Tag: pubmed

PLoS One. 2022 Jun 17;17(6):e0269097. doi: 10.1371/journal.pone.0269097. eCollection 2022.

ABSTRACT

BACKGROUND: One common way to share health data for secondary analysis while meeting increasingly strict privacy regulations is to de-identify it. To demonstrate that the risk of re-identification is acceptably low, re-identification risk metrics are used. There is a dearth of good risk estimators modeling the attack scenario where an adversary selects a record from the microdata sample and attempts to match it with individuals in the population.

OBJECTIVES: Develop an accurate risk estimator for the sample-to-population attack.

METHODS: A type of estimator based on creating a synthetic variant of a population dataset was developed to estimate the re-identification risk for an adversary performing a sample-to-population attack. The accuracy of the estimator was evaluated through a simulation on four different datasets in terms of estimation error. Two estimators were considered, a Gaussian copula and a d-vine copula. They were compared against three other estimators proposed in the literature.

RESULTS: Taking the average of the two copula estimates consistently had a median error below 0.05 across all sampling fractions and true risk values. This was significantly more accurate than existing methods. A sensitivity analysis of the estimator accuracy based on variation in input parameter accuracy provides further application guidance. The estimator was then used to assess re-identification risk and de-identify a large Ontario COVID-19 behavioral survey dataset.

CONCLUSIONS: The average of two copula estimators consistently provides the most accurate re-identification risk estimate and can serve as a good basis for managing privacy risks when data are de-identified and shared.

PMID:35714132 | DOI:10.1371/journal.pone.0269097

PLoS One. 2022 Jun 17;17(6):e0270168. doi: 10.1371/journal.pone.0270168. eCollection 2022.

ABSTRACT

Clinical testing of children in schools is challenging, with economic implications limiting its frequent use as a monitoring tool of the risks assumed by children and staff during the COVID-19 pandemic. Here, a wastewater-based epidemiology approach has been used to monitor 16 schools (10 primary, 5 secondary and 1 post-16 and further education) in England. A total of 296 samples over 9 weeks have been analysed for N1 and E genes using qPCR methods. Of the samples returned, 47.3% were positive for one or both genes with a detection frequency in line with the respective local community. WBE offers a low cost, non-invasive approach for supplementing clinical testing and can provide longitudinal insights that are impractical with traditional clinical testing.

PMID:35714109 | DOI:10.1371/journal.pone.0270168

PLoS One. 2022 Jun 17;17(6):e0270030. doi: 10.1371/journal.pone.0270030. eCollection 2022.

ABSTRACT

BACKGROUND: Hypertension is a disease that imposes risks of diseases on multi-system. Failure to control hypertension leads patients to end up with unavoidable complications, including death. Noncompliance to treatment is the main factor to develop such devastating complications whereas knowledge of patients about their disease is a key factor for better compliance. Thus, the purpose of this study is to assess the level of knowledge about hypertension and associated factors among hypertensive patients in public health facilities of Gondar city.

METHODS: Facility-based cross-sectional study was conducted between March and April 2019 in Gondar town. A systematic sampling technique was applied to select a total of 389 patients. A structured interview questionnaire was used to gather the data. The data were analyzed using STATA version 14. Ordinal logistic regression analysis was performed at P < 0.05 with a 95% confidence interval to identify statistically significant variables.

RESULTS: A total of 385 respondents participated giving a response rate of 98.9%. The majority (55.3%) of the patients had a low level of, 17.9% had a moderate level of knowledge whereas 26.8% had a high level of knowledge about hypertension. Those working in government organizations had 5.5 times higher odds of having a high level of knowledge than other groups (AOR = 5.5; 95%CI = 1.21, 25). Patients who received longer than four years of treatment showed twice larger odds of knowledge than those with below two years of treatment (AOR = 2; 95%CI = 1.29, 3.22) Moreover, patients residing proximate to the hospital increases the odds of having a higher level of knowledge by 1.64 times versus patients living far away from the hospital (AOR = 1.64, 95% CI = 1.07-2.63).

CONCLUSIONS: This finding revealed that knowledge about hypertension and risk factors among patients with hypertension was low. Employment in governmental organizations, longer duration of treatment, and residential proximity to hospitals/ health centers were statistically significant predictors of the participants’ knowledge about hypertension. Therefore, it is important to give health education to patients working in non-governmental organizations and self-employed individuals about diseases and risk factors. In addition, emphasis should be given to patients receiving less than two years of treatment and coming from remote areas to improve their knowledge of the disease.

PMID:35714113 | DOI:10.1371/journal.pone.0270030

PLoS One. 2022 Jun 17;17(6):e0270196. doi: 10.1371/journal.pone.0270196. eCollection 2022.

ABSTRACT

BACKGROUND: The ongoing COVID-19 pandemic has claimed >4 million lives globally, and these deaths often occurred in hospitalized patients with comorbidities. Therefore, the proposed review aims to distinguish the inpatient mortality and invasive mechanical ventilation risk in COVID-19 patients treated with the anti-SARS-CoV-2 monoclonal antibodies and/or the antiviral agents.

METHODS: A search in PubMed, Embase, and Scopus will ensue for the publications on randomized controlled trials testing the above, irrespective of the publication date or geographic boundary. Risk of bias assessment of the studies included in the review will occur using the Cochrane risk of bias tool for randomized trials (RoB 2). Frequentist method network meta-analyses (NMA) will compare each outcome’s risk across both types of anti-SARS-CoV-2 agents in one model and each in separate models. Additional NMA models will compare these in COVID-19 patients who were severely or critically ill, immunocompromised, admitted to the intensive care unit, diagnosed by nucleic acid amplification test, not treated with steroids, <18 years old, and at risk of infection due to variants of concern. The plan of excluding non-hospitalized patients from the proposed review is to minimize intransitivity risk. The acceptance of the network consistency assumption will transpire if the local and overall inconsistency assessment indicates no inconsistency. For each NMA model, the effect sizes (risk ratio) and their 95% confidence intervals will get reported in league tables. The best intervention prediction and quality of evidence grading will happen using the surface under the cumulative ranking curve values and the Grading of Recommendations Assessment, Development and Evaluation-based Confidence in Network Meta-Analysis approach, respectively. Sensitivity analysis will repeat the preliminary NMA while excluding the trials at high risk of bias. The Stata statistical software (v16) will be used for analysis. The statistical significance will get determined at p<0.05 and 95% confidence interval.

TRIAL REGISTRATION: PROSPERO Registration No: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021277663.

PMID:35714102 | DOI:10.1371/journal.pone.0270196

IEEE Trans Neural Netw Learn Syst. 2022 Jun 17;PP. doi: 10.1109/TNNLS.2022.3181378. Online ahead of print.

ABSTRACT

Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. However, existing methods often make the explicit or implicit assumption that the observed entries are noise-free to provide a theoretical guarantee of exact recovery of missing entries, which is quite restrictive in practice. To remedy such drawback, this article proposes a novel noisy tensor completion model, which complements the incompetence of existing works in handling the degeneration of high-order and noisy observations. Specifically, the tensor ring nuclear norm (TRNN) and least-squares estimator are adopted to regularize the underlying tensor and the observed entries, respectively. In addition, a nonasymptotic upper bound of estimation error is provided to depict the statistical performance of the proposed estimator. Two efficient algorithms are developed to solve the optimization problem with convergence guarantee, one of which is specially tailored to handle large-scale tensors by replacing the minimization of TRNN of the original tensor equivalently with that of a much smaller one in a heterogeneous tensor decomposition framework. Experimental results on both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed model in recovering noisy incomplete tensor data compared with state-of-the-art tensor completion models.

PMID:35714084 | DOI:10.1109/TNNLS.2022.3181378

IEEE Trans Neural Netw Learn Syst. 2022 Jun 17;PP. doi: 10.1109/TNNLS.2022.3174528. Online ahead of print.

ABSTRACT

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications.

PMID:35714085 | DOI:10.1109/TNNLS.2022.3174528

PLoS One. 2022 Jun 17;17(6):e0269306. doi: 10.1371/journal.pone.0269306. eCollection 2022.

ABSTRACT

We compare several popular methods of estimating the basic reproduction number, R0, focusing on the early stages of an epidemic, and assuming weekly reports of new infecteds. We study the situation when data is generated by one of three standard epidemiological compartmental models: SIR, SEIR, and SEAIR; and examine the sensitivity of the estimators to the model structure. As some methods are developed assuming specific epidemiological models, our work adds a study of their performance in both a well-specified (data generating model and method model are the same) and miss-specified (data generating model and method model differ) settings. We also study R0 estimation using Canadian COVID-19 case report data. In this study we focus on examples of influenza and COVID-19, though the general approach is easily extendable to other scenarios. Our simulation study reveals that some estimation methods tend to work better than others, however, no singular best method was clearly detected. In the discussion, we provide recommendations for practitioners based on our results.

PMID:35714080 | DOI:10.1371/journal.pone.0269306

J Anim Sci. 2022 Jun 17:skac222. doi: 10.1093/jas/skac222. Online ahead of print.

ABSTRACT

Precision feeding (PF) aims to provide the right amount of nutrients at the right time for each animal. Lactating sows generally receive the same diet, which either results in insufficient supply and body reserve mobilization, or excessive supply and high nutrient excretion. With the help of online measuring devices, computational methods, and smart feeders, we introduced the first PF decision support system (DSS) for lactating sows. Precision (PRE) and conventional (STD) feeding strategies were compared in commercial conditions. Every day each PRE sow received a tailored ration that had been computed by the DSS. This ration was obtained by blending a diet with a high AA and mineral content (13.00 g/kg SID Lys, 4.50 g/kg digestible P) and a diet low in AAs and minerals (6.50 g/kg SID Lys, 2.90 g/kg digestible P). All STD sows received a conventional diet (10.08 g/kg SID Lys, 3.78 g/kg digestible P). Before the trial, the DSS was fitted to farm performance for the prediction of piglet average daily gain (PADG) and sow daily feed intake (DFI), with data from 1,691 and 3,712 lactations, respectively. Sow and litter performance were analyzed for the effect of feeding strategy with ANOVA, with results considered statistically significant when P<0.05. The experiment involved 239 PRE and 240 STD sows. DFI was similarly high in both treatments (PRE: 6.59, STD: 6.45 kg/d; P=0.11). Litter growth was high (PRE: 2.96, STD: 3.06 kg/d), although it decreased slightly by about 3% in PRE compared to STD treatments (P<0.05). Sow body weight loss was low, although it was slightly higher in PRE sows (7.7 versus 2.1 kg, P<0.001), which might be due to insufficient AA supply in some sows. Weaning to estrus interval (5.6 d) did not differ. In PRE sows SID Lys intake (PRE: 7.7, STD: 10.0 g/kg; P<0.001) and digestible P intake (PRE: 3.2, STD: 3.8 g/kg; P<0.001) declined by 23% and 14%, respectively, and feed cost decreased by 12%. For PRE sows, excretion of N and P decreased by 28% and 42%, respectively. According to these results, PF appears to be a very promising strategy for lactating sows.

PMID:35714053 | DOI:10.1093/jas/skac222

JMIR Form Res. 2022 Jun 17;6(6):e33089. doi: 10.2196/33089.

ABSTRACT

BACKGROUND: Mobile games can be effective and motivating tools for promoting children’s health.

OBJECTIVE: We aimed to determine the comparative use of 2 prototype serious games for health and assess their effects on healthy lifestyle knowledge in youth aged 9-16 years at risk for type 2 diabetes (T2D).

METHODS: A 3-arm parallel pilot randomized controlled trial was undertaken to determine the feasibility and preliminary effectiveness of 2 serious games. Feasibility aspects included recruitment, participant attitudes toward the games, the amount of time the participants played each game at home, and the effects of the games on healthy lifestyle and T2D knowledge. Participants were allocated to play Diabetic Jumper (n=7), Ari and Friends (n=8), or a control game (n=8). All participants completed healthy lifestyle and T2D knowledge questionnaires at baseline, immediately after game play, and 4 weeks after game play. Game attitudes and preferences were also assessed. The primary outcome was the use of the game (specifically, the number of minutes played over 4 weeks).

RESULTS: In terms of feasibility, we were unable to recruit our target of 60 participants. In total, 23 participants were recruited. Participants generally viewed the games positively. There were no statistical differences in healthy lifestyle knowledge or diabetes knowledge over time or across games. Only 1 participant accessed the game for an extended period, playing the game for a total of 33 min over 4 weeks.

CONCLUSIONS: It was not feasible to recruit the target sample for this trial. The 2 prototype serious games were unsuccessful at sustaining long-term game play outside a clinic environment. Based on positive participant attitudes toward the games, it is possible to use these games or similar games as short-term stimuli to engage young people with healthy lifestyle and diabetes knowledge in a clinic setting; however, future research is required to explore this area.

TRIAL REGISTRATION: Australia New Zealand Clinical Trials Registry ACTRN12619000380190; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377123.

PMID:35713955 | DOI:10.2196/33089

JMIR Med Inform. 2022 Jun 17;10(6):e34204. doi: 10.2196/34204.

ABSTRACT

Real-world data (RWD) collected in routine health care processes and transformed to real-world evidence have become increasingly interesting within the research and medical communities to enhance medical research and support regulatory decision-making. Despite numerous European initiatives, there is still no cross-border consensus or guideline determining which qualities RWD must meet in order to be acceptable for decision-making within regulatory or routine clinical decision support. In the absence of guidelines defining the quality standards for RWD, an overview and first recommendations for quality criteria for RWD in pharmaceutical research and health care decision-making is needed in Austria. An Austrian multistakeholder expert group led by Gesellschaft für Pharmazeutische Medizin (Austrian Society for Pharmaceutical Medicine) met regularly; reviewed and discussed guidelines, frameworks, use cases, or viewpoints; and agreed unanimously on a set of quality criteria for RWD. This consensus statement was derived from the quality criteria for RWD to be used more effectively for medical research purposes beyond the registry-based studies discussed in the European Medicines Agency guideline for registry-based studies. This paper summarizes the recommendations for the quality criteria of RWD, which represents a minimum set of requirements. In order to future-proof registry-based studies, RWD should follow high-quality standards and be subjected to the quality assurance measures needed to underpin data quality. Furthermore, specific RWD quality aspects for individual use cases (eg, medical or pharmacoeconomic research), market authorization processes, or postmarket authorization phases have yet to be elaborated.

PMID:35713954 | DOI:10.2196/34204