scholarly journals Separating interviewer and area effects by using a cross-classified multilevel logistic model: simulation findings and implications for survey designs

2016 ◽  
Vol 180 (2) ◽  
pp. 531-550 ◽  
Author(s):  
Rebecca Vassallo ◽  
Gabriele Durrant ◽  
Peter Smith
Keyword(s):  
Logistic Model ◽  
Model Simulation ◽  
Area Effects ◽  
Multilevel Logistic Model ◽  
Survey Designs

A Goodness of Fit Test for the Multilevel Logistic Model

2015 ◽  
Vol 45 (2) ◽  
pp. 643-659 ◽  
Author(s):  
A. A. P. N. M. Perera ◽  
M. R. Sooriyarachchi ◽  
S. L. Wickramasuriya
Keyword(s):  
Logistic Model ◽  
Goodness Of Fit ◽  
Goodness Of Fit Test ◽  
Multilevel Logistic Model

Kinetic Modeling of 2-Keto-Gluconic Acid (2KGA) Production from Rice Starch Hydrolysate Using Pseudomonas fluorescens AR4

2012 ◽  
Vol 550-553 ◽  
pp. 1144-1150
Author(s):  
Wen Jing Sun ◽  
Lin Yu ◽  
Si Lian Yu ◽  
Feng Jie Cui ◽  
Yan Zheng Zhou ◽  
...  
Keyword(s):  
Experimental Data ◽  
Pseudomonas Fluorescens ◽  
Kinetic Modeling ◽  
Kinetic Models ◽  
Logistic Model ◽  
Model Simulation ◽  
Culture Broth ◽  
Rice Starch ◽  
Batch Production ◽  
Starch Hydrolysate

Kinetic models are proposed for the 2KGA batch production from rice starch hydrolysate containing 162 g/L of glucose by Pseudomonas fluorescens AR4. The models include terms accounting for both substrate and product inhibitions. Experimental data collected from the batch fermentations were used to estimate parameters and also to validate the models proposed. The growth of Ps. fluorescens could be expressed by a Logistic model wihout incorporating inhibitions of glucose and organic acids accumulated in the culture broth. The Luedeking–Piret model was able to describe the 2KGA formation as the fermentation proceeded with a mixed-growth-associated pattern. In all cases, the model simulation matched well with the experimental observations, which made it possible to elucidate the fermentation characteristics of Ps. fluorescens AR4 during efficient 2KGA production from glucose.

scholarly journals The association between loneliness during the COVID-19 pandemic and psychological distress

2021 ◽  
Author(s):  
Yusuke Konno ◽  
Masako Nagata ◽  
Ayako Hino ◽  
Seiichiro Tateishi ◽  
Mayumi Tsuji ◽  
...  
Keyword(s):  
Psychological Distress ◽  
Social Networking Sites ◽  
Educational Level ◽  
Logistic Model ◽  
Type Number ◽  
Cross Sectional ◽  
Family Time ◽  
Single Item ◽  
Online Study ◽  
Multilevel Logistic Model

The purpose of this study was to examine the association between loneliness and psychological distress during the COVID-19 pandemic in Japan. We conducted a cross-sectional, online study from 22 to 26 December 2020. A total of 27,036 participants, all employed at the time of the survey, were included in the analysis. Participants were asked if they felt loneliness in a single-item question. The Kessler 6 (K6) was used to assess psychological distress defined as K6 scores of 5 or higher, and 13 or higher. The odds ratios (ORs) of psychological distress associated with loneliness were estimated using a multilevel logistic model nested in the prefecture of residence, with adjustment for age, sex, marital status, equivalent income, educational level, smoking, alcohol consumption, job type, number of workplace employees, and cumulative incidence rate of COVID-19 in the prefecture. Communication with friends, acquaintances, and family was strongly associated with psychological distress, so we adjusted for these factors and eating meals alone. Results showed a significant association between loneliness and psychological distress (OR = 36.62, 95%CI = 32.95-40.69). Lack of friends to talk to, lack of acquaintances to ask for help, and lack of people to communicate with through social networking sites were all strongly associated with psychological distress, as were family time and solitary eating. Even after adjusting for these factors, loneliness was still strongly associated with psychological distress (OR = 29.36, 95%CI = 26.44-32.98). The association between loneliness during the COVID-19 pandemic and psychological distress indicates the need for intervention.

scholarly journals Impact of Government Interventions on Corona Virus Transmission in Nigeria

2020 ◽  
Vol 5 (7) ◽  
pp. 7-16
Author(s):  
Muhyideen Oloyede
Keyword(s):  
State Government ◽  
Logistic Model ◽  
Model Simulation ◽  
Virus Transmission ◽  
African Countries ◽  
Government Interventions ◽  
State Population ◽  
Intervention Measures ◽  
First Case ◽  
Before And After

Many measures such as multi-sectoral National Coronavirus Preparedness Group and social interventions were instituted by Nigerian governments before and after the first case of COVID-19 was recorded on February 27, 2020. Impacts of these intervention measures on COVID-19 transmission were assessed within the first 82 days in Nigeria and in the first 38 days in Kano state. These approaches toward containing the spread of COVID-19 include nonpharmaceutical intervention measures, multi-sectoral Emergency Operations Centre (EOC) was activated at Level 3, expansion of labouratories, RRT deployment etc which resulted in the slow transmission trajectory and low COVID-19 burden and death burden on the people. The analysis of the COVID-19 data obtained between February 27 and May 20, 2020 (82 days period) showed that Nigeria recorded lower death burden of one (1) per million population compare with other African countries like South Africa with death burden of 6 death per million population each and other countries, death burden increases (> 1) as COVID-19 cases increases. Similarly, COVID-19 burden of Nigeria is 33 cases per million which is also very low compared to other African countries and other highly affected countries like US and UK. By 20th May 2020, 6,677 confirmed cases and 202 deaths and 1,860 recovered from COVID-19 have occurred in Nigeria. At day 82, the exponential growth rate calculated was 0.05. The result of this study also provides epidemiological analysis of the first 40 days of COVID-19 outbreak in in the most populous state in Nigeria-Kano. Using this logistic model equation [y= 1.5*(1.3)x] generated from the curve, a total of 31,158 infections was averted in Kano state by the federal and state government intervention measures and citizen level of compliances as well as additional effort of WHO official which altogether brought the total infections to 900 as against 32,058 infections predicted by logistic model. Simulation of the same model predicts total infections of 6,092,642 by 9th of June more than half of state population in the absence of aforementioned interventions while by 11th of June 11,435,884 infections (state population). The results show COVID-19 positive cases are higher in the states with high population densities

scholarly journals Factors Associated with Neonatal Mortality in Bangladesh: A Multilevel Analysis

2021 ◽  
Vol 69 (1) ◽  
pp. 15-21
Author(s):  
Hemel Das ◽  
Nitai Chakraborty
Keyword(s):  
Neonatal Mortality ◽  
Neonatal Death ◽  
Logistic Model ◽  
Logistic Regression Model ◽  
Community Level ◽  
Bivariate Analysis ◽  
Factors Associated ◽  
Stratified Sample ◽  
Multilevel Logistic Regression Model ◽  
Multilevel Logistic Model

This study aims at disentangling the influence of both individual and community level factors on neonatal death in Bangladesh. For analysis purpose, data are extracted from Bangladesh Demographic and Health Survey 2014. Bivariate analysis is used to examine the differentials in neonatal mortality by selected background variables of both levels. As the survey is based on a two-stage stratified sample of households, multilevel logistic regression model is used to analyze the cluster effect and to determine the factors associated with neonatal mortality. Multilevel logistic model confirmed that there exists clustering impact on neonatal death. Moreover, the study has suggested implementation of intervention addressing several individual and community level factors to ameliorate the current neonatal health scenario in Bangladesh. Dhaka Univ. J. Sci. 69(1): 15-21, 2021 (January)

scholarly journals Association between work attendance when experiencing fever or cold symptoms and company characteristics and socioeconomic status in the COVID-19 pandemic in Japanese workers: a cross-sectional study

2021 ◽  
Author(s):  
Kazuyoshi Mizuki ◽  
Makoto Okawara ◽  
Ayako Hino ◽  
Hajime Ando ◽  
Tomohisa Nagata ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Logistic Model ◽  
Online Survey ◽  
Cross Sectional Study ◽  
Socioeconomic Background ◽  
Sectional Study ◽  
Odds Ratios ◽  
Cross Sectional ◽  
Japanese Workers ◽  
Multilevel Logistic Model

Abstract Objective: This study investigated the association between attending work while experiencing fever or cold symptoms and workers' socioeconomic background and company characteristics during the COVID-19 pandemic. Methods: A cross-sectional online survey was performed. Of a total of 33,302 participants, 3,676 workers who experienced fever or cold symptoms after April 2020 were included. The odds ratios (ORs) of attending work while sick associated with workers' socioeconomic background and company characteristics were evaluated using a multilevel logistic model. Results: The OR of attending work while sick associated with a lack of policy prohibiting workers from working when ill was 2.75 (95%CI: 2.28-3.20, P<0.001). Conclusion: This study suggests that clear company policies on work and illness can be effective for preventing employees from attending work while sick.

scholarly journals The Power of Crossing SIBTEST

2020 ◽  
Vol 44 (5) ◽  
pp. 393-408
Author(s):  
Zhushan Li
Keyword(s):  
Logistic Model ◽  
Model Simulation ◽  
Rejection Rate ◽  
Theoretical Formula ◽  
Test Statistic ◽  
Item Functioning ◽  
Alternative Hypotheses ◽  
Size Standard ◽  
Observed Rejection ◽  
3Pl Model

Crossing SIBTEST or CSIB is designed to detect crossing differential item functioning (DIF) as well as unidirectional DIF. A theoretical formula for the power of CSIB is derived based on the asymptotic distribution of the test statistic under the null and alternative hypotheses. The derived power formula provides insights on the factors that influence the CSIB power, including DIF effect size, standard error, and sample size. The power formula and those influencing factors are further discussed in the context of the item response theory (IRT) three parameter logistic model (3PL) model. Simulation results show the consistency between the theoretical power and the observed rejection rate. The power of CSIB is compared with the unidirectional SIBTEST in theory and through simulation.

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