Despite the trillions of dollars invested into health care annually, we rarely collect systematically the end results of care – outcomes. Lacking outcome data, providers are unable to learn how good they are compared to their peers and where they can improve. This general ignorance also affects patients and payers: patients are unable to select providers who can best treat their condition, and payment is based on activity rather than results.
Overdiagnosis occurs when a tumor is detected by screening but, in the absence of screening, that tumor would never have become symptomatic within the lifetime of the patient. Thus, an overdiagnosed tumor is a true extra diagnosis due solely to the existence of the screening test. Patients who are overdiagnosed cannot, by definition, be helped by the diagnosis, but they can be harmed, particularly if they are treated.
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.
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.
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.
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.
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.
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?
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).
Bayesian population reconstruction is a method for estimating past populations by age with fully probabilistic statements of uncertainty. It simultaneously estimates age-specific population counts, vital rates, and net migration from fragmentary data while formally accounting for measurement error.
This seminar reports on reproductive history interviews with 1,014 Tibetan women 40 years of age and older living at altitudes of 3,000+ meters in Gorkha and Mustang Districts, Nepal, as well as on ethnographic data from the regions.
This talk will cover key findings from perception studies and visualization research and will examine techniques for evaluating and understanding visualizations.
Stein Emil Vollset will give an overview of what is known and what remains to be learned about health, disease, and risk factors in Norway.
In countries with limited vital registration, adult mortality rates are frequently estimated using siblings’ survival histories (SSH) collected during nationally representative surveys such as the Demographic and Health Surveys. Such data may underestimate adult mortality because of reporting errors and omissions of deceased siblings. Dr.
Dr. Jewell’s arrival as an Assistant Professor at Berkeley in 1981 coincided with the peak of the HIV epidemic in the San Francisco Bay Area. From that moment on he was involved in many studies of the epidemiology of AIDS and subsequently intervention and treatment trials. He will discuss some of the statistical challenges associated with population studies of HIV and how methods developed to study the epidemic turned out to have much broader application.
Many health behaviors are difficult to measure. Estimates of illegal drug use are subject to substantial self-report and sampling biases. Municipal wastewater samples are routinely collected for 24-hour periods at the point of inflow to treatment plants, providing insights into substance consumption upstream that is anonymous, near real time, and relatively inexpensive.
This talk will highlight some of the benefits and challenges associated with harnessing the temporal structure present in many datasets.
Founder and CEO of Captricity, Kuang Chen, demonstrates Captricity and discusses ways to incorporate paper-based data into organizational workflows by transforming static data into structured, machine-readable formats for analysis, reporting, and other uses.