Predictor Of Health Outcomes Research Articles

Characteristics of balance performance in the Chinese elderly by age and gender

BackgroundPopulation aging has been an emerging public and health concern globally. Balance performance can be applied as an indicator of functional status and a predictor of health outcomes in the elderly. However, reference data of balance performance in the elderly generated from large scale studies have been very limited. In research and geriatric assessment settings, the age and gender specific data on balance performance are indispensable prerequisites for identifying subpopulation with and at risk of impairments and subsequently implementing targeted interventions in clinics and public health to improve their balance performance.MethodsA total of 1984 elderly subjects aged 60 to 97 years from community settings in urban China were investigated. The balance performances together with 3 individual domains and 16 items were evaluated using the X16 balance testing scale.ResultsIn the elderly, with age increases each item, individual domain, and overall balance performance scores decreased gradually. Meanwhile, individual variations of individual domains and overall balance performance were all increased over age. Relative to levels of 60- years, postural stability and overall balance performance decreased significantly since 65 years old, static balance and dynamic balance capacities started to decrease significantly since 70 years old. There was no significant difference in each balance domain and overall balance performance between men and women. Across age groups, portions of individuals able to perform task 4, 8 and 11 successfully were the lowest amongst their corresponding domains static balance, postural stability, and dynamic balance, respectively. Similar patterns were observed in both men and women. Balance performances were categorized into poor, fair, and good groups with scores of 0 to 10, 11 to 17, and 18 to 20, respectively. With increases of age, proportions with poor and fair balance capacities elevated stably.ConclusionsIn the elderly, with advances in age, abilities of overall balance performance, individual domains of static balance, postural stability, and dynamic balance, and successful performances on specific tasks declined gradually and stably. The deterioration started to be obvious since 65–75 years. Men and women had similar patterns.

Greater Risk of Negative Health Outcomes of Older Adults Living Alone in Vietnam: A Community Survey.

In modern Asian societies, there has been a shift in the living arrangements of older adults away from living with others. Knowing the health characteristics of individuals living alone can help identify high-risk groups. This cross-sectional study aimed to describe characteristics of the Vietnamese older adults and to investigate the association between living alone and their reported health outcomes by utilizing survey data of individuals aged ≥60 years in Vietnam in 2018. The community survey included questions about sociodemographic factors, living arrangement, and self-reported physical functional status. Multivariate logistic regression was used to examine whether or not living alone was a predictor of health outcomes. Of 725 study participants, 8.9% lived alone. These participants were more likely to be female, aged 70–79 years, living in rural areas, and currently single or previously married. After adjusting for covariates, older adults who were living alone were more likely to have arthritis (adjusted odds ratio [AOR] = 1.95, 95% confidence interval [CI]: 1.10–3.45), a history of falling (AOR = 2.44, 95% CI: 1.02–5.82), visual difficulties (AOR = 1.89, 95% CI: 1.04–3.41), feelings of loneliness (AOR = 1.95, 95% CI: 1.10–3.47), and high fear of falling (AOR = 1.88, 95% CI: 1.02–3.46). Older adults living alone in Vietnam were at greater risk of negative health consequences than those living with others. Screening and providing adequate social support for this specific population is important in preventing the adverse effects of solitary living among these older adults.

Multimorbidity measures from health administrative data using ICD system codes: A systematic review.

We aimed to identify and characterize adult population-based multimorbidity measures using health administrative data and the International Classification of Diseases (ICD) codes for disease identification. We performed a narrative systematic review of studies using or describing development or validation of multimorbidity measures. We compared the number of diseases included in the measures, the process of data extraction (case definition) and the validation process. We assessed the methodological robustness using eight criteria, five based on general criteria for indicators (AIRE instrument) and three multimorbidity-specific criteria. Twenty-two multimorbidity measures were identified. The number of diseases they included ranged from 5 to 84 (median=20), with 19 measures including both physical and mental conditions. Diseases were identified using ICD codes extracted from inpatient and outpatient data (18/22) and sometimes including drug claims (10/22). The validation process relied mainly on the capacity of the measures to predict health outcome (5/22), or on the validation of each individual disease against a gold standard (8/22). Six multimorbidity measures met at least six of the eight robustness criteria assessed. There is significant heterogeneity among the measures used to assess multimorbidity in administrative databases, and about a third are of low to moderate quality. A more consensual approach to the number of diseases or groups of diseases included in multimorbidity measures may improve comparison between regions, and potentially provide better control for multimorbidity-related confounding in studies.

Urban scaling of opioid analgesic sales in the United States.

Opioid misuse is a public health crisis in the United States. The origin of this crisis is associated with a sharp increase in opioid analgesic prescribing. We used the urban scaling framework to analyze opioid prescribing patterns in US commuting zones (CZs), i.e., groups of counties based on commuting patterns. The urban scaling framework postulates that a set of scaling relations can be used to predict health outcomes and behaviors in cities. We used data from the Drug Enforcement Administration's Automated Reports and Consolidated Ordering System (ARCOS) to calculate counts of oxycodone/hydrocodone pills distributed to 607 CZs in the continental US from 2006 to 2014. We estimated the scaling coefficient of opioid pill counts by regressing log(pills) on log(population) using a piecewise linear spline with a single knot at 82,363. Our results show that CZs with populations below the knot scaled superlinearly (β = 1.36), i.e., larger CZs had disproportionally larger pill counts compared to smaller CZs. On the other hand, CZs with populations above the knot scaled sublinearly (β = 0.92), i.e., larger CZs had disproportionally smaller pill counts compared to smaller CZs. This dual scaling pattern was consistent across US census regions. For CZs with population below the knot, the superlinear scaling of pills is consistent with the explanation that an increased number of successful matches between prescribers and users will lead to higher prescribing rates. The non-linear scaling behavior observed could be the result of a combination of factors, including stronger health care systems and prescribing regulation in largely populated commuting zones, as well as high availability of other opioids such as heroin in these commuting zones. Future research should explore potential mechanisms for the non-linearity of prescription opioid pills.

The effect of educational intervention based on Pender's health promotion model on quality of life and health promotion in patients with heart failure: an experimental study

BackgroundHeart failure is a common and chronic heart condition with high prevalence and mortality rates. This debilitating disease as an important predictor of health outcomes is directly related to patients' quality of life. Given that one of the main goals of heart failure treatment is to promote patients' quality of life and health status, conducting effective nursing interventions seems to be necessary in this regard. Therefore, the present study aimed to determine the effect of educational intervention based on Pender's health promotion model on quality of life and health promotion in patients with heart failure.MethodsThis is an experimental study in which a total of 80 patients with heart failure were recruited and randomly allocated to two groups of intervention and control (n = 40 in each group). The educational program was designed based on Pender's health promotion model and then provided for the patients in the intervention four subgroups (10 person in each group) during six sessions. Data were collected at three time-points of before, immediately after, and three months after the intervention using a demographic questionnaire, the Minnesota Living with Heart Failure Questionnaire (MLHFQ), and the Health-Promoting Lifestyle Profile II (HPLP-II). Data were then analyzed using SPSS Statistics for Windows, version 17.0 (SPSS Inc., Chicago, Ill., USA) and p value less than 0.05 was taken as statistically significant.ResultsBased on the results of the present study, no statistically significant difference was shown in terms of demographic characteristics between the two groups. It was also indicated that there was a statistically significant difference in the mean scores of all dimensions of quality of life (except in the physical dimension) between the two groups so that the overall mean score of quality of life increased significantly in the intervention group after the intervention (p < .05). Moreover, there were significant increases in the mean scores of health-promoting behaviors (except in the domain of physical activity) in the intervention group compared to the control group (p < .05) after intervention.ConclusionsThis study demonstrates a trend that Pender's health promotion model is effective in improving the quality of life of patients with heart failure except of the physical dimension, and strengthening their health-promoting behaviors in all dimensions except of the physical activity dimension.

Environment in Children's Health: A New Challenge for Risk Assessment.

In the last few years, many studies have focused on the effects of environmental contaminant exposure during the prenatal period or infancy as predictors of health outcomes in the future. In these time windows, due to their rapid growth, and physiologic and metabolic development, we can observe a higher vulnerability to the effects of environment, with respect to adulthood. The evidence of possible influences, partly mediated by epigenetic mechanisms, involve neurobehavioral responses and immune, endocrine, and respiratory systems, acting directly on the child or indirectly when mediated by placental transfer or breast feeding. In particular, due to a greater intake of air, food, and fluids relative to body weight, crawling behaviors and short stature, the risk of excessive exposure is greater in children. However, data on the long-term implications of early exposures are scarce. Additionally, so that physicians and institutions for child care and assistance of pregnant women can take actions to counteract the effects of chemical pollution (i.e., by educational opportunities), a risk assessment perspective that responds to the biocomplexity of the human being is needed. The present paper provides an overview of physiologic and behavioral characteristics during the perinatal period and in childhood, suggesting in a more integrated way, the need of a new risk-assessment approach to managing chronic disease in pediatric patients.

Use of artificial intelligence for public health surveillance: a case study to develop a machine Learning-algorithm to estimate the incidence of diabetes mellitus in France

BackgroundThe use of machine learning techniques is increasing in healthcare which allows to estimate and predict health outcomes from large administrative data sets more efficiently. The main objective of this study was to develop a generic machine learning (ML) algorithm to estimate the incidence of diabetes based on the number of reimbursements over the last 2 years.MethodsWe selected a final data set from a population-based epidemiological cohort (i.e., CONSTANCES) linked with French National Health Database (i.e., SNDS). To develop this algorithm, we adopted a supervised ML approach. Following steps were performed: i. selection of final data set, ii. target definition, iii. Coding variables for a given window of time, iv. split final data into training and test data sets, v. variables selection, vi. training model, vii. Validation of model with test data set and viii. Selection of the model. We used the area under the receiver operating characteristic curve (AUC) to select the best algorithm.ResultsThe final data set used to develop the algorithm included 44,659 participants from CONSTANCES. Out of 3468 variables from SNDS linked to CONSTANCES cohort were coded, 23 variables were selected to train different algorithms. The final algorithm to estimate the incidence of diabetes was a Linear Discriminant Analysis model based on number of reimbursements of selected variables related to biological tests, drugs, medical acts and hospitalization without a procedure over the last 2 years. This algorithm has a sensitivity of 62%, a specificity of 67% and an accuracy of 67% [95% CI: 0.66–0.68].ConclusionsSupervised ML is an innovative tool for the development of new methods to exploit large health administrative databases. In context of InfAct project, we have developed and applied the first time a generic ML-algorithm to estimate the incidence of diabetes for public health surveillance. The ML-algorithm we have developed, has a moderate performance. The next step is to apply this algorithm on SNDS to estimate the incidence of type 2 diabetes cases. More research is needed to apply various MLTs to estimate the incidence of various health conditions.

Development of Neural Network Models for Predicting Health Outcomes

Development of Neural Network Models for Predicting Health Outcomes

Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM2.5 Pollution.

This study evaluates numerous epidemiological, environmental, and economic factors affecting morbidity and mortality from PM2.5 exposure in the 27 member states of the European Union. This form of air pollution inflicts considerable social and economic damage in addition to loss of life and well-being. This study creates and deploys a comprehensive data pipeline. The first step consists of conventional linear models and supervised machine learning alternatives. Those regression methods do more than predict health outcomes in the EU-27 and relate those predictions to independent variables. Linear regression and its machine learning equivalents also inform unsupervised machine learning methods such as clustering and manifold learning. Lower-dimension manifolds of this dataset’s feature space reveal the relationship among EU-27 countries and their success (or failure) in managing PM2.5 morbidity and mortality. Principal component analysis informs further interpretation of variables along economic and health-based lines. A nonlinear environmental Kuznets curve may describe the fuller relationship between economic activity and premature death from PM2.5 exposure. The European Union should bridge the historical, cultural, and economic gaps that impair these countries’ collective response to PM2.5 pollution.

Association of Subjective Social Status With Life's Simple 7s Cardiovascular Health Index Among Hispanic/Latino People: Results From the HCHS/SOL

BackgroundEvidence suggests that subjective (perceived) social status (SSS) may predict health outcomes more strongly than objective social status, but little is known about the relationship between SSS and cardiovascular health (CVH). This study focuses on this relationship among diverse Hispanic/Latino adults because while poor CVH profiles are prevalent in this population, immigration complicates attempts to measure their social status.Methods and ResultsWe analyzed baseline HCHS/SOL (Hispanic Community Health Study/Study of Latinos) data on 15 374 Hispanic/Latino adults aged 18 to 74 years in 2008 to 2011. SSS was assessed using the McArthur Scale, a 10‐rung “social ladder.” CVH was based on levels of 7 metrics defined by the American Heart Association. Linear and logistic regressions were used to examine cross‐sectional associations of SSS with CVH (overall and single metrics) after adjusting for objective social status, demographic, and health factors. Less than half of the population (46%) had Ideal scores in ≥4 metrics of CVH. In multivariable‐adjusted models, an increase in SSS was associated with a higher overall CVH score (β=0.04; 95% CI, 0.01–0.06) and greater likelihood of Ideal levels of body mass index, physical activity, and fasting blood glucose levels. Nativity and time in the United States modified the association between SSS and Ideal smoking.ConclusionsSubjective measures of social status can enhance an understanding of CVH among Hispanic/Latino people. Future studies should explore the stability of SSS over time in comparison with objective social status and the mechanisms through which SSS may influence CVH.

Concordance of Children’s Adverse Childhood Experiences Amongst Child, Caregiver, and Caseworker

ABSTRACT Adverse childhood experiences (ACEs), often defined as potentially traumatic experiences such as child maltreatment and household difficulties (e.g., caregiver substance misuse) that occur prior to age 18, are associated with poor health and psychiatric illness across the life span and have been increasingly used as a predictor of health outcomes in both research and practice. However, limited research has explored who should be reporting ACEs for children or to what extent reporters agree on observations of ACE exposure. This study examines respondent agreement among children, caregiver, and caseworker using data drawn from the National Survey of Child and Adolescent Well-being (NSCAW II), a longitudinal, nationally representative survey of children who have been the subject of a child welfare investigation. A sub-sample of participants aged 8–17 was used (n = 1,652). The following ACE indicators had responses from all three respondents: sexual victimization, physical abuse, emotional abuse, and home violence. Analyses were completed to understand patterns of reporter’s responses to each of the ACEs selected using sampling weights. We calculated interrater agreement using the Kappa coefficient for each ACE indicator. Children, caregivers, and caseworkers differentially reported the number of ACEs the child experienced. Children reported experiencing just over two ACEs on average, while caregivers reported two and caseworkers reported an average of one. Overall, Kappa coefficients were extremely low with caseworker-caregiver agreement the highest at .41. Given the lack of concordance, we recommend asking multiple informants while carefully considering self-report by children when assessing for ACEs.

ImputEHR: A Visualization Tool of Imputation for the Prediction of Biomedical Data.

Electronic health records (EHRs) have been widely adopted in recent years, but often include a high proportion of missing data, which can create difficulties in implementing machine learning and other tools of personalized medicine. Completed datasets are preferred for a number of analysis methods, and successful imputation of missing EHR data can improve interpretation and increase our power to predict health outcomes. However, use of the most popular imputation methods mainly require scripting skills, and are implemented using various packages and syntax. Thus, the implementation of a full suite of methods is generally out of reach to all except experienced data scientists. Moreover, imputation is often considered as a separate exercise from exploratory data analysis, but should be considered as art of the data exploration process. We have created a new graphical tool, ImputEHR, that is based on a Python base and allows implementation of a range of simple and sophisticated (e.g., gradient-boosted tree-based and neural network) data imputation approaches. In addition to imputation, the tool enables data exploration for informed decision-making, as well as implementing machine learning prediction tools for response data selected by the user. Although the approach works for any missing data problem, the tool is primarily motivated by problems encountered for EHR and other biomedical data. We illustrate the tool using multiple real datasets, providing performance measures of imputation and downstream predictive analysis.

PS05: The role of disease severity and illness perceptions in predicting health outcomes in people with hidradenitis suppurativa: a longitudinal study

PS05: The role of disease severity and illness perceptions in predicting health outcomes in people with hidradenitis suppurativa: a longitudinal study

Predicting Obesity in Adults Using Machine Learning Techniques: An Analysis of Indonesian Basic Health Research 2018.

Obesity is strongly associated with multiple risk factors. It is significantly contributing to an increased risk of chronic disease morbidity and mortality worldwide. There are various challenges to better understand the association between risk factors and the occurrence of obesity. The traditional regression approach limits analysis to a small number of predictors and imposes assumptions of independence and linearity. Machine Learning (ML) methods are an alternative that provide information with a unique approach to the application stage of data analysis on obesity. This study aims to assess the ability of ML methods, namely Logistic Regression, Classification and Regression Trees (CART), and Naïve Bayes to identify the presence of obesity using publicly available health data, using a novel approach with sophisticated ML methods to predict obesity as an attempt to go beyond traditional prediction models, and to compare the performance of three different methods. Meanwhile, the main objective of this study is to establish a set of risk factors for obesity in adults among the available study variables. Furthermore, we address data imbalance using Synthetic Minority Oversampling Technique (SMOTE) to predict obesity status based on risk factors available in the dataset. This study indicates that the Logistic Regression method shows the highest performance. Nevertheless, kappa coefficients show only moderate concordance between predicted and measured obesity. Location, marital status, age groups, education, sweet drinks, fatty/oily foods, grilled foods, preserved foods, seasoning powders, soft/carbonated drinks, alcoholic drinks, mental emotional disorders, diagnosed hypertension, physical activity, smoking, and fruit and vegetables consumptions are significant in predicting obesity status in adults. Identifying these risk factors could inform health authorities in designing or modifying existing policies for better controlling chronic diseases especially in relation to risk factors associated with obesity. Moreover, applying ML methods on publicly available health data, such as Indonesian Basic Health Research (RISKESDAS) is a promising strategy to fill the gap for a more robust understanding of the associations of multiple risk factors in predicting health outcomes.

Estimating the health benefits of timely diagnosis and treatment of transthyretin amyloid cardiomyopathy

Aim: Delayed diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) represents a missed opportunity for intervention. This study estimates the health benefits of timely diagnosis and treatment with tafamidis. Methods: A disease simulation model was developed to predict health outcomes under scenarios of timely and delayed diagnosis and treatment. Efficacy and quality of life (QoL) profiles were derived from the pivotal tafamidis trial and diagnostic delay durations from the literature. Results: Timely diagnosis and treatment were predicted to extend mean life expectancy by 5.46 and 7.76years, relative to delayed diagnosis, for wild-type and hereditary ATTR-CM, respectively. Corresponding QALY gains were 4.50 and 6.22. Conclusion: Timely diagnosis and treatment with tafamidis are predicted to significantly improve survival andQoL for ATTR-CM patients.

Low Health Literacy is Associated with the Onset of CKD during the Life Course.

Health literacy, the ability to deal with information related to one's health, is a predictor of health outcomes in CKD. However, research has not explored whether low health literacy predicts the onset of CKD. We used data from participants of Lifelines, a prospective population-based cohort study of individuals living in The Netherlands, to assess the share of individuals with low health literacy by eGFR category, whether low health literacy is associated with CKD onset in the general population and in the subgroup of older adults, and whether established CKD risk factors mediate this association. In the total sample of 93,885 adults (mean follow-up 3.9 years), low health literacy was more likely among individuals in worse eGFR categories, increasing from 26.4% in eGFR category 1 to 50.0% in category 5 (P=0.02). Low health literacy, compared with adequate health literacy, was associated with the onset of CKD in the total sample (3.0% versus 2.1%) and in the subgroup of older adults (13.4% versus 11.3%), with odds ratios (ORs) of 1.44 (95% confidence interval (95% CI), 1.31 to 1.59) and 1.21 (95% CI, 1.04 to 1.41), respectively. After adjustment for sex, age, education, and income, health literacy was associated with CKD onset only in older adults (OR, 1.25; 95% CI, 1.04 to 1.50). This association was mediated by hypertension and high body mass index (BMI) in the crude model, but only by BMI after adjustment (with BMI explaining 18.8% of the association). Low health literacy is a risk factor for CKD onset among older adults, which suggests that CKD prevention might benefit from strategies to address low health literacy.

PDG35 Using Machine Learning to Classify Patients on Opioid Use

High-frequent opioid use tends to increase an individual’s risk of opioid use disorder, overdose, and death. Thus, it is important to predict an individuals’ opioid use frequency to improve opioid prescription utilization patterns. This study applied five machine learning techniques to predict opioid use frequency, including support vector machine, random forest, neural network, gradient boosting, and XGBoost (extreme gradient boosting). Additionally, this study compared the performance of these machine learning models with penalized logistic regression. This retrospective study included individuals receiving at least one opioid prescription from 2016-2018 in the national representative data, Medical Expenditure Panel Survey. The study outcome measured whether an individual lied in the upper 10 percent of the opioid prescription distribution. The predictors were selected based on Gelberg-Andersen’s Behavioral Model of Health Services Utilization. The prediction performance was assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) in the test data. Patient characteristics as predictors for high-frequency use of opioids were ranked by the relative importance in prediction in the test data. Random forest achieved the highest value of both AUROC (0.7726) and AUPRC (0.2871), outperforming logistic regression. In the best performing model, age, the number of chronic conditions, public insurance, and self-perceived health status had enormous predicting power in opioid use frequency. This study demonstrates that machine learning techniques can be a promising and powerful technique in predicting health outcomes.

Comparative Validation of Three DNA Methylation Algorithms of Ageing and a Frailty Index in Relation to Mortality: Results From the ESTHER Cohort Study

Background: Three DNA methylation (DNAm) based algorithms, DNAm PhenoAge acceleration (AgeAccelPheno), DNAm GrimAge acceleration (AgeAccelGrim), and mortality risk score (MRscore), based on methylation in 513, 1030 and 10 CpG sites, respectively, were recently established to predict health outcomes and mortality. We aimed to compare and validate the predictive ability of these scores and frailty in relation to mortality in a population-based cohort of older adults (50-75 years at baseline) from Germany. Methods: DNA methylation in whole blood was measured by the Infinium Methylation EPIC BeadChip kit (EPIC, Illumina, San Diego, CA, USA) and used to calculate the three methylation algorithms in two random subsets of the ESTHER cohort study (n = 741 and n = 1030). Frailty was assessed by a frailty index (FI) based on percentage of 34 selected deficits. Findings: During 14 years of follow-up, a total of 346 deaths were observed. AgeAccelPheno, AgeAccelGrim, MRscore and FI were positively correlated. Although each of them was strongly and independently associated with mortality, the association was strongest for MRscore [adjusted hazard ratio (95% CI) per SD increase = 1·73 (1·46-2·06]. Harrell’s C-statistic was 0·706 for a model predicting mortality by age and sex alone and increased to 0·752 in a model including MRscore and the FI. Interpretation: The combination of a DNA methylation score based on 10 CpGs only and an easy to ascertain frailty index may strongly enhance mortality prediction beyond age and sex. Funding Information: The ESTHER study was funded by grants from the Baden-Wurttemberg state Ministry of Science, Research and Arts (Stuttgart, Germany), the Federal Ministry of Education and Research (Berlin, Germany), the Federal Ministry of Family Affairs, Senior Citizens, Women and Youth (Berlin, Germany), and the Saarland State Ministry of Health, Social Affairs, Women and the Family (Saarbrucken, Germany). Declaration of Interests: No potential conflicts of interest were disclosed. Ethics Approval Statement: The ESTHER study was approved by the ethics committees of the medical faculty of the University of Heidelberg and the medical board of the state of Saarland. Written informed consent was obtained from each participant.

Association between sexual orientation acceptance and suicidal ideation, substance use, and internalised homophobia amongst the pink carpet Y cohort study of young gay, bisexual, and queer men in Singapore

BackgroundGay, bisexual and queer (GBQ) men are frequently subjected to minority stressors that have negative impacts on their health. Milestones that include the acceptance and disclosure of sexual identity amongst GBQ men are hence key instruments in understanding the prevalence of internalised homophobia and predicting health outcomes. As such, this work takes a novel approach to deduce the correlates of delayed acceptance of sexual orientation in young GBQ men as a measure of internalised homophobia through retrospective self-reporting and age-based analysis.MethodsParticipants were recruited as part of a cohort study exploring the syndemic risks associated with HIV acquisition among young GBQ men in Singapore. We examined their levels of internalised, perceived, experienced homophobia, as well as their health behaviours and suicidal tendencies. Two separate variables were also self-reported by the participants – the age of questioning of sexual orientation and the age of acceptance of sexual orientation. We subsequently recoded a new variable, delayed acceptance of sexual orientation, by taking the difference between these two variables, regressing it as an independent and dependent variable to deduce its psychosocial correlates, as well as its association with other measured instruments of health.ResultsAs a dependent variable, delayed acceptance of sexual orientation is positively associated with an increase of age and internalised homophobia, while being negatively associated with reporting as being gay, compared to being bisexual or queer. As an independent variable, delayed acceptance of sexual orientation was associated with a delayed age of coming out to siblings and parents, suicide ideation, historical use of substances including smoking tobacco cigarettes and consuming marijuana, as well as reporting higher levels of experienced, internalised and perceived homophobia.ConclusionGreater levels of early intervention and efforts are required to reduce the heightened experience of minority stress resulting from communal and institutional hostilities. Areas of improvement may include community-based counselling and psychological support for GBQ men, while not forsaking greater education of the social and healthcare sectors. Most importantly, disrupting the stigma narrative of a GBQ ‘lifestyle’ is paramount in establishing an accepting social environment that reduces the health disparity faced by GBQ men.

Cost-effective and efficient 3D human model creation and re-identification application for human digital twins

As health-care budgets are continuously under increasing demands, Artificial Intelligence resources such as digital heart twins could save millions of dollars by predicting results and preventing unnecessary surgery. Can we start to make digital human body twins to plant and predict health outcomes for a patient? By using a way to design competent simulation models from real objects, digital twins were created through IoT. But the digital twin is a complicated system and a very long-drawn step away from its possibilities. Researchers must design all components of entities or structures. There is a need to collect and merge various types of data. Many engineering researchers and participants aren’t sure about which technologies and resources to use. The 3D digital twin model offers a reference guide for digital twin comprehension and implementation. This paper aims to investigate and outline the recent technologies and tools used for digital twin applications from a 3-D digital model perspective, such as references to technologies and tools for future digital twin applications.