scholarly journals Evolution of the Initially Recruited SHARE Panel Sample Over the First Six Waves

2020 ◽  
Vol 36 (3) ◽  
pp. 507-527
Author(s):  
Sabine Friedel ◽  
Tim Birkenbach
Keyword(s):  
Retention Rates ◽  
Panel Survey ◽  
Face To Face ◽  
National Surveys ◽  
Sample Composition ◽  
Older Populations ◽  
The Impact ◽  
Second Wave ◽  
Large Decline ◽  
Over Time

AbstractAttrition is a frequently observed phenomenon in panel studies. The loss of panel members over time can hamper the analysis of panel survey data. Based on data from the Survey of Health, Ageing and Retirement in Europe (SHARE), this study investigates changes in the composition of the initially recruited first-wave sample in a multi-national face-to-face panel survey of an older population over waves. By inspecting retention rates and R-indicators, we found that, despite declining retention rates, the composition of the initially recruited panel sample in Wave 1 remained stable after the second wave. Thus, after the second wave there is no further large decline in representativeness with regard to the first wave sample. Changes in the composition of the sample after the second wave over time were due mainly to mortality-related attrition. Non-mortality-related attrition had a slight effect on the changes in sample composition with regard to birth in survey country, area of residence, education, and social activities. Our study encourages researchers to investigate further the impact of mortality- and non-mortality-related attrition in multi-national surveys of older populations.

The Influence of Inhomogeneous Input Data from Different Waves on Predictive Model Development for COVID-19 ICU Patients (Preprint)

2021 ◽  
Author(s):  
Sebastian Johannes Fritsch ◽  
Konstantin Sharafutdinov ◽  
Moein Einollahzadeh Samadi ◽  
Gernot Marx ◽  
Andreas Schuppert ◽  
...  
Keyword(s):  
Machine Learning ◽  
Convex Hull ◽  
Prediction Models ◽  
Model Development ◽  
Predictive Performance ◽  
Support Vector ◽  
Good Prediction ◽  
The Impact ◽  
Second Wave ◽  
Over Time

BACKGROUND During the course of the COVID-19 pandemic, a variety of machine learning models were developed to predict different aspects of the disease, such as long-term causes, organ dysfunction or ICU mortality. The number of training datasets used has increased significantly over time. However, these data now come from different waves of the pandemic, not always addressing the same therapeutic approaches over time as well as changing outcomes between two waves. The impact of these changes on model development has not yet been studied. OBJECTIVE The aim of the investigation was to examine the predictive performance of several models trained with data from one wave predicting the second wave´s data and the impact of a pooling of these data sets. Finally, a method for comparison of different datasets for heterogeneity is introduced. METHODS We used two datasets from wave one and two to develop several predictive models for mortality of the patients. Four classification algorithms were used: logistic regression (LR), support vector machine (SVM), random forest classifier (RF) and AdaBoost classifier (ADA). We also performed a mutual prediction on the data of that wave which was not used for training. Then, we compared the performance of models when a pooled dataset from two waves was used. The populations from the different waves were checked for heterogeneity using a convex hull analysis. RESULTS 63 patients from wave one (03-06/2020) and 54 from wave two (08/2020-01/2021) were evaluated. For both waves separately, we found models reaching sufficient accuracies up to 0.79 AUROC (95%-CI 0.76-0.81) for SVM on the first wave and up 0.88 AUROC (95%-CI 0.86-0.89) for RF on the second wave. After the pooling of the data, the AUROC decreased relevantly. In the mutual prediction, models trained on second wave´s data showed, when applied on first wave´s data, a good prediction for non-survivors but an insufficient classification for survivors. The opposite situation (training: first wave, test: second wave) revealed the inverse behaviour with models correctly classifying survivors and incorrectly predicting non-survivors. The convex hull analysis for the first and second wave populations showed a more inhomogeneous distribution of underlying data when compared to randomly selected sets of patients of the same size. CONCLUSIONS Our work demonstrates that a larger dataset is not a universal solution to all machine learning problems in clinical settings. Rather, it shows that inhomogeneous data used to develop models can lead to serious problems. With the convex hull analysis, we offer a solution for this problem. The outcome of such an analysis can raise concerns if the pooling of different datasets would cause inhomogeneous patterns preventing a better predictive performance.

scholarly journals Parental presence within households and the impact of antiretroviral therapy in Khayelitsha, Cape Town

2013 ◽  
Vol 14 (2) ◽  
pp. 70-74
Author(s):  
Catherine Jury ◽  
Nicoli Nattrass
Keyword(s):  
Antiretroviral Therapy ◽  
General Population ◽  
Panel Survey ◽  
Burden Of Care ◽  
Parental Presence ◽  
Gender And Education ◽  
Single Person ◽  
Effective Care ◽  
The Impact ◽  
Over Time

Background. While household support is an important component of effective care and treatment in HIV/AIDS, there are few insights from Southern Africa into how household support arrangements change over time for patients starting antiretroviral therapy (ART).Objective. We hypothesised that patients initiating ART are more likely to be living with family, especially their mothers, compared with the general population, but that over time these differences disappear.Methods. A panel survey of ART patients was matched by age, gender and education to a comparison sample drawn from adults in Khayelitsha, Cape Town.Results. The results show that there is a substantial potential burden of care on the families of patients starting ART, particularly mothers, and that the use of ART appears to reduce this burden over time. But, even after their health is restored, ART patients are significantly less likely to have a resident sexual partner and more likely to be living in single-person households than their counterparts in the general population.

scholarly journals Do electronic devices in face-to-face interviews change survey behavior? Evidence from a developing country

2019 ◽  
Vol 6 (2) ◽  
pp. 205316801984464 ◽  
Author(s):  
Sarah Sunn Bush ◽  
Lauren Prather
Keyword(s):  
Developing Country ◽  
New Technologies ◽  
Developing World ◽  
Electronic Devices ◽  
Wealth Effect ◽  
Tablet Computers ◽  
Panel Survey ◽  
Face To Face ◽  
Effect Hypothesis ◽  
Second Wave

A large literature shows that survey mode and survey technologies significantly affect item non-response and response distributions. Yet as researchers increasingly conduct surveys in the developing world, little attention has been devoted to understanding how new technologies—such as the use of electronic devices in face-to-face interviews—produce bias there. We hypothesize that using electronic devices instead of pen and paper can affect survey behavior via two pathways: a wealth effect and a surveillance effect. To test the hypotheses, we use data from a two-wave panel survey fielded in Tunisia. We investigate whether responses collected in Wave 1 with pen and paper changed when some individuals were interviewed in Wave 2 by interviewers using tablet computers. Consistent with the wealth effect hypothesis, more than half of the lowest income respondents reported a higher income in the second wave when interviewers used tablets. Conversely, we find little evidence that concerns about surveillance changed survey behavior.

scholarly journals Reflections on the Impact of Social Technologies on Lecturers in a Pathway Institution

2015 ◽  
pp. 131-154 ◽  
Author(s):  
Donna M Velliaris ◽  
Craig R Willis ◽  
Paul B Breen
Keyword(s):  
Higher Education ◽  
Academic Staff ◽  
New Technology ◽  
First Year ◽  
Professional Lives ◽  
Face To Face ◽  
Social Technologies ◽  
The Impact ◽  
Insight Into ◽  
Over Time

Education has evolved over time from face-to-face teaching to computer-supported learning, and now to even more sophisticated electronic tools. In particular, social technologies are being used to supplement the classroom experience and to ensure that students are becoming increasingly engaged in ways that appeal to them. No matter how educationally beneficial, however, new technology is affected by its users. To investigate this, lecturers at the Eynesbury Institute of Business and Technology (EIBT)—a Higher Education pathway provider—were surveyed to determine their perception and application of social technolog(ies) in their personal, but predominantly ‘professional' lives. Utilising a qualitative and autoethnographic approach, one author provides an insight into their own attitude toward social technologies, coupled with responses to three open-ended questions. Thereafter, the same questions were posed to EIBT academic staff to understand their willingness or reluctance to use social technologies in their practice as part of their first-year pathway course(s).

scholarly journals Socio-economic indicators correlate with daily mobility during the second-wave of the Covid-19 pandemic

2021 ◽  
Author(s):  
Jed Long ◽  
Chang Ren
Keyword(s):  
Positive Association ◽  
Economic Indicators ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Individual Mobility ◽  
Mobility Behaviour ◽  
The Impact ◽  
Second Wave ◽  
Pharmaceutical Interventions ◽  
Over Time

Non-pharmaceutical interventions are being used globally to limit the spread of Covid-19, which are in turn affecting individual mobility patterns. Mobility measures were found to be strongly associated with regional socio-economic indicators during the first wave of the pandemic. Here, we use network mobility data from an ~3.5 million person sample of individuals in Ontario, Canada to study the association between three different individual-mobility measures and four socio-economic indicators throughout the first and second wave of Covid-19 (January to December 2020). We demonstrate that understanding how mobility behaviours have changed in response to Covid-19 varies considerably depending on how mobility is measured. We find a strong positive association between different mobility levels and the economic deprivation index, which demonstrates that inequities in the changes to mobility across economic gradients observed during the initial lockdown have persisted into the later stages of the pandemic. However, the associations between mobility and other socio-economic indicators vary over time. We capture a strong day-of-week pattern of association between socio-economic indicators and mobility levels. Our findings have important implications for understanding if and how mobility data should be used to study the impact of non-pharmaceutical interventions on the socio-economic conditions across geographical space, and over time. Our results support that Covid-19 non-pharmaceutical interventions have resulted in geographically disparate responses to mobility behaviour, and quantifying mobility changes at fine geographical scales is crucial to understanding the impacts of Covid-19 on local populations.

scholarly journals The Regeneración Urbana, Calidad de Vida y Salud - RUCAS project: a Chilean multi-methods study to evaluate the impact of urban regeneration on resident health and wellbeing

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fernando Baeza ◽  
Alejandra Vives Vergara ◽  
Francisca González ◽  
Laura Orlando ◽  
Roxana Valdebenito ◽  
...  
Keyword(s):  
Health Effects ◽  
Urban Regeneration ◽  
Retention Rates ◽  
Health Determinants ◽  
Systematic Observation ◽  
Potential Health ◽  
Health And Wellbeing ◽  
Calidad De Vida ◽  
The Impact ◽  
Over Time

Abstract Background The available evidence of the health effects of urban regeneration is scarce In Latin America, and there are no studies focused on formal housing that longitudinally evaluate the impact of housing and neighborhood interventions on health. The “Regeneración Urbana, Calidad de Vida y Salud” (Urban Regeneration, Quality of Life, and Health) or RUCAS project is a longitudinal, multi-method study that will evaluate the impact of an intervention focused on dwellings, built environment and community on the health and wellbeing of the population in two social housing neighborhoods in Chile. Methods RUCAS consists of a longitudinal study where inhabitants exposed and unexposed to the intervention will be compared over time within the study neighborhoods (cohorts), capitalizing on interventions as a natural experiment. Researchers have developed a specific conceptual framework and identified potential causal mechanisms. Proximal and more distal intervention effects will be measured with five instruments, implemented pre- and post-interventions between 2018 and 2021: a household survey, an observation tool to evaluate dwelling conditions, hygrochrons for measuring temperature and humidity inside dwellings, systematic observation of recreational areas, and qualitative interviews. Survey baseline data (956 households, 3130 individuals) is presented to describe sociodemographics, housing and health characteristics of both cohorts, noting that neighborhoods studied show worse conditions than the Chilean population. Discussion RUCAS’ design allows for a comprehensive evaluation of the effects that the intervention could have on various dimensions of health and health determinants. RUCAS will face some challenges, like changes in the intervention process due to adjustments of the master plan, exogenous factors –including COVID-19 pandemic and associated lockdowns– and lost to follow-up. Given the stepped wedge design, that the study capitalizes on within household changes over time, the possibility of adjusting data collection process and complementarity of methods, RUCAS has the flexibility to adapt to these circumstances. Also, RUCAS’ outreach and retention strategy has led to high retention rates. RUCAS will provide evidence to inform regeneration processes, highlighting the need to consider potential health effects of regeneration in designing such interventions and, more broadly, health as a key priority in urban and housing policies.

A (supra)nationalist personality? The Big Five’s effects on political-territorial identification

2021 ◽  
pp. 146511652098890
Author(s):  
K Amber Curtis ◽  
Steven V Miller
Keyword(s):  
United Kingdom ◽  
Panel Survey ◽  
Systematic Relationship ◽  
The United Kingdom ◽  
Weight One ◽  
Static Effect ◽  
The Impact ◽  
Over Time ◽  
Eu Support ◽  
Cross National

Recent work suggests personality affects the subjective psychological weight one attaches to an identity. This study extends prior findings showing a static effect on European identification in a single country by investigating whether a similar systematic relationship exists for a wider range of political-territorial identities (regional, national, supranational, and exclusively nationalist) across different country contexts (Germany, Poland, and the United Kingdom) and over time (2012–2018). Original cross-national and panel survey data show that different traits predict both the type and degree of inclusivity of individuals’ identity attachments. These results contribute to the growing scholarship surrounding personality’s effects on EU support while underscoring the impact predispositions have on citizens’ sociopolitical orientations. They especially illuminate the contrasting profiles associated with those who identify as exclusively nationalist versus supranational European.

scholarly journals The interaction between social factors and adversities on self-harm during the COVID-19 pandemic: longitudinal analysis of 49 227 UK adults

BJPsych Open ◽  
2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Elise Paul ◽  
Daisy Fancourt
Keyword(s):  
Social Support ◽  
Social Factors ◽  
Fixed Effects ◽  
Social Study ◽  
Support Network ◽  
Face To Face ◽  
Self Harm ◽  
The Impact ◽  
Over Time

Background Little is known about which factors exacerbate and buffer the impact of coronavirus disease 2019 (COVID-19)-related adversities on changes in thinking about and engaging in self-harm over time. Aims To examine how changes in four social factors contribute to changes in self-harm thoughts and behaviours over time and how these factors in turn interact with adversities and worries about adversities to increase risk for these outcomes. Method Data from 49 227 UK adults in the UCL COVID-19 Social Study were analysed across the first 59 weeks of the pandemic. Fixed-effects logistic regressions examined time-varying associations between social support quality, loneliness, number of days of face-to-face contact for >15 min and number of days phoning/video calling for ≥15 min with self-harm thoughts and behaviours. We then examined how these four factors in turn interacted with the total number of adversities and worries about adversity and how this affected outcomes. Results Increases in the quality of social support were associated with decreases in the likelihood of both outcomes, whereas greater loneliness was associated with an increase in their likelihood. Associations were less clear for telephone/video contact and face-to-face contact with outcomes. Social support buffered and loneliness exacerbated the impact of adversity experiences on self-harm behaviours. Conclusions These findings suggest the importance of the quality of one's social support network, rather than the mere presence of contact, for reducing the likelihood of self-harm behaviours in the context of COVID-19 pandemic-related adversity and worry.

scholarly journals Cognitive Performance in Remote Work - Evidence from Professional Chess

2021 ◽  
Author(s):  
Steffen Künn ◽  
Christian Seel ◽  
Dainis Zegners
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Performance ◽  
Effect Size ◽  
Remote Work ◽  
Face To Face ◽  
The Impact ◽  
Remote Setting ◽  
Over Time

Abstract During the COVID-19 pandemic, traditional (offline) chess tournaments were prohibited and instead held online. We exploit this unique setting to assess the impact of remote–work policies on the cognitive performance of individuals. Using the artificial intelligence embodied in a powerful chess engine to assess the quality of chess moves and associated errors, we find a statistically and economically significant decrease in performance when an individual competes remotely versus offline in a face-to-face setting. The effect size decreases over time, suggesting an adaptation to the new remote setting.

scholarly journals The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Thomas Sigler ◽  
Sirat Mahmuda ◽  
Anthony Kimpton ◽  
Julia Loginova ◽  
Pia Wohland ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Total Population ◽  
Human Mobility ◽  
Household Size ◽  
Human Interaction ◽  
Population Characteristics ◽  
Disease Diffusion ◽  
Older Populations ◽  
The Impact ◽  
Over Time

Abstract Background COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of COVID-19 transmission, there remains an important gap in knowledge regarding the drivers of geographical diffusion between places, in particular at the global scale. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of reported COVID-19 diffusion over a six-week period in March and April 2020. Our exploratory analysis is based on reported COVID-19 data published by Johns Hopkins University which, despite its limitations, serves as the best repository of reported COVID-19 cases across nations. Results The quantile regression model suggests that globalisation, settlement, and population characteristics related to high human mobility and interaction predict reported disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total reported cases (per million) whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total reported cases (per million). Population density, and population characteristics such as total population, older populations, and household size are strong predictors in early weeks but have a muted impact over time on reported COVID-19 diffusion. In contrast, the impacts of interpersonal and trade globalisation are enhanced over time, indicating that human mobility may best explain sustained disease diffusion. Conclusions Model results confirm that globalisation, settlement and population characteristics, and variables tied to high human mobility lead to greater reported disease diffusion. These outcomes serve to inform suppression strategies, particularly as they are related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. It is likely that many of these processes are replicated at smaller geographical scales both within countries and within regions. Epidemiological strategies must therefore be tailored according to human mobility patterns, as well as countries’ settlement and population characteristics. We suggest that limiting human mobility to the greatest extent practical will best restrain COVID-19 diffusion, which in the absence of widespread vaccination may be one of the best lines of epidemiological defense.

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