Prediction Charts Research Articles

Synergistic modeling of hemorrhagic dengue fever: Passive immunity dynamics and time-delay neural network analysis.

Dengue fever poses a formidable epidemiological challenge, particularly for vulnerable groups such as infants. This research paper establishes a mathematical model to describe the dynamics of secondary immunity in infants against dengue hemorrhagic fever, who acquired primary immunity through maternal antibodies. The effect of passive immunity in the form of dengue immunoglobulin is analyzed for high-risk patients for different scenarios, including standard dengue infections, host with pre-existing immunity, delayed diagnosis or treatment, and end-stage dengue cases. Convergence analysis of the model is performed through disease free and disease endemic equilibrium points in terms of basic reproduction number R0 along with local stability of disease-free equilibrium point. Adams numerical approach is utilized to simulate dengue disease/immunity interactions. A time delay exogenous neural network approach coupled with Levenberg-Marquardt optimization is designed to characterize, model and simulate these curated scenarios. Exhaustive neural network procedures determine the efficacy of the neural network approach by means of mean square error (MSE) loss charts, error correlation graphs, error histogram analysis and time-series prediction charts. The impeccable characterization of the dengue fever scenarios is supported by extremely low MSE results of the order 10-9 to 10-11. To further showcase the competency of the neural network predictions, an exhaustive comparative study against the reference numerical solutions is illustrated with absolute errors in the range of 10-3 to 10-5. The novel development of mathematical model coupled with time-delay exogenous neural networks significantly enhances our ability to understand and predict the intricate dengue hemorrhagic fever dynamics allowing for targeted interventions for such infectious disease and epidemiological scenarios.

Feasibility of using a multivariate serum biomarker model in early pregnancy to predict gestational hypertension

Background With the increasing need for early prediction and intervention of Pregnancy-Induced Hypertension (PIH), researchers have begun to explore the use of multiserum biomarker models to improve the accuracy and reliability of predictions. It is estimated that between 5% and 8% of pregnant women worldwide experience pregnancy-induced hypertension, which is one of the leading causes of maternal death and adverse neonatal outcomes. Given the potential negative impact of pregnancy-induced hypertension on maternal and infant health, early identification of high-risk individuals and appropriate preventive measures are particularly important. Objective To assess the feasibility of using a multivariate serum biomarker model in early pregnancy to predict gestational hypertension. Methods Retrospective analysis was conducted on the clinical data of 125 pregnant women admitted to our hospital from January 2021 to December 2022. The occurrence of gestational hypertension was recorded and multiple serum biomarkers were collected and compared between the exposure and non-exposure groups. Logistic regression analysis was performed to identify influencing factors for gestational hypertension. Correlations between each factor and gestational hypertension were analyzed, and a line chart model was constructed. The discriminative ability of the model was evaluated using the C-index, and internal validation was conducted using ten-fold cross-validation and bootstrap validation. Results Out of 125 pregnant women, 35 (28.00%) developed gestational hypertension. β-HCG and Hcy were identified as independent risk factors, while PAPP-A, AFP, and uE3 were identified as independent protective factors. There was a positive correlation between Hcy, β-HCG, and gestational hypertension, and a negative correlation between PAPP-A, AFP, uE3, and gestational hypertension. The predictive line chart model had a C-index of 0.885 and an average AUC value of 0.853 after internal validation. Conclusion β-HCG and Hcy are risk factors, while PAPP-A, AFP, and uE3 are protective factors for gestational hypertension. A line chart model based on these factors can help identify pregnant women at risk of developing gestational hypertension in early pregnancy.

Cardio Vascular Disease Risk Assessment by Medical Officers According to the Guidelines at Healthy Lifestyle Centers in Western and North Western Provinces and the Factors Affecting Implementation of Guidelines

Introduction: Healthy Lifestyle Centers (HLC) were established in 2011 to carry out Cardiovascular Disease (CVD) Risk Assessments and follow up with patients. The capacity of primary care institutions (PCI) for CVD risk assessment has not been studied in the recent past.Objective: To evaluate the existing CVD risk assessment guideline and assess its application process by Medical Officers (MOs) and facilities available at HLCs for CVD risk assessment.Methodology: A descriptive study with three components was carried out. Component one – CVD risk assessment Guideline evaluated through Key informant interviews (KII); component two- assessment of the Knowledge and practices on CVD risk assessment of all MOs working in HLCs using a self-administered questionnaire and an observational checklist; component three- assessment of available facilities and resources to carry out CVD risk assessment using a checklist.Results and Discussion: The study was conducted in 141 HLCs accessible during the Covid period. Assessment of CVD risk assessment guidelines showed Problems in methodological standards and in the identification and summary of evidence. MOs need to gain Knowledge in screening those under 35 years, in reading the WHO/ISH Risk Prediction Chart and in advising on further management and follow-up. All HLCs have at least one medical officer, but there is a lack of nursing officers and health care assistants.Conclusions and recommendations: There was a gap in Knowledge in screening criteria and management of those under 35 years of age. It is recommended to carry out training at national level every six months so all staff newly appointed to primary care could be trained. It is also recommended to appoint at least one nursing officer or a Public Health Nursing Officer (PHNO) to an HLC.

Study on the Vertical Propagation Behavior of Hydraulic Fractures in Thin Interbedded Tight Sandstone

Hydraulic fracturing technology is vital for the efficient extraction of oil and gas from low-permeability tight sandstone reservoirs.Taking the central Bohai oilfield in China as an example, these fields are typically composed of thinly interbedded tight sandstone, characterized by low permeability and significant lithological heterogeneity between layers. Fractures may either be confined, limiting vertical growth and reducing production, or overextend into water-bearing zones, causing contamination and compromising reservoir integrity. Therefore, predicting vertical fracture propagation during field fracturing operations is critical for efficient resource extraction.However, there is still a lack of comprehensive understanding of the mechanisms governing vertical fracture growth offshore.This paper applies numerical simulations based on the finite element method to elucidate the interlayer fracture propagation behavior in low-permeability tight sandstone reservoirs. A fracture propagation model for thin interlayered tight sandstone formations is constructed, and the effects of various factors on hydraulic fracture propagation are systematically analyzed, including geological factors such as interlayer stress contrast, thickness, and differences in elastic modulus, as well as operational parameters including fracturing fluid viscosity and injection rate. This study clarifies the cross-layer propagation patterns of hydraulic fractures under the influence of multiple factors and yields a comprehensive prediction chart for fracture propagation thickness under the combination of complex factors. The results of this research can provide theoretical support for the design of reservoir stimulation operations in low-permeability tight sandstone oilfields.

Early Period Survival and Neurologic Prognosis in Newborns with Perinatal Asphyxia: A Tertiary Center Experience and a Mortality Chart

Background: Clinicians require more data regarding mortality and brain damage risk factors in perinatal asphyxia. Objectives: To assess early term outcomes and identify mortality risk factors in perinatal asphyxia. Methods: This study was conducted in a referral-center tertiary intensive care unit in Istanbul, Turkey, between 2016 and 2023. We included all patients who underwent therapeutic hypothermia treatment due to perinatal asphyxia. We recorded laboratory follow-up data, magnetic resonance imaging (MRI) findings, amplitude-integrated electroencephalograms (aEEG) results, mortality, and clinical outcomes. Both conventional frequentist statistical methods and Bayesian methods were used for analysis. Results: A total of 164 patients were included in the study, with an overall mortality rate of 9.8%. Risk factors for mortality included LDH, troponin I, INR, lactate, 2nd day creatinine, voltage anomalies, seizures, and male gender, as well as APGAR scores. A basic chart for mortality prediction was developed. The Sarnat score showed strong evidence, and APGAR 1 showed anecdotal evidence for association with brain damage, although brain damage was independent of laboratory results and other clinical findings, based on moderate and anecdotal evidence from Bayesian calculations. Cranial MRI findings revealed profound damage in 14.8% of Sarnat 1, 21.8% of Sarnat 2, and 50% of Sarnat 3 patients. Conclusions: This study presents prognostic factors for survival and brain damage in perinatal asphyxia. We recommend obtaining cranial MRI for all patients diagnosed with asphyxia, as most laboratory tests were independent of brain damage. Given that profound brain damage can occur even in Sarnat stage I patients, we emphasize the importance of therapeutic hypothermia for these patients.

The Role of Visualization in Estimating Cardiovascular Disease Risk: Scoping Review.

Supporting and understanding the health of patients with chronic diseases and cardiovascular disease (CVD) risk is often a major challenge. Health data are often used in providing feedback to patients, and visualization plays an important role in facilitating the interpretation and understanding of data and, thus, influencing patients' behavior. Visual analytics enable efficient analysis and understanding of large datasets in real time. Digital health technologies can promote healthy lifestyle choices and assist in estimating CVD risk. This review aims to present the most-used visualization techniques to estimate CVD risk. In this scoping review, we followed the Joanna Briggs Institute PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search strategy involved searching databases, including PubMed, CINAHL Ultimate, MEDLINE, and Web of Science, and gray literature from Google Scholar. This review included English-language articles on digital health, mobile health, mobile apps, images, charts, and decision support systems for estimating CVD risk, as well as empirical studies, excluding irrelevant studies and commentaries, editorials, and systematic reviews. We found 774 articles and screened them against the inclusion and exclusion criteria. The final scoping review included 17 studies that used different methodologies, including descriptive, quantitative, and population-based studies. Some prognostic models, such as the Framingham Risk Profile, World Health Organization and International Society of Hypertension risk prediction charts, Cardiovascular Risk Score, and a simplified Persian atherosclerotic CVD risk stratification, were simpler and did not require laboratory tests, whereas others, including the Joint British Societies recommendations on the prevention of CVD, Systematic Coronary Risk Evaluation, and Framingham-Registre Gironí del COR, were more complex and required laboratory testing-related results. The most frequently used prognostic risk factors were age, sex, and blood pressure (16/17, 94% of the studies); smoking status (14/17, 82%); diabetes status (11/17, 65%); family history (10/17, 59%); high-density lipoprotein and total cholesterol (9/17, 53%); and triglycerides and low-density lipoprotein cholesterol (6/17, 35%). The most frequently used visualization techniques in the studies were visual cues (10/17, 59%), followed by bar charts (5/17, 29%) and graphs (4/17, 24%). On the basis of the scoping review, we found that visualization is very rarely included in the prognostic models themselves even though technology-based interventions improve health care worker performance, knowledge, motivation, and compliance by integrating machine learning and visual analytics into applications to identify and respond to estimation of CVD risk. Visualization aids in understanding risk factors and disease outcomes, improving bioinformatics and biomedicine. However, evidence on mobile health's effectiveness in improving CVD outcomes is limited.

COMPARISON OF NAÏVE BAYES AND K-NEAREST NEIGHBOR MODELS FOR IDENTIFYING THE HIGHEST PREVALENCE OF STUNTING CASES IN EAST JAVA

Indonesia will experience a demographic bonus in 2030, where the productive age group will dominate the population and become a buffer for the economy. However, this potential is in vain if human resources experience stunting. According to WHO (2015), stunting is a disorder of child growth and development due to chronic malnutrition and repeated infections, characterized by below-standard length or height. Based on the background of the problem, the author wants to compare the prediction of the prevalence of the highest stunting cases in East Java using the Naive Bayes method and the K-Nearest Neighbor method. The stages carried out in this study are data collection, initial data processing, advanced data processing using the Naïve Bayes Method and K-Nearest Neighbor, and comparative analysis. The results of the implementation of the Naïve Bayes and K-Nearest Neighbor methods are in the form of stunting prevalence prediction charts with variables that affect LBW and TTD. The results of simulations conducted in 6 regions, the Naive Bayes method gets the highest accuracy value of 83.33% in simulation one and 66.67%. The smallest RMSE value is 0.382 simulation 1 and 0.469 simulation 2. This shows that the Naive Bayes method can predict well.

Analytical prediction model for edge water invasion flow rate in SAGD heavy oil reservoirs

Analytical prediction model for edge water invasion flow rate in SAGD heavy oil reservoirs

Accuracy of self-perceived cardiovascular disease risk and factors predicting risk underestimation in perimenopausal and postmenopausal women in Ismailia, Egypt.

Cardiovascular disease (CVD) is the leading cause of death globally, with women at higher risk after menopause. This increased risk is attributed to both aging and hormonal changes. Prior research has established a link between CVD risk perception and adopting healthy behaviors to prevent CVD. This study aimed to assess the accuracy of self-perceived CVD risk in perimenopausal and postmenopausal women, and to identify factors that predict CVD risk underestimation among them. A cross-sectional study was conducted in the administrative sectors of Suez Canal University campus in Ismailia, Egypt, over a period of eight months starting in July 2022. A total of 390 eligible women (employees and workers) were randomly selected. Participants were interviewed to obtain data on demographics, medical history, self-perceived risk of CVD, self-perceived general health, awareness of factors that increase the risk of developing CVD, perceived stress, health literacy, numeracy, and self-perceived 10-year risk of developing major cardiovascular events. They also underwent measurements of blood pressure, weight, and height. The updated 2019 WHO/CVD risk non-laboratory-based prediction chart for the North Africa and Middle East Region was used to predict the 10-year risk of major cardiovascular events for the study participants. Risk accuracy was measured by comparing self-perceived CVD risk with predicted CVD risk. The ratio of self-perceived to predicted moderate/high CVD risk was 27.7% to 44.3%, respectively. The accuracy of CVD risk perception was 68.2%. Kappa analysis results showed fair and significant agreement between self-perceived and predicted CVD risk (kappa ± SE = 35.9 ± 4.1%, p < 0 .001). The proportion of women who underestimated their risks was 24.1%. Of those in the high-risk group, 93.3% underestimated their CVD risk, compared to 50.6% in the moderate-risk group. Factors that significantly predicted CVD risk underestimation included being married (aOR 14.5; 95% CI 1.4-149.9), low income (aOR 2.321; 95% CI 1.09-4.909), high BMI (aOR 4.78; 95% CI 1.9-11.9), hypertension (aOR 3.5; 95% CI 2-6.2), and old age (aOR 1.46; 95% CI 1.3-1.6). Approximately one-third of our study participants misperceived their CVD risk; of those who did, 75.8% underestimated it. Marital status, old age, low income, high BMI, and hypertension strongly predicted CVD risk underestimation. These findings identified the menopausal women subgroups that could benefit from targeted health interventions designed to reduce CVD risk underestimation and improve risk accuracy.

Cardiovascular Risk across Glycemic Categories: Insights from a Nationwide Screening in Mongolia, 2022–2023

(1) Background: Diabetes mellitus is a significant risk factor for cardiovascular disease (CVD), a leading cause of death globally. Recent studies have highlighted the role of pre-diabetes, particularly impaired fasting glucose (IFG), in elevating CVD risk even before the onset of diabetes. The objective of this study was to assess cardiovascular disease (CVD) risk across specific glycemic categories, including normoglycemia, impaired fasting glucose (IFG), newly diagnosed diabetes, and long-standing diabetes, in a large Mongolian population sample. (2) Methods: This cross-sectional study utilized data from a nationwide health screening program in Mongolia between 2022 and 2023, involving 120,266 adults after applying inclusion criteria. The participants were categorized based on fasting plasma glucose levels (NGT): normoglycemia, IFG, newly diagnosed diabetes, and long-standing diabetes. CVD risk was assessed using WHO risk prediction charts, considering factors like age, blood pressure, smoking status, and diabetes status. (3) Results: CVD risk varied significantly with glycemic status. Among those with NGT, 62.9% were at low risk, while 31.2% were at moderate risk. In contrast, the IFG participants had 49.5% at low risk and 39.9% at moderate risk. Newly diagnosed diabetes showed 38.1% at low risk and 43.3% at moderate risk, while long-standing diabetes had 33.7% at low risk and 45.9% at moderate risk. Regression analysis indicated that glycemic status was independently associated with moderate to high CVD risk (OR in IFG: 1.13; 95% CI: 1.09–1.18), even after adjusting for age, gender, and central obesity. (4) Conclusions: This study emphasizes the need for early cardiovascular risk assessment and intervention, even in pre-diabetic stages like IFG.

10-year risk for cardiovascular diseases according to the WHO prediction chart: findings from the National Health and Morbidity Survey (NHMS) 2019

BackgroundThere has been a significant increase in the incidence of cardiovascular disease (CVD) in Malaysia. It is important to identify the group at high risk of CVD. This study aimed to assess the population distribution and factors associated with 10-year CVD risk among adults aged 40 to 74 years in Malaysia.MethodsThis study used secondary data from the NHMS 2019, a nationally representative cross-sectional population study. The following measurements were collected: anthropometric, systolic blood pressure, fasting blood glucose, total cholesterol, smoking, and sociodemographic. The 2019 WHO Southeast Asia laboratory-based charts were used to estimate individuals’ CVD risk. These charts predict significant cardiovascular events over ten years. Multiple logistic regression analysis was employed to ascertain the factors that are linked to elevated or extremely elevated risk of CVD.ResultsA total of 5,503 respondents were included in the analysis. Less than one-quarter of the respondents were current smokers and obese. Approximately 41.7%, 30.9%, and 22.5% of the participants had extremely low risk (less than 5%), low risk (between 5% and less than 10%), and moderate risk (between 10% and less than 20%), respectively. A total of 4.9% of the participants were categorised as having high (20% to < 30%) or very high (CVD) risk (≥ 30%). This classification was more prevalent among males (7.3%) than among females (2.5%; p < 0.001). The factors associated with high/very high CVD risk were unemployment (aOR = 1.88, 95% CI = 1.47–2.40), those with non-formal and primary education level (aOR = 2.36, 95% CI = 1.36 − 4.12 and aOR = 3.28, 95% CI = 2.10 − 5.12, respectively), and being physically inactive with obesity (aOR = 2.19, 95% CI = 1.18 − 4.08).ConclusionsThis study revealed that almost 5% of the population in Malaysia has a high 10-year CVD risk. These findings highlight Malaysia’s urgent need for comprehensive CVD prevention efforts.

A Retrospective Study Establishing a Nomogram Predictive Model for Postoperative High-Activity Delirium After Non-Cardiac Surgery.

Postoperative high-activity delirium (PDHA) manifests as a high alertness, restlessness, hallucinations, and delusions. Occurrence of PDHA represents an increased risk of poor prognosis for patients. To establish and validate a nomogram prediction model for high-activity delirium after non-cardiac surgery in a post-anesthesia care unit (PACU). This study retrospectively enrolled adult patients who underwent non-cardiac surgery and were observed in the PACU as training data. Patients were divided into PDHA (199 patients) and non-PDHA (396 patients) groups. Patients' general data, preoperative indicators, intraoperative conditions, and postoperative PACU conditions were collected. The risk factors for PDHA were identified using univariate and multivariate logistic regression analyses. A predictive column chart was created using R language. Adult patients who underwent non-cardiac surgery and entered the PACU for observation were randomly selected as the validation set data (198 cases) for model performance validation. The incidence rate of adult PDHA in the PACU was 0.275%. Sex, age, smoking history, low preoperative albumin level, Society of Anesthesiologists (ASA) classification, anesthesia duration, and postoperative PACU pain score were independent risk factors for hyperactive delirium in PACU adults. In this study, an adult PACU PDHA nomogram prediction model was developed. The training dataset verified that the ROC curve (area under the curve) and 95% confidence interval (95% CI) were 0.936 (0.917-0.955). The ROC curve of the validation data row showed that the area under the curve and 95% CI were 0.926 (0.885-0.967). The nomogram predictive model for PACU adult high-activity delirium constructed in this study showed good predictive performance. This model could enable the visualization and graphical prediction of adult high-activity delirium occurrence after PACU, which has clinical value.

P129 ASSESS THE ACCURACY OF FOUR RISK ASSESSMENT MODELS (RISKFRS, RISKWHO, RISKACC/AHA, AND RISKJBS) IN PREDICTING 10-YEAR CARDIOVASCULAR EVENT RISK IN INDIAN ACUTE MYOCARDIAL INFARCTION PATIENTS

Background: Despite numerous risk assessment models available for estimating the 10-year risk of cardiovascular (CV) events in individuals requiring primary prevention of CV disease, their applicability and accuracy in the Indian population remain inadequately studied. Objective of the study is to Assess and compare the predictive accuracy of four cardiovascular risk assessment models—RiskFRS, RiskWHO, RiskACC/AHA, and RiskJBS—in determining the 10-year cardiovascular event risk among Indian acute MI patients. Methods: In this study, 250 participants (mean age 59.4 ± 10.6 years; 205 males) without prior cardiovascular disease who experienced acute myocardial infarction (MI) were assessed using four established risk assessment models: Framingham Risk Score (RiskFRS), World Health Organization risk prediction charts (RiskWHO), American College of Cardiology/American Heart Association pooled cohort equations (RiskACC/AHA), and the 3rd Joint British Societies’ risk calculator (RiskJBS). These models estimated the participants’ predicted 10-year risk of cardiovascular events if assessed before the acute MI. Results: RiskWHO provided the lowest risk estimates, with 86.6% of patients estimated to have less than 20% 10-year risk. Conversely, RiskFRS and RiskACC/AHA yielded higher risk estimates (61.7% and 69.8% with less than 20% risk, respectively; p values &lt;0.001 compared to RiskWHO). However, RiskJBS identified the highest proportion of patients as high-risk (only 44.1% at less than 20% risk, p values &lt; 0.01 compared to all other three risk scores). Conclusions: In Indian patients experiencing acute MI, RiskJBS identifies a larger proportion of patients as ’high-risk’ compared to RiskWHO, RiskFRS, and RiskACC/AHA. However, further validation through large-scale prospective studies is necessary. These findings underscore the importance of tailoring risk assessment models to the unique characteristics of the Indian population and highlight the need for additional research to optimize cardiovascular risk prediction and management strategies in this demographic.

Performance of WHO-Updated Cardiovascular Disease Risk Prediction Charts among Doctors: Findings from a Tertiary Care Teaching Center in Puducherry, India.

The cardiovascular disease (CVD) risk prediction charts, updated by the World Health Organization for 21 regions in 2019. These charts-lab and non-lab versions-estimate a person's overall CVD risk; the non-lab version is intended for low-resource environments. Using the "non-lab" and "lab" versions of the WHO CVD risk prediction charts, we sought to estimate the burden of ten-year risk of a fatal or non-fatal CVD event in a tertiary care hospital of Puducherry and to assess the agreement between them. We included 255 doctors working in a tertiary care hospital in Puducherry. Age, gender, systolic blood pressure, and smoking status are all factors used in both charts. Moreover, a lab chart requires a person's total cholesterol and diabetes mellitus status, whereas a non-lab chart requires a person's body mass index. Proportions (95% confidence intervals) were used to portray the population at various CVD risk levels. Using Cohen's Kappa, the degree of agreement between the lab and non-lab charts was assessed (k). The majority of the study participants had <5% risk of CVD, and none had a risk of >20% in both the charts, which shows the better health-seeking behaviour of doctors. A good level of agreement was shown by the 95.2% (95%CI = 91.7 - 97.4 ) concordance in the risk categorization between the two charts (k = 0.934). When data are available and there is strong agreement between non-lab and lab-based charts, it is practical to apply WHO-updated CVD risk prediction charts.

Assessment of Cardiovascular Risk Using Who/Ish Risk Prediction Charts among Adult Population In a Rural Area in Thiruvallur District, Tamil Nadu

Assessment of Cardiovascular Risk Using Who/Ish Risk Prediction Charts among Adult Population In a Rural Area in Thiruvallur District, Tamil Nadu

Risk estimation of cardiovascular diseases using the World Health Organization/International Society of Hypertension risk prediction charts in the Azar cohort population: Cross-sectional study.

Cardiovascular disease (CVD) is one of the most important health problems and the leading cause of mortality worldwide. This study aimed to estimate the risk of CVD using the World Health Organization/International Society of Hypertension (WHO/ISH) risk prediction charts. The demographic characteristics of all participants of this study aged 40-70 years who did not have a prior coronary event were collected. The 10-year CVD risk was estimated using the laboratory version of the WHO/ISH risk score charts. The risk scores for 11678 participants of the Azar cohort population were calculated. Participants were classified as low risk, moderate risk, or high risk. According to the WHO/ISH charts, only 0.1 % of the population was classified as high-risk (≥40%), and 96.8% had a 10-year CVD risk of<10%. Also, participants with overweight (P=0.002), obesity, and abdominal obesity had higher CVD risk(P<0.001). There was a low burden of 10-year CVD risk among the Azar cohort population without prior coronary events. It appears the percentage of people in the high-risk group is underestimated in the WHO/ISH risk prediction charts, leading to delays in receiving appropriate management in the population concerned. Therefore, using other charts alongside the WHO/ISH risk prediction charts is advisable.

The Intersection of a Child's Demographics and Household Socioeconomic Status in the Multimorbidity of Malaria, Anaemia, and Malnutrition among Children Aged 6-59 Months in Nigeria.

Multimorbidity of malaria, anemia, and malnutrition (MAMM) is a condition in which an individual has two or more of these health conditions, and is becoming an emergent public health concern in sub-Saharan African countries. The independent associations of a child's demographic variables and household socioeconomic (HSE) disparities with a child's health outcomes have been established in the literature. However, the effects of the intersection of these factors on MAMM, while accounting for other covariates, have not been studied. Therefore, this study aimed to determine how children's sex, age, and household socioeconomic status interact to explain the variations in MAMM among children aged 6-59 months in Nigeria. Data from the 2018 Nigeria Demographic and Health Survey and the 2018 National Human Development Report (NHDR) were used. This study included weighted samples of 10,184 children aged 6-59 months in Nigeria. A three-level multilevel mixed effect ordinal logistic regression model was used, such that individual characteristics at level 1 were nested in communities at level 2 and nested in states at level 3. Subsequently, predictive probability charts and average adjusted probability tables were used to interpret the intersectional effects. Five models were created in this scenario. Model 1 is the interaction between the child's sex and household wealth status; model 2 is the interaction between the child's sex and age; model 3 is the interaction between the child's age and household wealth status; model 4 has the three two-way interactions of the child's sex, age, and household wealth status; and model 5 includes model 4 and the three-way interactions between a child's sex, age, and household wealth quintiles; while accounting for other covariates in each of the models. The prevalence of children with a 'none of the three diseases' outcome was 17.3% (1767/10,184), while 34.4% (3499/10,184) had 'only one of the diseases', and 48.3% (4918/10,184) had 'two or more' MAMMs. However, in the multivariate analyses, model 3 was the best fit compared with other models, so the two-way interaction effects of a child's age and household wealth status are significant predictors in the model. Children aged 36-47 months living in the poorest households had a probability of 0.11, 0.18, and 0.32 of existing with MAMM above the probability of children of the same age who live in the middle class, more prosperous, and richest households, respectively, while all other covariates were held constant. Thus, the variation in the prevalence of MAMM in children of different ages differs depending on the household wealth quintile. In other words, in older children, the variations in MAMM become more evident between the richer and the poorer household quintiles. Therefore, it is recommended that policies that are geared toward economic redistribution will help bridge the disparities observed in the prevalence of multiple diseases among children aged 6-59 months in Nigeria.

Prevalence of non-communicable chronic diseases in rural India amongst peri- and post-menopausal women: Can artificial intelligence help in early identification?

To identify peri- and post-menopausal women at risk of non-communicable diseases in rural India and to assess their prevalence amongst these groups via the use of artificial intelligence. An observational study conducted by the Indian Menopause Society in collaboration with the Government of Maharashtra. The study included rural women residents of three villages in the Latur district of Maharashtra, India. Accredited social health activist workers identified 400 peri- and post-menopausal women aged 45-60years. Specific symptoms able to predict the presence of a non-communicable disease were identified through the use of artificial intelligence. Descriptive statistics and predictive network charts analysis. The mean age of 316 women included in the analysis was 50.4years and the majority of them were illiterate (68%). The prevalence of dyslipidaemia, osteopenia, diabetes mellitus, obesity and hypertension were 58%, 50%, 25%, 25%, and 20% respectively. None of their symptoms or laboratory reports could be significantly correlated directly with any of these non-communicable diseases. Hence, we used a cluster of symptoms to suggest the presence of hypertension, diabetes mellitus, osteoporosis and hypothyroidism via predictive network analysis charts. Screening of at-risk women can be done using an artificial intelligence-based screening tool for early diagnosis, timely referral and treatment of non-communicable diseases with the support of community health workers.

Астрология как сакральное экологическое знание в тибетской буддийской традиции

Introduction. The identification of specific responses generated by religious traditions to present-day global challenges actualizes the rethinking of astrology as a traditional understanding of the world structure and influence of natural forces on man in Tibetan Buddhism. Goals. The study attempts an evaluation of astrology as a form of sacred knowledge in the Tibetan Buddhist tradition from the perspective of understanding its principles of ecology. Materials and methods. To facilitate this, the work employs a systematic approach of cultural anthropology according to which sacred astrological knowledge is viewed as an integral element to the specified religious tradition. The paper focuses on The White Beryl: A Treatise on [Tibetan] Astrology by Desi Sangye Gyatso, as well as works by modern Tibetan astrologers. Results. Tibetan astrology arose from integrated Indian, Chinese and ancient Tibetan ideas about the influence of natural elements on human life, and is a required tool in Tibetan medicine. Those ideas are based on some esoteric premises of the Buddhist Kalachakra Tantra which articulates the relationship between external natural cycles and internal human energy ones, as well as suggests methods of restoring the latter’s balance. In the Tibetan Buddhist tradition, sacred astrological knowledge is widely popular both in cult practices and non-religious activities. Astrologers draw prediction and natal charts, calculate favorable periods for rituals, healing practices, harvesting, weddings, funerals, etc. The three Lamrim-derived motives — to improve this lifetime, gain liberation from samsaric suffering, achieve enlightenment for the sake of universal well-being — may accordingly direct the intentions of Buddhists toward the use of sacred astrological knowledge to solve problematic situations, identify oneself, understand others, restore the balance of external and internal cycles. With due account of that followers of Tibetan Buddhism are basically oriented to altruism and rationalism, the paper presumes astrology can be considered a form of sacred knowledge instrumental in understanding (and implementing) principles of ecology. Restoration of balance in man-nature interaction through the rational use of astrological knowledge for the common good predetermines the sacredness of ecological knowledge in the Tibetan Buddhist tradition.

Optimizing Oxygen Delivery by Low-Flow Nasal Cannula to Small Infants: A Bench Study.

In infants treated with a low-flow nasal cannula (LFNC), the oxygen concentration delivered to the lungs (i.e., the effective FiO2) is difficult to estimate. The existing mathematical formulas rely on important assumptions regarding the values of respiratory parameters and, thus, may be inaccurate. We aimed to assess oxygen delivery by LFNC to small infants using realistic simulations on a mechanical breathing model. A mechanical breathing simulator (infant upper-airway replica, single-space breathing compartment, electric motor, microcontroller) was developed. Breathing simulations (n = 1200) were performed at various tidal volume (VT), inspiratory time (Ti), and respiratory rate (RR) combinations and different cannula flows. Minute ventilation (MV) was the most significant predictor of effective FiO2. FiO2 was higher at lower VT and higher Ti values. Benaron and Benitz's formula underestimated the effective FiO2 at lower MV values, while Finer's formula significantly overestimated it. A set of predictive FiO2 charts was developed based on cannula flow, infant body weight, and RR. The effective FiO2 delivered by LFNC to small infants critically depends on VT, Ti, and RR. However, since VT and Ti values are not available in clinical practice, the existing mathematical formulas may be inaccurate. Our novel predictive FiO2 charts could assist in optimizing oxygen delivery by LFNC using easy-to-obtain parameters, such as infant body weight and RR.