Tag: statistics

J Healthc Risk Manag. 2023 May 19. doi: 10.1002/jhrm.21542. Online ahead of print.

ABSTRACT

As supply chains experienced disruptions early in the COVID-19 pandemic, personal protective equipment (PPE) quickly became scarce. The purpose of this study was to examine the impact of perceptions of inadequate PPE, fear of COVID-19 infection, and self-reported direct COVID-19 exposure on health care workers. Data to assess distress, resilience, social-ecological factors, and work and nonwork-related stressors were collected from June to July 2020 at a large medical center. Stressors were analyzed by role using descriptive statistics and multivariate regression analysis. Our data indicate that job role influenced fear of infection and perceptions of inadequate PPE in the early phase of the COVID-19 pandemic. Perceived organizational support was also related to perceptions of inadequate PPE supply. Interestingly, work location, rather than job role, was predictive of direct COVID-19 exposure. Our data highlight a disconnect between the perception of safety in the health care setting with real risk of exposure to infectious disease. This study suggests that leaders in health care should focus on cultivating supportive organizational cultures, assessing both perceived and actual safety, and provide adequate training in safety practices may improve preparedness and organizational trust during times of both certainty and crisis particularly for clinical workers with less education and training.

PMID:37208959 | DOI:10.1002/jhrm.21542

Bioinformatics. 2023 May 4;39(5):btad324. doi: 10.1093/bioinformatics/btad324.

NO ABSTRACT

PMID:37208919 | DOI:10.1093/bioinformatics/btad324

Health Serv Res. 2023 May 19. doi: 10.1111/1475-6773.14183. Online ahead of print.

ABSTRACT

OBJECTIVE: To examine if income inequality, social cohesion, and neighborhood walkability are associated with physical activity among rural adults.

DATA SOURCE: Cross-sectional data came from a telephone survey (August 2020-March 2021) that examined food access, physical activity, and neighborhood environments across rural counties in a southeastern state.

STUDY DESIGN: Multinomial logistic regression models assessed the likelihood of being active versus inactive and insufficiently active versus inactive in this rural population. Coefficients are presented as relative risk ratios (RRRs). Statistical significance was determined using 95% confidence intervals (CIs). All analyses were performed in STATA 16.1.

DATA COLLECTION/EXTRACTION METHODS: Trained university students administered the survey. Students verbally obtained consent, read survey items, and recorded responses into Qualtrics software. Upon survey completion, respondents were mailed a $10 incentive card and printed informed consent form. Eligible participants were ≥18 years old and current residents of included counties.

PRINCIPAL FINDINGS: Respondents in neighborhoods with relatively high social cohesion versus low social cohesion were more likely to be active than inactive (RRR = 2.50, 95% CI: 1.27-4.90, p < 0.01), after accounting for all other variables in the model. Income inequality and neighborhood walkability were not associated with different levels of physical activity in the rural sample.

CONCLUSIONS: Study findings contribute to limited knowledge on the relationship between neighborhood environmental contexts and physical activity among rural populations. The health effects of neighborhood social cohesion warrant more attention in health equity research and consideration when developing multilevel interventions to improve the health of rural populations.

PMID:37208903 | DOI:10.1111/1475-6773.14183

Med Sci Monit. 2023 May 20;29:e940355. doi: 10.12659/MSM.940355.

ABSTRACT

BACKGROUND Improving the quality of life of scoliosis patients with appropriate preventive measures is critical. This study aimed to investigate the relationships among bone mass, Cobb angle, and complete blood count (CBC) parameters in patients with scoliosis. MATERIAL AND METHODS This joint study was conducted by the pediatric department and orthopedics clinics, which used the medical records of patients aged 10-18 years between 2018 and 2022. Patients were divided into 3 groups according to the Cobb angle. Patient blood count levels from medical records and bone mineral density (BMD) Z scores (g/cm²) were compared among groups. Notably, BMD Z scores were calculated using a (BMD) dataset from local Turkish children after adjusting for height and age. RESULTS A total of 184 individuals (120 females, 64 males) were included in the study. There were statistically significant differences among the groups in platelet-to-lymphocyte ratio (PLR). Significant differences in DXA Z scores among groups were found. There was a significantly strong and positive correlation between DXA Z scores and all CBC parameters in patients with severe scoliosis. CONCLUSIONS This study found that CBC parameters can predict BMD in adolescents. Furthermore, the association between vitamin D deficiency and low BMD may contribute to the follow-up of body adaptation in patients with scoliosis receiving conservative treatment.

PMID:37208892 | DOI:10.12659/MSM.940355

Am Surg. 2023 May 19:31348231177922. doi: 10.1177/00031348231177922. Online ahead of print.

ABSTRACT

INTRODUCTION: Venous thromboembolism (VTE) is a source of preventable morbidity and mortality in critically ill trauma patients. Age is one independent risk factor. Geriatric patients embody a population at high thromboembolic and hemorrhagic risk. Currently, there is little guidance between low molecular weight heparin (LMWH) and unfractionated heparin (UFH) for anticoagulant prophylaxis in the geriatric trauma patient.

METHODS: A retrospective review was conducted at an ACS verified, Level I Trauma center from 2014 to 2018. All patients 65 years or older, with high-risk injuries and admitted to the trauma service were included. Choice of agent was at provider discretion. Patients in renal failure, or those that received no chemoprophylaxis, were excluded. The primary outcomes were the diagnosis of deep vein thrombosis or pulmonary embolism and bleeding associated complications (gastrointestinal bleed, TBI expansion, hematoma development).

RESULTS: This study evaluated 375 subjects, 245 (65%) received enoxaparin and 130 (35%) received heparin. DVT developed in 6.9% of UFH patients, compared to 3.3% with LMWH (P = .1). PE was present in 3.8% of UFH group, but only .4% in the LMWH group (P = .01). Combined rate of DVT/PE was significantly lower (P = .006) with LMWH (3.7%) compared to UFH (10.8%). 10 patients had documented bleeding events, and there was no significant association between bleeding and the use of LMWH or UFH.

CONCLUSIONS: VTE events are more common in geriatric patients treated with UFH compared to LMWH. There was no associated increase in bleeding complications when LMWH was utilized. LMWH should be considered the chemoprophylatic agent of choice in high risk geriatric trauma patients.

PMID:37208855 | DOI:10.1177/00031348231177922

Pharmacoepidemiol Drug Saf. 2023 May 19. doi: 10.1002/pds.5639. Online ahead of print.

ABSTRACT

PURPOSE: Propensity score weighting is a popular approach for estimating treatment effects using observational data. Different sets of propensity score-based weights have proposed, including inverse probability of treatment (IPT) weights whose target estimand is the average treatment effect (ATE), weights whose target estimand is the average treatment effect in the treated (ATT), and, more recently, matching weights, overlap weights, and entropy weights. These latter three sets of weights focus on estimating the effect of treatment in those subjects for whom there is clinical equipoise. We conducted a series of simulations to explore differences in the value of the target estimands for these five sets of weights when the difference in means is the measure of treatment effect.

METHODS: We considered 648 scenarios defined by different values of the prevalence of treatment, the c-statistic of the propensity score model, the correlation between the linear predictors for treatment selection and the outcome, and by the magnitude of the interaction between treatment status and the linear predictor for the outcome in the absence of treatment.

RESULTS: We found that, when the prevalence of treatment was low or high and the c-statistic of the propensity score model was moderate to high, that matching weights, overlap weights, and entropy weights had target estimands that differed meaningfully from the target estimand of the ATE weights.

CONCLUSIONS: Researchers using matching weights, overlap weights, and entropy weights should not assume that the estimated treatment effect is comparable to the ATE. This article is protected by copyright. All rights reserved.

PMID:37208837 | DOI:10.1002/pds.5639

J Dent Educ. 2023 May 19. doi: 10.1002/jdd.13246. Online ahead of print.

ABSTRACT

PURPOSE/OBJECTIVES: Developing time management is an important aspect of a dental student’s passage to clinical care and in their growth as a professional. Suitable time management and preparedness can potentially impact the prognosis of a successful dental appointment. The objective of this study was to determine if a time management exercise could be effective to increase students’ preparedness, organization, time management, and reflection during simulated clinical care prior to transitioning to the dental clinic.

METHODS: Students completed five-time management exercises during the term preceding their entrance into the predoctoral restorative clinic which included appointment planning and organization, and reflection once finished. Pre- and post-term surveys were used to determine the impact of the experience. Quantitative data was analyzed using a paired t-test and the qualitative data was thematically coded by the researchers.

RESULTS: Students reported a statistically significant increase in their self-confidence of clinical readiness after completion of the time management series, and all students completed the surveys. The themes indicated by students through their comments in the post-survey question regarding the experience were as follows: planning and preparation, time management, the practice of procedures, concern about workload, faculty encouragement, and unclear. Most students also reported the exercise to be beneficial for their predoctoral clinical appointments.

CONCLUSIONS: It was determined that the time management exercises were effective for the students as they transitioned to treating patients in the predoctoral clinic and can be used for future classes to increase their success.

PMID:37208799 | DOI:10.1002/jdd.13246

J Chem Inf Model. 2023 May 19. doi: 10.1021/acs.jcim.3c00460. Online ahead of print.

ABSTRACT

While polymerization-induced self-assembly (PISA) has become a preferred synthetic route toward amphiphilic block copolymer self-assemblies, predicting their phase behavior from experimental design is extremely challenging, requiring time and work-intensive creation of empirical phase diagrams whenever self-assemblies of novel monomer pairs are sought for specific applications. To alleviate this burden, we develop here the first framework for a data-driven methodology for the probabilistic modeling of PISA morphologies based on a selection and suitable adaption of statistical machine learning methods. As the complexity of PISA precludes generating large volumes of training data with in silico simulations, we focus on interpretable low variance methods that can be interrogated for conformity with chemical intuition and that promise to work well with only 592 training data points which we curated from the PISA literature. We found that among the evaluated linear models, generalized additive models, and rule and tree ensembles, all but the linear models show a decent interpolation performance with around 0.2 estimated error rate and 1 bit expected cross entropy loss (surprisal) when predicting the mixture of morphologies formed from monomer pairs already encountered in the training data. When considering extrapolation to new monomer combinations, the model performance is weaker but the best model (random forest) still achieves highly nontrivial prediction performance (0.27 error rate, 1.6 bit surprisal), which renders it a good candidate to support the creation of empirical phase diagrams for new monomers and conditions. Indeed, we find in three case studies that, when used to actively learn phase diagrams, the model is able to select a smart set of experiments that lead to satisfactory phase diagrams after observing only relatively few data points (5-16) for the targeted conditions. The data set as well as all model training and evaluation codes are publicly available through the GitHub repository of the last author.

PMID:37208794 | DOI:10.1021/acs.jcim.3c00460

Adv Simul (Lond). 2023 May 19;8(1):15. doi: 10.1186/s41077-023-00253-4.

ABSTRACT

BACKGROUND: This methodological intersection article demonstrates a method to measure cognitive load in clinical simulations. Researchers have hypothesized that high levels of cognitive load reduce performance and increase errors. This phenomenon has been studied primarily by experimental designs that measure responses to predetermined stimuli and self-reports that reduce the experience to a summative value. Our goal was to develop a method to identify clinical activities with high cognitive burden using physiologic measures.

METHODS: Teams of emergency medical responders were recruited from local fire departments to participate in a scenario with a shockable pediatric out-of-hospital cardiac arrest (POHCA) patient. The scenario was standardized with the patient being resuscitated after receiving high-quality CPR and 3 defibrillations. Each team had a person in charge (PIC) who wore a functional near-infrared spectroscopy (fNIRS) device that recorded changes in oxygenated and deoxygenated hemoglobin concentration in their prefrontal cortex (PFC), which was interpreted as cognitive activity. We developed a data processing pipeline to remove nonneural noise (e.g., motion artifacts, heart rate, respiration, and blood pressure) and detect statistically significant changes in cognitive activity. Two researchers independently watched videos and coded clinical tasks corresponding to detected events. Disagreements were resolved through consensus, and results were validated by clinicians.

RESULTS: We conducted 18 simulations with 122 participants. Participants arrived in teams of 4 to 7 members, including one PIC. We recorded the PIC’s fNIRS signals and identified 173 events associated with increased cognitive activity. [Defibrillation] (N = 34); [medication] dosing (N = 33); and [rhythm checks] (N = 28) coincided most frequently with detected elevations in cognitive activity. [Defibrillations] had affinity with the right PFC, while [medication] dosing and [rhythm checks] had affinity with the left PFC.

CONCLUSIONS: FNIRS is a promising tool for physiologically measuring cognitive load. We describe a novel approach to scan the signal for statistically significant events with no a priori assumptions of when they occur. The events corresponded to key resuscitation tasks and appeared to be specific to the type of task based on activated regions in the PFC. Identifying and understanding the clinical tasks that require high cognitive load can suggest targets for interventions to decrease cognitive load and errors in care.

PMID:37208778 | DOI:10.1186/s41077-023-00253-4

Cardiooncology. 2023 May 19;9(1):25. doi: 10.1186/s40959-023-00178-x.

ABSTRACT

OBJECTIVE: To investigate the association between stages of QTc prolongation and the risk of cardiac events among patients on TKIs.

METHODS: This was a retrospective cohort study performed at an academic tertiary care center of cancer patients who were taking TKIs or not taking TKIs. Patients with two recorded ECGs between January 1, 2009, and December 31, 2019, were selected from an electronic database. The QTc duration > 450ms was determined as prolonged. The association between QTc prolongation progression and events of cardiovascular disease were compared.

RESULTS: This study included a total of 451 patients with 41.2% of patients taking TKIs. During a median follow up period of 3.1 years, 49.5% subjects developed CVD and 5.4% subjects suffered cardiac death in patient using TKIs (n = 186); the corresponding rates are 64.2% and 1.2% for patients not on TKIs (n = 265), respectively. Among patient on TKIs, 4.8% of subjects developed stroke, 20.4% of subjects suffered from heart failure (HF) and 24.2% of subjects had myocardial infarction (MI); corresponding incidence are 6.8%, 26.8% and 30.6% in non-TKIs. When patients were regrouped to TKIs versus non-TKIs with and without diabetes, there was no significant difference in the incidence of cardiac events among all groups. Adjusted Cox proportional hazards models were applied to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). There is a significant increased risk of HF events (HR, 95% CI: 2.12, 1.36-3.32) and MI events (HR, 95% CI: 1.78, 1.16-2.73) during the 1st visit. There are also trends for an increased incidence of cardiac adverse events associated with QTc prolongation among patient with QTc > 450ms, however the difference is not statistically significant. Increased cardiac adverse events in patients with QTc prolongation were reproduced during the 2nd visit and the incidence of heart failure was significantly associated with QTc prolongation(HR, 95% CI: 2.94, 1.73-5.0).

CONCLUSION: There is a significant increased QTc prolongation in patients taking TKIs. QTc prolongation caused by TKIs is associated with an increased risk of cardiac events.

PMID:37208762 | DOI:10.1186/s40959-023-00178-x