Tag: statistics

Nurse Educ Today. 2022 Apr 4;113:105362. doi: 10.1016/j.nedt.2022.105362. Online ahead of print.

ABSTRACT

BACKGROUND: Antibiotic resistant infections are a growing global public health threat poised to render antibiotics ineffective in treating even the most common infectious diseases. It is essential that future nurses have the knowledge and skills to keep patients safe from antibiotic harm in all health care settings, however, studies indicate that there is limited education provided in nursing schools regarding antibiotic use, antibiotic resistance, and antibiotic stewardship nursing practices.

OBJECTIVE: Evaluate the effect of a virtual, scenario-based simulation experience using simulated participants on pre-licensure baccalaureate nursing students’ antibiotic, antibiotic resistance, and antibiotic stewardship nursing practice knowledge.

METHODS: A quasi-experiential repeated measure pre-posttest design was used with a convenience sample of 165 pre-licensure baccalaureate nursing students enrolled in a health promotion course at a private university in the northeast region of the United States. The NLN Jeffries Simulation Theory guided the virtual simulation experience and used simulated participants methodology.

RESULTS: All students participated in the simulation experience. Statistically significant increases were noted (p < 0.005) in antibiotic, antibiotic use, and antibiotic resistance knowledge between the pre and post surveys. The most significant changes were in knowledge of antibiotic stewardship nursing practices.

CONCLUSION: Integration of virtual, scenario-based simulations provided students an active learning opportunity to practice antibiotic stewardship assessment and practice skills through real life-like situational experiences with simulated participants, resulting in improved antibiotic, antibiotic resistance, and antibiotic stewardship knowledge.

PMID:35421783 | DOI:10.1016/j.nedt.2022.105362

J Psychosom Res. 2022 Apr 3;157:110808. doi: 10.1016/j.jpsychores.2022.110808. Online ahead of print.

ABSTRACT

OBJECTIVE: Idiopathic Environmental Intolerance (IEI; i.e. the experience of somatic symptoms attributed to environmental agents) represents a functional somatic syndrome of unclear aetiology. Based on previous findings that suggest an association between IEI and perceptual anomalies, this study aimed to investigate the relationship between symptoms of IEI associated with chemicals (IEI-C) and facets of the schizotypy spectrum.

METHODS: A cross-sectional study design was used with N = 410 (78.3% female) persons responding to an online survey in which chemical odor sensitivity (COS) and modern health worries (MHW) that are associated with IEI-C, as well as schizotypal personality traits (SPQ), hallucination proneness (LSHS) and delusional ideation (PDI) as core components of the schizotypy spectrum were assessed.

RESULTS: Schizotypal traits were found to be significantly positively associated with MHWs (r = 0.20, p = .01), COS (r = 0.23, p = .01), and showed significant positive associations with hallucination proneness. Magical thinking was found to exhibit a significant positive relationship with both MHW (r = 0.17, p = .01) and COS (r = 0.21, p = .01). These small associations between IEI-C and facets of the psychosis spectrum remained significant even after statistically controlling for individual levels of trait anxiety and depression.

CONCLUSION: Schizotypal personality traits, particularly magical thinking, and hallucination proneness, appear positively related to facets of IEI-C. The findings are of relevance for the advancement of theoretical models of IEI.

PMID:35421699 | DOI:10.1016/j.jpsychores.2022.110808

Water Res. 2022 Mar 26;217:118333. doi: 10.1016/j.watres.2022.118333. Online ahead of print.

ABSTRACT

Preserving human and environmental health requires anthropogenic pollutants to be biologically degradable. Depending on concentration, both nutrients and pollutants induce and activate metabolic capacity in the endemic bacterial consortium, which in turn aids their degradation. Knowledge on such ‘acclimation’ is rarely implemented in risk assessment cost-effectively. As a result, an accurate description of the mechanisms and kinetics of biodegradation remains problematic. In this study, we defined a yield ‘effectivity’, comprising the effectiveness at which a pollutant (substrate) enhances its own degradation by inducing (biomass) cofactors involved therein. Our architecture for calculation represents the interplay between concentration and metabolism via both stoichiometric and thermodynamic concepts. The calculus for yield ‘effectivity’ is biochemically intuitive, implicitly embeds co-metabolism and distinguishes ‘endogenic’ from ‘exogenic’ substances’ reflecting various phenomena in biodegradation and bio-transformation studies. We combined data on half-lives of pollutants/nutrients in wastewater and surface water with transition-state rate theory to obtain also experimental values for effective yields. These quantify the state of acclimation: the portion of biodegradation kinetics attributable to (contributed by) ‘natural metabolism’, in view of similarity to natural substances. Calculated and experimental values showed statistically significant correspondence. Particularly, carbohydrate metabolism and nucleic acid metabolism appeared relevant for acclimation (R² = 0.11-0.42), affecting rates up to 10^4.9(±0.7) times: under steady-state acclimation, a compound stoichiometrically identical to carbohydrates or nucleic acids, is 10^3.2 to 10^4.9 times faster aerobically degraded than a compound marginally similar. Our new method, simulating (contribution by) the state of acclimation, supplements existing structure-biodegradation and kinetic models for predicting biodegradation in wastewater and surface water. The accuracy of prediction may increase when characterizing nutrients/co-metabolites in terms of, e.g., elemental analysis. We discuss strengths and limitations of our approach by comparison to empirical and mechanism-based methods.

PMID:35421691 | DOI:10.1016/j.watres.2022.118333

J Chromatogr B Analyt Technol Biomed Life Sci. 2022 Apr 9;1198:123250. doi: 10.1016/j.jchromb.2022.123250. Online ahead of print.

ABSTRACT

As a prescription for treating lung inflammation and intestinal diseases, Xuanbai Chengqi Decoction (XBCQD) in clinical practice can effectively treat COPD with excessive heat in the lung and fu-organs, which is characterized by phlegm-heat accumulation in the lung and constipation. This study aims to find the potential biomarkers of COPD with excessive heat in the lung and fu-organs from two aspects of lung and intestine based on metabolomics and microbiota analysis, and to evaluate the efficacy of XBCQD as well as to explore the mechanism of drug function according the regulating effect of drugs on these markers. The HPLC-Q-TOF-MS/MS, 16SrDNA technology and multiple statistical methods were used to trace the process of disease and curative effect with XBCQD. Results showed that the onset and development of disease was associated with the imbalance of 41 differential metabolites in plasma, bronchoalveolar lavage fluid and feces and 82 bacteria at the levels of phylum, class, order, family and genus from lung and intestine, including Escherichia-Shigella. However, after treatment with XBCQD, 30 differential metabolites mainly involving in the metabolism of linoleic acid, taurine and hypotaurine metabolism, arachidonic acid metabolism, biosynthesis of primary bile acids, tryptophan metabolism, arginine and proline metabolism and 65 pulmonary and intestinal bacteria at all levels were reversed in the drug group. In addition, the results of the correlation analysis showed that specific microbiota from lung and intestine and reversed differential metabolites had a significant correlation, and they could affect each other in the course of disease occurrence and treatment. This study preliminarily confirmed that XBCQD can be used to treat COPD with excessive heat in the lung and fu-organs through lung-intestine simultaneous treatment. It also provided new strategies for the treatment of lung diseases or intestinal diseases, and new research ideas for the evaluation of drug efficacy.

PMID:35421697 | DOI:10.1016/j.jchromb.2022.123250

J Health Econ. 2022 Mar 10;83:102612. doi: 10.1016/j.jhealeco.2022.102612. Online ahead of print.

ABSTRACT

We investigate the effects of incentivizing early prenatal care utilization on infant health by exploiting a reform that required expectant mothers to initiate prenatal care during the first ten weeks of gestation to obtain a one-time monetary transfer paid after childbirth. Applying a difference-in-differences design to individual-level data on the population of births and fetal deaths, we identify modest but statistically significant positive effects of the policy on neonatal health. We further provide suggestive evidence that improved maternal health-related knowledge and behaviors during pregnancy are plausible channels through which the reform might have affected fetal health.

PMID:35421668 | DOI:10.1016/j.jhealeco.2022.102612

Curr Probl Cancer. 2022 Mar 29;46(3):100861. doi: 10.1016/j.currproblcancer.2022.100861. Online ahead of print.

ABSTRACT

In clinical practice, a large number of patients have failed to receive chemotherapy or combination therapy because of drug resistance, recurrence and metastasis of specific sites. Therefore, how to choose the initial chemotherapy individually and reduce the occurrence of drug resistance is the key to cure high-risk GTN. This study investigated the efficacy of chemotherapy based on 5-fluorouracil (5-FU) regimen and EMA/CO regimen as the initial chemotherapy regimen in the treatment of high-risk gestational trophoblastic neoplasms (high-risk GTN). The treatment status of high-risk GTN patients who received primary chemotherapy using 5-Fu regimens (FAV and FA regimens) or EMA/CO regimens at Cancer Hospital of China Medical University from 2002 to 2019 was retrospective analyzed. Regular follow-up was conducted to evaluate its efficacy and to analyze prognostic factors. There were a total of 87 high-risk patients, 75 in the 5-FU-based group and 12 in the EMA/CO group. The clinical characteristics of patients in both groups were not statistically significant (P > 0.05). The overall survival rate of all patients was 87.4%, the rate of serological complete remission (SCR) was 87.4%, the SCR rate of initial treatment was 75.9%, the recurrence rate was 7.9%, and the mortality rate was 12.6%. There were no statistical differences in overall survival rate, SCR rate, SCR rate of initial treatment, drug resistance rate, recurrence rate and mortality in the 5-FU group and the EMA/CO group (P > 0.05). The median follow-up was 106 months. Kaplan-Meier analysis showed that the 1-year survival rate, 5-year survival rate and 10-year survival rate in the 5-FU group were 91.9%, 84.3% and 84.3% respectively. The 1-year survival rate, 5-year survival rate and 10-year survival rate of the EMA/CO group were all 91.7%, and there was no statistical difference in the overall survival time between the 2 groups (P > 0.05). COX proportional stepwise regression analysis showed that only clinical staging was an independent risk factor of the prognosis of high-risk GTN (P = 0.003). Conclusion Both 5-FU regimen and EMA / CO regimen can be used as the first-line treatment for high-risk GTN patients, and their effects are similar. For high-risk GTN patients with drug resistance, EMA / CO, FAEV and PEB can be used as second-line salvage chemotherapy.

PMID:35421635 | DOI:10.1016/j.currproblcancer.2022.100861

Food Chem. 2022 Apr 9;387:132932. doi: 10.1016/j.foodchem.2022.132932. Online ahead of print.

ABSTRACT

A rapid, simple, and efficient analysis methodology for screening specific quantitative peptides of beef was established based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) coupled with orthogonal partial least squares-discriminant analysis (OPLS-DA). The OPLS-DA model was built to select species-specific peptides that make a significant contribution to classification. Peptides with statistical significance were selected based on the variable importance in the projection (VIP) values and univariate P values. After the workflow of the statistical process, three specific quantitative peptides were identified by using homemade products with different beef contents. A quantification method for selected specific quantitative peptides was established by using LC-MS/MS. The quantitative results were applied to commercialized beef products. The developed method has high sensitivity, specificity, and repeatability. The results of this study proved that the integration of LC-MS/MS coupled with OPLS-DA is an efficient method for screening specific quantitative peptides and identification of the authenticity of meat products.

PMID:35421655 | DOI:10.1016/j.foodchem.2022.132932

J Exp Child Psychol. 2022 Apr 11;220:105430. doi: 10.1016/j.jecp.2022.105430. Online ahead of print.

ABSTRACT

Children’s everyday learning environment is semantically structured. For example, semantically related things (e.g., fork and spoon) usually co-occur in the same contexts. The current study examines the effects of semantically structured contexts on preschool-age children’s (N = 65, 33 girls, age range: 52-68 months) use of statistical information to learn novel word-object mappings. Children were assigned into one of two conditions, in which objects from the same semantic category repeatedly co-occurred in the same trials (Same-category condition) or objects from different categories repeatedly co-occurred in the same trials (Different-categories condition). Children’s word learning performance in the two conditions were comparable. However, their errors at test suggested that information extracted by children in the two conditions differed. Importantly, children in the Same-category condition extracted both statistical and semantic relationships from the stimuli.

PMID:35421627 | DOI:10.1016/j.jecp.2022.105430

J Acad Nutr Diet. 2022 Apr 11:S2212-2672(22)00199-X. doi: 10.1016/j.jand.2022.04.003. Online ahead of print.

ABSTRACT

BACKGROUND: Food marketing influences consumers’ preferences for and selection of marketed products. Although a substantial body of research has described food marketing practices in brick-and-mortar stores, no research has examined food marketing in online grocery retail despite its growing importance as a source of food-at-home purchases.

OBJECTIVE: To develop and apply a coding instrument to describe food marketing and the nutritional quality of marketed products in online grocery stores.

DESIGN: Quantitative content analysis and review of product Nutrition Facts labels and ingredients lists to calculate nutrient density and level of processing using the NOVA classification system.

PARTICIPANTS: /setting: Foods and beverages (n=3,473) marketed in the top revenue-generating online grocery retailers and those participating in the United States Department of Agriculture Supplemental Nutrition Assistance Program Online Purchasing Pilot (n=21) in 2019-2020.

MAIN OUTCOME MEASURES: Use of marketing mix strategies (i.e., product, placement, promotion, and pricing) across retailers and nutritional quality of marketed products. Products were considered of poor nutritional quality if they were ultra-processed (NOVA category 4) and excessive in sodium, saturated fat, free sugars, and/or other sweeteners. Products were also classified into 13 mutually exclusive food groups.

STATISTICAL TESTS PERFORMED: The proportion of retailers using each marketing strategy, proportion of products of poor nutritional quality, and proportion of products in each food group were calculated.

RESULTS: Retailers commonly used product recommendations, search result ordering, branded website content, user-generated content, and social media engagement to market products online. Candy, sweets, and snacks made up the largest percentage of marketed products (17.3%), followed by fruit, vegetables, and legumes (16.7%). Most (62%) marketed products were of poor nutritional quality. Staple food categories such as fruits, vegetables, and grains were frequently marketed, particularly through price reductions and product recommendations.

CONCLUSIONS: Online grocery retailers use a variety of customizable food marketing strategies on their websites. Although most marketed products are of poor nutritional quality, there is potential for marketing of staple food categories online that is not feasible in the brick-and-mortar store.

PMID:35421615 | DOI:10.1016/j.jand.2022.04.003

J Biomed Inform. 2022 Apr 11:104072. doi: 10.1016/j.jbi.2022.104072. Online ahead of print.

ABSTRACT

BACKGROUND: Medical decision-making impacts both individual and public health. Clinical scores are commonly used among various decision-making models to determine the degree of disease deterioration at the bedside. AutoScore was proposed as a useful clinical score generator based on machine learning and a generalized linear model. However, its current framework still leaves room for improvement when addressing unbalanced data of rare events.

METHODS: Using machine intelligence approaches, we developed AutoScore-Imbalance, which comprises three components: training dataset optimization, sample weight optimization, and adjusted AutoScore. Baseline techniques for performance comparison included the original AutoScore, full logistic regression, stepwise logistic regression, least absolute shrinkage and selection operator (LASSO), full random forest, and random forest with a reduced number of variables. These models were evaluated based on their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i.e., mean value of sensitivity and specificity). By utilizing a publicly accessible dataset from Beth Israel Deaconess Medical Center, we assessed the proposed model and baseline approaches to predict inpatient mortality.

RESULTS: AutoScore-Imbalance outperformed baselines in terms of AUC and balanced accuracy. The nine-variable AutoScore-Imbalance sub-model achieved the highest AUC of 0.786 (0.732-0.839), while the eleven-variable original AutoScore obtained an AUC of 0.723 (0.663-0.783), and the logistic regression with 21 variables obtained an AUC of 0.743 (0.685-0.800). The AutoScore-Imbalance sub-model (using a down-sampling algorithm) yielded an AUC of 0.771 (0.718-0.823) with only five variables, demonstrating a good balance between performance and variable sparsity. Furthermore, AutoScore-Imbalance obtained the highest balanced accuracy of 0.757 (0.702-0.805), compared to 0.698 (0.643-0.753) by the original AutoScore and the maximum of 0.720 (0.664-0.769) by other baseline models.

CONCLUSIONS: We have developed an interpretable tool to handle clinical data imbalance, presented its structure, and demonstrated its superiority over baselines. The AutoScore-Imbalance tool can be applied to highly unbalanced datasets to gain further insight into rare medical events and facilitate real-world clinical decision-making.

PMID:35421602 | DOI:10.1016/j.jbi.2022.104072