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.
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
Patterns in disease across space and time are important to epidemiologists and health professionals because they may indicate underlying elevated disease risk. In some cases, elevated risk may be driven by environmental exposures, infectious diseases or other factors where timely public health interventions are important. The spatial and spatio-temporal scan statistics identify a single most likely cluster or equivalently select a single correct model. We instead consider an ensemble of single cluster models. We use stacking, a model-averaging technique, to combine relative risk estimates from all of the single cluster models into a sequence of meta-models indexed by the effective number of parameters/clusters. The number of parameters/spatiotemporal clusters is chosen using information criteria. A simulation study is conducted to demonstrate the statistical properties of the stacking method. The method is illustrated using a dataset of female breast cancer incidence data at the municipality level in Japan.
Introduction: There are significant disparities in the rates of maternal and infant morbidity and mortality in the United States – a discrepancy of particular importance in Wisconsin, where Non-Hispanic Black women experience the highest mortality rates in the country. The adverse effects of neighborhood socioeconomic status and geographical distance to obstetrical care outcomes have been demonstrated previously, with poor neighborhood socioeconomic status having been linked to higher rates of preterm births and low birth weight infants, which both increase the risk of neonatal morbidity and mortality. The objective of this study was to investigate the contributions of Area Deprivation Index and geographic location on age-matched birth weight z-scores. Methods: We conducted a retrospective cohort study of all singleton births >22 weeks’ gestation in Dane County, Wisconsin, from January 2016 through June 2018. Generalized additive models were adjusted for race/ethnicity, cigarette use, delivery route, pregnancy-related or chronic hypertension, pregestational and gestational diabetes, number of prenatal visits, maternal age, total weight gain, and pre-pregnancy body mass index. Results: There is evidence of an association between birth weight z-score and spatial location (median P value 0.006). With area deprivation, we found no evidence of an association with birth weight z-score (-0.01; 95% CI, -0.03 to 0.01; P = 0.109). Mean birth weight z-scores were lowest (-0.72) in the urban center of Madison, while mean birth weight z-score was highest (0.18) in rural areas near the northeast, southeast, and southwest county borders. We found an effect of race/ethnicity on birth weight. Conclusions: We identified geographic variations in birth weight at a granular level using census block groups and a holistic measure of deprivation, which can inform targeted public health interventions. The lack of evidence of area deprivation on birth outcomes but significant spatial trends demonstrated continued geographic disparities in our health care systems.
Context: Population health rankings can be a catalyst for the improvement of health by drawing attention to areas in need of relative improvement and summarizing complex information in a manner understood by almost everyone. However, ranks also have unintended consequences, such as being interpreted as “hard truths”, where variations may not be significant. There is a need to improve communication about uncertainty in ranks, with accurate interpretation. The most common solutions discussed in the literature have included modeling approaches to minimize statistical noise or borrow strength from covariates. However, the use of complex models can limit communication and implementation, especially for broad audiences. Objectives: Explore data-informed grouping (cluster analysis) as an easier-to-understand, empirical technique to account for rank imprecision that can be effectively communicated both numerically and visually. Design: Cluster analysis, specifically k-means clustering with Wasserstein (earth mover’s) distance, was explored as an approach to identify natural and meaningful groupings and gaps in the data distribution for the County Health Rankings’ (CHR) health outcomes ranks. Setting: County-level health outcomes from the 2022 CHR Participants: 3,082 counties that were ranked in the 2022 CHR Main Outcome Measure: Data-informed health groups Results: Cluster analysis identified 30 health groupings among counties nationwide, with cluster size ranging from nine to 184 counties. On average, states had 16 identified clusters, ranging from 3 in Delaware and Hawaii to 27 in Virginia. Number of clusters per state was associated with number of counties per state and population of the state. The method helped address many of the issues that arise from providing rank estimates alone. Conclusions: Public health practitioners can use this information to understand uncertainty in ranks, visualize distances between county ranks, have context around which counties are not meaningfully different from one another, and compare county performance to peer counties.
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.
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.
Background Lifestyle factors have been studied for dementia risk, but few have comprehensively assessed both Alzheimer’s disease (AD) and cerebrovascular disease (CBVD) pathologies. Objective Our research aims to determine the relationships between lifestyle and various dementia pathologies, challenging conventional research paradigms. Methods Analyzing 1231 Wisconsin Registry for Alzheimer’s Prevention (WRAP) study participants, we focused on Life Simple Seven (LS7) score calculations from questionnaire data and clinical vitals. We assessed brain health indicators including CBVD, AD, and cognition. Results Higher (healthier) LS7 scores were associated with better CBVD outcomes, including lower percent white matter hyperintensities and higher cerebral blood flow, and higher Preclinical Alzheimer’s Composite 3 and Delayed Recall scores. No significant associations were observed between LS7 scores and AD markers of amyloid and tau accumulation. Conclusion This study provides evidence that the beneficial effects of LS7 on cognition are primarily through cerebrovascular pathways rather than direct influences on AD pathology.
Objective Nearly half of Wisconsin women live in rural areas, where access to obstetrical care remains a major concern. Rural communities are often disproportionately affected by national emergencies. This study aimed to evaluate the impact of the COVID-19 pandemic on severe maternal morbidity (SMM) rates across Wisconsin, with a focus on geographic variation. Study Design A retrospective cohort study was conducted using Wisconsin Hospital Association coding data for all births in Wisconsin from March 1, 2017 to March 31, 2023. The pre-pandemic period was defined as 3/1/2017 – 2/29/2020, and the pandemic period as 3/1/2020 to 3/31/2023. Generalized additive logistic regression models assessed changes in the SMM rates by ZIP code. Statistical analyses were performed using the mgcv package in R. Results Among 334,366 births (172,737 pre pandemic and 161,629 during the pandemic) there were 2,210 SMM events (0.7%). The SMM rate increased from 0.6% pre-pandemic to 0.8% during the pandemic (RR 1.33, 95% CI 1.23–1.45, p<0.0001). However, no significant geographic variation in the pandemic’s impact on SMM rates was observed (p=0.56). The most common SMM diagnoses during the pandemic included acute renal failure, disseminated intravascular coagulation, and acute respiratory distress syndrome. Conclusion There is strong evidence that the COVID-19 pandemic was associated with an increase in SMM events. Despite concerns about rural healthcare access, no geographic disparities were found, suggesting that further efforts are needed to analyze SMM events in rural areas.
Background Over-the-counter (OTC) medication misuse among older adults is a patient safety concern, exacerbated by limited patient engagement about potential risks. Senior Safe™, a pharmacy-based intervention using human-factors engineering and participatory design,specifically, shelf signage, product repositioning, and patient engagement to nudge safer choices. Despite its safety intent, and demonstrated effectiveness, it was important to determine the intervention’s impact on its financial sustainability. Methods This study evaluated Senior Safe’s effect on daily unit sales of OTC analgesic, sleep, and cough/cold/allergy products across 65 community pharmacies in a Midwestern health system. Using Generalized Linear Mixed Model regressions with Poisson distribution, the analyses compared daily unit sales pre- and post-intervention trends for products marked with Green Banners (safer), Red Stop Signs (high-risk), or Behind-the-Counter (BTC) signage (very high-risk), controlling for pharmacy type, size, location, open hours, and staff hours. Results Senior Safe was associated with increased sales of safer analgesics and cough/cold/allergy medications (IRR = 1.064 and 1.106), along with significant decreases in unit sales of BTC and Red Stop Sign products (IRR = 0.424-0.869). These findings suggest a substitution effect, where patients chose safer alternatives rather than forgoing OTC purchases. Operational factors, such as longer open hours and higher staffing levels, were positively associated with safer product unit sales. Conclusions Senior Safe successfully shifted consumer behavior toward safer OTC medication use without reducing overall sales volume, suggesting patient safety interventions can be financially sustainable in retail pharmacy settings. These results support broader implementation of low-cost, system-level interventions that align safety with business operations.
IMPORTANCE: Over seven million U.S. adults experience persistent health issues after COVID-19, known as “long COVID”. Although multiple guidelines recommend the inclusion of functional status in long COVID diagnostic criteria, more evidence is needed to guide this recommendation. This study explored the adjusted odds of developing long COVID by pre-infection symptoms and functional status, and the feasibility of estimating functional status using health records data. DESIGN & METHODS: Retrospective cohort study of U.S. adults with history of COVID-19 enrolled in a multicenter national cohort study through July 2022 (All of Us Controlled Tier CDR 7.0), using diagnostic, procedure, and billing codes from the health record, and baseline survey responses. The risk of long COVID was estimated using logistic regression by pre-infection (-5 years) incidences of (a) at least one symptom common in long COVID, and (b) functional status, and adjusted for disease and demographic characteristics. RESULTS: N = 65,464 met inclusion criteria; n=40,655 had post-infection occurrences of at least one symptom (long COVID group), n=24,809 had none (recovered). Adjusted odds ratios of developing long COVID increased with older age, female sex, Black racial identity, earlier variant, non-vaccination, lower pre-infection self-reported mental and cognitive health, and number of pre-infection symptoms. Adjusted odds were not significantly affected by any single pre-infection symptom, self-rated physical ability, or EHR-derived indicators of prior functional impairment. CONCLUSIONS. In this model, there was no significant difference in risk of long COVID based on either pre-infection total incidences of long COVID symptoms (compared to the average of 4) or pre-infection functional impairment. This suggests that long COVID was associated with a change from baseline functioning and health, including in people with pre-infection incident symptoms and functional impairments. The impacts of co-occurring pre-infection symptoms requires further investigation. Both harmonized electronic health records data and patient-reported outcomes contribute important data for developing the diagnostic utility of functional status changes in long COVID.
Motor difficulties are common in autistic individuals and may contribute to challenges in social development. Understanding the association between motor and social skills could inform interventions to improve developmental outcomes. Using data from the Study to Explore Early Development—a large, diverse sample of rigorously characterized preschool-aged autistic children in the United States—we aimed to (a) describe the frequency of motor challenges using multiple standardized instruments; and (b) evaluate associations between motor and social skills. Children were identified from health and education organizations and birth records. Caregivers completed standardized interviews and questionnaires, and children completed comprehensive developmental evaluations to determine autism status. Among 2,039 children meeting the study autism criteria, 67.3% exhibited motor scores ⩾2 standard deviations below the mean on at least one measure. Motor difficulties were more prevalent in the fine motor (up to 63.4%) than gross motor (14.2%) domain and among children with significant visual reception delays (up to 92.8%) than those without these delays (up to 32.0%). After adjusting for covariates, fine motor skills were significantly associated with social challenges in both functional and autism-specific domains. These findings highlight the importance of motor development in early autism evaluations.