Seminar Resources

Access to water and sanitation are not only critical to human health, but are also human rights. To date, initiatives have largely focused on increasing access to water sources that ‘have the potential to deliver safe water’ and sanitation facilities that ‘hygienically separate excreta from human contact’. While it is imperative to have safe water and safely managed excreta, these approaches have been too narrow. Water and sanitation are needed for far more than infectious disease prevention. In this talk, Dr. Caruso will discuss how water and sanitation research and programming have ignored the specific needs of women and girls, and, in some cases, have contributed to exploiting them. To enable the audience to think critically about gender in water and sanitation, Dr. Caruso will introduce the World Health Organization framework for assessing gender responsiveness, giving examples of water and sanitation initiatives that are gender harmful as well as those that are gender responsive. She will draw on her own research funded by the Bill & Melinda Gates Foundation, UNICEF, and the WHO, and will highlight simple actions students, researchers, and practitioners can take to ensure their work is gender aware. 

In this interactive talk, I will describe some GIS paradigms inherent to mapping people, administrative boundaries, and the physical environment. We will present a few creative problem solving / how-to exercises and work together in pairs / small groups to generate approaches to best model a spatial phenomenon. These exercises will include issues such as spatial data interpolation, capturing population in an area, measuring edge effects, raster (grid cell) combination, creating indexes, and gerrymandering. With each, I will comment on our collective answers vis-a-vis some best practices used in the GIS community.  Participants are expected to interact and work with others to think through these modeling techniques. The overall goal is to show how input data and choices can affect results of spatial data analyses, and that there are many different ways to model the same data.

The estimated global public health impacts of air pollution are enormous.  In the U.S. alone, approximately 94 thousand deaths and 1.5 million years of life loss are attributed to particulate matter annually as of 2019.  Traditionally, the scope of economics was limited to estimating and monetizing the market and nonmarket impacts of air pollution damages (e.g., morbidity, mortality, ecosystem damages, and materials damages).  In the last two decades, however, a considerable economic literature has emerged studying the direct health impacts of air pollution.  This new economic literature has focused on identifying the causal impacts of air pollution on health outcomes, moving beyond the correlational epidemiological studies that comprise virtually all the estimates used for regulatory impact analyses.  The causal framework employed by economists identifies natural experiments that create quasi-random exposure of individuals to pollutants, mimicking the randomized-control trials considered the gold standard for evidence in empirical work.  By identifying random variation in ambient air pollution, the economic literature has been able to derive causal estimates of the impacts of ambient pollutant exposures that cannot be estimated in a typical laboratory setting.  In this talk, we provide an overview of the causal inference framework through a specific research project that estimates the impacts of in utero exposure to air pollution from confined animal feeding operations (CAFOs) on birth outcomes.

Since the completion of the Human Genome Project, our ability to dissect the genetic component of complex traits, including disease susceptibility, has improved extraordinarily. Two examples are height, the genetic component of which has just been shown to be attributable to 12,000 polymorphisms contained within about 20% of the human genome; and educational attainment, for which 4,000 polymorphisms explain maybe a fifth of the variance for how long we stay in school. There is something of a rush to implement this knowledge into the development of so-called Polygenic Risk Scores (PRS) that evaluate a person’s likelihood of developing a disease or condition. However, there are three problems that I will discuss, which are extremely relevant to issues of health equity. The first is that the PRS are mostly based on European-ancestry cohorts and do not transfer well across populations. The second is that environment/culture/behavior/lifestyle interact in complex ways with PRS but are much harder to quantify. The third has to do with human fallibility in evaluating relative and absolute risk.

Climate change and increasing urbanization is expected to impact public health in the future, in particular by increasing the frequency of health emergencies like the COVID-19 pandemic. Several shortcomings in our public health response to such emergencies, such as limited testing availability and biases in testing populations, have underestimated infections and disease transmission, leading to severe health consequences. Thus to track disease and target resources in a more robust way in the future, better surveillance and monitoring techniques need to be developed, particularly those that are non-invasive, unbiased, and quickly implementable. To this end, sewage surveillance, which has been used sporadically in research settings for decades, has emerged as a tool to monitor community transmission of pathogens. The ability of sewage to provide a “snapshot” of community health, regardless of individuals’ symptomatic disease status, socioeconomic status, or neighborhood, makes it a promising tool in the public health response toolbox. In this talk, I will present the underlying principles of sewage surveillance for pathogens, how data from sewage surveillance has been utilized by public health officials during the COVID-19 pandemic, and future directions utilizing sewage surveillance for characterizing disease transmission within communities.

In this talk I will describe linkages between malaria outbreaks and interannual climate variability modes of the Indo-Pacific basin, and the influence of the tropical Atlantic. I will review current understanding, modeling challenges (what climate models can and cannot capture the observed variations), and the (very) uncertain climate change outlooks.