learning and data mining approach to enhance the effectiveness of stroke prediction. By employing the cross-industry standard process for data mining (CRISP-DM) methodology, various techniques, including random forest, ExtraTrees, XGBoost, artificial neural network (ANN), and genetic algorithm with ANN (GANN) were applied on two benchmark datasets to predict stroke based on several parameters, such as gender, age, various diseases, smoking status, BMI, HighCol, physical activity, hypertension, heart disease, lifestyle, and others. Due to dataset imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied to the datasets. Hyperparameter tuning optimized the models via grid search and randomized search cross-validation. The evaluation metrics included accuracy, precision, recall, F1-score, and area under the curve (AUC). The experimental results show that the ensemble ExtraTrees classifier achieved the highest accuracy (98.24%) and AUC (98.24%). Random forest also performed well, achieving 98.03% in both accuracy and AUC. Comparisons with state-of-the-art stroke prediction methods revealed that the proposed approach demonstrates superior performance, indicating its potential as a promising method for stroke prediction and offering substantial benefits to healthcare.

A comparative study of ProRithm and standard monitoring techniques for non-invasive blood pressure measurement using photoplethysmography and electrocardiography signals through artificial intelligence/machine learning methods

Background: Multi-parameter monitoring devices are essential for providing real-time patient data, which is crucial for effective healthcare interventions. This clinical trial evaluated the accuracy of the ProRithm beat-to-beat cuffless device for arterial blood pressure monitoring, comparing it with a standard sphygmomanometer. Methods: This observational study included 30 subjects aged 18 and above. Systolic and diastolic blood pressure measurements from both the ProRithm device and the Philips Monitor were compared using statistical analysis. Results: The analysis revealed no statistically significant differences between the ProRithm device and the manual method. In comparison with manual measurements using a sphygmomanometer, the mean systolic blood pressure was 131.2 mmHg with ProRithm it was 129.3 mmHg. Similarly, with the manual method, while the mean diastolic blood pressure was 76.2 mmHg and with ProRithm it was 75.9 mmHg. Conclusions: This study indicates that portable, small-sized devices like ProRithm, which facilitate remote monitoring, are effective for real-time blood pressure assessment in clinical settings.

Anaemia prevalence and risk factors in pregnant women in Oman: a retrospective case-control study

Background/Aims Anaemia is a multifactorial condition and understanding its prevalence and associated factors is crucial for effective healthcare planning and interventions. The aim of this study was to explore the prevalence of anaemia during pregnancy and its potential risk factors in Oman. Methods A retrospective approach was used to identify anaemia cases by examining medical records taken from 9321 women who gave birth at the hospital over 3 years. Chi-squared tests were used to establish differences in risk factors using a case-control design, with a 1:2 ratio. Results The crude prevalence of anaemia was 31.2%, dropping to 4.9% after excluding women with chronic and/or acute diseases. Significant differences were found in nationality (χ2=4.738, P=0.030), body mass index (χ2=6.392, P=0.041) and gestational age (χ2=4.329, P=0.037) for women with anaemia compared to those without. Conclusions These findings underscore the significance of assessing and managing anaemia in pregnancy, while considering the interaction of demographic and clinical variables in formulating anaemia prevention strategies.

Diabetes Prediction using Machine Learning

This study explores the application of machine learning for diabetes prediction. Leveraging a dataset of relevant features such as glucose levels, BMI, and family history, various algorithms are employed to develop predictive models. The goal is to enhance early detection and management of diabetes, contributing to more effective healthcare interventions. Results indicate promising accuracy and potential for real-world implementation in preventive healthcare systems. This presents an approach for predicting diabetes using machine learning techniques. With the increasing prevalence of diabetes worldwide, early detection and effective management are crucial for mitigating its impact on public health. Leveraging machine learning algorithms, such as decision trees, support vector machines, and neural networks, this research aims to develop predictive models based on various patient attributes and medical history data. The dataset used for model training and evaluation comprises demographic information, clinical measurements, and lifestyle factors collected from diabetic patients. Through extensive experimentation and evaluation, the performance of different machine learning algorithms is compared in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). The results demonstrate the efficacy of the proposed approach in accurately predicting diabetes risk, thereby offering valuable insights for preventive healthcare strategies and personalized treatment plans

Health-related quality of life and influencing factors of patients with paroxysmal nocturnal hemoglobinuria in China

BackgroundParoxysmal nocturnal hemoglobinuria (PNH) is a rare blood disorder, leading to various complications and impairments in patients’ health-related quality of life (HRQOL). Limited research has been conducted to evaluate the HRQOL of Chinese patients with PNH. Understanding the HRQOL in this specific population is crucial for providing effective healthcare interventions and improving patient’ health outcomes. This study aimed to assess HRQOL of Chinese patients with PNH, and identify key determinants.MethodsA cross-sectional study was conducted during 2022 to recruit patients with PNH in China. The study population was recruited from PNH China, one of the largest public welfare PNH patient mutual aid organization in China. Data were collected via an online questionnaire including the EQ-5D-5L (5L), and social-demographic and clinical characteristics. Descriptive statistics were employed to summarize the characteristics of the participants and their HRQOL. Multiple linear and logistic regression analyses were adopted to explore key factors affecting HRQOL.ResultsA total of 329 valid questionnaires were collected. The mean (SD) age of the patients was 35.3 (10.0) years, with 52.3% of them being male. The patients reported more problems in Anxiety/Depression (81.5%) and Pain/Discomfort (69.9%) dimensions compared to the other three 5L dimensions. The mean (SD) of 5L health utility score (HUS) and EQ-VAS score were 0.76 (0.21) and 62.61 (19.20), respectively. According to multiple linear regression, initial symptoms (i.e., Anemia [fatigue, tachycardia, shortness of breath, headache] and back pain) and complication of thrombosis were significant influencing factors affecting 5L HUS. Total personal income of the past year, initial symptom of hemoglobinuria and complication of thrombosis were significantly influencing factors of VAS score. Social-demographic and clinical characteristics, such as gender, income, and thrombosis, were also found to be significantly related to certain 5L health problems as well.ConclusionOur study manifested the HRQOL of PNH patients in China was markedly compromised, especially in two mental-health related dimensions, and revealed several socio-demographic and clinical factors of their HRQOL. These findings could be used as empirical evidence for enhancing the HRQOL of PNH patients in China.

A Qualitative evaluation in community settings in England exploring the experiences of coaches delivering the NHS Low Calorie Diet programme pilot to ethnically diverse participants

BackgroundThe management of type 2 diabetes (T2D) within diverse ethnic populations requires a culturally tailored approach. However, little is known about the experiences of coaches delivering interventions for T2D, such...

A Review On Early Detection Of Chronic Kidney Disease

Early detection of Chronic Kidney Disease (CKD) is critical for timely intervention and effective treatment. Deep learning algorithms have demonstrated promise in medical applications, including disease detection. In this study, we propose a deep learning-based system for early CKD detection using the Chronic Kidney Disease dataset from Kaggle. Additionally, we incorporate the Grasshopper Optimization Algorithm (GOA) for feature selection to enhance the system's performance and interpretability. Our system employs a convolutional neural network (CNN) architecture to analyze clinical and laboratory attributes from the CKD dataset, obtained from Kaggle, consisting of 4,000 instances with 25 attributes. These attributes encompass patient demographics, blood tests, and medical history, providing a comprehensive representation of CKD-related factors. To improve the system's performance, we integrate the GOA for feature selection. The GOA is a nature-inspired metaheuristic optimization algorithm that mimics the foraging behavior of grasshoppers. It aims to identify the most relevant attributes associated with CKD from the dataset. By selecting a subset of informative features, we enhance the model's predictive accuracy and reduce overfitting. During the training phase, the CNN learns to automatically extract relevant features and patterns associated with CKD from the selected attributes. Additionally, data preprocessing techniques such as normalization and feature scaling are applied to further improve the model's performance and generalizability. To evaluate the system's performance, we conduct experiments using a separate test dataset comprising 1,000 instances from the CKD dataset. The incorporation of the GOA for feature selection in our deep learning system not only improves its performance but also enhances interpretability. By identifying the most relevant attributes associated with CKD, we focus on key biomarkers and risk factors, enhancing the system's accuracy and providing valuable insights into the disease. Our research showcases the potential of deep learning algorithms, coupled with GOA-based feature selection, for early CKD detection. By leveraging the Kaggle CKD dataset and incorporating the GOA, we contribute to improving the accuracy and applicability of the system in real-world clinical settings. To handle Big data we are proposing to implement this problem on Pyspark one of the Big data computational environments for effective learning. In this platform, we can dynamically scale the infrastructure as per the demand of the data. Ultimately, our work aims to advance the early detection and management of CKD, leading to improved patient outcomes and more effective healthcare interventions.

Unraveling the distinction between depression and anxiety: A machine learning exploration of causal relationships

ObjectiveDepression and anxiety, prevalent coexisting mood disorders, pose a clinical challenge in accurate differentiation, hindering effective healthcare interventions. This research addressed this gap by employing a streamlined Symptom Checklist 90 (SCL-90) designed to minimize patient response burden. Utilizing machine learning algorithms, the study sought to construct classification models capable of distinguishing between depression and anxiety. MethodsThe study included 4262 individuals currently experiencing depression alone (n = 2998), anxiety alone (n = 716), or both depression and anxiety (n = 548). Counterfactual diagnosis was used to construct a causal network on the dataset. Employing a causal network, the SCL-90 was simplified. Items that have causality with only depression, only anxiety and both depression and anxiety were selected, and these streamlined items served as input features for four distinct machine learning algorithms, facilitating the creation of classification models for distinguishing depression and anxiety. ResultsCross-validation demonstrated the performance of the classification models with the following metrics: (1) K-nearest neighbors (AUC = 0.924, Acc = 92.81 %); (2) support vector machine (AUC = 0.937, Acc = 94.38 %); (3) random forest (AUC = 0.918, Acc = 94.38 %); and (4) adaptive boosting (AUC = 0.882, Acc = 94.38 %). Notably, the support vector machine excelled, with the highest AUC and superior accuracy. ConclusionIncorporating the simplified SCL-90 and machine learning presents a promising, efficient, and cost-effective tool for the precise identification of depression and anxiety.

IJCM_100A: Mountain Heights and Aging Insights: A Comprehensive Geriatric Assessment and Its Correlates in a Mesmerizing Hilly City of Northern India

Background: Global population is experiencing a rapid increase in the proportion of older adults, posing unique challenges to healthcare systems. Objective: To conduct a comprehensive geriatric assessment in Shimla, a hilly city in Northern India Methodology: A total of 408 participants aged 60 years and above were included in the study. Data were collected through structured interviews using a specifically designed questionnaire covering wide range of topics including socio-demographic characteristics, medical history, functional assessment, cognitive function assessment, psychosocial assessment, nutritional assessment, physical examination, fall risk assessment, and polypharmacy assessment. Results: The study involved 408 participants, with an average age of 70 years, comprising 53% males and 47% females. Functional assessment indicated theneed for assistance in various activities of daily living (ADLs) (16-32%) and instrumental activities of daily living (IADLs) (28-42%) Cognitive function assessment using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) revealed cognitive impairment in 32% and 42%, respectively. The Clock Drawing Test indicated difficulties in time representation and spatial organization in 28%. Psychosocial assessment highlighted the prevalence of symptoms of depression in 36% and f anxiety in 27%. Nutritional assessment revealed that 29% were malnourished, and 34% were at high risk of malnutrition according to the Malnutrition Universal Screening Tool (MUST). Musculoskeletal disorders were found in 42%, while sensory impairments were observed in 28%. The fall risk assessment using the Timed Up and Go (TUG) test and the Berg Balance Scale indicated an increased risk of falls in 36% and 41%, respectively. Polypharmacy assessment highlighted that 82 were taking multiple medications concurrently. Conclusion: The findings emphasize the importance of addressing the specific challenges faced by older adults in such geographical contexts. The results can guide the development of effective healthcare interventions and policies that are tailored to the unique needs of older adults in hilly areas.

Prevalence, Pattern and Outcome of Congenital Anomalies in Patients Admitted at a Tertiary Level Hospital in Bangladesh

Congenital anomalies are a major public health concern globally, contributing significantly to perinatal and neonatal mortality. Understanding their prevalence, patterns, and outcomes are essential for effective healthcare planning and interventions. This cross-sectional observational study aimed to investigate the distribution and immediate outcomes of congenital anomalies among infants delivered at Bangabandhu Sheikh Mujib Medical College Hospital, Faridpur, Bangladesh. Study population comprised admitted pregnant mothers who delivered infants with congenital anomalies from January to December 2022. Data were collected from medical records, including parity, gestational age at delivery, mode of delivery and outcomes of congenital anomalies. Total 3461 deliveries occurred during this period, of which 88 had congenital anomalies with a prevalence of 2.54%. Among the 88 infants with congenital anomalies, the most prevalent anomalies were Anencephaly and Hydrocephalus, affecting the central nervous system. Anencephaly had a 100% mortality rate, emphasizing its severity and poor prognosis. Anencephaly was more common in infants delivered between 12 to 28 weeks. Early gestational stages play critical role in its pathogenesis. Hydrocephalus showed a relatively higher proportion of live births. Vaginal delivery was the predominant mode of delivery for most congenital anomalies. Public health efforts targeting early detection, appropriate care and support for affected families can potentially reduce the impact of congenital anomalies on maternal and neonatal health. Bangabandhu Sheikh Mujib Med. Coll. J. 2024;3(1):27-31

Prevalence and Causes of Low Back Pain Among Women in the South-Western Region of Saudi Arabia

Aims & Objective: Lower back pain is a prevalent health issue that affects individuals worldwide, including women. In Saudi Arabia, women face unique challenges and factors that contribute to the development and experience of lower back pain. Understanding the specific context of lower back pain among women in Saudi Arabia is essential for effective prevention, management, and healthcare interventions. Methodology: The descriptive analytical method was relied upon to complete this study, where the research sample consisted of 82 women from the Jazan University community who were chosen randomly. The average age of participants with low back pain was 27.5 years, ranging from 18-35 years. Women with pain had a higher mean weight (56 kg) and body mass index (mean 23.04 kg/m) than the total sample. Result: Results showed that there is a relationship between the severity of pain and the type of profession in the samples, as it was found that the category that suffers from the severity of pain greater than average is the category of teachers and students compared to other professions. The results also showed that there was no correlation between the severity of pain and the number of children for the women in the study sample (P value = 0.153>0.05). The nonsignificant p value (0.153) indicates that there is no clear association in this sample. Conclusion: Our study sheds light on the complex interplay of factors influencing lower back pain (LBP) among women, revealing no significant correlation between LBP severity and both age and BMI, and similarly with the number of children.

Vitality of older adults through internal and external connectedness

BackgroundVitality is a relatively unresearched concept and defined in existing literature either on the premise of one's functioning, or in terms of feeling alive. There is, however, little known about the significance of a sense of connectedness to life for vitality, especially from the perspective of older adults. This research aims to explore the association between the concept of connectedness and the perspective of vitality as a sense of aliveness, in order to formulate a theoretical understanding grounded in the subjective perceptions that older adults themselves hold of vitality. MethodsFollowing a constructivist grounded theory approach and theoretical sampling, constant comparative analysis was performed on the transcripts of 15 semi-structured interviews, conducted in the Netherlands with older adults (mean age 73 years, eight females and seven males), regarding their vitality. Concurrently, empirical and theoretical findings were translated into a theoretical conceptualisation. ResultsAccording to the older participants, connecting to life itself enhanced their sense of vitality. This connection was achieved through internal connectedness (connecting to one's own life, intrinsic stimuli and intrinsic goals in an independent manner) and external connectedness (social connectedness, environmental connectedness, engagement with the external world). Furthermore, our findings revealed distinct interactions between internal and external connectedness and facilitating tools, such as freedom from physical constraints, financial freedom and adaptation. ConclusionsOur findings provide a holistic concept of vitality and connectedness and consolidate existing perspectives on vitality into an overarching framework, which can contribute to the development of effective care policy, healthcare interventions and welfare services.

Exploring the Epidemiology of Atrial Fibrillation in Nigeria: A Focus on Atrial Fibrillation in Heart Failure Patients and Its Association With Stroke.

Atrial fibrillation (AF) represents a significant global public health concern, particularly due to its association with adverse health outcomes such as stroke and heart failure. In Nigeria, where the burden of cardiovascular diseases is rising, understanding the prevalence and impact of AF is crucial for effective healthcare planning and intervention strategies. This review examines the epidemiology of AF in Nigeria, comparing it with global and African data. It explores demographic and regional variations, comorbidity factors, and the impact of AF on the healthcare system, mortality, and quality of life. Notably, the prevalence of AF in Nigeria generally falls just under 5%, but this figure rises to approximately 9% in stroke patients and 11-20% among those with heart failure (HF). Rheumatic heart disease (RHD) is identified as a significant AF risk factor within Africa, affecting around 20% of AF patients - a stark contrast to the 2% in North America. AF's association with higher mortality rates and functional deterioration highlights the urgent need for improved diagnostic and therapeutic approaches, alongside broader public health measures. In conclusion, the review emphasises the significant public health concern AF represents in Nigeria, especially among HF and stroke patients, and stresses the importance of tailored healthcare policies and interventions to mitigate AF's impact and improve patient outcomes.

Cultural variability in pain perception: A review of cross-cultural studies

Pain perception is a complex phenomenon influenced by biological, psychological, and socio-cultural factors. This review presents a review of cross-cultural studies investigating the cultural variability in pain perception. Understanding how different cultures experience and express pain is crucial for providing effective healthcare interventions and improving patient outcomes. Cross-cultural research reveals significant variations in pain perception across different cultural groups. Cultural beliefs, values, and practices shape individuals' interpretation and expression of pain. Studies have shown that cultural norms dictate whether individuals express pain openly or suppress it, impacting their willingness to seek medical help and their response to pain management strategies. Furthermore, cultural differences in pain expression extend to non-verbal cues and pain-related behaviors. For instance, some cultures may perceive stoicism as a virtue, leading individuals to underreport their pain levels, while others may encourage vocal expressions of pain. These cultural nuances influence healthcare professionals' interpretation of patients' pain experiences and may lead to disparities in pain assessment and treatment. Moreover, cultural factors interact with biological mechanisms to modulate pain perception. Genetic predispositions and physiological differences contribute to variations in pain sensitivity among different ethnic groups. However, cultural factors such as coping mechanisms, social support networks, and cultural rituals also play a significant role in shaping individuals' pain experiences. The impact of culture on pain perception extends beyond individual differences to influence healthcare systems and policies. Cultural competency training for healthcare providers is essential to address disparities in pain management and ensure culturally sensitive care. Additionally, integrating cultural factors into pain assessment tools and treatment protocols can improve the accuracy of pain assessment and enhance patient care outcomes. Cultural variability significantly influences pain perception, expression, and management. Cross-cultural studies provide valuable insights into the complex interplay between culture, biology, and socio-cultural factors in shaping individuals' experiences of pain. Recognizing and addressing cultural differences in pain perception is essential for delivering equitable and effective healthcare services worldwide.

Survival Analysis of Risk Factors for Mortality of Patients with Cardiovascular Diseases at Tikur Anbessa Specialized Tertiary Referral Hospital, Ethiopia: A Retrospective.

Cardiovascular diseases (CVD) represent a leading cause of global mortality, necessitating proactive identification of risk factors for preventive strategies. This study aimed to uncover prognostic factors influencing cardiovascular patient survival. This study, which used a sample size of 410, showed how to analyze data using simple random sampling. It was conducted at the Tikur Anbessa Specialist Hospital in Addis Ababa, Ethiopia, between September 2012 and April 2016. The Cox PH and stratified Cox regression models were used for the analysis. Findings disclosed a patient cohort where 200 patients (48.8%) persisted through subsequent evaluation, while 210 patients (51.2%) succumbed. Blood pressure (BP), specific CVD, and education levels (EL) exhibited nonproportionalities in scaled Schoenfeld residuals (P < 0.001), prompting necessary stratification. Inadequacies in the Cox proportional hazards model led to favoring the stratified Cox model. Notably, EL, BP, cholesterol level (CL), alcohol use (AU), smoking use (SU), and pulse rate (PR) exhibited statistical significance (P < 0.001). Acceptability of the absence of interaction in the model, with disease types as strata, was established. Different cardiovascular conditions served as distinct groups, where EL, AU, BP, PR, CL, and SU emerged as variables with statistically substantiated significance associated with the mortality of patients with CVD. Implications stress the imperative of widespread awareness among policymakers and the public concerning cardiovascular disease incidence. Such awareness is pivotal in mitigating identified risk factors, guiding more effective healthcare interventions tailored to the multifaceted challenges posed by cardiovascular health.

Carbon Monoxide Pollution and Limited Health Service Access in Third-world Countries

Despite its complexity, carbon monoxide is a key component of indoor and outdoor air pollution. While carbon monoxide arises from natural sources as well as human activities, the most detrimental exposure risks are within the domestic environment. In third-world countries like Guatemala, a notable positive correlation has been observed between severe respiratory diseases and the concentration of carbon monoxide in the air. This correlation can be attributed to these regions' housing conditions and daily lifestyle practices. Additionally, the limited healthcare services accessibility in third-world nations exacerbated the severity of domestic carbon monoxide poisoning cases. It is evident that effective healthcare interventions still need to be fully utilized in third-world countries, and thus, there is a need to move beyond identifying broad strategies and specific policies. Addressing the issues of carbon monoxide exposure and its impact on public health in these regions requires comprehensive approaches that take into account the challenges faced by these countries.&#x0D; To access carbon monoxide concentration in the household, EsayLog USB has been used in this study. The study reviews the amount of carbon monoxide present in certain villages with different conditions and high carbon monoxide concentrations, especially near fires using combustible fuels and houses at high altitudes. The study found that third-world countries' decision-makers should educate communities, build the environment to provide better medical access to the public and implement appropriate regulations to deliver better quality health care to people.

The Role of Primary Healthcare in Improving the Maternal and Infant Mortality in Pakistan

Background: Maternal and neonatal mortality remains a significant public health challenge globally, particularly in countries like Pakistan, which exhibit some of the highest rates of such mortalities. Effective primary healthcare interventions are crucial for mitigating these rates, focusing on enhancing the quality of care provided to mothers and newborns. Understanding the scope and impact of these interventions can inform future strategies to improve maternal and neonatal health outcomes. Objective: This review aims to evaluate the existing literature on primary healthcare interventions targeting maternal and neonatal mortality, identifying the types of interventions employed, their focus areas, and the outcomes achieved, with a specific interest in the context of Pakistan. Methods: A scoping review methodology was employed, analyzing studies that investigated primary healthcare interventions designed to reduce maternal and neonatal mortality. The review included studies published in English, utilizing a mix of research methodologies, and focused on interventions such as morbidity and mortality review processes, healthcare worker training, and community engagement strategies. Databases searched included Google Scholar, OVID, and the WHO Global Health Library, among others. Results: Out of 25 studies reviewed, a majority concentrated on healthcare team and program-level interventions rather than systemic healthcare reforms. Key areas of focus included enhancing review processes for maternity and neonatal care, training and education for healthcare workers, and community development initiatives. However, there was a noted lack of interventions directly improving the healthcare experiences of mothers and newborns. Mixed methods were the most common research approach, aligning with the complexity of evaluating health system interventions. Conclusion: To improve maternal and neonatal health outcomes in Pakistan, a comprehensive approach addressing the entire health system is required. This involves not only focusing on the delivery of healthcare services but also incorporating community insights, continuous education programs, and leadership modifications in healthcare services. Future strategies should prioritize the application of standardized and relevant methods to enhance the quality of maternity and newborn care initiatives, aiming for scalable improvements in healthcare outcomes.

Cardiovascular Comorbidities in COVID-19: Comprehensive Analysis of Key Topics.

The interrelation between COVID-19 and various cardiovascular and metabolic disorders has been a critical area of study. There is a growing need to understand how comorbidities such as cardiovascular diseases (CVDs) and metabolic disorders affect the risk and severity of COVID-19. The objective of this study is to systematically analyze the association between COVID-19 and cardiovascular and metabolic disorders. The focus is on comorbidity, examining the roles of CVDs such as embolism, thrombosis, hypertension, and heart failure, as well as metabolic disorders such as disorders of glucose and iron metabolism. Our study involved a systematic search in PubMed for literature published from 2000 to 2022. We established 2 databases: one for COVID-19-related articles and another for CVD-related articles, ensuring all were peer-reviewed. In terms of data analysis, statistical methods were applied to compare the frequency and relevance of MeSH (Medical Subject Headings) terms between the 2 databases. This involved analyzing the differences and ratios in the usage of these terms and employing statistical tests to determine their significance in relation to key CVDs within the COVID-19 research context. The study revealed that "Cardiovascular Diseases" and "Nutritional and Metabolic Diseases" were highly relevant as level 1 Medical Subject Headings descriptors in COVID-19 comorbidity research. Detailed analysis at level 2 and level 3 showed "Vascular Disease" and "Heart Disease" as prominent descriptors under CVDs. Significantly, "Glucose Metabolism Disorders" were frequently associated with COVID-19 comorbidities such as embolism, thrombosis, and heart failure. Furthermore, iron deficiency (ID) was notably different in its occurrence between COVID-19 and CVD articles, underlining its significance in the context of COVID-19 comorbidities. Statistical analysis underscored these differences, highlighting the importance of both glucose and iron metabolism disorders in COVID-19 research. This work lays the foundation for future research that utilizes a knowledge-based approach to elucidate the intricate relationships between these conditions, aiming to develop more effective health care strategies and interventions in the face of ongoing pandemic challenges.

Temporal Patterns of Diabetes in Pregnancy: Analyzing Pregestational and Gestational Diabetes Mellitus Among Women Aged 15-44 Years Using the United States Diabetes Surveillance System (USDDS) Database.

Diabetes in pregnancy, including pregestational diabetes mellitus (PGDM) and gestational diabetes mellitus (GDM), is a significant global health concern. Understanding its temporal patterns is crucial for effective healthcare planning and intervention strategies. This retrospective observational study utilized data from the United States Diabetes Surveillance System (USDDS) spanning 2000 to 2019. We conducted a comprehensive analysis of temporal trends in PGDM and GDM prevalence among people aged 15-44 years. Additionally, age and race-based subgroup analyses were performed to identify variations in diabetes patterns. Over the two-decade study period, PGDM and GDM exhibited distinct temporal patterns. PGDM prevalence remained stable initially (1.1% per 100 deliveries) but gradually increased to 1.6% by 2019. In contrast, GDM prevalence showed a consistent rise, reaching 9.6% per 100 deliveries by 2019. Age-specific analysis revealed higher prevalence rates in older age groups, peaking at 40-44 years. Race-based analyses unveiled significant disparities, with Asians having the highest GDM rates and Black individuals having the highest PGDM rates. The prevalence of diabetes in pregnancy in the United States has increased significantly from 2000 to 2019, emphasizing the need for continued surveillance and tailored interventions. Age and race-specific disparities highlight the importance of the growing impact of diabetes in pregnancy on maternal and fetal health.

Revealing associations between spatial time series trends of COVID-19 incidence and human mobility: an analysis of bidirectionality and spatiotemporal heterogeneity

BackgroundUsing human mobility as a proxy for social interaction, previous studies revealed bidirectional associations between COVID-19 incidence and human mobility. For example, while an increase in COVID-19 cases may affect mobility to decrease due to lockdowns or fear, conversely, an increase in mobility can potentially amplify social interactions, thereby contributing to an upsurge in COVID-19 cases. Nevertheless, these bidirectional relationships exhibit variations in their nature, evolve over time, and lack generalizability across different geographical contexts. Consequently, a systematic approach is required to detect functional, spatial, and temporal variations within the intricate relationship between disease incidence and mobility.MethodsWe introduce a spatial time series workflow to investigate the bidirectional associations between human mobility and disease incidence, examining how these associations differ across geographic space and throughout different waves of a pandemic. By utilizing daily COVID-19 cases and mobility flows at the county level during three pandemic waves in the US, we conduct bidirectional Granger causality tests for each county and wave. Furthermore, we employ dynamic time warping to quantify the similarity between the trends of disease incidence and mobility, enabling us to map the spatial distribution of trends that are either similar or dissimilar.ResultsOur analysis reveals significant bidirectional associations between COVID-19 incidence and mobility, and we develop a typology to explain the variations in these associations across waves and counties. Overall, COVID-19 incidence exerts a greater influence on mobility than vice versa, but the correlation between the two variables exhibits a stronger connection during the initial wave and weakens over time. Additionally, the relationship between COVID-19 incidence and mobility undergoes changes in direction and significance for certain counties across different waves. These shifts can be attributed to alterations in disease control measures and the presence of evolving confounding factors that differ both spatially and temporally.ConclusionsThis study provides insights into the spatial and temporal dynamics of the relationship between COVID-19 incidence and human mobility across different waves. Understanding these variations is crucial for informing the development of more targeted and effective healthcare policies and interventions, particularly at the city or county level where such policies must be implemented. Although we study the association between mobility and COVID-19 incidence, our workflow can be applied to investigate the associations between the time series trends of various infectious diseases and relevant contributing factors, which play a role in disease transmission.