Videos

Censuses and surveys have been the primary sources of information on mobility and migration. However, concerns with these data include sample size, detail, accuracy, and expense. In the past decade, large-scale mobile phone data have recently become available for the study of human movement patterns and hold immense promise for studying human behavior on a vast scale never before possible, with a precision and accuracy never before possible with surveys or other data collection techniques. Already a significant body of literature has made key inroads into understanding mobility using this exciting new data source, and several different measures of mobility have been used. However, there has been little discussion and analysis of these measures; it is unclear what exactly these measures measure. We argue that existing measures are contaminated by infrastructure and demographic and social characteristics of a population. These issues would be best addressed immediately, as they will influence future studies of mobility using mobile phone data. In this presentation, Dr. Dobra will describe new methods for measuring mobility with mobile phone data that address these concerns. Our measures are designed to address the spatial and social nature of human mobility. The practical relevance of massive geolocated data in the context of HIV research will also be discussed. This is joint work with Nathalie Williams, Tim Thomas, and Matt Dunbar.

Adolescents and young adults make up over a quarter of the global population. They can also be considered the most pervasively neglected group in global health. Yet a quiet revolution is now bringing a recognition that adolescents are central in almost every major challenge in global health. Bringing greater visibility to adolescents and their health has been an important facet of that recognition. This is also a central theme of the new Lancet Commission on Adolescent Health and Well-being. This presentation will track progress in our understandings of adolescent health and major influences on health over these years. It will also address currently available global data, gaps, and potential solutions. The Global Burden of Disease Study in particular has an important role in the new Commission and in guiding the development of better health information systems for this age group.

Much of what we take for granted in health care starts as a study published in a scientific journal. Studies can be complex, highly specific, and full of caveats, and yet, in order for them to be actionable, they need to be translated into real-world application. The editor of one of the world’s preeminent health journals will use examples from hypertension, obesity, and other areas to show how, in theory, health care is becoming more evidence-based, and yet how difficult it is to turn data into clinical guidelines that everyone will follow.

Professor LeVeque will introduce some of the techniques he has found most valuable in the context of Clawpack, an open-source software effort he has been leading for 20 years, and tsunami hazard assessment, one specific application of this software where accountability and reproducibility are particularly important.

The WHO Commission on the Social Determinants of Health presented evidence on the importance of a long list of social determinants of health in its final report in 2008, but policymakers find it difficult to translate the careful work of the Commission into concrete action because it remains unclear what interventions to prioritize. The objective of this paper is to determine a small set of social determinants for which there is empirical evidence of influence on population health, using Extreme Bound Analysis, a technique originally developed for models of economic growth. We estimate panel data models of life expectancy for countries of differing income levels using the World Bank’s World Development Indicators for the years 1990 to 2012. We address problems of missing data with multiple imputation techniques.

Comparing the burden of aging across countries hinges on the availability of valid and comparable indicators. The Old Age Dependency Ratio allows only a limited assessment of the challenges of aging, because it does not include information on any individual characteristics except age itself. Existing alternative indicators based on health or economic activity suffer from measurement and comparability problems. We propose an indicator based on age variation in cognitive functioning. We use newly released data from standardized tests of seniors’ cognitive abilities for countries from different world regions. In the wake of long-term advances in countries’ industrial composition and technological advances, the ability to handle new job procedures is now of high and growing importance, which increases the importance of cognition for work performance over time. In several countries with older populations, we find better cognitive performance on the part of populations aged 50+ than in countries with chronologically younger populations. This variation in cognitive functioning levels may be explained by the fact that seniors in some regions of the world experienced better conditions during childhood and adult life, including nutrition, duration and quality of schooling, lower exposure to disease, and physical and social activity patterns. Because of the slow process of cohort replacement, those countries whose seniors already have higher cognitive levels today are likely to continue to be at an advantage for several decades to come.

Armed with additional data, policymakers will be able to more effectively control tobacco in their countries. 

Explore trends in tobacco use worldwide and by country for the years 1980 to 2012 with our interactive data visualization tool. Use the settings below the graphics to focus on a demographic category or measure of interest (e.g., location, year, age, sex, metric).

Previous analyses of cause-specific mortality in the United States have either focused on just one or a few causes of death or have analyzed national trends. We extend this work to describe cause-specific mortality in the US by county, age, sex, year, and a collectively exhaustive set of conditions. First, we describe trends in causes of death across these five dimensions. Next, we use novel Bayesian small area modeling techniques to jointly estimate and forecast cause-specific mortality. Finally, we present cutting-edge data visualizations to explore the results and convey them to a broader audience.

Data analysis is a complex process with frequent shifts among data formats and models, and among textual and graphical media. We are investigating how to better support the life cycle of analysis by identifying critical bottlenecks and developing new methods at the intersection of visualization, machine learning, and computer systems. Can we empower users to transform and clean data without programming? Can we design scalable representations and systems to visualize and query big data in real time? How might we enable domain experts to guide machine learning methods to produce effective models? This talk will present selected projects that attempt to address these challenges and introduce new tools for interactive visual analysis.

In this talk, Dr. Welch will 1) define overdiagnosis: the detection of an “abnormality” that would have otherwise never become evident during the individual’s lifetime; 2) describe the proximate mechanisms for overdiagnosis: a) changing rules, b) seeing more, c) looking harder, and d) stumbling onto things; 3) explore the evidence for overdiagnosis and subsequent harm; and 4) consider approaches to mitigate the problem.

This study investigates empirically whether the population health benefits arising from progress toward universal health coverage (UHC) vary according to how equitable countries are in alternative domains, including access to care and socioeconomic conditions.

This talk examines the crowdsourcing phenomenon during natural disasters and other crisis events. 

This talk will present a simple approach to unifying these two approaches via a new graph, termed the Single-World Intervention Graph (SWIG).

A collaborative approach figured prominently in the United Arab Emirates’ (UAE) National Strategy and Action Plan for Environmental Health, designed to address the burden of disease from air pollution and other environmental causes.