Stephen John Mooney

Background: Extreme Risk Protection Orders (ERPOs) are civil orders that temporarily restrict firearm access, purchase, and possession for individuals who demonstrate high-risk behavior (“respondents”). We described demographic and case characteristics among respondents in Washington State by reason for ERPO petition filing, by year, and by county from December 2016 (when the law went into effect) through December 2023. Methods: We mapped the distribution of ERPO cases by county and summarized respondent and case characteristics. We plotted variables of interest across year and used the Cochran-Armitage test for trends in proportion to assess yearly changes in proportion from 2016 through 2023. We conducted a cluster analysis to describe how case characteristics relate to one another. Results: There were 1205 ERPO cases granted at the ex-parte level, representing 1160 unique respondents. The plurality of respondents were 40 to 54 years old (27.2%), male (85.1%) and White (76.9%). The plurality of ERPOs were filed for concern of harm to others ( n = 592, 49.1%), followed by concerns of harm to self ( n = 322, 26.7%). Of the 1180 cases with a final order hearing, 81.1% were granted, 11.9% were denied, and 7.0% were dismissed. There was a yearly increase in the proportion of respondents arrested around ERPO filing (average 4.2%-point increase per year), who demonstrated threats of violence (average 3.5%-point increase per year), and who brandished a weapon (average 3.2%-point increase per year). Three clusters of precipitating event characteristics emerged. The first cluster included suicidal ideation, threats, or plans; transport to a hospital for a mental health or chemical dependency evaluation; and suicide attempt or aborted attempt. The second cluster included threats of violence and “irrational” or “erratic” behavior. The third cluster included brandishing or reckless use of a weapon; respondent arrest; and use of violence. Conclusion: Washington State ERPOs were predominately filed for concerns of harm to others, but a range of high-risk behaviors were documented among ERPO respondents. The diversity of precipitating circumstances suggests that ERPOs may function as a flexible intervention with the potential to influence population health by reducing access to firearms during periods of acute risk.

Background: Neighborhood disinvestment, characterized by built environment disrepair and deterioration, has been linked to health behaviors and outcomes, including cancer survival. However, disinvestment temporal dynamics, including time-lagged exposure estimates among colorectal cancer (CRC) cases, remain underexplored. Objective: This study aimed to describe and validate a spatiotemporal neighborhood audit protocol using Google Street View imagery, develop and compare predictive spatiotemporal models of neighborhood disinvestment, and examine time-lagged associations between disinvestment and CRC survival. Methods: We conducted 8256 virtual audits of Franklin County, Ohio, streetscapes sampled across locations and dates from 2009 to 2022 using 7 disinvestment indicators: garbage, graffiti, abandoned buildings, building conditions, yard conditions, road verge conditions, and large dumpsters. Of these, 5751 eligible location date audits were included. A neighborhood disinvestment score (NDS) was derived using item response theory. We fit spatiotemporal regression Kriging models with a simplified sum-metric covariance structure and compared two candidate models using out-of-sample root mean square prediction error (RMSPE): (1) a model incorporating major highways, waterways, and railways to define neighborhood boundaries, and (2) a traditional Kriging model allowing NDS to vary continuously across space and time. The best-fitting model was used to estimate NDS at the geocoded address and diagnosis date of 2727 CRC cases diagnosed from 2012 to 2019 and recorded within the Ohio Cancer Incidence Surveillance System. Covariates included age, sex, minoritized race-ethnicity, marital status, health insurance, and cancer stage at diagnosis (localized, regional, and distant). We built accelerated failure time models to estimate time ratios and 95% CIs for NDS averaged over 0-, 3-, 6-, 12-, 18-, and 24-month time lags. Models were adjusted for covariates. We tested stage by NDS interactions. Those alive through December 31, 2020, were right censored. Results: NDS exhibited substantial spatial but modest temporal variation, with spatial correlation measurable within 2.8 kilometers and temporal correlation up to 270 days. Traditional spatiotemporal Kriging outperformed the boundary-based model (RMSPE _traditional =0.651 vs RMSPE _boundary =0.662). NDS varied by season, with lower disinvestment scores in spring and summer compared with fall. The accelerated failure time CRC survival model indicates an interaction between NDS and stage at diagnosis for all NDS time lags tested; higher prediagnosis NDS was significantly associated with shorter survival only among those with regional and not among those with localized or distant stage at diagnosis. For example, with a 6-month prediagnosis averaged time lag, each standard deviation increase in NDS was associated with a time ratio of 0.628 (95% CI 0.436-0.904). Associations were largely identical across time lags. Conclusions: The spatiotemporal neighborhood audit approach is feasible and efficient. NDS varies appreciably across season, was accurately estimated using regression Kriging, and time-lagged exposures from 0 to 24 months produced negligible change in the NDS-CRC survival association.

Background: Remediating vacant lots is associated with reductions in neighborhood violence and other adverse health outcomes, potentially via increasing visible cues-to-care that signal stewardship. However, accurately measuring these cues over time remains challenging, limiting our ability to measure the sustained effects of lot remediation. Methods: We developed a standardized audit protocol using subject-matter and community experts and assessed vacant lot cues-to-care from Google Street View imagery of vacant lots in Philadelphia from 2007 to 2023. We fit a two-parameter item response theory (IRT) model to combine the observable cues-to-care measured consistently into a single latent score representing the lot care condition. We validated the latent score by testing its sensitivity to a randomized controlled greening trial and a local lot maintenance program. Results: Five raters audited 3,419 images (518 vacant lots; 6,315 ratings) captured from July 2007 to October 2023. We selected reliable and conceptually aligned items to fit an IRT model representing a latent score of lot care condition and evaluated the validity of that score. Average pairwise Cohen’s for audited items was 0.31 (SD = 0.14) across all raters, and 0.55 (SD = 0.18) among our most concordant raters. Eight items with above-moderate reliability (K > 0.40) were retained for the final IRT model, which demonstrated high internal reliability (0.96). This IRT-derived care score was sensitive to improvements in lot conditions following greening interventions and enrollment in a lot maintenance program. Conclusion: Our IRT-derived score can be used to measure vacant lot care condition accurately over time. This can support long-term evaluation of vacant lot remediation interventions.

Excel spreadsheet showing the implementation of each the numerical examples in the paper.

Background:Analyses of etiologic heterogeneity use molecular markers to subtype cancers (e.g., breast cancer) and test whether an exposure is more strongly associated with one tumor subtype as opposed to other subtypes. However, these molecular markers (e.g., a mutated oncogene) may best be conceptualized as mediators of exposure–disease relationships.Methods:Seven model causal scenarios, based on data from the literature on postmenopausal breast cancer, were created with varying relationships between an exposure, a mediating or non-mediating molecular marker, and a disease status, with molecular marker used to subtype the disease. Using numerical examples, we assessed how well the analyses of etiologic heterogeneity identify the distinct etiologic pathways in these seven scenarios.Results:Etiologic heterogeneity analyses of the data from the four causal mediation models produced similar results despite the mediating role of molecular marker differing across scenarios, with heterogeneity effect sizes ranging from 1.47 to 1.60. Analyses of data from the two scenarios, in which molecular marker did not affect the risk of disease, also produced evidence of etiologic heterogeneity, with heterogeneity effect sizes of 1.53 and 2.66. The smallest heterogeneity effect size (1.33) was observed in the scenario in which molecular marker was a sufficient cause of disease.Conclusions:This methodologic research indicates that analyses of etiologic heterogeneity cannot distinguish between the modeled causal scenarios when a mediating or non-mediating molecular marker is used to subtype tumors.Impact:We discuss these results in the context of the literature on obesity and etiologic heterogeneity in breast tumors subtyped by sex hormone receptor status.

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Background: Physical activity (PA) interventions can increase levels of PA to help participants meet recommended levels. The impact of PA interventions may be affected by an individual’s neighborhood environment, including attributes such as walkability, crime rates, or greenspace availability, but research to date has lacked the power and geographic spread to adequately assess the role of the environment. Objective: The Interventions Supporting Physical Activity and the Environment (InSPACE) study used the Automatic Context Measurement Tool (ACMT) to gather environmental measures for participants around their home address in completed lifestyle intervention trials across the United States, then pooled and harmonized demographic and device-based activity data, creating a dataset for use in assessing the moderation effect of neighborhood attributes on interventions to increase PA. Methods: PA intervention trials were recruited from across the United States, and trialists were instructed in the use of the ACMT to geocode and collect prespecified environmental measures. The InSPACE research team gathered deidentified data from trialists, including demographics, raw accelerometry data, and ACMT-generated environmental measures and harmonized data to create a pooled dataset of PA intervention trial participants. Results: As of August 2025, a total of 39 PA intervention trials have been recruited and data from 31 of these trials have been processed and harmonized, creating a current pooled dataset of 4471 participants with any harmonized data, of whom 4360 (97.5%) have linked environmental data and 2208 (49.4%) have specified (3 days of at least 8 hours per day) accelerometry data for both baseline and postintervention. Results from the primary analysis for the InSPACE project are expected to be published in late 2026. Conclusion: InSPACE will contribute to understanding the role of the environment in moderating the effect of interventions to increase PA. The protocols and processes of InSPACE can inform future projects in pooled data harmonization and analysis.

Background : Physical activity (PA) may be a modifiable factor for chronic kidney disease (CKD) prevention, but evidence specific to Hispanic/Latino populations is limited. Our objective was to examine associations between PA type/volume with changes in kidney function among Hispanic/Latino adults. Methods : We included participants (mean age: 41.6 years, 55.2% women) in the Hispanic Community Health Study/Study of Latinos with complete data. PA was measured using both 7-day accelerometry and the Global Physical Activity Questionnaire. Primary outcomes were percent change in estimated glomerular filtration rate (eGFR) and percent change in urine albumin-to-creatinine ratio over a mean of 6.1 years (N = 7071); incident CKD was the secondary outcome (N = 7177). Results : The mean eGFR change was −3.6 mL/min/1.73 m ² , and mean urine albumin-to-creatinine ratio change was 4.0 mg/g. In multivariable-adjusted models accounting for demographic, occupational, and clinical factors, neither accelerometer-measured light PA or moderate to vigorous PA (MVPA) nor self-reported MVPA were significantly associated with changes in eGFR, urine albumin-to-creatinine ratio, or incident CKD ( P > .05). Diabetes status significantly modified associations between eGFR percent change and accelerometer-measured light PA (interaction P < .01), MVPA (interaction P = .02), and self-reported total MVPA (interaction P = .03). Among individuals with diabetes, higher self-reported total MVPA was associated with higher eGFR change (0.12% per 15 min/d; 95% CI, 0.02–0.22), whereas greater accelerometer-measured MVPA was linked to higher CKD risk (incidence rate ratio: 1.12; 95% CI, 1.02–1.23). Among those without diabetes, associations were inverse with 95% CIs that overlapped the null. Conclusions : PA types/volumes were not associated with kidney function decline or CKD incidence, though diabetes status may modify these relationships.

Background: Analyses of etiologic heterogeneity use molecular markers to subtype cancers (e.g., breast cancer) and test whether an exposure is more strongly associated with one tumor subtype as opposed to other subtypes. However, these molecular markers (e.g., a mutated oncogene) may best be conceptualized as mediators of exposure–disease relationships. Methods: Seven model causal scenarios, based on data from the literature on postmenopausal breast cancer, were created with varying relationships between an exposure, a mediating or non-mediating molecular marker, and a disease status, with molecular marker used to subtype the disease. Using numerical examples, we assessed how well the analyses of etiologic heterogeneity identify the distinct etiologic pathways in these seven scenarios. Results: Etiologic heterogeneity analyses of the data from the four causal mediation models produced similar results despite the mediating role of molecular marker differing across scenarios, with heterogeneity effect sizes ranging from 1.47 to 1.60. Analyses of data from the two scenarios, in which molecular marker did not affect the risk of disease, also produced evidence of etiologic heterogeneity, with heterogeneity effect sizes of 1.53 and 2.66. The smallest heterogeneity effect size (1.33) was observed in the scenario in which molecular marker was a sufficient cause of disease. Conclusions: This methodologic research indicates that analyses of etiologic heterogeneity cannot distinguish between the modeled causal scenarios when a mediating or non-mediating molecular marker is used to subtype tumors. Impact: We discuss these results in the context of the literature on obesity and etiologic heterogeneity in breast tumors subtyped by sex hormone receptor status.