Addressing selection bias in the UK Biobank neurological imaging cohort

The UK Biobank is a national prospective study of half a million participants between the ages of 40 and 69 at the time of recruitment between 2006 and 2010, established to facilitate research on diseases of aging. The imaging cohort is a subset of UK Biobank participants who have agreed to undergo extensive additional imaging assessments. However, Fry et al (2017) find evidence of "healthy volunteer bias" in the UK Biobank -- participants are less likely to smoke, be obese, consume alcohol daily than the target population of UK adults. Here we examine selection bias in the UK Biobank imaging cohort. We address two common misconceptions: first, that study size can compensate for bias in data collection, and second that selection bias does not affect estimates of associations, which are the primary interest of the UK Biobank. We introduce inverse probability weighting (IPW) as an approach commonly used in survey research that can be used to address selection bias in volunteer health studies like the UK Biobank. We discuss 6 such methods -- five existing and one novel --, assess relative performance in simulation studies, and apply them to the UK Biobank imaging cohort. We find that our novel method, BART for predicting the probability of selection combined with raking, performs well relative to existing methods, and helps alleviate selection bias in the UK Biobank imaging cohort.

Data-Enhancement Strategies in Weather-Related Health Studies

Although the relationship between weather and health is widely studied, there are still gaps in this knowledge. The present paper proposes data transformation as a way to address these gaps and discusses four different strategies designed to study particular aspects of a weather–health relationship, including (i) temporally aggregating the series, (ii) decomposing the different time scales of the data by empirical model decomposition, (iii) disaggregating the exposure series by considering the whole daily temperature curve as a single function, and (iv) considering the whole year of data as a single, continuous function. These four strategies allow studying non-conventional aspects of the mortality-temperature relationship by retrieving non-dominant time scale from data and allow to study the impact of the time of occurrence of particular event. A real-world case study of temperature-related cardiovascular mortality in the city of Montreal, Canada illustrates that these strategies can shed new lights on the relationship and outlines their strengths and weaknesses. A cross-validation comparison shows that the flexibility of functional regression used in strategies (iii) and (iv) allows a good fit of temperature-related mortality. These strategies can help understanding more accurately climate-related health.

Estimating Hourly Full-Coverage PM2.5 Concentrations Based On MODIS Data Over The Northeast of Thailand.

Particulate matter (PM2.5) pollutants are a significant health issue with impacts on human health; however, monitoring of PM2.5 is very limited in developing countries. Satellite remote sensing can expand spatial coverage, potentially enhancing our ability in a specific area for estimating PM2.5; however, some have reported poor predictive performance. An innovative combination of MODIS AOD was developed to fulfill all missing aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Therefore, hourly PM2.5 concentrations were obtained in Northeastern Thailand. A Linear mixed-effects (LME) model was used to predict location-specific hourly PM2.5 levels. Hourly PM2.5 concentrations measured at 20 PM2.5 monitoring sites and 10- fold cross-validation were addressed for model validation. The observed and predicted concentrations suggested that LME obtained from MODIS AOD data and other factors are a potentially useful predictor of hourly PM2.5 concentrations (R2 >0.70), providing more detailed spatial information for local scales studies. Interestingly, PM2.5 along the Mekong River area was observed higher than in the plain area. The finding can infer that the monsoon wind brings polluted air into the province from sources outside the region. The results will be helpful to analyze air pollution-related health studies.

Non-Probabilistic Sampling in Quantitative Clinical Research

Quantitative researchers need a probabilistic sample to generalise their findings, but research constraints often compel them to use non-probabilistic samples. The use of non-probability sampling methods in quantitative studies has therefore become a norm. Interestingly, even studies published in top-quality journals compromise best practices that the use of non-probabilistic samples requires. Based on a thorough review of relevant studies, we developed a typology of non-probability sampling methods used in quantitative health studies. An attempt was made to discuss the limit of inference under each type of non-probability sampling method. Non-probability sampling in quantitative research was also delineated as a way to maximise response rate. This study is expected to guide students and early career epidemiologists to understand how to apply non-probabilistic sampling methods in quantitative approaches and plausibly document or report their chosen methods.

Gamification and Health in a Holistic Perspective

Gamification and health are discussed from a one-sided perspective. Gamification and health studies focus on the use of gamification for health and overlook the perspective on how gamification affects health. This chapter discusses gamification and health in terms of organizations, individuals, and society, and addresses the effects of gamification on health and the use of gamification for health. Existing research on gamification and health addresses gamification practices developed for health and the health effects of gamification separately. Consequently, the aim of this chapter is to contribute to the original research collection organized into gamification studies in health from a holistic perspective.

What Is Deep Learning and How Has It Helped the COVID-19 Pandemic?

Since the beginning of the COVID-19 pandemic, researchers have been trying to find solutions through international collaborations and interdisciplinary studies. This chapter aims to present how deep learning is used for the COVID-19 pandemic. Within this scope, this chapter covers the fundamentals of deep learning in terms of definitions, key concepts, popular network types, and application areas. Then it gives a summary of the most recent studies in which deep learning models are used for COVID-19. The authors believe this chapter is a good start for researchers who want to study deep learning. In addition, the literature review section of this chapter, which was prepared by considering the COVID-19 pandemic, is expected to shed light on a broad range of health studies within the pandemic process and to provide better solutions to similar problems encountered in different sectors.

Harnessing the power of telemedicine to accomplish international pediatric outcome research during the COVID-19 pandemic

The COVID-19 pandemic occurred during planned neurodevelopmental follow-up of Colombian children with antenatal Zika-virus exposure. The objective of the study was to leverage the institution's telemedicine infrastructure to support international clinical child outcome research. In a prospective cohort study of child neurodevelopment (NCT04398901), we used synchronous telemedicine to remotely train a research team and perform live observational assessments of children in Sabanalarga, Colombia. An observational motor and conceptional standardized tool kit was mailed to Colombia; other materials were translated and emailed; team training was done virtually. Children were recruited by team on the ground. Synchronous activities were video-recorded directly to two laptops, each with a telehealth Zoom link to allow simultaneous evaluation of “table” and “standing” activities, and backup recordings were captured directly on the device in Colombia. The U.S. team attended live over Zoom from four states and five distinct locations, made observational notes, and provided real-time feedback. Fifty-seven, 3–4-year-old children with Zika-virus exposure and 70 non-exposed controls were studied during 10 daytrips. Direct laptop recording ensured complete record of child activities due to internet outages. Telemedicine can be used to successfully perform international neurodevelopmental outcome research in children during the COVID-19 pandemic. Telemedicine can benefit global health studies.

Sleep: the neglected life factor in adults with intellectual disabilities

Sleep is vital for our physical and mental health. Studies have shown that there is a high prevalence of sleep disorders and sleep difficulties amongst adults with intellectual disabilities. Despite this, sleep is often overlooked or its disorders are considered to be difficult to treat in adults with intellectual disabilities. There is a significant amount of research and guidance on management of sleep disorders in the general population. However, the evidence base for sleep disorders in adults with intellectual disabilities is limited. In this review paper, we look at the current evidence base for sleep disorders in adults with an intellectual disability, discuss collaborative working between intellectual disabilities psychiatrists and sleep medicine specialists to manage sleep disorders, and provide recommendations for future directions.

I am hated, therefore I am: The Enemy in Yorùbá Imaginary

This essay will study how the Yorùbá conceptualize “ọ̀tá” or the enemy, a trope that recurs in various cultural phenomena such as music, prayers, and other social rituals. The Yorùbá worldview of the enemy has profound implications on the way they frame issues that affect their mental, physical, social, and general well-being. Health studies, religious studies, and social ethics studies and analyses have mostly tried to investigate the enemy as a concept borne out of Yorùbá cosmology which serves as a conduit for superstition, fear, and other seemingly irrational behavior. In this essay, I frame the concept of “ọ̀tá” through the theatrical dialectic of antagonist/protagonist theory. The enemy, I argue, is the way the Yorùbá metaphorize all kinds of antagonism—material and immaterial ones—into an imaginative texture that gives it the tangibility they need to triumph against those situations. This essay will interrogate how this personification of antagonism is achieved by studying Ifá texts, Yorùbá popular music of a period, and contemporary Pentecostal prayers.