When: January 21, 2026 from 10 – 11 am
Where: Raitt 223 and on Zoom
Title: What do we lose if we lose the DHS? Quantifying research impact with digital trace data (Dennis Feehan)
The Demographic and Health Surveys (DHS) program has been a cornerstone of global health and demographic research for four decades, providing essential data in contexts where official statistics are sparse or unavailable. Following the unexpected termination of USAID funding in 2025, the future of the DHS is in flux. This study investigates what the global research community stands to lose if the DHS permanently ceases. We assembled a corpus of scholarly works that analyze or refer to DHS data, sourced from OpenAlex. We augment our corpus using methods taken from natural language processing and network analysis, and validate the results. We find that DHS data has had a large and sustained impact on scholarly research: we found over 70,000 research works spanning a wide range of social science and health disciplines. We also find that DHS papers have contributed disproportionately to UN Sustainable Development Goals and that they focus on understudied topics and regions. Our preliminary analysis offers just a glimpse of the insights made possible by this rich digital dataset. As this paper moves forward, we aim to more clearly demonstrate what makes the DHS uniquely valuable and what would be lost if it disappears.
Dennis Feehan is a demographer and a quantitative social scientists. My research interests lie at the intersection of networks, demography, and quantitative methodology. I’m an Associate Professor of Demography at UC Berkeley, and also the Director of Berkeley’s Social Science Data Lab (D-Lab).
The Computational Demography Working Group (CDWG) at the University of Washington meets weekly to provide an interdisciplinary forum for discussions of digital and computational approaches to demographic research. The workshop features a range of paper presentations, methods demonstrations, software tutorials and professional development. The CDWG is sponsored by the Center for Studies in Demography and Ecology (CSDE), the eScience Institute and OBSSR T32 Grant #1T32HD101442-01. We welcome anyone with interest in computational demography (broadly defined).
CSDE Affiliate
Laura Dwyer-Lindgren (Health Metrics Sciences) will deliver this week’s CSSS Seminar
in Savery 409 on Wednesday, January 21 from 12:30-1:30 pm. Seminars are available to anyone interested and are presented in a hybrid format. To attend a seminar virtually, please join
here.
An email with login information will be sent to you upon registration.
Mortality rates differ widely across racial and ethnic populations and locations, and these patterns vary by cause of death and over time. To better understand these patterns in the US, we estimated life expectancy and cause-specific mortality for approximately 175 causes of death by county, race and ethnicity, and year (2000–2023). This seminar will focus on the methods used for this analysis and present preliminary findings.
Laura Dwyer-Lindgren, PhD, is an Associate Professor of Health Metric Sciences at the Institute for Health Metrics and Evaluation (IHME) at the University of Washington. Her research focuses on quantifying and describing geographic patterns and disparities in health outcomes, and how other types of health disparities are patterned spatially and intersect with place-based disparities. At IHME, Dr. Dwyer-Lindgren leads the US Health Disparities team which focuses on describing levels and trends in burden of disease in the US by county, racial and ethnic group, and socioeconomic status.
CSDE Affiliate Spencer Wood (Principal Research Scientist, eScience Institute) and co-authors built and tested predictive models for estimating recreational visits to parks, open spaces, and other protected lands. The models incorporated multiple sources of digital mobility data including geotagged posts to social media platforms, community science observations, and a mobile device location dataset from a commercial vendor, alongside other covariates. Using observational visitation data series from the United States’ National Park Service, Forest Service, and Fish and Wildlife Service, the authors quantified the accuracy of the predictive models. Accuracy was highest when on-site visitation data was integrated, and when multiple data sources were integrated, as relying solely on mobile phone location data lead to errors in measurement.
NextGenPop is an undergraduate program in population research that trains and nurtures the next generation of population scientists. The program includes a 2-week, in-person, on-campus summer experience and subsequent virtual components focused on research and professional development. The University of Minnesota is hosting the summer 2026 program in Minneapolis, MN, from June 7 – 19. Participants receive a $1,000 stipend as well as funds to cover travel and living expenses. Classroom instruction and hands-on applications address contemporary social and policy issues in population research, including health disparities, family change, immigration, and social mobility. For more information, please visit the website and application page.
When: Friday, January 23 at 12:30 pm
Where: Parrington Hall Room 360 and on Zoom
One-on-One Meetings: Follow this link
We are looking forward to hosting Sarah Komisarow from the Sanford School of Public Policy at Duke University on Friday, January 23 in Parrington Hall 360 and on Zoom. This seminar is co-sponsored by the Population Health Initiative.
In this paper Komisarow estimates the impact of specialized instructional support personnel—school nurses and social workers—on student outcomes using quasi-experimental variation from North Carolina’s Child and Family Support Teams (CFST) program. The CFST program created two sources of natural variation: directly treated schools received substantial state-funded staffing increases in the form of highly trained two-person teams composed of a school nurse and a school social worker. At the same time, other schools in the same districts experienced more modest staffing increases indirectly through reallocations of existing personnel and district-led compensatory equalization. Using event-study and difference-in-differences designs, Komisarow shows that expanded access to support personnel reduced student absences and chronic absenteeism. In directly treated schools, annual absences decreased by 0.4 days (6%) and chronic absenteeism declined by 0.9 percentage points (12%) among high-risk students. In indirectly treated schools, absences fell by 0.2 days annually (3%) among all students, though effects on chronic absenteeism were not statistically significant. Thus, while large, specialized staffing increases generated substantial improvements for the highest-risk students, smaller and less specialized increases produced modest but broader benefits across all students. These findings demonstrate that school support staff play a critical role in addressing barriers to student learning.
Sarah Komisarow is an Assistant Professor of Public Policy and Economics in the Sanford School of Public Policy, a Faculty Affiliate of the Center for Child & Family Policy, and a Faculty Scholar at the Duke University Population Research Institute. She is an applied microeconomist with research interests in the economics of education and K-12 education policy. She graduated from Duke University with a B.A. in Public Policy Studies in 2008 and from the University of Chicago with a M.A. and Ph.D. in Economics in 2012 and 2016, respectively. Her personal website can be accessed here: https://sites.google.com/site/sarahkomisarow/home
of Population Research welcomes submissions for a Special Issue on “Demographic Perspectives on Migration”. Submit your manuscript until May 15, 2026
The editors invite contributions expanding the state-of-the-art knowledge and methodological approaches across a broad range of migration topics, including trends and spatial patterns, innovative data and methods, socio-economic inequalities, drivers of mobility and immobility, climate-related and crisis-driven migration, links between migration and family or health outcomes, emigration and return migration, migrant integration and labour-market impacts, as well as migration forecasting and scenario development.
We invite original unpublished contributions (empirical or theoretical) in form of Research articles, Review articles, Perspectives and shorter Data & Trends contributions. All submissions will be subject to external double-blind peer review.
For more information, please visit
Guest editors: Michaela Potančoková, Roman Hoffmann, Dilek Yildiz, Eleonora Mussino, James Raymer, Claudia Masferrer and Gregor Zens.
CSDE external affiliate and former CSDE T32 Fellow Delaney Glass (University of Toronto) and co-authors developed a Qualitative Framework for Operationalizing Respondent Sampling (Q-FORS), featured in Social Science and Medicine. Q-FORS focuses on data adequacy as the pivotal concept for determining a qualitative sample, especially with interviews and focus groups. It is a practical framework applicable across the spectrum of the research life cycle—from proposal writing and data collection to reporting—and is geared towards any researchers using qualitive methods, especially those publishing in medical and health related journals. The authors discuss the utility of this approach by providing two hypothetical case examples based on real-life studies being carried out by the co-authors.
SDE Affiliate Jing Xu (Anthropology) authored a chapter in a new book titled, Rethinking Childhood in Modern Chinese History. In this chapter, Xu draws on ethnographic records on Chinese children and childhood in the mid twentieth century that reflect three layers of marginality: social margins – ordinary, rural, working-class families in Hong Kong and Taiwan; historiographical margins – ethnographic materials are outside the conventional scope of ‘archival’ history on Chinese childhood; and intellectual margins – children at the periphery of these ethnographies. Xu also presents her rediscovery of a significant yet unpublished fieldnotes archive collected by the late anthropologists Arthur P. Wolf and Margery Wolf between 1958 and 1960 in the world’s first systematic, anthropological research on Han Chinese children. In a separate article, Xu draws on these fieldnotes and combines anthropological expertise and various AI technologies to analyze natural observation texts about children’s peer-interactions. Xu transformed raw fieldnotes into a text-as-data pipeline, discovered how ethnographic close-reading and AI technologies can complement and augment each other’s value, and shed light on the similarities and differences in how machines and humans learn and make sense of morality.