Tag: statistics

Vaccine. 2025 Apr 2;53:127081. doi: 10.1016/j.vaccine.2025.127081. Online ahead of print.

ABSTRACT

BACKGROUND: In 2024, the reported incidence of pertussis in China has increased sharply. As a vulnerable group, neonates continue to be at highest risk of severe outcomes from pertussis infection. However, there have been no serological assessment of population immunity since the 2024 pertussis outbreak in China. This study aimed to ascertain the PT-antibody status of pregnant women and their newborns during the epidemic period and offer insights to maternal immunization in China.

METHODS: From April to June 2024, 227 newborn-mother pairs in Xiamen City were recruited to test serum anti-PT IgG levels by ELISA. The geometric mean, undetectable rate (< 5 IU/mL), seropositivity rate (≥ 40 IU/mL), recent infection rate (≥ 100 IU/mL), and newborn-to-maternal ratio of anti-PT IgG were calculated. Statistical analysis was made based on maternal age, gravidity, parity, delivery mode, gestational age and newborn sex.

RESULTS: The anti-PT IgG levels were positively correlated (R² = 0.761) between pregnant women and their newborns. We found that 97.8 % of participants were seronegative, and 75.3 % of pregnant woman and 64.3 % of newborn had anti-PT IgG levels below 5 IU/mL. The seropositive prevalence of them were both only 2.2 % (95 % CI 0.9-5.1). The newborn-to-maternal ratio was stable in the range of 1.1 to 1.3 across all groups. There was no significant difference in the anti-PT IgG level of all participants across maternal and neonatal demographic characteristics. There was also no significant difference in the prevalence of seropositivity across demographic characteristics, except for delivery mode among newborns.

CONCLUSIONS: Almost all of the pregnant women and their newborns in this study had anti-PT IgG levels low enough to suggest susceptibility to pertussis infection, especially during an epidemic period. Infants are unprotected until the onset of primary immunization series, which would support the rationale for maternal immunization.

PMID:40179439 | DOI:10.1016/j.vaccine.2025.127081

Geriatr Nurs. 2025 Apr 2;63:113-122. doi: 10.1016/j.gerinurse.2025.03.010. Online ahead of print.

ABSTRACT

OBJECTIVE: The aim of this project was to establish an evidence-based practice program, and examine the effect on nurses’ knowledge levels, nurses’ compliance and patient satisfaction.

METHODS: We systematically searched six databases and five websites to summarize the best evidence. Next, we held stakeholder, EBP group and expert meetings to build the program. Finally, a noncontemporaneous controlled trial was applied.

RESULTS: After the implementation of the program, the knowledge scores of nurses improved significantly. The compliance with the nine audit criteria was over 80%, and the difference was statistically significant. The satisfaction of patients in the implementation group was significantly higher than that in the comparison group.

CONCLUSIONS: This was the first project of frailty screening and assessment in a clinical setting in China. The results showed that the implementation of the program could improve the knowledge level of nurses, regulate nursing behavior, and improve the satisfaction of patients.

PMID:40179436 | DOI:10.1016/j.gerinurse.2025.03.010

JMIR Res Protoc. 2025 Apr 3;14:e60790. doi: 10.2196/60790.

ABSTRACT

BACKGROUND: The human papillomavirus (HPV) vaccine is an effective way to prevent HPV and its associated cancers. Provider recommendation has been shown to be one of the most successful strategies for increasing the uptake of the HPV vaccine; however, more training and resources are needed to help boost health care providers’ confidence and communication skills in recommending the HPV vaccine to their patients, particularly in underserved Hispanic communities where vaccination rates among all ages are lower.

OBJECTIVE: This study aims to compare HPV educational and professional skills intervention effectiveness on improving provider recommendations and patient communication strategies with health care providers serving the El Paso United States-Mexico border region.

METHODS: We will conduct a randomized, blinded, multiple posttest-only controlled behavioral trial using a parallel group design that will examine the effectiveness of a fully automated, web-based, culturally tailored HPV education and professional skills intervention containing unique reading material and video role-play, as compared to a standard Centers for Disease Control and Prevention fact sheet and video about general communication skills. Participants were recruited using a purposive sampling technique, both internet-based and in-person outreach events. Study data are being collected and managed using REDCap (Research Electronic Data Capture; Vanderbilt University) hosted at the University of Texas at El Paso. Chi-square analyses, ANOVA, and other statistical tests will be used with 2-tail α to reject null hypotheses at .05 to analyze the self-assessed outcome data. The Mauchly test of sphericity for each ANOVA and the Huynh-Feldt epsilon test or Greenhouse-Geisser correction to the degrees of freedom of the F-ratio will be reported for each significant effect. We may use multiple imputation procedures to handle the missing data (if applicable). This study is being conducted in the west Texas or southeast New Mexico region of the United States. Chi-square analyses will be used to assess associations between variables reported on the baseline provider knowledge, attitudes, and practice scales. We seek to examine self-assessed changes in provider attitudes and behaviors regarding HPV vaccine recommendation 1 month after receiving our unique multimedia and culturally tailored intervention.

RESULTS: Research and data collection for this clinical trial began in December 2023. Participant recruitment was closed by May 2024 (N=128), with final data collection expected to be completed by December 2024.

CONCLUSIONS: This study team decided to report on the intervention protocol to help ensure transparency in the research process and facilitate the improvement of the research design. Tailored web-based educational programs for health care professionals, designed to address regional and patient population characteristics, may be a promising approach to enhancing the real-world implementation of clinical practice guidelines.

TRIAL REGISTRATION: ClinicalTrials.gov NCT05120869; http://clinicaltrials.gov/ct2/show/NCT05120869.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/60790.

PMID:40179382 | DOI:10.2196/60790

Anesthesiology. 2025 Apr 3. doi: 10.1097/ALN.0000000000005479. Online ahead of print.

ABSTRACT

BACKGROUND: The conscious state is maintained through intact communication between brain regions. However, studies on global and regional connectivity changes in unconscious state have been inconsistent. These inconsistencies could arise from unclear definition of unconsciousness, spatial and temporal limitations of neuroimaging modalities, and estimating only single connectivity measure. Here, we investigated global and regional changes in amplitude and phase based functional connectivity in propofol-induced unconsciousness, which is widely recognized as unconsciousness.

METHODS: We calculated amplitude and phase based functional connectivity using amplitude envelope correlation (AEC), weighted phase lag index (wPLI), and magnitude squared coherence (MSC) from intracranial electroencephalography data of 73 patients. Global changes in connectivity, complexity, and network efficiency were estimated. Regional connectivity changes between Brodmann areas, between 7 cortical lobes, and between resting state networks were assessed across all frequency bands. Additionally, we employed machine learning analysis to identify specific regions in classifying conscious and unconscious states.

RESULTS: In the unconscious state, global connectivity increased across all frequency bands, while global complexity and efficiency decreased, accompanied by increased delta and decreased high gamma power spectral density. Regional connectivity increased between entire cortical regions across all frequency bands. Machine learning analysis revealed that posterior connectivity was the most influential in classifying consciousness. Amplitude-based connectivity predominantly increased in the delta and theta bands, while phase-based connectivity predominantly increased from the beta to high gamma bands.

CONCLUSIONS: Propofol anesthesia suppresses cortical activity and induces oscillatory changes characterized by increased delta power and decreased high gamma power. These changes are accompanied by increased functional connectivity and reduced network complexity and efficiency. These changes limit the brain’s ability to generate a diverse repertoire of activity, ultimately leading to unconsciousness. Posterior connectivity, which showed high accuracy in predicting conscious states, would be crucial for sustaining consciousness.

PMID:40179374 | DOI:10.1097/ALN.0000000000005479

JMIR AI. 2025 Apr 3;4:e68809. doi: 10.2196/68809.

ABSTRACT

BACKGROUND: Most online and social media discussions about birth control methods for women center on side effects, highlighting a demand for shared experiences with these products. Online user reviews and ratings of birth control products offer a largely untapped supplementary resource that could assist women and their partners in making informed contraception choices.

OBJECTIVE: This study sought to analyze women’s online ratings and reviews of various birth control methods, focusing on side effects linked to low product ratings.

METHODS: Using natural language processing (NLP) for topic modeling and descriptive statistics, this study analyzes 19,506 unique reviews of female contraceptive products posted on the website Drugs.com.

RESULTS: Ratings vary widely across contraception types. Hormonal contraceptives with high systemic absorption, such as progestin-only pills and extended-cycle pills, received more unfavorable reviews than other methods and women frequently described menstrual irregularities, continuous bleeding, and weight gain associated with their administration. Intrauterine devices were generally rated more positively, although about 1 in 10 users reported severe cramps and pain, which were linked to very poor ratings.

CONCLUSIONS: While exploratory, this study highlights the potential of NLP in analyzing extensive online reviews to reveal insights into women’s experiences with contraceptives and the impact of side effects on their overall well-being. In addition to results from clinical studies, NLP-derived insights from online reviews can provide complementary information for women and health care providers, despite possible biases in online reviews. The findings suggest a need for further research to validate links between specific side effects, contraceptive methods, and women’s overall well-being.

PMID:40179373 | DOI:10.2196/68809

J Med Internet Res. 2025 Apr 3;27:e63090. doi: 10.2196/63090.

ABSTRACT

BACKGROUND: Floodlight Open is a global, open-access, fully remote, digital-only study designed to understand the drivers and barriers in deployment and persistence of use of a smartphone app for measuring functional impairment in a naturalistic setting and broad study population.

OBJECTIVE: This study aims to assess measurement equivalence properties of the Floodlight Open app across operating system (OS) platforms, OS versions, and smartphone device models.

METHODS: Floodlight Open enrolled adult participants with and without self-declared multiple sclerosis (MS). The study used the Floodlight Open app, a “bring-your-own-device” (BYOD) solution that remotely measured MS-related functional ability via smartphone sensor-based active tests. Measurement equivalence was assessed in all evaluable participants by comparing the performance on the 6 active tests (ie, tests requiring active input from the user) included in the app across OS platforms (iOS vs Android), OS versions (iOS versions 11-15 and separately Android versions 8-10; comparing each OS version with the other OS versions pooled together), and device models (comparing each device model with all remaining device models pooled together). The tests in scope were Information Processing Speed, Information Processing Speed Digit-Digit (measuring reaction speed), Pinching Test (PT), Static Balance Test, U-Turn Test, and 2-Minute Walk Test. Group differences were assessed by permutation test for the mean difference after adjusting for age, sex, and self-declared MS disease status.

RESULTS: Overall, 1976 participants using 206 different device models were included in the analysis. Differences in test performance between subgroups were very small or small, with percent differences generally being ≤5% on the Information Processing Speed, Information Processing Speed Digit-Digit, U-Turn Test, and 2-Minute Walk Test; <20% on the PT; and <30% on the Static Balance Test. No statistically significant differences were observed between OS platforms other than on the PT (P<.001). Similarly, differences across iOS or Android versions were nonsignificant after correcting for multiple comparisons using false discovery rate correction (all adjusted P>.05). Comparing the different device models revealed a statistically significant difference only on the PT for 4 out of 17 models (adjusted P≤.001-.03).

CONCLUSIONS: Consistent with the hypothesis that smartphone sensor-based measurements obtained with different devices are equivalent, this study showed no evidence of a systematic lack of measurement equivalence across OS platforms, OS versions, and device models on 6 active tests included in the Floodlight Open app. These results are compatible with the use of smartphone-based tests in a bring-your-own-device setting, but more formal tests of equivalence would be needed.

PMID:40179369 | DOI:10.2196/63090

Nutr Health. 2025 Apr 3:2601060251327714. doi: 10.1177/02601060251327714. Online ahead of print.

ABSTRACT

Background: The World Health Organization forecasts that depression will become the world’s second most common illness by 2030 and affect people of all ages. Meanwhile, in Indonesia, approximately 1 in 100 people experience depression, with the highest prevalence found in the age group of 15-24 years, at 2%. Adjusting one’s diet, as suggested by the Antidepressant Food Score (AFS) list, presents a promising method for managing and addressing depression. Aim: To find out the association between the AFS and depression levels. Methods: This cross-sectional study in Surabaya, Indonesia, included 374 participants aged 15-17 years. Antidepressant food intake was assessed using the Semi-Quantitative Food Frequency Questionnaire, while depression levels were measured with the Center for Epidemiologic Studies Depression Scale. Additionally, social and psychological factors were considered. The data were analyzed using STATA. Results: A significant difference in daily fruit and vegetable consumption was found between adolescents with mild and moderate depression. Those with mild depression had an average AFS of 86.03%, while those with moderate depression had 66.28%. Although the AFS was associated with depression (p = 0.031), it did not have a statistically significant impact on depression levels after adjusting for factors such as age, sex, social support, stress and problem-solving ability (odds ratio = 1.54, 95% confidence interval = 0.94, 2.50). Conclusion: This study identified a statistically significant association between AFS and depression levels. However, after adjusting for other predictors, this association did not remain statistically significant. Future research should focus on developing a more comprehensive database of antidepressant food lists in Indonesia.

PMID:40179358 | DOI:10.1177/02601060251327714

J Cardiovasc Nurs. 2025 Apr 3. doi: 10.1097/JCN.0000000000001203. Online ahead of print.

ABSTRACT

BACKGROUND: Research on the care needs of patients with heart failure (HF) has predominantly relied on cross-sectional studies. Consequently, there is limited understanding of how care needs evolve over time within this population.

OBJECTIVES: The aims of this study were to explore the trajectories of care needs in patients with HF 1 year after discharge and analyze the potential factors that can predict these trajectories.

METHODS: A total of 197 patients with HF were recruited and followed at 1, 3, 6, and 12 months postdischarge. Care needs were assessed using the care needs survey questionnaire, and potential factors were selected based on the Andersen Behavioral Model. A growth mixture model was used to identify the trajectories of care needs, whereas logistic regression analyses were used for statistical comparisons.

RESULTS: Three trajectories in the care needs of patients with HF were identified: (1) a mild increase trajectory, (2) a decline trajectory, and (3) a persistently high trajectory. Need factors were the most significant determinants of care needs trajectories, with higher New York Heart Association functional classification, left ventricular ejection fraction less than 40%, and lower self-reported health serving as key predictors of persistently high trajectory. In contrast, only lower self-efficacy and the absence of a spouse as predisposing factors were associated with an increased risk of maintaining persistently high levels of care needs.

CONCLUSION: Care needs after discharge in patients with HF can be characterized by 3 trajectories. Need factors will help clinicians with early identification of patients with persistently high level of care needs.

PMID:40179353 | DOI:10.1097/JCN.0000000000001203

PLoS Comput Biol. 2025 Apr 3;21(4):e1012878. doi: 10.1371/journal.pcbi.1012878. eCollection 2025.

ABSTRACT

The advent of 5′ single-cell RNA sequencing (scRNA-seq) technologies offers unique opportunities to identify and analyze transcription start sites (TSSs) at a single-cell resolution. These technologies have the potential to uncover the complexities of transcription initiation and alternative TSS usage across different cell types and conditions. Despite the emergence of computational methods designed to analyze 5′ RNA sequencing data, current methods often lack comparative evaluations in single-cell contexts and are predominantly tailored for paired-end data, neglecting the potential of single-end data. This study introduces scTSS, a computational pipeline developed to bridge this gap by accommodating both paired-end and single-end 5′ scRNA-seq data. scTSS enables joint analysis of multiple single-cell samples, starting with TSS cluster prediction and quantification, followed by differential TSS usage analysis. It employs a Binomial generalized linear mixed model to accurately and efficiently detect differential TSS usage. We demonstrate the utility of scTSS through its application in analyzing transcriptional initiation from single-cell data of two distinct diseases. The results illustrate scTSS’s ability to discern alternative TSS usage between different cell types or biological conditions and to identify cell subpopulations characterized by unique TSS-level expression profiles.

PMID:40179341 | DOI:10.1371/journal.pcbi.1012878

PLoS Comput Biol. 2025 Apr 3;21(4):e1012881. doi: 10.1371/journal.pcbi.1012881. eCollection 2025 Apr.

ABSTRACT

Understanding the spatial locations of cell within tissues is crucial for unraveling the organization of cellular diversity. Recent advancements in spatial resolved transcriptomics (SRT) have enabled the analysis of gene expression while preserving the spatial context within tissues. Spatial domain characterization is a critical first step in SRT data analysis, providing the foundation for subsequent analyses and insights into biological implications. Graph neural networks (GNNs) have emerged as a common tool for addressing this challenge due to the structural nature of SRT data. However, current graph-based deep learning approaches often overlook the instability caused by the high sparsity of SRT data. Masking mechanisms, as an effective self-supervised learning strategy, can enhance the robustness of these models. To this end, we propose SpaMask, dual masking graph autoencoder with contrastive learning for SRT analysis. Unlike previous GNNs, SpaMask masks a portion of spot nodes and spot-to-spot edges to enhance its performance and robustness. SpaMask combines Masked Graph Autoencoders (MGAE) and Masked Graph Contrastive Learning (MGCL) modules, with MGAE using node masking to leverage spatial neighbors for improved clustering accuracy, while MGCL applies edge masking to create a contrastive loss framework that tightens embeddings of adjacent nodes based on spatial proximity and feature similarity. We conducted a comprehensive evaluation of SpaMask on eight datasets from five different platforms. Compared to existing methods, SpaMask achieves superior clustering accuracy and effective batch correction.

PMID:40179332 | DOI:10.1371/journal.pcbi.1012881