Category: Nevin Manimala

J Osteopath Med. 2023 Mar 6. doi: 10.1515/jom-2022-0172. Online ahead of print.

NO ABSTRACT

PMID:36867732 | DOI:10.1515/jom-2022-0172

Am Surg. 2023 Mar 3:31348231161676. doi: 10.1177/00031348231161676. Online ahead of print.

ABSTRACT

Retrospective analysis, validating the brain injury guideline (BIG) in the management of traumatic head injury in our level II trauma center after implementation of the protocol, and compare the outcomes to those seen before the protocol, of 542 patients seen in the Emergency Department (ED), with head injury between 2017 and 2021 was completed. Those patients were divided into two groups: Group 1 (pre BIG protocol implementation) and Group 2 (post BIG protocol implementation). Data included age, race, length of stay (hospital and ICU), comorbid conditions, anticoagulant therapy, surgical intervention, GCS, ISS, findings of head CT and any subsequent progression, mortality, and readmission within one month. Student’s t-test and Chi-square test were used for statistical analysis. There were 314 patients in group 1 and 228 patients in group 2. Mean age of group 2 was significantly higher than group 1 (67 vs 59 years, p=0.0001), however their gender was similar. Data available on 526 patients were classified as BIG 1=122, BIG 2=73, and BIG 3=331 patients. Post-implementation group were older (70 vs 44 years, P=0.0001) with more females (67% vs 45%, P=0.05) and had significantly more than 4 comorbid conditions (29% vs 8%, P=0.004), with the majority presented with a size of 4 mm or less of acute subdural or subarachnoid hematoma. No patient in either group had progression of their neurological examination, neurosurgical intervention, or readmission.. Elderly trauma patients may benefit from implementation of BIG criteria protocol, thus reducing cost of patient care, however a larger sample size is needed.

PMID:36867721 | DOI:10.1177/00031348231161676

J Med Internet Res. 2023 Jan 31. doi: 10.2196/44242. Online ahead of print.

ABSTRACT

BACKGROUND: Telehealth has been increasingly adopted by healthcare systems since the start of the COVID-19 pandemic. Although telehealth may provide convenience for patients and clinicians, there are several barriers to accessing it and using it effectively to provide high quality patient care.

OBJECTIVE: This study, was part of a larger multisite community engaged study conducted to understand the impact of COVID-19 on diverse communities. The work described here explored the perceptions of and experience with telehealth use among diverse and underserved community members during COVID-19.

METHODS: We used mixed methods across three regions in the US (Midwest, Arizona, and Florida) from January 2021-November 2021. We promoted our study through social media and community partnerships, disseminating flyers in English and Spanish. We developed a moderator guide and conducted focus groups in English and Spanish mostly using a videoconferencing platform. Participants were placed in focus groups with others who shared similar demographic attributes and geographic location. Focus groups were audio-recorded and transcribed. We analyzed our qualitative data using the framework analytic approach. We developed our broader survey using validated scales and with input from community and scientific leaders and distributed it through social media in English and Spanish. We included a previously published questionnaire which had been used to assess perceptions about telehealth among patients with HIV. We analyzed our quantitative data using SAS software and standard statistical approaches. We examined the effect of region, age, ethnicity/race and education on use and perceptions of telehealth.

RESULTS: We included data from 47 focus groups. Due to our mode of dissemination, we cannot calculate a response rate for the survey. However, we received 3447 English language and 146 Spanish language responses. Over 90% of participants had internet access and 94% had used telehealth. About half of all participants agreed or strongly agreed that telehealth would be beneficial in the future because it better fit their schedules and they would not need to travel. However, about half also agreed or strongly agreed they would not be able to express themselves well and could not be examined when using telehealth. Indigenous participants were especially concerned about these issues when compared to other racial groups.

CONCLUSIONS: This work describes findings from a mixed methods community engaged research study about telehealth including perceived benefits and concerns. Although participants enjoyed the benefits of telehealth (not having to travel and easier scheduling) they also had concerns (not being able to express themselves well and not having a physical exam) about telehealth.. These sentiments were especially notable among the Indigenous population. Our work highlights the importance of fully understanding the impact of these novel health delivery modalities on the patient experience and actual or perceived quality of care received.

PMID:36867682 | DOI:10.2196/44242

Int J Biostat. 2023 Mar 6. doi: 10.1515/ijb-2022-0063. Online ahead of print.

ABSTRACT

Common statistical approaches are not designed to deal with so-called “short fat data” in biomarker pilot studies, where the number of biomarker candidates exceeds the sample size by magnitudes. High-throughput technologies for omics data enable the measurement of ten thousands and more biomarker candidates for specific diseases or states of a disease. Due to the limited availability of study participants, ethical reasons and high costs for sample processing and analysis researchers often prefer to start with a small sample size pilot study in order to judge the potential of finding biomarkers that enable – usually in combination – a sufficiently reliable classification of the disease state under consideration. We developed a user-friendly tool, called HiPerMAb that allows to evaluate pilot studies based on performance measures like multiclass AUC, entropy, area above the cost curve, hypervolume under manifold, and misclassification rate using Monte-Carlo simulations to compute the p-values and confidence intervals. The number of “good” biomarker candidates is compared to the expected number of “good” biomarker candidates in a data set with no association to the considered disease states. This allows judging the potential in the pilot study even if statistical tests with correction for multiple testing fail to provide any hint of significance.

PMID:36867668 | DOI:10.1515/ijb-2022-0063

PLoS Comput Biol. 2023 Mar 3;19(3):e1010941. doi: 10.1371/journal.pcbi.1010941. Online ahead of print.

ABSTRACT

As researchers develop computational models of neural systems with increasing sophistication and scale, it is often the case that fully de novo model development is impractical and inefficient. Thus arises a critical need to quickly find, evaluate, re-use, and build upon models and model components developed by other researchers. We introduce the NeuroML Database (NeuroML-DB.org), which has been developed to address this need and to complement other model sharing resources. NeuroML-DB stores over 1,500 previously published models of ion channels, cells, and networks that have been translated to the modular NeuroML model description language. The database also provides reciprocal links to other neuroscience model databases (ModelDB, Open Source Brain) as well as access to the original model publications (PubMed). These links along with Neuroscience Information Framework (NIF) search functionality provide deep integration with other neuroscience community modeling resources and greatly facilitate the task of finding suitable models for reuse. Serving as an intermediate language, NeuroML and its tooling ecosystem enable efficient translation of models to other popular simulator formats. The modular nature also enables efficient analysis of a large number of models and inspection of their properties. Search capabilities of the database, together with web-based, programmable online interfaces, allow the community of researchers to rapidly assess stored model electrophysiology, morphology, and computational complexity properties. We use these capabilities to perform a database-scale analysis of neuron and ion channel models and describe a novel tetrahedral structure formed by cell model clusters in the space of model properties and features. This analysis provides further information about model similarity to enrich database search.

PMID:36867658 | DOI:10.1371/journal.pcbi.1010941

PLoS Negl Trop Dis. 2023 Mar 3;17(3):e0011173. doi: 10.1371/journal.pntd.0011173. Online ahead of print.

ABSTRACT

BACKGROUND: Despite the use of numerous methods of control measures, mosquito populations and mosquito-borne diseases are still increasing globally. Evidence-based action thresholds to initiate or intensify control activities have been identified as essential in reducing mosquito populations to required levels at the correct/optimal time. This systematic review was conducted to identify different mosquito control action thresholds existing across the world and associated surveillance and implementation characteristics.

METHODOLOGY/PRINCIPAL FINDINGS: Searches for literature published from 2010 up to 2021 were performed using two search engines, Google Scholar and PubMed Central, according to PRISMA guidelines. A set of inclusion/exclusion criteria were identified and of the 1,485 initial selections, only 87 were included in the final review. Thirty inclusions reported originally generated thresholds. Thirteen inclusions were with statistical models that seemed intended to be continuously utilized to test the exceedance of thresholds in a specific region. There was another set of 44 inclusions that solely mentioned previously generated thresholds. The inclusions with “epidemiological thresholds” outnumbered those with “entomological thresholds”. Most of the inclusions came from Asia and those thresholds were targeted toward Aedes and dengue control. Overall, mosquito counts (adult and larval) and climatic variables (temperature and rainfall) were the most used parameters in thresholds. The associated surveillance and implementation characteristics of the identified thresholds are discussed here.

CONCLUSIONS/SIGNIFICANCE: The review identified 87 publications with different mosquito control thresholds developed across the world and published during the last decade. Associated surveillance and implementation characteristics will help organize surveillance systems targeting the development and implementation of action thresholds, as well as direct awareness towards already existing thresholds for those with programs lacking available resources for comprehensive surveillance systems. The findings of the review highlight data gaps and areas of focus to fill in the action threshold compartment of the IVM toolbox.

PMID:36867651 | DOI:10.1371/journal.pntd.0011173

PLoS One. 2023 Mar 3;18(3):e0282497. doi: 10.1371/journal.pone.0282497. eCollection 2023.

ABSTRACT

INTRODUCTION: Early evaluation of writing readiness is essential to predict and prevent handwriting difficulties and its negative influences on school occupations. An occupation-based measurement for kindergarten children has been previously developed: Writing Readiness Inventory Tool In Context (WRITIC). In addition, to assess fine motor coordination two tests are frequently used in children with handwriting difficulties: the modified Timed Test of In-Hand Manipulation (Timed TIHM) and the Nine-Hole Peg Test (9-HPT). However, no Dutch reference data are available.

AIM: To provide reference data for (1) WRITIC, (2) Timed-TIHM and (3) 9-HPT for handwriting readiness assessment in kindergarten children.

METHODS: Three hundred and seventy-four children from Dutch kindergartens in the age of 5 to 6.5 years (5.6±0.4 years, 190 boys/184 girls) participated in the study. Children were recruited at Dutch kindergartens. Full classes of the last year were tested, children were excluded if there was a medical diagnosis such as a visual, auditory, motor or intellectual impairment that hinder handwriting performance. Descriptive statistics and percentiles scores were calculated. The score of the WRITIC (possible score 0-48 points) and the performance time on the Timed-TIHM and 9-HPT are classified as percentile scores lower than the 15th percentile to distinguish low performance from adequate performance. The percentile scores can be used to identify children that are possibly at risk developing handwriting difficulties in first grade.

RESULTS: WRITIC scores ranged from 23 to 48 (41±4.4), Timed-TIHM ranged from 17.9 to 64.5 seconds (31.4± 7.4 seconds) and 9-HPT ranged from 18.2 to 48.3 seconds (28.4± 5.4). A WRITIC score between 0-36, a performance time of more than 39.6 seconds on the Timed-TIHM and more than 33.8 seconds on the 9-HPT were classified as low performance.

CONCLUSION: The reference data of the WRITIC allow to assess which children are possibly at risk developing handwriting difficulties.

PMID:36867627 | DOI:10.1371/journal.pone.0282497

PLoS One. 2023 Mar 3;18(3):e0282235. doi: 10.1371/journal.pone.0282235. eCollection 2023.

ABSTRACT

BACKGROUND: Internalizing and externalizing problems account for over 75% of the mental health burden in children and adolescents in the US, with higher burden among minority children. While complex interactions of multilevel factors are associated with these outcomes and may enable early identification of children in higher risk, prior research has been limited by data and application of traditional analysis methods. In this case example focused on Asian American children, we address the gap by applying data-driven statistical and machine learning methods to study clusters of mental health trajectories among children, investigate optimal predictions of children at high-risk cluster, and identify key early predictors.

METHODS: Data from the US Early Childhood Longitudinal Study 2010-2011 were used. Multilevel information provided by children, families, teachers, schools, and care-providers were considered as predictors. Unsupervised machine learning algorithm was applied to identify groups of internalizing and externalizing problems trajectories. For prediction of high-risk group, ensemble algorithm, Superlearner, was implemented by combining several supervised machine learning algorithms. Performance of Superlearner and candidate algorithms, including logistic regression, was assessed using discrimination and calibration metrics via crossvalidation. Variable importance measures along with partial dependence plots were utilized to rank and visualize key predictors.

FINDINGS: We found two clusters suggesting high- and low-risk groups for both externalizing and internalizing problems trajectories. While Superlearner had overall best discrimination performance, logistic regression had comparable performance for externalizing problems but worse for internalizing problems. Predictions from logistic regression were not well calibrated compared to those from Superlearner, however they were still better than few candidate algorithms. Important predictors identified were combination of test scores, child factors, teacher rated scores, and contextual factors, which showed non-linear associations with predicted probabilities.

CONCLUSIONS: We demonstrated the application of data-driven analytical approach to predict mental health outcomes among Asian American children. Findings from the cluster analysis can inform critical age for early intervention, while prediction analysis has potential to inform intervention programing prioritization decisions. However, to better understand external validity, replicability, and value of machine learning in broader mental health research, more studies applying similar analytical approach is needed.

PMID:36867610 | DOI:10.1371/journal.pone.0282235

PLoS One. 2023 Mar 3;18(3):e0282195. doi: 10.1371/journal.pone.0282195. eCollection 2023.

ABSTRACT

INTRODUCTION: Electronic health has the potential benefit to the health system by improving health service quality efficiency effectiveness and reducing the cost of care. Having good e-health literacy level is considered essential for improving healthcare delivery and quality of care as well as empowers caregivers and patients to influence control care decisions. Many studies have done on eHealth literacy and its determinants among adults, however, inconsistent findings from those studies were found. Therefore, this study was conducted to determine the pooled magnitude of eHealth literacy and to identify associated factors among adults in Ethiopia through systematic review and meta-analysis.

METHOD: Search of PubMed, Scopus, and web of science, and Google Scholar was conducted to find out relevant articles published from January 2028 to 2022. The Newcastle-Ottawa scale tool was used to assess the quality of included studies. Two reviewers extracted the data independently by using standard extraction formats and exported in to Stata version11 for meta-analysis. The degree of heterogeneity between studies was measured using I2 statistics. The publication bias between studies also checked by using egger test. The pooled magnitude of eHealth literacy was performed using fixed effect model.

RESULT: After go through 138 studies, five studies with total participants of 1758 were included in this systematic review and Meta-analysis. The pooled estimate of eHealth literacy in Ethiopia was found 59.39% (95%CI: 47.10-71.68). Perceived usefulness (AOR = 2.46; 95% CI: 1.36, 3.12),educational status(AOR = 2.28; 95% CI: 1.11, 4.68), internet access (AOR = 2.35; 95% CI: 1.67, 3.30), knowledge on electronic health information sources(AOR = 2.60; 95% CI: 1.78, 3.78), electronic health information sources utilization (AOR = 2.55; 95%CI: 1.85, 3.52), gender (AOR = 1.82; 95% CI: 1.38, 2.41) were identified significant predictors of e-health literacy.

CONCLUSION AND RECOMMENDATION: This systematic review and meta-analysis found that more than half of study participants were eHealth literate. This finding recommends that creating awareness about importance of eHealth usefulness and capacity building to enhance and encouraging to use electronic sources and availability of internet has para amount to solution to increase eHealth literacy level of study participants.

PMID:36867600 | DOI:10.1371/journal.pone.0282195

PLoS One. 2023 Mar 3;18(3):e0280496. doi: 10.1371/journal.pone.0280496. eCollection 2023.

ABSTRACT

OBJECTIVE: The heightened vulnerability of women to mental health issues during the period of pregnancy implies that seeking and receiving support for mental health services is a crucial factor in improving the emotional and mental well-being of pregnant women. The current study investigates the prevalence and correlates of seeking and receiving help for mental health services initiated by pregnant women and health professionals during pregnancy.

DESIGN: Using a cross-sectional design and self-report questionnaires, data were collected from 702 pregnant women in the first, second and third trimesters from four health facilities in the Greater Accra region of Ghana. Data were analyzed using descriptive and inferential statistics.

RESULTS: It was observed that 18.9% of pregnant women self-initiated help-seeking for mental health services whereas 64.8% reported that health professionals asked about their mental well-being, of which 67.7% were offered mental health support by health professionals. Diagnosis of medical conditions in pregnancy (i.e., hypertension and diabetes), partner abuse, low social support, sleep difficulty and suicidal ideation significantly predicted the initiation of help-seeking for mental health services by pregnant women. Fear of vaginal delivery and COVID-19 concerns predicted the provision of mental health support to pregnant women by health professionals.

CONCLUSION: The low prevalence of individual-initiated help-seeking implies that health professionals have a high responsibility of supporting pregnant women achieve their mental health needs.

PMID:36867597 | DOI:10.1371/journal.pone.0280496