Common Predictors Research Articles

Machine learning provides insights for spatially explicit pest management strategies by integrating information on population connectivity and habitat use in a key agricultural pest.

Insect pests have garnered increasing interest because of anthropogenic global change, and their sustainable management requires knowledge of population habitat use and spread patterns. To enhance this knowledge for the prevalent tea pest Empoasca onukii, we utilized a random forest algorithm and a bivariate map to develop and integrate models of its habitat suitability and genetic connectivity across China. Our modeling revealed heterogeneous spatial patterns in suitability and connectivity despite the common key environmental predictor of isothermality. Analyses indicated that tea cultivation in areas surrounding the Tibetan Plateau and the southern tip of China may be at low risk of population outbreaks because of their predicted low suitability and connectivity. However, regions along the middle and lower reaches of the Yangtze River should consider the high abundance and high recolonization potential of E. onukii, and thus the importance of control measures. Our results also emphasized the need to prevent dispersal from outside regions in the areas north of the Yangtze River and highlighted the effectiveness of internal management efforts in southwestern China and along the southeastern coast. Further projections under future conditions suggested the potential for increased abundance and spread in regions north of the Yangtze River and the southern tip of China, and indicated the importance of long-term monitoring efforts in these areas. These findings highlighted the significance of combining information on habitat use and spread patterns for spatially explicit pest management planning. In addition, the approaches we used have potential applications in the management of other pest systems and the conservation of endangered biological resources. © 2024 Society of Chemical Industry.

Effects of Middle-Aged Women's Demographics, Health Behavior, And Health Status on Health-Related Quality of Life by Economic Activity Participation: Panel Evidence from Korea.

Middle-aged women have lower health-related quality of life (HRQoL) than men, and women's participation in economic activities is an important issue. In this study, the middle-aged female population was classified by economic activity participation to explore HRQoL and to identify related factors. We obtained data from the 2017 Korea Health Panel Survey (N = 1857 women aged 50-64 years), analyzed with chi-squared tests, t-tests, analyses of variance, and multiple regression analyses. HRQoL was measured with the Euro QoL-5 Dimensions index, and four demographic factors, four health behavior factors, and ten health status factors were entered. HRQoL was lower in the economically inactive group compared to the active group. Education, physical activity, chronic disease, vision problem, dental problem, activity restriction, frustration, and anxiety were associated with HRQoL in both groups. Spouse and household income were additional predictors identified only in the economically inactive group, and the 10 factors explained 36.0% of the variance. In the economically active group, drinking, sleeping time, hearing problem, stress, and depression were additional predictors identified, and the 13 factors explained 23.0% of the variance. Healthcare providers should consider both common predictors between economically active and inactive middle-aged women as well as predictors that were unique to each group to improve their HRQoL.

Retrospective Analysis of Risk Factors in Geriatric Hip Fracture Patients Predictive of Surgical Intensive Care Unit Admission.

Although numerous risk factors and prediction models affecting morbidity and mortality in geriatric hip fracture patients have been previously identified, there are scant published data on predictors for perioperative Surgical Intensive Care Unit (SICU) admission in this patient population. Determining if a patient will need an SICU admission would not only allow for the appropriate allocation of resources and personnel but also permit targeted clinical management of these patients with the goal of improving morbidity and mortality outcomes. The purpose of this study was to identify specific risk factors predictive of SICU admission in a population of geriatric hip fracture patients. Unlike previous studies which have investigated predominantly demographic, comorbidity, and laboratory data, the present study also considered a frailty index and length of time from injury to presentation in the Emergency Department (ED). A total of 501 geriatric hip fracture patients admitted to a Level 1 trauma center were included in this retrospective, single-center, quantitative study from January 1, 2019, to December 31, 2022. Using a logistical regression analysis, more than 25 different variables were included in the regression model to identify values predictive of SICU admission. Predictive models of planned versus unplanned SICU admissions were also estimated. The discriminative ability of variables in the final models to predict SICU admission was assessed with receiver operating characteristic curves' area under the curve estimates. Frailty,serum lactate > 2, andpresentation to the ED > 12 hours after injurywere significant predictors of SICU admission overall (P = 0.03, 0.038, and 0.05 respectively). Additionally, the predictive model for planned SICU admission had no common significant predictors with unplanned SICU admission. Planned SICU admission significant predictors included an Injury Severity Score (ISS) of 15 and greater, a higher total serum protein, serum sodium <135, systolic blood pressure (BP) under 100, increased heart rate on admission to ED, thrombocytopenia (<120), and higher Anesthesia Society Association physical status classification (ASA) score (P = 0.007, 0.04, 0.05, 0.002, 0.041, 0.05, and 0.005 respectively). Each SICU prediction model (overall, planned, and unplanned) demonstrated sufficient discriminative ability with the area under the curve (AUC) values of 0.869, 0.601, and 0.866 respectively.Finally, mean hospital Length of Stay (LOS) and mortality were increased in SICU admissions when compared to non-SICU admissions. Of the three risk factors predictive of SICU admission identified in this study, two have not been extensively studied previously in this patient population. Frailty has been associated with increased mortality and postoperative complications in hip fracture patients, but this is the first study to date to use a novel frailty index specifically designed and validated for use in hip fracture patients. The other risk factor, time from injury to presentation to the ED serves as an indicator for time a hip fracture patient spent without receiving medical attention. This risk factor has not been investigated heavily in the past as a predictor of SICU admissions in this patient population.

Children born very or extremely preterm transitioning to school: a cross-sectional study examining predictors of school readiness, school adjustment, and support needs

ABSTRACT The transition to school is a sensitive developmental period for young children. Although children born very/extremely preterm have increased risk of health and developmental concerns, predictors of their school readiness and adjustment remain largely unexamined. Parents of very/extremely preterm-born children (aged 3–7 years; pre-transition n = 114, post-transition n = 112) completed an online survey assessing their perceptions of children’s school readiness (pre-transition) or adjustment (post-transition), support needs, child behaviour, parent distress, and parent confidence. Poorer school readiness and adjustment and greater needs for support correlated with child health/developmental condition/s; hyperactivity, peer problems, conduct problems, emotional symptoms, and less prosocial behaviour; and lower parent confidence. Using hierarchical linear regression, parent confidence emerged as the strongest common predictor of school readiness (pre-transition) and school adjustment (post-transition), followed by low hyperactivity, high parent education (tertiary), and no diagnosed child health/developmental condition/s. Results will be used to identify families needing support and develop tailored support strategies.

Blunt smoking during emerging adulthood: Characterizing transitions in cannabis and cigar co-use among a diverse cohort in Texas

ObjectiveTo characterize and compare transitions in blunt smoking behaviors among a diverse cohort of youth and young adults observed between Spring 2019 and Fall 2021. MethodsWe analyzed n = 14,152 observations (i.e., completed surveys) provided by n = 2,610 youth and young adults over six (6) waves from Spring 2019 to Fall 2021 via the Texas Adolescent Tobacco and Marketing Surveillance (TATAMS) system. Participants were recruited in age-cohorts, reflecting 16.5 years (0.5), 18.5 years (0.6), and 20.1 years (0.8) of age at baseline. We applied a three-state Markov model to estimate blunt initiation (never → ever), onset (never → current), continuation (ever → current), and discontinuation (current → ever). First, we compared transitions in blunt smoking by race/ethnicity, with non-Hispanic (NH) Whites as the referent. Second, we stratified the Markov models by race/ethnicity to identify common and unique predictors of blunt transitions, including sex, age, alcohol use, depression, anxiety, and tobacco cigar smoking. ResultsAt baseline, 73% of participants had never smoked blunts, 15.3% had ever smoked blunts s, and 11.7% currently smoked blunts. NHB (HR: 2.15; 95% CI: 1.21–3.84) and Hispanic (HR: 1.72; 95% CI: 1.08–2.72) participants had significantly greater risk of blunt smoking initiation, relative to NHWs. Similarly, NHBs had great risk for continuation (HR: 1.65; 95% CI: 1.16–2.34) and lower risk of discontinuation (HR: 0.57; 95% CI: 0.42–0.77), relative to NHWs. Alcohol use predicted greater risk for onset among NHW (HR: 5.22; 95% CI: 1.40–19.45), NHB (HR: 3.14; 95% CI: 1.32–7.46), and Hispanic (HR: 2.99; 95% CI: 1.80–4.97) participants. ConclusionsBlunt smoking initiation was most common among NHB and Hispanic youth and young adults while risk for sustained blunt smoking was higher in NHB youth and young adults. Research and interventions should investigate the link between alcohol use and elevated blunt smoking among young people.

Characteristics of early complications in isolated primary anterior cruciate ligament reconstruction surgery.

The early complications of isolated anterior cruciate ligament reconstruction surgery (ACLR) have not been well characterized using large databases. This study aims to characterize incidence, impact, and risk factors for short-term operative complications following elective, isolated ACLR surgery. We hypothesize that demographic and perioperative factors may predict 30-day complications after isolated ACLR. This case-control analysis of the American College of Surgeons National Surgical Quality Improvement Program Database (2005-2017) used Current Procedural Terminology codes to identify elective, isolated ACLR patients. Patients undergoing concomitant procedures were excluded. Complications were analyzed using bivariate analysis against demographic variables. Multiple stepwise logistic regression was used to identify independent risk factors for morbidity after ACLR. A total 12,790 patients (37.0% female, p = 0.674) were included with a mean age of 32.2years old (SD 10.7years, p < 0.001). Mean BMI was 27.8kg/m2 (6.5) where 28.9% of patients had a BMI > 30 (p = 0.064). The most common complications were wound-related (0.57%). In cases with complications, there were higher rates of (1.3% vs 0.8%, p = 0.004) prolonged operation (> 1.5h), higher rate (2.9% vs 1.8%, p = 0.004) of extended length of stay (≥ 1day), unplanned reoperation (15.8% vs 0.3%, p < 0.001), and unplanned readmission (17.5% vs 0.3%, p < 0.001). Multivariate analysis showed prolonged operative time (p = 0.001), dyspnea (p = 0.008), and non-ambulatory surgery (p = 0.034) to be predictive of any complication. Dependent functional status (p = 0.091), mFI-5 > 0.2 (= 0.173), female sex (p = 0.191), obesity (p = 0.101), and smoking (p = 0.113) were not risk factors for complications. ACLR is associated with low rates of morbidity and readmissions. The most common comorbidities, complications, and predictors of morbidities were identified to aid surgeons in further reducing adverse outcomes of ACLR. Operative time > 1.5h, dyspnea, and non-ambulatory surgery are predictive of complications.

Nutritional status of primary school children and their caregiver's knowledge on malnutrition in rural and urban communities of Ekiti State, Southwest Nigeria.

Nutritional imbalance is an underlying cause of 2.6million death annually and a third of child's death globally. This study assessed and compared the nutritional status of primary school children and their caregiver's knowledge on malnutrition in rural and urban communities of Ekiti State. This is a cross-sectional comparative study carried out among 983 urban and rural primary school children in Ekiti State (495 in urban and 488 in rural) using interviewer-administered semi-structured questionnaire. A multi-stage sampling technique was used and data collected was analyzed using SPSS 23 with level of statistical significance set at p < 0.05. Underweight and stunting were relatively higher in rural (6.5% and 22.7% respectively) than in urban (6.3% and 19.4% respectively) and these differences are not statistically significant (p = 0.898, p = 0.197). However, wasting, overweight and obesity were higher in urban (12.7%, 6.1% and 7.7% respectively) than rural (11.5%, 3.7% and 7.5% respectively) but the difference is not statistically significant. (p = 0.242). Majority of the caregivers in both settings had good knowledge of malnutrition though higher in urban mothers (89.5%) with statistical significance than their rural counterparts (71.5%). However, there is no significant association between caregiver's knowledge and malnutrition in this study. Being in lower primary school class, relationship with caregiver, educational status of caregiver and occupation of caregiver were the common predictors of malnutrition among the school children in both community settings. Generally, the prevalence of malnutrition was high in both urban and rural primary school children in this study. However, while underweight and stunting were more prevalent among the children in the rural communities, wasting, overweight and obesity were more prevalent in the urban. The caregivers in both communities had good knowledge of malnutrition (better in the urban) but this is not good enough to bring a significant relationship with the occurrence of malnutrition in the children. Common predictors of malnutrition in both community settings are being in lower primary school class, relationship with caregiver, educational status of caregiver and occupation of caregiver. It is therefore recommended that regular continuous public enlightenment, nutritional education programmes and other programmes targeted at improving the economic power of the caregivers are measures that will improve the nutritional status of the primary school children.

Externally validated prediction models for pre-eclampsia: systematic review and meta-analysis.

This systematic review and meta-analysis aimed to evaluate the performance of existing externally validated prediction models for pre-eclampsia (PE) (specifically, any-onset, early-onset, late-onset and pretermPE). A systematic search was conducted in five databases (MEDLINE, EMBASE, Emcare, CINAHL and Maternity & Infant Care Database) and using Google Scholar/reference search to identify studies based on the Population, Index prediction model, Comparator, Outcome, Timing and Setting (PICOTS) approach until 20 May 2023. We extracted data using the CHARMS checklist and appraised the risk of bias using the PROBAST tool. A meta-analysis of discrimination and calibration performance was conducted when appropriate. Twenty-three studies reported 52 externally validated prediction models for PE (one preterm, 20 any-onset, 17 early-onset and 14 late-onset PE models). No model had the same set of predictors. Fifteen any-onset PE models were validated externally once, two were validated twice and three were validated three times, while the Fetal Medicine Foundation (FMF) competing-risks model for preterm PE prediction was validated widely in 16 different settings. The most common predictors were maternal characteristics (prepregnancy body mass index, prior PE, family history of PE, chronic medical conditions and ethnicity) and biomarkers (uterine artery pulsatility index and pregnancy-associated plasma protein-A). The FMF model for preterm PE (triple test plus maternal factors) had the best performance, with a pooled area under the receiver-operating-characteristics curve (AUC) of 0.90 (95% prediction interval (PI), 0.76-0.96), and was well calibrated. The other models generally had poor-to-good discrimination performance (median AUC, 0.66 (range, 0.53-0.77)) and were overfitted on external validation. Apart from the FMF model, only two models that were validated multiple times for any-onset PE prediction, which were based on maternal characteristics only, produced reasonable pooled AUCs of 0.71 (95% PI, 0.66-0.76) and 0.73 (95% PI, 0.55-0.86). Existing externally validated prediction models for any-, early- and late-onset PE have limited discrimination and calibration performance, and include inconsistent input variables. The triple-test FMF model had outstanding discrimination performance in predicting preterm PE in numerous settings, but the inclusion of specialized biomarkers may limit feasibility and implementation outside of high-resource settings. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Intra-operative Predictors of Neurological Outcomes After Cardiac Surgery

Abstract Background: Despite the advancement in surgical techniques, neurological complications encountered post-cardiac surgery remain significant. Stroke, seizure, and deep coma are major postoperative neurological complications reported in numerous patients. This study aims to identify perioperative predictors for neurological complications and focus on the relationship between these predictors and major neurological complications, particularly stroke, seizure, and deep coma. Materials and Methods: This retrospective cohort study was conducted among patients who underwent cardiac surgery between January 2014 and December 2020 at King Fahd Hospital of University, Khobar, Saudi Arabia. Out of 432 patients who had surgery, only 331 patients met the inclusion criteria. A comparison was made between patients who developed postoperative neurological complications and those who did not. The collected data was analyzed using the Statistical Package of Social Sciences program version 29 (Armonk, NY: IBM Corp. USA). Results: There were 50 patients with postoperative neurological complications compared to 281 patients with no complications. The rate of perioperative haemodynamic instability was higher in the complication group (14/48, 29.2%) compared to the non-complication group (47/238, 19.7%). Stroke was the highest neurological complication encountered (14/50, 28%). Conclusions: Perioperative haemodynamic instability is more common in patients with postoperative neurological complications compared to patients with no complications. Additionally, stroke is the most common major neurological complication. Aortic surgeries, combined surgeries, and haemodynamic instability are the most common perioperative predictors of stroke.

Artificial intelligence informing clinical decision making on the risk of hospital readmissions in multi-morbid patients: a systematic review

Abstract Introduction Multi-morbid patients have complex care needs and are more likely to utilise healthcare services, which represents a financial burden on healthcare organisations. 30-day readmission of patients is a method of measuring the quality of the services provided within healthcare.[1] Artificial intelligence (AI) models can flag multi-morbidity patients at increased risk of readmission.[2] However, it is unclear which common predictors have been used to develop these models. Aim To systematically review the medical literature to identify predictors that have been used to develop AI models that predicted unplanned 30-day hospital readmissions of multi-morbid patients, and whether these predictors were modifiable or non-modifiable. Methods Four large databases: Medline, Embase, Web of Science and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched on 15th November 2022. Only publications that developed a machine learning algorithm to predict unplanned 30-day hospital readmission for patients with multi-morbidity were included. Articles published in English language were eligible for inclusion. A narrative synthesis of all eligible studies was undertaken. Key findings were identified through inductive strategy, which includes simplifying raw data in the included articles into a comprehensive format. This systematic review was registered with the PROSPRO database (CRD42022373937) and followed PRISMA guidelines. Results There were 1,906 articles extracted from all databases, 18 of which met our inclusion criteria. A total of 669 predictors of hospital readmission were found, and were divided into 103 modifiable (i.e., action could be taken to change the outcome) and 566 non-modifiable (i.e. age, gender, number of previous emergency admissions. The average number of modifiable predictors per prediction model was six, with the most common being length of hospital stay, multi-morbidity, obesity, hypertension, diabetes, depression, and anaemia. The most commonly used AI algorithm was the gradient boosting algorithm. This algorithm generally showed a better performance than other models reported. The performance of all models in the included studies was compared; 13 studies showed an average sensitivity of 72%. The average specificity was calculated at 73% from 11 studies. The highest AUC reported was above 0.9, which demonstrates an excellent performing model, while five models did not report a value for the AUC. Conclusion We identified modifiable and non-modifiable predictors used in previously developed AI algorithms that predict 30-day hospital readmissions of multi-morbid patients. Modifiable predictors in particular can help guide clinical decision-making by allowing early actions to be taken in primary care to potentially reduce the risk of readmission. However, one limitation of this review is that some included studies used balanced (i.e. synthetic) data which could have introduced a level of bias.

Longitudinal predictors of reading and arithmetic at different attainment levels

Reading and arithmetic are distinct academic skills that share similarities in skill acquisition and use. Previous research investigated the cognitive basis of associations and dissociations between reading and arithmetic by using either subtyping or dimensional approaches. In the current study, we aim to bridge the gap between these two approaches by investigating common and distinct predictors of reading and arithmetic at different performance levels with quantile regression models. This allowed us to look more closely at the lower tail of the ability distributions, and to test whether predictions for children with low reading and arithmetic fluency differed from the typical performance range. We analyzed longitudinal data of 357 children speaking English or German. Outcome variables were reading and arithmetic fluency assessed at the end of Grade 1, 2, and 3. Predictors were assessed in Grade 1. Results confirmed nonverbal IQ and working memory as domain-general predictors of reading and arithmetic. The association of reading and arithmetic was mainly explained by nonverbal IQ, phonological awareness, RAN and multi-digit transcoding. Across grades and performance levels, phonological awareness and RAN made a specific contribution to reading. Magnitude processing and multi-digit transcoding were specific predictors of arithmetic. Counting also made a specific prediction to arithmetic in Grade 3, but only in the low performance range. Our findings indicate partly distinct underlying cognitive mechanisms for reading and arithmetic. Shared predictors are involved in retrieval efficiency, language processing and cross-format integration. These results have important implications, as they suggest that most predictors are equally relevant for children with low, typical or even excellent reading and arithmetic fluency.

Development of a Better Nomogram for Prediction of Preoperative Microvascular Invasion and Postoperative Prognosis in Hepatocellular Carcinoma Patients: A Comparison Study.

Personalized precision medicine can be facilitated by clinically available preoperative microvascular invasion (MVI) prediction models that are reliable and postoperative MVI pathological grade-related recurrence prediction models that are accurate. In this study, we aimed to compare different mathematical models to derive the best preoperative prediction and postoperative recurrence prediction models for MVI. A total of 143 patients with hepatocellular carcinoma (HCC) whose clinical, laboratory, imaging, and pathological data were available were included in the analysis. Logistic regression, Cox proportional hazards regression, LASSO regression with 10-fold cross-validation, stepwise regression, and random forest methods were used for variable screening and predictive modeling. The accuracy and validity of seven preoperative MVI prediction models and five postoperative recurrence prediction models were compared in terms of C-index, net reclassification improvement, and integrated discrimination improvement. Multivariate logistic regression analysis revealed that a preoperative nomogram model with the variables cirrhosis diagnosis, alpha-fetoprotein > 400, and diameter, shape, and number of lesions can predict MVI in patients with HCC reliably. Postoperatively, a nomogram model with MVI grade, number of lesions, capsule involvement status, macrovascular invasion, and shape as the variables was selected after LASSO regression and 10-fold cross-validation analysis to accurately predict the prognosis for different MVI grades. The number and shape of the lesions were the most common predictors of MVI preoperatively and recurrence postoperatively. Our study identified the best statistical approach for the prediction of preoperative MVI as well as postoperative recurrence in patients with HCC based on clinical, imaging, and laboratory tests results. This could expedite preoperative treatment decisions and facilitate postoperative management.

The prevalence of functional dyspepsia using Rome IV questionnaire among chronic kidney disease patients

ABSTRACT: Background: Symptoms of dyspepsia are usually encountered by chronic kidney disease patients. Abdominal discomfort is commonly seen in CKD patients with no other causes of organic affection. Aim: to determine the prevalence of functional dyspepsia in CKD patients, and which subtype is predominant in them. Materials and patients: This observational study included 150 CKD patients. Clinical and laboratory data were recorded for every patient. All the patients were interviewed using the ROME IV questionnaire of functional dyspepsia. Patients fulfilling criteria for functional dyspepsia were exposed to upper GI endoscopy. Results: Overall, 73 (48.7%) of CKD patients were males and 77 (51.3%) were females with mean age of (45.71 ± 9.59) and mean BMI (26.58 ± 5.39). The frequency of functional dyspepsia among CKD patients was determined to be 14.7% (22 out of 150 patients). Among those affected by functional dyspepsia, the most prevalent subtype was found to be Epigastric Pain Syndrome (EPS), accounting for 59% (13 out of 22 cases). The most common predictor of FD in CKD patients was chronic HCV infection, hemodialysis, stage of CKD and eGFR as revealed by Univariate regression analysis. Conclusion: The prevalence of FD amongst CKD patients is 14.7% with EPS the predominant subtype. Male patients, HCV patients, patients with higher CKD stages and highly impaired eGFR (low eGFR) are more probable to have FD.

Identifying multilevel predictors of behavioral outcomes like park use: A comparison of conditional and marginal modeling approaches.

This study compared marginal and conditional modeling approaches for identifying individual, park and neighborhood park use predictors. Data were derived from the ParkIndex study, which occurred in 128 block groups in Brooklyn (New York), Seattle (Washington), Raleigh (North Carolina), and Greenville (South Carolina). Survey respondents (n = 320) indicated parks within one half-mile of their block group used within the past month. Parks (n = 263) were audited using the Community Park Audit Tool. Measures were collected at the individual (park visitation, physical activity, sociodemographic characteristics), park (distance, quality, size), and block group (park count, population density, age structure, racial composition, walkability) levels. Generalized linear mixed models and generalized estimating equations were used. Ten-fold cross validation compared predictive performance of models. Conditional and marginal models identified common park use predictors: participant race, participant education, distance to parks, park quality, and population >65yrs. Additionally, the conditional mode identified park size as a park use predictor. The conditional model exhibited superior predictive value compared to the marginal model, and they exhibited similar generalizability. Future research should consider conditional and marginal approaches for analyzing health behavior data and employ cross-validation techniques to identify instances where marginal models display superior or comparable performance.

Identifying the top predictors of student well-being across cultures using machine learning and conventional statistics

Alongside academic learning, there is increasing recognition that educational systems must also cater to students’ well-being. This study examines the key factors that predict adolescent students’ subjective well-being, indexed by life satisfaction, positive affect, and negative affect. Data from 522,836 secondary school students from 71 countries/regions across eight different cultural contexts were analyzed. Underpinned by Bronfenbrenner’s bioecological theory, both machine learning (i.e., light gradient-boosting machine) and conventional statistics (i.e., hierarchical linear modeling) were used to examine the roles of person, process, and context factors. Among the multiple predictors examined, school belonging and sense of meaning emerged as the common predictors of the various well-being dimensions. Different well-being dimensions also had distinct predictors. Life satisfaction was best predicted by a sense of meaning, school belonging, parental support, fear of failure, and GDP per capita. Positive affect was most strongly predicted by resilience, sense of meaning, school belonging, parental support, and GDP per capita. Negative affect was most strongly predicted by fear of failure, gender, being bullied, school belonging, and sense of meaning. There was a remarkable level of cross-cultural similarity in terms of the top predictors of well-being across the globe. Theoretical and practical implications are discussed.

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.

This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose. In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST). The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low. Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.

Prevalence and social determinants of depression: A cross-sectional survey of Myanmar migrant workers in Chiang Mai, Northern Thailand

ABSTRACT Background: Depression is a common mental disorder and the sixth leading cause of disability in Thailand. Chiang Mai has historically been a city of attraction for labour migration for Shan migrants from the adjacent Southern Shan State of Myanmar. Currently, only infectious diseases are screened during the pre-employment period. Prevention and early detection of noncommunicable diseases can improve a healthy workforce and reduce the healthcare burden on the host country. Therefore, we aimed to determine the prevalence of depression and associated factors. Methods: A cross-sectional survey was done among Myanmar migrant adults legally working in Chiang Mai, Thailand. Results: The final analysis included 360 participants and the prevalence of depression was 14.4%. Being female, duration of stay in Thailand of more than 10 years, co-residence with co-workers, and marginalisation pattern of acculturation were significant associated factors affecting depression in a multivariate regression analysis model. Conclusion: Although the culture and dialect were not different between the host and country of origin, Myanmar migrant workers of Shan ethnicity suffered from depression. Our findings highlighted the importance of social determinants beyond common predictors of depression among migrants and the need for public health measures to promote migrants' integration into the host culture.

Prognostic models and factors identifying end-of-life in non-cancer chronic diseases: a systematic review

BackgroundPrecise prognostic information, if available, is very helpful for guiding treatment decisions and resource allocation in patients with non-cancer non-communicable chronic diseases (NCDs). This study aimed to systematically review the...

BBOX1, LACC1, MMP7 and SSTR1 as common predictors in obesity and non-alcoholic fatty liver disease

BBOX1, LACC1, MMP7 and SSTR1 as common predictors in obesity and non-alcoholic fatty liver disease

Predictors and Clinical Significance of Myocardial Injury in Elderly Patients Under the COVID-19 Pandemic.

The elderly patients are at increased high risk of myocardial injury and mortality after the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. This study aims to investigate the prevalence, predictors and prognostic implications of myocardial injury in the elderly patients with SARS-CoV-2 infection. Patients aged over 65 years were consecutively recruited between April to May, 2022. Myocardial injury was assessed using the high-sensitivity cardiac troponin T (hs-cTnT) assay. The primary endpoint was in-hospital mortality. A total of 347 patients were recruited with a median age of 81 years. 45.8% were male and 18 (5.2%) deceased before discharge. Myocardial injury (hs-cTnT over 99% upper reference limit [URL]) was detected in 202 (58.2%) of patients. Predictors of myocardial injury included age (per 5-year increase), hypertension, vaccination, creatine, and neutrophil-to-lymphocyte ratio. hs-cTnT over 3 × URL was independently correlated with in-hospital mortality (adjusted odds ratio [adOR], 13.21; 95% confidence interval [CI], 2.11-87.1; p = 0.005) in comparison to hs-cTnT > URL (adOR, 0.66; 95% CI, 0.09-5.92; p = 0.680). Myocardial injury was a common phenomenon and prognostic predictor in elder patients after SARS-CoV-2 infection. Higher threshold of myocardial injury may be considered to improve risk stratification.