: Multilevel Modelling

2008 ◽  
pp. 337-368
Keyword(s):  
Multilevel Modelling

scholarly journals The impact of non-pharmaceutical interventions on COVID-19 epidemic growth in the 37 OECD member states

2021 ◽  
Author(s):  
Francisco Pozo-Martin ◽  
Heide Weishaar ◽  
Florin Cristea ◽  
Johanna Hanefeld ◽  
Thurid Bahr ◽  
...  
Keyword(s):  
Growth Rate ◽  
Early Phase ◽  
Policy Decision ◽  
Multilevel Modelling ◽  
Daily Growth ◽  
Public Places ◽  
Member States ◽  
Unit Increase ◽  
Policy Decision Making ◽  
The Impact

AbstractWe estimated the impact of a comprehensive set of non-pharmeceutical interventions on the COVID-19 epidemic growth rate across the 37 member states of the Organisation for Economic Co-operation and Development during the early phase of the COVID-19 pandemic and between October and December 2020. For this task, we conducted a data-driven, longitudinal analysis using a multilevel modelling approach with both maximum likelihood and Bayesian estimation. We found that during the early phase of the epidemic: implementing restrictions on gatherings of more than 100 people, between 11 and 100 people, and 10 people or less was associated with a respective average reduction of 2.58%, 2.78% and 2.81% in the daily growth rate in weekly confirmed cases; requiring closing for some sectors or for all but essential workplaces with an average reduction of 1.51% and 1.78%; requiring closing of some school levels or all school levels with an average reduction of 1.12% or 1.65%; recommending mask wearing with an average reduction of 0.45%, requiring mask wearing country-wide in specific public spaces or in specific geographical areas within the country with an average reduction of 0.44%, requiring mask-wearing country-wide in all public places or all public places where social distancing is not possible with an average reduction of 0.96%; and number of tests per thousand population with an average reduction of 0.02% per unit increase. Between October and December 2020 work closing requirements and testing policy were significant predictors of the epidemic growth rate. These findings provide evidence to support policy decision-making regarding which NPIs to implement to control the spread of the COVID-19 pandemic.

scholarly journals Multilevel modelling and multiple group analysis of disparities in continuity of care and viral suppression among adolescents and youths living with HIV in Nigeria

2020 ◽  
Vol 5 (11) ◽  
pp. e003269
Author(s):  
Okikiolu Badejo ◽  
Christiana Noestlinger ◽  
Toyin Jolayemi ◽  
Juliette Adeola ◽  
Prosper Okonkwo ◽  
...  
Keyword(s):  
Young Adults ◽  
Viral Suppression ◽  
Group Analysis ◽  
Age Groups ◽  
Multilevel Modelling ◽  
Retention In Care ◽  
Healthcare Facilities ◽  
Individual Level ◽  
Multiple Group ◽  
Facility Level

IntroductionSubstantial disparities in care outcomes exist between different subgroups of adolescents and youths living with HIV (AYLHIV). Understanding variation in individual and health facility characteristics could be key to identifying targets for interventions to reduce these disparities. We modelled variation in AYLHIV retention in care and viral suppression, and quantified the extent to which individual and facility characteristics account for observed variations.MethodsWe included 1170 young adolescents (10–14 years), 3206 older adolescents (15–19 years) and 9151 young adults (20–24 years) who were initiated on antiretroviral therapy (ART) between January 2015 and December 2017 across 124 healthcare facilities in Nigeria. For each age group, we used multilevel modelling to partition observed variation of main outcomes (retention in care and viral suppression at 12 months after ART initiation) by individual (level one) and health facility (level two) characteristics. We used multiple group analysis to compare the effects of individual and facility characteristics across age groups.ResultsFacility characteristics explained most of the observed variance in retention in care in all the age groups, with smaller contributions from individual-level characteristics (14%–22.22% vs 0%–3.84%). For viral suppression, facility characteristics accounted for a higher proportion of variance in young adolescents (15.79%), but not in older adolescents (0%) and young adults (3.45%). Males were more likely to not be retained in care (adjusted OR (aOR)=1.28; p<0.001 young adults) and less likely to achieve viral suppression (aOR=0.69; p<0.05 older adolescent). Increasing facility-level viral load testing reduced the likelihood of non-retention in care, while baseline regimen TDF/3TC/EFV or NVP increased the likelihood of viral suppression.ConclusionsDifferences in characteristics of healthcare facilities accounted for observed disparities in retention in care and, to a lesser extent, disparities in viral suppression. An optimal combination of individual and health services approaches is, therefore, necessary to reduce disparities in the health and well-being of AYLHIV.

scholarly journals Sampling weights in multilevel modelling: an investigation using PISA sampling structures

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Julia Mang ◽  
Helmut Küchenhoff ◽  
Sabine Meinck ◽  
Manfred Prenzel
Keyword(s):  
Hierarchical Models ◽  
Large Scale ◽  
Practical Importance ◽  
Multilevel Modelling ◽  
Likelihood Method ◽  
Multilevel Regression ◽  
Sampling Weights ◽  
International Student Assessment ◽  
Software Packages ◽  
Software Programs

Abstract Background Standard methods for analysing data from large-scale assessments (LSA) cannot merely be adopted if hierarchical (or multilevel) regression modelling should be applied. Currently various approaches exist; they all follow generally a design-based model of estimation using the pseudo maximum likelihood method and adjusted weights for the corresponding hierarchies. Specifically, several different approaches to using and scaling sampling weights in hierarchical models are promoted, yet no study has compared them to provide evidence of which method performs best and therefore should be preferred. Furthermore, different software programs implement different estimation algorithms, leading to different results. Objective and method In this study, we determine based on a simulation, the estimation procedure showing the smallest distortion to the actual population features. We consider different estimation, optimization and acceleration methods, and different approaches on using sampling weights. Three scenarios have been simulated using the statistical program R. The analyses have been performed with two software packages for hierarchical modelling of LSA data, namely Mplus and SAS. Results and conclusions The simulation results revealed three weighting approaches performing best in retrieving the true population parameters. One of them implies using only level two weights (here: final school weights) and is because of its simple implementation the most favourable one. This finding should provide a clear recommendation to researchers for using weights in multilevel modelling (MLM) when analysing LSA data, or data with a similar structure. Further, we found only little differences in the performance and default settings of the software programs used, with the software package Mplus providing slightly more precise estimates. Different algorithm starting settings or different accelerating methods for optimization could cause these distinctions. However, it should be emphasized that with the recommended weighting approach, both software packages perform equally well. Finally, two scaling techniques for student weights have been investigated. They provide both nearly identical results. We use data from the Programme for International Student Assessment (PISA) 2015 to illustrate the practical importance and relevance of weighting in analysing large-scale assessment data with hierarchical models.

scholarly journals Patient-level and hospital-level variation and related time trends in COVID-19 case fatality rates during the first pandemic wave in England: multilevel modelling analysis of routine data

2021 ◽  
pp. bmjqs-2021-012990
Author(s):  
Alex Bottle ◽  
Puji Faitna ◽  
Paul P Aylin
Keyword(s):  
Early Period ◽  
Total Mortality ◽  
Case Fatality ◽  
Routine Data ◽  
Multilevel Modelling ◽  
Primary Diagnosis ◽  
Hospital Death ◽  
Late Period ◽  
Hospital Level ◽  
Patient Level

BackgroundA report suggesting large between-hospital variations in mortality after admission for COVID-19 in England attracted much media attention but used crude rates. We aimed to quantify these variations between hospitals and over time during England’s first wave (March to July 2020) and assess available patient-level and hospital-level predictors to explain those variations.MethodsWe used administrative data for England, augmented by hospital-level information. Admissions were extracted with COVID-19 codes. In-hospital death was the primary outcome. Risk-adjusted mortality ratios (standardised mortality ratios) and interhospital variation were calculated using multilevel logistic regression. Early-wave (March to April) and late-wave (May to July) periods were compared.Results74 781 admissions had a primary diagnosis of COVID-19, with 21 984 in-hospital deaths (29.4%); the 30-day total mortality rate was 28.8%. The crude in-hospital death rate fell in all ages and overall from 32.9% in March to 13.4% in July. Patient-level predictors included age, male gender, non-white ethnic group (early period only) and several comorbidities (obesity early period only). The only significant hospital-level predictor was daily COVID-19 admissions in the late period; we did not find a relation with staff absences for COVID-19, mechanical ventilation bed occupancies, total bed occupancies or bed occupancies for COVID-19 admissions in either period. Just 4 (3%) and 2 (2%) hospitals were high, and 5 (4%) and 0 hospitals were low funnel plot mortality outliers at 3 SD for early and late periods, respectively, after risk adjustment. We found no strong correlation between early and late hospital-level mortality (r=0.17, p=0.06).ConclusionsThere was modest variation in mortality following admission for COVID-19 between English hospitals after adjustment for risk and random variation, in marked contrast to early media reports. Early-period mortality did not predict late-period mortality.

O.014 Multilevel modelling of intracranial pressure after endoscopic third ventriculostomy

2008 ◽  
Vol 110 ◽  
pp. S4
Author(s):  
D. Santamarta ◽  
J. Martín-Vallejo ◽  
J. Fernández ◽  
J. Ibáñez ◽  
J. García-Cosamalón
Keyword(s):  
Intracranial Pressure ◽  
Endoscopic Third Ventriculostomy ◽  
Multilevel Modelling ◽  
Third Ventriculostomy

Allometric Multilevel Modelling of Agility and Dribbling Speed by Skeletal Age and Playing Position in Youth Soccer Players

2014 ◽  
Vol 35 (09) ◽  
pp. 762-771 ◽  
Author(s):  
J. Valente-dos-Santos ◽  
M. Coelho-e-Silva ◽  
J. Duarte ◽  
J. Pereira ◽  
R. Rebelo-Gonçalves ◽  
...  
Keyword(s):  
Multilevel Modelling ◽  
Skeletal Age ◽  
Soccer Players ◽  
Youth Soccer

scholarly journals The demand of part-time in European companies: a multilevel modelling approach

2012 ◽  
Vol 44 (8) ◽  
pp. 1057-1066 ◽  
Author(s):  
Dominique Anxo ◽  
Shakir Hussain ◽  
Ghazi Shukur
Keyword(s):  
Multilevel Modelling ◽  
Part Time ◽  
Modelling Approach
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