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Ronald Gangnon

Professor of Biostatistics

University of Wisconsin-Madison

Biography

I am a Professor in the Department of Biostatistics and Medical Informatics and the Department of Population Health Sciences in the School of Medicine and Public Health at the University of Wisconsin-Madison. I have an affiliate appointment in the Department of Statistics.

I grew up in Duluth, Minnesota. I graduated from East High School in 1988. I received a BA in Mathematics and Economics in 1992 from the University of Minnesota-Duluth and an MS in Statistics in 1994 and a PhD in Statistics with emphasis in Biostatistics in 1998 from the University of Wisconsin-Madison. My PhD advisor was Murray Clayton.

I was a research scientist in the Statistical Data Analysis Center(SDAC) in the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison, 1998-2005. I joined the faculty with a joint appointment in the Department of Biostatistics and Medical Informatics and the Department of Population Health Sciences in 2005.

I am an applied biostatistician focusing on problems in clinical and epidemiologic research. Current methodologic areas of interest include (1) multi-state models for incidence, progression and regression of ocular (and other) diseases, (2) small area estimation problems, particularly ranking, (3) spatial and spatio-temporal modeling, particularly cluster detection and high-dimensional variable selection and (4) age-period-cohort modeling.

Outside of the office, I’m an avid cyclist. You can check out my recent rides on Strava. I’m also a big movie fan. You’ll definitely see me at the Wisconsin Film Festival, one of my favorite events every year, and you can see lists of my favorite films and what I’ve been watching recently on Letterboxd.

Interests

  • Spatial and Spatio-Temporal Modeling
  • Age-Period-Cohort Models
  • Ranking
  • Multi-State Models

Education

  • PhD in Statistics (emphasis in Biostatistics), 1998

    University of Wisconsin-Madison

  • MS in Statistics, 1994

    University of Wisconsin-Madison

  • BA in Mathematics and Economics, 1992

    University of Minnesota-Duluth

Publications

A flexible method for identifying spatial clusters of breast cancer using individual-level data

Prior research has shown that cancer risk varies by geography, but scan statistic methods for identifying cancer clusters in case-control studies have been limited in their ability to identify multiple clusters and adjust for participant-level risk factors. We develop a method to identify geographic patterns of breast cancer odds using the Wisconsin Women’s Health Study, a series of 5 population-based case-control studies of female Wisconsin residents aged 20-79 enrolled in 1988-2004 (cases=16,076, controls=16,795). We create sets of potential clusters by overlaying a 1 km grid over each county-neighborhood and enumerating a series of overlapping circles. Using a two-step approach, we fi=t a penalized binomial regression model to the number of cases and trials in each grid cell, penalizing all potential clusters by the least absolute shrinkage and selection operator (Lasso). We use BIC to select the number of clusters, which are included in a participant-level logistic regression model. We identify 15 geographic clusters, resulting in 23 areas of unique geographic odds ratios. After adjustment for known risk factors, confidence intervals narrowed but breast cancer odds ratios did not meaningfully change; one additional hotspot was identified. By considering multiple overlapping spatial clusters simultaneously, we discern gradients of spatial odds across Wisconsin.

Risk of COVID-19 Illness among In-Person K-12 School Educators and Its Association with School District Prevention Policies – Wisconsin, September 2-November 24, 2021

Objectives: To assess the rate of COVID-19 among in-person K-12 educators and its association with various COVID-19 prevention policies in school districts. Methods: Actively working, in-person K-12 educators in Wisconsin were linked to COVID-19 cases with onset during September 2–November 24, 2021. A mixed-effects Cox proportional hazards model, adjusted for pertinent person- and community-level confounders, compared the hazards rate of COVID-19 among educators working in districts with and without specific COVID-19 prevention policies. Results: In-person educators working in school districts that required masking for students and staff experienced 19% lower hazards of COVID-19 compared with those in districts without any masking policy (hazards ratio = 0.81, 95% confidence interval = 0.72 to 0.92). Reduced COVID-19 hazards were consistent and remained statistically significant when educators were stratified by elementary, middle, and high school environments. Conclusions: In Wisconsin’s K-12 school districts, during the Fall 2021 academic semester, a policy that required both students and staff to mask was associated with significantly reduced risk of COVID-19 among in-person educators across all grade levels.

Examining the Association between the Gastrointestinal Microbiome and Gulf War Illness: A Prospective Cohort Study

Gulf War Illness (GWI) affects approximately 25-35% of the 1991 Gulf War Veteran population. Those affected experience a wide range of symptoms including pain, fatigue, cognitive impairments, gastrointestinal dysfunction, skin disorders, and respiratory symptoms. Longitudinal studies have shown patients with GWI have had little to improvement in symptoms since their diagnosis. The gut microbiome and diet have been shown to play an important role in overall health and preliminary research has shown the gut microbiota may play a role in GWI as well. To examine the relationship between the gut microbiota, diet, and GWI, we conducted an eight week prospective cohort study collecting stool samples, medication and health history, and dietary data. Stool samples were analyzed using 16S rRNA sequencing on the Illumina MiSeq. Sixty-nine participants were enrolled into the study, 36 of which met the case definition for GWI. The gut microbiome of participants was very stable over the duration of the study and showed no within person (alpha diversity) differences between groups though beta diversity was statistically different between those with and without GWI. Several taxonomic lineages were identified as differentially abundant between those with and without GWI (n=9).

US Mass public shootings since Columbine: victims per incident by race and ethnicity of the perpetrator

White individuals in the United States (US) have historically had disproportionate access to firearms. The real-life availability of firearms, including those most lethal, may still be greater among White populations, manifesting in the number of victims in shootings. We compared the severity of US mass public shootings since Columbine by race and/or ethnicity of the perpetrator using The Violence Project Database of Mass Shooters, assessing fatalities (minimum four), total victims, type, and legal status of guns used. We used data visualization and Quasi-Poisson regression of victims minus four – accounting for truncation at 4 fatalities – to assess fatality and total victim rates comparing Non-Hispanic (NH) White with NH Black shooters, using winsorization to account for outlier bias from the 2017 Las Vegas shooting. In 104 total mass public shootings until summer 2021, NH White shooters had higher median fatalities (6 [IQR 5-9] versus 5 [IQR 4-6]) and total victims (9 [IQR 6-19] versus 7 [IQR 5-12]) per incident. Confidence intervals of NH Black versus NH White fatalities rate ratios (RR) ranged from 0.17-1.15, and of total victim RRs from 0.15-1.04. White shooters were overrepresented in mass public shootings with the most victims, typically involving legally owned assault rifles. To better understand the consequences when firearms are readily available, including assault rifles, we need a database of all US gun violence. Our assessment of total victims beyond fatalities emphasizes the large number of US gun violence survivors and the need to understand their experiences to capture the full impact of gun violence.

Regularized spatial and spatio-temporal cluster detection

Spatial and spatio-temporal cluster detection are important tools in public health and many other areas of application. Cluster detection can be approached as a multiple testing problem, typically using a space and time scan statistic. We recast the spatial and spatio-temporal cluster detection problem in a high-dimensional data analytical framework with Poisson or quasi-Poisson regression with the Lasso penalty. We adopt a fast and computationally-efficient method using a novel sparse matrix representation of the effects of potential clusters. The number of clusters and tuning parameters are selected based on (quasi-)information criteria. We evaluate the performance of our proposed method including the false positive detection rate and power using a simulation study. Application of the method is illustrated using breast cancer incidence data from three prefectures in Japan.