scholarly journals Modeling of the Weekly Variation of the Reported COVID-19 Cases as A Potential Indicator of the Surveillance System Accuracy

2021 ◽  
Author(s):  
Zahra Zamaninasab ◽  
Hamid Sharifi ◽  
Ehsan Mostafavi ◽  
Leila Mounesan ◽  
Ali Akbar Haghdoost
Keyword(s):  
Surveillance System ◽  
Regression Models ◽  
Positive Association ◽  
Absolute Intensity ◽  
P Value ◽  
Potential Indicator ◽  
Minimum Number ◽  
System Data ◽  
Harmonic Regression ◽  
Common Observation

Abstract Periodical daily variation in the number of reported COVID-19 cases within weeks is a common observation in global and national statistics. This variation may imply that the day of week has a significant role in the number of reported cases. We compared the pattern in some countries with an acceptable surveillance system. Data of 18 European and North American countries between 6 Mar and 8 Nov 2020 were extracts. Harmonic regression models were used to quantify the peak day, the absolute intensity and the average of coefficient of variation within weeks (ACVW) classified by country. In eight countries, the within week variation was statistically significant, the maximum and minimum number reported cases were in Saturday and Monday respectively, however, this pattern varied among countries. The maximum of ACVW was observed in Belgium and France, while it was minimum in Russia. The level of intensity of infection had a positive association with the ACVW (r = 0.54, p-value = 0.021). The observed variation and its pattern may show that the coverage or the tidiness of COVID-19 surveillance system fluctuates in different days of week. In addition, we suggest that the level of this fluctuation might be used as an accuracy indicator of the surveillance system.

scholarly journals Association of University Reopening Policies with New Confirmed COVID-19 Cases in the United States

2020 ◽  
Author(s):  
Yang Li ◽  
Cheng Ma ◽  
Weijing Tang ◽  
Xuefei Zhang ◽  
Ji Zhu ◽  
...  
Keyword(s):  
Multivariate Regression ◽  
Regression Models ◽  
Positive Association ◽  
The United States ◽  
P Value ◽  
County Level ◽  
Hybrid Mode ◽  
County Population ◽  
Public Health Officials ◽  
The Mean

AbstractReopening of universities in the U.S. has been controversial in the setting of the coronavirus disease 2019 (COVID-19) pandemic. We leveraged several publicly available data sources to study the association of county-level new confirmed COVID-19 case rates since September 1st and the number of students returning to campus across 2,893 U.S. counties with and without universities. In 1,069 U.S. counties with universities, we also studied the association of different reopening policies (online, in-person, hybrid) on new confirmed COVID-19 cases. Multivariate regression models estimated both effects of university reopening and different reopening policies. Mean number of daily confirmed cases per 10,000 county population was 1.51 from August 1st to August 31st, and 1.98 from September 1st to October 22nd. Mean number of students returning to universities was 2.1% (95% CI, 1.8% to 2.3%) of the county population and the number of students returning to campus had a positive association (β = 2.006, p-value < 0.001) with new confirmed COVID-19 cases within the local county region where the institution resided. For U.S. counties with universities, the mean proportion of online enrollment within each county was 40.1% (95% CI, 37.4% to 42.8%), with most students enrolling in-person or hybrid mode. In comparison to holding class in-person, reopening universities online (β = -0.329, p-value < 0.001) or in a hybrid mode (β = -0.272, p-value = 0.012) was negatively associated with new confirmed COVID-19 cases. These findings could help public health officials consider policies to mitigate additional waves of infection during the upcoming winter.Significance StatementOur study finds that higher numbers of students returning to campus was associated with an increase in new confirmed COVID-19 cases; reopening online or partially online was associated with slower spread of the virus, in comparison to in-person reopening. These findings could provide guidance for policymakers on universities’ reopening in upcoming semesters.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

scholarly journals CANTARE: finding and visualizing network-based multi-omic predictive models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Janet C. Siebert ◽  
Martine Saint-Cyr ◽  
Sarah J. Borengasser ◽  
Brandie D. Wagner ◽  
Catherine A. Lozupone ◽  
...  
Keyword(s):  
Random Forests ◽  
Predictive Models ◽  
Regression Models ◽  
Dynamic Range ◽  
P Value ◽  
Published Data ◽  
Logistic Regression Models ◽  
Pairwise Models ◽  
Biological Insight ◽  
Analytical Approaches

Abstract Background One goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be “ome aware.” Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs. Methods We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting “top table” of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements). Results We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and  penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10–5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User’s Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/. Conclusion CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.

scholarly journals Recommendations for Abnormal Behaviour Ethograms in Monkey Research

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1461
Author(s):  
Andrea Polanco ◽  
Brenda McCowan ◽  
Lee Niel ◽  
David L. Pearl ◽  
Georgia Mason
Keyword(s):  
Macaca Mulatta ◽  
Regression Models ◽  
Convergent Validity ◽  
P Value ◽  
Morphological Similarity ◽  
Abnormal Behaviour ◽  
Logistic Regression Models ◽  
The Third ◽  
Welfare Implications ◽  
Self Stimulation

Laboratory monkey ethograms currently include subcategories of abnormal behaviours that are based on superficial morphological similarity. Yet, such ethograms may be misclassifying behaviour, with potential welfare implications as different abnormal behaviours are likely to have distinct risk factors and treatments. We therefore investigated the convergent validity of four hypothesized subcategories of abnormal behaviours (‘motor’, e.g., pacing; ‘self-stimulation’, e.g., self-sucking; ‘postural’, e.g., hanging; and ‘self-abuse’, e.g., self-biting). This hypothesis predicts positive relationships between the behaviours within each subcategory. Rhesus macaque (Macaca mulatta) data on 19 abnormal behaviours were obtained from indoor-housed animals (n = 1183). Logistic regression models, controlling for sex, age, and the number of observations, revealed that only 1/6 ‘motor’ behaviours positively predicted pacing, while 2/3 ‘self-abuse’ behaviours positively predicted self-biting (one-tailed p-value < 0.05). Furthermore, ‘self-stimulation’ behaviours did not predict self-sucking, and none of the ‘postural’ behaviours predicted hanging. Thus, none of the subcategories fully met convergent validity. Subsequently, we created four new valid subcategories formed of comorbid behaviours. The first consisted of self-biting, self-hitting, self-injurious behaviour, floating limb, leg-lifting, and self-clasping. The second comprised twirling, bouncing, rocking, swinging, and hanging. The third comprised pacing and head-twisting, while the final subcategory consisted of flipping and eye-poking. Self-sucking, hair-plucking, threat-biting, and withdrawn remained as individual behaviours. We encourage laboratories to replicate the validation of these subcategories first, and for scientists working with other species to validate their ethograms before using them in welfare assessments.

scholarly journals The Differential Effects of Caregiving Intensity on Overnight Hospitalization in the Previous 2 Years

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 76-76
Author(s):  
Kylie Meyer ◽  
Zachary Gassoumis ◽  
Kathleen Wilber
Keyword(s):  
Health Services ◽  
Fixed Effects ◽  
Regression Models ◽  
P Value ◽  
Logistic Regression Models ◽  
Spousal Caregiving ◽  
Health Insurance Status ◽  
Race Ethnicity ◽  
Self Assessed Health ◽  
Retirement Study

Abstract Caregiving for a spouse is considered a major stressor many Americans will encounter during their lifetimes. Although most studies indicate caregiving is associated with experiencing diminished health outcomes, little is known about how this role affects caregivers’ use of acute health services. To understand how spousal caregiving affects the use of acute health services, we use data from the Health and Retirement Study. We apply fixed effects (FE) logistic regression models to examine odds of experiencing an overnight hospitalization in the previous two years according to caregiving status, intensity, and changes in caregiving status and intensity. Models controlled for caregiver gender, age, race, ethnicity, educational attainment, health insurance status, the number of household residents, and self-assessed health. Overall, caregivers were no more likely to experience an overnight hospitalization compared to non-caregivers (OR 0.92; CI 0.84 to 1.00; p-value=0.057). However, effects varied according to the intensity of caregiving and the time spent in this role. Compared to non-caregivers, for example, spouses who provided care to someone with no need for assistance with activities of daily living had lower odds of experiencing a hospitalization (OR 0.77; CI 0.66 to 0.89). In contrast, caregivers who provided care to someone with dementia for 4 to &lt;6 years had 3.29 times the odds of experiencing an overnight hospitalization (CI 1.04 to 10.38; p-value=0.042). Findings indicate that, although caregivers overall appear to use acute health services about as much as non-caregivers, large differences exist between caregivers. Results emphasize the importance of recognizing diversity within caregiving experiences.

Plasma glycerophospholipid profile, erythrocyte n–3 PUFAs, and metabolic syndrome incidence: a prospective study in Chinese men and women

2021 ◽  
Author(s):  
Shuangshuang Chen ◽  
Qingqing Wu ◽  
Li Zhu ◽  
Geng Zong ◽  
Huaixing Li ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Fatty Acids ◽  
Polyunsaturated Fatty Acids ◽  
Animal Studies ◽  
Plasma Concentrations ◽  
Positive Association ◽  
Community Dwelling ◽  
P Value ◽  
Total N ◽  
Long Chain

ABSTRACT Background Animal studies have highlighted critical roles of glycerophospholipid (GP) metabolism in various metabolic syndrome (MetS)-related features such as dyslipidemia, obesity, and insulin resistance. However, human prospective studies of associations between circulating GPs and risks of MetS are scarce. Objectives We aimed to investigate whether GPs are associated with incidence of MetS in a well-established cohort. Methods A total of 1243 community-dwelling Chinese aged 50–70 y without MetS at baseline and followed up for 6 y were included in current analyses. A total of 145 plasma GPs were quantified by high-throughput targeted lipidomics. MetS was defined using the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian Americans. Results After 6 y, 429 participants developed MetS. Eleven GPs, especially those with long-chain polyunsaturated fatty acids (LCPUFAs) or very-long-chain polyunsaturated fatty acids (VLCPUFAs) at the sn-2 position, including 1 phosphatidylcholine (PC) [PC(18:0/22:6)], 9 phosphatidylethanolamines (PEs) [PE(16:0/22:6), PE(18:0/14:0), PE(18:0/18:1), PE(18:0/18:2), PE(18:0/20:3), PE(18:0/22:5), PE(18:0/22:6), PE(18:1/22:6), and PE(18:2/22:6)], and 1 phosphatidylserine (PS) [PS(18:0/18:0)], were positively associated with incident MetS (RRs: 1.16–1.30 per SD change; Bonferroni-corrected P &lt; 0.05). In network analysis, the strongest positive association for MetS incidence was evidenced in a module mainly composed of PEs containing C22:6 and PSs [RR: 1.21; 95% CI: 1.12, 1.31 per SD change; Bonferroni-corrected P &lt; 0.05]. This association was more pronounced in participants with lower erythrocyte total n–3 PUFA concentrations [Bonferroni-corrected Pinter(P value for the interaction)&lt; 0.05]. Conclusions Elevated plasma concentrations of GPs, especially PEs with LCPUFAs or VLCPUFAs at the sn-2 position, are associated with higher risk of incident MetS. Future studies are merited to confirm our findings.

scholarly journals Case Study of Pediatric Cerebral Sinus Venous Thrombosis Center of a Low Middle-Income Country

2021 ◽  
Vol 27 ◽  
pp. 107602962110228
Author(s):  
Bushra Moiz ◽  
Ronika Devi Ukrani ◽  
Aiman Arif ◽  
Inaara Akbar ◽  
Muhammed Wahhaab Sadiq ◽  
...  
Keyword(s):  
Sinus Thrombosis ◽  
Care Center ◽  
Tertiary Care ◽  
Age Groups ◽  
Activated Protein C ◽  
Positive Association ◽  
Cerebral Venous Sinus Thrombosis ◽  
P Value ◽  
Middle Income ◽  
Duration Of Symptoms

Pediatric cerebral venous sinus thrombosis (CVST) is rare but a potentially fatal disease requiring its understanding in local setting. In this study, we observed the clinical course, management, and outcome of pediatric patients with sinus thrombosis in a tertiary care center at Pakistan. Patients between age 0 to 18 years of both genders diagnosed with sinus thrombosis during 2011 to 2020 were included. Data was collected through in-house computerized system and SPSS version 19 was used for analysis. Of 143492 pediatric admissions, 32 (21 males and 11 females) patients with a median (IQR) age of 4.5 years (0-16) had CVST. This is equivalent to 18.5 CVST events per million pediatric admissions. Adolescents were mostly affected, and the overall mortality was 7%. Primary underlying disorders were infections (59%), hematological neoplasms (12.5%), thrombotic thrombocytopenic purpura (3%) and antiphospholipid syndrome (3%). Activated protein C resistance (44%) was the most common inherited thrombophilia. Twenty-one (66%) patients were anemic with a mean (±SD) hemoglobin of 9.0 g/dL (±2.3). Regression analysis showed a positive association of anemia with multiple sinus involvement ( P-value 0.009) but not with duration of symptoms ( P-value 0.344), hospital stay ( P-value 0.466), age ( P-value 0.863) or gender ( P-value 0.542) of the patients. SARS-COV2 was negative in patients during 2020. Adolescents were primarily affected by sinus thrombosis and infections was the predominant risk factor for all age groups, with a low all-cause mortality. A high index of clinical suspicion is required for prompt diagnosis and intervention.

Trends in the prevalence of metabolic syndrome and its components in Mexican adults, 2006-2018

2021 ◽  
Vol 63 (6, Nov-Dic) ◽  
pp. 713-724
Author(s):  
Rosalba Rojas-Martínez ◽  
Carlos A Aguilar-Salinas ◽  
Martín Romero-Martínez ◽  
Lilia Castro-Porras ◽  
Donaji Gómez-Velasco ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Metabolic Syndrome ◽  
Regression Models ◽  
Significant Risk ◽  
P Value ◽  
Logistic Regression Models ◽  
Mexican Adults ◽  
Diabetes Risk Score ◽  
Linear Trends

Objective. To examine trends in the prevalence of metabolic syndrome (MS) and its components. Materials and methods. Data from 27 800 Mexican adults who participated in Ensanut 2006, 2012, 2016 and 2018 were analyzed. Linear regression was used across each Ensanut period to assess temporal linear trends in the prevalence of MS. Logistic regression models were obtained to calculate the percentage change, p-value for the trend and the association between the presence of MS and the risk of developing type 2 diabetes mellitus (T2DM) over 10 years using the Finnish Diabetes Risk Score (FINDRISC) and cardiovascular disease (CVD) using Globorisk. Results. The prevalence of MS in Mexican adults according to the harmonized definition was: 40.2, 57.3, 59.99 and 56.31%, in 2006, 2012, 2016 and 2018 respectively (p for trend <0.0001). In 2018, 7.62% of metabolic syndrome cases had a significant risk for incident DM2 and 11.6% for CVD. Conclusion. It is estimated that there are 36.5 million Mexican adults living with metabolic syndrome, of which 2 million and 2.5 million have a high risk of developing T2DM or cardiovascular disease respectively, over the next 10 years.

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