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Health disparities are ongoing challenges in the United States, with one of the underlying factors being the variation in health risk behaviors across different socioeconomic and demographic communities. In this cross-sectional ecological study, utilizing data from 26,781 US zip codes provided by the Centers for Disease Control and Prevention (CDC), we conducted generalized linear model regressions to explore the associations between race, poverty, education, and urban/rural status in areas. Our analysis indicated that areas with a higher prevalence of Black population and higher poverty levels are associated with a more significant number of unique health risk behaviors. Conversely, other racial groups were associated with fewer unique health risk behaviors, although they may still engage in higher levels of specific behaviors. These insights underscore the need for tailored public health strategies to address the disparities in health risk behaviors across different sub-populations. Additionally, we employed Finite Mixture Modeling (FMM) to identify distinct sub-populations in the United States based on health risk behaviors, specifically binge drinking, smoking, sleep deprivation, and physical inactivity, which are essential risk factors for chronic diseases such as cancer, diabetes, and cardiovascular diseases. We identified four clusters and conducted an in-depth analysis of the demographics and socioeconomic characteristics of the identified clusters. Our findings reveal that one cluster, characterized by the worst health risk behaviors, exhibited the highest poverty rate, the least favorable health insurance profile, and the lowest frequencies of post-secondary education attainment compared to the other three clusters. This cluster, therefore, warrants targeted public health interventions. [ABSTRACT FROM AUTHOR]

Psychological distress in women represents a critical aspect; however, few studies have comprehensively addressed how health management strategies, consideration of future consequences (CFC), cognitive reappropriation, and self-efficacy may influence this phenomenon, especially in contexts that require a comprehensive approach to women's mental health. This study aimed to evaluate the effects of future consequence consideration, cognitive reappraisal, and self-efficacy in health management on psychological distress in American women, as well as to demonstrate their equivalence according to age. Data from 2788 women responding to the 2019 Health Information National Trends Survey 5, cycle 3 were used. A predictive model was considered using structural equation modeling (SEM). Cognitive reappraisal and self-efficacy in health management (SEHM) have a significant mediating effect between CFC and psychological distress. In addition, the invariance of the SEM model was presented as a function of age. The findings underscore the importance of incorporating cognitive components into mental health interventions for women, focusing on modifying thoughts and perceptions about the efficacy of health care to mitigate negative emotions. [ABSTRACT FROM AUTHOR]

Purpose: Target word activation in picture naming was explored in children who stutter (CWS) and typically fluent children (TFC) using event-related potentials (ERPs). Method: A total of 18 CWS and 16 TFC completed a task combining picture naming and probe word identification. On each trial, a picture-to-be-named was followed by an auditory probe word-to-be-identified; the probe was identical (Identity condition) or Unrelated to the picture name. ERPs were recorded from probe onset. Attenuation of the N400 ERP component was predicted to Identity versus Unrelated trials (N400 priming). Between-groups differences in amplitude, timing, and topography of N400 priming were explored. Results: Naming was more accurate on Identity versus Unrelated trials. Probe word identification accuracy was not affected by Condition. N400 priming was detected, indicating that self-generated picture names facilitated semantic processing of identical probes. This effect was larger in amplitude in CWS versus TFC. Unexpectedly, an N400-preceding, frontally maximal, positive-going ERP component--associated with expectancy processing--was larger in amplitude to Unrelated versus Identity trials. This effect was smaller in CWS versus TFC. Conclusions: A larger N400 priming effect in CWS versus TFC reflects a tendency toward more extensive semantic processing in picture naming in CWS. A smaller Condition effect on frontally maximal, positive-going, N400-preceding ERP activity in CWS versus TFC indicates a reduced ability to form expectancies about the lexical and/or phonological identity of probe words in CWS. Both effects may point to inefficient activation of target words in picture naming in CWS. [ABSTRACT FROM AUTHOR]

BACKGROUND: The American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Values (TLVs) for Lifting is a manual material handling (MMH) assessment method to identify weight limits that nearly all workers may be exposed to without developing work-related low back disorders (LBD). However, this assessment method only applies to lifting with the torso within 30° asymmetry of the sagittal plane. OBJECTIVE: Estimate TLV weight limits while lifting with torso asymmetry greater than 30° beyond the sagittal plane. METHODS: Lifting tasks were performed from various horizontal and vertical locations, at torso asymmetry angles of 0°, 15°, 30°, 45°, 60°, 75° and 90°, using ACGIH identified TLVs. Validated MMH assessment methods (NIOSH Lifting Equation, Ohio State University LBD Risk Model) were utilized to estimate TLVs at torso asymmetries greater than 30°. RESULTS: The current ACGIH TLVs resulted in low- to moderate-risk risk levels for torso asymmetries from 0° to 30°, and the risk incrementally increased as torso asymmetry increased to 90°. With the intention to keep the risk levels to that found at 30° torso asymmetry, lower TLV weight limits in the vertical and horizontal zones investigated were estimated for torso asymmetries from 45° to 90°. The resulting adjusted TLVs were consistent with weight limits identified for similar lifting conditions from other assessment methods that account for torso asymmetry. CONCLUSIONS: This research found current ACGIH-defined TLVs possess less than high-risk for LBD, and provided guidance to practitioners for reduced TLVs when torso asymmetry is greater than 30° from the sagittal plane. [ABSTRACT FROM AUTHOR]

Simple Summary: As the treatment modalities for spinal metastatic tumors continue to evolve, the goal of improving the quality of life of patients with spinal metastases becomes more easily attainable. The best patient care is a combination of multiple treatment modalities, surgical and nonsurgical, that maximizes the advantages of each modality and should be individualized according to their unique clinical presentation, pathology, and life expectancy. In this review article, we highlight various new treatment options that have shown promising improvements in patient outcomes. Notably, we discuss the potential clinical applications of AI and NGS in the treatment of spinal metastases. Spinal metastases occur in up to 40% of patients with cancer. Of these cases, 10% become symptomatic. The reported incidence of spinal metastases has increased in recent years due to innovations in imaging modalities and oncological treatments. As the incidence of spinal metastases rises, so does the demand for improved treatments and treatment algorithms, which now emphasize greater multidisciplinary collaboration and are increasingly customized per patient. Uniquely, we discuss the potential clinical applications of AI and NGS in the treatment of spinal metastases. Material and Methods: A PubMed search for articles published from 2000 to 2023 regarding spinal metastases and artificial intelligence in healthcare was completed. After screening for relevance, the key findings from each study were summarized in this update. Results: This review summarizes the evidence from studies reporting on treatment modalities for spinal metastases, including minimally invasive surgery (MIS), external beam radiation therapy (EBRT), stereotactic radiosurgery (SRS), CFR-PEEK instrumentation, radiofrequency ablation (RFA), next-generation sequencing (NGS), artificial intelligence, and predictive models. [ABSTRACT FROM AUTHOR]

Background Virtually all cases of hepatitis C virus (HCV) infection in children in the United States occur through vertical transmission, but it is unknown how many children are infected. Cases of maternal HCV infection have increased in the United States, which may increase the number of children vertically infected with HCV. Infection has long-term consequences for a child's health, but treatment options are now available for children ≥3 years old. Reducing HCV infections in adults could decrease HCV infections in children. Methods Using a stochastic compartmental model, we forecasted incidence of HCV infections in children in the United States from 2022 through 2027. The model considered vertical transmission to children <13 years old and horizontal transmission among individuals 13–49 years old. We obtained model parameters and initial conditions from the literature and the Centers for Disease Control and Prevention's 2021 Viral Hepatitis Surveillance Report. Results Model simulations assuming direct-acting antiviral treatment for children forecasted that the number of acutely infected children would decrease slightly and the number of chronically infected children would decrease even more. Alone, treatment and early screening in individuals 13–49 years old reduced the number of forecasted cases in children and, together, these policy interventions were even more effective. Conclusions Based on our simulations, acute and chronic cases of HCV infection are remaining constant or slightly decreasing in the United States. Improving early screening and increasing access to treatment in adults may be an effective strategy for reducing the number of HCV infected children in the United States. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Consciousness assessment is an important component in the prehospital care of ill or injured children. Both the Glasgow Coma Scale (GCS) and the Alert, Verbal, Pain, Unresponsive (AVPU) scale are used for this purpose. We sought to identify cut points for the GCS to correspond to the AVPU scale for pediatric emergency medical services (EMS) encounters. METHODS: We conducted a retrospective cross-sectional analysis using the 2019-2022 National EMS Information System data set, including children (<18 years) with a GCS and AVPU score. We evaluated several approaches to develop cut points for the GCS within the AVPU scale and reported measures of performance. RESULTS: Of 6 186 663 pediatric encounters, 4 311 598 with both GCS and AVPU documentation were included (median age was 10 years [interquartile range 3-15]; 50.9% boys). Lower AVPU scores correlated with life-sustaining procedures, including those for airway management, seizure, and cardiac arrest. Optimal GCS cut points obtained via a grid-based search were 14 to 15 for alert, 11 to 13 for verbal, 7 to 10 for pain, and 3 to 6 for unresponsive. Overall accuracy was 0.95, with kappa of 0.61. Intraclass F1 statistics were lower for verbal (0.37) and pain (0.50) categories compared with alert (0.98) and unresponsive (0.78). CONCLUSIONS: We developed a cross-walking between the AVPU and GCS scales. Overall performance was high, though performance within the verbal and pain categories was lower. These findings can be useful to enhance clinician handovers and to aid in the development of EMS-based prediction models. [ABSTRACT FROM AUTHOR]

The organ shortage continues to present problems around the world including the United States. In response, some countries have switched from an opt-in organ donor registry to an opt-out registry. The United States currently utilizes an opt-in registry where an individual is not considered an organ donor until they register their intentions. In the current study, U.S. adults were randomly assigned to a 2 (message valence: promotional, refutational) x 2 (autonomy restoration postscript: present, not present) x 2 (social proof: high likes, low likes) posttest only control group design evaluating social media message regarding an opt-out organ donor registry for implementation in the U.S. Results revealed an interaction between message valence and autonomy on freedom threat perceptions toward the message. Consistent with psychological reactance theory, freedom threat perceptions were positively associated with reactance, which in turn was positively associated with an intention to sign a petition and call a representative to voice disapproval of the opt-out organ donor registry. The discussion is focused on pragmatic recommendations for organ donor practitioners and advocates as well as the theoretical contributions to reactance theory. [ABSTRACT FROM AUTHOR]

Millions of people around the world are affected by mental illness, yet a substantive portion of individuals with mental illness do not regularly seek help from medical professionals or utilize mental health services. Help-seeking involves an individual's communicative intention and behaviors to solicit advice and/or treatment and thus is essentially a communicative process. This manuscript reports a longitudinal study that examined predictors and outcomes of help-seeking in mental illness contexts. Three-wave data from 223 participants clinically diagnosed with one or more mental illness conditions were collected and analyzed. Results indicated that expected outcomes of help-seeking from counselors were significantly associated with participants' communication efficacy and target efficacy at Wave 1, which subsequently influenced motivation to seek help and actual help-seeking behaviors at Wave 2. Actual help-seeking behaviors predicted post-traumatic growth and cognitive reappraisal at Wave 3. Importantly, communication efficacy and target efficacy interacted in their effects on help-seeking motivation and behaviors. Theoretical implications for help-seeking communication processes and practical implications for mental health help-seeking are discussed. [ABSTRACT FROM AUTHOR]

Background: For the past 50 years, standard guidelines have recommended the use of sex-adjusted mid-parental height to predict a child's final height. Here, we studied the accuracy of this procedure. Methods: We used height data in a cohort of 23 very large nuclear families (mean = 11 adult children per family). We compared the actual final height of the children to their height predicted by the standard procedure, as well as to alternative height predictions that incorporate corrections of mid-parental height for age, sex, and regression to the mean. Results: Standard mid-parental height explained 36% of the variance in children's heights, with a heritability of 74%, and children were on average 2.7 cm taller than predicted by their target heights. When we introduced a nonlinear correction for the age of the parents, employed a multiplicative (rather than additive) correction for sex, and accounted for regression to the mean, the variance explained increased to 40%, heritability increased to 80%, and prediction bias was reduced from 2.7 cm to 0.14 cm (representing an improvement in prediction by half a standard deviation of the height distribution). We further measured the empirical distribution of the heights of adult children around their predicted height. We describe how this distribution can be used to estimate the probability that a child's height is within the normal expected range. Conclusions and Relevance: Based on these observations, we propose an improved method for predicting children's target heights. Our procedure for determining whether the deviation of a child's projected height from the target height is in the normal range can be used to assess whether the child should be tested further for potential medical abnormalities. [ABSTRACT FROM AUTHOR]

Aims: We aimed to develop an elaborative nomogram that predicts cancer-specific survival (CSS) in American and Chinese octogenarians treated with radical resection for CRC. Methods: The patient data of newly diagnosed patients aged 80 years or older who underwent radical resection for CRC from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and then randomly divided into a training cohort and a validation cohort. The patients collected from our hospital were defined as the external validation cohort. Univariate and multivariate Cox regression was used to select independent predictive factors for the construction of a nomogram to predict 1-, 2- and 3-year CSS. Results: The multivariate Cox regression model identified age, T stage, N stage, perineural invasion, chemotherapy, tumour deposits, carcinoembryonic antigen level, number of lymph node metastases, and number of solid organ metastases as independent predictors of survival. The C-index of the nomogram for 1-, 2- and 3-year CSS was 0.758, 0.762, and 0.727, respectively, demonstrating significant clinical value and substantial reliability compared to the TNM stage. The calibration curve and area under the curve also indicated considerable predictive accuracy. In addition, decision curve analysis demonstrated desirable net benefits in clinical application. Conclusion: We constructed a nomogram for predicting the CSS of individual octogenarian patients with CRC who underwent radical resection. The nomogram performed better than the TNM staging system in this particular population and could guide clinicians in clinical follow-up and individual therapeutic plan formulation. Highlights: This study explored the exclusive characteristics of octogenarians treated with radical resection for colorectal cancer. This study is the first to develop a nomogram for predicting the cancer-specific survival of individual octogenarian patients after radical resection for colorectal cancer with the substantial reliability (C-index: 0.758 compared to the AJCC pathological staging system (C-index: 0.713). Internal validation of this nomogram in a cohort of American patients (N = 1644) and external validation in a cohort of Chinese patients (N = 368) confirmed its accuracy. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the potential role of Caprini risk assessment model (RAM) in predicting the risk of venous thromboembolism (VTE) in patients undergoing total hip or knee arthroplasty (THA/TKA). No national study has investigated the role of Caprini RAM after primary THA/TKA. Methods: Data from The National Sample of Healthcare Cost and Utilization Project (HCUP) in 2019 were utilized for this study. The dataset consisted of 229,134 patients who underwent primary THA/TKA. Deep vein thrombosis (DVT) and pulmonary embolism (PE) were considered as VTE. The incidence of thrombosis was calculated based on different Caprini scores, and the risk of the Caprini indicator for VTE events was evaluated using a forest plot. Results: The prevalence of VTE after primary THA/TKA in the U.S. population in 2019 was found to be 4.7 cases per 1000 patients. Age, body mass index (BMI), and Caprini score showed a positive association with the risk of VTE (P < 0.05). The receiver operating characteristic (ROC) curve analysis indicated that a Caprini score of 9.5 had a sensitivity of 47.2% and a specificity of 82.7%, with an area under the curve (AUC) of 0.693 (95% CI, 0.677−0.710). The highest Youden index was 0.299. Multivariate logistic regression analysis revealed that malignancy, varicose vein, positive blood test for thrombophilia, history of thrombosis, COPD, hip fracture, blood transfusion, and age were significant risk factors for VTE. Based on these findings, a new risk stratification system incorporating the Caprini score was proposed. Conclusions: Although the Caprini score does not seem to be a good predictive model for VTE after primary THA/TKA, new risk stratification for the Caprini score is proposed to increase its usefulness. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) based predictive models for early detection of cardiovascular disease (CVD) risk are increasingly being utilised. However, AI based risk prediction models that account for right-censored data have been overlooked. This systematic review (PROSPERO protocol CRD42023492655) includes 33 studies that utilised machine learning (ML) and deep learning (DL) models for survival outcome in CVD prediction. We provided details on the employed ML and DL models, eXplainable AI (XAI) techniques, and type of included variables, with a focus on social determinants of health (SDoH) and gender-stratification. Approximately half of the studies were published in 2023 with the majority from the United States. Random Survival Forest (RSF), Survival Gradient Boosting models, and Penalised Cox models were the most frequently employed ML models. DeepSurv was the most frequently employed DL model. DL models were better at predicting CVD outcomes than ML models. Permutation-based feature importance and Shapley values were the most utilised XAI methods for explaining AI models. Moreover, only one in five studies performed gender-stratification analysis and very few incorporate the wide range of SDoH factors in their prediction model. In conclusion, the evidence indicates that RSF and DeepSurv models are currently the optimal models for predicting CVD outcomes. This study also highlights the better predictive ability of DL survival models, compared to ML models. Future research should ensure the appropriate interpretation of AI models, accounting for SDoH, and gender stratification, as gender plays a significant role in CVD occurrence. [ABSTRACT FROM AUTHOR]

Background: Drawing on prospective data from the National Health and Nutrition Examination Survey (NHANES), our goal was to construct and validate a 5-year survival prediction model for individuals with cognitive impairment (CI). Methods: This study entailed a prospective cohort design utilizing information from the 2011-2014 NHANES dataset, encompassing individuals aged 40 years or older, with updated mortality status as of December 31, 2019. Predictive models within the derivation and validation cohorts were assessed using logistic proportional risk regression, column-line plots, and least absolute shrinkage and selection operator (LASSO) binomial regression models. Results: The study enrolled a total of 1,439 participants (677 men, mean age 69.75 ± 6.71 years), with the derivation and validation cohorts consisting of 1,007 (538 men) and 432 (239 men) individuals, respectively. The 5-year mortality rate stood at 16.12% (n = 232). We devised a 5-item column-line graphical model incorporating age, race, stroke, cardiovascular disease (CVD), and blood urea nitrogen (BUN). The model exhibited an area under the curve (AUC) of 0.772 with satisfactory calibration. Internal validation demonstrated that the column-line graph model displayed strong discrimination, yielding an AUC of 0.733, and exhibited good calibration. Conclusion: To sum up, our study successfully developed and internally validated a 5-item nomogram integrating age, race, stroke, cardiovascular disease, and blood urea nitrogen. This nomogram exhibited robust predictive performance for 5-year mortality in individuals with CI, offering a valuable tool for prognostic evaluation and personalized care planning. [ABSTRACT FROM AUTHOR]

Background: Early medical attention after concussion may minimize symptom duration and burden; however, many concussions are undiagnosed or have a delay in diagnosis after injury. Many concussion symptoms (eg, headache, dizziness) are not visible, meaning that early identification is often contingent on individuals reporting their injury to medical staff. A fundamental understanding of the types and levels of factors that explain when concussions are reported can help identify promising directions for intervention. Purpose: To identify individual and institutional factors that predict immediate (vs delayed) injury reporting. Study Design: Case-control study; Level of evidence, 3. Methods: This study was a secondary analysis of data from the Concussion Assessment, Research and Education (CARE) Consortium study. The sample included 3213 collegiate athletes and military service academy cadets who were diagnosed with a concussion during the study period. Participants were from 27 civilian institutions and 3 military institutions in the United States. Machine learning techniques were used to build models predicting who would report an injury immediately after a concussive event (measured by an athletic trainer denoting the injury as being reported "immediately" or "at a delay"), including both individual athlete/cadet and institutional characteristics. Results: In the sample as a whole, combining individual factors enabled prediction of reporting immediacy, with mean accuracies between 55.8% and 62.6%, depending on classifier type and sample subset; adding institutional factors improved reporting prediction accuracies by 1 to 6 percentage points. At the individual level, injury-related altered mental status and loss of consciousness were most predictive of immediate reporting, which may be the result of observable signs leading to the injury report being externally mediated. At the institutional level, important attributes included athletic department annual revenue and ratio of athletes to athletic trainers. Conclusion: Further study is needed on the pathways through which institutional decisions about resource allocation, including decisions about sports medicine staffing, may contribute to reporting immediacy. More broadly, the relatively low accuracy of the machine learning models tested suggests the importance of continued expansion in how reporting is understood and facilitated. [ABSTRACT FROM AUTHOR]

Era studies are key to estimate the historic rate of genetic gain for commercial traits of a crop. We evaluated for the first time nearly all maize hybrids released (269) in five relative maturity markets (95, 100, 105, 110, and 115) from Bayer Crop Science Dekalb brand over the last 20 years. We focused on grain yield, plant height, moisture, and test weight. We then compared the predicted gains derived from the genome‐wide prediction models with the estimated ones. We found that genetic gain was positive and significant for grain yield across the five relative maturities, ranging from 0.077 to 0.147 Mg ha−1 year−1, while it was negative (desirable) and significant for plant height, with a decrease from −0.259 to −0.456 cm year−1. We did not find significant changes for grain harvest moisture and test weight. The genetic gain estimates were consistent with predictions using the company's genomic estimated breeding values. We concluded that breeding has delivered increased yield and decreased plant height over this period for Dekalb hybrids, and the company's genomewide prediction model can be used to predict the direction of future maize genetic gains. This era study includes a larger number of hybrids, broadens the range of relative maturities, and incorporates comparisons with genomewide predictions compared to previous era studies. [ABSTRACT FROM AUTHOR]

Introduction: Healthcare organizations are under increasing pressure from policymakers, payers, and advocates to screen for and address patients’ health-related social needs (HRSN). The emergency department (ED) presents several challenges to HRSN screening, and patients are frequently not screened for HRSNs. Predictive modeling using machine learning and artificial intelligence, approaches may address some pragmatic HRSN screening challenges in the ED. Because predictive modeling represents a substantial change from current approaches, in this study we explored the acceptability of HRSN predictive modeling in the ED. Methods: Emergency clinicians, ED staff, and patient perspectives on the acceptability and usage of predictive modeling for HRSNs in the ED were obtained through in-depth semi-structured interviews (eight per group, total 24). All participants practiced at or had received care from an urban, Midwest, safety-net hospital system. We analyzed interview transcripts using a modified thematic analysis approach with consensus coding. Results: Emergency clinicians, ED staff, and patients agreed that HRSN predictive modeling must lead to actionable responses and positive patient outcomes. Opinions about using predictive modeling results to initiate automatic referrals to HRSN services were mixed. Emergency clinicians and staff wanted transparency on data inputs and usage, demanded high performance, and expressed concern for unforeseen consequences. While accepting, patients were concerned that prediction models can miss individuals who required services and might perpetuate biases. Conclusion: Emergency clinicians, ED staff, and patients expressed mostly positive views about using predictive modeling for HRSNs. Yet, clinicians, staff, and patients listed several contingent factors impacting the acceptance and implementation of HRSN prediction models in the ED. [ABSTRACT FROM AUTHOR]

Introduction: The global incidence of total joint arthroplasty (TJA) has consistently risen over time, and while various forecasts differ in magnitude, future projections suggest a continued increase in these procedures. Differences in future United States projections may arise from the modeling method selected, the nature of the national arthroplasty registry employed, or the representativeness of the specific hospital discharge records utilized. In addition, many models have not accounted for ambulatory surgery as well as all payer types. Therefore, to attempt to make a more accurate model, we utilized a national representative sample that included outpatient arthroplasties and all insurance types to predict the volumes of primary TJA in the USA from 2019 to 2060. Methods: A national, all-payer database was queried. All patients who underwent primary total hip arthroplasty (THA) and total knee arthroplasty (TKA) from January 1, 2010, to December 31, 2019, were identified using international classification of disease Ninth Revision (9) and Tenth Revision (10) codes and current procedure terminology codes. Absolute frequencies and incidence rates were calculated per 100,000 for both THA and TKA procedures, with 95% confidence intervals. Mean growth in absolute frequency and incidence rates were calculated for each procedure from 2010 to 2014, and 2010 to 2019, with 95% confidence intervals (CI). Results: The overall increase in THA and TKA procedures are expected to grow + 10 and + 36%, respectively, using linear regressions and + 9 and + 37%, respectively. The most positive mean growth in procedure frequency occurred from 2010 to 2014 for THA (+ 24, 95% Confidence Interval (CI): + 21, + 27) and 2010–2019 for TKA (+ 11%, 95% CI: + 9, + 14). There positive trend patterns in incidence rate growth for both procedures, with similar 2010–2019 incidence rates + 6%) for THA (+ 3%, 95% CI: + 0, + 6%) and TKA (+ 3%, 95% CI: + 1%, + 6%). Conclusion: Utilizing a nationally representative database, we demonstrated that TJA procedures would continue with an increased growth pattern to 2060, though slightly decreased from the surge from 2014 to 2019. While this finding applies to the representativeness of the population at hand, the inclusion of outpatient arthroplasty and all payer types validates an approach that has not been undertaken in previous projection studies. [ABSTRACT FROM AUTHOR]

This study identified the process and agency characteristics associated with poor utilization outcomes – higher percentages of patients (i) admitted to an acute care organization and (ii) visited an emergency room (ER) unplanned without hospitalization – for home health agencies (HHAs) in the United States. We conducted a secondary analysis of data about HHAs' various characteristics, process adherence levels, and utilization outcomes collected from disparate public repositories for 2010–2022. We developed descriptive tree-based models using HHAs' hospital admission or ER visit percentages as response variables. Across the board, hospital admission percentages have steadily improved while ER percentages deteriorated for an extended period. Recently, checking for fall risks and depression was associated with improved outcomes for urban agencies. In general, rural HHAs had worse utilization outcomes than urban HHAs. Targeted investments and improvement initiatives can help rural HHAs close the urban-rural gap in the future. [ABSTRACT FROM AUTHOR]

A reliable, user-friendly, and multidimensional prediction tool can help to identify children at high risk for ADHD and facilitate early recognition and family management of ADHD. We aimed to develop and validate a risk nomogram for ADHD in children aged 3–17 years in the United States based on clinical manifestations and complex environments. A total of 141,356 cases were collected for the prediction model. Another 54,444 cases from a new data set were utilized for performing independent external validation. The LASSO regression was used to control possible variables. A final risk nomogram for ADHD was established based on logistic regression, and the discrimination and calibration of the established nomogram were evaluated by bootstrapping with 1000 resamples. A final risk nomogram for ADHD was established based on 13 independent predictors, including behavioral problems, learning disabilities, age, intellectual disabilities, anxiety symptoms, gender, premature birth, maternal age at childbirth, parent-child interaction patterns, etc. The C-index of this model was 0.887 in the training set, and 0.862 in the validation set. Internal and external validation proved that the model was reliable. A nomogram, a statistical prediction tool that assesses individualized ADHD risk for children is helpful for the early identification of children at high risk for ADHD and the construction of a conceptual model of society-family-school collaborative diagnosis, treatment, and management of ADHD. To construct a conceptual model of society-family-school collaborative diagnosis, treatment, and management of ADHD. We employed machine learning algorithms to identify 13 independent prognostic factors of ADHD from multi-dimensional predictors. We successfully developed a highly accurate early ADHD prediction model with a C-index of 0.887. This reliable and user-friendly tool incorporates multiple dimensions of prediction, enabling g parents, teachers, and physicians to identify children at a high risk of ADHD. By empowering parents, teachers, and physicians with the ability to identify children at high risk, we can facilitate early recognition and family management, leading to improved outcomes for children with ADHD. [Display omitted] • We developed a nomogram for ADHD in children aged 3-17, which can aid in early recognition and family management of ADHD. • The nomogram was validated externally by using another database, proving its reliability in accurately predicting ADHD risk. • This nomogram forms a collaborative diagnostic, therapeutic, and management model involving society, family, and school. [ABSTRACT FROM AUTHOR]

Physical inactivity and loneliness are both associated with health risks and can affect each other through various social and behavioral mechanisms. However, current evidence on this relationship is equivocal and mostly based on cross-sectional data. This longitudinal study aimed to determine whether current levels of physical activity (moderate and vigorous intensity) and loneliness are associated with future respective states of themselves and each other. We used data from waves 6-14 (2002-2018) of the Health and Retirement Study (n  = 20 134) in a mixed-effects and random-intercept cross-lagged panel model. Analysis showed that current loneliness and physical activity were associated with each future respective state. Additionally, weekly participation in moderate-intensity, but not vigorous-intensity, physical activity was associated with a lower likelihood of becoming lonely in the future (relative risk [RR] = 0.94; 95% CI, 0.90-0.99). However, changes in physical activity were not associated with deviation from a person's typical level of loneliness (for vigorous intensity, mean deviation [MD] = 0.00; 95% CI: −0.04 to 0.03; for moderate-intensity, MD = 0.01; 95% CI: −0.03 to 0.04). Loneliness was not associated with moderate- or vigorous-intensity physical activity in subsequent waves. This suggests that while lower physical activity levels can be associated with future loneliness, changing levels of physical activity has little impact on loneliness at the individual level. [ABSTRACT FROM AUTHOR]

Suicide is the second leading cause of death among individuals aged 5 to 24 in the United States (US). However, the precursors to suicide often do not surface, making suicide prevention challenging. This study aims to develop a machine learning model for predicting suicide ideation (SI), suicide planning (SP), and suicide attempts (SA) among adolescents in the US during the coronavirus pandemic. We used the 2021 Adolescent Behaviors and Experiences Survey Data. Class imbalance was addressed using the proposed data augmentation method tailored for binary variables, Modified Synthetic Minority Over-Sampling Technique. Five different ML models were trained and compared. SHapley Additive exPlanations analysis was conducted for explainability. The Logistic Regression model, identified as the most effective, showed superior performance across all targets, achieving high scores in recall: 0.82, accuracy: 0.80, and area under the Receiver Operating Characteristic curve: 0.88. Variables such as sad feelings, hopelessness, sexual behavior, and being overweight were noted as the most important predictors. Our model holds promise in helping health policymakers design effective public health interventions. By identifying vulnerable sub-groups within regions, our model can guide the implementation of tailored interventions that facilitate early identification and referral to medical treatment. [ABSTRACT FROM AUTHOR]

Objective Maternal risk stratification systems are increasingly employed in predicting and preventing obstetric complications. These systems focus primarily on maternal morbidity, and few tools exist to stratify neonatal risk. We sought to determine if a maternal risk stratification score was associated with neonatal morbidity. Study Design Retrospective cohort study of patients with liveborn infants born at ≥24 weeks at four hospitals in one health system between January 1, 2020, and December 31, 2020. The Expanded Obstetric Comorbidity Score (EOCS) is used as the maternal risk score. The primary neonatal outcome was 5-minute Apgar <7. Logistic regression models determined associations between EOCS and neonatal morbidity. Secondary analyses were performed, including stratifying outcomes by gestational age and limiting analysis to "low-risk" term singletons. Model discrimination assessed using the area under the receiver operating characteristic curves (AUC) and calibration via calibration plots. Results A total of 14,497 maternal–neonatal pairs were included; 236 (1.6%) had 5-minute Apgar <7; EOCS was higher in 5-minute Apgar <7 group (median 41 vs. 11, p < 0.001). AUC for EOCS in predicting Apgar <7 was 0.72 (95% Confidence Interval (CI) 0.68, 0.75), demonstrating relatively good discrimination. Calibration plot revealed that those in the highest EOCS decile had higher risk of neonatal morbidity (7.6 vs. 1.7%, p < 0.001). When stratified by gestational age, discrimination weakened with advancing gestational age: AUC 0.70 for <28 weeks, 0.63 for 28 to 31 weeks, 0.64 for 32 to 36 weeks, and 0.61 for ≥37 weeks. When limited to term low-risk singletons, EOCS had lower discrimination for predicting neonatal morbidity and was not well calibrated. Conclusion A maternal morbidity risk stratification system does not perform well in most patients giving birth, at low risk for neonatal complications. The findings suggest that the association between EOCS and 5-minute Apgar <7 likely reflects a relationship with prematurity. This study cautions against intentional or unintentional extrapolation of maternal morbidity risk for neonatal risk, especially for term deliveries. Key Points EOCS had moderate discrimination for Apgar <7. Predictive performance declined when limited to low-risk term singletons. Relationship between EOCS and Apgar <7 was likely driven by prematurity. [ABSTRACT FROM AUTHOR]

Objective: This study examines Chinese international students' underlying beliefs and overall perception of the flu vaccination to inform effective health promotion efforts on U.S. college campuses. Participants: Data were collected in March 2020. Participants (N = 189) were recruited via email at a southeastern university in the U.S. Methods: Incorporating theory recommendations and recent vaccine-related research findings, this study designs a Four-Factor Measurement Model through confirmatory factor analysis (CFA) to examine Chinese international students' perception of flu vaccination. Results: Chinese international students' health beliefs, particularly their perceived susceptibility of the influenza virus, was the most important factor to explain their perception of flu vaccination. While normative beliefs were the least influential factor to account for Chinese international students' perception of flu vaccination, these students valued healthcare providers' opinions immensely. Conclusions: This study suggests the Four-Factor Measurement Model will help measure Chinese international students' perception of flu vaccination and can be applied to future vaccine-related research. [ABSTRACT FROM AUTHOR]

Low health literacy (LHL) significantly impacts patients' ability to participate actively in their healthcare. Registered nurses (RNs) play a crucial role in identifying LHL and addressing patient knowledge gaps and skill deficits. This correlational study examined the relationship between RNs' predictions of patients' health literacy levels (HLL) and the actual HLL of a predominately Hispanic patient population. In addition, personal factors (i.e., demographics) were analyzed to determine their influence on the nurse's predictions and patients' HLL. Data were collected from 84 participant patient–nurse couplets admitted to a medical-surgical unit in a rural setting located on the United States–Mexico border. In addition to demographic information collected via survey, RNs were asked to predict their patient's health literacy abilities while the Newest Vital Sign, a health literacy assessment tool, was deployed to determine the actual HLL of patients participating in the study. Data were analyzed using descriptive statistics, t -tests, and chi-square tests while a Spearman correlational model was used to examine the relationship between predicted HLL and actual HLL. Finally, a logistic regression model was used to analyze the relationship between personal factors and HL data for RNs and patients. Analysis of the data revealed that RNs consistently overestimated patients' abilities, as evidenced by the disparity between patients' actual HLL (mean 1.71) and predicted HLL (mean 4.26) by RNs, with a moderately strong positive relationship (rs =.418). Notably, higher academic preparation and years of experience did not enhance the RNs' ability to identify LHL while the highest level of education completed was the only statistically significant predictor of adequate health literacy in the patient population sampled. These findings emphasize the need to prioritize effective health literacy education in RN academic preparation and clinical practice to support the detection of LHL when a standardized health literacy assessment tool is not utilized in the clinical setting. By recognizing the presence of LHL, healthcare professionals can better support patients' needs and bridge the knowledge gap, ultimately improving patient outcomes. [ABSTRACT FROM AUTHOR]

Background: The electronic health record (EHR) presents new opportunities for the timely identification of patients at high risk of critical illness and the implementation of preventive strategies. This study aims to externally validate an EHR‐based Elders Risk Assessment (ERA) score to identify older patients at high risk of future critical illness during a primary care visit. Methods: This historical cohort study included patients aged ≥65 years who had primary care visits at Mayo Clinic Rochester, MN, between July 2019 and December 2021. The ERA score at the time of the primary care visit was used to predict critical illness, defined as death or ICU admission within 1 year of the visit. Results: A total of 12,885 patients were included in the analysis. The median age at the time of the primary care visit was 75 years, with 44.6% being male. 93.7% of participants were White, and 64.2% were married. The median (25th, 75th percentile) ERA score was 4 (0, 9). 11.3% of study participants were admitted to the ICU or died within 1 year of the visit. The ERA score predicted critical illness within 1 year of a primary care visit with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.83–0.85), which indicates good discrimination. An ERA score of 9 was identified as optimal for implementing and testing potential preventive strategies, with the odds ratio of having the primary outcome in patients with ERA score ≥9 being 11.33 (95%CI 9.98–12.87). Conclusions: This simple EHR‐based risk assessment model can predict critical illness within 1 year of primary care visits in older patients. The findings of this study can serve as a basis for testing and implementation of preventive strategies to promote the well‐being of older adults at risk of critical illness and its consequences. See related article by Haimovich and Deardorff. [ABSTRACT FROM AUTHOR]

Simple Summary: Neuroendocrine carcinomas (NECs) of the tubular gastrointestinal tract are rare and associated with worse clinical outcomes. Data from 10,387 patients were collected from the SEER database with a median age of 63 years. The most common primary tumor site was the small intestine. women had a superior 5-year survival rate. Age > 65 years, descending colon and anorectal sites were associated with worse prognosis. Surgical intervention and tumors located in the small intestine and appendix showed a better prognosis. Background: Neuroendocrine carcinomas (NECs) of the tubular gastrointestinal tract (GI-NECs) are rare and associated with worse clinical outcomes. This population-based study aims to highlight key demographics, clinicopathological factors, and survival outcomes in the US population. Methods: Data from 10,387 patients with GI-NECs were extracted from the Surveillance, Epidemiology, and End Result (SEER) database from 2000 to 2020. Results: Most patients were >40 years old at the time of presentation with a median age of 63 years old, with almost equal ethnic distribution per US population data. The most common primary tumor site was the small intestine (33.6%). The metastatic spread was localized in 34.8%, regional in 27.8%, and distant in 37.3% of cases, and the liver was the most common site of metastasis (19.9%) in known cases of metastases. Most NEC patients underwent surgery, presenting the highest 5-year overall survival of 73.2% with a 95% confidence interval (CI) (95% CI 72.0–74.4%), while chemotherapy alone had the lowest 5-year survival of 8.0% (95% CI 6.4–10.0%). Compared to men, women had a superior 5-year survival rate of 59.0% (95% CI 57.6–60.5%). On multivariate analysis, age > 65 (HR 2.49, 95% CI 2.36–2.54%, p ≤ 0.001), distant metastasis (HR 2.57, 95% CI 2.52–2.62%, p ≤ 0.001), tumor size > 4 mm (HR 1.98, 95%, CI 1.70–2.31%, p ≤ 0.001), esophageal (HR 1.49, 95% CI 0.86–2.58%, p ≤ 0.001), transverse colon (HR 1.95, 95% CI 1.15–3.33%, p ≤ 0.01), descending colon (HR 2.12, 95% CI 1.12, 3.97%, p = 0.02) anorectal sites, and liver or lung metastases were associated with worse survival. Surgical intervention and tumors located in the small intestine or appendix showed a better prognosis. Conclusion: GI-NECs are a group of rare malignancies associated with a poor prognosis. Therefore, epidemiological studies analyzing national databases may be the best alternative to have a more comprehensive understanding of this condition, assess the impact of current practices, and generate prognosis tools. [ABSTRACT FROM AUTHOR]

In this research note, we describe the results of the first validation study of the U.S. Census Bureau's new Community Resilience Estimates (CRE), which uses Census microdata to develop a tract-level vulnerability index for the United States. By employing administrative microdata to link Social Security Administration mortality records to CRE, we show that CRE quartiles provide more stable predictions of COVID-19 excess deaths than single demographic categorizations such as race or age, as well as other vulnerability measures including the U.S. Centers for Disease Control and Prevention's Social Vulnerability Index (SVI) and the Federal Emergency Management Agency's National Risk Index (NRI). We also use machine learning techniques to show that CRE provides more predictive power of COVID-19 excess deaths than standard socioeconomic predictors of vulnerability such as poverty and unemployment, as well as SVI and NRI. We find that a 10-percentage-point increase in a key CRE risk measure is associated with one additional death per neighborhood during the initial outbreak of COVID-19 in the United States. We conclude that, compared with alternative measures, CRE provides a more accurate predictor of community vulnerability to a disaster such as a pandemic. [ABSTRACT FROM AUTHOR]

Public health campaigns have turned to the Health Belief Model (HBM) as a guiding framework for the past six decades. Carpenter’s 2010 HBM meta-analysis revealed important shortcomings as well as a path forward that has largely been ignored by recent COVID-19 research using this framework. Consistent with Carpenter’s recommendations, this study on the uptake of the first COVID-19 booster vaccine focused on the overlooked interactional processes of the original HBM founders. Our study used SEM and Hayes’s PROCESS 4.1 to explore the possibilities of the interdependent nature of the core three beliefs to form a model that is integrated. The study indicated that the core variables of the original HBM were significant predictors of the intent to take the first COVID-19 booster vaccine when considered in an interactional process framework. Our study results have implications for those designing public health advocacy campaigns regarding COVID-19 as it enters an endemic stage with future vaccines and medications. [ABSTRACT FROM AUTHOR]

Background: The introduction of laparoscopy in 1989 revolutionized surgical practices, reducing post-operative complications, and enhancing outcomes. Despite its benefits, limitations in laparoscopic tools have led to continued use of open surgery. Robotic-assisted surgery emerged to address these limitations, but its adoption trends and potential impact on open and laparoscopic surgery require analysis. Methods: A retrospective analysis used the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) databases from 2012 to 2021. The study encompassed various abdominal procedures, employing Vector Autoregressive (VAR) models to analyze the dynamic relationships between surgical techniques. The models predicted future trends in open, laparoscopic, and robotic surgery until Q2 of 2025. Results: The analysis included 360,171 patients across diverse procedures. In urology, robotic surgery dominated prostatectomies (83.1% in 2021) and nephrectomies (55.1% in 2021), while the open approach remained the predominant surgical technique for cystectomies (72.5% in 2021). In general surgery, robotic colectomies were forecasted to surpass laparoscopy, becoming the primary approach by 2024 (45.7% in 2025). Proctectomies also showed a shift towards robotic surgery, predicted to surpass laparoscopy and open surgery by 2025 (32.3%). Pancreatectomies witnessed a steady growth in robotic surgery, surpassing laparoscopy in 2021, with forecasts indicating further increase. While hepatectomies remained predominantly open (70.0% in 2025), esophagectomies saw a rise in robotic surgery, predicted to become the primary approach by 2025 (52.3%). Conclusions: The study suggests a transformative shift towards robotic-assisted surgery, poised to dominate various minimally invasive procedures. The forecasts indicate that robotic surgery may surpass laparoscopy and open surgery in colectomies, proctectomies, pancreatectomies, and esophagectomies by 2025. This anticipated change emphasizes the need for proactive adjustments in surgical training programs to align with evolving surgical practices. The findings have substantial implications for future healthcare practices, necessitating a balance between traditional laparoscopy and the burgeoning role of robotic-assisted surgery. [ABSTRACT FROM AUTHOR]

The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings that can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different weather conditions, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions [i.e., different near-surface heating rates ( d T / d t ), incoming shortwave radiation (SWd) regimes, and 5-cm soil moisture (SM05)] to evaluate the High-Resolution Rapid Refresh (HRRR) Model, which is a 3-km model used for operational weather forecasting in the United States. On days with small (large) d T / d t , we found afternoon T biases of about 2°C (−1°C) and afternoon SWd biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM05 biases. On days with small (large) SWd, we found daytime temperature biases of about 3°C (−2.5°C) and daytime SWd biases of up to 190 W m−2 (80 W m−2). Whereas different SM05 had little impact on T and SWd biases, dry (wet) conditions had positive (negative) SM05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identify model deficiencies in order to support improvements to the parameterization schemes used therein. A similar, regime-specific verification approach may also be used to help evaluate other geophysical models. Significance Statement: Improving weather forecasting models requires careful evaluations against high-quality observations. We used observations from the U.S. Climate Reference Network (USCRN) and found that the performance of the High-Resolution Rapid Refresh (HRRR) Model varies as a function of differences in near-surface heating and solar radiation. This finding indicates that model evaluations need to be conducted under varying near-surface weather conditions rather than averaging across multiple weather types. This new approach will allow for model developers to better identify model deficiencies and is a useful step to helping improve weather forecasts. [ABSTRACT FROM AUTHOR]

Child corporal punishment (CP) may lead to child physical abuse, which is a public health concern in the United States. The present study examined major risk factors predicting attitudes toward CP among a sample of Black parents (N = 394), including frequency and valence of experiences of CP during childhood, outcome expectancies of CP, and perceptions of self-efficacy and response efficacy of non-physical discipline strategies. Structural equation modeling results revealed that the indirect associations between CP frequency and attitudes through self-efficacy and response efficacy were moderated by CP valence. Results extend the literature and point to the need for incorporating information about efficacy of evidence-based non-physical discipline strategies into intervention messages targeting prevention of child physical abuse. [ABSTRACT FROM AUTHOR]

BACKGROUND: The goals of this study were to develop a model that predicts the risk of 30-d all-cause readmission in hospitalized Medicaid patients diagnosed with COPD and to create a predictive model in a retrospective study of a population cohort. METHODS: We analyzed 2016--2019 Medicaid claims data from 7 United States states. A COPD admission was one in which either the admission diagnosis or the first or second clinical (discharge) diagnosis bore an International Classification of Diseases, Tenth Revision code for COPD. A readmission was an admission for any condition (not necessarily COPD) that occurred within 30 d of a COPD discharge. We estimated a mixed-effects logistic model to predict 30-d readmission from patient demographic data, comorbidities, past health care utilization, and features of the index hospitalization. We evaluated model fit graphically and measured predictive accuracy by the area under the receiver operating characteristic (ROC) curve. RESULTS: Among 12,283 COPD hospitalizations contributed by 9,437 subjects, 2,534 (20.6%) were 30-d readmissions. The final model included demographics, comorbidities, claims history, admission and discharge variables, length of stay, and seasons of admission and discharge. The observed versus predicted plot showed reasonable fit, and the estimated area under the ROC curve of 0.702 was robust in sensitivity analyses. CONCLUSIONS: Our model identified with acceptable accuracy hospitalized Medicaid patients with a diagnosis of COPD who are at high risk of readmission. One can use the model to develop post-discharge management interventions for reducing readmissions, for adjusting comparisons of readmission rates between sites/providers or over time, and to guide a patient-centered approach to patient care. [ABSTRACT FROM AUTHOR]

Introduction: In dental hygiene education, a combination of ethics and professionalism, critical thinking, and problem‐solving skills are utilized while managing conflict in academic settings, especially in academic clinical patient care settings. Limited evidence exists in determining whether emotional intelligence is related to conflict management styles (CMS). Purpose: The purpose of this study was to determine whether relationships exist between emotional intelligence and CMS among undergraduate dental hygiene students. Methods: This quantitative cross‐sectional survey research study involved a non‐probabilistic sample of undergraduate dental hygiene students in the Western United States. The survey instrument consisted of three parts: 1. The Rahim Organizational Conflict Inventory‐II (ROCI‐II) included 28 questions measuring 5 CMS; 2. The Emotional Quotient Self‐Assessment Checklist (EQSAC) included 30 questions measuring 6 domains of emotional intelligence (EI); and 3. Demographic questions. The data were analysed using descriptive statistics, correlational statistics and multiple linear regression. Results: The responses from 92 participants were analysed. Most dental hygiene participants had EI scores in the moderate range and mainly used the collaborative and compromising CMS. Total EI scores were significant predictors of collaborative, compromising, accommodating and competing CMS. Conclusion: Improving EI scores may contribute to the increased use of the collaborative and compromising CMS in dental hygiene education. [ABSTRACT FROM AUTHOR]

A key focus of sports science research is the identification of quantitative assessments that can predict players' on-field performance and developmental potential. Despite efforts to establish predictive models, there are few validated measures that show reliable associations and large gaps in understanding. Here, we test a multidimensional battery of assessments developed through the USA Baseball, Prospect Development Pipeline that capture strength and functional movement abilities, and anthropometric characteristics, in a two-year cohort of collegiate baseball players from the Appalachian League. Swing propensity metrics for Zone Contact Percentage (ZCP: proportion pitches in strike zone swung at and hit) and Hard-Hit Percentage (HHP: proportion in-play balls with exit velocity ≥ 95 mph) were calculated on 189 players. Models testing hierarchical combinations of anthropometric and anthropometric plus assessment data were implemented using nested cross-validation with random forest and elastic net regression. Results indicate that anthropometric features account for 29% of variance in ZCP and 50–55% of HHP, while the addition of assessment contributed an additional 1–3% to ZCP and 5–12% to HHP, with top predictors coming from PDP strength and power assessments. These findings delineate contributions of andromorphic and physical abilities to in-game baseball performance using a validated assessment battery and advanced game statistics. [ABSTRACT FROM AUTHOR]

Hidradenitis suppurativa (HS) is a chronic inflammatory follicular skin condition that is associated with significant psychosocial and economic burden and a diminished quality of life and work productivity. Accurate diagnosis of HS is challenging due to its unknown etiology, which can lead to underdiagnosis or misdiagnosis that results in increased patient and healthcare system burden. We applied machine learning (ML) to a medical and pharmacy claims database using data from 2000 through 2018 to develop a novel model to better understand HS underdiagnosis on a healthcare system level. The primary results demonstrated that high-performing models for predicting HS diagnosis can be constructed using claims data, with an area under the curve (AUC) of 81%-82% observed among the top-performing models. The results of the models developed in this study could be input into the development of an impact of inaction model that determines the cost implications of HS diagnosis and treatment delay to the healthcare system. [ABSTRACT FROM AUTHOR]

There were 9.7 million Native Americans (American Indian, Alaska Native-AI/AN- these acronyms will be used interchangeably with Native Americans throughout the paper) in 2019 comprising 2.9% of the U.S. population. Native American populations have disproportionately higher rates of mental illnesses compared to other racial groups in the U.S. Mental health is a significant public health concern for this population, impacting different areas of their lives including employment. Additionally, Native Americans continue to experience significant disparities in access to Vocational Rehabilitation (VR) services and have poor employment outcomes. However, little is known about the relationships among demographic factors, vocational rehabilitation services, and employment outcomes of Native Americans with mental illness. Consequently, the current study examined how demographic factors and VR services are related to successful employment outcomes for Native American VR clients with mental illnesses using data from the Rehabilitation Services Administration (RSA) program year (2019) Case Service Report (9–11). Both descriptive analysis and data mining approaches were used to answer the research questions. Chi-square Automatic Interaction Detector (CHAID) analysis was used to determine which of the VR services could best predict the successful employment outcome of Native Americans with mental illness. The findings of the data mining approach revealed that among all the vocational rehabilitation services, job placement assistance was the strongest predictor of successful employment among Native American clients with mental illnesses. The second most important service predicting successful employment for those who received job placement assistance was shown to be maintenance. Implications for rehabilitation counselors and future research are discussed. [ABSTRACT FROM AUTHOR]

Background: Racial and socioeconomic status (SES) disparities in preterm delivery (PTD) have existed in the United States for decades. Disproportionate maternal exposures to adverse childhood experiences (ACEs) may increase the risk for adverse birth outcomes. Moreover, racial and SES disparities exist in the prevalence of ACEs, underscoring the need for research that examines whether ACEs contribute to racial and SES disparities in PTD. Methods: We examined the relationship between ACEs and PTD in a longitudinal sample of N = 3,884 women from the National Longitudinal Study of Adolescent to Adult Health (1994–2018). We applied latent class analysis to (1) identify subgroups of women characterized by patterns of ACE occurrence; (2) estimate the association between latent class membership (LCM) and PTD, and (3) examine whether race and SES influence LCM or the association between LCM and PTD. Results: Two latent classes were identified, with women in the high ACEs class characterized by a higher probability of emotional abuse, physical abuse, sexual abuse, and foster care placement compared with the low ACEs class, but neither class was associated with PTD. Race and SES did not predict LCM. Conclusions: Our findings suggest that ACEs may not impact PTD risk in previously hypothesized ways. Future research should assess the impact of ACEs on the probability of having live birth pregnancies as well as the role of potential protective factors in mitigating the impact of ACEs on PTD. [ABSTRACT FROM AUTHOR]

Although parent–child conversations about race are recommended to curb White U.S. children's racial biases, little work has tested their influence. We designed a guided racism discussion task for U.S. White parents and their 8–12-year-old White children. We explored whether children's and parents' (a) pro-White implicit biases changed pre to postconversation, (b) racial socialization messages (color conscious, external attributions for prejudiced behavior and colorblind racial ideology [CBRI]) predicted changes in each other's implicit biases, and (c) associations varied by the type of racism (subtle vs. blatant) discussed. Children's and parents' biases significantly declined, pre to postdiscussion. Parents' color conscious messages predicted greater declines and messages reflecting CBRI and external attributions predicted smaller declines in children's bias. These patterns were observed during discussions of subtle, but not blatant bias. Effects of children's messages on parents' bias were mixed. Our findings suggest that color conscious parent–child discussions may effectively reduce implicit pro-White bias in White children. Public Significance Statement: Although they believe it to be beneficial, many White U.S. parents avoid discussing racism with their children or downplay the importance of race when they have these conversations. In the current study, we developed a method to encourage color conscious conversations in White families and found that parents' and children's racial biases reduced after the discussion. Our findings suggest that parent–child discussions about racism may effectively reduce racial biases in White families. [ABSTRACT FROM AUTHOR]

Background: Non-alcoholic steatohepatitis (NASH) is one of the leading indications for liver transplantation (LT) in the United States. As with the current obesity epidemic, the incidence of NASH continues to rise. However, the impact of broad utilization of bariatric surgery (BS) for patients with NASH is unknown, particularly in regard to mitigating the need for LT. Methods: Markov decision analysis was performed to simulate the lives of 20,000 patients with obesity and concomitant NASH who were deemed ineligible to be waitlisted for LT unless they achieved a body mass index (BMI) < 35 kg/m2. Life expectancy following medical weight management (MWM) and sleeve gastrectomy (SG) were estimated. Base case patients were defined as having NASH without fibrosis and a pre-intervention BMI of 45 kg/m2. Sensitivity analysis of initial BMI was performed. Results: Simulated base case analysis patients who underwent SG gained 14.3 years of life compared to patients who underwent MWM. One year after weight loss intervention, 9% of simulated MWM patients required LT compared to only 5% of SG patients. Survival benefit for SG was observed above a BMI of 32.2 kg/m2. Conclusion: In this predictive model of 20,000 patients with obesity and concomitant NASH, surgical weight loss is associated with a reduction in the progression of NASH, thereby reducing the need for LT. A reduced BMI threshold of 32 kg/m2 for BS may offer survival benefit for patients with obesity and NASH. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus (T2DM) is a prevalent global health issue that has been linked to an increased risk of depression. The objective of this study was to construct a nomogram model for predicting depression in T2DM patients. A total of 4280 patients with T2DM were included in this study from the 2007–2014 NHANES. The entire dataset was split randomly into training set comprising 70 % of the data and a validation set comprising 30 % of the data. LASSO and multivariate logistic regression analyses identified predictors significantly associated with depression, and the nomogram was constructed with these predictors. The model was assessed by C-index, calibration curve, the hosmer–lemeshow test and decision curve analysis (DCA). The nomogram model comprised of 9 predictors, namely age, gender, PIR, BMI, education attainment, smoking status, LDL-C, sleep duration and sleep disorder. The C-index of the training set was 0.780, while that of the validation set was 0.752, indicating favorable discrimination for the model. The model exhibited excellent clinical applicability and calibration in both the training and validation datasets. Moreover, the cut-off value of the nomogram is 223. This study has shortcomings in data collection, lack of external validation, and results non-extrapolation. Our nomogram exhibits high clinical predictability, enabling clinicians to utilize this tool in identifying high-risk depressed patients with T2DM. It has the potential to decrease the incidence of depression and significantly improve the prognosis of patients with T2DM. • This is the first nomogram model to predict depression in patients with T2DM among the United States population. • The model showed favorable discrimination, clinical applicability, and calibration in both training and validation datasets. • The nomogram has the potential to identify high-risk depressed patients and improve the prognosis of patients with T2DM. [ABSTRACT FROM AUTHOR]

Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) pose significant burdens on individuals and society, necessitating accurate prediction methods. Machine learning (ML) algorithms utilizing electronic health records and survey data offer promising tools for forecasting these conditions. However, potential bias and inaccuracies inherent in subjective survey responses can undermine the precision of such predictions. This research investigates the reliability of five prominent ML algorithms—a Convolutional Neural Network (CNN), Random Forest, XGBoost, Logistic Regression, and Naive Bayes—in predicting MDD and GAD. A dataset rich in biomedical, demographic, and self-reported survey information is used to assess the algorithms' performance under different levels of subjective response inaccuracies. These inaccuracies simulate scenarios with potential memory recall bias and subjective interpretations. While all algorithms demonstrate commendable accuracy with high-quality survey data, their performance diverges significantly when encountering erroneous or biased responses. Notably, the CNN exhibits superior resilience in this context, maintaining performance and even achieving enhanced accuracy, Cohen's kappa score, and positive precision for both MDD and GAD. This highlights the CNN's superior ability to handle data unreliability, making it a potentially advantageous choice for predicting mental health conditions based on self-reported data. These findings underscore the critical importance of algorithmic resilience in mental health prediction, particularly when relying on subjective data. They emphasize the need for careful algorithm selection in such contexts, with the CNN emerging as a promising candidate due to its robustness and improved performance under data uncertainties. [ABSTRACT FROM AUTHOR]

Background: Breakthrough cancer pain (BTCP) is primarily managed at home and can stem from physical exertion and emotional distress triggers. Beyond these triggers, the impact of ambient environment on pain occurrence and intensity has not been investigated. This study explores the impact of environmental factors on the frequency and severity of breakthrough cancer pain (BTCP) in the home context from the perspective of patients with advanced cancer and their primary family caregiver. Methods: A health monitoring system was deployed in the homes of patient and family caregiver dyads to collect self-reported pain events and contextual environmental data (light, temperature, humidity, barometric pressure, ambient noise.) Correlation analysis examined the relationship between environmental factors with: 1) individually reported pain episodes and 2) overall pain trends in a 24-hour time window. Machine learning models were developed to explore how environmental factors may predict BTCP episodes. Results: Variability in correlation strength between environmental variables and pain reports among dyads was found. Light and noise show moderate association (r = 0.50–0.70) in 66% of total deployments. The strongest correlation for individual pain events involved barometric pressure (r = 0.90); for pain trends over 24-hours the strongest correlations involved humidity (r = 0.84) and barometric pressure (r = 0.83). Machine learning achieved 70% BTCP prediction accuracy. Conclusion: Our study provides insights into the role of ambient environmental factors in BTCP and offers novel opportunities to inform personalized pain management strategies, remotely support patients and their caregivers in self-symptom management. This research provides preliminary evidence of the impact of ambient environmental factors on BTCP in the home setting. We utilized real-world data and correlation analysis to provide an understanding of the relationship between environmental factors and cancer pain which may be helpful to others engaged in similar work. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To analyze US commercial insurance payments associated with COVID-19 as a function of severity and duration of disease. STUDY DESIGN: Retrospective database analysis. METHODS: Patients with COVID-19 between April1, 2020, and June 30, 2021, in the Merative MarketScan Commercial database were identified and stratified as having asymptomatic, mild, moderate (with and without lower respiratory disease), or severe/critical (S/C) disease based on the severity of the acute COVID-19 infection. Duration of disease (DOD) was estimated for all patients. Patients with DOD longer than 12 weeks were defined as having post--COVID-19 condition (PCC). Outcomes were all-cause payments (ACP) and disease-specific payments (DSP) for the entire DOD. Variables included demographic and comorbidities at the time of acute disease. Adjusted payments by disease severity were estimated using generalized linear models (γ distribution with log link). RESULTS: A total of 738,339 patients were included (374,401 asymptomatic, 156,220 mild, 180,213 moderate, and 27,505 S/C cases). DSP increased from $217 (95% CI, $214-221) for asymptomatic cases to $2744 (95% CI, $2678- $2811) for moderate cases with lower respiratory disease and $28,250 (95% CI, $26,963-$29,538) for S/C cases. ACP increased from $505 (95% CI, $497-$512) for asymptomatic cases to $46,538 (95% CI, $44,096-$48,979) for S/C cases. The DSP and ACP further increased by $50,736 (95% CI, $45,337-$56,136) and $94,839 (95% CI, $88,029-$101,649), respectively, in S/C cases with PCC vs a DOD of fewer than4weeks. CONCLUSIONS: COVID-19 payments for S/C cases were more than 10-fold greater than those of moderate cases and further increased by nearly $95,000 in S/C cases with PCC vs a DOD of fewer than 4 weeks. [ABSTRACT FROM AUTHOR]

Policy PointsPromoting healthy public policies is a national priority, but state policy adoption is driven by a complex set of internal and external factors.This study employs new social network methods to identify underlying connections among states and to predict the likelihood of new firearm‐related policy adoption given changes to this interstate network.This approach could be used to assess the likelihood that a given state will adopt a specific new firearm‐related law and to identify points of influence that could either inhibit or promote wider diffusion of specific laws. Context: US states are largely responsible for the regulation of firearms within their borders. Each state has developed a different legal environment with regard to firearms based on different values and beliefs of citizens, legislators, governors, and other stakeholders. Predicting the types of firearm laws that states may adopt is therefore challenging. Methods: We propose a parsimonious model for this complex process and provide credible predictions of state firearm laws by estimating the likelihood they will be passed in the future. We employ a temporal exponential‐family random graph model to capture the bipartite state law–state network data over time, allowing for complex interdependencies and their temporal evolution. Using data on all state firearm laws over the period 1979–2020, we estimate these models' parameters while controlling for factors associated with firearm law adoption, including internal and external state characteristics. Predictions of future firearm law passage are then calculated based on a number of scenarios to assess the effects of a given type of firearm law being passed in the future by a given state. Findings: Results show that a set of internal state factors are important predictors of firearm law adoption, but the actions of neighboring states may be just as important. Analysis of scenarios provide insights into the mechanics of how adoption of laws by specific states (or groups of states) may perturb the rest of the network structure and alter the likelihood that new laws would become more (or less) likely to continue to diffuse to other states. Conclusions: The methods used here outperform standard approaches for policy diffusion studies and afford predictions that are superior to those of an ensemble of machine learning tools. The proposed framework could have applications for the study of policy diffusion in other domains. [ABSTRACT FROM AUTHOR]

BACKGROUND: In recent years, the adoption of electric scooters has been accompanied by a surge of scooter-related injuries in the US, raising concerns for their severity and associated healthcare costs. This study aimed to assess temporal trends and outcomes of scooter-related hospital admissions compared with bicycle-related hospitalizations. STUDY DESIGN: This was a retrospective cohort study using the 2016 to 2020 National Inpatient Sample for patients younger than 65 years who were hospitalized after bicycle- and scooter-related injuries. The Trauma Mortality Prediction Model was used to quantify injury severity. The primary outcomes of interest were temporal trends of micromobility injuries. In-hospital mortality, rates of long bone fracture, traumatic brain injury, paralysis, length of stay, hospitalization costs, and nonhome discharge were secondarily assessed. RESULTS: Among 92,815 patients included in the study, 6,125 (6.6%) had scooter-related injuries. Compared with patients with bicycle-related injuries, patients with scooter-related injuries were more commonly younger than 18 years (26.7% vs 16.4%, p < 0.001) and frequently underwent major operations (55.8% vs 48.1%, p < 0.001). After risk adjustment, scooterrelated injuries were associated with greater risks of long bone fracture (adjusted odds ratio 1.40, 95% CI 1.15 to 1.70) and paralysis (adjusted odds ratio 2.06, 95% CI 1.16 to 3.69) compared with bicycle-related injuries. Additionally, patients with bicycle- or scooter-related injuries had comparable index hospitalization durations of stay and costs. CONCLUSIONS: The prevalence and severity of scooter-related injuries have significantly increased in the US, thereby attributing to a substantial cost burden on the healthcare system. Multidisciplinary efforts to inform safety policies and enact targeted interventions are warranted to reduce scooter-related injuries. (J Am Coll Surg 2024;238:254-260. © 2024 by the American College of Surgeons. [ABSTRACT FROM AUTHOR]

Background Large vessel recanalization (LVR) before endovascular therapy (EVT) for acute large vessel ischemic strokes is a poorly understood phenomenon. Better understanding of predictors for LVR is important for optimizing stroke triage and patient selection for bridging thrombolysis. Methods In this retrospective cohort study, consecutive patients presenting to a comprehensive stroke center for EVT treatment were identified from 2018 to 2022. Demographic information, clinical characteristics, intravenous thrombolysis (IVT) use, and LVR before EVT were recorded. Factors independently associated with different rates of LVR were identified, and a prediction model for LVR was constructed. Results 640 patients were identified. 57 (8.9%) patients had LVR before EVT. A minority (36.4%) of LVR patients had significant improvements in National Institutes of Health Stroke Scale. Independent predictors for LVR were identified and used to construct the 8-point HALT score: hyperlipidemia (1 point), atrial fibrillation (1 point), location of vascular occlusion (internal carotid: 0 points, M1: 1 point, M2: 2 points, vertebral/basilar: 3 points), and thrombolysis at least 1.5 hours before angiography (3 points). The HALT score had an area under the receiver-operating curve (AUC) of 0.85 (95% CI 0.81 to 0.90, P<0.001) for predicting LVR. LVR before EVT occurred in only 1 of 302 patients (0.3%) with low (0-2) HALT scores. Conclusions IVT at least 1.5 hours before angiography, site of vascular occlusion, atrial fibrillation, and hyperlipidemia are independent predictors for LVR. The 8-point HALT score proposed in this study may be a valuable tool for predicting LVR before EVT. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is the most common joint disease in the US and can increase the risk of depression. Both depression and OA disproportionately affect women, yet this study is one of few on depression prevalence, treatment across age groups, and predictors in women with OA. Data were extracted from the 2011–March 2020 National Health and Nutrition Examination Survey (NHANES). Women aged ≥ 45 years with self-reported osteo- or degenerative arthritis were included. Outcomes were depression (assessed with PHQ-9) and treatment (self-reported pharmacotherapy and mental health services). Logistic regression was used to examine associations between age group, covariates, and outcomes. Overall, depression prevalence was 8%, with higher proportions among those 45–64 years old. Aging was associated with reduced odds of depression (Age 65–79: OR 0.68 (95% CI: 0.52–0.89); Age 80+: OR 0.49 (95% CI: 0.33–0.74); vs. Age 45–54). Of those with a positive depression screen, 21.6% documented some form of treatment. Age group was not statistically different between those treated and those not treated. Women aged 45–64 with osteoarthritis may be at increased risk of depression, and most are not treated. As depression is related to increased pain and risk of rehospitalization, future research should prioritize interventions to increase uptake of depression treatment. [ABSTRACT FROM AUTHOR]

Objectives: Our aim was to assess the trend in gynaecologic cancer (GC) mortality in the period from 2010 to 2022 in the United States, with focus on the impact of the pandemic on increased deaths. Methods: GC mortality data were extracted from the Center for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) platform. We analysed mortality trends and evaluated observed vs. predicted mortality for the period from 2020 to 2022 with joinpoint regression and prediction modelling analyses. Results: A total of 334,382 deaths among adults aged 25 years and older with gynaecologic cancer were documented from 2010 to 2022. The overall age-standardised mortality rate (ASMR, per 100,000 persons) for ovarian cancer-related death decreased gradually from 7.189 in 2010 to 5.517 in 2019, yielding an APC (annual percentage change) of -2.8%. However, the decrease in ovarian cancer-related mortality slowed down by more than 4-fold during the pandemic. Cervical cancer -related mortality decreased slightly prior to the pandemic and increased during the pandemic with an APC of 0.6%, resulting in excess mortality of 4.92%, 9.73% and 2.03% in 2020, 2021 and 2022, respectively. For uterine corpus cancer, the ASMR increased from 1.905 in 2010 to 2.787 in 2019, and increased sharply to 3.079 in 2021 and 3.211 in 2022. The ASMR rose steadily between 2013 and 2022, yielding an APC of 6.9%. Conclusions: Overall, we found that GC-related mortality increased during the COVID-19 pandemic, and this increase was not specific to age, race, or ethnicity. [ABSTRACT FROM AUTHOR]