Suchergebnisse

One of the key questions in the use of mixture models concerns the choice of the number of components most suitable for a given data set. In this paper we investigate answers to this problem in the context of likelihood-based clustering of the rows of a matrix of ordinal data modelled by the ordered stereotype model. Two methodologies for selecting the best model are demonstrated and compared. The first approach fits a separate model to the data for each possible number of clusters, and then uses an information criterion to select the best model. The second approach uses a Bayesian construction in which the parameters and the number of clusters are estimated simultaneously from their joint posterior distribution. Simulation studies are presented which include a variety of scenarios in order to test the reliability of both approaches. Finally, the results of the application of model selection to two real data sets are shown. ; The authors are sincerely grateful to Prof. Shirley Pledger for making many valuablesuggestions and stimulating discussions about this work. We are grateful to two referees and an asso-ciate editor for helpful comments and references. Daniel Fernández acknowledges Victoria University ofWellington for providing financial support through Victoria Doctoral Scholarship program. This researchwas supported by the Marsden Fund Council from Government funding, administered by the Royal Societyof New Zealand, Grant E2317-2922 ; Peer Reviewed ; Postprint (author's final draft)

Background: On January 21, 2020, the World Health Organization reported the first case of severe acute respiratory syndrome coronavirus 2, which rapidly evolved to the COVID-19 pandemic. Since then, the virus has also rapidly spread among Latin American, Caribbean, and African countries. Objective: The first aim of this study is to identify new emerging COVID-19 clusters over time and space (from January 21 to mid-May 2020) in Latin American, Caribbean, and African regions, using a prospective space–time scan measurement approach. The second aim is to assess the impact of real-time population mobility patterns between January 21 and May 18, 2020, under the implemented government interventions, measurements, and policy restrictions on COVID-19 spread among those regions and worldwide. Methods: We created a global COVID-19 database, of 218 countries and territories, merging the World Health Organization daily case reports with other measures such as population density and country income levels for January 21 to May 18, 2020. A score of government policy interventions was created for low, intermediate, high, and very high interventions. The population's mobility patterns at the country level were obtained from Google community mobility reports. The prospective space–time scan statistic method was applied in five time periods between January and May 2020, and a regression mixed model analysis was used. Results: We found that COVID-19 emerging clusters within these five periods of time increased from 7 emerging clusters to 28 by mid-May 2020. We also detected various increasing and decreasing relative risk estimates of COVID-19 spread among Latin American, Caribbean, and African countries within the period of analysis. Globally, population mobility to parks and similar leisure areas during at least a minimum of implemented intermediate-level control policies (when compared to low-level control policies) was related to accelerated COVID-19 spread. Results were almost consistent when regional stratified analysis was applied. In addition, worldwide population mobility due to working during high implemented control policies and very high implemented control policies, when compared to low-level control policies, was related to positive COVID-19 spread. Conclusions: The prospective space–time scan is an approach that low-income and middle-income countries could use to detect emerging clusters in a timely manner and implement specific control policies and interventions to slow down COVID-19 transmission. In addition, real-time population mobility obtained from crowdsourced digital data could be useful for current and future targeted public health and mitigation policies at a global and regional level. ; DF is a Serra Húnter Fellow and was supported by Marsden grant E2987-3648 administrated by the Royal Society of New Zealand, and by grant 2017 SGR 622 (GRBIO) administrated by the Departament d'Economia i Coneixement de la Generalitat de Catalunya (Spain) ; Peer Reviewed ; Postprint (published version)

Objectives: Our aim was to determine clusters of non-communicable diseases (NCDs) in a very large, population-based sample of middle-aged and older adults from low- and middle-income (LMICs) and high-income (HICs) regions. Additionally, we explored the associations with several covariates. Design: The total sample was 72¿140 people aged 50+ years from three population-based studies (English Longitudinal Study of Ageing, Survey of Health, Ageing and Retirement in Europe Study and Study on Global Ageing and Adult Health) included in the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project and representing eight regions with LMICs and HICs. Variables were previously harmonised using an ex-post strategy. Eight NCDs were used in latent class analysis. Multinomial models were made to calculate associations with covariates. All the analyses were stratified by age (50–64 and 65+ years¿old). Results Three clusters were identified: 'cardio-metabolic' (8.93% in participants aged 50–64 years and 27.22% in those aged 65+ years), 'respiratory-mental-articular' (3.91% and 5.27%) and 'healthy' (87.16% and 67.51%). In the younger group, Russia presented the highest prevalence of the 'cardio-metabolic' group (18.8%) and England the 'respiratory-mental-articular' (5.1%). In the older group, Russia had the highest proportion of both classes (48.3% and 9%). Both the younger and older African participants presented the highest proportion of the 'healthy' class. Older age, being woman, widowed and with low levels of education and income were related to an increased risk of multimorbidity. Physical activity was a protective factor in both age groups and smoking a risk factor for the 'respiratory-mental-articular'. Conclusion: Multimorbidity is common worldwide, especially in HICs and Russia. Health policies in each country addressing coordination and support are needed to face the complexity of a pattern of growing multimorbidity. ; This work was supported by the 5-year Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project and the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM). The ATHLOS project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 635 316. DF's work has been supported by grant RTI2018-100927-J-I00 Retos Investigación from Ministerio de Ciencia e Innovación (MCI), by Marsden grant E2987-3648 (Royal Society of New Zealand), and by grant 2017 SGR 622 (GRBIO) from the Departament d'Economia i Coneixement de la Generalitat de Catalunya (Spain). This work, grant number RTI2018-100927-J-I00, is supported by the Ministerio de Ciencia e Innovación (MCI, Spain), by the Agencia Estatal de Investigación (AEI, Spain) and by the European Regional Development Fund FEDER (FEDER, UE). BO's work is supported by the PERIS programme 2016–2020 'Ajuts per a la Incorporació de Científics i Tecnòlegs' (grant number SLT006/17/00066), with the support of the Health Department from the Generalitat de Catalunya. ; Peer Reviewed ; Postprint (published version)

On March 12th, 2020, the WHO declared COVID-19 as a pandemic. The collective impact of environ-mental and ecosystem factors, as well as biodiversity, on the spread of COVID-19 and its mortalityevolution remain empirically unknown, particularly in regions with a wide ecosystem range. The aim ofour study is to assess how those factors impact on the COVID-19 spread and mortality by country. Thisstudy compiled a global database merging WHO daily case reports with other publicly available measuresfrom January 21st to May 18th, 2020. We applied spatio-temporal models to identify the influence ofbiodiversity, temperature, and precipitation andfitted generalized linear mixed models to identify theeffects of environmental variables. Additionally, we used count time series to characterize the associationbetween COVID-19 spread and air quality factors. All analyses were adjusted by social demographic,country-income level, and government policy intervention confounders, among 160 countries, globally.Our results reveal a statistically meaningful association between COVID-19 infection and several factorsof interest at country and city levels such as the national biodiversity index, air quality, and pollutantselements (PM10,PM2.5,and O3). Particularly, there is a significant relationship of loss of biodiversity, highlevel of air pollutants, and diminished air quality with COVID-19 infection spread and mortality. Ourfindings provide an empirical foundation for future studies on the relationship between air qualityvariables, a country's biodiversity, and COVID-19 transmission and mortality. The relationships measuredin this study can be valuable when governments plan environmental and health policies, as alternativestrategy to respond to new COVID-19 outbreaks and prevent future crises ; Daniel Fernández supported by grant 2017 SGR 622 (GRBIO) administrated by the Departament d'Economia i Coneixement de la Generalitat de Catalunya (Spain) ; Peer Reviewed ; Postprint (author's final draft)

(1) Cognitive decline differs among individuals and cognition function domains. We sought to identify distinct groups of immediate and delayed verbal memory in two age subsamples(50–64, 65+years), and to analyze associated factors. (2) Latent class mixed models were used to identify verbal memory trajectories in a sample of Spanish community-dwelling individuals over 8 years' follow up. Chi-square and Kruskal–Wallis tests were used to assess differences among trajectories. (3) Different trajectories were identified. In the case of immediate verbal memory, these were: very low/decline (6.3%), low/stable (38.2%), medium/slow decline (43.4%), and high/slowdecline (12.2%) in the middle-aged group, and low/decline (20.4%), medium/slow decline (60.4%),and high/slow decline (19.2%) in the older subsample. In delayed verbal memory, more distinct patterns were found: very low/decline (12.4%), low/stable (51.4%), medium/accelerated decline (24.7%),and high/slow increase (11.4%) in the younger group, and low/slow decline (34.4%), medium/decline(52.7%), and high/slow decline (12.9%) in the older group. (4) Overall, low initial performance and decline were associated with older age, lower education, and higher diabetes/stroke prevalence.Differences found suggests heterogeneity in cognitive ageing. The high prevalence of cardiovascular diseases in those with worse cognition suggests that early interventions to prevent those conditions should be targeted in midlife to delay cognitive decline. ; This work was supported by the European Community's Seventh Framework Programme [grant number223071-COURAGE Study]; the Instituto de Salud Carlos III-FIS [grant numbers PS09/00295, PS09/01845, PI12/01490,and PI13/00059. Projects PI12/01490 and PI13/00059 have been co-funded by the European Union EuropeanRegional Development Fund (ERDF) "A Way to Build Europe"]; the European Union's Horizon 2020 Research andInnovation Programme [grant agreement 635316]; and the Centro de Investigación Biomédica en Red de SaludMental (CIBERSAM), Instituto de Salud Carlos III. NMM is supported by the programme 'Contratos predoctoralespara Formación de Personal Investigador, FPI-UAM', Universidad Autónoma de Madrid, Spain. EL's work issupported by the Sara Borrell postdoctoral program (CD18/00099) of the Instituto de Salud Carlos III (Spain)and co-funded by the European Union (ERDF/ESF, "Investing in your future"). DM's work is supported by theSpanish Ministry of Education, Culture, and Sports [grant number FPU15/02634]. DF's work has been supportedby grant RTI2018-100927-J-I00 administrated by Ministerio de Ciencia e Innovación (MCI, Spain), by the AgenciaEstatal de Investigación (AEI, Spain), and by the European Regional Development Fund (FEDER, UE), by Marsdengrant E2987-3648 administrated by the Royal Society of New Zealand), and by grant 2017 SGR 622 (GRBIO)administrated by the Departament d'Economia i Coneixement de la Generalitat de Catalunya (Spa ; Peer Reviewed ; Postprint (published version)