Nevin Manimala

Health Rep. 2022 Sep 15;33(9):3-10. doi: 10.25318/82-003-x202200900001-eng.

ABSTRACT

BACKGROUND: Throughout the COVID-19 pandemic, Canadian public health officials have mandated and recommended precautions to slow the spread of COVID-19. This study examined which population groups were less compliant with precautions, such as mask-wearing and self-isolating, and where they were located in Canada.

DATA AND METHODS: Results are from the Canadian COVID-19 Antibody and Health Survey, a national survey aimed at estimating how many Canadians who were older than one year and living in private households had antibodies in their blood against the SARS-CoV-2 virus. Questionnaire data were collected in the 10 provinces and 3 territorial capitals, from November 2020 to April 2021. Respondents were asked about compliance with precautions related to COVID-19. Weighted prevalences and logistic regression models were used to identify which population groups were less compliant with precautions to prevent the spread of COVID-19, and where they were located in Canada.

RESULTS: Significant differences in compliance with precautions were found by sex, region, urban versus rural location, age, income, presence of chronic conditions, household size and work status. With covariate adjustment, Canadians who were less compliant with precautions were males, those living in the territorial capitals, those in rural areas, and people aged 34 and younger (compared with people aged 65 and older). Additional differences were found when analyzing compliance with consistently recommended precautions compared with those usually recommended.

INTERPRETATION: As Canada continues to navigate the waves of the pandemic, and with the emergence of new variants, precautions are still being mandated or recommended in many jurisdictions and locations. Continuing to understand which population groups were less compliant in earlier waves and where they were located in Canada can be beneficial to ongoing and future public health efforts to slow the transmission of COVID-19.

PMID:36153709 | DOI:10.25318/82-003-x202200900001-eng

J Clin Nurs. 2022 Sep 25. doi: 10.1111/jocn.16545. Online ahead of print.

ABSTRACT

AIMS AND OBJECTIVES: The study was conducted to describe long-term perceived health among patients after a percutaneous coronary intervention as well as clarify the associations between perceived health and various factors.

BACKGROUND: Perceived health is an important outcome for coronary heart disease patients who have undergone percutaneous coronary intervention. Poor perceived health predicts low adherence to treatment, morbidity and mortality.

DESIGN: An explanatory and descriptive survey with a six-year follow-up (STROBE Statement: File S1).

METHODS: Baseline data (n = 416) were collected in 2013, with follow-up data collected from the same study group in 2019 (n = 154) at two university hospitals and three central hospitals in Finland. The employed self-reported questionnaire was based on the EuroQoL visual analogue scale and EuroQol five-dimensional scale. Data were analysed using descriptive statistics and multivariate methods.

RESULTS: Perceived health did not significantly differ four months or six years after percutaneous coronary intervention. The respondents most commonly reported pain and discomfort (62.1%), problems in mobility (50.3%), issues with usual activities (27.5%), and anxiety and depression (24.0%). Managing self-care (8.5%) was least likely to be an issue for the respondents. A majority of the reported problems were of a mild nature. The consumption of an adequate amount of vegetables, lower systolic blood pressure, regular follow-up treatment, lack of prior invasive procedures, and younger age predicted better scores for both perceived health and its separate dimensions.

CONCLUSION: Regular follow-up is important to ensure after percutaneous coronary intervention to identify patients with pain and discomfort, mobility problems, depression and anxiety. Healthcare professionals should pay particular attention to elderly patients, who have undergone severe invasive procedures.

RELEVANCE TO CLINICAL PRACTICE: This study confirms the importance of regular follow-ups for post-percutaneous coronary intervention patients.

PATIENT OR PUBLIC CONTRIBUTION: Patients have completed a self-reported questionnaire based on informed consent.

PMID:36153702 | DOI:10.1111/jocn.16545

BMC Biol. 2022 Sep 24;20(1):208. doi: 10.1186/s12915-022-01398-w.

ABSTRACT

BACKGROUND: Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.

RESULTS: In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.

CONCLUSIONS: We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

PMID:36153614 | DOI:10.1186/s12915-022-01398-w

Parasit Vectors. 2022 Sep 24;15(1):336. doi: 10.1186/s13071-022-05471-9.

ABSTRACT

BACKGROUND: Mathematical modelling of host-parasite systems has seen tremendous developments and broad applications in theoretical and applied ecology. The current study focuses on the infection dynamics of a gyrodactylid-fish system. Previous experimental studies have explored the infrapopulation dynamics of co-infecting ectoparasites, Gyrodactylus turnbulli and G. bullatarudis, on their fish host, Poecilia reticulata, but questions remain about parasite microhabitat preferences, host survival and parasite virulence over time. Here, we use more advanced statistics and a sophisticated mathematical model to investigate these questions based on empirical data to add to our understanding of this gyrodactylid-fish system.

METHODS: A rank-based multivariate Kruskal-Wallis test coupled with its post-hoc tests and graphical summaries were used to investigate the spatial and temporal parasite distribution of different gyrodactylid strains across different host populations. By adapting a multi-state Markov model that extends the standard survival models, we improved previous estimates of survival probabilities. Finally, we quantified parasite virulence of three different strains as a function of host mortality and recovery across different fish stocks and sexes.

RESULTS: We confirmed that the captive-bred G. turnbulli and wild G. bullatarudis strains preferred the caudal and rostral regions respectively across different fish stocks; however, the wild G. turnbulli strain changed microhabitat preference over time, indicating microhabitat preference of gyrodactylids is host and time dependent. The average time of host infection before recovery or death was between 6 and 14 days. For this gyrodactylid-fish system, a longer period of host infection led to a higher chance of host recovery. Parasite-related mortalities are host, sex and time dependent, whereas fish size is confirmed to be the key determinant of host recovery.

CONCLUSION: From existing empirical data, we provided new insights into the gyrodactylid-fish system. This study could inform the modelling of other host-parasite interactions where the entire infection history of the host is of interest by adapting multi-state Markov models. Such models are under-utilised in parasitological studies and could be expanded to estimate relevant epidemiological traits concerning parasite virulence and host survival.

PMID:36153606 | DOI:10.1186/s13071-022-05471-9

Orphanet J Rare Dis. 2022 Sep 24;17(1):365. doi: 10.1186/s13023-022-02513-4.

ABSTRACT

BACKGROUND: Glut1 deficiency syndrome (Glut1-DS) is a rare metabolic encephalopathy. Familial forms are poorly investigated, and no previous studies have explored aspects of Glut1-DS over the course of life: clinical pictures, intelligence, life achievements, and quality of life in adulthood. Clinical, biochemical and genetic data in a cohort of familial Glut1-DS cases were collected from medical records. Intelligence was assessed using Raven’s Standard Progressive Matrices and Raven’s Colored Progressive Matrices in adults and children, respectively. An ad hoc interview focusing on life achievements and the World Health Organization Quality of Life Questionnaire were administered to adult subjects.

RESULTS: The clinical picture in adults was characterized by paroxysmal exercise-induced dyskinesia (PED) (80%), fatigue (60%), low intelligence (60%), epilepsy (50%), and migraine (50%). However, 20% of the adults had higher-than-average intelligence. Quality of Life (QoL) seemed unrelated to the presence of PED or fatigue in adulthood. An association of potential clinical relevance, albeit not statistically significant, was found between intelligence and QoL. The phenotype of familial Glut1-DS in children was characterized by epilepsy (83.3%), intellectual disability (50%), and PED (33%).

CONCLUSION: The phenotype of familial Glut1-DS shows age-related differences: epilepsy predominates in childhood; PED and fatigue, followed by epilepsy and migraine, characterize the condition in adulthood. Some adults with familial Glut1-DS may lead regular and fulfilling lives, enjoying the same QoL as unaffected individuals. The disorder tends to worsen from generation to generation, with new and more severe symptoms arising within the same family. Epigenetic studies might be useful to assess the phenotypic variability in Glut1-DS.

PMID:36153584 | DOI:10.1186/s13023-022-02513-4

BioData Min. 2022 Sep 24;15(1):20. doi: 10.1186/s13040-022-00308-8.

ABSTRACT

BACKGROUND: Under-five mortality is a matter of serious concern for child health as well as the social development of any country. The paper aimed to find the accuracy of machine learning models in predicting under-five mortality and identify the most significant factors associated with under-five mortality.

METHOD: The data was taken from the National Family Health Survey (NFHS-IV) of Uttar Pradesh. First, we used multivariate logistic regression due to its capability for predicting the important factors, then we used machine learning techniques such as decision tree, random forest, Naïve Bayes, K- nearest neighbor (KNN), logistic regression, support vector machine (SVM), neural network, and ridge classifier. Each model’s accuracy was checked by a confusion matrix, accuracy, precision, recall, F1 score, Cohen’s Kappa, and area under the receiver operating characteristics curve (AUROC). Information gain rank was used to find the important factors for under-five mortality. Data analysis was performed using, STATA-16.0, Python 3.3, and IBM SPSS Statistics for Windows, Version 27.0 software.

RESULT: By applying the machine learning models, results showed that the neural network model was the best predictive model for under-five mortality when compared with other predictive models, with model accuracy of (95.29% to 95.96%), recall (71.51% to 81.03%), precision (36.64% to 51.83%), F1 score (50.46% to 62.68%), Cohen’s Kappa value (0.48 to 0.60), AUROC range (93.51% to 96.22%) and precision-recall curve range (99.52% to 99.73%). The neural network was the most efficient model, but logistic regression also shows well for predicting under-five mortality with accuracy (94% to 95%)., AUROC range (93.4% to 94.8%), and precision-recall curve (99.5% to 99.6%). The number of living children, survival time, wealth index, child size at birth, birth in the last five years, the total number of children ever born, mother’s education level, and birth order were identified as important factors influencing under-five mortality.

CONCLUSION: The neural network model was a better predictive model compared to other machine learning models in predicting under-five mortality, but logistic regression analysis also shows good results. These models may be helpful for the analysis of high-dimensional data for health research.

PMID:36153553 | DOI:10.1186/s13040-022-00308-8

Cost Eff Resour Alloc. 2022 Sep 24;20(1):52. doi: 10.1186/s12962-022-00386-9.

ABSTRACT

BACKGROUND: Accurate information on the cost determinants in the COVID-19 patients could provide policymakers a valuable planning tool for dealing with the future COVID-19 crises especially in the health systems with limited resources.

OBJECTIVES: This study aimed to determine the factors affecting direct medical cost of COVID-19 patients in Hamadan, the west of Iran.

METHODS: This study considered 909 confirmed COVID-19 patients with positive real-time reverse-transcriptase polymerase-chain-reaction test which were hospitalized from 1 March to 31 January 2021 in Farshchian (Sina) hospital in Hamadan, Iran. A checklist was utilized to assess the relationship of demographic characteristics, clinical presentation, medical laboratory findings and the length of hospitalization to the direct hospitalization costs in two groups of patients (patients with hospitalization ≤ 9 days and > 9 days). Statistical analysis was performed using chi-square, median test and multivariable quantile regression model at 0.05 significance levels with Stata 14 software program.

RESULTS: The median cost of hospitalization in patients was totally 134.48 dollars (Range: 19.19-2397.54) and respectively 95.87 (Range: 19.19-856.63) and 507.30 dollars (Range: 68.94-2397.54) in patients with hospitalization ≤ 9 days and > 9 days. The adjusted estimates presented that in patients with 9 or less hospitalization days history of cardiovascular disease, wheezing pulmonary lung, SPO2 lower than 90%, positive CRP, LDH higher than 942 U/L, NA lower than 136 mEq/L, lymphosite lower than 20% and patients with ICU experience had significantly positive relationship to the median of cost. Moreover, in patients with more than 9 hospitalization days, history of cardiovascular disease and ICU experience was statistically positive association and age older than 60 years and WBC lower than 4.5 mg/dL had statistically negative relationship to the median of hospitalization cost.

CONCLUSION: As the length of hospital stay, which can be associated with the severity of the disease, increases, health systems become more vulnerable in terms of resource utilization, which in turn can challenge their responsiveness and readiness to meet the specialized treatment needs of individuals.

PMID:36153533 | DOI:10.1186/s12962-022-00386-9

Genet Sel Evol. 2022 Sep 24;54(1):65. doi: 10.1186/s12711-022-00756-0.

ABSTRACT

BACKGROUND: Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset from seven intensely selected lines to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays and to identify scenarios in which WGS provides the largest advantage.

METHODS: We sequenced 6931 individuals from seven commercial pig lines with different numerical sizes. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a standard marker array or variants preselected from WGS based on association tests.

RESULTS: The accuracies of genomic predictions based on preselected WGS variants were not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to standard marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and standard marker arrays were augmented with preselected variants with statistically significant associations to the trait. With this method and training sets of around 80k individuals, the accuracy of within-line genomic predictions was on average improved by 0.025. With multi-line training sets, improvements of 0.04 compared to marker arrays could be expected.

CONCLUSIONS: Our results showed that WGS has limited potential to improve the accuracy of genomic predictions compared to marker arrays in intensely selected pig lines. Thus, although we expect that larger improvements in accuracy from the use of WGS are possible with a combination of larger training sets and optimised pipelines for generating and analysing such datasets, the use of WGS in the current implementations of genomic prediction should be carefully evaluated against the cost of large-scale WGS data on a case-by-case basis.

PMID:36153511 | DOI:10.1186/s12711-022-00756-0

BMC Oral Health. 2022 Sep 24;22(1):426. doi: 10.1186/s12903-022-02456-z.

ABSTRACT

BACKGROUND: This in vitro study evaluated the effect of amine-free initiator system and polymerization type on long-term color change of amine-free light-cure and dual-cure resin cements.

METHODS: Sixty disk-shaped specimens (10 × 1 mm) were prepared from six different amine-free resin cements; NX3 Nexus light-cure (LC) and dual-cure (DC), Variolink Veneer (LC) and Variolink II (DC), Relyx Veneer (LC) and Rely X Ultimate (DC). A feldspathic porcelain specimen (12 × 14 × 0.8 mm) was obtained from a CAD/CAM block (Cerec Blocks; Sirona Dental Systems GmbH, Bensheim, Germany) for color testing. The feldspathic specimen was placed on the resin cement disk and all measurements were performed without cementation. A spectrophotometer was used for color measurements. Specimens were subjected to thermal aging (5 °C and 55 °C; 5000 and 20,000 cycles). Specific color coordinate differences (ΔL, Δa, and Δb) and the total color differences (ΔE₀₀) were calculated after immersion in distilled water for different periods. Normality of data distribution was tested by using the Kolmogorov-Smirnov test. Data were statistically in a model of repeated measures, using multivariate tests and Tukey’s multiple comparison tests at a significance level of p < 0.05.

RESULTS: ∆E₀₀ values of resin cements were influenced by cycle periods, significantly (p < 0.05). The highest ΔE00 values for long term were obtained in the NX3 (DC) (3.49 ± 0.87) and the lowest in the NX3 (LC) (1.41 ± 0.81). NX3 (LC), Variolink (DC), RELY X (LC) resin cements showed clinically acceptable color change after long-term aging (∆E₀₀ < 1.8).

CONCLUSION: Light-cure resin cements should be preferred for long-term color stability of full ceramic restorations.

PMID:36153495 | DOI:10.1186/s12903-022-02456-z

BMC Med Res Methodol. 2022 Sep 24;22(1):250. doi: 10.1186/s12874-022-01727-1.

ABSTRACT

BACKGROUND: Missing data are ubiquitous in randomised controlled trials. Although sensitivity analyses for different missing data mechanisms (missing at random vs. missing not at random) are widely recommended, they are rarely conducted in practice. The aim of the present study was to demonstrate sensitivity analyses for different assumptions regarding the missing data mechanism for randomised controlled trials using latent growth modelling (LGM).

METHODS: Data from a randomised controlled brief alcohol intervention trial was used. The sample included 1646 adults (56% female; mean age = 31.0 years) from the general population who had received up to three individualized alcohol feedback letters or assessment-only. Follow-up interviews were conducted after 12 and 36 months via telephone. The main outcome for the analysis was change in alcohol use over time. A three-step LGM approach was used. First, evidence about the process that generated the missing data was accumulated by analysing the extent of missing values in both study conditions, missing data patterns, and baseline variables that predicted participation in the two follow-up assessments using logistic regression. Second, growth models were calculated to analyse intervention effects over time. These models assumed that data were missing at random and applied full-information maximum likelihood estimation. Third, the findings were safeguarded by incorporating model components to account for the possibility that data were missing not at random. For that purpose, Diggle-Kenward selection, Wu-Carroll shared parameter and pattern mixture models were implemented.

RESULTS: Although the true data generating process remained unknown, the evidence was unequivocal: both the intervention and control group reduced their alcohol use over time, but no significant group differences emerged. There was no clear evidence for intervention efficacy, neither in the growth models that assumed the missing data to be at random nor those that assumed the missing data to be not at random.

CONCLUSION: The illustrated approach allows the assessment of how sensitive conclusions about the efficacy of an intervention are to different assumptions regarding the missing data mechanism. For researchers familiar with LGM, it is a valuable statistical supplement to safeguard their findings against the possibility of nonignorable missingness.

TRIAL REGISTRATION: The PRINT trial was prospectively registered at the German Clinical Trials Register (DRKS00014274, date of registration: 12th March 2018).

PMID:36153489 | DOI:10.1186/s12874-022-01727-1