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.

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).

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.

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 Schools are primary venues of influenza amplification with secondary spread to communities. We assessed K-12 student absenteeism monitoring as a means for early detection of influenza activity in the community. Materials and methods Between September 2014 and March 2020, we conducted a prospective observational study of all-cause (a-TOT), illness-associated (a-I), and influenza-like illness–associated (a- ILI) absenteeism within the Oregon School District (OSD), Dane County, Wisconsin. Absenteeism was reported through the electronic student information system. Students were visited at home where pharyngeal specimens were collected for influenza RT-PCR testing. Surveillance of medically-attended laboratory-confirmed influenza (MAI) occurred in five primary care clinics in and adjoining the OSD. Poisson general additive log linear regression models of daily counts of absenteeism and MAI were compared using correlation analysis. Findings Influenza was detected in 723 of 2,378 visited students, and in 1,327 of 4,903 MAI patients. Over six influenza seasons, a-ILI was significantly correlated with MAI in the community (r =0.57; 95% CI: 0.53–0.63) with a one-day lead time and a-I was significantly correlated with MAI in the community (r = 0.49; 0.44–0.54) with a 10-day lead time, while a-TOT performed poorly (r = 0.27; 0.21–0.33), following MAI by six days. Discussion Surveillance using cause-specific absenteeism was feasible and performed well over a study period marked by diverse presentations of seasonal influenza. Monitoring a-I and a- ILI can provide early warning of seasonal influenza in time for community mitigation efforts.