scholarly journals Basic Reproduction Number of the 2019 Novel Coronavirus Disease in the Major Endemic Areas of China: A Latent Profile Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Honglv Xu ◽  
Yi Zhang ◽  
Min Yuan ◽  
Liya Ma ◽  
Meng Liu ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Reproduction Number ◽  
Profile Analysis ◽  
Hunan Province ◽  
Initial Value ◽  
Endemic Areas ◽  
Novel Coronavirus ◽  
Latent Profile

Objective: The aim of this study is to analyze the latent class of basic reproduction number (R0) trends of the 2019 novel coronavirus disease (COVID-19) in the major endemic areas of China.Methods: The provinces that reported more than 500 cases of COVID-19 till February 18, 2020 were selected as the major endemic areas. The Verhulst model was used to fit the growth rate of cumulative confirmed cases. The R0 of COVID-19 was calculated using the parameters of severe acute respiratory syndrome (SARS) and COVID-19. The latent class of R0 was analyzed using the latent profile analysis (LPA) model.Results: The median R0 calculated from the SARS and COVID-19 parameters were 1.84–3.18 and 1.74–2.91, respectively. The R0 calculated from the SARS parameters was greater than that calculated from the COVID-19 parameters (Z = −4.782 to −4.623, p < 0.01). Both R0 can be divided into three latent classes. The initial value of R0 in class 1 (Shandong Province, Sichuan Province, and Chongqing Municipality) was relatively low and decreased slowly. The initial value of R0 in class 2 (Anhui Province, Hunan Province, Jiangxi Province, Henan Province, Zhejiang Province, Guangdong Province, and Jiangsu Province) was relatively high and decreased rapidly. Moreover, the initial R0 value of class 3 (Hubei Province) was in the range between that of classes 1 and 2, but the higher R0 level lasted longer and decreased slowly.Conclusion: The results indicated that the overall R0 trend is decreased with the strengthening of comprehensive prevention and control measures of China for COVID-19, however, there are regional differences.

scholarly journals Basic reproduction number of 2019 Novel Coronavirus Disease in Major Endemic Areas of China: A latent profile analysis

2020 ◽  
Author(s):  
Honglv Xu ◽  
Min Yuan ◽  
Liya Ma ◽  
Meng Liu ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Reproduction Number ◽  
Profile Analysis ◽  
Initial Value ◽  
Class 1 ◽  
Endemic Areas ◽  
Novel Coronavirus ◽  
Latent Profile

AbstractObjectiveThe aim of the study is to analyze the latent class of basic reproduction number (R0) trend of 2019 novel coronavirus disease (COVID-19) in major endemic areas of China.MethodsThe provinces that reported more than 500 cases of COVID-19 till February 18, 2020 were selected as the major endemic area. The Verhulst model was used to fit the growth rate of cumulative confirmed cases. The R0 of COVID-19 was calculated using the parameters of severe acute respiratory syndrome (SARS) and COVID-19, respectively. The latent class of R0 was analyzed using a latent profile analysis model.ResultsThe median R0 calculated from SARS and COVID-19 parameters were 1.84 - 3.18 and 1.74 - 2.91, respectively. The R0 calculated from the SARS parameters was greater than that of calculated from the COVID-19 parameters (Z = −4.782 - −4.623, P < 0.01). Both R0 can be divided into three latent classes. The initial value of R0 in class 1 (Shandong Province, Sichuan Province and Chongqing Municipality) was relatively low and decreases slowly. The initial value of R0 in class 2 (Anhui Province, Hunan Province, Jiangxi Province, Henan Province, Zhejiang Province, Guangdong Province and Jiangsu Province) was relatively high and decreases rapidly. Moreover, the initial value of R0 of class 3 (Hubei Province) was between that of class 1 and class 2, but the higher level of R0 lasts longer and decreases slowly.ConclusionThe results indicated that overall trend of R0 has been falling with the strengthening of China’s comprehensive prevention and control measures for COVID-19, however, presents regional differences.

scholarly journals Phenotyping Chronic Pelvic Pain Based on Latent Class Modeling of Physical Examination

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
B. W. Fenton ◽  
S. F. Grey ◽  
M. Reichenbach ◽  
M. McCarroll ◽  
V. Von Gruenigen
Keyword(s):  
Physical Examination ◽  
Pelvic Pain ◽  
Chronic Pelvic Pain ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Profile Analysis ◽  
Wall Region ◽  
Uterosacral Ligaments ◽  
Pain Pressure Threshold ◽  
Latent Profile

Introduction. Defining clinical phenotypes based on physical examination is required for clarifying heterogeneous disorders such as chronic pelvic pain (CPP). The objective of this study was to determine the number of classes within 4 examinable regions and then establish threshold and optimal exam criteria for the classes discovered. Methods. A total of 476 patients meeting the criteria for CPP were examined using pain pressure threshold (PPT) algometry and standardized numeric scale (NRS) pain ratings at 30 distinct sites over 4 pelvic regions. Exploratory factor analysis, latent profile analysis, and ROC curves were then used to identify classes, optimal examination points, and threshold scores. Results. Latent profile analysis produced two classes for each region: high and low pain groups. The optimal examination sites (and high pain minimum thresholds) were for the abdominal wall region: the pair at the midabdomen (PPT threshold depression of > 2); vulvar vestibule region: 10:00 position (NRS > 2); pelvic floor region: puborectalis (combined NRS > 6); vaginal apex region: uterosacral ligaments (combined NRS > 8). Conclusion. Physical examination scores of patients with CPP are best categorized into two classes: high pain and low pain. Standardization of the physical examination in CPP provides both researchers and general gynecologists with a validated technique.

scholarly journals Latent Profile Analyses of Depressive Symptoms in Younger and Older Oncology Patients

Assessment ◽  
2018 ◽  
Vol 27 (7) ◽  
pp. 1383-1398 ◽  
Author(s):  
Rebecca M. Saracino ◽  
Heining Cham ◽  
Barry Rosenfeld ◽  
Christian J. Nelson
Keyword(s):  
Depressive Symptoms ◽  
Older Patients ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Profile Analysis ◽  
Depression Scale ◽  
Epidemiological Studies ◽  
Class Differences ◽  
Patients With Cancer ◽  
Latent Profile

The aging of America will include a significant increase in the number of older patients with cancer, many of whom will experience significant depressive symptoms. Although geriatric depression is a well-studied construct, its symptom presentation in the context of cancer is less clear. Latent profile analysis was conducted on depressive symptoms in younger (40-64 years) and older (≥65 years) patients with cancer ( N = 636). The sample was clinically heterogeneous (i.e., included all stages, dominated by advanced stage disease). Participants completed questionnaires including the Center for Epidemiological Studies Depression Scale, which was used for the latent profile analysis. A four-class pattern was supported for each age group. However, the four-class pattern was significantly different between the younger and older groups in terms of the item means within each corresponding latent class; differences were primarily driven by severity such that across classes, older adults endorsed milder symptoms. An unexpected measurement issue was uncovered regarding reverse-coded items, suggesting that they may generate unreliable scores on the Center for Epidemiological Studies Depression Scale for a significant subset of patients. The results indicate that cancer clinicians can expect to see depressive symptoms along a continuum of severity for patients of any age, with less severe symptoms among older patients.

scholarly journals Heterogeneity of social anxiety in treatment-seeking adults who stutter: A latent profile analysis

2021 ◽  
Author(s):  
Tomosumi Haitani ◽  
Naomi Sakai ◽  
Koichi Mori ◽  
Tomohito Houjou
Keyword(s):  
Social Anxiety ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Profile Analysis ◽  
Demographic Data ◽  
Data Communication ◽  
Anxiety Scale ◽  
Situational Factors ◽  
Adults Who Stutter ◽  
Latent Profile

Purpose: Adults who stutter (AWS) often experience social anxiety. Social anxiety is explained by several situational factors, one of which is a factor for telephone, which is unique to AWS. This unique social anxiety, which has not been observed in individuals with social anxiety disorder (SAD), may lead to heterogeneity or distinct subtypes of AWS. The present study aimed to investigate the heterogeneity of social anxiety in AWS in terms of feared social situations.Methods: Social anxiety was measured using the fear/anxiety scale of the Liebowitz Social Anxiety Scale (LSAS). The scores of the five subscales in the LSAS in 562 AWS were analyzed using latent profile analysis. First, the number of latent classes (subtypes) was determined through statistical criteria and interpretability. Next, the profiles of social anxiety, demographic data, communication attitudes, and the overall severity of social anxiety of the subtypes were investigated.Results: Five latent class solutions led to good classifications. About one-quarter of AWS (156) were included in a subtype with sub-clinical levels of overall severity of social anxiety but severe social anxiety in telephone situations. Among them, 100 AWS showed severe social anxiety only in telephone situations. Psychosocial factors, including employment status and communication attitude, were related to extracted subtypes.Conclusions: Some AWS have severe social anxiety specific to telephone situations, which is not proportional to the overall severity of social anxiety. The telephone-specific subtype of social anxiety has not been empirically extracted in principal diagnosis of SAD and can be unique in AWS.

scholarly journals Sources of COVID-19-Related Information in People with Various Levels of Risk Perception and Preventive Behaviors in Taiwan: A Latent Profile Analysis

2021 ◽  
Vol 18 (4) ◽  
pp. 2091
Author(s):  
Peng-Wei Wang ◽  
Yi-Lung Chen ◽  
Yu-Ping Chang ◽  
Chia-Fen Wu ◽  
Wei-Hsin Lu ◽  
...  
Keyword(s):  
Risk Perception ◽  
Online Survey ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Information Sources ◽  
Profile Analysis ◽  
Multinomial Logistic Regression ◽  
Preventive Behaviors ◽  
Related Information ◽  
Latent Profile

The present study aimed to identify the distinct levels of risk perception and preventive behaviors during the coronavirus disease 2019 (COVID-19) outbreak among people in Taiwan and to examine the roles of information sources in various levels of risk perception and preventive behavior. The online survey recruited 1984 participants through a Facebook advertisement. Their self-reported risk perception, adopted preventive behaviors and COVID-19-related information were collected. We analyzed individuals’ risk perception and adopted preventive behaviors by using latent profile analysis and conducted multinomial logistic regression of latent class membership on COVID-19-related information sources. Four latent classes were identified, including the risk neutrals with high preventive behaviors, the risk exaggerators with high preventive behaviors, the risk deniers with moderate preventive behaviors, and the risk deniers with low preventive behaviors. Compared with the risk neutrals, the risk exaggerators with high preventive behaviors were more likely to obtain COVID-19 information from multiple sources, whereas the risk deniers with moderate preventive behaviors and risk deniers with low preventive behaviors were less likely to obtain COVID-19 information compared with the risk neutrals. Governments and health professions should take the variety of risk perception and adopted preventive behaviors into consideration when disseminating information on COVID-19 to the general public.

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