Video archive

Postdoctoral scholar Christian Razo discusses the burden of disease attributable to high LDL-cholesterol as part of the Health Metrics Sciences lecture series.

Maria de Fatima Marinho de Souza presents on verbal autopsy in Brazil as part of the Health Metrics Sciences lecture series. 
 

Jeff Stanaway, Assistant Professor, Department of Health Metrics Sciences discusses blood lead and mortality risk. 

Assistant Professor Katrin Burkart discusses estimating the present and future burden of disease attributable to temperature, as part of the Health Metrics Sciences lecture series.

Research Professor Marcia Weaver presents on cause-specific spending per disability-adjusted life-year averted from 1996 to 2016, as part of the Health Metrics Sciences lecture series.

For this iteration of the Health Metrics Sciences lecture series, Professor Christopher J. L. Murray and Associate Professor Haidong Wang explore estimating global total COVID-19 mortality.

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.

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.

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 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.