Tag: statistics

PLoS One. 2022 Apr 5;17(4):e0266245. doi: 10.1371/journal.pone.0266245. eCollection 2022.

ABSTRACT

BACKGROUND: Colchicine has been used an effective anti-inflammatory drug to treat gout diseases. Owing to its pharmacodynamic of inhibiting interleukins, it has been repurposed to target the cytokine storm post-SARS-CoV-2 invasion. The goal of this meta-analysis was to evaluate the safety profile of colchicine in COVID-19 patients using the gold-standard randomised-control trials.

METHODS: Electronic databases (Pubmed, Google Scholar, and Cochrane) were systematically searched until June 2021 and RCTs were extracted. Outcomes of interest included all-cause mortality, COVID-19 severity, mechanical ventilation, C-reactive protein and D-dimer levels. Using a random-effects model, dichotomous outcomes were pooled using odds ratios (OR) through the generic inverse variance formula while weighted mean differences were calculated using the Wan’s method. P-values < 0.05 were considered statistically significant for all outcomes.

RESULTS: A total population of 16,048 from five RCTs were included in the analysis. Of this, 7957 were randomized to colchicine, and 8091 received standard care, with an average age of 60.67 years. Colchicine was observed to significantly reduce COVID-19 severity (OR: 0.41, 95% CI [0.22, 0.76]; p = 0.005), and CRP levels (WMD: -19.99, 95% CI [-32.09, -7.89]; p = 0.001). However, there was no significant difference in D-dimer levels (WMD: 0.31, 95% CI [-0.61, 1.23]; p = 0.51), mechanical ventilation (OR: 0.42, 95% CI [0.17, 1.03]; p = 0.06; I2 = 74%) and all-cause mortality (OR: 0.98, 95% CI [0.83, 1.16]; p = 0.84) among patients receiving colchicine or standard care.

CONCLUSION: Colchicine treatment decreased CRP levels and COVID-19 severity, with dimer levels, all-cause mortality and mechanical ventilation remaining seemingly unaffected. Thus, clinical trials need to be carried out that allow effective evaluation of colchicine in COVID-19 patients.

PMID:35381033 | DOI:10.1371/journal.pone.0266245

PLoS One. 2022 Apr 5;17(4):e0266232. doi: 10.1371/journal.pone.0266232. eCollection 2022.

ABSTRACT

BACKGROUND: As of July 2021, there has been more than 185 million documented cases of the novel coronavirus (SARS-CoV-2) infections and more than 4 million deaths globally. Despite more than 90% of documented cases being classified as “recovered” from SARS-CoV-2 infection, a proportion of patients reported a wide variety of persisting symptoms after the initial onset or acute phase of the infection, often referred to as “Long Covid”. As data on the symptomatology of post-acute SARS-CoV-2 infection gradually becomes available, there is an urgent need to organise and synthesise the data in order to define what constitutes Long Covid and assist with its management in clinical and community settings.

METHODS: This protocol follows the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. A comprehensive literature search strategy will be developed in accordance with the Cochrane highly sensitive search guidelines. The following electronic databases will be searched for studies to include in the systematic review and meta-analysis: MEDLINE (via PubMed), Scopus, Google Scholar, Web of Science (Web of Knowledge), Science direct, EMBASE, Mednar, Psych INFO, and EBSCOhost. Dual screening will be applied at every screening stage. Two reviewers will independently screen titles, abstracts and full text of potentially eligible studies following the predefined inclusion and exclusion criteria in order to select studies to include in the review. As heterogeneity is anticipated between the included studies, data will be pooled in a meta-analysis using a random effects model. A clustering analytic approach will be applied to identify symptoms groupings and assign the symptoms into clusters. R statistical software will be used for the meta-analysis. Highly heterogenous data will be synthesised narratively. The studies will be assessed, for quality using quality assessment tools appropriate for each study design. Two reviewers will independently undertake the quality of studies assessments.

DISSEMINATION PLANS: Findings of the systematic review will be disseminated through a peer-reviewed publication and presentation of findings at conferences, workshops and government and private sector stakeholder engagement meetings.

CLINICAL TRIAL REGISTRATION: PROSPERO registration number: CRD4202126589. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD4202126589.

PMID:35381027 | DOI:10.1371/journal.pone.0266232

PLoS One. 2022 Apr 5;17(4):e0266010. doi: 10.1371/journal.pone.0266010. eCollection 2022.

ABSTRACT

Spatial event data is heavily used in contemporary research on political violence. Such data are oftentimes mapped onto grid-cells or administrative regions to draw inference about the determinants of conflict intensity. This setup can identify geographic determinants of violence, but is also prone to methodological issues. Problems resulting from spatial aggregation and dependence have been raised in methodological studies, but are rarely accounted for in applied research. As a consequence, we know little about the empirical relevance of these general problems and the trustworthiness of a popular research design. We address these questions by simulating conflict events based on spatial covariates from seven high-profile conflicts. We find that standard designs fail to deliver reliable inference even under ideal conditions at alarming rates. We also test a set of statistical remedies which strongly improve the results: Controlling for the geographic area of spatial units eliminates an important source of spurious correlation. In time-series analyses, the same result can be achieved with unit-level fixed effects. Under outcome diffusion, spatial lag models with area controls produce most reliable inference. When those are computationally intractable, geographically larger aggregations lead to similar improvements. Generally, all analyses should be performed at two separate levels of geographic aggregation. To facilitate future research into geographic methods, we release the Simple Conflict Event Generator (SCEG) developed for this analysis.

PMID:35381020 | DOI:10.1371/journal.pone.0266010

PLoS One. 2022 Apr 5;17(4):e0265471. doi: 10.1371/journal.pone.0265471. eCollection 2022.

ABSTRACT

When dealing with longitudinal data, linear mixed-effects models (LMMs) are often used by researchers. However, LMMs are not always the most adequate models, especially if we expect a nonlinear relationship between the outcome and a continuous covariate. To allow for more flexibility, we propose the use of a semiparametric mixed-effects model to evaluate the overall treatment effect on the hemodynamic responses during bone graft healing and build a prediction model for the healing process. The model relies on a closed-form expectation-maximization algorithm, where the unknown nonlinear function is estimated using a Lasso-type procedure. Using this model, we were able to estimate the effect of time for individual mice in each group in a nonparametric fashion and the effect of the treatment while accounting for correlation between observations due to the repeated measurements. The treatment effect was found to be statistically significant, with the autograft group having higher total hemoglobin concentration than the allograft group.

PMID:35381007 | DOI:10.1371/journal.pone.0265471

PLoS One. 2022 Apr 5;17(4):e0264626. doi: 10.1371/journal.pone.0264626. eCollection 2022.

ABSTRACT

BACKGROUND: There was limited data on treatment outcomes among patients with diabetic ketoacidosis (DKA) in Ethiopia.

OBJECTIVE: The aim of the study was to determine the treatment outcomes of DKA patients attending Debre Tabor General Hospital.

METHOD: A retrospective study was conducted at Debre Tabor General Hospital and data were collected from June 1 to June 30 of 2018. Participants included in the study were all diabetic patients with DKA admitted from August 2010 to May 31, 2018. The primary outcomes were the treatment outcomes of DKA including (in-hospital glycemic control, the length of hospital stay and in-hospital mortality). The statistical analysis was carried out using Statistical Package for Social Sciences (SPSS) version 22. Descriptive statistics was presented in the form of means with standard deviation and binary regression was conducted to determine factors that affect length of hospital stay among DKA patients.

RESULT: 387 patients were included in the study. The mean age of patients was 33.30± 14.96 years. The most common precipitating factor of DKA was new onset diabetes mellitus 150(38.8%). The mean length of hospital stay was 4.64(±2.802) days. The mean plasma glucose at admission and discharge was 443.63(±103.33) and 172.94 (±80.60) mg/dL, respectively. The majority 370 (95.60%) of patients improved and discharged whereas 17 (4.40%) patients died in the hospital. Patients with mild and moderate DKA showed short hospital stay; AOR: 0.16 [0.03-0.78] and AOR:0.17[0.03-0.96] compared with severe DKA. Diabetic ketoacidosis precipitated by infection were nearly five times more likely to have long hospital stay than DKA precipitated by other causes; AOR: 4.59 [1.08-19.42]. In addition, serum glucose fluctuation during hospitalization increased the likelihood of long hospital stay, AOR: 2.15[1.76-2.63].

CONCLUSIONS: New onset type 1 diabetes was the major precipitating factor for DKA. Admitted DKA patients remained in hospital for a duration of approximately five days. About five out of hundred DKA patients ended up with death in the hospital. Infection, serum glucose fluctuations and severity of DKA were determinants of long hospital stay. Early prevention of precipitating factors and adequate management of DAK are warranted to reduce length of hospital stay and mortality.

PMID:35381004 | DOI:10.1371/journal.pone.0264626

J Behav Addict. 2022 Apr 4. doi: 10.1556/2006.2022.00009. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Data regarding the association between ultra-processed food (UPF) consumption and eating disorders (ED) are scarce. Our aim was to investigate whether UPF intake was associated with different ED types in a large population-based study.

METHODS: 43,993 participants (mean age = 51.0 years; 76.1% women) of the French NutriNet-Santé web-cohort who were screened for ED in 2014 via the Sick-Control-One stone-Fat-Food (SCOFF) questionnaire, were included in the analysis. The clinical algorithm Expali TM tool was used to identify four ED types: restrictive, bulimic, binge eating, and other (not otherwise specified). Mean dietary intake was evaluated from at least 2 self-administered 24-h dietary records (2013-2015); categorization of food as ultra-processed or not relied on the NOVA classification. The associations between UPF intake (as percent and reflecting mean daily UPF quantity (g/d) within the dietary intake, %UPF) and ED types were evaluated using polytomous logistic regression models.

RESULTS: 5,967 participants (13.6%) were categorized as likely ED (restrictive n = 444; bulimic n = 1,575; binge eating n = 3,124; other ED n = 824). The fully-adjusted analyses revealed a positive association between UPF intake and bulimic, binge eating, and other ED: ED risk (odds ratio, OR) for an absolute 10-percentage point incremental increase in %UPF intake were 1.08 (1.01-1.14; P = 0.02), 1.21 (1.16-1.26; P < 0.0001), and 1.11 (1.02-1.20; P = 0.02), respectively. No significant association was detected for restrictive ED.

DISCUSSION AND CONCLUSION: This study revealed an association of UPF intake with different ED types among French adults. Future research is needed to elucidate the direction of the observed associations.

PMID:35380986 | DOI:10.1556/2006.2022.00009

Am J Respir Crit Care Med. 2022 Apr 5. doi: 10.1164/rccm.202106-1439OC. Online ahead of print.

ABSTRACT

RATIONALE: Outdoor air pollution is a potential risk factor for lower lung function and chronic obstructive pulmonary disease (COPD). Little is known on how airway abnormalities and lung growth might modify this relationship.

OBJECTIVE: To evaluate the associations of ambient air pollution exposure with lung function and COPD, and to examine possible interactions with dysanapsis.

METHODS: We made use of cross-sectional post-bronchodilator spirometry data from 1452 individuals enrolled in the Canadian Cohort Obstructive Lung Disease (CanCOLD) with linked ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) air pollution estimates. Dysanapsis, or the ratio of airway-to-lung volume calculated from thoracic computed tomography images, was used to examine possible interactions.

MEASUREMENTS AND MAIN RESULTS: In adjusted models, 101.7 mL [95% CI: -166.2, -37.2] and 115.0 mL [95% CI: -196.5, -33.4] lower forced expiratory volume in one second (FEV1) were demonstrated per increase of 2.4 ug/m3 PM2.5, and 9.2 ppb NO2, respectively. Interaction between air pollution and dysanapsis was not statistically significant when modelling airway-to-lung ratio as a continuous variable. However, a 109.8 mL [95% CI: -209.0, -10.5] lower FEV1 and an 87% [95% CI: 12%; 213%] higher odds of COPD was observed amongst individuals in the lowest, relative to highest, airway-to-lung ratio, per 2.4 μg/m3 increment of PM2.5.

CONCLUSION: Ambient air pollution exposure was associated with lower lung function, even at relatively low concentrations. Individuals with dysanaptic lung growth might be particularly susceptible to inhaled ambient air pollutants, especially those at the extremes of dysanapsis.

PMID:35380941 | DOI:10.1164/rccm.202106-1439OC

BMJ Open. 2021 Dec 30;11(12):e055013. doi: 10.1136/bmjopen-2021-055013.

ABSTRACT

OBJECTIVES: To examine the associations between self-stigma and diabetes duration in a sample of Japanese people with type 2 diabetes.

DESIGN: A secondary analysis of a cross-sectional study.

SETTING: Two university hospitals, one general hospital and one clinic in Tokyo, Japan.

PARTICIPANTS: Outpatients with type 2 diabetes aged 20-74 years and receiving treatment from diabetes specialist physicians (n=209) completed a self-administered questionnaire.

PRIMARY AND SECONDARY OUTCOME MEASURES: Self-stigma was measured as the primary outcome. Patient Activation Measure, body mass index and haemoglobin A1c were measured as secondary outcomes.

RESULTS: One-way analysis of covariance showed significant differences in self-stigma levels between the five groups of diabetes duration (≤5 years, 6-10 years, 11-15 years, 16-21 years and 22 years or more) after controlling for age, gender, education, marital status, diabetes treatment (insulin use) and diabetes-related complications, F(4,198)=2.83, p=0.026. Multiple comparisons using Bonferroni correction showed statistically significant differences in self-stigma levels between the groups with ≤5 years (95% CI 59.63 to 69.73) and 11-15 years with diabetes (95% CI 71.12 to 80.82; p=0.020). The highest mean level of self-stigma was observed in the group having diabetes for 11-15 years.

CONCLUSIONS: Self-stigma was associated with diabetes duration and was lowest after diagnosis and gradually increased, with its highest levels being observed in those having diabetes for 11-15 years. Self-stigma takes time to develop and gradually increases in individuals as it is learnt through direct experiences of diabetes-related stigma after self-administering treatment in everyday social situations.

PMID:35380981 | DOI:10.1136/bmjopen-2021-055013

Am J Respir Cell Mol Biol. 2022 Apr 5. doi: 10.1165/rcmb.2021-0346OC. Online ahead of print.

ABSTRACT

Chronic alcohol drinking is associated with increased susceptibility to viral and bacterial respiratory pathogens. In this study, we utilize a rhesus macaque model of voluntary ethanol self-administration to study the effects of long-term alcohol drinking on the immunological landscape of the lung. We report heightened inflammatory state in alveolar macrophages (AM) obtained from ethanol (EtOH) drinking animals that is accompanied by increased chromatin accessibility in intergenic regions that regulate inflammatory genes and contain binding motifs for transcription factors AP-1, IRF8, and NFKB p-65. In line with these transcriptional and epigenetic changes at basal state, AM from EtOH drinking animals generate elevated inflammatory mediator responses to LPS and respiratory syncytial virus (RSV). However, transcriptional analysis revealed an inefficient induction of interferon stimulated genes with EtOH in response to RSV, suggesting disruption of anti-microbial defenses. Correspondingly, AM from EtOH drinking animals exhibited transcriptional shifts indicative of increased oxidative stress and oxidative phosphorylation which was coupled with higher cytosolic reactive oxygen species (ROS) and mitochondrial potential. This heightened oxidative stress state was accompanied by decreased ability to phagocytose bacteria. Bulk RNA and ATAC sequencing data further revealed reduced expression and chromatin accessibility of loci associated with tissue repair and maintenance with chronic EtOH drinking. Similarly, analysis of scRNA-Seq data revealed shifts in cell states from tissue maintenance to inflammatory responses with EtOH. Collectively, these data provide novel insight into mechanisms by which chronic EtOH drinking increases susceptibility to infection in patients with alcohol use disorders.

PMID:35380939 | DOI:10.1165/rcmb.2021-0346OC

Environ Sci Pollut Res Int. 2022 Apr 5. doi: 10.1007/s11356-022-19730-w. Online ahead of print.

ABSTRACT

We researched China’s climate and sustainable development goal with relevant and susceptible instruments capable of inducing and mitigating carbon emissions. Amidst the contributor to the global carbon emissions, China is caught in between mitigating its carbon emission and aiming towards placing its national contribution of emissions to the acceptable levels of 1.5 °C and below 2 °C. Following the intricacies surrounding China’s sustainable development as it contains its economic and environmental performance, we adopt China’s data of 1980 and 2018 with different scientific approaches (nonlinear autoregressive distributed lag (NARDL), dynamic ordinary least square test, and bootstrap Granger causality) with different instruments (such as economic growth, financial development, renewable energy, and innovation policies) to research China’s sustainable development. For clear exposition and insight into our findings with policies attached, we draw a conclusion from the outcomes of the mentioned approaches. From NARDL and dynamic ordinary least squares (DOLS), we find that economic growth through economic activities is statistically significant in determining the trend (increase) of carbon emissions in China in both periods (short run and long run). However, other selected instruments (financial, renewable, and innovation policies) tend towards controlling and moderating the carbon emissions in China. Thus, China has good prospects to mitigate its carbon emissions if considered tailoring its policies towards favorable instruments. From bootstrap Granger causality, we find similar inferential results that support previous findings thereby confirming the positive implication of the selected instruments to China’s sustainable development. Hence, the nexus that is established among the selected instruments clearly show the importance of technological innovation and renewable energy in mitigating carbon emissions.

PMID:35380330 | DOI:10.1007/s11356-022-19730-w