Search

Introduction: Duplex ultrasound (DUS) is the modality of choice for surveillance of popliteal artery aneurysms (PAAs). However, noninvasive vascular laboratories have no standard guidelines for reporting results. This study assessed reports of PAA DUS for inclusion of information pertinent to operative decision-making and timing of surveillance. Methods: This study was a retrospective review of a multi-institutional repository that was queried for all patients with a PAA from 2008 to 2022 and confirmed via manual chart review. DUS reports were abstracted and images were individually annotated for features of interest including dimensions, flow abnormalities, and percent thrombus burden. Results: A total of 166 PAAs in 130 patients had at least one DUS available for viewing. Postoperative surveillance of PAAs was performed at several intervals: the first at 30 months (IQR 3.7–113, n = 44), the second at 64 months (IQR 20–172, n = 31), and the third at 152 months (IQR 46–217, n = 16) after the operation. The largest diameter of operative PAAs (median 27.5 mm, IQR 21.8–38.0) was significantly greater than nonoperative PAAs (median 20.9 mm, IQR 16.7–27.3); p < 0.01. Fewer than 33 (21%) reports commented on patency of distal runoff. We calculated an average percent thrombus of 60% (IQR 19–81) in nonoperative PAAs, which is significantly smaller than 75% (IQR 58–89) in operative PAAs; p < 0.01. Conclusion: In this multi-institutional retrospective study, PAAs are often not followed at intervals recommended by the Society for Vascular Surgery guidelines and do not include all measurements necessary for clinical decision-making in the multi-institutional repository studied. There should be standardization of PAA DUS protocols performed by all noninvasive vascular laboratories to ensure completeness of PAA DUS images and inclusion of characteristics pertinent to clinical decision-making in radiology reports. [ABSTRACT FROM AUTHOR]

DUS measurements for popliteal artery aneurysms (PAAs) specifically can be time-consuming, error-prone, and operator-dependent. To eliminate this subjectivity and provide efficient segmentation, we applied artificial intelligence (AI) to accurately delineate inner and outer lumen on DUS. DUS images were selected from a cohort of patients with PAAs from a multi-institutional platform. Encord is an easy-to-use, readily available online AI platform that was used to segment both the inner lumen and outer lumen of the PAA on DUS images. A model trained on 20 images and tested on 80 images had a mean Average Precision of 0.85 for the outer polygon and 0.23 for the inner polygon. The outer polygon had a higher recall score than precision score at 0.90 and 0.85, respectively. The inner polygon had a score of 0.25 for both precision and recall. The outer polygon false-negative rate was the lowest in images with the least amount of blur. This study demonstrates the feasibility of using the widely available Encord AI platform to identify standard features of PAAs that are critical for operative decision making. [ABSTRACT FROM AUTHOR]

In this study, we examined how mental health inequities by gender and sexual identity changed from before the COVID-19 pandemic to during the early phase of the pandemic. Sexual and gender minority (SGM) adults and non-SGM adults were recruited through social media to complete online questionnaires. Results indicate a worsening change in depression from before the pandemic to during the early phase of the pandemic that was significantly greater for gender minorities compared to cisgender (cis) women. Anxiety and loneliness scores did not change differentially for gender minorities (vs cis women) or sexual minorities (vs heterosexuals). Overall, COVID-19 had a greater negative impact on depression for gender minorities compared with cis women. [ABSTRACT FROM AUTHOR]

The early phases of the coronavirus 19 disease (COVID-19) pandemic were associated with changes in psychological well-being and alcohol use. However, it is unclear whether these changes are artifacts of psychological well-being and alcohol use prior to the pandemic across different sociodemographic groups. We received surveys from 247 adult residents of Allegheny County, Pennsylvania (United States), with an oversampling of sexual- and gender-minority individuals. Responses included measures of psychological well-being, substance use, and sociodemographic characteristics. Unadjusted mean depression scores, anxiety scores, and number of drinking days increased for all age and income groups during COVID-19, while average number of drinks per drinking day and days intoxicated differentially increased or decreased by age and income groups. Using Bayesian seemingly unrelated regression, we assessed depression and anxiety symptoms and alcohol use during the early stages of the pandemic and one month before COVID-19 was first identified in Allegheny County concurrently. Those in the youngest (18–24) group drank on more days during (but not before) the pandemic than those in the 25–44 age group. Compared to cisgender women, gender-minority adults had higher depression scores during the early stages of the pandemic. Employed adults had lower anxiety scores during (but not before) the pandemic than adults who were unemployed. Those with past-year annual incomes above $80,000 had fewer drinks on average drinking occasions than those in the $40,000 or below group before (but not during) the pandemic. Patterns of psychological distress and alcohol use associated with the COVID-19 pandemic differ by subgroup compared to patterns prior to the pandemic. Interventions addressing worsening mental health outcomes and shifting alcohol use patterns must be sensitive to the needs of vulnerable groups, such as younger adults and those experiencing poverty or unemployment. [ABSTRACT FROM AUTHOR]

Intimate partner violence (IPV) and child maltreatment outcomes are markedly associated with substance abuse disorders. However, few studies have explored these serious family violence outcomes in connection to the opioid epidemic or population-level geographic connections between these variables. This study assesses associations of ZIP code-level IPV and child maltreatment hospitalization outcomes with opioid- and alcohol-related diagnoses as well as economic and demographic neighborhood characteristics. We used 11 years (2004–2014) of ZIP code-level Pennsylvania hospital discharge data and U.S. Census neighborhood characteristics data. As nearby ZIP codes are more likely to be similar than those that are distant, we incorporated spatial autocorrelation using conditionally autoregressive Bayesian hierarchical space–time models. There was a positive relationship between ZIP code-level opioid-related diagnoses and both IPV (relative risk 1.061; 95% credible interval [1.015, 1.106]) and child maltreatment (relative risk 1.055; 95% credible interval [1.035, 1.070]) hospitalizations. There was a positive relationship between alcohol-related diagnoses and IPV but not child maltreatment. Higher median household incomes were associated with lower counts of both IPV and child maltreatment hospitalizations. To illustrate geographic heterogeneity of model estimates, posterior distributions were used to compare variability of effects across ZIP codes. Our findings emphasize the secondary implications of the opioid epidemic in the form of family violence within communities. [ABSTRACT FROM AUTHOR]

Population analyses of the correlates of neighborhood crime implicitly assume that a single spatial unit can be used to assess neighborhood effects. However, no single spatial unit may be suitable for analyses of the many social determinants of crime. Instead, effects may appear at multiple spatial resolutions, with some determinants acting broadly, others locally, and still others as some function of both global and local conditions. We provide a multiresolution spatial analysis that simultaneously examines US Census block, block group, and tract effects of alcohol outlets and drug markets on violent crimes in Oakland, California, incorporating spatial lag effects at the 2 smaller spatial resolutions. Using call data from the Oakland Police Department from 2010–2015, we examine associations of assaults, burglaries, and robberies with multiple resolutions of alcohol outlet types and compare the performance of single (block-level) models with that of multiresolution models. Multiresolution models performed better than the block models, reflected in improved deviance and Watanabe-Akaike information criteria and well-supported multiresolution associations. By considering multiple spatial scales and spatial lags in a Bayesian framework, researchers can explore multiresolution processes, providing more detailed tests of expectations from theoretical models and leading the way to more effective intervention efforts. [ABSTRACT FROM AUTHOR]

Background: The rapid growth of opioid abuse and the related mortality across the United States has spurred the development of predictive models for the allocation of public health resources. These models should characterize heterogeneous growth across states using a drug epidemic framework that enables assessments of epidemic onset, rates of growth, and limited capacities for epidemic growth.Methods: We used opioid overdose mortality data for 146 North and South Carolina counties from 2001 through 2014 to compare the retrodictive and predictive performance of a logistic growth model that parameterizes onsets, growth, and carrying capacity within a traditional Bayesian Poisson space-time model.Results: In fitting the models to past data, the performance of the logistic growth model was superior to the standard Bayesian Poisson space-time model (deviance information criterion: 8,088 vs. 8,256), with reduced spatial and independent errors. Predictively, the logistic model more accurately estimated fatality rates 1, 2, and 3 years in the future (root mean squared error medians were lower for 95.7% of counties from 2012 to 2014). Capacity limits were higher in counties with greater population size, percent population age 45-64, and percent white population. Epidemic onset was associated with greater same-year and past-year incidence of overdose hospitalizations.Conclusion: Growth in annual rates of opioid fatalities was capacity limited, heterogeneous across counties, and spatially correlated, requiring spatial epidemic models for the accurate and reliable prediction of future outcomes related to opioid abuse. Indicators of risk are identifiable and can be used to predict future mortality outcomes. [ABSTRACT FROM AUTHOR]

Objective: The shelter-in-place mandates enacted early in the COVID-19 pandemic resulted in changes in alcohol use and consequent outcomes. We assessed changes in six categories of season-specific alcohol-attributable mortality from before to during the early phases of the COVID-19 pandemic in the United States., Method: We used logistic regression models to assess alcohol-attributable mortality in the United States from 2017 through 2020 ( n = 11,632,725 decedents ages 18 and older). Outcomes included chronic fully alcohol-attributable deaths, poisonings, motor vehicle accidents, suicides, homicides, and falls. Exposure variables included year, season, the interaction between the year 2020 and season, rurality, the interaction between the year 2020 and rurality, decedent age, sex, race, ethnicity, marital status, and education., Results: Compared with 2019, season-specific mortality age-adjusted rates of chronic fully alcohol-attributable deaths, homicides, poisonings, and falls increased during the COVID-19 pandemic. Suicide rates decreased in most 2020 seasons relative to the same seasons in 2019. Motor vehicle deaths decreased in spring 2020 versus 2019. Relative to dying by any other cause, the odds of death by chronic fully alcohol-attributable causes and poisonings were higher across seasons in 2020 versus 2019. The odds of death by suicide were higher among residents of rural counties in spring 2020 versus 2019., Conclusions: There were distinct temporal changes in six types of alcohol-attributable deaths during the early phases of the COVID-19 pandemic.