We seek to train scientists to understand complex health problems and health disparities as resulting from multiple interacting layers of influence that unfold over chronological, biological, and historical time. This exciting new program at the University of Minnesota, housed in the Minnesota Population Center, features cross-training in the biology and etiology of disease, as well as in the social sciences. The program includes engagement in independent and collaborative population health research supervised by interdisciplinary teams of faculty, and intensive professional socialization designed to integrate trainees from diverse disciplinary backgrounds and prepare them to have outstanding careers as population health scientists.
Interested candidates can read more about our program online (https://pop.umn.edu/training/postdoctoral) and can download an application in the “How to Apply” section. We are accepting applications now.
Please direct all questions to Lindsey Fabian (fabian@umn.edu).
At the upcoming 2019 PAA meeting, 4 CSDE Fellows, 9 CSDE Trainees, and 28 CSDE Affiliates are scheduled to participate as presenters, chairs, coauthors, and discussants. Our scholars tackle a wide range of demographic issues, represented in the varied presentations listed here.
CSDE will also sponsor an IAPHS panel discussion and reception on community-engaged research titled “Population Health Reception: The Perils and Promise of Community Engaged Research” (Wednesday, 4/10/2019, 6:00-7:30 PM, JW Marriott, Brazos 206). Organized by Chris Bachrach and Dawn Upchurch, this panel will feature comments by Mark Hayward, University of Texas-Austin, Lourdes Rodriguez, University of Texas-Austin, David Vlahov, Yale School of Nursing, and Rachel Kimbro, Rice University. There will be plenty of time for networking, refreshments, and a lively audience discussion.
CSDE has supported Trainees and Fellows by awarding PAA travel grants to Ian Kennedy, Christine Leibbrand, Connor Gilroy, and Hilary Wething. CSDE Trainees also participated in a PAA workshop led by Matt Hall last Fall, where they received feedback from CSDE Affiliates on drafts of their submissions.
CRAFTING EFFECTIVE DIVERSITY STATEMENTS & COVER LETTERS
A panel and workshop for postdocs and graduate students.
Thursday, Apr 4th, 3:00-5:00 p.m.
Samuel E. Kelly Ethnic Cultural Center, Unity Suite
What is a diversity statement? Why is it important? How do I describe my potential to contribute to diversity, inclusion and equity in academia and industry?
Panelists:
- Rickey Hall, Vice President and University Diversity Officer, UW
- Evangelina Shreeve, Director, STEM Education and Outreach, PNNL
- Butch de Castro, Professor and Associate Dean, Nursing, UW
- Cynthia del Rosario, Diversity, Equity and Access Officer, Information School, UW
Hear panelists talk about what they look for in a diversity statement when they are hiring; discuss expectations for a diversity statement in your job application; and begin writing one as part of the workshop.
RSVP: http://bit.ly/diversityStatement
In celebration of 2019 Graduate and Professional Student Appreciation Week, this event is generously co-hosted by:
- UW Postdoc Diversity Alliance
- Center for Teaching and Learning
- Office of Minority Affairs & Diversity
- Office of Postdoctoral Affairs
- Core Programs, Graduate School
Interested in applying to the Doctoral Consortium at the International Conference on Quantitative Ethnography (ICQE) 2019? The call for participation is now out!
The Doctoral Consortium is designed to support the development of graduate students who are in the early stages of working on their dissertations. Doctoral students will meet and discuss their work with peers and mentors from the field of Quantitative Ethnography. Graduate students accepted into the Doctoral Consortium will receive free conference registration, hotel accommodations, and travel stipends.
Interested? Check out the ICQE schedule and the call for participation below for more information about this chance to present and get feedback on your research and to learn more about new research and developments in Quantitative Ethnography!
CSDE is happy to share the exciting 2019 Spring Seminar Schedule, starting with the PAA Practice Presentations this Friday and closing with the CSDE community end-of-year reception, in June. The impressive lineup of speakers includes Jenna Nobles (University of Wisconsin-Madison), Mary Pattillo (Northwestern), Daniel Belsky (Columbia), Anna Gassman-Pines (Duke), Alan Griffith (UW) and Fabian Pfeffer (University of Michigan). A big highlight is the UW Data Collective launch party on April 19, 2019. You can find all of the details in the Seminar Series page or by downloading and printing the poster. Feel free to put it up in your office and department!
CSDE is hosting a practice presentation session to help prepare for PAA and improve presentation skills. The format is informal and is a great opportunity to hone your presentation and get feedback from friends and colleagues. Presenters include Hilary Wething, Public Policy, Christine Leibbrand, Sociology, Ian Kennedy, Sociology, Lauren Wyszynski, IHME, Yuan Hsiao, Sociology & Statistics, and Neal Marquez, Sociology. Everyone is invited to discuss or observe! Lunch will be served.
CSDE Affiliate Amelia Gavin, Associate Professor at the School of Social Work, was recently featured in a UW News story about a Journal of Health Disparities Research and Practice paper in which she connects racial discrimination to PTSD, and thus to preterm birth.
According to Gavin, “Pregnancy is a stress test for the body. If you’ve been stressed during your life through discrimination, poverty and residential segregation, then the likelihood of having a healthy birth outcome has been compromised.”
Gavin’s article finds that African-American women are nearly twice as likely to give birth prematurely as white women. Such births often coincide with low birth weight, and together are linked to other developmental delays and health effects believed responsible for almost one-fifth of infant deaths nationwide. The trend holds up regardless of socioeconomic factors.
Lee Fiorio, Geography doctoral student, and Connor Gilroy, Sociology doctoral student, are CSDE Trainees, former Funded Fellows, and leaders of the Computational Demography Working Group. The UW College of Arts & Sciences has recently published a story featuring their incredible research at the leading edge of data science and social science.
Both Fiorio, who focuses on migration across the US and the globe, and Gilroy, who looks at our willingness to share information about our sexual identity, use traditional sources like the US Census, but they find that the massive quantities of data generated through social media or cell phone use — digital trace data — provide a particularly rich snapshot of society. “The data are generated in real time, they are generated very quickly, and that’s very different from a traditional demographic data source like the US Census, which comes out once every ten years,” says Gilroy. According to Fiorio, “the research is about methodology — seeing what digital trace data can tell us that might be missing when we go about estimating migration in the standard, traditional way.”
The NIH Office of Behavioral and Social Sciences Research (OBSSR) invites Early Stage Investigators (ESI, within 10 years of their terminal degree) to participate in the NIH Matilda White Riley Honors Behavioral and Social Sciences Research Paper Competition. Initiated as an annual distinguished scholar lecture, OBSSR expanded the Matilda White Riley Honors event in 2016 to recognize emerging scientists with a competition for peer-reviewed articles by ESIs. OBSSR will pay the travel expenses for up to four ESI honorees to present the findings from their accepted paper and participate in a moderated discussion of future behavioral and social sciences research possibilities during the 12th annual meeting, to be held on Thursday, June 6.
ESIs can submit one research article meeting the following criteria:
– The first author of the paper is an Early Stage Investigator (as of the deadline for this paper competition submission), defined by the NIH as someone who has completed their terminal research degree or end of post-graduate clinical training, whichever date is later, within the past 10 years and who has not previously competed successfully as PD/PI for a substantial NIH independent research award.
– The article was published or accepted and in-press between 01/01/18 and 12/31/18.
– The article involves original research published in a peer-review journal. (Note: conceptual, review, or meta-analysis papers are not eligible for this competition).
The submission deadline is Sunday, March 31, 2019. Submission link: https://mwr.obssr.od.nih.gov/Contestant
The 12th NIH Matilda White Riley Behavioral and Social Sciences Honors will be held on Thursday, June 6, 2019, from 8:00 am to 12 noon on NIH’s Main Campus – Wilson Hall, Building 1. https://www.scgcorp.com/obssr12thmatilda/
If you have any questions, please contact NIHMWRHonors@nih.gov.
For more information about past NIH Matilda White Riley Behavioral and Social Sciences Honors, visit the OBSSR website: https://obssr.od.nih.gov
Purpose
The National Library of Medicine is issuing this Notice to highlight its interest in receiving grant applications through NLM Research Grants in Biomedical Informatics and Data Science (R01 Clinical Trial Optional) (PAR 18-896), focused on research to reduce or mitigate gaps and errors in health data sets.
Background
Recent successes with the use of data-centric artificial intelligence (AI) methods such as deep learning are stimulating interest in the promise of harnessing large and complex digital health data sets to advance the goals of precision medicine. Applying AI methods to large health data sets promises to provide new powers of discovery, diagnosis, prediction, and decision support aimed at improving health outcomes and reducing healthcare costs. Numerous public datasets of human and non-human data are available, and a rich array of specialized tools and platforms can be used in studies and applications. However, recent work in identifying and addressing systematic biases and blind spots in data, and in the AI systems derived from that data, have highlighted an array of potential problems with fairness, accuracy, safety, and reproducibility of inferences and conclusions. Work on bias and incompleteness in health data sets includes studies that find poor representation of minority groups, seniors, and women. (See, for example, https://www.eurekalert.org/pub_releases/2016-10/uoms-nsr100716.php, or https://datasociety.net/output/fairness-in-precision-medicine/?utm_source=STAT+Newsletters&utm_campaign=436e1528c5-Readout&utm_medium=email&utm_term=0_8cab1d7961-436e1528c5-150097429). A recent Wall Street Journal article (https://www.wsj.com/articles/a-crucial-step-for-avoiding-ai-disasters-11550069865) noted that computational tools developed by a diverse team can help avoid bias in algorithms. Beyond problems with biases and other gaps in data, research using health data from humans requires special care to protect the sources and the data (see https://www.ncbi.nlm.nih.gov/pubmed?term=Barocas%2C%20Solon%5BAuthor%5D ). The All of Us Research Program (https://allofus.nih.gov/) aims to develop an unbiased, representative health data resource, but there are many other health data sets already in use or being constructed. Tools developed using biased and incomplete data sets may contribute to erroneous analyses. Statistical fallacies and representational errors unrelated to the research question at hand could introduce systematic errors. The core questions for understanding and mitigating these and other problems in health data research are: “What can be done, computationally and/or statistically, to reduce or mitigate gaps and errors in data sets used for health research?” and, “How can we improve the tools used for discovery, understanding, and visualization in health data sets and their analyses?” Whether the problem is due to incomplete health data or inadequate tools, approaches are needed to strengthen the reproducibility and applicability of data-centered research on the etiology, epidemiology and treatment of health conditions.
Research Objectives
NLM invites research grant applications that propose state of the art methods and approaches to address problems with large health data sets or tools used to analyze them, whether the data are drawn from electronic health records or public health data sets, biomedical imaging, omics repositories or other biomedical or social/behavioral data sets. Areas of interest include but are not limited to (1) developing and testing computational or statistical approaches applied to large and/or merged health data sets holding human or non-human data, with a focus on understanding and characterizing the gaps, errors, biases, and other limitations in the data or inferences based on the data; (2) exploring approaches to correcting biases or compensating for missing data, including the introduction of debiasing techniques and policies or the use of synthetic data; (3) testing new statistical algorithms or other computational approaches to strengthen research designs for use with specific types of biomedical and social/behavioral data; (4) generating metadata that adequately characterizes the data, including its provenance, intended use, and processes by which it was collected and verified; (5) improving approaches for integrating, mining, and analyzing health data that preserve the confidentiality, accuracy, completeness and overall security of the data. Applicants should address ethical issues that might arise from their proposed approach.