scholarly journals Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China

2020 ◽  
Vol 17 (11) ◽  
pp. 3903 ◽  
Author(s):  
Yongzhu Xiong ◽  
Yunpeng Wang ◽  
Feng Chen ◽  
Mingyong Zhu
Keyword(s):  
Spatial Autocorrelation ◽  
Influencing Factors ◽  
Multiple Scales ◽  
Open Data ◽  
Rank Correlation ◽  
Hubei Province ◽  
Spatiotemporal Patterns ◽  
County Level ◽  
Negatively Associated ◽  
Spearman’S Rank Correlation

The coronavirus disease 2019 (COVID-19) epidemic has had a crucial influence on people’s lives and socio-economic development. An understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic on multiple scales could benefit the control of the outbreak. Therefore, we used spatial autocorrelation and Spearman’s rank correlation methods to investigate these two topics, respectively. The COVID-19 epidemic data reported publicly and relevant open data in Hubei province were analyzed. The results showed that (1) at both prefecture and county levels, the global spatial autocorrelation was extremely significant for the cumulative confirmed COVID-19 cases (CCC) in Hubei province from 30 January to 18 February 2020. Further, (2) at both levels, the significant hotspots and cluster/outlier areas were observed solely in Wuhan city and most of its districts/sub-cities from 30 January to 18 February 2020. (3) At the prefecture level in Hubei province, the number of CCC had a positive and extremely significant correlation (p < 0.01) with the registered population (RGP), resident population (RSP), Baidu migration index (BMI), regional gross domestic production (GDP), and total retail sales of consumer goods (TRS), respectively, from 29 January to 18 February 2020 and had a negative and significant correlation (p < 0.05) with minimum elevation (MINE) from 2 February to 18 February 2020, but no association with the land area (LA), population density (PD), maximum elevation (MAXE), mean elevation (MNE), and range of elevation (RAE) from 23 January to 18 February 2020. (4) At the county level, the number of CCC in Hubei province had a positive and extremely significant correlation (p < 0.01) with PD, RGP, RSP, GDP, and TRS, respectively, from 27 January to 18 February 2020, and was negatively associated with MINE, MAXE, MNE, and RAE, respectively, from 26 January to 18 February 2020, and negatively associated with LA from 30 January to 18 February 2020. It suggested that (1) the COVID-19 epidemics at both levels in Hubei province had evident characteristics of significant global spatial autocorrelations and significant centralized high-risk outbreaks. (2) The COVID-19 epidemics were significantly associated with the natural factors, such as LA, MAXE, MNE, and RAE, -only at the county level, not at the prefecture level, from 2 February to 18 February 2020. (3) The COVID-19 epidemics were significantly related to the socioeconomic factors, such as RGP, RSP, TRS, and GDP, at both levels from 26 January to 18 February 2020. It is desired that this study enrich our understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic and benefit classified prevention and control of the COVID-19 epidemic for policymakers.

scholarly journals Spatial statistics and influencing factors of the novel coronavirus pneumonia 2019 epidemic in Hubei Province, China

2020 ◽  
Author(s):  
Yongzhu Xiong ◽  
Yunpeng Wang ◽  
Feng Chen ◽  
Mingyong Zhu
Keyword(s):  
Spatial Autocorrelation ◽  
Influencing Factors ◽  
Pearson Correlation ◽  
Hubei Province ◽  
County Level ◽  
The Novel ◽  
Land Area ◽  
Novel Coronavirus ◽  
And Control ◽  
Global Spatial Autocorrelation

Abstract An in-depth understanding of the spatiotemporal dynamic characteristics of infectious diseases could be helpful for epidemic prevention and control. Based on the novel coronavirus pneumonia (NCP) data published on official websites, GIS spatial statistics and Pearson correlation methods were used to analyze the spatial autocorrelation and influencing factors of the 2019 NCP epidemic from January 30, 2020 to February 18, 2020. The following results were obtained. (1) During the study period, Hubei Province was the only significant cluster area and hotspot of cumulative confirmed cases of NCP infection at the provincial level in China. (2) The NCP epidemic in China had a very significant global spatial autocorrelation at the prefecture-city level, and Wuhan was the significant hotspot and cluster city for cumulative confirmed NCP cases in the whole country. (3) The cumulative confirmed NCP cases had a very significant global spatial autocorrelation at the county level in Hubei Province, and the county-level districts under the jurisdiction of Wuhan and neighboring Huangzhou district in Huanggang City were the significant hotspots and spatial clusters of cumulative confirmed NCP cases. (4) Based on Pearson correlation analysis, the number of cumulative confirmed NCP cases in Hubei Province had very significant and positive correlations (p<0.01) at the prefecture-city and the county levels with four population indexes (registered population, resident population, regional GDP and total retail sales of consumer goods) during the study period. (5) The number of the cumulative confirmed NCP cases in Hubei Province also had a very significant and positive correlation (p<0.01) on the prefecture-city scale with the Baidu migration index and population density but not with land area, whereas that in Hubei Province had a significant and positive correlation (p<0.05) at the county level with land area but not with population density from January 30, 2020, to February 18, 2020. It was found that the NCP epidemic in Hubei Province had distinctive characteristics of a significant centralized outbreak, significant spatial autocorrelation and complex influencing factors and that the spatial scale had a significant effect on the global spatial autocorrelation of the NCP epidemic. The findings help to deepen the understanding of spatial distribution patterns and transmission trends of the NCP epidemic and may also benefit scientific prevention and control of epidemics such as COVID-19.

scholarly journals Spatial Statistics and Influencing Factors of the Epidemic of Novel Coronavirus Pneumonia 2019 in Hubei Province, China

2020 ◽  
Author(s):  
Yongzhu Xiong ◽  
Yunpeng Wang ◽  
Feng Chen ◽  
Mingyong Zhu
Keyword(s):  
Spatial Autocorrelation ◽  
Influencing Factors ◽  
Hot Spot ◽  
Pearson Correlation ◽  
Hubei Province ◽  
Land Area ◽  
Novel Coronavirus ◽  
And Control ◽  
Global Spatial Autocorrelation ◽  
County Scale

Abstract An in-depth understanding of spatiotemporal dynamic characteristics of infectious diseases could be helpful to an epidemic prevention and control. Based on the novel coronavirus pneumonia (NCP) data published on official websites, GIS spatial statistics and Pearson correlation methods were used to analyze the spatial autocorrelation and influencing factors of the 2019 NCP epidemic from January 30, 2020 to February 18, 2020. The results of the study showed that: (1) During the study period, Hubei Province was the only significant cluster area and hot spot of the cumulative cases confirmed with the NCP infection in China on the provincial scale; (2) The epidemic of the NCP infection in China on the prefecture-city scale had a very significant global spatial autocorrelation, and Wuhan had always been the significant hot spot and cluster city of the cumulative cases confirmed with the NCP infection in the whole country; (3) The cumulative cases confirmed with the NCP infection in Hubei Province on the county scale had a very significant global spatial autocorrelation, and the county-level districts under the jurisdiction of Wuhan and its neighboring Huangzhou district in Huanggang City were the significant hot spots and spatial clusters of the cumulative cases confirmed with the NCP infection; (4) Based on Pearson correlation analysis, the number of the accumulative cases confirmed with the NCP infection in Hubei Province on the prefecture-city scale and also on the county scale had very significant and positive correlations (p < 0.01) with the four indexes of population of registration population, resident population, regional GDP and total retail sales of consumer goods, respectively, during the study period; (5) The number of the cumulative cases confirmed with the NCP infection in Hubei Province on the prefecture-city scale also had a very significant and positive correlation (p < 0.01) with Baidu migration index and population density, respectively, but not with land area, whereas that in Hubei Province on the county scale had a significant and positive correlation (p < 0.05) with land area, but not with population density from January 30, 2020 to February 18, 2020. It is found that the NCP epidemic in Hubei Province has the distinctive characteristics of significantly centralized outbreak, significantly spatial autocorrelation and complex influencing factors and that the spatial scale has a significant effect on the global spatial autocorrelation of the NCP epidemic. The findings help to deepen the understanding of spatial distribution patterns and transmission trends of the NCP epidemic and may also benefit scientific prevention and control of epidemics such as NCP 2019.

scholarly journals Association between concentrations of chromium in drinking water and mortality due to suicide in Alabama

2020 ◽  
Vol 18 (5) ◽  
pp. 835-842
Author(s):  
Caitlyn C. Whitaker ◽  
Marshall E. Cates ◽  
Danielle L. Cruthirds ◽  
Gregory S. Gorman
Keyword(s):  
Drinking Water ◽  
Statistical Significance ◽  
Case Series ◽  
Rank Correlation ◽  
Caucasian Population ◽  
County Level ◽  
Total Chromium ◽  
Spearman’S Rank Correlation ◽  
Antidepressant Effects ◽  
Demographic Subgroups

Abstract Preclinical studies and clinical data from case series and placebo-controlled trials suggest that chromium might have antidepressant effects. We conducted an observational study in order to assess the association between concentrations of chromium in drinking water and mortality due to suicide in Alabama. Publicly available databases were used to determine both county-level concentrations of chromium in drinking water and county-level rates of mortality due to suicide in the years 2005–2015. Data analyses comparing county-level concentrations of total chromium in drinking water with mortality rate due to suicide were conducted using a two-tailed nonparametric Spearman's rank correlation, with statistical significance set at p ≤ 0.01 and 99% confidence interval. Sub-analyses were conducted examining males, females, whites, and blacks/other minorities. There were no statistically significant findings concerning concentrations of chromium and suicide rate in the general population (p = 0.35, r = −0.12); however, there was a statistically significant inverse relationship between the concentration of chromium and suicide deaths in whites (p = 0.009, r = −0.32). There were no statistically significant findings in the remaining demographic subgroups. Chromium in drinking water might have a protective effect against mortality due to suicide, at least in the Caucasian population.

A Study of the Relationships between Nursing Students’ Meanings of Life, Positive Beliefs, and Well-Being

2018 ◽  
Vol 3 (01) ◽  
Author(s):  
Fu-Ju Tsai ◽  
Cheng-Yu Chen ◽  
Gwo-Liang Yeh ◽  
Yih-Jin Hu ◽  
Chie-Chien Tseng ◽  
...  
Keyword(s):  
Health Promotion ◽  
Nursing Students ◽  
Analysis Of Variance ◽  
Rank Correlation ◽  
Meaning Of Life ◽  
Well Being ◽  
Social Health ◽  
Cross Sectional ◽  
Nursing Educators ◽  
Spearman’S Rank Correlation

Background: Nursing educators should train nursing students to pursue physical, psychological, spiritual, and social health promotion. The purpose of this study was to explore relationships between nursing students’ meaning of life, positive beliefs, and well-being. Methods: A cross-sectional correlational study with a quantitative approach was adopted. Purposive sampling was used. A total of 170 nursing students voluntarily participated in this study. A 56-item questionnaire was used to examine nursing students’ meaning of life (1-25 items), positive beliefs (1-11 items), and well-being (1-20 items). The content validity index (CVI) of the study questionnaire was established as 0.95 by seven expert scholars. The reliability values for the three parts of the measure were as follows: meaning of life, Cronbach’s α 0.96; positive beliefs, Cronbach’s α 0.93; and well-being, Cronbach’s α 0.95. Percentages, frequencies, means, SDs, Kruskal-Wallis one-way analysis of variance by rank, Spearman’s rank correlation, one-way analysis of variance, Spearman’s rho correlation, and regression analysis were used for the data analysis. Results: Nursing students had the following mean scores: meaning of life with 4.02 (SD 0.56); positive beliefs with 3.92 (SD 0.62); and well-being with 3.95 (SD 0.57). The results indicate that for all nursing students, meaning of life was positively correlated with positive beliefs, r=0.83 (P<.01); similarly, all nursing students had positive beliefs that were positively correlated with meaning of life, r=0.83 (P<.01). In the results of the study, the nursing students’ background, meaning of life and positive beliefs explained 63% of the variance in well-being (Adjusted R2 squared =0.63, F=33.41, P<.001). Conclusions: Nursing students’ sense of meaning of life and positive beliefs may impact their well-being. Therefore, nursing educators can promote meaning of life and positive beliefs to nursing students as a way to increase their well-being for physical, psychological, spiritual, and social health promotion.

scholarly journals Deregulation of lncRNA HIST1H2AG-6 and AIM1-3 in peripheral blood mononuclear cells is associated with newly diagnosed type 2 diabetes

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Hui Jiang ◽  
Peian Lou ◽  
Xiaoluo Chen ◽  
Chenguang Wu ◽  
Shihe Shao
Keyword(s):  
Roc Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Rank Correlation ◽  
Differentially Expressed ◽  
Healthy Controls ◽  
Biological Functions ◽  
Multivariate Logistic Regression ◽  
Spearman’S Rank Correlation ◽  
Spearman’S Rank Correlation Coefficient

Abstract Background Type 2 diabetes mellitus (T2DM) is mainly affected by genetic and environmental factors; however, the correlation of long noncoding RNAs (lncRNAs) with T2DM remains largely unknown. Methods Microarray analysis was performed to identify the differentially expressed lncRNAs and messenger RNAs (mRNAs) in patients with T2DM and healthy controls, and the expression of two candidate lncRNAs (lnc-HIST1H2AG-6 and lnc-AIM1-3) were further validated using quantitative real-time polymerase chain reaction (qRT-PCR). Spearman’s rank correlation coefficient was used to measure the degree of association between the two candidate lncRNAs and differentially expressed mRNAs. Furthermore, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and GO (Gene Ontology) enrichment analysis were used to reveal the biological functions of the two candidate lncRNAs. Additionally, multivariate logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed. Results The microarray analysis revealed that there were 55 lncRNAs and 36 mRNAs differentially expressed in patients with T2DM compared with healthy controls. Notably, lnc-HIST1H2AG-6 was significantly upregulated and lnc-AIM1-3 was significantly downregulated in patients with T2DM, which was validated in a large-scale qRT-PCR examination (90 controls and 100 patients with T2DM). Spearman’s rank correlation coefficient revealed that both lncRNAs were correlated with 36 differentially expressed mRNAs. Furthermore, functional enrichment (KEGG and GO) analysis demonstrated that the two lncRNA-related mRNAs might be involved in multiple biological functions, including cell programmed death, negative regulation of insulin receptor signal, and starch and sucrose metabolism. Multivariate logistic regression analysis revealed that lnc-HIST1H2AG-6 and lnc-AIM1-3 were significantly correlated with T2DM (OR = 5.791 and 0.071, respectively, both P = 0.000). Furthermore, the ROC curve showed that the expression of lnc-HIST1H2AG-6 and lnc-AIM1-3 might be used to differentiate patients with T2DM from healthy controls (area under the ROC curve = 0.664 and 0.769, respectively). Conclusion The profiles of lncRNA and mRNA were significantly changed in patients with T2DM. The expression levels of lnc-HIST1H2AG-6 and lnc-AIM1-3 genes were significantly correlated with some features of T2DM, which may be used to distinguish patients with T2DM from healthy controls and may serve as potential novel biomarkers for diagnosis in the future.

scholarly journals Magnetic resonance cisternography imaging findings related to the leakage of Gadolinium into the subarachnoid space

2021 ◽  
Author(s):  
Rei Nakamichi ◽  
Toshiaki Taoka ◽  
Hisashi Kawai ◽  
Tadao Yoshida ◽  
Michihiko Sone ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Subarachnoid Space ◽  
Area Under The Curve ◽  
Rank Correlation ◽  
Predictive Performance ◽  
Roc Curves ◽  
Imaging Findings ◽  
Spearman’S Rank Correlation ◽  
Sagittal Sinus ◽  
Magnetic Resonance Cisternography

Abstract Purpose To identify magnetic resonance cisternography (MRC) imaging findings related to Gadolinium-based contrast agent (GBCA) leakage into the subarachnoid space. Materials and methods The number of voxels of GBCA leakage (V-leak) on 3D-real inversion recovery images was measured in 56 patients scanned 4 h post-intravenous GBCA injection. Bridging veins (BVs) were identified on MRC. The numbers of BVs with surrounding cystic structures (BV-cyst), with arachnoid granulations protruding into the superior sagittal sinus (BV-AG-SSS) and the skull (BV-AG-skull), and including any of these factors (BV-incl) were recorded. Correlations between these variables and V-leak were examined based on the Spearman’s rank correlation coefficient. Receiver-operating characteristic (ROC) curves were generated to investigate the predictive performance of GBCA leakage. Results V-leak and the number of BV-incl were strongly correlated (r = 0.609, p < 0.0001). The numbers of BV-cyst and BV-AG-skull had weaker correlations with V-leak (r = 0.364, p = 0.006; r = 0.311, p = 0.020, respectively). The number of BV-AG-SSS was not correlated with V-leak. The ROC curve for contrast leakage exceeding 1000 voxels and the number of BV-incl had moderate accuracy, with an area under the curve of 0.871. Conclusion The number of BV-incl may be a predictor of GBCA leakage and a biomarker for waste drainage function without using GBCA.

scholarly journals Predicting presenteeism using measures of health status

2021 ◽  
Author(s):  
Cheryl Jones ◽  
Katherine Payne ◽  
Alexander Thompson ◽  
Suzanne M. M. Verstappen
Keyword(s):  
Health Status ◽  
Online Survey ◽  
Activity Index ◽  
Work Productivity ◽  
Rank Correlation ◽  
Ordinary Least Squares ◽  
Statistical Correlation ◽  
Least Squares Regression ◽  
Mapping Algorithm ◽  
Spearman’S Rank Correlation

Abstract Objectives To identify whether it is feasible to develop a mapping algorithm to predict presenteeism using multiattribute measures of health status. Methods Data were collected using a bespoke online survey in a purposive sample (n = 472) of working individuals with a self-reported diagnosis of Rheumatoid arthritis (RA). Survey respondents were recruited using an online panel company (ResearchNow). This study used data captured using two multiattribute measures of health status (EQ5D-5 level; SF6D) and a measure of presenteeism (WPAI, Work Productivity Activity Index). Statistical correlation between the WPAI and the two measures of health status (EQ5D-5 level; SF6D) was assessed using Spearman’s rank correlation. Five regression models were estimated to quantify the relationship between WPAI and predict presenteeism using health status. The models were specified based in index and domain scores and included covariates (age; gender). Estimated and observed presenteeism were compared using tenfold cross-validation and evaluated using Root mean square error (RMSE). Results A strong and negative correlation was found between WPAI and: EQ5D-5 level and WPAI (r = − 0.64); SF6D (r =− 0.60). Two models, using ordinary least squares regression were identified as the best performing models specifying health status using: SF6D domains with age interacted with gender (RMSE = 1.7858); EQ5D-5 Level domains and age interacted with gender (RMSE = 1.7859). Conclusions This study provides indicative evidence that two existing measures of health status (SF6D and EQ5D-5L) have a quantifiable relationship with a measure of presenteeism (WPAI) for an exemplar application of working individuals with RA. A future study should assess the external validity of the proposed mapping algorithms.

Multi-Method Vs Single Method Appraisal of Clinical Quality Indicators for the Emergency Medical Services

2020 ◽  
Author(s):  
Ian Howard ◽  
Peter Cameron ◽  
Maaret Castrén ◽  
Lee Wallis ◽  
Veronica Lindström
Keyword(s):  
Group Discussion ◽  
Rank Correlation ◽  
Healthcare Setting ◽  
Prehospital Emergency Care ◽  
Spearman’S Rank Correlation ◽  
Prehospital Emergency ◽  
Single Method ◽  
Multi Method Approach ◽  
The Individual ◽  
Method Approach

ABSTRACT Background Quality Indicator (QI) appraisal protocols are a novel methodology that combines multiple appraisal methods to comprehensively assess the "appropriateness" of QIs for a particular healthcare setting. However, they remain inadequately explored compared to the single appraisal method approach. This paper aimed to describe and test a QI appraisal protocol versus the single method approach, against a series of QIs potentially relevant to the South African Prehospital Emergency Care setting. Methods An appraisal protocol was developed consisting of two categorical-based appraisal methods, combined with the qualitative analysis of the discussion generated during the consensus application of each method. The output of the protocol was assessed and compared with the application and output of each method. Inter-rater reliability of each particular method was evaluated prior to group consensus rating. Variation in the number of non-valid QIs and the proportion of non-valid QIs identified between each method and the protocol were compared and assessed. Results There was mixed IRR of the individual methods. There was similarly low to moderate correlation of the results obtained between the particular methods (Spearman’s rank correlation=0.42,p&lt;0.001). From a series of 104 QIs, 11 non-valid QIs were identified that were shared between the individual methods. A further 19 non-valid QIs were identified and not shared by each method, highlighting the benefits of a multi-method approach. The outcomes were additionally evident in the group discussion analysis, which in and of itself added further input that would not have otherwise been captured by the individual methods alone. Conclusion The utilization of a multi-method appraisal protocol offers multiple benefits, when compared to the single appraisal approach, and can provide the confidence that the outcomes of the appraisal will ensure a strong foundation on which the QI framework can be successfully implemented.

scholarly journals Is HBA1C reliable in diagnosing diabetes in the acute phase of acute coronary syndromes?

2021 ◽  
Vol 10 (Supplement_1) ◽  
Author(s):  
M Stratinaki ◽  
K Milaki ◽  
S Stavrakis ◽  
M Pitarokoilis ◽  
E Charitakis ◽  
...  
Keyword(s):  
Acute Phase ◽  
Acute Coronary Syndromes ◽  
Rank Correlation ◽  
Mean Value ◽  
Gensini Score ◽  
Funding Sources ◽  
Spearman’S Rank Correlation ◽  
Coronary Syndromes ◽  
One Way Anova

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Hospitalization due to acute coronary syndromes (ACS) usually is the occasion that leads to diagnosis of type 2 diabetes mellitus (T2DM). Current literature suggests that the severity of the ACS could be associated with the presence and the severity of DM. Purpose To study the reliability of HbA1c in the diagnosis of T2DM in the acute phase of ACS, as well as the presence of possible correlation between the HbA1c and the severity of ACS. Methods We evaluated 160 consecutive patients admitted due to ACS. HbA1c was measured on day 1 and day 90. HbA1c &gt;6.5% was used to diagnose T2DM and HbA1c 5.7-6.4% was used to diagnose pre-diabetes. The severity of ACS was assessed via Gensini score. Results are interpreted as mean ± SD. Comparisons were made by one way ANOVA(p &lt; 0.05 was regarded statistically significant).Spearman’s rank correlation was used to study the correlation between Gensini score and the other parameters. Results Mean age was 59.73 ± 12.21 years. 103/160(64.37%) were male and 57/160(35.63%) were female. 19/160(11.87%) were diagnosed as STEMI and 141/160(88.13%) as NSTEMI. Mean BMI was 29.55 ± 8.41 kg/m2 and mean Hb 12.62 ± 2.08 g/dl. On day 1, 43/160 (26.87%) had HbA1c &gt; 6.5% and 41/160(25.62%) HbA1c 5.7-6.4%. On day 90, 28/160 (17.5%) had HbA1c &gt; 6.5% and 52/160(32.5%) HbA1c 5.7-6.4%. Gensini score varied between 0-144 with mean value 40.26 ±35.9. A strong correlation was found between Gensini score and HbA1c on admission as well as on day 90 (rho-0.36, p &lt; 0.05 and rho = 0.32, p &lt; 0.05 respectively). Conclusion HbA1c seems to be reliable in the identification of pre-diabetes but not T2DM in the acute phase of ACS. The correlation of the severity between ACS and HbA1c seems to relate with the worst prognosis of T2DM patients.

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