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Chen J, Yang J, Liu S, Zhou H, Yin X, Luo M, Wu Y, Chang J. Risk profiles for smoke behavior in COVID-19: a classification and regression tree analysis approach. BMC Public Health 2023; 23:2302. [PMID: 37990320 PMCID: PMC10664606 DOI: 10.1186/s12889-023-17224-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 11/14/2023] [Indexed: 11/23/2023] Open
Abstract
BACKGROUND COVID-19 pandemic emerged worldwide at the end of 2019, causing a severe global public health threat, and smoking is closely related to COVID-19. Previous studies have reported changes in smoking behavior and influencing factors during the COVID-19 period, but none of them explored the main influencing factor and high-risk populations for smoking behavior during this period. METHODS We conducted a nationwide survey and obtained 21,916 valid data. Logistic regression was used to examine the relationships between each potential influencing factor (sociodemographic characteristics, perceived social support, depression, anxiety, and self-efficacy) and smoking outcomes. Then, variables related to smoking behavior were included based on the results of the multiple logistic regression, and the classification and regression tree (CART) method was used to determine the high-risk population for increased smoking behavior during COVID-19 and the most profound influencing factors on smoking increase. Finally, we used accuracy to evaluated the performance of the tree. RESULTS The strongest predictor of smoking behavior during the COVID-19 period is acceptance degree of passive smoking. The subgroup with a high acceptation degree of passive smoking, have no smokers smoked around, and a length of smoking of ≥ 30 years is identified as the highest smoking risk (34%). The accuracy of classification and regression tree is 87%. CONCLUSION The main influencing factor is acceptance degree of passive smoking. More knowledge about the harm of secondhand smoke should be promoted. For high-risk population who smoke, the "mask protection" effect during the COVID-19 pandemic should be fully utilized to encourage smoking cessation.
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Affiliation(s)
- Jiangyun Chen
- School of Health Management, Southern Medical University, No.1023-1063 Shatai Road, Baiyun District, Guangzhou City, Guangdong Province, China
- Institute of Health Management, Southern Medical University, No.1023-1063 Shatai Road, Baiyun District, Guangzhou City, Guangdong Province, China
- Institute for Hospital Management of Henan Province, No. 1, Longhu Middle Ring Road, Jinshui District, Zhengzhou City, Henan Province, China
| | - Jiao Yang
- School of Public Health, Capital Medical University, 10 Xitoutiao, Youanmen, Beijing, China
| | - Siyuan Liu
- School of Public Health, Southern Medical University, No.1023-1063 Shatai Road, Baiyun District, Guangzhou City, Guangdong Province, China
| | - Haozheng Zhou
- School of Public Health, Southern Medical University, No.1023-1063 Shatai Road, Baiyun District, Guangzhou City, Guangdong Province, China
| | - Xuanhao Yin
- School of Public Health, Southern Medical University, No.1023-1063 Shatai Road, Baiyun District, Guangzhou City, Guangdong Province, China
| | - Menglin Luo
- School of Pharmaceutical, Southern Medical University, Guangzhou, China
| | - Yibo Wu
- School of Public Health, Peking University, No.38 Xueyuan Road, Haidian District, Beijing City, China.
| | - Jinghui Chang
- School of Health Management, Southern Medical University, No.1023-1063 Shatai Road, Baiyun District, Guangzhou City, Guangdong Province, China.
- Institute of Health Management, Southern Medical University, No.1023-1063 Shatai Road, Baiyun District, Guangzhou City, Guangdong Province, China.
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Johns H, Campbell B, Bernhardt J, Churilov L. Generalised pairwise comparisons for trend: An extension to the win ratio and win odds for dose-response and prognostic variable analysis with arbitrary statements of outcome preference. Stat Methods Med Res 2023; 32:609-625. [PMID: 36573043 DOI: 10.1177/09622802221146306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The win ratio is a novel approach for handling complex patient outcomes that have seen considerable interest in the medical statistics literature, and operates by considering all-to-all pairwise statements of preference on outcomes. Recent extensions to the method have focused on the two-group case, with few developments made for considering the impact of a well-ordered explanatory variable, which would allow for dose-response analysis or the analysis of links between complex patient outcomes and prognostic variables. Where such methods have been developed, they are semiparametric methods that can only be applied to survival outcomes. In this article, we introduce the generalised pairwise comparison for trend, a modified form of Agresti's generalised odds ratio. This approach is capable of considering arbitrary statements of preference, thus enabling its use across all types of outcome data. We provide a simulation study validating the approach and illustrate it with three clinical applications in stroke research.
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Affiliation(s)
- Hannah Johns
- Melbourne Medical School, University of Melbourne, Melbourne, Australia
| | - Bruce Campbell
- Department of Medicine and Neurology, Melbourne Brain Centre and Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia
| | - Julie Bernhardt
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Leonid Churilov
- Melbourne Medical School, University of Melbourne, Melbourne, Australia
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Gao X, Ma K, Yang H, Wang K, Fu B, Zhu Y, She X, Cui B. A rapid, non-invasive method for fatigue detection based on voice information. Front Cell Dev Biol 2022; 10:994001. [PMID: 36176279 PMCID: PMC9513181 DOI: 10.3389/fcell.2022.994001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 08/24/2022] [Indexed: 11/19/2022] Open
Abstract
Fatigue results from a series of physiological and psychological changes due to continuous energy consumption. It can affect the physiological states of operators, thereby reducing their labor capacity. Fatigue can also reduce efficiency and, in serious cases, cause severe accidents. In addition, it can trigger pathological-related changes. By establishing appropriate methods to closely monitor the fatigue status of personnel and relieve the fatigue on time, operation-related injuries can be reduced. Existing fatigue detection methods mostly include subjective methods, such as fatigue scales, or those involving the use of professional instruments, which are more demanding for operators and cannot detect fatigue levels in real time. Speech contains information that can be used as acoustic biomarkers to monitor physiological and psychological statuses. In this study, we constructed a fatigue model based on the method of sleep deprivation by collecting various physiological indexes, such as P300 and glucocorticoid level in saliva, as well as fatigue questionnaires filled by 15 participants under different fatigue procedures and graded the fatigue levels accordingly. We then extracted the speech features at different instances and constructed a model to match the speech features and the degree of fatigue using a machine learning algorithm. Thus, we established a method to rapidly judge the degree of fatigue based on speech. The accuracy of the judgment based on unitary voice could reach 94%, whereas that based on long speech could reach 81%. Our fatigue detection method based on acoustic information can easily and rapidly determine the fatigue levels of the participants. This method can operate in real time and is non-invasive and efficient. Moreover, it can be combined with the advantages of information technology and big data to expand its applicability.
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Affiliation(s)
| | | | | | | | | | | | | | - Bo Cui
- *Correspondence: Xiaojun She, ; Bo Cui,
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Using Data Mining Techniques for Detecting Dependencies in the Outcoming Data of a Web-Based System. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12126115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The increasing amount of data from web systems data is becoming one of the most valuable resources for information retrieval and knowledge discovery. The huge content of information makes it an important area for data mining research. To analyze the dependencies of the outcoming data, expressed as query scenarios, we present a new approach for evaluating the behavior of interactive web systems by applying different data mining techniques to solve the problem. We propose tools that take outcoming logs as input, analyze them, and provide information about web client actions. Qualitative and quantitative automatic evaluation of the data can explain the connections between the most significant parameters of the system in particular scenarios. In this paper, we propose a new method, which can be used to efficiently verify the type of client behavior of a web system or design of the system. The analysis of results demonstrates the possibility of efficient pattern search.
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Chen K, Wu W, Lan Y, Liu Z, Han F, Xu Y. Assessment and Prediction of Free Fatty Acids Changes in Maize Based on Effective Accumulated Temperature in Large Granaries. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2022. [DOI: 10.1080/10942912.2022.2074031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Kai Chen
- School of Biological and Agricultural Engineering, Jilin University, Changchun, China
| | - Wenfu Wu
- School of Biological and Agricultural Engineering, Jilin University, Changchun, China
- School of Grain Science and Technology, Jilin Business and Technology College, Changchun, China
| | - Yankun Lan
- Warehousing Department, China Grain Reserves Group LTD. Company Jilin Branch, Changchun, China
| | - Zhe Liu
- School of Biological and Agricultural Engineering, Jilin University, Changchun, China
| | - Feng Han
- School of Biological and Agricultural Engineering, Jilin University, Changchun, China
| | - Yan Xu
- School of Biological and Agricultural Engineering, Jilin University, Changchun, China
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Data Mining and Meta-Analysis of Psoriasis Based on Association Rules. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:9188553. [PMID: 35126954 PMCID: PMC8813247 DOI: 10.1155/2022/9188553] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 12/22/2021] [Indexed: 01/01/2023]
Abstract
Psoriasis is a common chronic and recurrent disease in dermatology, which has a great impact on the physical and mental health of patients. Meta-analysis can evaluate the effectiveness and safety of defubao in the treatment of psoriasis vulgaris. This article observes psoriasis skin lesions treated with topical defubao and the changes in blood vessels under dermoscopy. Considering that the Apriori algorithm and the existing improved algorithm have the problems of ignoring the weight and repeatedly scanning the database, this paper proposes a matrix association rule method based on random forest weighting. This method uses the random forest algorithm to assign weights to each item in the data set, and introduces matrix theory to convert the transaction data set into a matrix form and store it, thereby improving operating efficiency. This article included 11 studies, of which 7 studies used the indicator "Researcher's Overall Assessment" (IGA) to evaluate the efficacy, 5 studies used the "Patient Overall Assessment" (PGA) as the efficacy evaluation index, and Loss Area and Severity Index (PASI) was used as an observation index to evaluate the efficacy. Seven studies conducted safety comparisons. In this paper, IGA and PGA were used as evaluation indicators. The treatment effect of the defubao group was better than the calcipotriol group and the betamethasone group. The differences were statistically significant. The effect of the Fubao treatment for 8 weeks is significantly better than that of 4 weeks and 2 weeks, and the differences are statistically different. Using PASI as the evaluation index, a descriptive study was carried out, and it was found that after 4 weeks of treatment for psoriasis vulgaris, the average PASI reduction rate of patients was higher than that of the calcipotriol group and the betamethasone group. The safety evaluation found that after 8 weeks of treatment, the incidence of adverse events in the defubao group was significantly lower than that in the calcipotriol group.
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